The Impact of Covid-19 on Stunting Cases in Indonesia: A Bayesian Spatial Modeling Approach - Ankaz As Sikib; Aswi, Aswi; Ruliana (Journal of Mathematics, Computations and Statistics, 2024)
The high number of COVID-19 cases has impacted various sectors. One of the notable consequences of the COVID-19 pandemic is its effect on food security and nutrition. Social restrictions implemented to curb the spread of the virus have resulted in worsening economic conditions, limited access to healthcare facilities, difficulties in obtaining nutritious food, and school closures. Changes in the routines and activities of COVID-19 patients may contribute to an increase in the prevalence of stunting in Indonesia. While research has been conducted on the impact of COVID-19 on the rise of stunting cases in Indonesia, previous studies have typically focused on individual provinces and have not utilized the Bayesian Conditional Autoregressive (CAR) model. This study aims to investigate the relationship between COVID-19 and the increase in stunting cases across Indonesia. We analyze data on stunting cases in each Indonesian province and the number of COVID-19 patients between March 23, 2020, and December 31, 2021. To assess the relationship, we employ the Bayesian spatial CAR Leroux model with several Inverse-Gamma hyperpriors. We compare these models using various fit criteria. The results indicate that the Bayesian spatial CAR Leroux model with Inverse-Gamma hyperpriors (0.1;0.1) performs best, as it yields the smallest Deviance Information Criterion (DIC) and Watanabe-Akaike Information Criterion (WAIC) values. In conclusion, our analysis reveals a positive correlation between the number of COVID-19 cases and the increase in stunting cases in Indonesia. Approximately 50% of the regions in Indonesia face a high relative risk of stunting, with Nusa Tenggara Timur having the highest relative risk, followed by Kalimantan Barat and Sulawesi Barat