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51. Supplement to: West African Ebola epidemic after one year — slowing but not yet under control.

Author

Agua-Agum, Junerlyn, Ariyarajah, Archchun, Aylward, Bruce, Blake, Isobel M., Brennan, Richard, Cori, Anne, Donnelly, Christl A, Dorigatti, Ilaria, Dye, Christopher, Eckmanns, Tim, Ferguson, Neil M, Formenty, Pierre, Fraser, Christophe, Garcia, Erika, Garske, Tini, Hinsley, Wes, Holmes, David, Hugonnet, Stéphane, Iyengar, Swathi, Jombart, Thibaut, Krishnan, Ravi, Meijers, Sascha, Mills, Harriet L., Mohamed, Yasmine, Nedjati-Gilani, Gemma, Newton, Emily, Nouvellet, Pierre, Pelletier, Louise, Perkins, Devin, Riley, Steven, Sagrado, Maria, Schnitzler, Johannes, Schumacher, Dirk, Shah, Anita, Van Kerkhove, Maria D, Varsaneux, Olivia, and Kannangarage, Niluka Wijekoon

Published
2015

53. Rapid SARS-CoV-2 Intra-Host and Within-Household Emergence of Novel Haplotypes

Author

Manuto, Laura, primary, Grazioli, Marco, additional, Spitaleri, Andrea, additional, Fontana, Paolo, additional, Bianco, Luca, additional, Bertolotti, Luigi, additional, Bado, Martina, additional, Mazzotti, Giorgia, additional, Bianca, Federico, additional, Onelia, Francesco, additional, Lorenzin, Giovanni, additional, Simeoni, Fabio, additional, Lazarevic, Dejan, additional, Franchin, Elisa, additional, Vecchio, Claudia Del, additional, Dorigatti, Ilaria, additional, Tonon, Giovanni, additional, Cirillo, Daniela Maria, additional, Lavezzo, Enrico, additional, Crisanti, Andrea, additional, and Toppo, Stefano, additional

Published
2022
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54. Rapid longitudinal SARS-CoV-2 intra-host emergence of novel haplotypes regardless of immune deficiencies

Author

Manuto, Laura, primary, Grazioli, Marco, additional, Spitaleri, Andrea, additional, Fontana, Paolo, additional, Bianco, Luca, additional, Bertolotti, Luigi, additional, Bado, Martina, additional, Mazzotti, Giorgia, additional, Bianca, Federico, additional, Onelia, Francesco, additional, Lorenzin, Giovanni, additional, Simeoni, Fabio, additional, Lazarevic, Dejan, additional, Franchin, Elisa, additional, Del Vecchio, Claudia, additional, Dorigatti, Ilaria, additional, Tonon, Giovanni, additional, Cirillo, daniela, additional, Lavezzo, Enrico, additional, Crisanti, Andrea, additional, and Toppo, Stefano, additional

Published
2021
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55. Estimating the number of undetected COVID-19 cases among travellers from mainland China

Author

Bhatia, Sangeeta, primary, Imai, Natsuko, additional, Cuomo-Dannenburg, Gina, additional, Baguelin, Marc, additional, Boonyasiri, Adhiratha, additional, Cori, Anne, additional, Cucunubá, Zulma, additional, Dorigatti, Ilaria, additional, FitzJohn, Rich, additional, Fu, Han, additional, Gaythorpe, Katy, additional, Ghani, Azra, additional, Hamlet, Arran, additional, Hinsley, Wes, additional, Laydon, Daniel, additional, Nedjati-Gilani, Gemma, additional, Okell, Lucy, additional, Riley, Steven, additional, Thompson, Hayley, additional, van Elsland, Sabine, additional, Volz, Erik, additional, Wang, Haowei, additional, Wang, Yuanrong, additional, Whittaker, Charles, additional, Xi, Xiaoyue, additional, Donnelly, Christl A., additional, and Ferguson, Neil M., additional

Published
2021
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64. Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’

Author

Lavezzo, Enrico, Franchin, Elisa, Ciavarella, Constanze, Cuomo-Dannenburg, Gina, Barzon, Luisa, Del Vecchio, Claudia, Rossi, Lucia, Manganelli, Riccardo, Loregian, Arianna, Navarin, Nicolò, Abate, Davide, Sciro, Manuela, Merigliano, Stefano, De Canale, Ettore, Vanuzzo, Maria Cristina, Besutti, Valeria, Saluzzo, Francesca, Onelia, Francesco, Pacenti, Monia, Parisi, Saverio G., Carretta, Giovanni, Donato, Daniele, Flor, Luciano, Cocchio, Silvia, Masi, Giulia, Sperduti, Alessandro, Cattarino, Lorenzo, Salvador, Renato, Nicoletti, Michele, Caldart, Federico, Castelli, Gioele, Nieddu, Eleonora, Labella, Beatrice, Fava, Ludovico, Drigo, Matteo, Gaythorpe, Katy A. M., Brazzale, Alessandra R., Toppo, Stefano, Trevisan, Marta, Baldo, Vincenzo, Donnelly, Christl A., Ferguson, Neil M., Dorigatti, Ilaria, Crisanti, Andrea, Ainslie, Kylie E. C., Baguelin, Marc, Bhatt, Samir, Boonyasiri, Adhiratha, Boyd, Olivia, Coupland, Helen L., Cucunubá, Zulma, Djafaara, Bimandra A., van Elsland, Sabine L., FitzJohn, Rich, Flaxman, Seth, Green, Will D., Hallett, Timothy, Hamlet, Arran, Haw, David, Imai, Natsuko, Jeffrey, Benjamin, Knock, Edward, Laydon, Daniel J., Mellan, Thomas, Mishra, Swapnil, Nedjati-Gilani, Gemma, Nouvellet, Pierre, Okell, Lucy C., Parag, Kris V., Riley, Steven, Thompson, Hayley A., Unwin, H. Juliette T., Verity, Robert, Vollmer, Michaela A. C., Walker, Patrick G. T., Walters, Caroline E., Wang, Haowei, Wang, Yuanrong, Watson, Oliver J., Whittaker, Charles, Whittles, Lilith K., Xi, Xiaoyue, Medical Research Council (MRC), Medical Research Council, The Royal Society, Bill & Melinda Gates Foundation, Imperial College Healthcare NHS Trust- BRC Funding, The Academy of Medical Sciences, National Institute for Health Research, UK Research and Innovation, and Wellcome Trust

