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1. Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource

Author

Krawczyk, Konrad, Chelkowski, Tadeusz, Laydon, Daniel J, Mishra, Swapnil, Xifara, Denise, Flaxman, Seth, Mellan, Thomas, Schwämmle, Veit, Röttger, Richard, Hadsund, Johannes T, and Bhatt, Samir

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundBefore the advent of an effective vaccine, nonpharmaceutical interventions, such as mask-wearing, social distancing, and lockdowns, have been the primary measures to combat the COVID-19 pandemic. Such measures are highly effective when there is high population-wide adherence, which requires information on current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place. ObjectiveHere we analyzed online news media coverage of COVID-19. We quantified the total volume of COVID-19 articles, their sentiment polarization, and leading subtopics to act as a reference to inform future communication strategies. MethodsWe collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available online at SciRide). Using topic detection, we identified COVID-19–related content to quantify the proportion of total coverage the pandemic received in 2020. The sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 coverage to reveal the leading themes in pandemic reporting and their respective emotional polarizations. ResultsWe found that COVID-19 coverage accounted for approximately 25.3% of all front-page online news articles between January and October 2020. Sentiment analysis of English-language sources revealed that overall COVID-19 coverage was not exclusively negatively polarized, suggesting wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16% of COVID-19 news articles (or 4% of all English-language articles) can be classified as highly negatively polarized, citing issues such as death, fear, or crisis. ConclusionsThe goal of COVID-19 public health communication is to increase understanding of distancing rules and to maximize the impact of governmental policy. The extent to which the quantity and quality of information from different communication channels (eg, social media, government pages, and news) influence public understanding of public health measures remains to be established. Here we conclude that a quarter of all reporting in 2020 covered COVID-19, which is indicative of information overload. In this capacity, our data and analysis form a quantitative basis for informing health communication strategies along traditional news media channels to minimize the risks of COVID-19 while vaccination is rolled out.

Published
2021
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4. The effectiveness of Hope Groups, a mental health, parenting support, and violence prevention program for families affected by the war in Ukraine: Findings from a pre-post study

Author

Hillis, Susan, Tucker, Sydney, Baldonado, Nicole, Taradaika, Evgenia, Bryn, Lyudmyla, Kharchenko, Svitlana, Machabelii, Tetiana, Taylor, Roisin, Green, Phil, Goldman, Philip, Awah, Isang, Baldonado, Joshua, Gomez, Praveen, Flaxman, Seth, Ratmann, Oliver, Lachman, Jamie M., Villaveces, Andres, Sherr, Lorraine, and Cluver, Lucie

Published
2024
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6. Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals

Author

Brizzi, Andrea, Whittaker, Charles, Servo, Luciana M. S., Hawryluk, Iwona, Prete, Jr, Carlos A., de Souza, William M., Aguiar, Renato S., Araujo, Leonardo J. T., Bastos, Leonardo S., Blenkinsop, Alexandra, Buss, Lewis F., Candido, Darlan, Castro, Marcia C., Costa, Silvia F., Croda, Julio, de Souza Santos, Andreza Aruska, Dye, Christopher, Flaxman, Seth, Fonseca, Paula L. C., Geddes, Victor E. V., Gutierrez, Bernardo, Lemey, Philippe, Levin, Anna S., Mellan, Thomas, Bonfim, Diego M., Miscouridou, Xenia, Mishra, Swapnil, Monod, Mélodie, Moreira, Filipe R. R., Nelson, Bruce, Pereira, Rafael H. M., Ranzani, Otavio, Schnekenberg, Ricardo P., Semenova, Elizaveta, Sonabend, Raphael, Souza, Renan P., Xi, Xiaoyue, Sabino, Ester C., Faria, Nuno R., Bhatt, Samir, and Ratmann, Oliver

Published
2022
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7. Effective refractive error coverage in adults aged 50 years and older: estimates from population-based surveys in 61 countries

Author

Bron, Alain, Cheng, Ching-Yu, Fernandes, Arthur, Friedman, David, Gazzard, Andrew, Kahloun, Rim, Kempen, John, Khairallah, Moncef, Lansingh, Van C, Leasher, Janet, Leveziel, Nicolas, Limburg, Hans, Nowak, Michal, Pesudovs, Konrad, Peto, Tunde, Rossetti, Luca, Tahhan, Nina, Varma, Rohit, Alemayehu, Wondu, Arditi, Aries, Dana, Reza, Del Monte, Monte, Deva, jenny, Dreer, Laura, Ehrlich, Josh, Ellwein, Leon, Hammond, Billy, Hartnett, Mary E, Ingram, April, Khanna, Rohit, Kim, Judy, Lim, Jennifer, Morse, Alan, Musch, David, Parodi, Maurizio B, Ramulu, Pradeep, Robin, Alan, Serle, Janet, Shen, Tueng, Sitorus, Rita S, Stambolian, Dwight, Topouzis, Fotis, Tsilimbaris, Miltiadis, Virgili, Gianni, West, Sheila, Ababora, Jafer K, AlSawahli, Heba, Andriamanjato, Hery Harimanitra, Barrenechea, Rosario, Batlle, Juan F, Burnett, Anthea M, Finger, Robert P, Gallarreta, Marcelo, Gomez-Bastar, Pedro A, Gurung, Reeta, Jain, Elesh, Kabona, George E, Kalua, Khumbo, Kandeke, Levi, Karimurio, Jefitha, Kikira, Susan A, Kulkarni, Sucheta, Mathenge, Wanjiku, Mishra, Sailesh Kumar, Mohammadi, Seyed Farzad, Mörchen, Manfred, Muhammad, Nasiru, Mutati, Grace C, Nano, Maria Eugenia, Németh, János, Paduca, Ala, Páez, Alexander, Rabiu, M Mansur, Rif'ati, Lutfah, Salowi, Mohamad Aziz, Sapkota, Yuddha D, Sargent, Nicholas, Thoufeeq, Ubeydulla, Villalobos, Astrid V, Xiao, Biaxiang, Yee Melgar, Mariano, Zhang, Xiu Juan, Bourne, Rupert Richard Alexander, Cicinelli, Maria Vittoria, Sedighi, Tabassom, Tapply, Ian H, McCormick, Ian, Jonas, Jost B, Congdon, Nathan G, Ramke, Jacqueline, Naidoo, Kovin S, Fricke, Timothy R, Burton, Matthew J, Müller, Andreas, Bikbov, Mukharram M, Furtado, João M, Kyari, Fatima, He, Mingguang, Wang, Ya Xing, Vijaya, Lingam, Nangia, Vinay, Brian, Garry, Emamian, Mohammad Hassan, Fotouhi, Akbar, Hashemi, Hassan, Khandekar, Rajiv B, Marmamula, Srinivas, Salomão, Solange, George, Ronnie, Kazakbaeva, Gyulli, Braithwaite, Tasanee, Casson, Robert J, Iwase, Aiko, Gupta, Noopur, Abdianwall, Mohammad H, Wong, Tien Y, Wang, Ningli, Taylor, Hugh R, Flaxman, Seth R, Keel, Stuart, and Resnikoff, Serge

Published
2022
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9. Orphanhood and Caregiver Loss Among Children Based on New Global Excess COVID-19 Death Estimates

Author

Hillis, Susan, Ntwali N'konzi, Joel-Pascal, Msemburi, William, Cluver, Lucie, Villaveces, Andres, Flaxman, Seth, and Unwin, H. Juliette T.

