A Bayesian Hierarchical Model for COVID-19 Cases in Mindanao Philippines

Year: 2022       Vol.: 71       No.: 2      

Authors: Jejemae D. Nacion and Bernadette F. Tubo

Abstract:

A Bayesian hierarchical modelling approach is utilized to nowcast COVID-19 cases in Mindanao, Philippines for the year 2020 to 2021. A spatio-temporal model is considered and the proposed methodology explores the possibility of a flexible way of correcting the time and space delayed reports of the COVID-19 cases for a duration of 4 weeks for the 27 provinces in Mindanao via a Bayesian approach. The goal of the modelling approach is to include parameters that will correct reporting delays in the dataset and derive a model using the Integrated Nested Laplace Approximation (INLA). The study shows that the proposed model was able to capture the increasing trend of the COVID-19 disease counts, that is, the prediction counts derived are closer to the true count compared to the currently reported counts of COVID-19 cases which showed a decreasing behavior. The ability of the proposed model to nowcast statistically significant estimates, particularly, for epidemic counts of COVD-19 in the presence of report delays may aid health authorities to have effective control measures and issuance of warnings to the public.

Keywords: Bayesian inference, spatio-temporal model, reporting delay, nowcasting

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