Subjects
0301 basic medicine, Male, CORONAVIRUS, Viral Nonstructural Proteins, medicine.disease_cause, Disease Outbreaks, 0302 clinical medicine, Viral Envelope Proteins, Prevalence, 030212 general & internal medicine, Child, Asymptomatic Infections, Coronavirus, Aged, 80 and over, education.field_of_study, Coronavirus RNA-Dependent RNA Polymerase, Multidisciplinary, Middle Aged, Viral Load, 3. Good health, Multidisciplinary Sciences, Policy, Italy, Child, Preschool, Science & Technology - Other Topics, Female, medicine.symptom, Coronavirus Infections, Viral load, Serial interval, Adult, medicine.medical_specialty, Adolescent, General Science & Technology, Population, Pneumonia, Viral, Policy and public health in microbiology, Asymptomatic, 03 medical and health sciences, Betacoronavirus, Coronavirus Envelope Proteins, Young Adult, Internal medicine, medicine, Imperial College COVID-19 Response Team, Humans, Author Correction, education, Pandemics, Aged, Science & Technology, business.industry, SARS-CoV-2, Infant, Newborn, Outbreak, COVID-19, Infant, medicine.disease, RNA-Dependent RNA Polymerase, Confidence interval, Pneumonia, 030104 developmental biology, business
Abstract

On 21 February 2020, a resident of the municipality of Vo', a small town near Padua (Italy), died of pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection1. This was the first coronavirus disease 19 (COVID-19)-related death detected in Italy since the detection of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province2. In response, the regional authorities imposed the lockdown of the whole municipality for 14 days3. Here we collected information on the demography, clinical presentation, hospitalization, contact network and the presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo' at two consecutive time points. From the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI): 2.1-3.3%). From the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI: 0.8-1.8%). Notably, 42.5% (95% CI: 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (that is, did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI: 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (P = 0.62 and 0.74 for E and RdRp genes, respectively, exact Wilcoxon-Mann-Whitney test). This study sheds light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides insights into its transmission dynamics and the efficacy of the implemented control measures.

Published
2020
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65. MOESM2 of Using cluster analysis to reconstruct dengue exposure patterns from cross-sectional serological studies in Singapore

Author

Sorawat Sangkaew, Tan, Li, Ng, Lee, Ferguson, Neil, and Dorigatti, Ilaria

Abstract

Additional file 2: Figure S1. Correlation matrix of the eight variables in the data set (see Additional file 2: Table S1 for the variable definitions). The size and colour represent the correlation between any two variables, with larger circles representing higher correlation. Figure S2. Age distribution of the samples collected in the 2009 and 2013 serosurveys separately, by cluster. Age was classified as belonging to one of the following age-groups: 16–20; 21–25; 26–30; 31–35; 36–40; 41–45; 46–50; 51–55; 56–60 years. Figure S3. Median (bold line), interquantiles (box), range (vertical line) and outliers (points) of the log10 PRNT50 titres against DENV1-4 characterising the five clusters obtained with the agglomerative hierarchical clustering algorithm applied to the 2009 dataset. The colours blue, grey, green and brown represent DENV1, DENV2, DENV3 and DENV4, respectively. Figure S4. Median (bold line), interquantiles (box), range (vertical line) and outliers (points) of the log10 PRNT50 titres against DENV1-4 characterising the six clusters obtained with the agglomerative hierarchical clustering algorithm applied to the 2013 dataset. The colours blue, grey, green and brown represent DENV1, DENV2, DENV3 and DENV4, respectively.

Published
2020
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67. SARS-CoV-2 Antibody Dynamics, Within-Household Transmission and the Impact of Contact Tracing from Community-Wide Serological Testing in the Italian Municipality of Vo’

Author

Dorigatti, Ilaria, primary, Lavezzo, Enrico, additional, Manuto, Laura, additional, Ciavarella, Constanze, additional, Pacenti, Monia, additional, Boldrin, Caterina, additional, Cattai, Margherita, additional, Saluzzo, Francesca, additional, Franchin, Elisa, additional, Del Vecchio, Claudia, additional, Caldart, Federico, additional, Castelli, Gioele, additional, Nicoletti, Michele, additional, Nieddu, Eleonora, additional, Salvadoretti, Elisa, additional, Labella, Beatrice, additional, Fava, Ludovico, additional, Guglielmo, Simone, additional, Fascina, Mariateresa, additional, Alvisi, Gualtiero, additional, Vanuzzo, Maria Cristina, additional, Zupo, Tiziano, additional, Calandrin, Reginetta, additional, Lisi, Vittoria, additional, Rossi, Lucia, additional, Castigliuolo, Ignazio, additional, Merigliano, Stefano, additional, Unwin, H. Juliette T., additional, Plebani, Mario, additional, Padoan, Andrea, additional, Brazzale, Alessandra R., additional, Toppo, Stefano, additional, Ferguson, Neil M., additional, Donnelly, Christl, additional, and Crisanti, Andrea, additional

