COVID-19 Incidence Prediction Model Based on Community Behavior With Neural Networks

Abstract BACKGROUNDS : The COVID-19 pandemic has created a global health emergency that requires a public health response to prevent the spread of the virus. AIM: The purpose of this study was to determine the prediction model for the incidence of COVID-19 based on community behavior. METHODS: This study used a cross-sectional study design. The study population was all people aged >18 years in Medan City and obtained a sample of 395 people with stratified random sampling technique. The research instrument used a questionnaire in google form, then, using Microsoft Office Excel, we transferred the data from the survey to a computer program. Furthermore, the data was analyzed using the neural networks method. Then the features importance will be calculated using the Random Forest with Mean Decrease Impurity (RF-MDI) method. RESULT: The results showed that based on the confusion matrix, the prediction value for those who did not suffer from COVID-19 was correct from negative data = 8, the correct prediction value for COVID-19 from positive data = 8. While the incorrect prediction value for machines that predicted negative results but the actual data was positive = 2, and predicts a positive result but the actual data is negative = 4. Thus, based on the neural net classification method, the accuracy value is 72%. The results of this study indicate that poor preventive behavior by the community greatly affects the spread of COVID-19 cases. CONCLUSION: Poor community behavior, such as not limiting their interaction/contact with other people, not exercising frequently, leaving the house without keeping a safe distance, and not washing hands regularly, can all impact COVID-19 transmission in the community Keywords: Behavior Prediction Model, COVID-19 Incidence, Neural Network