Prediction of Death Counts Based on Short-term Mortality Fluctuations Data Series using Multi-output Regression Models

Abstract

Effective public health responses to unexpected epidemiological hazards or disasters need rapid and reliable monitoring. But, monitoring fast-changing situations and ac-quiring timely, accurate, and cross-national statistics to address short-term mortality fluctuations due to these hazards is very challenging. Estimating weekly excess deaths is the most solid and accurate way to measure the mortality burden caused by short-term risk factors. The Short-term Mortality Fluctuations (STMF) data series is one of the significant collections of the Human Mortality Database (HMD) that provides the weekly death counts and rates by age and sex of a country. Sometimes, the data collected from the sources are not always represented in specific age groups rather represented by the the total number of individual death records per week. However, the researchers reclassified their dataset based on the ranges of age and sex distributions of every country so that one can easily find out how many people died in per week of each country based on an equation and earlier distribution data. The paper focuses on the implementation of multi-output regression models such as logistic regression, decision tree, random forest, k nearest neighbors, lasso, support vector regressor, artificial neural network, and recurrent neural network to correctly predict death counts for specific age groups. According to the results, random forest delivered the highest performance with an R squared coefficient value of 0.9975, root mean square error of 43.2263, and mean absolute error of 16.4069.