Published
2021
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72. Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment

Author

Ainslie, Kylie E. C., primary, Walters, Caroline E., additional, Fu, Han, additional, Bhatia, Sangeeta, additional, Wang, Haowei, additional, Xi, Xiaoyue, additional, Baguelin, Marc, additional, Bhatt, Samir, additional, Boonyasiri, Adhiratha, additional, Boyd, Olivia, additional, Cattarino, Lorenzo, additional, Ciavarella, Constanze, additional, Cucunuba, Zulma, additional, Cuomo-Dannenburg, Gina, additional, Dighe, Amy, additional, Dorigatti, Ilaria, additional, van Elsland, Sabine L, additional, FitzJohn, Rich, additional, Gaythorpe, Katy, additional, Ghani, Azra C, additional, Green, Will, additional, Hamlet, Arran, additional, Hinsley, Wes, additional, Imai, Natsuko, additional, Jorgensen, David, additional, Knock, Edward, additional, Laydon, Daniel, additional, Nedjati-Gilani, Gemma, additional, Okell, Lucy C, additional, Siveroni, Igor, additional, Thompson, Hayley A, additional, Unwin, H. Juliette T., additional, Verity, Robert, additional, Vollmer, Michaela, additional, Walker, Patrick G T, additional, Wang, Yuanrong, additional, Watson, Oliver J, additional, Whittaker, Charles, additional, Winskill, Peter, additional, Donnelly, Christl A, additional, Ferguson, Neil M, additional, and Riley, Steven, additional

Published
2020
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73. Environment influences SARS-CoV-2 transmission in the absence of non-pharmaceutical interventions

Author

Smith, Thomas P., primary, Flaxman, Seth, additional, Gallinat, Amanda S., additional, Kinosian, Sylvia P., additional, Stemkovski, Michael, additional, Unwin, H. Juliette T., additional, Watson, Oliver J., additional, Whittaker, Charles, additional, Cattarino, Lorenzo, additional, Dorigatti, Ilaria, additional, Tristem, Michael, additional, and Pearse, William D., additional

Published
2020
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75. SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China

Author

Thompson, Hayley A, primary, Imai, Natsuko, additional, Dighe, Amy, additional, Ainslie, Kylie E C, additional, Baguelin, Marc, additional, Bhatia, Sangeeta, additional, Bhatt, Samir, additional, Boonyasiri, Adhiratha, additional, Boyd, Olivia, additional, Brazeau, Nicholas F, additional, Cattarino, Lorenzo, additional, Cooper, Laura V, additional, Coupland, Helen, additional, Cucunuba, Zulma, additional, Cuomo-Dannenburg, Gina, additional, Djaafara, Bimandra, additional, Dorigatti, Ilaria, additional, van Elsland, Sabine, additional, FitzJohn, Richard, additional, Fu, Han, additional, Gaythorpe, Katy A M, additional, Green, Will, additional, Hallett, Timothy, additional, Hamlet, Arran, additional, Haw, David, additional, Hayes, Sarah, additional, Hinsley, Wes, additional, Jeffrey, Benjamin, additional, Knock, Edward, additional, Laydon, Daniel J, additional, Lees, John, additional, Mangal, Tara D, additional, Mellan, Thomas, additional, Mishra, Swapnil, additional, Mousa, Andria, additional, Nedjati-Gilani, Gemma, additional, Nouvellet, Pierre, additional, Okell, Lucy, additional, Parag, Kris V, additional, Ragonnet-Cronin, Manon, additional, Riley, Steven, additional, Unwin, H Juliette T, additional, Verity, Robert, additional, Vollmer, Michaela, additional, Volz, Erik, additional, Walker, Patrick G T, additional, Walters, Caroline, additional, Wang, Haowei, additional, Wang, Yuanrong, additional, Watson, Oliver J, additional, Whittaker, Charles, additional, Whittles, Lilith K, additional, Winskill, Peter, additional, Xi, Xiaoyue, additional, Donnelly, Christl A, additional, and Ferguson, Neil M, additional

Published
2020
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76. The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries

