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

Author

Julius R. Migriño, Ani Regina U. Batangan, and Rizal Michael R. Abello

Subjects
covid-19, decision trees, machine learning, mortality, surveillance, Public aspects of medicine, RA1-1270
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.

Published
2024
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2. Using machine learning to create a decision tree model to predict outcomes of COVID-19 cases in the Philippines

Author

Ani Regina U Batangan and Julius R Migriño

Subjects
Male, Coronavirus disease 2019 (COVID-19), Cross-sectional study, Philippines, Decision tree, National capital region, Machine learning, computer.software_genre, Machine Learning, Case fatality rate, Medicine, Humans, Working age, Prognostic models, Aged, business.industry, SARS-CoV-2, Decision Trees, COVID-19, General Medicine, Cross-Sectional Studies, Research-Article, Artificial intelligence, business, computer, Decision tree model
Abstract

Objective: The aim of this study was to create a decision tree model with machine learning to predict the outcomes of COVID-19 cases from data publicly available in the Philippine Department of Health (DOH) COVID Data Drop. Methods: The study design was a cross-sectional records review of the DOH COVID Data Drop for 25 August 2020. Resolved cases that had either recovered or died were used as the final data set. Machine learning processes were used to generate, train and validate a decision tree model. Results: A list of 132 939 resolved COVID-19 cases was used. The notification rates and case fatality rates were higher among males (145.67 per 100 000 and 2.46%, respectively). Most COVID-19 cases were clustered among people of working age, and older cases had higher case fatality rates. The majority of cases were from the National Capital Region (590.20 per 100 000), and the highest case fatality rate (5.83%) was observed in Region VII. The decision tree model prioritized age and history of hospital admission as predictors of mortality. The model had high accuracy (81.42%), sensitivity (81.65%), specificity (81.41%) and area under the curve (0.876) but a poor F-score (16.74%). Discussion: The model predicted higher case fatality rates among older people. For cases aged >51 years, a history of hospital admission increased the probability of COVID-19-related death. We recommend that more comprehensive primary COVID-19 data sets be used to create more robust prognostic models.

Published
2021

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