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A data-driven model for COVID-19 pandemic – Evolution of the attack rate and prognosis for Brazil

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  • Rocha Filho, T.M.
  • Moret, M.A.
  • Chow, C.C.
  • Phillips, J.C.
  • Cordeiro, A.J.A.
  • Scorza, F.A.
  • Almeida, A.-C.G.
  • Mendes, J.F.F.

Abstract

We introduce a compartmental model SEIAHRV (Susceptible, Exposed, Infected, Asymptomatic, Hospitalized, Recovered, Vaccinated) with age structure for the spread of the SARAS-CoV virus. In order to model current different vaccines we use compartments for individuals vaccinated with one and two doses without vaccine failure and a compartment for vaccinated individual with vaccine failure. The model allows to consider any number of different vaccines with different efficacies and delays between doses. Contacts among age groups are modeled by a contact matrix and the contagion matrix is obtained from a probability of contagion pc per contact. The model uses known epidemiological parameters and the time dependent probability pc is obtained by fitting the model output to the series of deaths in each locality, and reflects non-pharmaceutical interventions. As a benchmark the output of the model is compared to two good quality serological surveys, and applied to study the evolution of the COVID-19 pandemic in the main Brazilian cities with a total population of more than one million. We also discuss with some detail the case of the city of Manaus which raised special attention due to a previous report of We also estimate the attack rate, the total proportion of cases (symptomatic and asymptomatic) with respect to the total population, for all Brazilian states since the beginning of the COVID-19 pandemic. We argue that the model present here is relevant to assessing present policies not only in Brazil but also in any place where good serological surveys are not available.

Suggested Citation

  • Rocha Filho, T.M. & Moret, M.A. & Chow, C.C. & Phillips, J.C. & Cordeiro, A.J.A. & Scorza, F.A. & Almeida, A.-C.G. & Mendes, J.F.F., 2021. "A data-driven model for COVID-19 pandemic – Evolution of the attack rate and prognosis for Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:chsofr:v:152:y:2021:i:c:s096007792100713x
    DOI: 10.1016/j.chaos.2021.111359
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    References listed on IDEAS

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    Cited by:

    1. Rocha Filho, T.M. & Mendes, J.F.F. & Lucio, M.L. & Moret, M.A., 2023. "COVID-19 data, mitigation policies and Newcomb–Benford law," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Zhu, Ligang & Li, Xiang & Xu, Fei & Yin, Zhiyong & Jin, Jun & Liu, Zhilong & Qi, Hong & Shuai, Jianwei, 2022. "Network modeling-based identification of the switching targets between pyroptosis and secondary pyroptosis," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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