COVID-19 Infection Wave Mortality from Surveillance Data in The Philippines Using Machine Learning

Abstract

The Philippines had several COVID-19 infection waves brought about by different strains and variants of SARS-CoV-2. This study aimed to describe COVID-19 outcomes by infection waves using machine learning. A cross-sectional surveillance data review design was employed using the DOH COVID Data Drop dataset as of September 24, 2022. The predominant variant(s) of concern divided the dataset into time intervals representing the infection waves: ancestral (A0), Alpha/Beta (AB), Delta (D), and Omicron (O). Descriptive statistics and machine learning models were generated from each infection. The final data set consisted of 3,896,206 cases wherein 98.39% of cases recovered while 1.61% died. The highest and lowest CFR was observed during the ancestral wave (2.49) and the Omicron wave (0.61%), respectively. In all four data sets, higher age groups had higher CFRs, and F-score and specificity were highest using naïve Bayes. Area under the curve (AUC) was highest in the naïve Bayes models for the A0, AB and D models, while sensitivity was highest in the decision tree models for the A0, AB and O models. The ancestral, Alpha/Beta and Delta variants seem to have similar transmission and mortality profiles, while the Omicron variant caused lesser deaths despite increased transmissibility.

Keywords

• covid-19
• decision trees
• machine learning
• mortality
• surveillance