Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

Since December 2019, the novel coronavirus(COVID-19) has caused over 700,000 deaths with more than 10 million people being infected. Bangladesh, the most densely populated country in the world, is now under community trans-mission of the COVID-19 outbreak. This has created huge health, social, and economic burdens. Till the 10th of February 2020, Bangladesh has reported over 500,000 infected cases and 8000 deaths. To prevent further detriment in our scenario, predicting future consequences are very important. Studies have shown that machine learning(ML) models work extremely well in providing precise information regarding COVID-19 to the authorities thus enabling them to make decisions accordingly. However, to the best of our knowledge, no ML models have been applied that can help in determining the pandemic circumstance for Bangladesh demographics. In this study, we explore different machine learning algorithms that can provide more accurate estimations for predicting future cases which includes infections and deaths due to COVID-19 for Bangladesh. Based on this the government and policymakers can make a decision about the lockdown, resource mobilization, etc. Our study shows that in predicting the pandemic situations, amidst many predicting models the Facebook Prophet Model provided the best accuracy. We believe that using this information the authorities can take decisions that will lead to the saving of countless lives of the people. Additionally, this will also help to reduce the immeasurable economic burden our country is facing due to the present status quo. Furthermore, this study will help analysts to construct predicting models for future explorations.