A Computational Intelligence Approach to Detect Future Trends of COVID-19 in France by Analyzing Chinese Data

Aims: Due to the terrible effects of 2019 novel coronavirus (COVID19) on health systems and the global economy, the necessity to study future trends of the virus outbreaks around the world is seriously felt. Since geographical mobility is a risk factor of the disease, it has spread to most of the countries recently. It, therefore, necessitates to design a decision support model to 1) identify the spread pattern of coronavirus and, 2) provide reliable information for the detection of future trends of the virus outbreaks. Materials Methods: The present study adopts a computational intelligence approach to detect the possible trends in the spread of 2019-nCoV in China for a onemonth period. Then, a validated model for detecting future trends in the spread of the virus in France is proposed. It uses ANN (Artificial Neural Network) and a combination of ANN and GA (Genetic Algorithm), PSO (Particle Swarm Optimization), and ICA (Imperialist Competitive Algorithm) as predictive models. Findings: The models work on the basis of data released from the past and the present days from WHO (World Health Organization). By comparing four proposed models, ANN and GAANN achieve a high degree of accuracy in terms of performance indicators. Conclusion: The models proposed in the present study can be used as decision support tools for managing and controlling of 2019-nCoV outbreaks.