A Study on Future Lockdown Predictions using ANN

Abstract

More than three years have passed since the first detection of COVID-19, and our generation had no clue as to what was coming or how dangerous the disease was. Unfortunately, this is not the end of such diseases, and in order to minimize losses, we may have to deal with many more lockdowns over new diseases. With the data from the recent pandemic, we investigated how to anticipate the potential lockdown date in the future. We took into account the first detection, the first death by COVID-19, the WHO emergency declaration, the total GDP of the various nations, the GDP growth, the population density, etc. This paperwork predicts the day passed after the first COVID-19 positive detection inside the country when the country's government declared a lockdown (FDL) and the day when the WHO announced COVID-19 as an emergency of international concern (WEL). This research uses a variety of machine learning models, including a modified Artificial Neural Network (ANN), for the prediction. This study presents the successful tuning and training of an ANN model using the lockdown dataset, ultimately achieving an impressive accuracy rate of 96% for FDL (First Detection Lockdown) and 98% for WEL (World Emergency Lockdown), which is more accurate than any other applied traditional and/or tree-based machine-learning models