Kriging interpolation model: The problem of predicting the number of deaths due to COVID-19 over time in Vietnam

Abstract

The COVID-19 pandemic can be considered a human disaster, it has claimed the lives of many people. We only know the number of deaths due to COVID-19 through government statistics, but on days when there are no statistics, how do we know whether people died that day or not? This study aims to predict the number of new deaths per day due to COVID 19 in Vietnam on days when observational data is not available and predict the number of deaths in the future. The study used COVID-19 data from the World Health Organization (WHO). A total of 260 days were collected and the author processed and standardized the data. Based on available data, the author uses Kriging interpolation statistical method to build a forecast model. As a result, the author has selected a prediction model suitable for a highly reliable data set, the regression coefficient and correlation coefficient are close to 1, the error between the model’s prediction results compared to data. There are days when the prediction error is almost zero. The study has built a future forecast map of the number of new deaths per day due to COVID-19. The article concludes that applying the Kriging statistical methodis appropriate for COVID-19 data. This research opens up new research directions for related fields such as earthquakes, mining, groundwater, environment, etc.