54 results on '"Nedjati Gilani, G"'
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2. Author Correction: Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’ (Nature, (2020), 584, 7821, (425-429), 10.1038/s41586-020-2488-1)
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Lavezzo, E., Franchin, E., Ciavarella, C., Cuomo-Dannenburg, G., Barzon, L., Del Vecchio, C., Rossi, L., Manganelli, R., Loregian, A., Navarin, N., Abate, D., Sciro, M., Merigliano, S., De Canale, E., Vanuzzo, M. C., Besutti, V., Saluzzo, F., Onelia, F., Pacenti, M., Parisi, S. G., Carretta, G., Donato, D., Flor, L., Cocchio, S., Masi, G., Sperduti, A., Cattarino, L., Salvador, R., Nicoletti, M., Caldart, F., Castelli, G., Nieddu, E., Labella, B., Fava, L., Drigo, M., Gaythorpe, K. A. M., Ainslie, K. E. C., Baguelin, M., Bhatt, S., Boonyasiri, A., Boyd, O., Coupland, H. L., Cucunuba, Z., Djafaara, B. A., van Elsland, S. L., Fitzjohn, R., Flaxman, S., Green, W. D., Hallett, T., Hamlet, A., Haw, D., Imai, N., Jeffrey, B., Knock, E., Laydon, D. J., Mellan, T., Mishra, S., Nedjati-Gilani, G., Nouvellet, P., Okell, L. C., Parag, K. V., Riley, S., Thompson, H. A., Unwin, H. J. T., Verity, R., Vollmer, M. A. C., Walker, P. G. T., Walters, C. E., Wang, H., Wang, Y., Watson, O. J., Whittaker, C., Whittles, L. K., Xi, X., Brazzale, A. R., Toppo, S., Trevisan, M., Baldo, V., Donnelly, C. A., Ferguson, N. M., Dorigatti, I., and Crisanti, A.
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- 2021
3. Report 33: Modelling the allocation and impact of a COVID-19 vaccine
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Hogan, A, Winskill, P, Watson, O, Walker, P, Whittaker, C, Baguelin, M, Haw, D, Lochen, A, Gaythorpe, K, Ainslie, K, Bhatt, S, Boonyasiri, A, Boyd, O, Brazeau, N, Cattarino, L, Charles, G, Cooper, L, Coupland, H, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Donnelly, C, Dorigatti, I, Eales, O, Van Elsland, S, Ferreira Do Nascimento, F, Fitzjohn, R, Flaxman, S, Green, W, Hallett, T, Hamlet, A, Hinsley, W, Imai, N, Jauneikaite, E, Jeffrey, B, Knock, E, Laydon, D, Lees, J, Mellan, T, Mishra, S, Nedjati Gilani, G, Nouvellet, P, Ower, A, Parag, K, Ragonnet-Cronin, M, Siveroni, I, Skarp, J, Thompson, H, Unwin, H, Verity, R, Vollmer, M, Volz, E, Walters, C, Wang, H, Wang, Y, Whittles, L, Xi, X, Muhib, F, Smith, P, Hauck, K, Ferguson, N, Ghani, A, Medical Research Council (MRC), and Abdul Latif Jameel Foundation
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Coronavirus ,COVID19 ,COVID-19 ,Vaccine - Abstract
Several SARS-CoV-2 vaccine candidates are now in late-stage trials, with efficacy and safety results expected by the end of 2020. Even under optimistic scenarios for manufacture and delivery, the doses available in 2021 are likely to be limited. Here we identify optimal vaccine allocation strategies within and between countries to maximise health (avert deaths) under constraints on dose supply. We extended an existing mathematical model of SARS-CoV-2 transmission across different country settings to model the public health impact of potential vaccines, using a range of target product profiles developed by the World Health Organization. We show that as supply increases, vaccines that reduce or block infection – and thus transmission – in addition to preventing disease have a greater impact than those that prevent disease alone, due to the indirect protection provided to high-risk groups. We further demonstrate that the health impact of vaccination will depend on the cumulative infection incidence in the population when vaccination begins, the duration of any naturally acquired immunity, the likely trajectory of the epidemic in 2021 and the level of healthcare available to effectively treat those with disease. Within a country, we find that for a limited supply (doses for
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- 2020
4. Report 32: Targeting interventions to age groups that sustain COVID-19 transmission in the United States
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Monod, M, Blenkinsop, A, Xi, X, Herbert, D, Bershan, S, Tietze, S, Bradley, V, Chen, Y, Coupland, H, Filippi, S, Ish-Horowicz, J, McManus, M, Mellan, T, Gandy, A, Hutchinson, M, Unwin, H, Vollmer, M, Weber, S, Zhu, H, Bezancon, A, Ferguson, N, Mishra, S, Flaxman, S, Bhatt, S, Ratmann, O, Ainslie, K, Baguelin, M, Boonyasiri, A, Boyd, O, Cattarino, L, Cooper, L, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Djaafara, A, Dorigatti, I, Van Elsland, S, Fitzjohn, R, Gaythorpe, K, Geidelberg, L, Green, W, Hamlet, A, Jeffrey, B, Knock, E, Laydon, D, Nedjati Gilani, G, Nouvellet, P, Parag, K, Siveroni, I, Thompson, H, Verity, R, Walters, C, Donnelly, C, Okell, L, Bhatia, S, Brazeau, N, Eales, O, Haw, D, Imai, N, Jauneikaite, E, Lees, J, Mousa, A, Olivera Mesa, D, Skarp, J, Whittles, L, Medical Research Council (MRC), and Abdul Latif Jameel Foundation
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Coronavirus ,COVID19 ,COVID-19 ,USA - Abstract
Following inial declines, in mid 2020, a resurgence in transmission of novel coronavirus disease (COVID-19) has occurred in the United States and parts of Europe. Despite the wide implementaon of non-pharmaceucal inter-venons, it is sll not known how they are impacted by changing contact paerns, age and other demographics. As COVID-19 disease control becomes more localised, understanding the age demographics driving transmission and how these impact the loosening of intervenons such as school reopening is crucial. Considering dynamics for the United States, we analyse aggregated, age-specific mobility trends from more than 10 million individuals and link these mechaniscally to age-specific COVID-19 mortality data. In contrast to previous approaches, we link mobility to mortality via age specific contact paerns and use this rich relaonship to reconstruct accurate trans-mission dynamics. Contrary to anecdotal evidence, we find lile support for age-shis in contact and transmission dynamics over me. We esmate that, unl August, 63.4% [60.9%-65.5%] of SARS-CoV-2 infecons in the United States originated from adults aged 20-49, while 1.2% [0.8%-1.8%] originated from children aged 0-9. In areas with connued, community-wide transmission, our transmission model predicts that re-opening kindergartens and el-ementary schools could facilitate spread and lead to considerable excess COVID-19 aributable deaths over a 90-day period. These findings indicate that targeng intervenons to adults aged 20-49 are an important con-sideraon in halng resurgent epidemics, and prevenng COVID-19-aributable deaths when kindergartens and elementary schools reopen.
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- 2020
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5. Report 31: Estimating the burden of COVID-19 in Damascus, Syria: an analysis of novel data sources to infer mortality under-ascertainment
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Van Elsland, S, Watson, O, Alhaffar, M, Mehchy, Z, Whittaker, C, Akil, Z, Ainslie, K, Baguelin, M, Bhatt, S, Boonyasiri, A, Boyd, O, Brazeau, N, Cattarino, L, Charles, G, Ciavarella, C, Cooper, L, Coupland, H, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Djaafara, A, Donnelly, C, Dorigatti, I, Eales, O, Nascimento, F, Fitzjohn, R, Flaxman, S, Forna, A, Fu, H, Gaythorpe, K, Green, W, Hamlet, A, Hauck, K, Haw, D, Hayes, S, Hinsley, W, Imai, N, Jeffrey, B, Johnson, R, Jorgensen, D, Knock, E, Laydon, D, Lees, J, Mellan, T, Mishra, S, Nedjati Gilani, G, Nouvellet, P, Okell, L, Olivera Mesa, D, Pons Salort, M, Ragonnet-Cronin, M, Siveroni, I, Stopard, I, Thompson, H, Unwin, H, Verity, R, Vollmer, M, Volz, E, Walters, C, Wang, H, Wang, Y, Whittles, L, Winskill, P, Xi, X, Ferguson, N, Beals, E, Walker, P, Anonymous Authors, Medical Research Council (MRC), and Abdul Latif Jameel Foundation
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Coronavirus ,Syria ,COVID19 ,COVID-19 - Abstract
The COVID-19 pandemic has resulted in substantial mortality worldwide. However, to date, countries in the Middle East and Africa have reported substantially lower mortality rates than in Europe and the Americas. One hypothesis is that these countries have been ‘spared’, but another is that deaths have been under-ascertained (deaths that have been unreported due to any number of reasons, for instance due to limited testing capacity). However, the scale of under-ascertainment is difficult to assess with currently available data. In this analysis, we estimate the potential under-ascertainment of COVID-19 mortality in Damascus, Syria, where all-cause mortality data has been reported between 25th July and 1st August. We fit a mathematical model of COVID-19 transmission to reported COVID-19 deaths in Damascus since the beginning of the pandemic and compare the model-predicted deaths to reported excess deaths. Exploring a range of different assumptions about under-ascertainment, we estimate that only 1.25% of deaths (sensitivity range 1% - 3%) due to COVID-19 are reported in Damascus. Accounting for under-ascertainment also corroborates local reports of exceeded hospital bed capacity. To validate the epidemic dynamics inferred, we leverage community-uploaded obituary certificates as an alternative data source, which confirms extensive mortality under-ascertainment in Damascus between July and August. This level of under-ascertainment suggests that Damascus is at a much later stage in its epidemic than suggested by surveillance reports, which have repo. We estimate that 4,340 (95% CI: 3,250 - 5,540) deaths due to COVID-19 in Damascus may have been missed as of 2nd September 2020. Given that Damascus is likely to have the most robust surveillance in Syria, these findings suggest that other regions of the country could have experienced similar or worse mortality rates due to COVID-19.
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- 2020
6. Report 30: The COVID-19 epidemic trends and control measures in mainland China
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Fu, H, Xi, X, Wang, H, Boonyasiri, A, Wang, Y, Hinsley, W, Fraser, K, McCabe, R, Olivera Mesa, D, Skarp, J, Ledda, A, Dewe, T, Dighe, A, Winskill, P, Van Elsland, S, Ainslie, K, Baguelin, M, Bhatt, S, Boyd, O, Brazeau, N, Cattarino, L, Charles, G, Coupland, H, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Donnelly, C, Dorigatti, I, Green, W, Hamlet, A, Hauck, K, Haw, D, Jeffrey, B, Laydon, D, Lees, J, Mellan, T, Mishra, S, Nedjati Gilani, G, Nouvellet, P, Okell, L, Parag, K, Ragonnet-Cronin, M, Riley, S, Schmit, N, Thompson, H, Unwin, H, Verity, R, Vollmer, M, Volz, E, Walker, P, Walters, C, Watson, O, Whittaker, C, Whittles, L, Imai, N, Bhatia, S, Ferguson, N, and Medical Research Council (MRC)
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Coronavirus ,China ,COVID19 ,COVID-19 - Published
- 2020
7. Estimating the number of undetected COVID-19 cases among travellers from mainland China
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Bhatia, S, Imai, N, Cuomo-Dannenburg, G, Baguelin, M, Boonyasiri, A, Cori, A, Cucunuba Perez, Z, Dorigatti, I, Fitzjohn, R, Fu, H, Gaythorpe, K, Ghani, A, Hamlet, A, Hinsley, W, Laydon, D, Nedjati Gilani, G, Okell, L, Riley, S, Thompson, H, Van Elsland, S, Volz, E, Wang, H, Wang, Y, Whittaker, C, Xi, X, Donnelly, CA, Ferguson, NM, and Medical Research Council (MRC)
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Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that more than two thirds (70%, 95% CI: 54% - 80%, compared to Singapore; 75%, 95% CI: 66% - 82%, compared to multiple countries) of cases exported from mainland China have remained undetected. Conclusions: These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China.
