Impact of Weather Conditions on COVID-19 Incidence in Select Regions of the Philippines: National Capital Region (NCR), IV-A (CALABARZON), and VII (Central Visayas)

Abstract

Studies have indicated a significant correlation between weather parameters and Coronavirus Disease (COVID-19) incidence; however, few have investigated the relationship within the Philippines and used such to predict COVID-19 trends. This study aimed to determine and correlate weather parameters (temperature, rainfall, and humidity) of the National Capital Region (NCR), Region IV-A (CALABARZON), and Region VII (Central Visayas) to their respective COVID-19 incidence rates; and to assess the ability of such to predict COVID-19 incidence trends. Descriptive statistics was used to assess COVID-19 cases and weather conditions in the regions, while Pearson correlation and time series analysis were used to determine the correlation between the variables and to evaluate the ability of the former to predict incidence trends. Recorded weather parameters and COVID-19 incidence are reflective of the country's climate and the population density of the three regions selected for study. The strength and direction of the correlation between the meteorological factors and COVID-19 cases varied between locations; but generally, COVID-19 cases were found to have a positive relationship with a weak correlation with rainfall and temperature, while a weak negative correlation is seen with respect to relative humidity. Generated predictive models yielded r-square values of 0.67, 0.65, and 0.57 for NCR, CALABARZON, and Central Visayas implying the ability of said weather parameters to predict historical data accurately. These findings ultimately suggest that weather conditions and the number of COVID-19 cases are correlated and that models utilizing such can predict COVID-19 incidence trends with relative accuracy.  Despite the significant findings, this study has its limitations, which must be addressed for the betterment of future research. The authors recommend the inclusion of other regions within the country to obtain more comprehensive results. In addition, it was suggested that the time frame of data collection be extended to cover longer periods to provide stronger conclusions. The inclusion of other factors related to viral transmission and personal health (i.e. virus resistance, lock-down protocols, limited testing capacities, population endurance, population mobility, sanitation practices, hand washing habits, and personal hygiene) must also be considered.