  To accurately predict the regional spread of Covid-19 infection, this study
proposes a novel hybrid model which combines a Long short-term memory (LSTM)
artificial recurrent neural network with dynamic behavioral models. Several
factors and control strategies affect the virus spread, and the uncertainty
arisen from confounding variables underlying the spread of the Covid-19
infection is substantial. The proposed model considers the effect of multiple
factors to enhance the accuracy in predicting the number of cases and deaths
across the top ten most-affected countries and Australia. The results show that
the proposed model closely replicates test data. It not only provides accurate
predictions but also estimates the daily behavior of the system under
uncertainty. The hybrid model outperforms the LSTM model accounting for limited
available data. The parameters of the hybrid models were optimized using a
genetic algorithm for each country to improve the prediction power while
considering regional properties. Since the proposed model can accurately
predict Covid-19 spread under consideration of containment policies, is capable
of being used for policy assessment, planning and decision-making.