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- 2020
8. Report 26: Reduction in mobility and COVID-19 transmission
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Nouvellet, P, Bhatia, S, Cori, A, Ainslie, K, Baguelin, M, Bhatt, S, Boonyasiri, A, Brazeau, N, Cattarino, L, Cooper, L, Coupland, H, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dighe, A, Djaafara, A, Dorigatti, I, Eales, O, Van Elsland, S, Nscimento, F, Fitzjohn, R, Gaythorpe, K, Geidelberg, L, Grassly, N, Green, W, Hamlet, A, Hauck, K, Hinsley, W, Imai, N, Jeffrey, B, Knock, E, Laydon, D, Lees, J, Mangal, T, Mellan, T, Nedjati Gilani, G, Parag, K, Pons Salort, M, Ragonnet-Cronin, M, Riley, S, Unwin, H, Verity, R, Vollmer, M, Volz, E, Walker, P, Walters, C, Wang, H, Watson, O, Whittaker, C, Whittles, L, Xi, X, Ferguson, N, Donnelly, C, and Medical Research Council (MRC)
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Mobility ,COVID19 ,Transmissibility ,COVID-19 - Abstract
In response to the COVID-19 pandemic, countries have sought to control transmission of SARS-CoV-2 by restricting population movement through social distancing interventions, reducing the number of contacts. Mobility data represent an important proxy measure of social distancing. Here, we develop a framework to infer the relationship between mobility and the key measure of population-level disease transmission, the reproduction number (R). The framework is applied to 53 countries with sustained SARS-CoV-2 transmission based on two distinct country-specific automated measures of human mobility, Apple and Google mobility data. For both datasets, the relationship between mobility and transmission was consistent within and across countries and explained more than 85% of the variance in the observed variation in transmissibility. We quantified country-specific mobility thresholds defined as the reduction in mobility necessary to expect a decline in new infections (R
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- 2020
9. Report 23: State-level tracking of COVID-19 in the United States
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Unwin, H, Mishra, S, Bradley, VC, Gandy, A, Vollmer, M, Mellan, T, Coupland, H, Ainslie, K, Whittaker, C, Ish-Horowicz, J, Filippi, S, Xi, X, Monod, M, Ratmann, O, Hutchinson, M, Valka, F, Zhu, H, Hawryluk, I, Milton, P, Baguelin, M, Boonyasiri, A, Brazeau, N, Cattarino, L, Charles, G, Cooper, L, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Djaafara, A, Dorigatti, I, Eales, O, Eaton, J, Van Elsland, S, Fitzjohn, R, Gaythorpe, K, Green, W, Hallett, T, Hinsley, W, Imai, N, Jeffrey, B, Knock, E, Laydon, D, Lees, J, Nedjati Gilani, G, Nouvellet, P, Okell, L, Ower, A, Parag, K, Siveroni, I, Thompson, H, Verity, R, Walker, P, Walters, C, Wang, Y, Watson, O, Whittles, L, Ghani, A, Ferguson, N, Riley, S, Donnelly, C, Bhatt, S, Flaxman, S, and Medical Research Council (MRC)
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Coronavirus ,COVID19 ,COVID-19 ,United States - Abstract
our estimates show that the percentage of individuals that have been infected is 4.1% [3.7%-4.5%], with wide variation between states. For all states, even for the worst affected states, we estimate that less than a quarter of the population has been infected; in New York, for example, we estimate that 16.6% [12.8%-21.6%] of individuals have been infected to date. Our attack rates for New York are in line with those from recent serological studies [1] broadly supporting our choice of infection fatality rate. There is variation in the initial reproduction number, which is likely due to a range of factors; we find a strong association between the initial reproduction number with both population density (measured at the state level) and the chronological date when 10 cumulative deaths occurred (a crude estimate of the date of locally sustained transmission). Our estimates suggest that the epidemic is not under control in much of the US: as of 17 May 2020 the reproduction number is above the critical threshold (1.0) in 24 [95% CI: 20-30] states. Higher reproduction numbers are geographically clustered in the South and Midwest, where epidemics are still developing, while we estimate lower reproduction numbers in states that have already suffered high COVID-19 mortality (such as the Northeast). These estimates suggest that caution must be taken in loosening current restrictions if effective additional measures are not put in place. We predict that increased mobility following relaxation of social distancing will lead to resurgence of transmission, keeping all else constant. We predict that deaths over the next two-month period could exceed current cumulative deaths by greater than two-fold, if the relationship between mobility and transmission remains unchanged. Our results suggest that factors modulating transmission such as rapid testing, contact tracing and behavioural precautions are crucial to offset the rise of transmission associated with loosening of social distancing. Overall, we show that while all US states have substantially reduced their reproduction numbers, there is little evidence that any states are approaching herd immunity and thus the epidemic is close to over in any state.
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- 2020
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10. Report 22: Equity in response to the COVID-19 pandemic: an assessment of the direct and indirect impacts on disadvantaged and vulnerable populations in low- and lower middle-income countries
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Winskill, P, Whittaker, C, Walker, P, Watson, O, Laydon, D, Imai, N, Cuomo-Dannenburg, G, Ainslie, K, Baguelin, M, Bhatt, S, Boonyasiri, A, Cattarino, L, Ciavarella, C, Cooper, L, Coupland, H, Cucunuba Perez, Z, Van Elsland, S, Fitzjohn, R, Flaxman, S, Gaythorpe, K, Green, W, Hallett, T, Hamlet, A, Hinsley, W, Knock, E, Lees, J, Mellan, T, Mishra, S, Nedjati Gilani, G, Nouvellet, P, Okell, L, Parag, K, Thompson, H, Unwin, H, Wang, Y, Whittles, L, Xi, X, Ferguson, N, Donnelly, C, Ghani, A, and Medical Research Council (MRC)
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Coronavirus ,COVID19 ,COVID-19 ,Equity - Abstract
The impact of the COVID-19 pandemic in low-income settings is likely to be more severe due to limited healthcare capacity. Within these settings, however, there exists unfair or avoidable differences in health among different groups in society – health inequities – that mean that some groups are particularly at risk from the negative direct and indirect consequences of COVID-19. The structural determinants of these are often reflected in differences by income strata, with the poorest populations having limited access to preventative measures such as handwashing. Their more fragile income status will also mean that they are likely to be employed in occupations that are not amenable to social-distancing measures, thereby further reducing their ability to protect themselves from infection. Furthermore, these populations may also lack access to timely healthcare on becoming ill. We explore these relationships by using large-scale household surveys to quantify the differences in handwashing access, occupation and hospital access with respect to wealth status in low-income settings. We use a COVID-19 transmission model to demonstrate the impact of these differences. Our results demonstrate clear trends that the probability of death from COVID-19 increases with increasing poverty. On average, we estimate a 32.0% (2.5th-97.5th centile 8.0%-72.5%) increase in the probability of death in the poorest quintile compared to the wealthiest quintile from these three factors alone. We further explore how risk mediators and the indirect impacts of COVID-19 may also hit these same disadvantaged and vulnerable the hardest. We find that larger, inter-generational households that may hamper efforts to protect the elderly if social distancing are associated with lower-income countries and, within LMICs, lower wealth status. Poorer populations are also more susceptible to food security issues - with these populations having the highest levels under-nourishment whilst also being most dependent on their own food production. We show that timing of the COVID-19 epidemic in low-resource settings has the potential to interrupt planting and harvesting seasons for staple crops, thereby accentuating this vulnerability. These enhanced risks and key vulnerabilities – alongside the broader concerns surrounding displaced or conflict-affected populations - demonstrate the challenges that the most marginalised populations face during the ongoing COVID-19 pandemic.
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- 2020
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11. Report 21: Estimating COVID-19 cases and reproduction number in Brazil
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Mellan, T, Hoeltgebaum, H, Mishra, S, Whittaker, C, Schnekenberg, R, Gandy, A, Unwin, H, Vollmer, M, Coupland, H, Hawryluk, I, Rodrigues Faria, N, Vesga, J, Zhu, H, Hutchinson, M, Ratmann, O, Monod, M, Ainslie, K, Baguelin, M, Bhatia, S, Boonyasiri, A, Brazeau, N, Charles, G, Cooper, L, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dighe, A, Djaafara, A, Eaton, J, Van Elsland, S, Fitzjohn, R, Fraser, K, Gaythorpe, K, Green, W, Hayes, S, Imai, N, Jeffrey, B, Knock, E, Laydon, D, Lees, J, Mangal, T, Mousa, A, Nedjati Gilani, G, Nouvellet, P, Olivera Mesa, D, Parag, K, Pickles, M, Thompson, H, Verity, R, Walters, C, Wang, H, Wang, Y, Watson, O, Whittles, L, Xi, X, Okell, L, Dorigatti, I, Walker, P, Ghani, A, Riley, S, Ferguson, N, Donnelly, C, Flaxman, S, Bhatt, S, and Medical Research Council (MRC)
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Coronavirus ,0303 health sciences ,03 medical and health sciences ,0302 clinical medicine ,COVID19 ,COVID-19 ,030212 general & internal medicine ,Brazil ,3. Good health ,030304 developmental biology - Abstract
Brazil is an epicentre for COVID-19 in Latin America. In this report we describe the Brazilian epidemic using three epidemiological measures: the number of infections, the number of deaths and the reproduction number. Our modelling framework requires sufficient death data to estimate trends, and we therefore limit our analysis to 16 states that have experienced a total of more than fifty deaths. The distribution of deaths among states is highly heterogeneous, with 5 states—São Paulo, Rio de Janeiro, Ceará, Pernambuco and Amazonas—accounting for 81% of deaths reported to date. In these states, we estimate that the percentage of people that have been infected with SARS-CoV-2 ranges from 3.3% (95% CI: 2.8%-3.7%) in São Paulo to 10.6% (95% CI: 8.8%-12.1%) in Amazonas. The reproduction number (a measure of transmission intensity) at the start of the epidemic meant that an infected individual would infect three or four others on average. Following non-pharmaceutical interventions such as school closures and decreases in population mobility, we show that the reproduction number has dropped substantially in each state. However, for all 16 states we study, we estimate with high confidence that the reproduction number remains above 1. A reproduction number above 1 means that the epidemic is not yet controlled and will continue to grow. These trends are in stark contrast to other major COVID19 epidemics in Europe and Asia where enforced lockdowns have successfully driven the reproduction number below 1. While the Brazilian epidemic is still relatively nascent on a national scale, our results suggest that further action is needed to limit spread and prevent health system overload.
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- 2020
12. Report 20: A sub-national analysis of the rate of transmission of Covid-19 in Italy
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Vollmer, M, Mishra, S, Unwin, H, Gandy, A, Melan, T, Bradley, V, Zhu, H, Coupland, H, Hawryluk, I, Hutchinson, M, Ratmann, O, Monod, M, Walker, P, Whittaker, C, Cattarino, L, Ciavarella, C, Cilloni, L, Ainslie, K, Baguelin, M, Bhatia, S, Boonyasiri, A, Brazeau, N, Charles, G, Cooper, L, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dighe, A, Djaafara, A, Eaton, J, Van Elsland, S, Fitzjohn, R, Fraser, K, Gaythorpe, K, Green, W, Hayes, S, Imai, N, Jeffrey, B, Knock, E, Laydon, D, Lees, J, Mangal, T, Mousa, A, Nedjati Gilani, G, Nouvellet, P, Olivera Mesa, D, Parag, K, Pickles, M, Thompson, H, Verity, R, Walters, C, Wang, H, Wang, Y, Watson, O, Whittles, L, Xi, X, Ghani, A, Riley, S, Okell, L, Donnelly, C, Ferguson, N, Dorigatti, I, Flaxman, S, Bhatt, S, and Medical Research Council (MRC)
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Coronavirus ,Italy ,COVID19 ,Lockdown ,COVID-19 ,Transmission - Abstract
Italy was the first European country to experience sustained local transmission of COVID-19. As of 1st May 2020, the Italian health authorities reported 28; 238 deaths nationally. To control the epidemic, the Italian government implemented a suite of non-pharmaceutical interventions (NPIs), including school and university closures, social distancing and full lockdown involving banning of public gatherings and non essential movement. In this report, we model the effect of NPIs on transmission using data on average mobility. We estimate that the average reproduction number (a measure of transmission intensity) is currently below one for all Italian regions, and significantly so for the majority of the regions. Despite the large number of deaths, the proportion of population that has been infected by SARS-CoV-2 (the attack rate) is far from the herd immunity threshold in all Italian regions, with the highest attack rate observed in Lombardy (13.18% [10.66%-16.70%]). Italy is set to relax the currently implemented NPIs from 4th May 2020. Given the control achieved by NPIs, we consider three scenarios for the next 8 weeks: a scenario in which mobility remains the same as during the lockdown, a scenario in which mobility returns to pre-lockdown levels by 20%, and a scenario in which mobility returns to pre-lockdown levels by 40%. The scenarios explored assume that mobility is scaled evenly across all dimensions, that behaviour stays the same as before NPIs were implemented, that no pharmaceutical interventions are introduced, and it does not include transmission reduction from contact tracing, testing and the isolation of confirmed or suspected cases. We find that, in the absence of additional interventions, even a 20% return to pre-lockdown mobility could lead to a resurgence in the number of deaths far greater than experienced in the current wave in several regions. Future increases in the number of deaths will lag behind the increase in transmission intensity and so a second wave will not be immediately apparent from just monitoring of the daily number of deaths. Our results suggest that SARS-CoV-2 transmission as well as mobility should be closely monitored in the next weeks and months. To compensate for the increase in mobility that will occur due to the relaxation of the currently implemented NPIs, enhanced community surveillance including swab testing, contact tracing and the early isolation of infections are of paramount importance to reduce the risk of resurgence in transmission.
