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. 2023 Jun 8;18(6):e0286643.
doi: 10.1371/journal.pone.0286643. eCollection 2023.

Hybrid prediction of infections and deaths due to COVID-19 in two Colombian data series

Mónica Paola de la Cruz  1 , Diana Milena Galvis  1 , Gladys Elena Salcedo  1
Affiliations

Hybrid prediction of infections and deaths due to COVID-19 in two Colombian data series

Mónica Paola de la Cruz et al. PLoS One. .

Abstract

The prediction of the number of infected and dead due to COVID-19 has challenged scientists and government bodies, prompting them to formulate public policies to control the virus' spread and public health emergency worldwide. In this sense, we propose a hybrid method that combines the SIRD mathematical model, whose parameters are estimated via Bayesian inference with a seasonal ARIMA model. Our approach considers that notifications of both, infections and deaths are realizations of a time series process, so that components such as non-stationarity, trend, autocorrelation and/or stochastic seasonal patterns, among others, must be taken into account in the fitting of any mathematical model. The method is applied to data from two Colombian cities, and as hypothesized, the prediction outperforms the obtained with the fit of only the SIRD model. In addition, a simulation study is presented to assess the quality of the estimators of SIRD model in the inverse problem solution.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Real data (blue squares), predictions with the SIRD model (black curves) and SIRD + SARIMA hybrid (red lines), Calarcá—Colombia.
(a) Days since the first case. (b) Days since the first case.
Fig 2
Fig 2. Real data (blue squares) and predictions with the SIRD model (black curves) and the SIRD + SARIMA hybrid (red curves), Pasto-Colombia.
(a) Days since the first case. (b) Days since the first case.
See this image and copyright information in PMC

References

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