Modelling the unexpected dynamics of COVID-19 in Manaus,Brazil  

作　　者：Daihai He Yael Artzy-Randrup Salihu S.Musa Tiago Gräf Felipe Naveca Lewi Stone 

机构地区：[1]Department of Applied Mathematics,Hong Kong Polytechnic University,Hong Kong SAR,China [2]Department of Theoretical and Computational Ecology,IBED,University of Amsterdam,Amsterdam,Netherlands [3]Department of Genomics and Computational Biology,University of Massachusetts Chan Medical School,Worcester,MA,01605,USA [4]Department of Mathematics,Aliko Dangote University of Science and Technology,Kano,Nigeria [5]Instituto Gonçalo Moniz,Fiocruz,Salvador,Bahia,Brazil [6]Instituto Leônidas e Maria Deane,Fiocruz,Manaus,Brazil [7]Mathematical Sciences,School of Science,RMIT University,Melbourne,Australia [8]Biomathematics Unit,School of Zoology,Faculty of Life Sciences,Tel Aviv University,Tel Aviv,Israel

出　　处：《Infectious Disease Modelling》2024年第2期557-568,共12页传染病建模（英文）

基　　金：DH was supported by Hong Kong Research Grants Council Collaborative Research Fund(C5079-21G).

摘　　要：In late March 2020,SARS-CoV-2 arrived in Manaus,Brazil,and rapidly developed into a large-scale epidemic that collapsed the local health system and resulted in extreme death rates.Several key studies reported that∼76%of residents of Manaus were infected(attack rate AR≃76%)by October 2020,suggesting protective herd immunity had been reached.Despite this,an unexpected second wave of COVID-19 struck again in November and proved to be larger than the first,creating a catastrophe for the unprepared population.It has been suggested that this could be possible if the second wave was driven by reinfections.However,it is widely reported that reinfections were at a low rate(before the emergence of Omicron),and reinfections tend to be mild.Here,we use novel methods to model the epidemic from mortality data without considering reinfection-caused deaths and evaluate the impact of interventions to explain why the second wave appeared.The method fits a“flexible”reproductive numberR_(0)(t)that changes over the epidemic,and it is demonstrated that the method can successfully reconstruct R_(0)(t)from simulated data.For Manaus,the method finds AR≃34%by October 2020 for the first wave,which is far less than required for herd immunity yet in-line with seroprevalence estimates.The work is complemented by a two-strain model.Using genomic data,the model estimates transmissibility of the new P.1 virus lineage as 1.9 times higher than that of the non-P.1.Moreover,an age class model variant that considers the high mortality rates of older adults show very similar results.These models thus provide a reasonable explanation for the two-wave dynamics in Manaus without the need to rely on large reinfection rates,which until now have only been found in negligible to moderate numbers in recent surveillance efforts.

关 键 词：MODELLING COVID-19 Reproduction number Herd immunity REINFECTION 

分 类 号：R563.1[医药卫生—呼吸系统]