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A non-central beta model to forecast and evaluate pandemics time series

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  • Firmino, Paulo Renato Alves
  • de Sales, Jair Paulino
  • Gonçalves Júnior, Jucier
  • da Silva, Taciana Araújo

Abstract

Government, researchers, and health professionals have been challenged to model, forecast, and evaluate pandemics time series (e.g. new coronavirus SARS-CoV-2, COVID-19). The main difficulty is the level of novelty imposed by these phenomena. Information from previous epidemics is only partially relevant. Further, the spread is local-dependent, reflecting a number of social, political, economic, and environmental dynamic factors. The present paper aims to provide a relatively simple way to model, forecast, and evaluate the time incidence of a pandemic. The proposed framework makes use of the non-central beta (NCB) probability density function. Specifically, a probabilistic optimisation algorithm searches for the best NCB model of the pandemic, according to the mean square error metric. The resulting model allows one to infer, among others, the general peak date, the ending date, and the total number of cases as well as to compare the level of difficult imposed by the pandemic among territories. Case studies involving COVID-19 incidence time series from countries around the world suggest the usefulness of the proposed framework in comparison with some of the main epidemic models from the literature (e.g. SIR, SIS, SEIR) and established time series formalisms (e.g. exponential smoothing - ETS, autoregressive integrated moving average - ARIMA).

Suggested Citation

  • Firmino, Paulo Renato Alves & de Sales, Jair Paulino & Gonçalves Júnior, Jucier & da Silva, Taciana Araújo, 2020. "A non-central beta model to forecast and evaluate pandemics time series," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
  • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s096007792030607x
    DOI: 10.1016/j.chaos.2020.110211
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    References listed on IDEAS

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    1. Arjun Gupta & Johanna Orozco-Castañeda & Daya Nagar, 2011. "Non-central bivariate beta distribution," Statistical Papers, Springer, vol. 52(1), pages 139-152, February.
    2. Xingyu Zhang & Tao Zhang & Alistair A Young & Xiaosong Li, 2014. "Applications and Comparisons of Four Time Series Models in Epidemiological Surveillance Data," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-16, February.
    3. Alexander Shapiro & Jos Berge, 2002. "Statistical inference of minimum rank factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 67(1), pages 79-94, March.
    4. Carol Y. Lin, 2008. "Modeling Infectious Diseases in Humans and Animals by KEELING, M. J. and ROHANI, P," Biometrics, The International Biometric Society, vol. 64(3), pages 993-993, September.
    5. Tsallis, Constantino & Stariolo, Daniel A., 1996. "Generalized simulated annealing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 233(1), pages 395-406.
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    Cited by:

    1. Gonçalves, Alan D.S. & Fernandes, Leonardo H.S. & Nascimento, Abraão D.C., 2022. "Dynamics diagnosis of the COVID-19 deaths using the Pearson diagram," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).

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