Author

Walker, Patrick G. T., primary, Whittaker, Charles, additional, Watson, Oliver J., additional, Baguelin, Marc, additional, Winskill, Peter, additional, Hamlet, Arran, additional, Djafaara, Bimandra A., additional, Cucunubá, Zulma, additional, Olivera Mesa, Daniela, additional, Green, Will, additional, Thompson, Hayley, additional, Nayagam, Shevanthi, additional, Ainslie, Kylie E. C., additional, Bhatia, Sangeeta, additional, Bhatt, Samir, additional, Boonyasiri, Adhiratha, additional, Boyd, Olivia, additional, Brazeau, Nicholas F., additional, Cattarino, Lorenzo, additional, Cuomo-Dannenburg, Gina, additional, Dighe, Amy, additional, Donnelly, Christl A., additional, Dorigatti, Ilaria, additional, van Elsland, Sabine L., additional, FitzJohn, Rich, additional, Fu, Han, additional, Gaythorpe, Katy A. M., additional, Geidelberg, Lily, additional, Grassly, Nicholas, additional, Haw, David, additional, Hayes, Sarah, additional, Hinsley, Wes, additional, Imai, Natsuko, additional, Jorgensen, David, additional, Knock, Edward, additional, Laydon, Daniel, additional, Mishra, Swapnil, additional, Nedjati-Gilani, Gemma, additional, Okell, Lucy C., additional, Unwin, H. Juliette, additional, Verity, Robert, additional, Vollmer, Michaela, additional, Walters, Caroline E., additional, Wang, Haowei, additional, Wang, Yuanrong, additional, Xi, Xiaoyue, additional, Lalloo, David G., additional, Ferguson, Neil M., additional, and Ghani, Azra C., additional

Published
2020
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77. State-level tracking of COVID-19 in the United States

Author

Unwin, H. Juliette T., primary, Mishra, Swapnil, additional, Bradley, Valerie C., additional, Gandy, Axel, additional, Mellan, Thomas A., additional, Coupland, Helen, additional, Ish-Horowicz, Jonathan, additional, Vollmer, Michaela A. C., additional, Whittaker, Charles, additional, Filippi, Sarah L., additional, Xi, Xiaoyue, additional, Monod, Mélodie, additional, Ratmann, Oliver, additional, Hutchinson, Michael, additional, Valka, Fabian, additional, Zhu, Harrison, additional, Hawryluk, Iwona, additional, Milton, Philip, additional, Ainslie, Kylie E. C., additional, Baguelin, Marc, additional, Boonyasiri, Adhiratha, additional, Brazeau, Nick F., additional, Cattarino, Lorenzo, additional, Cucunuba, Zulma, additional, Cuomo-Dannenburg, Gina, additional, Dorigatti, Ilaria, additional, Eales, Oliver D., additional, Eaton, Jeffrey W., additional, van Elsland, Sabine L., additional, FitzJohn, Richard G., additional, Gaythorpe, Katy A. M., additional, Green, William, additional, Hinsley, Wes, additional, Jeffrey, Benjamin, additional, Knock, Edward, additional, Laydon, Daniel J., additional, Lees, John, additional, Nedjati-Gilani, Gemma, additional, Nouvellet, Pierre, additional, Okell, Lucy, additional, Parag, Kris V., additional, Siveroni, Igor, additional, Thompson, Hayley A., additional, Walker, Patrick, additional, Walters, Caroline E., additional, Watson, Oliver J., additional, Whittles, Lilith K., additional, Ghani, Azra C., additional, Ferguson, Neil M., additional, Riley, Steven, additional, Donnelly, Christl A., additional, Bhatt, Samir, additional, and Flaxman, Seth, additional

Published
2020
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78. Estimating the number of undetected COVID-19 cases among travellers from mainland China

Author

Bhatia, Sangeeta, primary, Imai, Natsuko, additional, Cuomo-Dannenburg, Gina, additional, Baguelin, Marc, additional, Boonyasiri, Adhiratha, additional, Cori, Anne, additional, Cucunubá, Zulma, additional, Dorigatti, Ilaria, additional, FitzJohn, Rich, additional, Fu, Han, additional, Gaythorpe, Katy, additional, Ghani, Azra, additional, Hamlet, Arran, additional, Hinsley, Wes, additional, Laydon, Daniel, additional, Nedjati-Gilani, Gemma, additional, Okell, Lucy, additional, Riley, Steven, additional, Thompson, Hayley, additional, van Elsland, Sabine, additional, Volz, Erik, additional, Wang, Haowei, additional, Wang, Yuanrong, additional, Whittaker, Charles, additional, Xi, Xiaoyue, additional, Donnelly, Christl A., additional, and Ferguson, Neil M., additional

Published
2020
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79. Subnational analysis of the COVID-19 epidemic in Brazil

Author

Mellan, Thomas A, primary, Hoeltgebaum, Henrique H, additional, Mishra, Swapnil, additional, Whittaker, Charlie, additional, Schnekenberg, Ricardo P, additional, Gandy, Axel, additional, Unwin, H Juliette T, additional, Vollmer, Michaela A C, additional, Coupland, Helen, additional, Hawryluk, Iwona, additional, Faria, Nuno Rodrigues, additional, Vesga, Juan, additional, Zhu, Harrison, additional, Hutchinson, Michael, additional, Ratmann, Oliver, additional, Monod, Mélodie, additional, Ainslie, Kylie E C, additional, Baguelin, Marc, additional, Bhatia, Sangeeta, additional, Boonyasiri, Adhiratha, additional, Brazeau, Nicholas, additional, Charles, Giovanni, additional, Cucunuba, Zulma, additional, Cuomo-Dannenburg, Gina, additional, Dighe, Amy, additional, Eaton, Jeff, additional, Elsland, Sabine L van, additional, Gaythorpe, Katy A M, additional, Green, Will, additional, Knock, Edward, additional, Laydon, Daniel, additional, Lees, John A, additional, Mousa, Andria, additional, Nedjati-Gilani, Gemma, additional, Nouvellet, Pierre, additional, Parag, Kris V, additional, Thompson, Hayley A, additional, Verity, Robert, additional, Walters, Caroline E, additional, Wang, Haowei, additional, Wang, Yuanrong, additional, Watson, Oliver J, additional, Whittles, Lilith, additional, Xi, Xiaoyue, additional, Dorigatti, Ilaria, additional, Walker, Patrick, additional, Ghani, Azra C, additional, Riley, Steven, additional, Ferguson, Neil M, additional, Donnelly, Christl A, additional, Flaxman, Seth, additional, and Bhatt, Samir, additional

