مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 11 42 2023 6 10 An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest An Analysis of Covid-19 Pandemic Outbreak on Economy using Neural Network and Random Forest 163 175 10.52547/jist.34246.11.42.163 en Md. Nahid Hasan Brac University Tanvir Ahmed Brac University Md. Ashik Brac University Md. Jahid Hasan Brac University Tahaziba Azmin Brac University Jia Uddin Ulsan University 2022 2 23 The pandemic disease outbreaks are causing a significant financial crisis affecting the worldwide economy. Machine learning techniques are urgently required to detect, predict and analyze the economy for early economic planning and growth. Consequently, in this paper, we use machine learning classifiers and regressors to construct an early warning model to tackle economic recession due to the cause of covid-19 pandemic outbreak. A publicly available database created by the National Bureau of Economic Research (NBER) is used to validate the model, which contains information about national revenue, employment rate, and workers' earnings of the USA over 239 days (1 January 2020 to 12 May 2020). Different techniques such as missing value imputation, k-fold cross validation have been used to pre-process the dataset. Machine learning classifiers- Multi-layer Perceptron- Neural Network (MLP-NN) and Random Forest (RF) have been used to predict recession. Additionally, machine learning regressors-Long Short-Term Memory (LSTM) and Random Forest (RF) have been used to detect how much recession a country is facing as a result of positive test cases of covid-19 pandemic. Experimental results demonstrate that the MLP-NN and RF classifiers have exhibited average 88.33% and 85% of recession (where 95%, 81%, 89% and 85%, 81%, 89% for revenue, employment rate and workers earnings, respectively) and average 90.67% and 93.67% of prediction accuracy for LSTM and RF regressors (where 92%, 90%, 90%, and 95%, 93%, 93% respectively). http://jist.ir/en/Article/Download/34246