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- 2020
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13. Report 19: The potential impact of the COVID-19 epidemic on HIV, TB and malaria in low- and middle-income countries
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Hogan, A, Jewell, B, Sherrard-Smith, E, Vesga, J, Watson, O, Whittaker, C, Hamlet, A, Smith, J, Ainslie, K, Baguelin, M, Bhatt, S, Boonyasiri, A, Brazeau, N, Cattarino, L, Charles, G, Cooper, L, Coupland, H, Cuomo-Dannenburg, G, Dighe, A, Djaafara, A, Donnelly, C, Dorigatti, I, Eaton, J, Van Elsland, S, Fitzjohn, R, Fu, H, Gaythorpe, K, Green, W, Haw, D, Hayes, S, Hinsley, W, Imai, N, Knock, E, Laydon, D, Lees, J, Mangal, T, Mellan, T, Mishra, S, Nedjati Gilani, G, Nouvellet, P, Okell, L, Ower, A, Parag, K, Pickles, M, Stopard, I, Thompson, H, Unwin, H, Verity, R, Vollmer, M, Walters, C, Wang, H, Wang, Y, Whittles, L, Winskill, P, Xi, X, Ferguson, N, Churcher, T, Arinaminpathy, N, Ghani, A, Walker, P, Hallett, T, and Medical Research Council (MRC)
- Abstract
COVID-19 has the potential to cause disruptions to health services in different ways; through the health system becoming overwhelmed with COVID-19 patients, through the intervention used to slow transmission of COVID-19 inhibiting access to preventative interventions and services, and through supplies of medicine being interrupted. We aim to quantify the extent to which such disruptions in services for HIV, TB and malaria in high burden low- and middle-income countries could lead to additional loss of life. In high burden settings, HIV, TB and malaria related deaths over 5 years may be increased by up to 10%, 20% and 36%, respectively, compared to if there were no COVID-19 epidemic. We estimate the greatest impact on HIV to be from interruption to ART, which may occur during a period of high or extremely high health system demand; for TB, we estimate the greatest impact is from reductions in timely diagnosis and treatment of new cases, which may result from a long period of COVID-19 suppression interventions; for malaria, we estimate that the greatest impact could come from reduced prevention activities including interruption of planned net campaigns, through all phases of the COVID-19 epidemic. In high burden settings, the impact of each type of disruption could be significant and lead to a loss of life-years over five years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV/TB epidemics. Maintaining the most critical prevention activities and healthcare services for HIV, TB and malaria could significantly reduce the overall impact of the COVID-19 epidemic.
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- 2020
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14. Report 16: Role of testing in COVID-19 control
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Grassly, N, Pons Salort, M, Parker, E, White, P, Ainslie, K, Baguelin, M, Bhatt, S, Boonyasiri, A, Boyd, O, Brazeau, N, Cattarino, L, Ciavarella, C, Cooper, L, Coupland, H, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dighe, A, Djaafara, A, Donnelly, C, Dorigatti, I, Van Elsland, S, Ferreira Do Nascimento, F, Fitzjohn, R, Fu, H, Gaythorpe, K, Geidelberg, L, Green, W, Hallett, T, Hamlet, A, Hayes, S, Hinsley, W, Imai, N, Jorgensen, D, Knock, E, Laydon, D, Lees, J, Mangal, T, Mellan, T, Mishra, S, Nedjati Gilani, G, Nouvellet, P, Okell, L, Ower, A, Parag, K, Pickles, M, Ragonnet-Cronin, M, Stopard, I, Thompson, H, Unwin, H, Verity, R, Vollmer, M, Volz, E, Walker, P, Walters, C, Wang, H, Wang, Y, Watson, O, Whittaker, C, Whittles, L, Winskill, P, Xi, X, Ferguson, N, and Medical Research Council (MRC)
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Coronavirus ,COVID19 ,Testing ,COVID-19 - Abstract
The World Health Organization has called for increased molecular testing in response to the COVID-19 pandemic, but different countries have taken very different approaches. We used a simple mathematical model to investigate the potential effectiveness of alternative testing strategies for COVID-19 control. Weekly screening of healthcare workers (HCWs) and other at-risk groups using PCR or point-of-care tests for infection irrespective of symptoms is estimated to reduce their contribution to transmission by 25-33%, on top of reductions achieved by self-isolation following symptoms. Widespread PCR testing in the general population is unlikely to limit transmission more than contact-tracing and quarantine based on symptoms alone, but could allow earlier release of contacts from quarantine. Immunity passports based on tests for antibody or infection could support return to work but face significant technical, legal and ethical challenges. Testing is essential for pandemic surveillance but its direct contribution to the prevention of transmission is likely to be limited to patients, HCWs and other high-risk groups.
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- 2020
15. Report 13: Estimating the number of infections and the impact of non-pharmaceutical interventions on COVID-19 in 11 European countries
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Flaxman, S, Mishra, S, Gandy, A, Unwin, H, Coupland, H, Mellan, T, Zhu, H, Berah, T, Eaton, J, Perez Guzman, P, Schmit, N, Cilloni, L, Ainslie, K, Baguelin, M, Blake, I, Boonyasiri, A, Boyd, O, Cattarino, L, Ciavarella, C, Cooper, L, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dighe, A, Djaafara, A, Dorigatti, I, Van Elsland, S, Fitzjohn, R, Fu, H, Gaythorpe, K, Geidelberg, L, Grassly, N, Green, W, Hallett, T, Hamlet, A, Hinsley, W, Jeffrey, B, Jorgensen, D, Knock, E, Laydon, D, Nedjati Gilani, G, Nouvellet, P, Parag, K, Siveroni, I, Thompson, H, Verity, R, Volz, E, Walters, C, Wang, H, Wang, Y, Watson, O, Winskill, P, Xi, X, Whittaker, C, Walker, P, Ghani, A, Donnelly, C, Riley, S, Okell, L, Vollmer, M, Ferguson, N, Bhatt, S, Medical Research Council (MRC), and The Royal Society
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Europe ,COVID19 ,Non-pharmaceutical Interventions ,Pneumonia, Viral ,Coronavirus Infections ,CoronaVirus - Abstract
Following the emergence of a novel coronavirus (SARS-CoV-2) and its spread outside of China, Europe is now experiencing large epidemics. In response, many European countries have implemented unprecedented non-pharmaceutical interventions including case isolation, the closure of schools and universities, banning of mass gatherings and/or public events, and most recently, widescale social distancing including local and national lockdowns. In this report, we use a semi-mechanistic Bayesian hierarchical model to attempt to infer the impact of these interventions across 11 European countries. Our methods assume that changes in the reproductive number – a measure of transmission - are an immediate response to these interventions being implemented rather than broader gradual changes in behaviour. Our model estimates these changes by calculating backwards from the deaths observed over time to estimate transmission that occurred several weeks prior, allowing for the time lag between infection and death. One of the key assumptions of the model is that each intervention has the same effect on the reproduction number across countries and over time. This allows us to leverage a greater amount of data across Europe to estimate these effects. It also means that our results are driven strongly by the data from countries with more advanced epidemics, and earlier interventions, such as Italy and Spain. We find that the slowing growth in daily reported deaths in Italy is consistent with a significant impact of interventions implemented several weeks earlier. In Italy, we estimate that the effective reproduction number, Rt, dropped to close to 1 around the time of lockdown (11th March), although with a high level of uncertainty. Overall, we estimate that countries have managed to reduce their reproduction number. Our estimates have wide credible intervals and contain 1 for countries that have implemented all interventions considered in our analysis. This means that the reproduction number may be above or below this value. With current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels. We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-2 up to 28th March, representing between 1.88% and 11.43% of the population. The proportion of the population infected to date – the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics. Given the lag of 2-3 weeks between when transmission changes occur and when their impact can be observed in trends in mortality, for most of the countries considered here it remains too early to be certain that recent interventions have been effective. If interventions in countries at earlier stages of their epidemic, such as Germany or the UK, are more or less effective than they were in the countries with advanced epidemics, on which our estimates are largely based, or if interventions have improved or worsened over time, then our estimates of the reproduction number and deaths averted would change accordingly. It is therefore critical that the current interventions remain in place and trends in cases and deaths are closely monitored in the coming days and weeks to provide reassurance that transmission of SARS-Cov-2 is slowing.
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- 2020
16. Report 12: The global impact of COVID-19 and strategies for mitigation and suppression
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Walker, P, Whittaker, C, Watson, O, Baguelin, M, Ainslie, K, Bhatia, S, Bhatt, S, Boonyasiri, A, Boyd, O, Cattarino, L, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dighe, A, Donnelly, C, Dorigatti, I, Van Elsland, S, Fitzjohn, R, Flaxman, S, Fu, H, Gaythorpe, K, Geidelberg, L, Grassly, N, Green, W, Hamlet, A, Hauck, K, Haw, D, Hayes, S, Hinsley, W, Imai, N, Jorgensen, D, Knock, E, Laydon, D, Mishra, S, Nedjati Gilani, G, Okell, L, Riley, S, Thompson, H, Unwin, H, Verity, R, Vollmer, M, Walters, C, Wang, H, Wang, Y, Winskill, P, Xi, X, Ferguson, N, Ghani, A, Medical Research Council (MRC), and The Royal Society
- Subjects
Coronavirus ,COVID19 ,Global Burden - Abstract
The world faces a severe and acute public health emergency due to the ongoing COVID-19 global pandemic. How individual countries respond in the coming weeks will be critical in influencing the trajectory of national epidemics. Here we combine data on age-specific contact patterns and COVID-19 severity to project the health impact of the pandemic in 202 countries. We compare predicted mortality impacts in the absence of interventions or spontaneous social distancing with what might be achieved with policies aimed at mitigating or suppressing transmission. Our estimates of mortality and healthcare demand are based on data from China and high-income countries; differences in underlying health conditions and healthcare system capacity will likely result in different patterns in low income settings. We estimate that in the absence of interventions, COVID-19 would have resulted in 7.0 billion infections and 40 million deaths globally this year. Mitigation strategies focussing on shielding the elderly (60% reduction in social contacts) and slowing but not interrupting transmission (40% reduction in social contacts for wider population) could reduce this burden by half, saving 20 million lives, but we predict that even in this scenario, health systems in all countries will be quickly overwhelmed. This effect is likely to be most severe in lower income settings where capacity is lowest: our mitigated scenarios lead to peak demand for critical care beds in a typical low-income setting outstripping supply by a factor of 25, in contrast to a typical high-income setting where this factor is 7. As a result, we anticipate that the true burden in low income settings pursuing mitigation strategies could be substantially higher than reflected in these estimates. Our analysis therefore suggests that healthcare demand can only be kept within manageable levels through the rapid adoption of public health measures (including testing and isolation of cases and wider social distancing measures) to suppress transmission, similar to those being adopted in many countries at the current time. If a suppression strategy is implemented early (at 0.2 deaths per 100,000 population per week) and sustained, then 38.7 million lives could be saved whilst if it is initiated when death numbers are higher (1.6 deaths per 100,000 population per week) then 30.7 million lives could be saved. Delays in implementing strategies to suppress transmission will lead to worse outcomes and fewer lives saved. We do not consider the wider social and economic costs of suppression, which will be high and may be disproportionately so in lower income settings. Moreover, suppression strategies will need to be maintained in some manner until vaccines or effective treatments become available to avoid the risk of later epidemics. Our analysis highlights the challenging decisions faced by all governments in the coming weeks and months, but demonstrates the extent to which rapid, decisive and collective action now could save millions of lives.
