Mathematics and Computer Science, Volume 1

27Prediction of Seasonal Ailments Using Big Data: A Case Study

K. Indhumathi¹* and K. Sathesh Kumar²

¹Department of Computer Application, Kalasalingam Academy of Research and Education, Kalasalingam University, Krishnan Kovil, Tamil Nadu, India

²Department of Computer Science and Information Technology, Kalasalingam University, Krishnan Kovil, Tamil Nadu, India

Abstract

Infection outbreaks are influenced by the state of the environment and climatic conditions. Depending on the temperature, some illnesses might influence how quickly they spread. Dengue fever, for example, spreads more during the monsoon and winter seasons due to the fact that these are wet seasons. As a result, damp conditions can encourage the generation of dengue mosquitoes. Atmospheric variations are an important factor in the onset of a new illness. It also creates new infections. Big Data is an effective component for forecasting seasonal infectious illnesses that are affected by climatic variations. The researcher addresses three deadly diseases in this paper: COVID-19, dengue fever, and flu. Along with how the weather pattern can influence the spread of these three outbreaks, researchers also forecast which season has an impact on which disease. In addition, numerous disease prediction methods will be discussed. Based on this analysis, it appears that the big data approach is best for predicting seasonal infectious illnesses. This research offers numerous important insights toward seasonal aliment prediction ...