Published
2020
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81. A sub-national analysis of the rate of transmission of COVID-19 in Italy

Author

Vollmer, Michaela A. C., primary, Mishra, Swapnil, additional, Unwin, H Juliette T, additional, Gandy, Axel, additional, Mellan, Thomas A, additional, Bradley, Valerie, additional, Zhu, Harrison, additional, Coupland, Helen, additional, Hawryluk, Iwona, additional, Hutchinson, Michael, additional, Ratmann, Oliver, additional, Monod, Melodie, additional, Walker, Patrick, additional, Whittaker, Charlie, additional, Cattarino, Lorenzo, additional, Ciavarella, Constance, additional, Cilloni, Lucia, additional, Ainslie, Kylie, additional, Baguelin, Marc, additional, Bhatia, Sangeeta, additional, Boonyasiri, Adhiratha, additional, Brazeau, Nicholas, additional, Charles, Giovanni, additional, Cooper, Laura V, additional, Cucunuba, Zulma, additional, Cuomo-Dannenburg, Gina, additional, Dighe, Amy, additional, Djaafara, Bimandra, additional, Eaton, Jeff, additional, van Elsland, Sabine L, additional, FitzJohn, Richard, additional, Fraser, Keith, additional, Gaythorpe, Katy, additional, Green, Will, additional, Hayes, Sarah, additional, Imai, Natsuko, additional, Jeffrey, Ben, additional, Knock, Edward, additional, Laydon, Daniel, additional, Lees, John, additional, Mangal, Tara, additional, Mousa, Andria, additional, Nedjati-Gilani, Gemma, additional, Nouvellet, Pierre, additional, Olivera, Daniela, additional, Parag, Kris V, additional, Pickles, Michael, additional, Thompson, Hayley A, additional, Verity, Robert, additional, Walters, Caroline, additional, Wang, Haowei, additional, Wang, Yuanrong, additional, Watson, Oliver J, additional, Whittles, Lilith, additional, Xi, Xiaoyue, additional, Ghani, Azra A M, additional, Riley, Steven, additional, Okell, Lucy C, additional, Donnelly, Christl A, additional, Ferguson, Neil M, additional, Dorigatti, Ilaria, additional, Flaxman, Seth, additional, and Bhatt, Samir, additional

Published
2020
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82. Estimates of the severity of COVID-19 disease

Author

Verity, Robert, primary, Okell, Lucy C, additional, Dorigatti, Ilaria, additional, Winskill, Peter, additional, Whittaker, Charles, additional, Imai, Natsuko, additional, Cuomo-Dannenburg, Gina, additional, Thompson, Hayley, additional, Walker, Patrick GT, additional, Fu, Han, additional, Dighe, Amy, additional, Griffin, Jamie T, additional, Baguelin, Marc, additional, Bhatia, Sangeeta, additional, Boonyasiri, Adhiratha, additional, Cori, Anne, additional, Cucunubá, Zulma, additional, FitzJohn, Rich, additional, Gaythorpe, Katy, additional, Green, Will, additional, Hamlet, Arran, additional, Hinsley, Wes, additional, Laydon, Daniel, additional, Nedjati-Gilani, Gemma, additional, Riley, Steven, additional, van Elsland, Sabine, additional, Volz, Erik, additional, Wang, Haowei, additional, Wang, Yuanrong, additional, Xi, Xiaoyue, additional, Donnelly, Christl A, additional, Ghani, Azra C, additional, and Ferguson, Neil M, additional

Published
2020
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84. A quantitative comparison of West Nile virus incidence from 2013 to 2018 in Emilia-Romagna, Italy

Author

Marini, Giovanni, primary, Calzolari, Mattia, additional, Angelini, Paola, additional, Bellini, Romeo, additional, Bellini, Silvia, additional, Bolzoni, Luca, additional, Torri, Deborah, additional, Defilippo, Francesco, additional, Dorigatti, Ilaria, additional, Nikolay, Birgit, additional, Pugliese, Andrea, additional, Rosà, Roberto, additional, and Tamba, Marco, additional

Published
2020
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86. Exposure Patterns Driving Ebola Transmission in West Africa: A Retrospective Observational Study

Author

Agua-Agum, Junerlyn, Ariyarajah, Archchun, Aylward, Bruce, Bawo, Luke, Bilivogui, Pepe, Blake, Isobel M., Brennan, Richard J., Cawthorne, Amy, Cleary, Eilish, Clement, Peter, Conteh, Roland, Cori, Anne, Dafae, Foday, Dahl, Benjamin, Dangou, Jean-Marie, Diallo, Boubacar, Donnelly, Christl A., Dorigatti, Ilaria, Dye, Christopher, Eckmanns, Tim, Fallah, Mosoka, Ferguson, Neil M., Fiebig, Lena, Fraser, Christophe, Garske, Tini, Gonzalez, Lice, Hamblion, Esther, Hamid, Nuha, Hersey, Sara, Hinsley, Wes, Jambei, Amara, Jombart, Thibaut, Kargbo, David, Keita, Sakoba, Kinzer, Michael, George, Fred Kuti, Godefroy, Beatrice, Gutierrez, Giovanna, Kannangarage, Niluka, Mills, Harriet L., Moller, Thomas, Meijers, Sascha, Mohamed, Yasmine, Morgan, Oliver, Nedjati-Gilani, Gemma, Newton, Emily, Nouvellet, Pierre, Nyenswah, Tolbert, Perea, William, Perkins, Devin, Riley, Steven, Rodier, Guenael, Rondy, Marc, Sagrado, Maria, Savulescu, Camelia, Schafer, Ilana J., Schumacher, Dirk, Seyler, Thomas, Shah, Anita, Van Kerkhove, Maria D., Wesseh, C. Samford, and Yoti, Zabulon

