ArticlePhysica A2021
Count regression models for COVID-19.
Article in Physica A, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Assessment of uncertainty quantification in universal differential equations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2025Article
- An ensemble approach improves the prediction of the COVID-19 pandemic in South Korea.Journal of global health · 2025Article
- Article
- Spatio-temporal epidemiology and associated indicators of COVID-19 (wave-I and II) in India.Scientific reports · 2024Article
- A Stochastic Mobility-Driven Spatially Explicit SEIQRD covid-19 Model with VOCs, Seasonality, and Vaccines.Applied mathematical modelling · 2023Article
- Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infectious contacts.Epidemics · 2023Article
- Analysis of reporting lag in daily data of COVID-19 in Japan.Letters in spatial and resource sciences · 2023Article
- The distribution of COVID-19 mortality.Infectious Disease Modelling · 2022Article
- Re-visiting the COVID-19 analysis using the class of high ordered integer-valued time series models with harmonic features.Healthcare analytics (New York, N.Y.) · 2022Article
- Modeling COVID-19 daily cases in Senegal using a generalized Waring regression model.Physica A · 2022Article
- Population density impact on COVID-19 mortality rate: A multifractal analysis using French data.Physica A · 2022Article
- One year of COVID-19 in Italy: are containment policies enough to shape the pandemic pattern?Socio-economic planning sciences · 2022Article
- Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis.Journal of medical Internet research · 2021Article
- The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation.PeerJ. Computer science · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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