Dataset on the COVID-19 Pandemic Situation in Tunisia with application to SIR Model

Lassoued, Abir; Ben Saad, Afef; Lassoued, Hela; Ketata, Raouf; Boubaker, Olfa

April 9, 2020 marks 100 days since first cases of the Coronavirus Disease 2019 in China. In this crucial day with 1 436 198 confirmed infected cases in the world (New confirmed infected cases 82 837) and 85 521 deaths (6286 New daily deaths), the Global Level of the Covid-19 pandemic is evaluated at very high according to the World Health Organization (WHO) situation report. For most people, COVID-19 infection will cause mild illness (fever and at least one sign/symptom of respiratory disease), however for more than 3.4% of people, it can be fatal. Older people and those with pre-existing medical conditions (such as cardiovascular disease, chronic respiratory disease or diabetes) are at risk for severe disease and mortality. The incubation period of the virus is estimated to be between 2 and 14 days but longer incubation are reported. Furthermore, Data published by world authorities shows that statistics are different for different geographical regions and depends on many social and environmental factors. The sad reality of the COVID-19 is that there are currently no medications or vaccines proven to be effective for the treatment or prevention of the disease. The pandemic spread is consequenctly followed by a worldwide panic. Facing this dramatic uncertain situation, implementing a country wide strategy for social distancing and a general logistic policy for critical and life-saving supplies is an urgency for governomental and sanitaire authorities. Several mathematical models have been proposed for developed countries to predict the epidemic spread. However these models can't be adapted to developing countries due to the major differences in geographic, societal, economic and political adopted strategies. Here, we propose the application of the well known SIR model to the case study of Tunisia where Data are collected from three official databases showing differences in reporting methods. The proposed data could be useful to predict the spread of COVID-2019 and design a more reliable model that can estimate the finale size of the spread and help in monitoring infection control.