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- 2020
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17. Report 11: Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment
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Ainslie, K, Walters, C, Fu, H, Bhatia, S, Wang, H, Baguelin, M, Bhatt, S, Boonyasiri, A, Boyd, O, Cattarino, L, Ciavarella, C, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dighe, A, Dorigatti, I, Van Elsland, S, Fitzjohn, R, Gaythorpe, K, Geidelberg, L, Ghani, A, Green, W, Hamlet, A, Hauck, K, Hinsley, W, Imai, N, Jorgensen, D, Knock, E, Laydon, D, Nedjati Gilani, G, Okell, L, Siveroni, I, Thompson, H, Unwin, H, Verity, R, Vollmer, M, Walker, P, Wang, Y, Watson, O, Whittaker, C, Winskill, P, Xi, X, Donnelly, C, Ferguson, N, Riley, S, Medical Research Council (MRC), and The Royal Society
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Coronavirus ,COVID19 ,Containment ,Social Distancing - Abstract
The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. As of 20 March 2020, over 254,000 cases and 10,000 deaths had been reported worldwide. The outbreak began in the Chinese city of Wuhan in December 2019. In response to the fast-growing epidemic, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. At the peak of the outbreak in China (early February), there were between 2,000 and 4,000 new confirmed cases per day. For the first time since the outbreak began there have been no new confirmed cases caused by local transmission in China reported for five consecutive days up to 23 March 2020. This is an indication that the social distancing measures enacted in China have led to control of COVID-19 in China. These interventions have also impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic is not yet clear. Here, we estimate transmissibility from reported cases and compare those estimates with daily data on within-city movement, as a proxy for economic activity. Initially, within-city movement and transmission were very strongly correlated in the 5 provinces most affected by the epidemic and Beijing. However, that correlation is no longer apparent even though within-city movement has started to increase. A similar analysis for Hong Kong shows that intermediate levels of local activity can be maintained while avoiding a large outbreak. These results do not preclude future epidemics in China, nor do they allow us to estimate the maximum proportion of previous within-city activity that will be recovered in the medium term. However, they do suggest that after very intense social distancing which resulted in containment, China has successfully exited their stringent social distancing policy to some degree. Globally, China is at a more advanced stage of the pandemic. Policies implemented to reduce the spread of COVID-19 in China and the exiting strategies that followed can inform decision making processes for countries once containment is achieved.
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- 2020
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18. Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand
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Ferguson, N, Laydon, D, Nedjati Gilani, G, Imai, N, Ainslie, K, Baguelin, M, Bhatia, S, Boonyasiri, A, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dighe, A, Dorigatti, I, Fu, H, Gaythorpe, K, Green, W, Hamlet, A, Hinsley, W, Okell, L, Van Elsland, S, Thompson, H, Verity, R, Volz, E, Wang, H, Wang, Y, Walker, P, Walters, C, Winskill, P, Whittaker, C, Donnelly, C, Riley, S, Ghani, A, Medical Research Council (MRC), and The Royal Society
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Coronavirus ,COVID19 ,Non-pharmaceutical interventions ,healthcare demand ,Mortality - Abstract
The global impact of COVID-19 has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 H1N1 influenza pandemic. Here we present the results of epidemiological modelling which has informed policymaking in the UK and other countries in recent weeks. In the absence of a COVID-19 vaccine, we assess the potential role of a number of public health measures – so-called non-pharmaceutical interventions (NPIs) – aimed at reducing contact rates in the population and thereby reducing transmission of the virus. In the results presented here, we apply a previously published microsimulation model to two countries: the UK (Great Britain specifically) and the US. We conclude that the effectiveness of any one intervention in isolation is likely to be limited, requiring multiple interventions to be combined to have a substantial impact on transmission. Two fundamental strategies are possible: (a) mitigation, which focuses on slowing but not necessarily stopping epidemic spread – reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (b) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely. Each policy has major challenges. We find that that optimal mitigation policies (combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease) might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems (most notably intensive care units) being overwhelmed many times over. For countries able to achieve it, this leaves suppression as the preferred policy option. We show that in the UK and US context, suppression will minimally require a combination of social distancing of the entire population, home isolation of cases and household quarantine of their family members. This may need to be supplemented by school and university closures, though it should be recognised that such closures may have negative impacts on health systems due to increased absenteeism. The major challenge of suppression is that this type of intensive intervention package – or something equivalently effective at reducing transmission – will need to be maintained until a vaccine becomes available (potentially 18 months or more) – given that we predict that transmission will quickly rebound if interventions are relaxed. We show that intermittent social distancing – triggered by trends in disease surveillance – may allow interventions to be relaxed temporarily in relative short time windows, but measures will need to be reintroduced if or when case numbers rebound. Last, while experience in China and now South Korea show that suppression is possible in the short term, it remains to be seen whether it is possible long-term, and whether the social and economic costs of the interventions adopted thus far can be reduced.
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- 2020
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19. Report 8: Symptom progression of COVID-19
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Gaythorpe, K, Imai, N, Cuomo-Dannenburg, G, Baguelin, M, Bhatia, S, Boonyasiri, A, Cori, A, Cucunuba Perez, Z, Dighe, A, Dorigatti, I, Fitzjohn, R, Fu, H, Green, W, Griffin, J, Hamlet, A, Hinsley, W, Hong, N, Kwun, M, Laydon, D, Nedjati Gilani, G, Okell, L, Riley, S, Thompson, H, Van Elsland, S, Verity, R, Volz, E, Walker, P, Wang, H, Wang, Y, Walters, C, Whittaker, C, Winskill, P, Xi, X, Donnelly, C, Ghani, A, Ferguson, N, Medical Research Council (MRC), and The Royal Society
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Coronavirus ,COVID19 ,Symptom - Abstract
The COVID-19 epidemic was declared a Public Health Emergency of International Concern (PHEIC) by WHO on 30th January 2020 [1]. As of 8 March 2020, over 107,000 cases had been reported. Here, we use published and preprint studies of clinical characteristics of cases in mainland China as well as case studies of individuals from Hong Kong, Japan, Singapore and South Korea to examine the proportional occurrence of symptoms and the progression of symptoms through time. We find that in mainland China, where specific symptoms or disease presentation are reported, pneumonia is the most frequently mentioned, see figure 1. We found a more varied spectrum of severity in cases outside mainland China. In Hong Kong, Japan, Singapore and South Korea, fever was the most frequently reported symptom. In this latter group, presentation with pneumonia is not reported as frequently although it is more common in individuals over 60 years old. The average time from reported onset of first symptoms to the occurrence of specific symptoms or disease presentation, such as pneumonia or the use of mechanical ventilation, varied substantially. The average time to presentation with pneumonia is 5.88 days, and may be linked to testing at hospitalisation; fever is often reported at onset (where the mean time to develop fever is 0.77 days).
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- 2020
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20. Report 7: Estimating infection prevalence in Wuhan City from repatriation flights
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Thompson, H, Imai, N, Dighe, A, Baguelin, M, Bhatia, S, Boonyasiri, A, Cori, A, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Dorigatti, I, Fitzjohn, R, Fu, H, Gaythorpe, K, Ghani, A, Green, W, Hamlet, A, Hinsley, W, Laydon, D, Nedjati Gilani, G, Okell, L, Riley, S, Van Elsland, S, Volz, E, Wang, H, Yuanrong, W, Whittaker, C, Xi, X, Donnelly, C, Ferguson, N, and Medical Research Council (MRC)
- Subjects
Coronavirus ,Prevalence ,COVID-19 ,Repatriation flights ,health care economics and organizations - Abstract
Since the end of January 2020, in response to the growing COVID-19 epidemic, 55 countries have repatriated over 8000 citizens from Wuhan City, China. In addition to quarantine measures for returning citizens, many countries implemented PCR screening to test for infection regardless of symptoms. These flights therefore give estimates of infection prevalence in Wuhan over time. Between 30th January and 1st February (close to the peak of the epidemic in Wuhan), infection prevalence was 0.87% (95% CI: 0.32% - 1.89%). As countries now start to repatriate citizens from Iran and northern Italy, information from repatriated citizens could help inform the level of response necessary to help control the outbreaks unfolding in newly affected areas.
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- 2020
21. Report 6: Relative sensitivity of international surveillance
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Bhatia, S, Imai, N, Cuomo-Dannenburg, G, Baguelin, M, Boonyasiri, A, Cori, A, Cucunuba Perez, Z, Dorigatti, I, Fitzjohn, R, Fu, H, Gaythorpe, K, Ghani, A, Hamlet, A, Hinsley, W, Laydon, D, Nedjati Gilani, G, Thompson, H, Okell, L, Riley, S, Van Elsland, S, Volz, E, Wang, H, Wang, Y, Whittaker, C, Xi, X, Donnelly, C, Ferguson, N, and Medical Research Council (MRC)
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Surveillance ,COVID-19 - Abstract
Since the start of the COVID-19 epidemic in late 2019, there are now 29 affected countries with over 1000 confirmed cases outside of mainland China. In previous reports, we estimated the likely epidemic size in Wuhan City based on air traffic volumes and the number of detected cases internationally. Here we analysed COVID-19 cases exported from mainland China to different regions and countries, comparing the country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different countries. Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that about two thirds of COVID-19 cases exported from mainland China have remained undetected worldwide, potentially resulting in multiple chains of as yet undetected human-to-human transmission outside mainland China.
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- 2020
22. Report 5: Phylogenetic analysis of SARS-CoV-2
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Volz, E, Baguelin, M, Bhatia, S, Boonyasiri, A, Cori, A, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Donnelly, C, Dorigatti, I, Fitzjohn, R, Fu, H, Gaythorpe, K, Ghani, A, Hamlet, A, Hinsley, W, Imai, N, Laydon, D, Nedjati Gilani, G, Okell, L, Riley, S, Van Elsland, S, Wang, H, Wang, Y, Xi, X, Ferguson, N, Medical Research Council (MRC), and The Royal Society
- Subjects
Phylogenetics ,COVID-19 - Abstract
Genetic diversity of SARS-CoV-2 (formerly 2019-nCoV), the virus which causes COVID-19, provides information about epidemic origins and the rate of epidemic growth. By analysing 53 SARS-CoV-2 whole genome sequences collected up to February 3, 2020, we find a strong association between the time of sample collection and accumulation of genetic diversity. Bayesian and maximum likelihood phylogenetic methods indicate that the virus was introduced into the human population in early December and has an epidemic doubling time of approximately seven days. Phylodynamic modelling provides an estimate of epidemic size through time. Precise estimates of epidemic size are not possible with current genetic data, but our analyses indicate evidence of substantial heterogeneity in the number of secondary infections caused by each case, as indicated by a high level of over-dispersion in the reproduction number. Larger numbers of more systematically sampled sequences – particularly from across China – will allow phylogenetic estimates of epidemic size and growth rate to be substantially refined.
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- 2020
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23. Report 4: Severity of 2019-novel coronavirus (nCoV)
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Dorigatti, I, Okell, L, Cori, A, Imai, N, Baguelin, M, Bhatia, S, Boonyasiri, A, Cucunuba Perez, Z, Cuomo-Dannenburg, G, Fitzjohn, R, Fu, H, Gaythorpe, K, Hamlet, A, Hinsley, W, Hong, N, Kwun, M, Laydon, D, Nedjati Gilani, G, Riley, S, Van Elsland, S, Volz, E, Wang, H, Walters, C, Xi, X, Donnelly, C, Ghani, A, Ferguson, N, Medical Research Council (MRC), and The Royal Society
- Subjects
CFR ,COVID-19 ,Severity - Abstract
We present case fatality ratio (CFR) estimates for three strata of 2019-nCoV infections. For cases detected in Hubei, we estimate the CFR to be 18% (95% credible interval: 11%-81%). For cases detected in travellers outside mainland China, we obtain central estimates of the CFR in the range 1.2-5.6% depending on the statistical methods, with substantial uncertainty around these central values. Using estimates of underlying infection prevalence in Wuhan at the end of January derived from testing of passengers on repatriation flights to Japan and Germany, we adjusted the estimates of CFR from either the early epidemic in Hubei Province, or from cases reported outside mainland China, to obtain estimates of the overall CFR in all infections (asymptomatic or symptomatic) of approximately 1% (95% confidence interval 0.5%-4%). It is important to note that the differences in these estimates does not reflect underlying differences in disease severity between countries. CFRs seen in individual countries will vary depending on the sensitivity of different surveillance systems to detect cases of differing levels of severity and the clinical care offered to severely ill cases. All CFR estimates should be viewed cautiously at the current time as the sensitivity of surveillance of both deaths and cases in mainland China is unclear. Furthermore, all estimates rely on limited data on the typical time intervals from symptom onset to death or recovery which influences the CFR estimates.
