Predictive Models for Forecasting Coronavirus Disease 2019  Cases: Relevance to Public Health Services

Alvin O Cayogyog; Reynaldo O Cuizon; Felix C Chavez Jr; Randy A Tudy

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Predictive Models for Forecasting Coronavirus Disease 2019 Cases: Relevance to Public Health Services

Author: Alvin O. Cayogyog ¹ ; Reynaldo O. Cuizon ² ; Felix C. Chavez Jr ³ ; Randy A. Tudy ⁴
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1. Agusan del Sur State College of Agriculture and Technology, Bunawan, Agusan del Sur, Philippines
2. Holy Cross of Davao College, Sta. Ana Avenue, Davao City, 8000 Davao del Sur, Philippines.
3. Jose Maria College Foundation, Inc., Philippine-Japan Friendship Highway, Davao City, 8000 Davao del Sur, Philippines.
4. University of Southeastern Philippines, Bo. Obrero, Davao City
Publication Type:Journal Article
Keywords: COVID-19; SIR; ARIMA; Enhanced Community Quarantine; Philippines
From:International Journal of Public Health Research 2024;14(no.1):1876-1887
CountryMalaysia
Language:English
Abstract: Introduction: The global impact of the coronavirus disease 2019 (COVID-19) pandemic has continually jeopardized vulnerable populations encompassing children, youth, elders, and individuals with immunodeficiency and comorbidities.
Methods:In recognizing the crucial role of predictive analytics in shaping public health decisions, this study utilizes a predictive design, drawing on official data from the Department of Health (DOH) in the Davao Region, Philippines, spanning 57 days from March 15 to May 10, 2020. By comparing the Susceptible, Infected, Recovered (SIR) model and the Autoregressive Integrated Moving Average (ARIMA) model, the research aims to provide a scientific foundation for informed decision-making by public health authorities.
Result:Analysis revealed that the SIR model emerged as the most effective in identifying trends and forecasting future cases. Despite both models indicating a substantial reduction in infection rates, caution is advised against discontinuing control and preventive measures due to the latent potential for another surge. The findings underscore the necessity for scientifically forecasted data to guide decision-makers in enhancing the responsiveness of public health services during similar and potentially worsening conditions.
Conclusions:Hence, this study contributes to the ongoing pandemic preparedness and responsiveness discourse. Its emphasis on predictive analytics, particularly the SIR model, offers valuable insights for authorities tasked with safeguarding public health. The significance lies in addressing the current situation in the Davao region and providing a template for future scenarios. As the world grapples with the unpredictable nature of infectious diseases, informed decision-making based on scientific forecasts becomes imperative for effective public health management.
Full text:20240723084912245021.2024my0315.pdf