Predicting COVID-19 Trends with Comparative Analysis of ARIMA and ANN Models

Abstract

This study uses Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks to predict COVID-19 in Pakistan. The pandemic epidemic that hit Wuhan, China, in December 2019 and affected millions of people worldwide. As of March 1, 2023, Pakistan had 296,149 confirmed cases, 6,298 deaths, and 280,970 recoveries. The predictive algorithms above will predict confirmed cases, deaths, and recoveries for 30 days. The approach collects time-series data on confirmed cases, deaths, and recoveries. The data is processed and analyzed using ARIMA and ANN models. These models were chosen because they can handle non-linear and complex time series data, making them excellent for pandemic prediction. The research hypothesis is that ARIMA and ANN models can accurately anticipate Pakistani COVID-19 case trends over the next 30 days. Correlation and MSE are used to compare models. Early results reveal that ARIMA and ANN models accurately estimate COVID-19 prevalence in Pakistan. An in-depth study of the methodology suggests adjustments to improve forecast accuracy. This study has significant ramifications. Accurate projections can help policymakers choose public health initiatives, saving lives and money. Successfully using these machine learning models could lead to their usage in epidemic prediction.