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- 2020
24. Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013 – 2016
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Garske, T, Cori, A, Ariyarajah, A, Blake, I, Dorigatti, I, Eckmanns, T, Fraser, C, Hinsley, W, Jombart, T, Mills, H, Nedjati-Gilani, G, Newton, E, Nouvellet, P, Perkins, D, Riley, S, Schumacher, D, Shah, A, Van Kerkhove, M, Dye, C, Ferguson, N, Donnelly, C, Medical Research Council (MRC), and National Institute for Health Research
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Life Sciences & Biomedicine - Other Topics ,OUTCOMES ,Evolutionary Biology ,Science & Technology ,FEATURES ,CONAKRY ,spatial heterogeneity ,Ebola virus disease ,case fatality ratio ,severity ,11 Medical And Health Sciences ,outlier detection ,PERFORMANCE ,06 Biological Sciences ,mortality ,SIERRA-LEONE ,SURVIVAL ,GUINEA ,EPIDEMIOLOGY ,Life Sciences & Biomedicine ,Biology ,VIRUS DISEASE - Abstract
The 2013–2016 Ebola outbreak in West Africa is the largest on record with 28 616 confirmed, probable and suspected cases and 11 310 deaths officially recorded by 10 June 2016, the true burden probably considerably higher. The case fatality ratio (CFR: proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits, if estimated accurately. We analysed individual-level clinical outcome data from Guinea, Liberia and Sierra Leone officially reported to the World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%) among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalized also played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres (TCs), adjusting for known factors influencing survival and identified eight districts and three TCs with a CFR significantly different from the average. From the current dataset, we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors or was caused by differences in reporting practices or case ascertainment.
- Published
- 2016
25. A ranking of diffusion MRI compartment models with in vivo human brain data
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Ferizi, U, Schneider, T, Panagiotaki, E, Nedjati-Gilani, G, Zhang, H, Wheeler-Kingshott, CA, and Alexander, DC
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microstructure imaging ,Models, Neurological ,Computer Processing and Modeling—Note ,Brain ,Reproducibility of Results ,brain imaging ,Sensitivity and Specificity ,Diffusion ,Nuclear Medicine & Medical Imaging ,Diffusion Magnetic Resonance Imaging ,0903 Biomedical Engineering ,Body Water ,Image Interpretation, Computer-Assisted ,Humans ,Computer Simulation ,white matter - Abstract
Purpose Diffusion magnetic resonance imaging (MRI) microstructure imaging provides a unique noninvasive probe into tissue microstructure. The technique relies on biophysically motivated mathematical models, relating microscopic tissue features to the magnetic resonance (MR) signal. This work aims to determine which compartment models of diffusion MRI are best at describing measurements from in vivo human brain white matter. Methods Recent work shows that three compartment models, designed to capture intra-axonal, extracellular, and isotropically restricted diffusion, best explain multi-b-value data sets from fixed rat corpus callosum. We extend this investigation to in vivo by using a live human subject on a clinical scanner. The analysis compares models of one, two, and three compartments and ranks their ability to explain the measured data. We enhance the original methodology to further evaluate the stability of the ranking. Results As with fixed tissue, three compartment models explain the data best. However, a clearer hierarchical structure and simpler models emerge. We also find that splitting the scanning into shorter sessions has little effect on the ranking of models, and that the results are broadly reproducible across sessions. Conclusion Three compartments are required to explain diffusion MR measurements from in vivo corpus callosum, which informs the choice of model for microstructure imaging applications in the brain. Magn Reson Med 72:1785–1792, 2014. © 2013 The authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance.
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- 2013
26. Ebola Virus Disease among Male and Female Persons in West Africa
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Agua-Agum, J, Ariyarajah, A, Blake, IM, Cori, A, Donnelly, CA, Dorigatti, I, Dye, C, Eck-Manns, T, Ferguson, NM, Fraser, C, Garske, T, Hinsley, W, Jombart, T, Mills, HL, Nedjati-Gilani, G, Newton, E, Nouvellet, P, Perkins, D, Riley, S, Schumacher, D, Shah, A, Thomas, LJ, Van Kerkhove, MD, Medical Research Council (MRC), and National Institute for Health Research
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Male ,0301 basic medicine ,medicine.medical_specialty ,viruses ,MEDLINE ,Disease ,medicine.disease_cause ,Article ,West africa ,03 medical and health sciences ,Medicine, General & Internal ,Sex Factors ,Sex factors ,General & Internal Medicine ,medicine ,Humans ,Ebolavirus ,Science & Technology ,Ebola virus ,business.industry ,11 Medical And Health Sciences ,General Medicine ,Hemorrhagic Fever, Ebola ,Virology ,Hospitalization ,Survival Rate ,Africa, Western ,030104 developmental biology ,Family medicine ,Female ,business ,Life Sciences & Biomedicine ,WHO Ebola Response Team - Abstract
The Ebola virus has caused substantial illness in West Africa during the past 2 years. In this report, potential differences in the burden of illness between male and female persons are investigated.
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- 2016
27. A simple approach to measure transmissibility and forecast incidence
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Nouvellet, P, Cori, A, Garske, T, Blake, I, Dorigatti, I, Hinsley, W, Jombart, T, Mills, H, Nedjati-Gilani, G, Kerkhove, V, Fraser, C, Donnelly, C, Ferguson, N, Riley, S, Wellcome Trust, Medical Research Council (MRC), National Institute for Health Research, and National Institutes of Health
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MCMC ,Epidemiology ,EPIDEMICS ,Branching process ,Microbiology ,Communicable Diseases ,Article ,lcsh:Infectious and parasitic diseases ,EBOLA-VIRUS DISEASE ,Renewal equation ,Virology ,Humans ,lcsh:RC109-216 ,Physics::Atmospheric and Oceanic Physics ,WEST-AFRICA ,Retrospective Studies ,NUMBERS ,Science & Technology ,Incidence ,Public Health, Environmental and Occupational Health ,1103 Clinical Sciences ,Infectious Diseases ,Rapid response ,1117 Public Health And Health Services ,Parasitology ,Life Sciences & Biomedicine ,OUTBREAKS ,Forecasting - Abstract
Highlights • Our simple approach relies on very few parameters and minimal assumptions • Subjective choice of best training period improved forecasts • Despites its simplicity, our model forecasted well under a range scenarios. • This approach can be a natural 'null model' for comparison with methods., Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen “future” simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes – other than the widespread depletion of susceptible individuals – that produce non-exponential patterns of incidence.
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28. Fiber Bundle Segmentation Using Spectral Embedding and Supervised Learning
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Stefan Sunaert, Dorothée Vercruysse, Frederik Maes, Paul Suetens, Daan Christiaens, O'Donnell, L, Nedjati-Gilani, G, Rathi, Y, Reisert, M, and Schneider, T
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business.industry ,Fiber (mathematics) ,Supervised learning ,Pattern recognition ,Overfitting ,Machine learning ,computer.software_genre ,Random forest ,Support vector machine ,PSI_MIC ,Embedding ,Segmentation ,Artificial intelligence ,business ,computer ,Mathematics ,Tractography - Abstract
Diffusion-weighted imaging and tractography offer a unique approach to probe the microarchitecture of brain tissue noninvasively. Whole brain tractography, however, produces an unstructured set of fiber trajectories, whereas clinical applications often demand targeted tracking of specific bundles. This work presents a novel, hybrid approach to fiber bundle segmentation, using spectral embedding and supervised learning. Training data of 20 healthy subjects is labeled with a parcellation-based method, and used to train support vector machine and random forest classifiers. Cross-validation was used to avoid overfitting. Results on testing data of 5 independent subjects show a clear improvement over unsupervised methods. Moreover, estimating the label probabilities allows to reduce the effect of outliers. ispartof: pages:103-114 ispartof: Computational Diffusion MRI vol:39 pages:103-114 ispartof: MICCAI 2014 Workshop on Computational Diffusion MRI - CDMRI 2014 location:Cambridge, MA, USA date:18 Sep - 18 Sep 2014 status: published
- Published
- 2014
29. Estimating the number of undetected COVID-19 cases among travellers from mainland China.
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Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunubá Z, Dorigatti I, FitzJohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly CA, and Ferguson NM
- Abstract
Background: As of August 2021, every region of the world has been affected by the COVID-19 pandemic, with more than 196,000,000 cases worldwide. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that up to 70% (95% CI: 54% - 80%) of imported cases could remain undetected relative to the sensitivity of surveillance in Singapore. The percentage of undetected imported cases rises to 75% (95% CI 66% - 82%) when comparing to the surveillance sensitivity in multiple countries. Conclusions: Our analysis shows that a large number of COVID-19 cases remain undetected across the world. These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China., Competing Interests: No competing interests were disclosed., (Copyright: © 2021 Bhatia S et al.)
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- 2021
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30. The impact of a COVID-19 lockdown on work productivity under good and poor compliance.
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Ohrnberger J, Segal AB, Forchini G, Miraldo M, Skarp J, Nedjati-Gilani G, Laydon DJ, Ghani A, Ferguson NM, and Hauck K
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- Communicable Disease Control, Government, Humans, SARS-CoV-2, COVID-19, Pandemics
- Abstract
Background: In response to the COVID-19 pandemic, governments across the globe have imposed strict social distancing measures. Public compliance to such measures is essential for their success, yet the economic consequences of compliance are unknown. This is the first study to analyze the effects of good compliance compared with poor compliance to a COVID-19 suppression strategy (i.e. lockdown) on work productivity., Methods: We estimate the differences in work productivity comparing a scenario of good compliance with one of poor compliance to the UK government COVID-19 suppression strategy. We use projections of the impact of the UK suppression strategy on mortality and morbidity from an individual-based epidemiological model combined with an economic model representative of the labour force in Wales and England., Results: We find that productivity effects of good compliance significantly exceed those of poor compliance and increase with the duration of the lockdown. After 3 months of the lockdown, work productivity in good compliance is £398.58 million higher compared with that of poor compliance; 75% of the differences is explained by productivity effects due to morbidity and non-health reasons and 25% attributed to avoided losses due to pre-mature mortality., Conclusion: Good compliance to social distancing measures exceeds positive economic effects, in addition to health benefits. This is an important finding for current economic and health policy. It highlights the importance to set clear guidelines for the public, to build trust and support for the rules and if necessary, to enforce good compliance to social distancing measures., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Public Health Association.)
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- 2021
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31. Efficacy profile of the CYD-TDV dengue vaccine revealed by Bayesian survival analysis of individual-level phase III data.
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Laydon DJ, Dorigatti I, Hinsley WR, Nedjati-Gilani G, Coudeville L, and Ferguson NM
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- Adolescent, Bayes Theorem, Child, Child, Preschool, Dengue pathology, Dengue Vaccines adverse effects, Humans, Serogroup, Survival Analysis, Dengue prevention & control, Dengue Vaccines immunology, Dengue Virus classification, Models, Biological
- Abstract
Background: Sanofi-Pasteur's CYD-TDV is the only licensed dengue vaccine. Two phase three trials showed higher efficacy in seropositive than seronegative recipients. Hospital follow-up revealed increased hospitalisation in 2-5- year-old vaccinees, where serostatus and age effects were unresolved., Methods: We fit a survival model to individual-level data from both trials, including year 1 of hospital follow-up. We determine efficacy by age, serostatus, serotype and severity, and examine efficacy duration and vaccine action mechanism., Results: Our modelling indicates that vaccine-induced immunity is long-lived in seropositive recipients, and therefore that vaccinating seropositives gives higher protection than two natural infections. Long-term increased hospitalisation risk outweighs short-lived immunity in seronegatives. Independently of serostatus, transient immunity increases with age, and is highest against serotype 4. Benefit is higher in seropositives, and risk enhancement is greater in seronegatives, against hospitalised disease than against febrile disease., Conclusions: Our results support vaccinating seropositives only. Rapid diagnostic tests would enable viable 'screen-then-vaccinate' programs. Since CYD-TDV acts as a silent infection, long-term safety of other vaccine candidates must be closely monitored., Funding: Bill & Melinda Gates Foundation, National Institute for Health Research, UK Medical Research Council, Wellcome Trust, Royal Society., Clinical Trial Number: NCT01373281 and NCT01374516., Competing Interests: DL, ID, WH, GN, NF No competing interests declared, LC Laurent Coudeville is employed by Sanofi-Pasteur, (© 2021, Laydon et al.)
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- 2021
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32. Reduction in mobility and COVID-19 transmission.
