Dynamics of COVID-19 in Amazonia: a history of government denialism and the risk of a third wave

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dc.contributorLab. Biologia Estruturalpt_BR
dc.contributor.authorFerrante, Lucaspt_BR
dc.contributor.authorDuczmal, Luiz Henriquept_BR
dc.contributor.authorCapanema, Eduardopt_BR
dc.contributor.authorSteinmetz, Wilhelm Alexander Cardosopt_BR
dc.contributor.authorAlmeida, Alexandre Celestino Leitept_BR
dc.contributor.authorLeão, Jeremiaspt_BR
dc.contributor.authorVassão, Ruth Camargopt_BR
dc.contributor.authorFearnside, Philip Martinpt_BR
dc.contributor.authorTupinambás, Unaípt_BR
dc.date.accessioned2022-03-08T16:25:19Z-
dc.date.available2022-03-08T16:25:19Z-
dc.date.issued2022pt_BR
dc.identifier.citationFerrante L, Duczmal LH, Capanema E, Steinmetz WAC, Almeida ACL, Leão J, et al. Dynamics of COVID-19 in Amazonia: a history of government denialism and the risk of a third wave. Prev. Med. Rep. 2022 Apr;26:101752. doi:https://doi.org/10.1016/j.pmedr.2022.101752.pt_BR
dc.identifier.urihttps://repositorio.butantan.gov.br/handle/butantan/4245-
dc.description.abstractThe city of Manaus (the capital of Brazil’s state of Amazonas) has become a key location for understanding the dynamics of the global pandemic of COVID-19. Different groups of scientists have foreseen different scenarios, such as the second wave or that Manaus could escape such a wave by having reached herd immunity. Here we test five hypotheses that explain the second wave of COVID-19 in Manaus: 1) The greater transmissibility of the Amazonian (gamma or P.1) variant is responsible for the second wave; 2) SARS-CoV-2 infection levels during the first wave were overestimated by those foreseeing herd immunity, and the population remained below this threshold when the second wave began at the beginning of December 2020; 3) Antibodies acquired from infection by one lineage do not confer immunity against other lineages; 4) Loss of immunity has generated a feedback phenomenon among infected people, which could generate future waves, and 5) A combination of the foregoing hypotheses. We also evaluated the possibility of a third wave in Manaus despite advances in vaccination, the new wave being due to the introduction of the delta variant in the region and the loss of immunity from natural contact with the virus. We developed a multi-strain SEIRS (Susceptible-Exposed-Infected-Removed-Susceptible) model and fed it with data for Manaus on mobility, COVID-19 hospitalizations, numbers of cases and deaths. Our model contemplated the current vaccination rates for all vaccines applied in Manaus and the individual protection rates already known for each vaccine. Our results indicate that the SARS-CoV-2 gamma (P.1) strain that originated in the Amazon region is not the cause of the second wave of COVID-19 in Manaus, but rather this strain originated during the second wave and became predominant in January 2021. Our multi-strain SEIRS model indicates that neither the doubled transmission rate of the gamma variant nor the loss of immunity alone is sufficient to explain the sudden rise of hospitalizations in late December 2020. Our results also indicate that the most plausible explanation for the current second wave is a SARS-CoV-2 infection level at around 50% of the population in early December 2020, together with loss of population immunity and early relaxation of restrictive measures. The most-plausible model indicates that contact with one strain does not provide protection against other strains and that the gamma variant has a transmissibility rate twice that of the original SARS-CoV-2 strain. Our model also shows that, despite the advance of vaccination, and even if future vaccination advances at a steady pace, the introduction of the delta variant or other new variants could cause a new wave of COVID-19.pt_BR
dc.description.sponsorship(CNPq) Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.description.sponsorship(CAPES) Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorpt_BR
dc.description.sponsorship(FAPEMIG) Fundação de Amparo à Pesquisa do Estado de Minas Geraispt_BR
dc.description.sponsorship(FAPEAM) Fundação de Amparo à Pesquisa do Estado do Amazonaspt_BR
dc.format.extent101752pt_BR
dc.language.isoEnglishpt_BR
dc.relation.ispartofPreventive Medicine Reportspt_BR
dc.rightsOpen accesspt_BR
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_BR
dc.titleDynamics of COVID-19 in Amazonia: a history of government denialism and the risk of a third wavept_BR
dc.typeArticlept_BR
dc.rights.licenseCC BYpt_BR
dc.identifier.url10.1016/j.pmedr.2022.101752pt_BR
dc.contributor.external(INPA) Instituto Nacional de Pesquisas da Amazôniapt_BR
dc.contributor.external(UFMG) Universidade Federal de Minas Geraispt_BR
dc.contributor.external(UFAM) Universidade Federal do Amazonaspt_BR
dc.contributor.external(UFSJ) Universidade Federal de São João del-Reipt_BR
dc.identifier.citationvolume26pt_BR
dc.subject.keywordBrazilpt_BR
dc.subject.keywordcoronaviruspt_BR
dc.subject.keywordDelta variantpt_BR
dc.subject.keywordepidemiologypt_BR
dc.subject.keywordGamma variantpt_BR
dc.subject.keywordimmunity losspt_BR
dc.subject.keywordManauspt_BR
dc.subject.keywordmulti-strain SEIRS modelpt_BR
dc.subject.keywordSARS-CoV-2pt_BR
dc.subject.keywordpandemicpt_BR
dc.subject.keywordpublic healthpt_BR
dc.subject.keywordpublic health policypt_BR
dc.subject.keywordreinfectionpt_BR
dc.subject.keywordtransmissibilitypt_BR
dc.relation.ispartofabbreviatedPrev Med Reppt_BR
dc.identifier.citationabntv. 26, 101752, abr. 2022pt_BR
dc.identifier.citationvancouver2022 Apr;26:101752pt_BR
dc.contributor.butantanVassão, Ruth Camargo|:Pesquisador|:Lab. Biologia Estruturalpt_BR
dc.sponsorship.butantan(CNPq) Conselho Nacional de Desenvolvimento Científico e Tecnológico¦¦pt_BR
dc.sponsorship.butantan(CAPES) Coordenação de Aperfeiçoamento de Pessoal de Nível Superior¦¦pt_BR
dc.sponsorship.butantan(FAPEMIG) Fundação de Amparo à Pesquisa do Estado de Minas Gerais¦¦pt_BR
dc.sponsorship.butantan(FAPEAM) Fundação de Amparo à Pesquisa do Estado do Amazonas¦¦pt_BR
dc.identifier.bvsccBR78.1pt_BR
dc.identifier.bvsdbIBProdpt_BR
dc.description.dbindexedYespt_BR
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