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Report 21: Estimating COVID-19 Cases and Reproduction Number in Brazil 8th May 2020 Imperial College COVID-19 Response Team Report 21: Estimating COVID-19 cases and reproduction number in Brazil Thomas A Mellan3∗, Henrique H Hoeltgebaum∗, Swapnil Mishra∗, Charlie Whittaker∗, Ricardo P Schnekenberg∗, Axel Gandy∗, H Juliette T Unwin, Michaela A C Vollmer, Helen C oupland, Iwona H awryluk, Nuno Ro-drigues Faria, Juan Vesga, Harrison Zhu, Michael Hutchinson, Oliver Ratmann, Melodie Monod, Kylie Ainslie, Marc Baguelin, Sangeeta Bhatia, Adhiratha Boonyasiri, Nicholas Brazeau, Giovanni Charles, Laura V Cooper, Zulma Cucunuba, Gina Cuomo-Dannenburg, Amy Dighe, Bimandra Djaafara, Jeff Eaton, Sabine L van Elsland, Richard FitzJohn, Keith Fraser, Katy Gaythorpe, Will Green, Sarah Hayes, Natsuko Imai, Ben Jeffrey, Edward Knock, Daniel Laydon, John Lees, Tara Mangal, Andria Mousa, Gemma Nedjati-Gilani, Pierre Nouvellet, Daniela Olivera, Kris V Parag, Michael Pickles, Hayley A Thompson, Robert Verity, Caro- line Walters, Haowei Wang, Yuanrong Wang, Oliver J Watson, Lilith Whittles, Xiaoyue Xi, Lucy Okell, Ilaria Dorigatti, Patrick Walker, Azra Ghani, Steven Riley, Neil M Ferguson1, Christl A. Donnelly, Seth Flaxman∗ and Samir Bhatt2∗ Department of Infectious Disease Epidemiology, Imperial College London Department of Mathematics, Imperial College London WHO Collaborating Centre for Infectious Disease Modelling MRC Centre for Global Infectious Disease Analytics Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London Department of Statistics, University of Oxford Nuffield Department of Clinical Neurosciences, University of Oxford ∗Contributed equally. Correspondence: [email protected], [email protected],3 t.mel- [email protected] SUGGESTED CITATION TA Mellan, HH Hoeltgebaum, S Mishra et al. Estimating COVID-19 cases and reproduction number in Brazil. Imperial College London (08-05-2020), doi: https://doi.org/10.25561/78872. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. DOI: https://doi.org/10.25561/78872 Page 1 of 24 8th May 2020 Imperial College COVID-19 Response Team 1 Summary Brazil is an epicentre for COVID-19 in Latin America. In this report we describe the Brazilian epidemic using three epidemiological measures: the number of infections, the number of deaths and the repro- duction number. Our modelling framework requires sufficient death data to estimate trends,andwe therefore limit our analysis to 16 states that have experienced a total of more than fifty deaths. The distribution of deaths among states is highly heterogeneous, with 5 states—São Paulo, Rio deJaneiro, Ceará, Pernambuco and Amazonas—accounting for 81% of deaths reported to date. In these states, we estimate that the percentage of people that have been infected with SARS-CoV-2 ranges from 3.3%(95% CI: 2.8%-3.7%) in São Paulo to 10.6% (95% CI: 8.8%-12.1%) in Amazonas. The reproduction number (a measure of transmission intensity) at the start of the epidemic meant that an infected individual would infect three or four others on average. Following non-pharmaceutical interventions such as school clo- sures and decreases in population mobility, we show that the reproduction number has dropped sub- stantially in each state. However, for all 16 states we study, we estimate with high confidence thatthe reproduction number remains above 1. A reproduction number above 1 means that the epidemic is not yet controlled and will continue to grow. These trends are in stark contrast to other major COVID- 19 epidemics in Europe and Asia where enforced lockdowns have successfully driven the reproduction number below 1. While the Brazilian epidemic is still relatively nascent on a national scale, our results suggest that further action is needed to limit spread and prevent health system overload. 2 Introduction The world faces an unprecedented public health emergency in the COVID-19 pandemic. Since the emer- gence of the novel coronavirus (SARS-CoV-2) in China in December 2019, global spread has been rapid, with over 3.5 million cases and almost 250 thousand deaths reported from 187 countries as of the 6th May [13]. Though transmission of the disease beyond Asia was initially centred around Western Europe and North America, significant spread is now seen in other parts of the world, including many countries across Sub-Saharan Africa and Latin America. One such area of concern is Brazil - since report of its first case on 25th February, its epidemic has grown quickly, with