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Nouvellet P, Bhatia S, Cori A, Ainslie KEC, Baguelin M, Bhatt S, Boonyasiri A, Brazeau NF, Cattarino L, Cooper LV, Coupland H, Cucunuba ZM, Cuomo-Dannenburg G, Dighe A, Djaafara BA, Dorigatti I, Eales OD, van Elsland SL, Nascimento FF, FitzJohn RG, Gaythorpe KAM, Geidelberg L, Green WD, Hamlet A, Hauck K, Hinsley W, Imai N, Jeffrey B, Knock E, Laydon DJ, Lees JA, Mangal T, Mellan TA, Nedjati-Gilani G, Parag KV, Pons-Salort M, Ragonnet-Cronin M, Riley S, Unwin HJT, Verity R, Vollmer MAC, Volz E, Walker PGT, Walters CE, Wang H, Watson OJ, Whittaker C, Whittles LK, Xi X, Ferguson NM, and Donnelly CA
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- Algorithms, COVID-19 epidemiology, COVID-19 virology, Communicable Disease Control statistics & numerical data, Global Health, Humans, Models, Theoretical, Physical Distancing, Quarantine methods, SARS-CoV-2 physiology, COVID-19 transmission, Communicable Disease Control methods, Pandemics prevention & control, SARS-CoV-2 isolation & purification
- Abstract
In response to the COVID-19 pandemic, countries have sought to control SARS-CoV-2 transmission by restricting population movement through social distancing interventions, thus reducing the number of contacts. Mobility data represent an important proxy measure of social distancing, and here, we characterise the relationship between transmission and mobility for 52 countries around the world. Transmission significantly decreased with the initial reduction in mobility in 73% of the countries analysed, but we found evidence of decoupling of transmission and mobility following the relaxation of strict control measures for 80% of countries. For the majority of countries, mobility explained a substantial proportion of the variation in transmissibility (median adjusted R-squared: 48%, interquartile range - IQR - across countries [27-77%]). Where a change in the relationship occurred, predictive ability decreased after the relaxation; from a median adjusted R-squared of 74% (IQR across countries [49-91%]) pre-relaxation, to a median adjusted R-squared of 30% (IQR across countries [12-48%]) post-relaxation. In countries with a clear relationship between mobility and transmission both before and after strict control measures were relaxed, mobility was associated with lower transmission rates after control measures were relaxed indicating that the beneficial effects of ongoing social distancing behaviours were substantial.
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- 2021
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33. Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China.
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Fu H, Wang H, Xi X, Boonyasiri A, Wang Y, Hinsley W, Fraser KJ, McCabe R, Olivera Mesa D, Skarp J, Ledda A, Dewé T, Dighe A, Winskill P, van Elsland SL, Ainslie KEC, Baguelin M, Bhatt S, Boyd O, Brazeau NF, Cattarino L, Charles G, Coupland H, Cucunuba ZM, Cuomo-Dannenburg G, Donnelly CA, Dorigatti I, Eales OD, FitzJohn RG, Flaxman S, Gaythorpe KAM, Ghani AC, Green WD, Hamlet A, Hauck K, Haw DJ, Jeffrey B, Laydon DJ, Lees JA, Mellan T, Mishra S, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Ragonnet-Cronin M, Riley S, Schmit N, Thompson HA, Unwin HJT, Verity R, Vollmer MAC, Volz E, Walker PGT, Walters CE, Watson OJ, Whittaker C, Whittles LK, Imai N, Bhatia S, and Ferguson NM
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- COVID-19 prevention & control, China epidemiology, Contact Tracing, Databases, Factual, Humans, COVID-19 epidemiology, SARS-CoV-2
- Abstract
Objectives: In this data collation study, we aimed to provide a comprehensive database describing the epidemic trends and responses during the first wave of coronavirus disease 2019 (COVID-19) throughout the main provinces in China., Methods: From mid-January to March 2020, we extracted publicly available data regarding the spread and control of COVID-19 from 31 provincial health authorities and major media outlets in mainland China. Based on these data, we conducted descriptive analyses of the epidemic in the six most-affected provinces., Results: School closures, travel restrictions, community-level lockdown, and contact tracing were introduced concurrently around late January but subsequent epidemic trends differed among provinces. Compared with Hubei, the other five most-affected provinces reported a lower crude case fatality ratio and proportion of critical and severe hospitalised cases. From March 2020, as the local transmission of COVID-19 declined, switching the focus of measures to the testing and quarantine of inbound travellers may have helped to sustain the control of the epidemic., Conclusions: Aggregated indicators of case notifications and severity distributions are essential for monitoring an epidemic. A publicly available database containing these indicators and information regarding control measures is a useful resource for further research and policy planning in response to the COVID-19 epidemic., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
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- 2021
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34. SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China.
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Thompson HA, Imai N, Dighe A, Ainslie KEC, Baguelin M, Bhatia S, Bhatt S, Boonyasiri A, Boyd O, Brazeau NF, Cattarino L, Cooper LV, Coupland H, Cucunuba Z, Cuomo-Dannenburg G, Djaafara B, Dorigatti I, van Elsland S, FitzJohn R, Fu H, Gaythorpe KAM, Green W, Hallett T, Hamlet A, Haw D, Hayes S, Hinsley W, Jeffrey B, Knock E, Laydon DJ, Lees J, Mangal TD, Mellan T, Mishra S, Mousa A, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Ragonnet-Cronin M, Riley S, Unwin HJT, Verity R, Vollmer M, Volz E, Walker PGT, Walters C, Wang H, Wang Y, Watson OJ, Whittaker C, Whittles LK, Winskill P, Xi X, Donnelly CA, and Ferguson NM
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- COVID-19 Nucleic Acid Testing methods, COVID-19 Nucleic Acid Testing statistics & numerical data, China epidemiology, Epidemiologic Measurements, Humans, International Health Regulations organization & administration, Prevalence, Travel Medicine methods, Travel Medicine trends, United Kingdom epidemiology, Air Travel statistics & numerical data, COVID-19 epidemiology, COVID-19 prevention & control, COVID-19 transmission, Communicable Disease Control methods, Epidemiological Monitoring, SARS-CoV-2 isolation & purification
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- 2020
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35. State-level tracking of COVID-19 in the United States.
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Unwin HJT, Mishra S, Bradley VC, Gandy A, Mellan TA, Coupland H, Ish-Horowicz J, Vollmer MAC, Whittaker C, Filippi SL, Xi X, Monod M, Ratmann O, Hutchinson M, Valka F, Zhu H, Hawryluk I, Milton P, Ainslie KEC, Baguelin M, Boonyasiri A, Brazeau NF, Cattarino L, Cucunuba Z, Cuomo-Dannenburg G, Dorigatti I, Eales OD, Eaton JW, van Elsland SL, FitzJohn RG, Gaythorpe KAM, Green W, Hinsley W, Jeffrey B, Knock E, Laydon DJ, Lees J, Nedjati-Gilani G, Nouvellet P, Okell L, Parag KV, Siveroni I, Thompson HA, Walker P, Walters CE, Watson OJ, Whittles LK, Ghani AC, Ferguson NM, Riley S, Donnelly CA, Bhatt S, and Flaxman S
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- Bayes Theorem, COVID-19 transmission, Humans, Models, Statistical, United States epidemiology, Virus Diseases epidemiology, COVID-19 epidemiology, Pandemics statistics & numerical data
- Abstract
As of 1st June 2020, the US Centres for Disease Control and Prevention reported 104,232 confirmed or probable COVID-19-related deaths in the US. This was more than twice the number of deaths reported in the next most severely impacted country. We jointly model the US epidemic at the state-level, using publicly available death data within a Bayesian hierarchical semi-mechanistic framework. For each state, we estimate the number of individuals that have been infected, the number of individuals that are currently infectious and the time-varying reproduction number (the average number of secondary infections caused by an infected person). We use changes in mobility to capture the impact that non-pharmaceutical interventions and other behaviour changes have on the rate of transmission of SARS-CoV-2. We estimate that R
t was only below one in 23 states on 1st June. We also estimate that 3.7% [3.4%-4.0%] of the total population of the US had been infected, with wide variation between states, and approximately 0.01% of the population was infectious. We demonstrate good 3 week model forecasts of deaths with low error and good coverage of our credible intervals.- Published
- 2020
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36. Response to COVID-19 in South Korea and implications for lifting stringent interventions.
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Dighe A, Cattarino L, Cuomo-Dannenburg G, Skarp J, Imai N, Bhatia S, Gaythorpe KAM, Ainslie KEC, Baguelin M, Bhatt S, Boonyasiri A, Brazeau NF, Cooper LV, Coupland H, Cucunuba Z, Dorigatti I, Eales OD, van Elsland SL, FitzJohn RG, Green WD, Haw DJ, Hinsley W, Knock E, Laydon DJ, Mellan T, Mishra S, Nedjati-Gilani G, Nouvellet P, Pons-Salort M, Thompson HA, Unwin HJT, Verity R, Vollmer MAC, Walters CE, Watson OJ, Whittaker C, Whittles LK, Ghani AC, Donnelly CA, Ferguson NM, and Riley S
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- Bayes Theorem, COVID-19, COVID-19 Testing, Clinical Laboratory Techniques, Contact Tracing trends, Coronavirus Infections diagnosis, Disease Outbreaks prevention & control, Humans, Pneumonia, Viral diagnosis, Quarantine trends, Republic of Korea epidemiology, SARS-CoV-2, Betacoronavirus, Contact Tracing methods, Coronavirus Infections epidemiology, Coronavirus Infections prevention & control, Pandemics prevention & control, Pneumonia, Viral epidemiology, Pneumonia, Viral prevention & control, Quarantine methods
- Abstract
Background: After experiencing a sharp growth in COVID-19 cases early in the pandemic, South Korea rapidly controlled transmission while implementing less stringent national social distancing measures than countries in Europe and the USA. This has led to substantial interest in their "test, trace, isolate" strategy. However, it is important to understand the epidemiological peculiarities of South Korea's outbreak and characterise their response before attempting to emulate these measures elsewhere., Methods: We systematically extracted numbers of suspected cases tested, PCR-confirmed cases, deaths, isolated confirmed cases, and numbers of confirmed cases with an identified epidemiological link from publicly available data. We estimated the time-varying reproduction number, R
t , using an established Bayesian framework, and reviewed the package of interventions implemented by South Korea using our extracted data, plus published literature and government sources., Results: We estimated that after the initial rapid growth in cases, Rt dropped below one in early April before increasing to a maximum of 1.94 (95%CrI, 1.64-2.27) in May following outbreaks in Seoul Metropolitan Region. By mid-June, Rt was back below one where it remained until the end of our study (July 13th). Despite less stringent "lockdown" measures, strong social distancing measures were implemented in high-incidence areas and studies measured a considerable national decrease in movement in late February. Testing the capacity was swiftly increased, and protocols were in place to isolate suspected and confirmed cases quickly; however, we could not estimate the delay to isolation using our data. Accounting for just 10% of cases, individual case-based contact tracing picked up a relatively minor proportion of total cases, with cluster investigations accounting for 66%., Conclusions: Whilst early adoption of testing and contact tracing is likely to be important for South Korea's successful outbreak control, other factors including regional implementation of strong social distancing measures likely also contributed. The high volume of testing and the low number of deaths suggest that South Korea experienced a small epidemic relative to other countries. Caution is needed in attempting to replicate the South Korean response in populations with larger more geographically widespread epidemics where finding, testing, and isolating cases that are linked to clusters may be more difficult.- Published
- 2020
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37. Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment.
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Ainslie KEC, Walters CE, Fu H, Bhatia S, Wang H, Xi X, Baguelin M, Bhatt S, Boonyasiri A, Boyd O, Cattarino L, Ciavarella C, Cucunuba Z, Cuomo-Dannenburg G, Dighe A, Dorigatti I, van Elsland SL, FitzJohn R, Gaythorpe K, Ghani AC, Green W, Hamlet A, Hinsley W, Imai N, Jorgensen D, Knock E, Laydon D, Nedjati-Gilani G, Okell LC, Siveroni I, Thompson HA, Unwin HJT, Verity R, Vollmer M, Walker PGT, Wang Y, Watson OJ, Whittaker C, Winskill P, Donnelly CA, Ferguson NM, and Riley S
- Abstract
Background : The COVID-19 epidemic was declared a Global Pandemic by WHO on 11 March 2020. By 24 March 2020, over 440,000 cases and almost 20,000 deaths had been reported worldwide. In response to the fast-growing epidemic, which began in the Chinese city of Wuhan, Hubei, China imposed strict social distancing in Wuhan on 23 January 2020 followed closely by similar measures in other provinces. These interventions have impacted economic productivity in China, and the ability of the Chinese economy to resume without restarting the epidemic was not clear. Methods : Using daily reported cases from mainland China and Hong Kong SAR, we estimated transmissibility over time and compared it to daily within-city movement, as a proxy for economic activity. Results : Initially, within-city movement and transmission were very strongly correlated in the five mainland provinces most affected by the epidemic and Beijing. However, that correlation decreased rapidly after the initial sharp fall in transmissibility. In general, towards the end of the study period, the correlation was no longer apparent, despite substantial increases in within-city movement. A similar analysis for Hong Kong shows that intermediate levels of local activity were maintained while avoiding a large outbreak. At the very end of the study period, when China began to experience the re-introduction of a small number of cases from Europe and the United States, there is an apparent up-tick in transmission. Conclusions: Although these results do not preclude future substantial increases in incidence, they suggest that after very intense social distancing (which resulted in containment), China successfully exited its lockdown to some degree. Elsewhere, movement data are being used as proxies for economic activity to assess the impact of interventions. The results presented here illustrate how the eventual decorrelation between transmission and movement is likely a key feature of successful COVID-19 exit strategies., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Ainslie KEC et al.)
