Time Series Forecasting of HIV/AIDS in the Philippines Using Deep
  Learning: Does COVID-19 Epidemic Matter?
Sales G. Aribe Jr. Bobby D. Gerardo Ruji P. Medina
With a 676% growth rate in HIV incidence between 2010 and 2021, the HIV/AIDS
epidemic in the Philippines is the one that is spreading the quickest in the
western Pacific. Although the full effects of COVID-19 on HIV services and
development are still unknown, it is predicted that such disruptions could lead
to a significant increase in HIV casualties. Therefore, the nation needs some
modeling and forecasting techniques to foresee the spread pattern and enhance
the governments prevention, treatment, testing, and care program. In this
study, the researcher uses Multilayer Perceptron Neural Network to forecast
time 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 the
predicted cumulative cases in the nation by 2030 will reach 145,273.
Additionally, there is very little difference between observed and anticipated
HIV epidemic levels, as evidenced by reduced RMSE, MAE, and MAPE values as well
as a greater coefficient of determination. Further research revealed that the
Philippines seems far from achieving Sustainable Development Goal 3 of Project
2030 due to an increase in the nations rate of new HIV infections. Despite the
detrimental effects of COVID-19 spread on HIV/AIDS efforts nationwide, the
Philippine government, under the Marcos administration, must continue to adhere
to the United Nations 90-90-90 targets by enhancing its ART program and
ensuring that all vital health services are readily accessible and available.