Subjects
Epidemics -- Social aspects -- United States, Child welfare -- Management, Orphans -- Statistics -- Health aspects, Pediatric research, Company business management, Health
Abstract

The availability of new excess mortality data enables us to update global minimum estimates of COVID-19 orphanhood and caregiver death among children (The Economist, 2021; Unwin et al., 2022; Wang, [...]

Published
2022

11. A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe

Author

Altman, George, Ahuja, Janvi, Monrad, Joshua Teperowski, Dhaliwal, Gurpreet, Rogers-Smith, Charlie, Leech, Gavin, Snodin, Benedict, Sandbrink, Jonas B., Finnveden, Lukas, Norman, Alexander John, Oehm, Sebastian B., Sandkühler, Julia Fabienne, Kulveit, Jan, Flaxman, Seth, Gal, Yarin, Mishra, Swapnil, Bhatt, Samir, Sharma, Mrinank, Mindermann, Sören, and Brauner, Jan Markus

Published
2022
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12. Global disparities in SARS-CoV-2 genomic surveillance

Author

Brito, Anderson F., Semenova, Elizaveta, Dudas, Gytis, Hassler, Gabriel W., Kalinich, Chaney C., Kraemer, Moritz U. G., Ho, Joses, Tegally, Houriiyah, Githinji, George, Agoti, Charles N., Matkin, Lucy E., Whittaker, Charles, Howden, Benjamin P., Sintchenko, Vitali, Zuckerman, Neta S., Mor, Orna, Blankenship, Heather M., de Oliveira, Tulio, Lin, Raymond T. P., Siqueira, Marilda Mendonça, Resende, Paola Cristina, Vasconcelos, Ana Tereza R., Spilki, Fernando R., Aguiar, Renato Santana, Alexiev, Ivailo, Ivanov, Ivan N., Philipova, Ivva, Carrington, Christine V. F., Sahadeo, Nikita S. D., Branda, Ben, Gurry, Céline, Maurer-Stroh, Sebastian, Naidoo, Dhamari, von Eije, Karin J., Perkins, Mark D., van Kerkhove, Maria, Hill, Sarah C., Sabino, Ester C., Pybus, Oliver G., Dye, Christopher, Bhatt, Samir, Flaxman, Seth, Suchard, Marc A., Grubaugh, Nathan D., Baele, Guy, and Faria, Nuno R.

Published
2022
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14. Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study

Author

Nyberg, Tommy, Ferguson, Neil M, Nash, Sophie G, Webster, Harriet H, Flaxman, Seth, Andrews, Nick, Hinsley, Wes, Lopez Bernal, Jamie, Kall, Meaghan, Bhatt, Samir, Blomquist, Paula, Zaidi, Asad, Volz, Erik, Abdul Aziz, Nurin, Harman, Katie, Funk, Sebastian, Abbott, Sam, Hope, Russell, Charlett, Andre, Chand, Meera, Ghani, Azra C, Seaman, Shaun R, Dabrera, Gavin, De Angelis, Daniela, Presanis, Anne M, Thelwall, Simon, Bernal, Jamie Lopez, and Aziz, Nurin Abdul

Published
2022
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15. Unrepresentative big surveys significantly overestimated US vaccine uptake

Author

Bradley, Valerie C., Kuriwaki, Shiro, Isakov, Michael, Sejdinovic, Dino, Meng, Xiao-Li, and Flaxman, Seth

Subjects
Epidemics -- Control -- United States -- United Kingdom, Health behavior -- Forecasts and trends, Market trend/market analysis, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Surveys are a crucial tool for understanding public opinion and behaviour, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the effect of survey bias: an instance of the Big Data Paradox.sup.1. Here we demonstrate this paradox in estimates of first-dose COVID-19 vaccine uptake in US adults from 9 January to 19 May 2021 from two large surveys: Delphi-Facebook.sup.2,3 (about 250,000 responses per week) and Census Household Pulse.sup.4 (about 75,000 every two weeks). In May 2021, Delphi-Facebook overestimated uptake by 17 percentage points (14-20 percentage points with 5% benchmark imprecision) and Census Household Pulse by 14 (11-17 percentage points with 5% benchmark imprecision), compared to a retroactively updated benchmark the Centers for Disease Control and Prevention published on 26 May 2021. Moreover, their large sample sizes led to miniscule margins of error on the incorrect estimates. By contrast, an Axios-Ipsos online panel.sup.5 with about 1,000 responses per week following survey research best practices.sup.6 provided reliable estimates and uncertainty quantification. We decompose observed error using a recent analytic framework.sup.1 to explain the inaccuracy in the three surveys. We then analyse the implications for vaccine hesitancy and willingness. We show how a survey of 250,000 respondents can produce an estimate of the population mean that is no more accurate than an estimate from a simple random sample of size 10. Our central message is that data quality matters more than data quantity, and that compensating the former with the latter is a mathematically provable losing proposition. An analysis of three surveys of COVID-19 vaccine behaviour shows that larger surveys overconfidently overestimated vaccine uptake, a demonstration of how larger sample sizes can paradoxically lead to less accurate estimates., Author(s): Valerie C. Bradley [sup.1] , Shiro Kuriwaki [sup.2] , Michael Isakov [sup.3] , Dino Sejdinovic [sup.1] , Xiao-Li Meng [sup.4] , Seth Flaxman [sup.5] Author Affiliations: (1) Department of [...]

Published
2021
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17. Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England

Author

Mishra, Swapnil, Mindermann, Sören, Sharma, Mrinank, Whittaker, Charles, Mellan, Thomas A, Wilton, Thomas, Klapsa, Dimitra, Mate, Ryan, Fritzsche, Martin, Zambon, Maria, Ahuja, Janvi, Howes, Adam, Miscouridou, Xenia, Nason, Guy P, Ratmann, Oliver, Semenova, Elizaveta, Leech, Gavin, Sandkühler, Julia Fabienne, Rogers-Smith, Charlie, Vollmer, Michaela, Unwin, H Juliette T, Gal, Yarin, Chand, Meera, Gandy, Axel, Martin, Javier, Volz, Erik, Ferguson, Neil M, Bhatt, Samir, Brauner, Jan M, and Flaxman, Seth

Published
2021
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21. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study

