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Scale-free dynamics of COVID-19 in a Brazilian city.
Policarpo, J M P; Ramos, A A G F; Dye, C; Faria, N R; Leal, F E; Moraes, O J S; Parag, K V; Peixoto, P S; Buss, L; Sabino, E C; Nascimento, V H; Deppman, A.
  • Policarpo JMP; Instituto de Física - Universidade de São Paulo, Brazil.
  • Ramos AAGF; Instituto de Física - Universidade de São Paulo, Brazil.
  • Dye C; Department of Biology, University of Oxford, UK.
  • Faria NR; Department of Biology, University of Oxford, UK.
  • Leal FE; Imperial Coll London, MRC Ctr Global Infect Dis Anal, Sch Publ Helth, London, England, UK.
  • Moraes OJS; Faculdade de Medicina - Universidade de São Paulo, Brazil.
  • Parag KV; Universidade de São Caetano do Sul, São Caetano do Sul and Programa de Oncovirologia - Instituto Nacional de Câncer, Rio de Janeiro, Brazil.
  • Peixoto PS; Instituto de Física - Universidade de São Paulo, Brazil.
  • Buss L; MRC Centre for Global Infectious Disease Analysis, J-IDEA, Imperial College London, London W2 1PG, UK.
  • Sabino EC; Instituto de Matemática e Estatística, Universidade de São Paulo, Brazil.
  • Nascimento VH; Faculdade de Medicina - Universidade de São Paulo, Brazil.
  • Deppman A; Faculdade de Medicina - Universidade de São Paulo, Brazil.
Appl Math Model ; 121: 166-184, 2023 Sep.
Article in English | MEDLINE | ID: covidwho-2310430
ABSTRACT
A common basis to address the dynamics of directly transmitted infectious diseases, such as COVID-19, are compartmental (or SIR) models. SIR models typically assume homogenous population mixing, a simplification that is convenient but unrealistic. Here we validate an existing model of a scale-free fractal infection process using high-resolution data on COVID-19 spread in São Caetano, Brazil. We find that transmission can be described by a network in which each infectious individual has a small number of susceptible contacts, of the order of 2-5. This model parameter correlated tightly with physical distancing measured by mobile phone data, such that in periods of greater distancing the model recovered a lower average number of contacts, and vice versa. We show that the SIR model is a special case of our scale-free fractal process model in which the parameter that reflects population structure is set at unity, indicating homogeneous mixing. Our more general framework better explained the dynamics of COVID-19 in São Caetano, used fewer parameters than a standard SIR model and accounted for geographically localized clusters of disease. Our model requires further validation in other locations and with other directly transmitted infectious agents.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Country/Region as subject: South America / Brazil Language: English Journal: Appl Math Model Year: 2023 Document Type: Article Affiliation country: J.apm.2023.03.039

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Prognostic study Country/Region as subject: South America / Brazil Language: English Journal: Appl Math Model Year: 2023 Document Type: Article Affiliation country: J.apm.2023.03.039