Detalhes do Documento

Percolation across households in mechanistic models of non-pharmaceutical interventions in SARS-CoV-2 disease dynamics

Autor(es): Franco, Caroline [UNESP] ; Ferreira, Leonardo Souto [UNESP] ; Sudbrack, Vítor [UNESP] ; Borges, Marcelo Eduardo ; Poloni, Silas [UNESP] ; Prado, Paulo Inácio ; White, Lisa J. ; Águas, Ricardo ; Kraenkel, Roberto André [UNESP] ; Coutinho, Renato Mendes

Data: 2022

Identificador Persistente: http://hdl.handle.net/11449/230597

Origem: Oasisbr

Assunto(s): Compartmental model; COVID-19; Percolation; SEIR

Descrição

Made available in DSpace on 2022-04-29T08:40:54Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-06-01

Li Ka Shing Foundation

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Bill and Melinda Gates Foundation

Since the emergence of the novel coronavirus disease 2019 (COVID-19), mathematical modelling has become an important tool for planning strategies to combat the pandemic by supporting decision-making and public policies, as well as allowing an assessment of the effect of different intervention scenarios. A proliferation of compartmental models were developed by the mathematical modelling community in order to understand and make predictions about the spread of COVID-19. While compartmental models are suitable for simulating large populations, the underlying assumption of a well-mixed population might be problematic when considering non-pharmaceutical interventions (NPIs) which have a major impact on the connectivity between individuals in a population. Here we propose a modification to an extended age-structured SEIR (susceptible–exposed–infected–recovered) framework, with dynamic transmission modelled using contact matrices for various settings in Brazil. By assuming that the mitigation strategies for COVID-19 affect the connections among different households, network percolation theory predicts that the connectivity among all households decreases drastically above a certain threshold of removed connections. We incorporated this emergent effect at population level by modulating home contact matrices through a percolation correction function, with the few additional parameters fitted to hospitalisation and mortality data from the city of São Paulo. Our model with percolation effects was better supported by the data than the same model without such effects. By allowing a more reliable assessment of the impact of NPIs, our improved model provides a better description of the epidemiological dynamics and, consequently, better policy recommendations.

Institute of Theoretical Physics São Paulo State University

Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Medicine University of Oxford

Observatório COVID-19 BR

Department of Ecology and Evolution University of Lausanne

Instituto de Biociências Universidade de São Paulo

Nuffield Department of Medicine University of Oxford Centre for Tropical Medicine and Global Health

Centro de Matemática Computação e Cognição - Universidade Federal do ABC

Institute of Theoretical Physics São Paulo State University

CAPES: 001

FAPESP: 2016/01343-7

FAPESP: 2017/26770-8

FAPESP: 2018/23984-0