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Abstract Background A protracted Ebola Virus Disease (EVD) epidemic in the eastern Ituri, North and South Kivu provinces of the Democratic Republic of Congo (DRC) caused 3470 confirmed and probable cases between July 2018 and April 2020. During the epidemic, the International Federation of Red Cross and Red Crescent Societies (IFRC) supported the DRC Red Cross and other local actors to offer safe and dignified burials (SDB) for suspected and confirmed EVD cases, so as to reduce transmission associated with infectious dead bodies. We conducted a retrospective cohort study of the SDB service’s performance in order to inform future applications of this intervention. Methods We analysed data on individual SDB responses to quantify performance based on key indicators and against pre-specified service standards. Specifically, we defined SDB timeliness as response within 24 h and success as all components of the service being implemented. Combining the database with other information sources, we also fit generalised linear mixed binomial models to explore factors associated with unsuccessful SDB. Results Out of 14,624 requests for SDB, 99% were responded to, 89% within 24 h. Overall, 61% of SDBs were successful, somewhat below target (80%), with failures clustered during a high-insecurity period. Factors associated with increased odds of unsuccessful SDB included reported community and/or family nonacceptance, insecurity and suspensions of the EVD response, low health facility coverage and high coverage of radio and telephony. Burials supported by mobile Civil Protection (local authorities) and/or static, community-based ‘harm reduction’ teams were associated with lower odds of failure. Conclusions A large-scale, timely and moderately performant SDB service proved feasible during the challenging eastern DRC EVD response. Burial teams that are managed by community actors and operate locally, and supported rather than owned by the Red Cross or other humanitarian organisations, are a promising modality of delivering this pillar of EVD control.

Background: A protracted Ebola Virus Disease (EVD) epidemic in the eastern Ituri, North and South Kivu provinces of the Democratic Republic of Congo (DRC) caused 3470 confirmed and probable cases between July 2018 and April 2020. During the epidemic, the International Federation of Red Cross and Red Crescent Societies (IFRC) supported the DRC Red Cross and other local actors to offer safe and dignified burials (SDB) for suspected and confirmed EVD cases, so as to reduce transmission associated with infectious dead bodies. We conducted a retrospective cohort study of the SDB service's performance in order to inform future applications of this intervention. Methods: We analysed data on individual SDB responses to quantify performance based on key indicators and against pre-specified service standards. Specifically, we defined SDB timeliness as response within 24 h and success as all components of the service being implemented. Combining the database with other information sources, we also fit generalised linear mixed binomial models to explore factors associated with unsuccessful SDB. Results: Out of 14,624 requests for SDB, 99% were responded to, 89% within 24 h. Overall, 61% of SDBs were successful, somewhat below target (80%), with failures clustered during a high-insecurity period. Factors associated with increased odds of unsuccessful SDB included reported community and/or family nonacceptance, insecurity and suspensions of the EVD response, low health facility coverage and high coverage of radio and telephony. Burials supported by mobile Civil Protection (local authorities) and/or static, community-based 'harm reduction' teams were associated with lower odds of failure. Conclusions: A large-scale, timely and moderately performant SDB service proved feasible during the challenging eastern DRC EVD response. Burial teams that are managed by community actors and operate locally, and supported rather than owned by the Red Cross or other humanitarian organisations, are a promising modality of delivering this pillar of EVD control. [ABSTRACT FROM AUTHOR]

Abstract Background Epidemics of infectious disease occur frequently in low-income and humanitarian settings and pose a serious threat to populations. However, relatively little is known about responses to these epidemics. Robust evaluations can generate evidence on response efforts and inform future improvements. This systematic review aimed to (i) identify epidemics reported in low-income and crisis settings, (ii) determine the frequency with which evaluations of responses to these epidemics were conducted, (iii) describe the main typologies of evaluations undertaken and (iv) identify key gaps and strengths of recent evaluation practice. Methods Reported epidemics were extracted from the following sources: World Health Organization Disease Outbreak News (WHO DON), UNICEF Cholera platform, Reliefweb, PROMED and Global Incidence Map. A systematic review for evaluation reports was conducted using the MEDLINE, EMBASE, Global Health, Web of Science, WPRIM, Reliefweb, PDQ Evidence and CINAHL Plus databases, complemented by grey literature searches using Google and Google Scholar. Evaluation records were quality-scored and linked to epidemics based on time and place. The time period for the review was 2010–2019. Results A total of 429 epidemics were identified, primarily in sub-Saharan Africa, the Middle East and Central Asia. A total of 15,424 potential evaluations records were screened, 699 assessed for eligibility and 132 included for narrative synthesis. Only one tenth of epidemics had a corresponding response evaluation. Overall, there was wide variability in the quality, content as well as in the disease coverage of evaluation reports. Conclusion The current state of evaluations of responses to these epidemics reveals large gaps in coverage and quality and bears important implications for health equity and accountability to affected populations. The limited availability of epidemic response evaluations prevents improvements to future public health response. The diversity of emphasis and methods of available evaluations limits comparison across responses and time. In order to improve future response and save lives, there is a pressing need to develop a standardized and practical approach as well as governance arrangements to ensure the systematic conduct of epidemic response evaluations in low-income and crisis settings.

