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With a 676% growth rate in HIV incidence between 2010 and 2021, the HIV/AIDSepidemic in the Philippines is the one that is spreading the quickest in thewestern Pacific. Although the full effects of COVID-19 on HIV services anddevelopment are still unknown, it is predicted that such disruptions could leadto a significant increase in HIV casualties. Therefore, the nation needs somemodeling and forecasting techniques to foresee the spread pattern and enhancethe governments prevention, treatment, testing, and care program. In thisstudy, the researcher uses Multilayer Perceptron Neural Network to forecasttime series during the period when the COVID-19 pandemic strikes the nation,using statistics taken from the HIV/AIDS and ART Registry of the Philippines.After training, validation, and testing of data, the study finds that thepredicted cumulative cases in the nation by 2030 will reach 145,273.Additionally, there is very little difference between observed and anticipatedHIV epidemic levels, as evidenced by reduced RMSE, MAE, and MAPE values as wellas a greater coefficient of determination. Further research revealed that thePhilippines seems far from achieving Sustainable Development Goal 3 of Project2030 due to an increase in the nations rate of new HIV infections. Despite thedetrimental effects of COVID-19 spread on HIV/AIDS efforts nationwide, thePhilippine government, under the Marcos administration, must continue to adhereto the United Nations 90-90-90 targets by enhancing its ART program andensuring that all vital health services are readily accessible and available.

Time Series Forecasting of HIV/AIDS in the Philippines Using Deep Learning: Does COVID-19 Epidemic Matter?