Data analysis and mathematical model: control measures and prediction to prevent COVID-19 outbreak

Background. The recent COVID-19 outbreak is now an ongoing global health emergency. The world is dealing with the epidemic with no vaccine or medication to treat the disease, the only effectual measure for now is to implement quarantine methods. We designed a quarantine mathematical model with data analysis to predict the outcome of this pandemic. Methods. We collected available online data of four different countries China, Italy, Spain and USA. First, we have analyzed the real-life data and abridged data. Then, fitting analysis of the data was done in comparison with the outcome of our mathematical results. Results. It is found that disease progression in this model is determined by the basic reproductive ratio, . If > 1, the number of latently infected individuals grows exponentially; (in a case with enough public mobility). If < 1 then the infection rate decays exponentially i.e. government ensures the social isolation through quarantine. Data analysis of different countries show that the possible dynamics are growth, growth-decay and growth-decay-growth dynamics. After imposition of a quarantine on March 9, 2020 in Italy, within 13 days of lock-down, the maximum number of infection was observed after 42 days (from Feb 15, 2020) before decreasing. The quarantine model approximates that the disease in Italy could be under control by mid-May. Similar results present for Spain (growth-decay) and USA (growth only). Conclusion. The  of COVID-19 may vary from country to country. To control the pandemic,  and incubation period play an important role in spreading and controlling of the disease.