Abstract Background Since the first cluster of cases was identified in Wuhan City, China, in December 2019, coronavirus disease 2019 (COVID-19) rapidly spreads globally. Scientists have made strides in estimating key transmission and epidemiological parameters. In particular, accumulating evidence points to a substantial fraction of asymptomatic or subclinical infections, which influences our understanding of the transmission potential and severity of this emerging disease. In this study, we derive estimates of the transmissibility and virulence of COVID-19 in Wuhan City, China, by reconstructing the underlying transmission dynamics using multiple data sources. Methods We employ statistical methods and publicly available epidemiological datasets to jointly derive estimates of transmissibility and severity associated with the novel coronavirus. For this purpose, the daily series of laboratory-confirmed COVID-19 cases and deaths in Wuhan City together with epidemiological data of Japanese repatriated from Wuhan City on board government-chartered flights were integrated into our analysis. Results Our posterior estimates of basic reproduction number (R) in Wuhan City, China, in 2019–2020 reached values at 3.49 (95% CrI 3.39–3.62) with a mean serial interval of 6.0 days, and the enhanced public health intervention after January 23 in 2020 was associated with a significantly reduced R at 0.84 (95% CrI 0.81–0.88), with the total number of infections (i.e., cumulative infections) estimated at 1,906,634 (95% CrI 1,373,500–2,651,124) in Wuhan City, elevating the overall proportion of infected individuals to 19.1% (95% CrI 13.5–26.6%). We also estimated the most recent crude infection fatality ratio (IFR) and time–delay adjusted IFR at 0.04% (95% CrI 0.03–0.06%) and 0.12% (95% CrI 0.08–0.17%), respectively, estimates that are substantially smaller than the crude CFR estimated at 4.06%. Conclusions We have estimated key epidemiological parameters of the transmissibility and virulence of COVID-19 in Wuhan, China, during January–February 2020 using an ecological modeling approach that is suitable to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance systems. Our estimate of time–delay adjusted IFR falls in the range of the median IFR estimates based on multiple serological studies conducted in several areas of the world.

Background: COVID-19 spread may have a dramatic impact in countries with vulnerable economies and limited availability of, and access to, healthcare resources and infrastructures. However, in sub-Saharan Africa, a low prevalence and mortality have been observed so far.Methods: We collected data on individuals' social contacts in the South West Shewa Zone (SWSZ) of Ethiopia across geographical contexts characterized by heterogeneous population density, work and travel opportunities, and access to primary care. We assessed how socio-demographic factors and observed mixing patterns can influence the COVID-19 disease burden, by simulating SARS-CoV-2 transmission in remote settlements, rural villages, and urban neighborhoods, under school closure mandate.Results: From national surveillance data, we estimated a net reproduction number of 1.62 (95% CI 1.55-1.70). We found that, at the end of an epidemic mitigated by school closure alone, 10-15% of the population residing in the SWSZ would have been symptomatic and 0.3-0.4% of the population would require mechanical ventilation and/or possibly result in a fatal outcome. Higher infection attack rates are expected in more urbanized areas, but the highest incidence of critical disease is expected in remote subsistence farming settlements. School closure contributed to reduce the reproduction number by 49% and the attack rate of infections by 28-34%.Conclusions: Our results suggest that the relatively low burden of COVID-19 in Ethiopia observed so far may depend on social mixing patterns, underlying demography, and the enacted school closures. Our findings highlight that socio-demographic factors can also determine marked heterogeneities across different geographical contexts within the same region, and they contribute to understand why sub-Saharan Africa is experiencing a relatively lower attack rate of severe cases compared to high-income countries. [ABSTRACT FROM AUTHOR]

