Vikas Kasina
The early diagnosis of public health hazards necessitates the monitoring of infectious diseases. A wide range of human and environmental factors influence the emergence of novel diseases. These include population density, travel, and trade. One of the most exciting developments in molecular diagnostics is the rising number of new technologies being developed. Risk assessment and outbreak detection can be improved through web-based surveillance systems and epidemic intelligence methodologies employed by all major public health institutes. Since its discovery on Chinese soil in December of this year, the COVID-19 virus has made its way to every continent but Antarctica. The World Health Organization has identified SARS-CoV-2-caused Coronavirus Disease 19 (COVID-19) as a global health emergency (WHO). If the rate of transmission is higher than that of SARS or the usual flu, then the fight against this disease must continue. To further understand this conflict, this essay focuses on the role of data science. A wide range of fields, such as epidemiology, drug discovery, and molecular design, benefit from the application of data science in conjunction with statistical analysis, computer science, and computational biology. Models based on data and mathematics have been developed for COVID-19, including correlations and forecasts. Data science methods are used to analyse huge COVID-19 epidemiological datasets in this study. COVID-19 confirmed cases can be better understood with the help of this solution. Our data science approach has been shown to be effective in gleaning meaningful information from the massive COVID-19 dataset.
Data Science, Real Time Disease Outbreak