Today, 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.

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<span>Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.</span>

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this hour, coronavirus is a threat to our life and especially to our close family and the elderly person, who have more or less poor health, it will harm human beings and economics, so we have to find a method to limit the risk of this devastating virus and to discover a way to decrease its propagation. Our concern is to compute people density on many different locations of interest in the whole city with intention of dispatching people to different areas by avoiding as much as possible congestions. The support of using big data technologies is very crucial to process data with a fast manner and in real time. Today traditional database management tools is unable to manage the voluminous data generated by different system sources, that's why big data technologies will give us its support to manage this mass of data to extract crucial information from this enormous data, and without big data technologies, the process turn out very difficult to control. In the present paper, we first propose a method that will calculate densities of people in different city's places in real time, this will allow us to prevent people locations to avoid where there are congestions and reorient them to another lightened safe locations. And then we will store in a database all human contacts that have taken place to warn people who have been in contact or who have been close to the contaminated ones.