Subjects
Health facilities -- Management, Ebola hemorrhagic fever -- Care and treatment, Medical care -- Management -- Africa, Medical personnel -- Practice, Company business management, Biological sciences, World Health Organization
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 [less than or equal to]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., Author(s): International Ebola Response Team, Junerlyn Agua-Agum 1, Archchun Ariyarajah 1, Bruce Aylward 1, Luke Bawo 2, Pepe Bilivogui 3, Isobel M. Blake 4, Richard J. Brennan 1, Amy Cawthorne [...]

Published
2016
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87. A Comparative Analysis of Statistical Methods to Estimate the Reproduction Number in Emerging Epidemics, With Implications for the Current Coronavirus Disease 2019 (COVID-19) Pandemic.

Author

O'Driscoll, Megan, Harry, Carole, Donnelly, Christl A, Cori, Anne, and Dorigatti, Ilaria

Subjects
COVID-19, COMPARATIVE studies, COVID-19 pandemic
Abstract

Background As the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic continues its rapid global spread, quantification of local transmission patterns has been, and will continue to be, critical for guiding the pandemic response. Understanding the accuracy and limitations of statistical methods to estimate the basic reproduction number, R0, in the context of emerging epidemics is therefore vital to ensure appropriate interpretation of results and the subsequent implications for control efforts. Methods Using simulated epidemic data, we assess the performance of 7 commonly used statistical methods to estimate R0 as they would be applied in a real-time outbreak analysis scenario: fitting to an increasing number of data points over time and with varying levels of random noise in the data. Method comparison was also conducted on empirical outbreak data, using Zika surveillance data from the 2015–2016 epidemic in Latin America and the Caribbean. Results We find that most methods considered here frequently overestimate R0 in the early stages of epidemic growth on simulated data, the magnitude of which decreases when fitted to an increasing number of time points. This trend of decreasing bias over time can easily lead to incorrect conclusions about the course of the epidemic or the need for control efforts. Conclusions We show that true changes in pathogen transmissibility can be difficult to disentangle from changes in methodological accuracy and precision in the early stages of epidemic growth, particularly for data with significant over-dispersion. As localized epidemics of SARS-CoV-2 take hold around the globe, awareness of this trend will be important for appropriately cautious interpretation of results and subsequent guidance for control efforts. [ABSTRACT FROM AUTHOR]

Published
2021
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93. Yellow fever in Asia-a risk analysis.

Author

Daniels, Bethan Cracknell, Gaythorpe, Katy, Imai, Natsuko, Dorigatti, Ilaria, and Cracknell Daniels, Bethan

Subjects
YELLOW fever, NEGATIVE binomial distribution, RISK assessment, BRANCHING processes, MOSQUITOES
Abstract

Background: There is concern about the risk of yellow fever (YF) establishment in Asia, owing to rising numbers of urban outbreaks in endemic countries and globalisation. Following an outbreak in Angola in 2016, YF cases were introduced into China. Prior to this, YF had never been recorded in Asia, despite climatic suitability and the presence of mosquitoes. An outbreak in Asia could result in widespread fatalities and huge economic impact. Therefore, quantifying the potential risk of YF outbreaks in Asia is a public health priority.Methods: Using international flight data and YF incidence estimates from 2016, we quantified the risk of YF introduction via air travel into Asia. In locations with evidence of a competent mosquito population, the potential for autochthonous YF transmission was estimated using a temperature-dependent model of the reproduction number and a branching process model assuming a negative binomial distribution.Results: In total, 25 cities across Asia were estimated to be at risk of receiving at least one YF viraemic traveller during 2016. At their average temperatures, we estimated the probability of autochthonous transmission to be <50% in all cities, which was primarily due to the limited number of estimated introductions that year.Conclusion: Despite the rise in air travel, we found low support for travel patterns between YF endemic countries and Asia resulting in autochthonous transmission during 2016. This supports the historic absence of YF in Asia and suggests it could be due to a limited number of introductions in previous years. Future increases in travel volumes or YF incidence can increase the number of introductions and the risk of autochthonous transmission. Given the high proportion of asymptomatic or mild infections and the challenges of YF surveillance, our model can be used to estimate the introduction and outbreak risk and can provide useful information to surveillance systems. [ABSTRACT FROM AUTHOR]

Published
2021
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94. Case Fatality Ratio Estimates for the 2013–2016 West African Ebola Epidemic: Application of Boosted Regression Trees for Imputation.