Abstract Background Zika virus (ZIKV) emerged in Latin America and the Caribbean (LAC) region in 2013, with serious implications for population health in the region. In 2016, the World Health Organization declared the ZIKV outbreak a Public Health Emergency of International Concern following a cluster of associated neurological disorders and neonatal malformations. In 2017, Zika cases declined, but future incidence in LAC remains uncertain due to gaps in our understanding, considerable variation in surveillance and the lack of a comprehensive collation of data from affected countries. Methods Our analysis combines information on confirmed and suspected Zika cases across LAC countries and a spatio-temporal dynamic transmission model for ZIKV infection to determine key transmission parameters and projected incidence in 90 major cities within 35 countries. Seasonality was determined by spatio-temporal estimates of Aedes aegypti vectorial capacity. We used country and state-level data from 2015 to mid-2017 to infer key model parameters, country-specific disease reporting rates, and the 2018 projected incidence. A 10-fold cross-validation approach was used to validate parameter estimates to out-of-sample epidemic trajectories. Results There was limited transmission in 2015, but in 2016 and 2017 there was sufficient opportunity for wide-spread ZIKV transmission in most cities, resulting in the depletion of susceptible individuals. We predict that the highest number of cases in 2018 would present within some Brazilian States (Sao Paulo and Rio de Janeiro), Colombia and French Guiana, but the estimated number of cases were no more than a few hundred. Model estimates of the timing of the peak in incidence were correlated (p

Abstract Background Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data. Methods We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data. Results We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting. Conclusions Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.

Abstract Background Refugees may have an increased vulnerability to infectious diseases, and the consequences of an outbreak are more severe in a refugee camp. When an outbreak is suspected, access to clinical information is critical for investigators to verify that an outbreak is occurring, to determine the cause and to select interventions to control it. Experience from previous outbreaks suggests that the accuracy and completeness of this information is poor. This study is the first to assess the adequacy of clinical characterisation of acute medical illnesses in refugee camps. The objective is to direct improvements in outbreak identification and management in this vulnerable setting. Methods We collected prospective data in 13 refugee camps in Greece. We passively observed consultations where patients presented with syndromes that might warrant inclusion into an existing syndromic surveillance system and then undertook a structured assessment of routine clinical data collection to examine the extent to which key clinical parameters required for an outbreak response were ascertained and then documented. Results A total of 528 patient consultations were included. The most common presenting condition was an acute respiratory illness. Clinicians often made a comprehensive clinical assessment, especially for common syndromes of respiratory and gastrointestinal conditions, but documented their findings less frequently. For fewer than 5% of patients were a full set of vital signs ascertained and so the severity of patient illnesses was largely unknown. In only 11% of consultations was it verified that a patient who met the case criteria for syndromic surveillance reporting based on an independent assessment was reported into the system. Discussion Opportunities exist to strengthen clinical data capture and recording in refugee camps, which will produce a better calibrated and directed public health response. Conclusion Information of significant utility for outbreak response is collected at the clinical interface and we recommend improving how this information is recorded and linked into surveillance systems.

Background: Epidemics of infectious disease occur frequently in low-income and humanitarian settings and pose a serious threat to populations. However, relatively little is known about responses to these epidemics. Robust evaluations can generate evidence on response efforts and inform future improvements. This systematic review aimed to (i) identify epidemics reported in low-income and crisis settings, (ii) determine the frequency with which evaluations of responses to these epidemics were conducted, (iii) describe the main typologies of evaluations undertaken and (iv) identify key gaps and strengths of recent evaluation practice.Methods: Reported epidemics were extracted from the following sources: World Health Organization Disease Outbreak News (WHO DON), UNICEF Cholera platform, Reliefweb, PROMED and Global Incidence Map. A systematic review for evaluation reports was conducted using the MEDLINE, EMBASE, Global Health, Web of Science, WPRIM, Reliefweb, PDQ Evidence and CINAHL Plus databases, complemented by grey literature searches using Google and Google Scholar. Evaluation records were quality-scored and linked to epidemics based on time and place. The time period for the review was 2010-2019.Results: A total of 429 epidemics were identified, primarily in sub-Saharan Africa, the Middle East and Central Asia. A total of 15,424 potential evaluations records were screened, 699 assessed for eligibility and 132 included for narrative synthesis. Only one tenth of epidemics had a corresponding response evaluation. Overall, there was wide variability in the quality, content as well as in the disease coverage of evaluation reports.Conclusion: The current state of evaluations of responses to these epidemics reveals large gaps in coverage and quality and bears important implications for health equity and accountability to affected populations. The limited availability of epidemic response evaluations prevents improvements to future public health response. The diversity of emphasis and methods of available evaluations limits comparison across responses and time. In order to improve future response and save lives, there is a pressing need to develop a standardized and practical approach as well as governance arrangements to ensure the systematic conduct of epidemic response evaluations in low-income and crisis settings. [ABSTRACT FROM AUTHOR]