Author

Steinmetz, Jaimie D, Bourne, Rupert R A, Briant, Paul Svitil, Flaxman, Seth R, Taylor, Hugh R B, Jonas, Jost B, Abdoli, Amir Aberhe, Abrha, Woldu Aberhe, Abualhasan, Ahmed, Abu-Gharbieh, Eman Girum, Adal, Tadele Girum, Afshin, Ashkan, Ahmadieh, Hamid, Alemayehu, Wondu, Alemzadeh, Sayyed Amirpooya Samir, Alfaar, Ahmed Samir, Alipour, Vahid, Androudi, Sofia, Arabloo, Jalal, Arditi, Aries Berhe, Aregawi, Brhane Berhe, Arrigo, Alessandro, Ashbaugh, Charlie, Ashrafi, Elham Debalkie, Atnafu, Desta Debalkie, Bagli, Eleni Amin, Baig, Atif Amin Winfried, Bärnighausen, Till Winfried, Battaglia Parodi, Maurizio, Beheshti, Mahya Srikanth, Bhagavathula, Akshaya Srikanth, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhattacharyya, Krittika, Bijani, Ali, Bikbov, Mukharram, Bottone, Michele, Braithwaite, Tasanee M, Bron, Alain M, Burugina Nagaraja, Sharath A, Butt, Zahid A, Caetano dos Santos, Florentino Luciano L, Carneiro, Vera L James, Casson, Robert James, Cheng, Ching-Yu Jasmine, Choi, Jee-Young Jasmine, Chu, Dinh-Toi, Cicinelli, Maria Vittoria M, Coelho, João M G, Congdon, Nathan G A, Couto, Rosa A A, Cromwell, Elizabeth A M, Dahlawi, Saad M, Dai, Xiaochen, Dana, Reza, Dandona, Lalit, Dandona, Rakhi A, Del Monte, Monte A, Derbew Molla, Meseret, Dervenis, Nikolaos Alemayehu, Desta, Abebaw Alemayehu P, Deva, Jenny P, Diaz, Daniel, Djalalinia, Shirin E, Ehrlich, Joshua R, Elayedath, Rajesh Rashad, Elhabashy, Hala Rashad B, Ellwein, Leon B, Emamian, Mohammad Hassan, Eskandarieh, Sharareh, Farzadfar, Farshad G, Fernandes, Arthur G, Fischer, Florian S, Friedman, David S M, Furtado, João M, Gaidhane, Shilpa, Gazzard, Gus, Gebremichael, Berhe, George, Ronnie, Ghashghaee, Ahmad, Gilani, Syed Amir, Golechha, Mahaveer, Hamidi, Samer Randall, Hammond, Billy Randall R, Hartnett, Mary Elizabeth R Kusuma, Hartono, Risky Kusuma, Hashi, Abdiwahab I, Hay, Simon I, Hayat, Khezar, Heidari, Golnaz, Ho, Hung Chak, Holla, Ramesh, Househ, Mowafa J, Huang, John J Emmanuel, Ibitoye, Segun Emmanuel M, Ilic, Irena M D, Ilic, Milena D D, Ingram, April D Naghibi, Irvani, Seyed Sina Naghibi, Islam, Sheikh Mohammed Shariful, Itumalla, Ramaiah, Jayaram, Shubha Prakash, Jha, Ravi Prakash, Kahloun, Rim, Kalhor, Rohollah, Kandel, Himal, Kasa, Ayele Semachew, Kavetskyy, Taras A, Kayode, Gbenga A H, Kempen, John H, Khairallah, Moncef, Khalilov, Rovshan Ahmad, Khan, Ejaz Ahmad C, Khanna, Rohit C, Khatib, Mahalaqua Nazli Ahmed, Khoja, Tawfik Ahmed E, Kim, Judy E, Kim, Yun Jin, Kim, Gyu Ri, Kisa, Sezer, Kisa, Adnan, Kosen, Soewarta, Koyanagi, Ai, Kucuk Bicer, Burcu, Kulkarni, Vaman P, Kurmi, Om P, Landires, Iván Charles, Lansingh, Van Charles L, Leasher, Janet L E, LeGrand, Kate E, Leveziel, Nicolas, Limburg, Hans, Liu, Xuefeng, Madhava Kunjathur, Shilpashree, Maleki, Shokofeh, Manafi, Navid, Mansouri, Kaweh, McAlinden, Colm Gebremichael, Meles, Gebrekiros Gebremichael M, Mersha, Abera M, Michalek, Irmina Maria R, Miller, Ted R, Misra, Sanjeev, Mohammad, Yousef, Mohammadi, Seyed Farzad Abdu, Mohammed, Jemal Abdu H, Mokdad, Ali H, Moni, Mohammad Ali Al, Montasir, Ahmed Al R, Morse, Alan R Fentaw, Mulaw, Getahun Fentaw C, Naderi, Mehdi, Naderifar, Homa S, Naidoo, Kovin S, Naimzada, Mukhammad David, Nangia, Vinay, Narasimha Swamy, Sreenivas Muhammad, Naveed, Dr Muhammad, Negash, Hadush Lan, Nguyen, Huong Lan, Nunez-Samudio, Virginia Akpojene, Ogbo, Felix Akpojene, Ogundimu, Kolawole T, Olagunju, Andrew T E, Onwujekwe, Obinna E, Otstavnov, Nikita O, Owolabi, Mayowa O, Pakshir, Keyvan, Panda-Jonas, Songhomitra, Parekh, Utsav, Park, Eun-Cheol, Pasovic, Maja, Pawar, Shrikant, Pesudovs, Konrad, Peto, Tunde Quang, Pham, Hai Quang, Pinheiro, Marina, Podder, Vivek, Rahimi-Movaghar, Vafa, Rahman, Mohammad Hifz Ur Y, Ramulu, Pradeep Y, Rathi, Priya, Rawaf, Salman Laith, Rawaf, David Laith, Rawal, Lal, Reinig, Nickolas M, Renzaho, Andre M, Rezapour, Aziz L, Robin, Alan L, Rossetti, Luca, Sabour, Siamak, Safi, Sare, Sahebkar, Amirhossein, Sahraian, Mohammad Ali M, Samy, Abdallah M, Sathian, Brijesh, Saya, Ganesh Kumar, Saylan, Mete A, Shaheen, Amira A Ali, Shaikh, Masood Ali T, Shen, Tueng T, Shibuya, Kenji Shibabaw, Shiferaw, Wondimeneh Shibabaw, Shigematsu, Mika, Shin, Jae Il, Silva, Juan Carlos, Silvester, Alexander A, Singh, Jasvinder A, Singhal, Deepika S, Sitorus, Rita S, Skiadaresi, Eirini Yurievich, Skryabin, Valentin Yurievich Aleksandrovna, Skryabina, Anna Aleksandrovna, Soheili, Amin Bekele, Sorrie, Muluken Bekele A R C, Sousa, Raúl A R C T, Sreeramareddy, Chandrashekhar T, Stambolian, Dwight Girma, Tadesse, Eyayou Girma, Tahhan, Nina Ismail, Tareque, Md Ismail, Topouzis, Fotis Xuan, Tran, Bach Xuan, Tsegaye, Gebiyaw K, Tsilimbaris, Miltiadis K, Varma, Rohit, Virgili, Gianni, Vongpradith, Avina Thu, Vu, Giang Thu, Wang, Ya Xing, Wang, Ningli Hailay, Weldemariam, Abrha Hailay K, West, Sheila K Gebeyehu, Wondmeneh, Temesgen Gebeyehu Y, Wong, Tien Y, Yaseri, Mehdi, Yonemoto, Naohiro, Yu, Chuanhua Sergeevich, Zastrozhin, Mikhail Sergeevich, Zhang, Zhi-Jiang R, Zimsen, Stephanie R, Resnikoff, Serge, and Vos, Theo

