Effective Demarcation of Coronavirus Disease Transmission Rate Using Rule Based Techniques

ABSTRACT

COVID-19 is a real life-threatening disease which has locked up the whole Globe. In spite of the rapid sharing of information and inculcating social responsibilities by multiple factor such as personal hygiene, social distancing and self-isolation the spread of COVID19 is increasing day by day. Controlling the spread of disease in early stage becomes the primary responsibility of the government. The judicial administration along with the health-care workers struggles tirelessly to flatten the curve by taking measure to reduce the spread of disease. The primary objective of this paper is to help the government in this aspect of buying time to control the spread of virus. World Health Organisation notified that “COVID-19 is transmitted via droplets and fomites during close unprotected contacts between an infector and infected.” Hence the key strategy should focus on preventing the virus before becoming a community transmission. Locating the area of very high possibilities of COVID victims becomes the highest priority to block transmission. This can be done by identifying the parameters which influence the spread of disease and applying on the large volume of data obtained by the area wise survey information. In this paper a decision tree is built using Iterative Dichotomiser (ID3) and it is further compared using Naive Bayes classification algorithm to check the accuracy of the result obtained by ID3. The obtained result of tested data is further solved and an improvised optimal decision tree is build which is used to identify and block the area of highest risk factor. © The Electrochemical Society