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During a pandemic, early prognostication of patient infected rates can reducethe death by ensuring treatment facility and proper resource allocation. Inrecent months, the number of death and infected rates has increased moredistinguished than before in Bangladesh. The country is struggling to providemoderate medical treatment to many patients. This study distinguishes machinelearning models and creates a prediction system to anticipate the infected anddeath rate for the coming days. Equipping a dataset with data from March 1,2020, to August 10, 2021, a multi-layer perceptron (MLP) model was trained. Thedata was managed from a trusted government website and concocted manually fortraining purposes. Several test cases determine the model's accuracy andprediction capability. The comparison between specific models assumes that theMLP model has more reliable prediction capability than the support vectorregression (SVR) and linear regression model. The model presents a report aboutthe risky situation and impending coronavirus disease (COVID-19) attack.According to the prediction produced by the model, Bangladesh may sufferanother COVID-19 attack, where the number of infected cases can be between 929to 2443 and death cases between 19 to 57.

Coronavirus disease situation analysis and prediction using machine learning: a study on Bangladeshi population