Published
2021
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22. Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the Global Burden of Disease Study

Author

Bourne, Rupert, Steinmetz, Jaimie D, Flaxman, Seth, Briant, Paul Svitil, Taylor, Hugh R, Resnikoff, Serge, Casson, Robert James, Abdoli, Amir, Abu-Gharbieh, Eman, Afshin, Ashkan, Ahmadieh, Hamid, Akalu, Yonas, Alamneh, Alehegn Aderaw, Alemayehu, Wondu, Alfaar, Ahmed Samir, Alipour, Vahid, Anbesu, Etsay Woldu, Androudi, Sofia, Arabloo, Jalal, Arditi, Aries, Asaad, Malke, Bagli, Eleni, Baig, Atif Amin, Bärnighausen, Till Winfried, Battaglia Parodi, Maurizio, Bhagavathula, Akshaya Srikanth, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhattacharyya, Krittika, Bijani, Ali, Bikbov, Mukharram, Bottone, Michele, Braithwaite, Tasanee, Bron, Alain M, Butt, Zahid A, Cheng, Ching-Yu, Chu, Dinh-Toi, Cicinelli, Maria Vittoria, Coelho, João M, Dagnew, Baye, Dai, Xiaochen, Dana, Reza, Dandona, Lalit, Dandona, Rakhi, Del Monte, Monte A, Deva, Jenny P, Diaz, Daniel, Djalalinia, Shirin, Dreer, Laura E, Ehrlich, Joshua R, Ellwein, Leon B, Emamian, Mohammad Hassan, Fernandes, Arthur G, Fischer, Florian, Friedman, David S, Furtado, João M, Gaidhane, Abhay Motiramji, Gaidhane, Shilpa, Gazzard, Gus, Gebremichael, Berhe, George, Ronnie, Ghashghaee, Ahmad, Golechha, Mahaveer, Hamidi, Samer, Hammond, Billy Randall, Hartnett, Mary Elizabeth R, Hartono, Risky Kusuma, Hay, Simon I, Heidari, Golnaz, Ho, Hung Chak, Hoang, Chi Linh, Househ, Mowafa, Ibitoye, Segun Emmanuel, Ilic, Irena M, Ilic, Milena D, Ingram, April D, Irvani, Seyed Sina Naghibi, Jha, Ravi Prakash, Kahloun, Rim, Kandel, Himal, Kasa, Ayele Semachew, Kempen, John H, Keramati, Maryam, Khairallah, Moncef, Khan, Ejaz Ahmad, Khanna, Rohit C, Khatib, Mahalaqua Nazli, Kim, Judy E, Kim, Yun Jin, Kisa, Sezer, Kisa, Adnan, Koyanagi, Ai, Kurmi, Om P, Lansingh, Van Charles, Leasher, Janet L, Leveziel, Nicolas, Limburg, Hans, Majdan, Marek, Manafi, Navid, Mansouri, Kaweh, McAlinden, Colm, Mohammadi, Seyed Farzad, Mohammadian-Hafshejani, Abdollah, Mohammadpourhodki, Reza, Mokdad, Ali H, Moosavi, Delaram, Morse, Alan R, Naderi, Mehdi, Naidoo, Kovin S, Nangia, Vinay, Nguyen, Cuong Tat, Nguyen, Huong Lan Thi, Ogundimu, Kolawole, Olagunju, Andrew T, Ostroff, Samuel M, Panda-Jonas, Songhomitra, Pesudovs, Konrad, Peto, Tunde, Quazi Syed, Zahiruddin, Rahman, Mohammad Hifz Ur, Ramulu, Pradeep Y, Rawaf, Salman, Rawaf, David Laith, Reinig, Nickolas, Robin, Alan L, Rossetti, Luca, Safi, Sare, Sahebkar, Amirhossein, Samy, Abdallah M, Saxena, Deepak, Serle, Janet B, Shaikh, Masood Ali, Shen, Tueng T, Shibuya, Kenji, Shin, Jae Il, Silva, Juan Carlos, Silvester, Alexander, Singh, Jasvinder A, Singhal, Deepika, Sitorus, Rita S, Skiadaresi, Eirini, Skirbekk, Vegard, Soheili, Amin, Sousa, Raúl A R C, Spurlock, Emma Elizabeth, Stambolian, Dwight, Taddele, Biruk Wogayehu, Tadesse, Eyayou Girma, Tahhan, Nina, Tareque, Md Ismail, Topouzis, Fotis, Tran, Bach Xuan, Travillian, Ravensara S, Tsilimbaris, Miltiadis K, Varma, Rohit, Virgili, Gianni, Wang, Ya Xing, Wang, Ningli, West, Sheila K, Wong, Tien Y, Zaidi, Zoubida, Zewdie, Kaleab Alemayehu, Jonas, Jost B, and Vos, Theo

Published
2021
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23. Database of epidemic trends and control measures during the first wave of COVID-19 in mainland China

Author

Fu, Han, Wang, Haowei, Xi, Xiaoyue, Boonyasiri, Adhiratha, Wang, Yuanrong, Hinsley, Wes, Fraser, Keith J., McCabe, Ruth, Olivera Mesa, Daniela, Skarp, Janetta, Ledda, Alice, Dewé, Tamsin, Dighe, Amy, Winskill, Peter, van Elsland, Sabine L., Ainslie, Kylie E.C., Baguelin, Marc, Bhatt, Samir, Boyd, Olivia, Brazeau, Nicholas F., Cattarino, Lorenzo, Charles, Giovanni, Coupland, Helen, Cucunuba, Zulma M., Cuomo-Dannenburg, Gina, Donnelly, Christl A., Dorigatti, Ilaria, Eales, Oliver D., FitzJohn, Richard G., Flaxman, Seth, Gaythorpe, Katy A.M., Ghani, Azra C., Green, William D., Hamlet, Arran, Hauck, Katharina, Haw, David J., Jeffrey, Benjamin, Laydon, Daniel J., Lees, John A., Mellan, Thomas, Mishra, Swapnil, Nedjati-Gilani, Gemma, Nouvellet, Pierre, Okell, Lucy, Parag, Kris V., Ragonnet-Cronin, Manon, Riley, Steven, Schmit, Nora, Thompson, Hayley A., Unwin, H.Juliette T., Verity, Robert, Vollmer, Michaela A.C., Volz, Erik, Walker, Patrick G.T., Walters, Caroline E., Watson, Oliver J., Whittaker, Charles, Whittles, Lilith K., Imai, Natsuko, Bhatia, Sangeeta, and Ferguson, Neil M.