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- 2020
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38. Potential impact of the COVID-19 pandemic on HIV, tuberculosis, and malaria in low-income and middle-income countries: a modelling study.
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Hogan AB, Jewell BL, Sherrard-Smith E, Vesga JF, Watson OJ, Whittaker C, Hamlet A, Smith JA, Winskill P, Verity R, Baguelin M, Lees JA, Whittles LK, Ainslie KEC, Bhatt S, Boonyasiri A, Brazeau NF, Cattarino L, Cooper LV, Coupland H, Cuomo-Dannenburg G, Dighe A, Djaafara BA, Donnelly CA, Eaton JW, van Elsland SL, FitzJohn RG, Fu H, Gaythorpe KAM, Green W, Haw DJ, Hayes S, Hinsley W, Imai N, Laydon DJ, Mangal TD, Mellan TA, Mishra S, Nedjati-Gilani G, Parag KV, Thompson HA, Unwin HJT, Vollmer MAC, Walters CE, Wang H, Wang Y, Xi X, Ferguson NM, Okell LC, Churcher TS, Arinaminpathy N, Ghani AC, Walker PGT, and Hallett TB
- Subjects
- COVID-19, HIV Infections epidemiology, HIV Infections mortality, Humans, Malaria epidemiology, Malaria mortality, Models, Theoretical, Tuberculosis epidemiology, Tuberculosis mortality, Coronavirus Infections epidemiology, Developing Countries, HIV Infections prevention & control, Health Services Accessibility, Malaria prevention & control, Pandemics, Pneumonia, Viral epidemiology, Tuberculosis prevention & control
- Abstract
Background: COVID-19 has the potential to cause substantial disruptions to health services, due to cases overburdening the health system or response measures limiting usual programmatic activities. We aimed to quantify the extent to which disruptions to services for HIV, tuberculosis, and malaria in low-income and middle-income countries with high burdens of these diseases could lead to additional loss of life over the next 5 years., Methods: Assuming a basic reproduction number of 3·0, we constructed four scenarios for possible responses to the COVID-19 pandemic: no action, mitigation for 6 months, suppression for 2 months, or suppression for 1 year. We used established transmission models of HIV, tuberculosis, and malaria to estimate the additional impact on health that could be caused in selected settings, either due to COVID-19 interventions limiting activities, or due to the high demand on the health system due to the COVID-19 pandemic., Findings: In high-burden settings, deaths due to HIV, tuberculosis, and malaria over 5 years could increase by up to 10%, 20%, and 36%, respectively, compared with if there was no COVID-19 pandemic. The greatest impact on HIV was estimated to be from interruption to antiretroviral therapy, which could occur during a period of high health system demand. For tuberculosis, the greatest impact would be from reductions in timely diagnosis and treatment of new cases, which could result from any prolonged period of COVID-19 suppression interventions. The greatest impact on malaria burden could be as a result of interruption of planned net campaigns. These disruptions could lead to a loss of life-years over 5 years that is of the same order of magnitude as the direct impact from COVID-19 in places with a high burden of malaria and large HIV and tuberculosis epidemics., Interpretation: Maintaining the most critical prevention activities and health-care services for HIV, tuberculosis, and malaria could substantially reduce the overall impact of the COVID-19 pandemic., Funding: Bill & Melinda Gates Foundation, Wellcome Trust, UK Department for International Development, and Medical Research Council., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
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- 2020
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39. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries.
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Walker PGT, Whittaker C, Watson OJ, Baguelin M, Winskill P, Hamlet A, Djafaara BA, Cucunubá Z, Olivera Mesa D, Green W, Thompson H, Nayagam S, Ainslie KEC, Bhatia S, Bhatt S, Boonyasiri A, Boyd O, Brazeau NF, Cattarino L, Cuomo-Dannenburg G, Dighe A, Donnelly CA, Dorigatti I, van Elsland SL, FitzJohn R, Fu H, Gaythorpe KAM, Geidelberg L, Grassly N, Haw D, Hayes S, Hinsley W, Imai N, Jorgensen D, Knock E, Laydon D, Mishra S, Nedjati-Gilani G, Okell LC, Unwin HJ, Verity R, Vollmer M, Walters CE, Wang H, Wang Y, Xi X, Lalloo DG, Ferguson NM, and Ghani AC
- Subjects
- COVID-19, Coronavirus Infections transmission, Humans, Patient Acceptance of Health Care, Pneumonia, Viral transmission, Public Health, Coronavirus Infections epidemiology, Coronavirus Infections prevention & control, Developing Countries, Global Health, Pandemics prevention & control, Pneumonia, Viral epidemiology, Pneumonia, Viral prevention & control, Poverty
- Abstract
The ongoing coronavirus disease 2019 (COVID-19) pandemic poses a severe threat to public health worldwide. We combine data on demography, contact patterns, disease severity, and health care capacity and quality to understand its impact and inform strategies for its control. Younger populations in lower-income countries may reduce overall risk, but limited health system capacity coupled with closer intergenerational contact largely negates this benefit. Mitigation strategies that slow but do not interrupt transmission will still lead to COVID-19 epidemics rapidly overwhelming health systems, with substantial excess deaths in lower-income countries resulting from the poorer health care available. Of countries that have undertaken suppression to date, lower-income countries have acted earlier. However, this will need to be maintained or triggered more frequently in these settings to keep below available health capacity, with associated detrimental consequences for the wider health, well-being, and economies of these countries., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)
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- 2020
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40. Estimating the number of undetected COVID-19 cases among travellers from mainland China.
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Bhatia S, Imai N, Cuomo-Dannenburg G, Baguelin M, Boonyasiri A, Cori A, Cucunubá Z, Dorigatti I, FitzJohn R, Fu H, Gaythorpe K, Ghani A, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Okell L, Riley S, Thompson H, van Elsland S, Volz E, Wang H, Wang Y, Whittaker C, Xi X, Donnelly CA, and Ferguson NM
- Abstract
Background: Since the start of the COVID-19 epidemic in late 2019, there have been more than 152 affected regions and countries with over 110,000 confirmed cases outside mainland China. Methods: We analysed COVID-19 cases among travellers from mainland China to different regions and countries, comparing the region- and country-specific rates of detected and confirmed cases per flight volume to estimate the relative sensitivity of surveillance in different regions and countries. Results: Although travel restrictions from Wuhan City and other cities across China may have reduced the absolute number of travellers to and from China, we estimated that more than two thirds (70%, 95% CI: 54% - 80%, compared to Singapore; 75%, 95% CI: 66% - 82%, compared to multiple countries) of cases exported from mainland China have remained undetected. Conclusions: These undetected cases potentially resulted in multiple chains of human-to-human transmission outside mainland China., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Bhatia S et al.)
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- 2020
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41. Estimates of the severity of coronavirus disease 2019: a model-based analysis.
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Verity R, Okell LC, Dorigatti I, Winskill P, Whittaker C, Imai N, Cuomo-Dannenburg G, Thompson H, Walker PGT, Fu H, Dighe A, Griffin JT, Baguelin M, Bhatia S, Boonyasiri A, Cori A, Cucunubá Z, FitzJohn R, Gaythorpe K, Green W, Hamlet A, Hinsley W, Laydon D, Nedjati-Gilani G, Riley S, van Elsland S, Volz E, Wang H, Wang Y, Xi X, Donnelly CA, Ghani AC, and Ferguson NM
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- Adolescent, Adult, Aged, Aged, 80 and over, Betacoronavirus, COVID-19, Child, Child, Preschool, China epidemiology, Hospitalization statistics & numerical data, Humans, Incidence, Infant, Infant, Newborn, Middle Aged, Models, Statistical, SARS-CoV-2, Young Adult, Coronavirus Infections mortality, Pandemics statistics & numerical data, Pneumonia, Viral mortality
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Background: In the face of rapidly changing data, a range of case fatality ratio estimates for coronavirus disease 2019 (COVID-19) have been produced that differ substantially in magnitude. We aimed to provide robust estimates, accounting for censoring and ascertainment biases., Methods: We collected individual-case data for patients who died from COVID-19 in Hubei, mainland China (reported by national and provincial health commissions to Feb 8, 2020), and for cases outside of mainland China (from government or ministry of health websites and media reports for 37 countries, as well as Hong Kong and Macau, until Feb 25, 2020). These individual-case data were used to estimate the time between onset of symptoms and outcome (death or discharge from hospital). We next obtained age-stratified estimates of the case fatality ratio by relating the aggregate distribution of cases to the observed cumulative deaths in China, assuming a constant attack rate by age and adjusting for demography and age-based and location-based under-ascertainment. We also estimated the case fatality ratio from individual line-list data on 1334 cases identified outside of mainland China. Using data on the prevalence of PCR-confirmed cases in international residents repatriated from China, we obtained age-stratified estimates of the infection fatality ratio. Furthermore, data on age-stratified severity in a subset of 3665 cases from China were used to estimate the proportion of infected individuals who are likely to require hospitalisation., Findings: Using data on 24 deaths that occurred in mainland China and 165 recoveries outside of China, we estimated the mean duration from onset of symptoms to death to be 17·8 days (95% credible interval [CrI] 16·9-19·2) and to hospital discharge to be 24·7 days (22·9-28·1). In all laboratory confirmed and clinically diagnosed cases from mainland China (n=70 117), we estimated a crude case fatality ratio (adjusted for censoring) of 3·67% (95% CrI 3·56-3·80). However, after further adjusting for demography and under-ascertainment, we obtained a best estimate of the case fatality ratio in China of 1·38% (1·23-1·53), with substantially higher ratios in older age groups (0·32% [0·27-0·38] in those aged <60 years vs 6·4% [5·7-7·2] in those aged ≥60 years), up to 13·4% (11·2-15·9) in those aged 80 years or older. Estimates of case fatality ratio from international cases stratified by age were consistent with those from China (parametric estimate 1·4% [0·4-3·5] in those aged <60 years [n=360] and 4·5% [1·8-11·1] in those aged ≥60 years [n=151]). Our estimated overall infection fatality ratio for China was 0·66% (0·39-1·33), with an increasing profile with age. Similarly, estimates of the proportion of infected individuals likely to be hospitalised increased with age up to a maximum of 18·4% (11·0-37·6) in those aged 80 years or older., Interpretation: These early estimates give an indication of the fatality ratio across the spectrum of COVID-19 disease and show a strong age gradient in risk of death., Funding: UK Medical Research Council., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)
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- 2020
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42. A simple approach to measure transmissibility and forecast incidence.
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Nouvellet P, Cori A, Garske T, Blake IM, Dorigatti I, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Van Kerkhove MD, Fraser C, Donnelly CA, Ferguson NM, and Riley S
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- Forecasting, Humans, Incidence, Retrospective Studies, Communicable Diseases epidemiology, Communicable Diseases transmission, Epidemics statistics & numerical data
- Abstract
Outbreaks of novel pathogens such as SARS, pandemic influenza and Ebola require substantial investments in reactive interventions, with consequent implementation plans sometimes revised on a weekly basis. Therefore, short-term forecasts of incidence are often of high priority. In light of the recent Ebola epidemic in West Africa, a forecasting exercise was convened by a network of infectious disease modellers. The challenge was to forecast unseen "future" simulated data for four different scenarios at five different time points. In a similar method to that used during the recent Ebola epidemic, we estimated current levels of transmissibility, over variable time-windows chosen in an ad hoc way. Current estimated transmissibility was then used to forecast near-future incidence. We performed well within the challenge and often produced accurate forecasts. A retrospective analysis showed that our subjective method for deciding on the window of time with which to estimate transmissibility often resulted in the optimal choice. However, when near-future trends deviated substantially from exponential patterns, the accuracy of our forecasts was reduced. This exercise highlights the urgent need for infectious disease modellers to develop more robust descriptions of processes - other than the widespread depletion of susceptible individuals - that produce non-exponential patterns of incidence., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)
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- 2018
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43. Using Wolbachia for Dengue Control: Insights from Modelling.
