The Interplay between Clusters, Covariates and Spatial Priors in Spatial Modelling of Covid-19 in South Sulawesi Province, Indonesia
Daftar Isi:
- A number of previous studies on modelling Covid-19 using a Bayesian spatial Conditional Autoregressive (CAR) have been carried out. However, this model has not been used in modeling Covid-19 with the inclusion of covariates. This study aims to evaluate the most suitable Bayesian spatial CAR localised models in modelling the number of Covid-19 cases without and with the inclusion of covariates (the distance to the capital city and population density), examine the impact of covariates, and spatial priors on the identified clusters and factors that affect the Covid-19 risk in South Sulawesi Province. Data on the number of confirmed cases of Covid-19 (19 March 2020 -25 February 2022) were analyzed using the Bayesian spatial CAR localised model with a different number of clusters and priors. The results show that the Bayesian spatial CAR localised model with the inclusion of population density fits the data better than a corresponding model without the inclusion of covariates. There was a positive correlation between the Covid-19 risk and population density. The interplay between covariates, spatial priors, and clustering structure influenced the performance of models. Makassar city and Bone have the highest and the lowest relative risk (RR) of Covid-19 respectively