Published
2021
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24. Comparison of molecular testing strategies for COVID-19 control: a mathematical modelling study

Author

Ainslie, Kylie, Baguelin, Marc, Bhatt, Samir, Boonyasiri, Adhiratha, Brazeau, Nick, Cattarino, Lorenzo, Coupland, Helen, Cucunuba, Zulma, Cuomo-Dannenburg, Gina, Dighe, Amy, Donnelly, Christl, van Elsland, Sabine L, FitzJohn, Richard, Flaxman, Seth, Fraser, Keith, Gaythorpe, Katy, Green, Will, Hamlet, Arran, Hinsley, Wes, Imai, Natsuko, Knock, Edward, Laydon, Daniel, Mellan, Thomas, Mishra, Swapnil, Nedjati-Gilani, Gemma, Nouvellet, Pierre, Okell, Lucy, Ragonnet-Cronin, Manon, Thompson, Hayley A, Unwin, H. Juliette T, Vollmer, Michaela, Volz, Erik, Walters, Caroline, Wang, Yuanrong, Watson, Oliver J, Whittaker, Charles, Whittles, Lilith, Xi, Xiaoyue, Grassly, Nicholas C, Pons-Salort, Margarita, Parker, Edward P K, White, Peter J, and Ferguson, Neil M

Published
2020
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25. Reply to: The effect of interventions on COVID-19

Author

Flaxman, Seth, Mishra, Swapnil, Scott, James, Ferguson, Neil, Gandy, Axel, and Bhatt, Samir

Subjects
Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Author(s): Seth Flaxman [sup.1] , Swapnil Mishra [sup.2] , James Scott [sup.1] , Neil Ferguson [sup.2] , Axel Gandy [sup.1] , Samir Bhatt [sup.2] Author Affiliations: (1) Department of Mathematics, [...]

Published
2020
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31. Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe

Author

Sharma, Mrinank, Mindermann, Sören, Rogers-Smith, Charlie, Leech, Gavin, Snodin, Benedict, Ahuja, Janvi, Sandbrink, Jonas B., Monrad, Joshua Teperowski, Altman, George, Dhaliwal, Gurpreet, Finnveden, Lukas, Norman, Alexander John, Oehm, Sebastian B., Sandkühler, Julia Fabienne, Aitchison, Laurence, Gavenčiak, Tomáš, Mellan, Thomas, Kulveit, Jan, Chindelevitch, Leonid, Flaxman, Seth, Gal, Yarin, Mishra, Swapnil, Bhatt, Samir, and Brauner, Jan Markus

Published
2021
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33. Author Correction: Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals

Author

Brizzi, Andrea, Whittaker, Charles, Servo, Luciana M. S., Hawryluk, Iwona, Prete, Jr, Carlos A., de Souza, William M., Aguiar, Renato S., Araujo, Leonardo J. T., Bastos, Leonardo S., Blenkinsop, Alexandra, Buss, Lewis F., Candido, Darlan, Castro, Marcia C., Costa, Silvia F., Croda, Julio, de Souza Santos, Andreza Aruska, Dye, Christopher, Flaxman, Seth, Fonseca, Paula L. C., Geddes, Victor E. V., Gutierrez, Bernardo, Lemey, Philippe, Levin, Anna S., Mellan, Thomas, Bonfim, Diego M., Miscouridou, Xenia, Mishra, Swapnil, Monod, Mélodie, Moreira, Filipe R. R., Nelson, Bruce, Pereira, Rafael H. M., Ranzani, Otavio, Schnekenberg, Ricardo P., Semenova, Elizaveta, Sonabend, Raphael, Souza, Renan P., Xi, Xiaoyue, Sabino, Ester C., Faria, Nuno R., Bhatt, Samir, and Ratmann, Oliver

Published
2022
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35. Mapping changes in housing in sub-Saharan Africa from 2000 to 2015

Author

Tusting, Lucy S., Bisanzio, Donal, Alabaster, Graham, Cameron, Ewan, Cibulskis, Richard, Davies, Michael, and Flaxman, Seth

Subjects
Sub-Saharan Africa -- Economic aspects, Housing development -- Planning -- Economic aspects -- Reports, Africans, Nutrition, Housing, Water resource management, Human rights, Urban population, Water, Sustainable development, Geostatistics, Metropolitan areas, Company business planning, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals.sup.1,2. Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10-12%) to 23% (21-25%). However, 53 (50-57) million urban Africans (47% (44-50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals. The prevalence of improved housing (with improved drinking water and sanitation, sufficient living area and durable construction) in urban and rural sub-Saharan Africa doubled between 2000 and 2015., Author(s): Lucy S. Tusting [sup.1] , Donal Bisanzio [sup.2] [sup.3] , Graham Alabaster [sup.4] , Ewan Cameron [sup.5] , Richard Cibulskis [sup.6] , Michael Davies [sup.7] , Seth Flaxman [sup.8] [...]

Published
2019
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37. State-level tracking of COVID-19 in the United States

Author

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

Published
2020
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38. Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis

Author

Bourne, Rupert, Ackland, Peter, Arditi, Aries, Barkana, Yaniv, Bozkurt, Banu, Braithwaite, Tasanee, Bron, Alain, Budenz, Donald, Cai, Feng, Casson, Robert, Chakravarthy, Usha, Choi, Jaewan, Cicinelli, Maria Vittoria, Congdon, Nathan, Dana, Reza, Dandona, Rakhi, Dandona, Lalit, Das, Aditi, Dekaris, Iva, Del Monte, Monte, deva, Jenny, Dreer, Laura, Ellwein, Leon, Frazier, Marcela, Frick, Kevin, Friedman, David, Furtado, Joao, Gao, Hua, Gazzard, Gus, George, Ronnie, Gichuhi, Stephen, Gonzalez, Victor, Hammond, Billy, Hartnett, Mary Elizabeth, He, Minguang, Hejtmancik, James, Hirai, Flavio, Huang, John, Ingram, April, Javitt, Jonathan, Jonas, Jost, Joslin, Charlotte, Keeffe, Jill, Kempen, John, Khairallah, Moncef, Khanna, Rohit, Kim, Judy, Lambrou, George, Lansingh, Van Charles, Lanzetta, Paolo, Leasher, Janet, Lim, Jennifer, LIMBURG, Hans, Mansouri, Kaweh, Mathew, Anu, Morse, Alan, Munoz, Beatriz, Musch, David, Naidoo, Kovin, Nangia, Vinay, Palaiou, Maria, Parodi, Maurizio Battaglia, Pena, Fernando Yaacov, Pesudovs, Konrad, Peto, Tunde, Quigley, Harry, Raju, Murugesan, Ramulu, Pradeep, Rankin, Zane, Resnikoff, Serge, Reza, Dana, Robin, Alan, Rossetti, Luca, Saaddine, Jinan, Sandar, Mya, Serle, Janet, Shen, Tueng, Shetty, Rajesh, Sieving, Pamela, Silva, Juan Carlos, Silvester, Alex, Sitorus, Rita S., Stambolian, Dwight, Stevens, Gretchen, Taylor, Hugh, Tejedor, Jaime, Tielsch, James, Tsilimbaris, Miltiadis, van Meurs, Jan, Varma, Rohit, Virgili, Gianni, Wang, Ya Xing, Wang, Ning-Li, West, Sheila, Wiedemann, Peter, Wong, Tien, Wormald, Richard, Zheng, Yingfeng, Flaxman, Seth R, Bourne, Rupert R A, Cicinelli, Maria V, Jonas, Jost B, Kempen, John H, Limburg, Hans, Stevens, Gretchen A, Tahhan, Nina, Wong, Tien Y, and Taylor, Hugh R