the country now reporting over 135,000 cases and over 7,000 deaths [14].In response to significant spread and community transmission of the virus within the country, Brazilian stateand city officials have mandated extensive public health measures to reduce the transmission ofCOVID- 19, including declaring a state of emergency, mandating the closure of retail and service businesses, DOI: https://doi.org/10.25561/78872 Page 2 of 24 8th May 2020 Imperial College COVID-19 Response Team restricting transportation, and closing schools. Specific packages of interventions have been decidedat the state level, with substantial variation between states in the extent to which measures have been adopted, and their comparative timing [4]. Importantly, interventions employed to date remain short of the widespread and mandatory lockdowns implemented across parts of Asia and Europe and which have proved to be highly effective at containing spread of the virus [7, 19]. 0 500 1000 1500 2000 3000+ 0 50 100 150 200 300+ (a) (b) Figure 1: Map of cases (a) and deaths (b) in Brazil by state level. Data source: Painel Coronavirus at https://covid.saude.gov.br, accessed on 7th May 2020. It remains unclear the extent to which these measures have been effective in reducing transmission across the country. Reported cases across Brazil have more than doubled over the past 10 days and show little sign of slowing. Given this rapid growth, a better understanding of the current epidemiologi- cal situation and the impact of interventions deployed to date is required to guide policy decisions aimed at preventing worsening of the public health emergency the country faces. Motivated by this, weex- tend a previously published semi-mechanistic Bayesian hierarchical model of COVID-19 epidemiological dynamics [7, 19] to assess the impact of interventions aimed at curbing transmission of COVID-19 across Brazil. In our framework we estimate the number of deaths, infections and transmission as afunction of patterns in human mobility. We utilise this framework to explore the epidemiological situationin detail at the state level and understand the spread of the virus across the country to date. These results include both estimates of the proportion of individuals infected so far as well as the impact ofdiffer- ent control interventions, and provide insight into possible future epidemic trajectories should further control measures be employed, or current interventions relaxed. DOI: https://doi.org/10.25561/78872 Page 3 of 24 8th May 2020 Imperial College COVID-19 Response Team State IFR % Population Deaths Deaths per million Infections (thousands) Attack rate % Reproduction number SP 0.7 46,289,333 3,045 65.80 1,530 [1,310, 1,700] 3.30 [2.83, 3.68] 1.47 [1.34, 1.59] RJ 0.8 17,366,189 1,205 69.40 582 [492, 657] 3.35 [2.83, 3.78] 1.44 [1.28, 1.60] CE 1.1 9,187,886 848 92.30 410 [343, 464] 4.46 [3.73, 5.05] 1.61 [1.42, 1.81] PE 1.1 9,617,072 803 83.50 288 [239, 328] 3.00 [2.49, 3.41] 1.32 [1.14, 1.53] AM 0.8 4,207,714 751 178 448 [372, 509] 10.60 [8.84, 12.10] 1.58 [1.38, 1.81] PA 0.9 8,690,745 392 45.10 439 [339, 513] 5.05 [3.90, 5.90] 1.90 [1.57, 2.31] MA 1.0 7,114,598 291 40.90 147 [118, 170] 2.07 [1.65, 2.40] 1.55 [1.32, 1.80] BA 1.1 14,930,424 160 10.70 59.5 [46.5, 69.6] 0.40 [0.31, 0.47] 1.37 [1.14, 1.63] ES 0.9 4,064,052 145 35.70 91 [69.4, 107] 2.24 [1.71, 2.64] 1.57 [1.29, 1.90] PR 0.9 11,516,840 101 8.77 28.4 [21.6, 33.6] 0.25 [0.19, 0.29] 1.16 [0.95, 1.39] MG 1.0 21,292,666 97 4.56 28.1 [21, 33.4] 0.13 [0.10, 0.16] 1.30 [1.05, 1.57] PB 1.2 4,039,277 92 22.80 25.7 [19.4, 30.4] 0.64 [0.48, 0.75] 1.23 [0.97, 1.52] AL 1.1 3,351,092 89 26.60 40.1 [29, 48.1] 1.20 [0.87, 1.44] 1.27 [0.94, 1.67] RS 0.9 11,422,973 87 7.62 48.2 [36.3, 57.1] 0.42 [0.32, 0.50] 1.44 [1.15, 1.77] RN 1.1 3,534,165 72 20.40 19.9 [14.7, 23.7] 0.56 [0.42, 0.67] 1.18 [0.92, 1.45] SC 0.8 7,252,502 59 8.14 16.5 [12.2, 19.7] 0.23 [0.17, 0.27] 1.14 [0.91, 1.38] Table 1: Estimated infection fatality ratio (IFR), state population, reported deaths and deaths permillion population, estimated number of infections in thousands, attack rate (AR), and time-varying reproduc- tion number on 6 May 2020 with 95% credible intervals, for São Paulo (SP), Rio de Janeiro (RJ), Pernam- buco (PE), Ceará (CE), Amazonas (AM), Pará (PA), Maranhão (MA), Bahia (BA), Espírito Santo (ES), Paraná (PR), Minas Gerais (MG), Paraíba (PB), Rio Grande do Sul (RS), Rio Grande do Norte (RN), Alagoas (AL), Santa Catarina (SC).
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