Author

Forna, Alpha, Nouvellet, Pierre, Dorigatti, Ilaria, and Donnelly, Christl A

Subjects
CONFIDENCE intervals, DECISION trees, EBOLA virus disease, EPIDEMICS, HOSPITAL care, MACHINE learning, SURVIVAL, RECEIVER operating characteristic curves, STATISTICAL models, DESCRIPTIVE statistics
Abstract

Background The 2013–2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported. Methods Using a boosted regression tree model, we imputed survival outcomes (ie, survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR. Results The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5–75.6%); specificity, 69.8% (54.1–75.6%); percentage correctly classified, 69.9% (53.7–75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8–82.1%). The adjusted CFR estimates (95% CI) for the 2013–2016 West African epidemic were 82.8% (45.6–85.6%) overall and 89.1% (40.8–91.6%), 65.6% (61.3–69.6%), and 79.2% (45.4–84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR. Conclusions The adjusted CFR estimates improved the naive CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemics, and help better allocate resources and evaluate the effectiveness of future inventions. [ABSTRACT FROM AUTHOR]

Published
2020
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95. Spatiotemporal variability in dengue transmission intensity in Jakarta, Indonesia.

Author

O'Driscoll, Megan, Imai, Natsuko, Ferguson, Neil M., Hadinegoro, Sri Rezeki, Satari, Hindra Irawan, Tam, Clarence C., and Dorigatti, Ilaria

Subjects
DENGUE, DENGUE hemorrhagic fever, MATHEMATICAL models, MEGALOPOLIS, VACCINATION
Abstract

Background: Approximately 70% of the global burden of dengue disease occurs on the Asian continent, where many large urban centres provide optimal environments for sustained endemic transmission and periodic epidemic cycles. Jakarta, the capital of Indonesia, is a densely populated megacity with hyperendemic dengue transmission. Characterization of the spatiotemporal distribution of dengue transmission intensity is of key importance for optimal implementation of novel control and prevention programmes, including vaccination. In this paper we use mathematical models to provide the first detailed description of spatial and temporal variability in dengue transmission intensity in Jakarta. Methodology/Principal findings: We applied catalytic models in a Bayesian framework to age-stratified dengue case notification data to estimate dengue force of infection and reporting probabilities in 42 subdistricts of Jakarta. The model was fitted to yearly and average annual data covering a 10-year period between 2008 and 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129–0.131) per year in Jakarta province, ranging from 0.090 (95%CrI: 0.077–0.103) to 0.164 (95%CrI: 0.153–0.174) across subdistricts. Annual average transmission intensity in Jakarta province during the 10-year period ranged from 0.012 (95%CrI: 0.011–0.013) in 2017 to 0.124 (95%CrI: 0.121–0.128) in 2016. Conclusions/Significance: While the absolute number of dengue case notifications cannot be relied upon as a measure of endemicity, the age-distribution of reported dengue cases provides valuable insights into the underlying nature of transmission. Our estimates from yearly and average annual case notification data represent the first detailed estimates of dengue transmission intensity in Jakarta's subdistricts. These will be important to consider when assessing the population-level impact and cost-effectiveness of potential control and prevention programmes in Jakarta province, such as the controlled release of Wolbachia-carrying mosquitoes and vaccination. Author summary: Characterization of the spatiotemporal distribution of dengue transmission intensity, a key measure of population infection risk, can inform the optimal use and deployment of control and prevention programmes. Jakarta, the capital of Indonesia, is a large urban centre with hyperendemic dengue transmission. We fitted catalytic models to age-stratified dengue surveillance data reported in Jakarta's subdistricts from 2008 to 2017. We estimated a long-term average annual transmission intensity of 0.130 (95%CrI: 0.129–0.131) per year in Jakarta province, which varied across subdistricts from 0.090 (95%CrI: 0.077–0.103) per year in Sawah Besar to 0.164 (95%CrI: 0.153–0.174) per year in Pasar Rebo. We observed significant spatiotemporal variation and clustering of transmission intensity in Jakarta. Our estimates obtained from the analysis of yearly and cumulative case-notification data reported between 2008 and 2017 represent the first detailed estimates of average dengue transmission intensity, which will be key to assess the potential impact of future control and prevention programmes in Jakarta province. [ABSTRACT FROM AUTHOR]

Published
2020
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97. Outbreak of Ebola virus disease in the Democratic Republic of the Congo, April–May, 2018: an epidemiological study