Background: Since the first cluster of cases was identified in Wuhan City, China, in December 2019, coronavirus disease 2019 (COVID-19) rapidly spreads globally. Scientists have made strides in estimating key transmission and epidemiological parameters. In particular, accumulating evidence points to a substantial fraction of asymptomatic or subclinical infections, which influences our understanding of the transmission potential and severity of this emerging disease. In this study, we derive estimates of the transmissibility and virulence of COVID-19 in Wuhan City, China, by reconstructing the underlying transmission dynamics using multiple data sources.Methods: We employ statistical methods and publicly available epidemiological datasets to jointly derive estimates of transmissibility and severity associated with the novel coronavirus. For this purpose, the daily series of laboratory-confirmed COVID-19 cases and deaths in Wuhan City together with epidemiological data of Japanese repatriated from Wuhan City on board government-chartered flights were integrated into our analysis.Results: Our posterior estimates of basic reproduction number (R) in Wuhan City, China, in 2019-2020 reached values at 3.49 (95% CrI 3.39-3.62) with a mean serial interval of 6.0 days, and the enhanced public health intervention after January 23 in 2020 was associated with a significantly reduced R at 0.84 (95% CrI 0.81-0.88), with the total number of infections (i.e., cumulative infections) estimated at 1,906,634 (95% CrI 1,373,500-2,651,124) in Wuhan City, elevating the overall proportion of infected individuals to 19.1% (95% CrI 13.5-26.6%). We also estimated the most recent crude infection fatality ratio (IFR) and time-delay adjusted IFR at 0.04% (95% CrI 0.03-0.06%) and 0.12% (95% CrI 0.08-0.17%), respectively, estimates that are substantially smaller than the crude CFR estimated at 4.06%.Conclusions: We have estimated key epidemiological parameters of the transmissibility and virulence of COVID-19 in Wuhan, China, during January-February 2020 using an ecological modeling approach that is suitable to infer epidemiological parameters with quantified uncertainty from partial observations collected by surveillance systems. Our estimate of time-delay adjusted IFR falls in the range of the median IFR estimates based on multiple serological studies conducted in several areas of the world. [ABSTRACT FROM AUTHOR]

Background: Emerging and epidemic infectious disease outbreaks are a significant public health problem and global health security threat. As an outbreak begins, epidemiological investigations and traditional public health responses are generally mounted very quickly. However, patient-centred research is usually not prioritised when planning and enacting the response. Instead, the clinical research response occurs subsequent to and separate from the public health response, and is inadequate for evidence-based decision-making at the bedside or in the offices of public health policymakers. Discussion: The deficiencies of the clinical research response to severe acute respiratory syndrome, pandemic influenza, Middle East respiratory syndrome coronavirus and Ebola virus demonstrate that current research models do not adequately inform and improve the quality of clinical care or public health response. Three suggestions for improvements are made. First, integrate the data and sample collection needs for clinical and public health decision-making within a unified framework, combined with a risk-based, rather than a discipline-based, approach to ethical review and consent. Second, develop clinical study methods and tools that are specifically designed to meet the epidemiological and contextual challenges of emerging and epidemic infectious diseases. Third, invest in investigator-led clinical research networks that are primed and incentivised to respond to outbreak infections, and which can call on the support and resources of a central centre of excellence. Conclusions: It is crucial that the field of epidemic science matures to place patients at the heart of the response. This can only be achieved when patient-centred research is integrated in the outbreak response from day one and practical steps are taken to reduce the barriers to the generation of reliable and useful evidence. [ABSTRACT FROM AUTHOR]

