| Title |
A Bayesian Hierarchical Model for COVID-19 Cases in Mindanao Philippines |
|
Authors |
Nacion, Jejemae D. and Tubo, Bernadette F. |
|
Publication date |
2022 |
|
Journal |
The Philippine Statistician |
| Volume |
71 |
| Issue |
2 |
| Pages |
25-36 |
| Publisher |
Philippine Statistical Association INC |
| 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. |
| Index terms / Keywords |
Bayesian inference, spatio-temporal model, reporting delay, nowcasting |
| DOI |
|
Mindanao State
University - Iligan Institute of Technology