Author

Barry, Ahmadou, primary, Ahuka-Mundeke, Steve, additional, Ali Ahmed, Yahaya, additional, Allarangar, Yokouide, additional, Anoko, Julienne, additional, Archer, Brett Nicholas, additional, Aruna Abedi, Aaron, additional, Bagaria, Jayshree, additional, Belizaire, Marie Roseline Darnycka, additional, Bhatia, Sangeeta, additional, Bokenge, Théophile, additional, Bruni, Emanuele, additional, Cori, Anne, additional, Dabire, Ernest, additional, Diallo, Amadou Mouctar, additional, Diallo, Boubacar, additional, Donnelly, Christl Ann, additional, Dorigatti, Ilaria, additional, Dorji, Tshewang Choden, additional, Escobar Corado Waeber, Aura Rocio, additional, Fall, Ibrahima Socé, additional, Ferguson, Neil M, additional, FitzJohn, Richard Gareth, additional, Folefack Tengomo, Gervais Leon, additional, Formenty, Pierre Bernard Henri, additional, Forna, Alpha, additional, Fortin, Anne, additional, Garske, Tini, additional, Gaythorpe, Katy AM, additional, Gurry, Celine, additional, Hamblion, Esther, additional, Harouna Djingarey, Mamoudou, additional, Haskew, Christopher, additional, Hugonnet, Stéphane Alexandre Louis, additional, Imai, Natsuko, additional, Impouma, Benido, additional, Kabongo, Guylain, additional, Kalenga, Oly Ilunga, additional, Kibangou, Emerencienne, additional, Lee, Theresa Min-Hyung, additional, Lukoya, Charles Okot, additional, Ly, Ousmane, additional, Makiala-Mandanda, Sheila, additional, Mamba, Augustin, additional, Mbala-Kingebeni, Placide, additional, Mboussou, Franck Fortune Roland, additional, Mlanda, Tamayi, additional, Mondonge Makuma, Vital, additional, Morgan, Oliver, additional, Mujinga Mulumba, Anastasie, additional, Mukadi Kakoni, Patrick, additional, Mukadi-Bamuleka, Daniel, additional, Muyembe, Jean-Jaques, additional, Bathé, Ndjoloko Tambwe, additional, Ndumbi Ngamala, Patricia, additional, Ngom, Roland, additional, Ngoy, Guillaume, additional, Nouvellet, Pierre, additional, Nsio, Justus, additional, Ousman, Kevin Babila, additional, Peron, Emilie, additional, Polonsky, Jonathan Aaron, additional, Ryan, Michael J., additional, Touré, Alhassane, additional, Towner, Rodney, additional, Tshapenda, Gaston, additional, Van De Weerdt, Reinhilde, additional, Van Kerkhove, Maria, additional, Wendland, Annika, additional, Yao, N'Da Konan Michel, additional, Yoti, Zabulon, additional, Yuma, Etienne, additional, Kalambayi Kabamba, Guy, additional, Lukwesa Mwati, Jean de Dieu, additional, Mbuy, Gisele, additional, Lubula, Leopold, additional, Mutombo, Anny, additional, Mavila, Oscar, additional, Lay, Yyonne, additional, and Kitenge, Emma, additional

Published
2018
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98. After Ebola in West Africa--Unpredictable Risks, Preventable Epidemics

Author

WHO Ebola Response Team, Agua-Agum, Junerlyn, Allegranzi, Benedetta, Ariyarajah, Archchun, Aylward, R Bruce, Blake, Isobel M, Barboza, Philippe, Bausch, Daniel, Brennan, Richard J, Clement, Peter, Coffey, Pasqualina, Cori, Anne, Donnelly, Christl A, Dorigatti, Ilaria, Drury, Patrick, Durski, Kara, Dye, Christopher, Eckmanns, Tim, Ferguson, Neil M, Fraser, Christophe, Garcia, Erika, Garske, Tini, Gasasira, Alex, Gurry, Céline, Hamblion, Esther, Hinsley, Wes, Holden, Robert, Holmes, David, Hugonnet, Stéphane, Jaramillo Gutierrez, Giovanna, Jombart, Thibaut, Kelley, Edward, Santhana, Ravi, Mahmoud, Nuha, Mills, Harriet L, Mohamed, Yasmine, Musa, Emmanuel, Naidoo, Dhamari, Nedjati-Gilani, Gemma, Newton, Emily, Norton, Ian, Nouvellet, Pierre, Perkins, Devin, Perkins, Mark, Riley, Steven, Schumacher, Dirk, Shah, Anita, Tang, Minh, Varsaneux, Olivia, and Van Kerkhove, Maria D

Abstract

Between December 2013 and April 2016, the largest epidemic of Ebola virus disease (EVD) to date generated more than 28,000 cases and more than 11,000 deaths in the large, mobile populations of Guinea, Liberia, and Sierra Leone. Tracking the rapid rise and slower decline of the West African epidemic has reinforced some common understandings about the epidemiology and control of EVD but has also generated new insights. Despite having more information about the geographic distribution of the disease, the risk of human infection from animals and from survivors of EVD remains unpredictable over a wide area of equatorial Africa. Until human exposure to infection can be anticipated or avoided, future outbreaks will have to be managed with the classic approach to EVD control - extensive surveillance, rapid detection and diagnosis, comprehensive tracing of contacts, prompt patient isolation, supportive clinical care, rigorous efforts to prevent and control infection, safe and dignified burial, and engagement of the community. Empirical and modeling studies conducted during the West African epidemic have shown that large epidemics of EVD are preventable - a rapid response can interrupt transmission and restrict the size of outbreaks, even in densely populated cities. The critical question now is how to ensure that populations and their health services are ready for the next outbreak, wherever it may occur. Health security across Africa and beyond depends on committing resources to both strengthen national health systems and sustain investment in the next generation of vaccines, drugs, and diagnostics.

Published
2016

100. Ebola virus disease among children in West Africa

Author

WHO Ebola Response Team, Agua-Agum, Junerlyn, Ariyarajah, Archchun, Blake, Isobel M, Cori, Anne, Donnelly, Christl A, Dorigatti, Ilaria, Dye, Christopher, Eckmanns, Tim, Ferguson, Neil M, Fowler, Robert A, Fraser, Christophe, Garske, Tini, Hinsley, Wes, Jombart, Thibaut, Mills, Harriet L, Murthy, Srinivas, Nedjati Gilani, Gemma, Nouvellet, Pierre, Pelletier, Louise, Riley, Steven, Schumacher, Dirk, Shah, Anita, and Van Kerkhove, Maria D

Published
2015

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