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

Details

Ressource 1Download: 35325705_BIB_9CBBD61263C9.pdf (1012.19 [Ko])
State: Public
Version: Final published version
License: Not specified
Serval ID
serval:BIB_9CBBD61263C9
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Percolation across households in mechanistic models of non-pharmaceutical interventions in SARS-CoV-2 disease dynamics.
Journal
Epidemics
Author(s)
Franco C., Ferreira L.S., Sudbrack V., Borges M.E., Poloni S., Prado P.I., White L.J., Águas R., Kraenkel R.A., Coutinho R.M.
ISSN
1878-0067 (Electronic)
ISSN-L
1878-0067
Publication state
Published
Issued date
06/2022
Peer-reviewed
Oui
Volume
39
Pages
100551
Language
english
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
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.
Keywords
Brazil, COVID-19/epidemiology, Communicable Disease Control, Humans, Models, Theoretical, Pandemics/prevention & control, SARS-CoV-2, COVID-19, Compartmental model, Percolation, SEIR
Pubmed
Web of science
Open Access
Yes
Create date
09/04/2022 20:05
Last modification date
25/01/2024 8:41
Usage data