Published
2017
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39. Magnitude, temporal trends, and projections of the global prevalence of blindness and distance and near vision impairment: a systematic review and meta-analysis

Author

Bourne, Rupert, Ackland, Peter, Arditi, Aries, Barkana, Yaniv, Bozkurt, Banu, BRAITHWAITE, TASANEE, Bron, Alain, Budenz, Donald, Cai, Feng, Casson, Robert, Chakravarthy, Usha, Choi, Jaewan, Cicinelli, Maria Vittoria, Congdon, Nathan, Dana, Reza, Dandona, Rakhi, Dandona, Lalit, Das, Aditi, Dekaris, Iva, Del Monte, Monte, Deva, Jenny, Dreer, Laura, Ellwein, Leon, Frazier, Marcela, Frick, Kevin, Friedman, David, Furtado, Joao, Gao, Hua, Gazzard, Gus, George, Ronnie, Gichuhi, Stephen, Gonzalez, Victor, Hammond, Billy, Hartnett, Mary Elizabeth, He, Minguang, Hejtmancik, James, Hirai, Flavio, Huang, John, Ingram, April, Javitt, Jonathan, Jonas, Jost, Joslin, Charlotte, Keeffe, Jill, Kempen, John, Khairallah, Moncef, Khanna, Rohit, Kim, Judy, Lambrou, George, Lansingh, Van Charles, Lanzetta, Paolo, Leasher, Janet, Lim, Jennifer, LIMBURG, Hans, Mansouri, Kaweh, Mathew, Anu, Morse, Alan, Munoz, Beatriz, Musch, David, Naidoo, Kovin, Nangia, Vinay, PALAIOU, MARIA, Parodi, Maurizio Battaglia, Pena, Fernando Yaacov, Pesudovs, Konrad, Peto, Tunde, Quigley, Harry, Raju, Murugesan, Ramulu, Pradeep, Resnikoff, Serge, Robin, Alan, Rossetti, Luca, Saaddine, Jinan, SANDAR, MYA, Serle, Janet, Shen, Tueng, Shetty, Rajesh, Sieving, Pamela, Silva, Juan Carlos, Silvester, Alex, Sitorus, Rita S, Stambolian, Dwight, Stevens, Gretchen, Taylor, Hugh, Tejedor, Jaime, Tielsch, James, Tsilimbaris, Miltiadis, van Meurs, Jan, Varma, Rohit, Virgili, Gianni, Volmink, Jimmy, Wang, Ya Xing, Wang, Ning-Li, West, Sheila, Wiedemann, Peter, Wong, Tien, Wormald, Richard, Zheng, Yingfeng, Bourne, Rupert R A, Flaxman, Seth R, Braithwaite, Tasanee, Cicinelli, Maria V, Jonas, Jost B, Kempen, John H, Limburg, Hans, Stevens, Gretchen A, Tahhan, Nina, Wong, Tien Y, and Taylor, Hugh R

Published
2017
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40. Deep learning and MCMC with aggVAE for shifting administrative boundaries: mapping malaria prevalence in Kenya

Author

Semenova, Elizaveta, Mishra, Swapnil, Bhatt, Samir, Flaxman, Seth, and Unwin, H Juliette T

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Statistics - Machine Learning, Machine Learning (stat.ML), Machine Learning (cs.LG)
Abstract

Model-based disease mapping remains a fundamental policy-informing tool in the fields of public health and disease surveillance. Hierarchical Bayesian models have emerged as the state-of-the-art approach for disease mapping since they are able to both capture structure in the data and robustly characterise uncertainty. When working with areal data, e.g.~aggregates at the administrative unit level such as district or province, current models rely on the adjacency structure of areal units to account for spatial correlations and perform shrinkage. The goal of disease surveillance systems is to track disease outcomes over time. This task is especially challenging in crisis situations which often lead to redrawn administrative boundaries, meaning that data collected before and after the crisis are no longer directly comparable. Moreover, the adjacency-based approach ignores the continuous nature of spatial processes and cannot solve the change-of-support problem, i.e.~when estimates are required to be produced at different administrative levels or levels of aggregation. We present a novel, practical, and easy to implement solution to solve these problems relying on a methodology combining deep generative modelling and fully Bayesian inference: we build on the recently proposed PriorVAE method able to encode spatial priors over small areas with variational autoencoders by encoding aggregates over administrative units. We map malaria prevalence in Kenya, a country in which administrative boundaries changed in 2010.

Published
2023

42. Semi-mechanistic Bayesian modelling of COVID-19 with renewal processes.

Author

Bhatt, Samir, Ferguson, Neil, Flaxman, Seth, Gandy, Axel, Mishra, Swapnil, and Scott, James A

Subjects
LATENT infection, COVID-19, COVID-19 pandemic, MULTILEVEL models, SEROPREVALENCE, BASIC reproduction number
Abstract

We propose a general Bayesian approach to modelling epidemics such as COVID-19. The approach grew out of specific analyses conducted during the pandemic, in particular, an analysis concerning the effects of non-pharmaceutical interventions (NPIs) in reducing COVID-19 transmission in 11 European countries. The model parameterises the time-varying reproduction number R t through a multilevel regression framework in which covariates can be governmental interventions, changes in mobility patterns, or other behavioural measures. Bayesian multilevel modelling allows a joint fit across regions, with partial pooling to share strength. This innovation was critical to our timely estimates of the impact of lockdown and other NPIs in the European epidemics: estimates from countries at later stages in their epidemics informed those of countries at earlier stages. Originally released as Imperial College Reports, the validity of this approach was borne out by the subsequent course of the epidemic. Our framework provides a fully generative model for latent infections and derived observations, including deaths, cases, hospitalizations, ICU admissions, and seroprevalence surveys. In this article, we additionally explore the confounded nature of NPIs and mobility. Versions of our model were used by New York State, Tennessee, and Scotland to estimate the current epidemic situation and make policy decisions. [ABSTRACT FROM AUTHOR]

Published
2023
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44. Regularised B-splines Projected Gaussian Process Priors to Estimate Time-trends in Age-specific COVID-19 Deaths.