We discuss models for rapidly disseminating infectious diseases during mass gatherings (MGs), using influenza as a case study. Recent innovations in modeling and forecasting influenza transmission dynamics at local, regional, and global scales have made influenza a particularly attractive model scenario for MG. We discuss the behavioral, medical, and population factors for modeling MG disease transmission, review existing model formulations, and highlight key data and modeling gaps related to modeling MG disease transmission. We argue that the proposed improvements will help integrate infectious-disease models in MG health contingency plans in the near future, echoing modeling efforts that have helped shape influenza pandemic preparedness plans in recent years. [ABSTRACT FROM AUTHOR]

The number of reports in the literature on dengue outbreaks in various parts of south China is increasing. This trend is likely contributed to by multiple factors, chief among which is the increase in trade and human movement in and out of China from the Southeast Asian region where dengue is firmly endemic. However, a holistic picture of dengue in China and how the public health authorities are responding to this global health challenge has been missing. In a research article published in, Lai have now filled this gap in knowledge by BMC Medicine et al. analysing statutorily mandated national dengue surveillance data from 1990 till 2014. They also conducted time series analyses to identify key drivers of dengue transmission in south China as well as from south China to the other parts of this vast and populous country. Their findings, as well as the description of surveillance and disease control activities in China, highlight urgent steps that need to be taken if China wishes to prevent itself from becoming another country that experiences large and frequent cycles of epidemic dengue. [ABSTRACT FROM AUTHOR]

Abstract Providing valid and reliable estimates of the transmissibility and severity of pandemic influenza in real time is key to guide public health policymaking. In particular, early estimates of the transmissibility are indispensable for determining the type and intensity of interventions. A recent study by House and colleagues in BMC Medicine devised a stochastic transmission model to estimate the unbiased risk of transmission within households, applying the method to datasets of the 2009 A/H1N1 influenza pandemic. Here, we discuss future challenges in household transmission studies and underscore the need to systematically collect epidemiological data to decipher the household transmission dynamics. We emphasize the need to consider three critical issues for future improvements: (i) capturing age-dependent heterogeneity within households calls for intensive modeling efforts, (ii) the timeline of observation during the course of an epidemic and the length of follow-up should be aligned with study objectives, and (iii) the use of laboratory methods, especially molecular techniques, is encouraged to distinguish household transmissions from those arising in the community. See related article: http://www.biomedcentral.com/1741-7015/10/117

Background: Zika virus (ZIKV) emerged in Latin America and the Caribbean (LAC) region in 2013, with serious implications for population health in the region. In 2016, the World Health Organization declared the ZIKV outbreak a Public Health Emergency of International Concern following a cluster of associated neurological disorders and neonatal malformations. In 2017, Zika cases declined, but future incidence in LAC remains uncertain due to gaps in our understanding, considerable variation in surveillance and the lack of a comprehensive collation of data from affected countries.Methods: Our analysis combines information on confirmed and suspected Zika cases across LAC countries and a spatio-temporal dynamic transmission model for ZIKV infection to determine key transmission parameters and projected incidence in 90 major cities within 35 countries. Seasonality was determined by spatio-temporal estimates of Aedes aegypti vectorial capacity. We used country and state-level data from 2015 to mid-2017 to infer key model parameters, country-specific disease reporting rates, and the 2018 projected incidence. A 10-fold cross-validation approach was used to validate parameter estimates to out-of-sample epidemic trajectories.Results: There was limited transmission in 2015, but in 2016 and 2017 there was sufficient opportunity for wide-spread ZIKV transmission in most cities, resulting in the depletion of susceptible individuals. We predict that the highest number of cases in 2018 would present within some Brazilian States (Sao Paulo and Rio de Janeiro), Colombia and French Guiana, but the estimated number of cases were no more than a few hundred. Model estimates of the timing of the peak in incidence were correlated (p < 0.05) with the reported peak in incidence. The reporting rate varied across countries, with lower reporting rates for those with only confirmed cases compared to those who reported both confirmed and suspected cases.Conclusions: The findings suggest that the ZIKV epidemic is by and large over within LAC, with incidence projected to be low in most cities in 2018. Local low levels of transmission are probable, but the estimated rate of infection suggests that most cities have a population with high levels of herd immunity. [ABSTRACT FROM AUTHOR]

Background: Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data.Methods: We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data.Results: We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting.Conclusions: Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns. [ABSTRACT FROM AUTHOR]