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Count regression models for COVID-19

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  • Chan, Stephen
  • Chu, Jeffrey
  • Zhang, Yuanyuan
  • Nadarajah, Saralees

Abstract

At the end of 2019, the current novel coronavirus emerged as a severe acute respiratory disease that has now become a worldwide pandemic. Future generations will look back on this difficult period and see how our society as a whole united and rose to this challenge. Many reports have suggested that this new virus is becoming comparable to the Spanish flu pandemic of 1918. We provide a statistical study on the modelling and analysis of the daily incidence of COVID-19 in eighteen countries around the world. In particular, we investigate whether it is possible to fit count regression models to the number of daily new cases of COVID-19 in various countries and make short term predictions of these numbers. The results suggest that the biggest advantage of these methods is that they are simplistic and straightforward allowing us to obtain preliminary results and an overall picture of the trends in the daily confirmed cases of COVID-19 around the world. The best fitting count regression model for modelling the number of new daily COVID-19 cases of all countries analysed was shown to be a negative binomial distribution with log link function. Whilst the results cannot solely be used to determine and influence policy decisions, they provide an alternative to more specialised epidemiological models and can help to support or contradict results obtained from other analysis.

Suggested Citation

  • Chan, Stephen & Chu, Jeffrey & Zhang, Yuanyuan & Nadarajah, Saralees, 2021. "Count regression models for COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    • Handle: RePEc:eee:phsmap:v:563:y:2021:i:c:s0378437120307743
    DOI: 10.1016/j.physa.2020.125460
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    References listed on IDEAS

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    3. Fokianos, Konstantinos & Tjøstheim, Dag, 2011. "Log-linear Poisson autoregression," Journal of Multivariate Analysis, Elsevier, vol. 102(3), pages 563-578, March.
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    Cited by:

    1. Taro Kanatani & Kuninori Nakagawa, 2023. "Analysis of reporting lag in daily data of COVID-19 in Japan," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-20, December.
    2. Panarello, Demetrio & Tassinari, Giorgio, 2022. "One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern?," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    3. Gning, Lucien & Ndour, Cheikh & Tchuenche, J.M., 2022. "Modeling COVID-19 daily cases in Senegal using a generalized Waring regression model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    4. Pascoal, R. & Rocha, H., 2022. "Population density impact on COVID-19 mortality rate: A multifractal analysis using French data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    5. Ilya V. Naumov & Sergey S. Krasnykh & Yulia S. Otmakhova, 2022. "Scenario forecasting of the socio-economic consequences of the COVID-19 pandemic in Russian regions," R-Economy, Ural Federal University, Graduate School of Economics and Management, vol. 8(1), pages 5-20.

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