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Dorigatti I, McCormack C, Nedjati-Gilani G, and Ferguson NM
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- Animals, Dengue transmission, Insect Vectors microbiology, Aedes microbiology, Biological Control Agents, Dengue prevention & control, Dengue Virus physiology, Models, Theoretical, Wolbachia physiology
- Abstract
Dengue is the most common arboviral infection of humans, responsible for a substantial disease burden across the tropics. Traditional insecticide-based vector-control programmes have limited effectiveness, and the one licensed vaccine has a complex and imperfect efficacy profile. Strains of the bacterium Wolbachia, deliberately introduced into Aedes aegyptimosquitoes, have been shown to be able to spread to high frequencies in mosquito populations in release trials, and mosquitoes infected with these strains show markedly reduced vector competence. Thus, Wolbachia represents an exciting potential new form of biocontrol for arboviral diseases, including dengue. Here, we review how mathematical models give insight into the dynamics of the spread of Wolbachia, the potential impact of Wolbachia on dengue transmission, and we discuss the remaining challenges in evaluation and development., (Copyright © 2017 Elsevier Ltd. All rights reserved.)
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- 2018
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44. Heterogeneities in the case fatality ratio in the West African Ebola outbreak 2013-2016.
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Garske T, Cori A, Ariyarajah A, Blake IM, Dorigatti I, Eckmanns T, Fraser C, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Newton E, Nouvellet P, Perkins D, Riley S, Schumacher D, Shah A, Van Kerkhove MD, Dye C, Ferguson NM, and Donnelly CA
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- Guinea epidemiology, Hemorrhagic Fever, Ebola mortality, Humans, Liberia epidemiology, Mortality, Public Health statistics & numerical data, Sierra Leone epidemiology, World Health Organization, Epidemics statistics & numerical data, Hemorrhagic Fever, Ebola epidemiology
- Abstract
The 2013-2016 Ebola outbreak in West Africa is the largest on record with 28 616 confirmed, probable and suspected cases and 11 310 deaths officially recorded by 10 June 2016, the true burden probably considerably higher. The case fatality ratio (CFR: proportion of cases that are fatal) is a key indicator of disease severity useful for gauging the appropriate public health response and for evaluating treatment benefits, if estimated accurately. We analysed individual-level clinical outcome data from Guinea, Liberia and Sierra Leone officially reported to the World Health Organization. The overall mean CFR was 62.9% (95% CI: 61.9% to 64.0%) among confirmed cases with recorded clinical outcomes. Age was the most important modifier of survival probabilities, but country, stage of the epidemic and whether patients were hospitalized also played roles. We developed a statistical analysis to detect outliers in CFR between districts of residence and treatment centres (TCs), adjusting for known factors influencing survival and identified eight districts and three TCs with a CFR significantly different from the average. From the current dataset, we cannot determine whether the observed variation in CFR seen by district or treatment centre reflects real differences in survival, related to the quality of care or other factors or was caused by differences in reporting practices or case ascertainment.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'., (© 2017 The Authors.)
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- 2017
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45. Key data for outbreak evaluation: building on the Ebola experience.
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Cori A, Donnelly CA, Dorigatti I, Ferguson NM, Fraser C, Garske T, Jombart T, Nedjati-Gilani G, Nouvellet P, Riley S, Van Kerkhove MD, Mills HL, and Blake IM
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- Africa, Western epidemiology, Checklist, Epidemics prevention & control, Humans, Public Health, Communicable Diseases, Emerging epidemiology, Communicable Diseases, Emerging prevention & control, Communicable Diseases, Emerging transmission, Communicable Diseases, Emerging virology, Hemorrhagic Fever, Ebola epidemiology, Hemorrhagic Fever, Ebola prevention & control, Hemorrhagic Fever, Ebola transmission, Hemorrhagic Fever, Ebola virology
- Abstract
Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue 'The 2013-2016 West African Ebola epidemic: data, decision-making and disease control'., (© 2017 The Authors.)
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- 2017
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46. Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study.
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Agua-Agum J, Ariyarajah A, Aylward B, Bawo L, Bilivogui P, Blake IM, Brennan RJ, Cawthorne A, Cleary E, Clement P, Conteh R, Cori A, Dafae F, Dahl B, Dangou JM, Diallo B, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Fallah M, Ferguson NM, Fiebig L, Fraser C, Garske T, Gonzalez L, Hamblion E, Hamid N, Hersey S, Hinsley W, Jambei A, Jombart T, Kargbo D, Keita S, Kinzer M, George FK, Godefroy B, Gutierrez G, Kannangarage N, Mills HL, Moller T, Meijers S, Mohamed Y, Morgan O, Nedjati-Gilani G, Newton E, Nouvellet P, Nyenswah T, Perea W, Perkins D, Riley S, Rodier G, Rondy M, Sagrado M, Savulescu C, Schafer IJ, Schumacher D, Seyler T, Shah A, Van Kerkhove MD, Wesseh CS, and Yoti Z
- Subjects
- Guinea epidemiology, Hemorrhagic Fever, Ebola transmission, Hemorrhagic Fever, Ebola virology, Humans, Liberia epidemiology, Retrospective Studies, Risk Factors, Sierra Leone epidemiology, Disease Outbreaks, Ebolavirus physiology, Hemorrhagic Fever, Ebola epidemiology
- Abstract
Background: The ongoing West African Ebola epidemic began in December 2013 in Guinea, probably from a single zoonotic introduction. As a result of ineffective initial control efforts, an Ebola outbreak of unprecedented scale emerged. As of 4 May 2015, it had resulted in more than 19,000 probable and confirmed Ebola cases, mainly in Guinea (3,529), Liberia (5,343), and Sierra Leone (10,746). Here, we present analyses of data collected during the outbreak identifying drivers of transmission and highlighting areas where control could be improved., Methods and Findings: Over 19,000 confirmed and probable Ebola cases were reported in West Africa by 4 May 2015. Individuals with confirmed or probable Ebola ("cases") were asked if they had exposure to other potential Ebola cases ("potential source contacts") in a funeral or non-funeral context prior to becoming ill. We performed retrospective analyses of a case line-list, collated from national databases of case investigation forms that have been reported to WHO. These analyses were initially performed to assist WHO's response during the epidemic, and have been updated for publication. We analysed data from 3,529 cases in Guinea, 5,343 in Liberia, and 10,746 in Sierra Leone; exposures were reported by 33% of cases. The proportion of cases reporting a funeral exposure decreased over time. We found a positive correlation (r = 0.35, p < 0.001) between this proportion in a given district for a given month and the within-district transmission intensity, quantified by the estimated reproduction number (R). We also found a negative correlation (r = -0.37, p < 0.001) between R and the district proportion of hospitalised cases admitted within ≤4 days of symptom onset. These two proportions were not correlated, suggesting that reduced funeral attendance and faster hospitalisation independently influenced local transmission intensity. We were able to identify 14% of potential source contacts as cases in the case line-list. Linking cases to the contacts who potentially infected them provided information on the transmission network. This revealed a high degree of heterogeneity in inferred transmissions, with only 20% of cases accounting for at least 73% of new infections, a phenomenon often called super-spreading. Multivariable regression models allowed us to identify predictors of being named as a potential source contact. These were similar for funeral and non-funeral contacts: severe symptoms, death, non-hospitalisation, older age, and travelling prior to symptom onset. Non-funeral exposures were strongly peaked around the death of the contact. There was evidence that hospitalisation reduced but did not eliminate onward exposures. We found that Ebola treatment units were better than other health care facilities at preventing exposure from hospitalised and deceased individuals. The principal limitation of our analysis is limited data quality, with cases not being entered into the database, cases not reporting exposures, or data being entered incorrectly (especially dates, and possible misclassifications)., Conclusions: Achieving elimination of Ebola is challenging, partly because of super-spreading. Safe funeral practices and fast hospitalisation contributed to the containment of this Ebola epidemic. Continued real-time data capture, reporting, and analysis are vital to track transmission patterns, inform resource deployment, and thus hasten and maintain elimination of the virus from the human population., Competing Interests: The authors have declared that no competing interests exist.
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- 2016
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47. After Ebola in West Africa--Unpredictable Risks, Preventable Epidemics.
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Agua-Agum J, Allegranzi B, Ariyarajah A, Aylward R, Blake IM, Barboza P, Bausch D, Brennan RJ, Clement P, Coffey P, Cori A, Donnelly CA, Dorigatti I, Drury P, Durski K, Dye C, Eckmanns T, Ferguson NM, Fraser C, Garcia E, Garske T, Gasasira A, Gurry C, Hamblion E, Hinsley W, Holden R, Holmes D, Hugonnet S, Jaramillo Gutierrez G, Jombart T, Kelley E, Santhana R, Mahmoud N, Mills HL, Mohamed Y, Musa E, Naidoo D, Nedjati-Gilani G, Newton E, Norton I, Nouvellet P, Perkins D, Perkins M, Riley S, Schumacher D, Shah A, Tang M, Varsaneux O, and Van Kerkhove MD
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- Africa, Western epidemiology, Disaster Planning, Hemorrhagic Fever, Ebola transmission, Humans, Public Health Administration, Ebolavirus, Epidemics, Hemorrhagic Fever, Ebola epidemiology
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- 2016
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48. Ebola Virus Disease among Male and Female Persons in West Africa.
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Agua-Agum J, Ariyarajah A, Blake IM, Cori A, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Ferguson NM, Fraser C, Garske T, Hinsley W, Jombart T, Mills HL, Nedjati-Gilani G, Newton E, Nouvellet P, Perkins D, Riley S, Schumacher D, Shah A, Thomas LJ, and Van Kerkhove MD
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- Africa, Western epidemiology, Ebolavirus, Female, Hemorrhagic Fever, Ebola mortality, Hospitalization, Humans, Male, Sex Factors, Survival Rate, Hemorrhagic Fever, Ebola epidemiology
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- 2016
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49. The role of rapid diagnostics in managing Ebola epidemics.
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Nouvellet P, Garske T, Mills HL, Nedjati-Gilani G, Hinsley W, Blake IM, Van Kerkhove MD, Cori A, Dorigatti I, Jombart T, Riley S, Fraser C, Donnelly CA, and Ferguson NM
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- Africa, Western epidemiology, Humans, Time Factors, Triage, Diagnostic Tests, Routine, Hemorrhagic Fever, Ebola diagnosis, Hemorrhagic Fever, Ebola epidemiology, Hemorrhagic Fever, Ebola prevention & control, Hemorrhagic Fever, Ebola transmission
- Abstract
Ebola emerged in West Africa around December 2013 and swept through Guinea, Sierra Leone and Liberia, giving rise to 27,748 confirmed, probable and suspected cases reported by 29 July 2015. Case diagnoses during the epidemic have relied on polymerase chain reaction-based tests. Owing to limited laboratory capacity and local transport infrastructure, the delays from sample collection to test results being available have often been 2 days or more. Point-of-care rapid diagnostic tests offer the potential to substantially reduce these delays. We review Ebola rapid diagnostic tests approved by the World Health Organization and those currently in development. Such rapid diagnostic tests could allow early triaging of patients, thereby reducing the potential for nosocomial transmission. In addition, despite the lower test accuracy, rapid diagnostic test-based diagnosis may be beneficial in some contexts because of the reduced time spent by uninfected individuals in health-care settings where they may be at increased risk of infection; this also frees up hospital beds. We use mathematical modelling to explore the potential benefits of diagnostic testing strategies involving rapid diagnostic tests alone and in combination with polymerase chain reaction testing. Our analysis indicates that the use of rapid diagnostic tests with sensitivity and specificity comparable with those currently under development always enhances control, whether evaluated at a health-care-unit or population level. If such tests had been available throughout the recent epidemic, we estimate, for Sierra Leone, that their use in combination with confirmatory polymerase chain-reaction testing might have reduced the scale of the epidemic by over a third.
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- 2015
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50. Ebola virus disease among children in West Africa.
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Agua-Agum J, Ariyarajah A, Blake IM, Cori A, Donnelly CA, Dorigatti I, Dye C, Eckmanns T, Ferguson NM, Fowler RA, Fraser C, Garske T, Hinsley W, Jombart T, Mills HL, Murthy S, Nedjati Gilani G, Nouvellet P, Pelletier L, Riley S, Schumacher D, Shah A, and Van Kerkhove MD
- Subjects
- Adolescent, Adult, Africa, Western epidemiology, Age Factors, Child, Child, Preschool, Disease Progression, Epidemics, Female, Humans, Incidence, Infant, Infant, Newborn, Male, Cost of Illness, Hemorrhagic Fever, Ebola epidemiology, Hemorrhagic Fever, Ebola mortality, Infectious Disease Incubation Period
- Published
- 2015
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