Estimation of People Density to Reduce Coronavirus Propagation

Abstract

AbstractToday, the spread of coronavirus has become the number one concern of countries as it threatens human life and economics; therefore, the scientific community tries hard to discover the treatment to deal with this virus or at least find out a method to reduce its propagation. In this context, our concern is to estimate density of people being inside all different places of interest in the city in the purpose of distributing users of our application to different places by avoiding congestions of people. The use of big data is very important for data treatment for fast execution. In this time, oldest relational database technologies cannot anymore handle the enormous data created by various application sources, in this direction big data tools permit us to handle this enormous data in the purpose to mine important data from the voluminous data, if we rely on the support of big data tools, the treatment will be complicated to administer. In this chapter, firstly, we define a system that calculates number of people in diverse city’s areas; it will help to inform users’ best places to alleviate areas where there are congestions and redirect people to other place with low density. And secondly, we keep trace in a database all people contacts that have been near each other to prevent people who have been close to the positive cases.KeywordsBig dataDensity managementCongestions handlingSpread of coronavirus