Author

Monod, Mélodie, Blenkinsop, Alexandra, Brizzi, Andrea, Yu Chen, Perello, Carlos Cardoso Correia, Jogarah, Vidoushee, Yuanrong Wang, Flaxman, Seth, Bhatt, Samir, and Ratmann, Oliver

Subjects
GAUSSIAN function, COVID-19 pandemic, KERNEL functions, EPIDEMICS
Abstract

The COVID-19 pandemic has caused severe public health consequences in the United States. In this study, we use a hierarchical Bayesian model to estimate the age-specific COVID-19 attributable deaths over time in the United States. The model is specified by a novel non-parametric spatial approach over time and age, a low-rank Gaussian Process (GP) projected by regularised Bsplines. We show that this projection defines a GP with attractive smoothness and computational efficiency properties, derive its kernel function, and discuss the penalty terms induced by the projected GP. Simulation analyses and benchmark results show that the B-splines projected GP may perform better than standard B-splines and Bayesian P-splines, and equivalently well as a standard GP at considerably lower runtimes. We apply the model to weekly, age-stratified COVID-19 attributable deaths reported by the US Centers for Disease Control, which are subject to censoring and reporting biases. Using the B-splines projected GP, we can estimate longitudinal trends in COVID-19 associated deaths across the US by 1-year age bands. These estimates are instrumental to calculate age-specific mortality rates, describe variation in age-specific deaths across the US, and for fitting epidemic models. Here, we couple the model with age-specific vaccination rates to show that vaccination rates were significantly associated with the magnitude of resurgences in COVID-19 deaths during the summer 2021. With counterfactual analyses, we quantify the avoided COVID-19 deaths under lower vaccination rates and avoidable COVID-19 deaths under higher vaccination rates. The Bsplines projected GP priors that we develop are likely an appealing addition to the arsenal of Bayesian regularising priors. [ABSTRACT FROM AUTHOR]

Published
2023
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45. Application of referenced thermodynamic integration to Bayesian model selection.

Author

Hawryluk, Iwona, Mishra, Swapnil, Flaxman, Seth, Bhatt, Samir, and Mellan, Thomas A.

Subjects
STATISTICAL learning, COVID-19
Abstract

Evaluating normalising constants is important across a range of topics in statistical learning, notably Bayesian model selection. However, in many realistic problems this involves the integration of analytically intractable, high-dimensional distributions, and therefore requires the use of stochastic methods such as thermodynamic integration (TI). In this paper we apply a simple but under-appreciated variation of the TI method, here referred to as referenced TI, which computes a single model's normalising constant in an efficient way by using a judiciously chosen reference density. The advantages of the approach and theoretical considerations are set out, along with pedagogical 1 and 2D examples. The approach is shown to be useful in practice when applied to a real problem —to perform model selection for a semi-mechanistic hierarchical Bayesian model of COVID-19 transmission in South Korea involving the integration of a 200D density. [ABSTRACT FROM AUTHOR]

Published
2023
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46. Trends and mortality effects of vitamin A deficiency in children in 138 low-income and middle-income countries between 1991 and 2013: a pooled analysis of population-based surveys

Author

Stevens, Gretchen A, Bennett, James E, Hennocq, Quentin, Lu, Yuan, De-Regil, Luz Maria, Rogers, Lisa, Danaei, Goodarz, Li, Guangquan, White, Richard A, Flaxman, Seth R, Oehrle, Sean-Patrick, Finucane, Mariel M, Guerrero, Ramiro, Bhutta, Zulfiqar A, Then-Paulino, Amarilis, Fawzi, Wafaie, Black, Robert E, and Ezzati, Majid

Published
2015
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47. City-Wide Perceptions of Neighbourhood Quality using Street View Images

Author

Muller, Emily, Gemmell, Emily, Choudhury, Ishmam, Nathvani, Ricky, Metzler, Antje Barbara, Bennett, James, Denton, Emily, Flaxman, Seth, and Ezzati, Majid

Subjects
FOS: Computer and information sciences, Computer Science - Computers and Society, Computer Vision and Pattern Recognition (cs.CV), Computers and Society (cs.CY), Computer Science - Computer Vision and Pattern Recognition
Abstract

The interactions of individuals with city neighbourhoods is determined, in part, by the perceived quality of urban environments. Perceived neighbourhood quality is a core component of urban vitality, influencing social cohesion, sense of community, safety, activity and mental health of residents. Large-scale assessment of perceptions of neighbourhood quality was pioneered by the Place Pulse projects. Researchers demonstrated the efficacy of crowd-sourcing perception ratings of image pairs across 56 cities and training a model to predict perceptions from street-view images. Variation across cities may limit Place Pulse's usefulness for assessing within-city perceptions. In this paper, we set forth a protocol for city-specific dataset collection for the perception: 'On which street would you prefer to walk?'. This paper describes our methodology, based in London, including collection of images and ratings, web development, model training and mapping. Assessment of within-city perceptions of neighbourhoods can identify inequities, inform planning priorities, and identify temporal dynamics. Code available: https://emilymuller1991.github.io/urban-perceptions/.

Published
2022

50. SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion

Author

Mlcochova, Petra, Kemp, Steven A., Dhar, Mahesh Shanker, Papa, Guido, Meng, Bo, Ferreira, Isabella A. T. M., Datir, Rawlings, Collier, Dami A., Albecka, Anna, Singh, Sujeet, Pandey, Rajesh, Brown, Jonathan, Zhou, Jie, Goonawardane, Niluka, Mishra, Swapnil, Whittaker, Charles, Mellan, Thomas, Marwal, Robin, Datta, Meena, Sengupta, Shantanu, Ponnusamy, Kalaiarasan, Radhakrishnan, Venkatraman Srinivasan, Abdullahi, Adam, Charles, Oscar, Chattopadhyay, Partha, Devi, Priti, Caputo, Daniela, Peacock, Tom, Wattal, Chand, Goel, Neeraj, Satwik, Ambrish, Vaishya, Raju, Agarwal, Meenakshi, Chauhan, Himanshu, Dikid, Tanzin, Gogia, Hema, Lall, Hemlata, Verma, Kaptan, Singh, Manoj K., Soni, Namita, Meena, Namonarayan, Madan, Preeti, Singh, Priyanka, Sharma, Ramesh, Sharma, Rajeev, Kabra, Sandhya, Kumar, Sattender, Kumari, Swati, Sharma, Uma, Chaudhary, Urmila, Sivasubbu, Sridhar, Scaria, Vinod, Oberoi, J. K., Raveendran, Reena, Datta, S., Das, Saumitra, Maitra, Arindam, Chinnaswamy, Sreedhar, Biswas, Nidhan Kumar, Parida, Ajay, Raghav, Sunil K., Prasad, Punit, Sarin, Apurva, Mayor, Satyajit, Ramakrishnan, Uma, Palakodeti, Dasaradhi, Seshasayee, Aswin Sai Narain, Thangaraj, K., Bashyam, Murali Dharan, Dalal, Ashwin, Bhat, Manoj, Shouche, Yogesh, Pillai, Ajay, Abraham, Priya, Potdar, Varsha Atul, Cherian, Sarah S., Desai, Anita Sudhir, Pattabiraman, Chitra, Manjunatha, M. V., Mani, Reeta S., Udupi, Gautam Arunachal, Nandicoori, Vinay, Tallapaka, Karthik Bharadwaj, Sowpati, Divya Tej, Kawabata, Ryoko, Morizako, Nanami, Sadamasu, Kenji, Asakura, Hiroyuki, Nagashima, Mami, Yoshimura, Kazuhisa, Ito, Jumpei, Kimura, Izumi, Uriu, Keiya, Kosugi, Yusuke, Suganami, Mai, Oide, Akiko, Yokoyama, Miyabishara, Chiba, Mika, Saito, Akatsuki, Butlertanaka, Erika P., Tanaka, Yuri L., Ikeda, Terumasa, Motozono, Chihiro, Nasser, Hesham, Shimizu, Ryo, Yuan, Yue, Kitazato, Kazuko, Hasebe, Haruyo, Nakagawa, So, Wu, Jiaqi, Takahashi, Miyoko, Fukuhara, Takasuke, Shimizu, Kenta, Tsushima, Kana, Kubo, Haruko, Shirakawa, Kotaro, Kazuma, Yasuhiro, Nomura, Ryosuke, Horisawa, Yoshihito, Takaori-Kondo, Akifumi, Tokunaga, Kenzo, Ozono, Seiya, Baker, Stephen, Dougan, Gordon, Hess, Christoph, Kingston, Nathalie, Lehner, Paul J., Lyons, Paul A., Matheson, Nicholas J., Owehand, Willem H., Saunders, Caroline, Summers, Charlotte, Thaventhiran, James E. D., Toshner, Mark, Weekes, Michael P., Maxwell, Patrick, Shaw, Ashley, Bucke, Ashlea, Calder, Jo, Canna, Laura, Domingo, Jason, Elmer, Anne, Fuller, Stewart, Harris, Julie, Hewitt, Sarah, Kennet, Jane, Jose, Sherly, Kourampa, Jenny, Meadows, Anne, O'Brien, Criona, Price, Jane, Publico, Cherry, Rastall, Rebecca, Ribeiro, Carla, Rowlands, Jane, Ruffolo, Valentina, Tordesillas, Hugo, Bullman, Ben, Dunmore, Benjamin J., Fawke, Stuart, Graf, Stefan, Hodgson, Josh, Huang, Christopher, Hunter, Kelvin, Jones, Emma, Legchenko, Ekaterina, Matara, Cecilia, Martin, Jennifer, Mescia, Federica, O'Donnell, Ciara, Pointon, Linda, Pond, Nicole, Shih, Joy, Sutcliffe, Rachel, Tilly, Tobias, Treacy, Carmen, Tong, Zhen, Wood, Jennifer, Wylot, Marta, Bergamaschi, Laura, Betancourt, Ariana, Bower, Georgie, Cossetti, Chiara, De Sa, Aloka, Epping, Madeline, Gleadall, Nick, Grenfell, Richard, Hinch, Andrew, Huhn, Oisin, Jackson, Sarah, Jarvis, Isobel, Krishna, Ben, Lewis, Daniel, Marsden, Joe, Nice, Francesca, Okecha, Georgina, Omarjee, Ommar, Perera, Marianne, Potts, Martin, Richoz, Nathan, Romashova, Veronika, Yarkoni, Natalia Savinykh, Sharma, Rahul, Stefanucci, Luca, Stephens, Jonathan, Strezlecki, Mateusz, Turner, Lori, De Bie, Eckart M. D. D., Bunclark, Katherine, Josipovic, Masa, Mackay, Michael, Rossi, Sabrina, Selvan, Mayurun, Spencer, Sarah, Yong, Cissy, Allison, John, Butcher, Helen, Clapham-Riley, Debbie, Dewhurst, Eleanor, Furlong, Anita, Graves, Barbara, Gray, Jennifer, Ivers, Tasmin, Kasanicki, Mary, Le Gresley, Emma, Linger, Rachel, Meloy, Sarah, Muldoon, Francesca, Ovington, Nigel, Papadia, Sofia, Phelan, Isabel, Stark, Hannah, Stirrups, Kathleen E., Townsend, Paul, Walker, Neil, Webster, Jennifer, Scholtes, Ingrid, Hein, Sabine, King, Rebecca, Mavousian, Antranik, Lee, Joo Hyeon, Bassi, Jessica, Silacci-Fegni, Chiara, Saliba, Christian, Pinto, Dora, Irie, Takashi, Yoshida, Isao, Hamilton, William L., Sato, Kei, Bhatt, Samir, Flaxman, Seth, James, Leo C., Corti, Davide, Piccoli, Luca, Barclay, Wendy S., Rakshit, Partha, Agrawal, Anurag, Gupta, Ravindra K., (INSACOG), Indian SARS-CoV-2 Genomics Consortium, Consortium, Genotype to Phenotype Japan (G2P-Japan), Collaboration, CITIID-NIHR BioResource COVID-19, Gupta, Ravindra K [0000-0001-9751-1808], Apollo - University of Cambridge Repository, and Gupta, Ravindra K. [0000-0001-9751-1808]

Subjects
Male, COVID-19 Vaccines, medicine.drug_class, Health Personnel, India, Monoclonal antibody, Virus Replication, Antibodies, Cell Line, Cell Fusion, Immune system, 13/100, medicine, Humans, Neutralizing antibody, Antibodies, Neutralizing, Female, Kinetics, SARS-CoV-2, Spike Glycoprotein, Coronavirus, Vaccination, Immune Evasion, Neutralizing, 631/326/596/4130, Syncytium, Multidisciplinary, Cell fusion, biology, article, Vaccine efficacy, 631/250/254, Virology, Spike Glycoprotein, Coronavirus, 13/31, biology.protein, Antibody, Infection
Abstract

The B.1.617.2 (Delta) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in the state of Maharashtra in late 2020 and spread throughout India, outcompeting pre-existing lineages including B.1.617.1 (Kappa) and B.1.1.7 (Alpha)1. In vitro, B.1.617.2 is sixfold less sensitive to serum neutralizing antibodies from recovered individuals, and eightfold less sensitive to vaccine-elicited antibodies, compared with wild-type Wuhan-1 bearing D614G. Serum neutralizing titres against B.1.617.2 were lower in ChAdOx1 vaccinees than in BNT162b2 vaccinees. B.1.617.2 spike pseudotyped viruses exhibited compromised sensitivity to monoclonal antibodies to the receptor-binding domain and the amino-terminal domain. B.1.617.2 demonstrated higher replication efficiency than B.1.1.7 in both airway organoid and human airway epithelial systems, associated with B.1.617.2 spike being in a predominantly cleaved state compared with B.1.1.7 spike. The B.1.617.2 spike protein was able to mediate highly efficient syncytium formation that was less sensitive to inhibition by neutralizing antibody, compared with that of wild-type spike. We also observed that B.1.617.2 had higher replication and spike-mediated entry than B.1.617.1, potentially explaining the B.1.617.2 dominance. In an analysis of more than 130 SARS-CoV-2-infected health care workers across three centres in India during a period of mixed lineage circulation, we observed reduced ChAdOx1 vaccine effectiveness against B.1.617.2 relative to non-B.1.617.2, with the caveat of possible residual confounding. Compromised vaccine efficacy against the highly fit and immune-evasive B.1.617.2 Delta variant warrants continued infection control measures in the post-vaccination era., A study of SARS-CoV-2 variants examining their transmission, infectivity, and potential resistance to therapies provides insights into the biology of the Delta variant and its role in the global pandemic.

Published
2022

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