A new design of an adaptive model of infectious diseases based on artificial intelligence approach: monitoring and forecasting of COVID-19 epidemic cases

BackgroundMathematical infectious disease models available in literature, mostly take in their design that the parameters of basic reproduction number R0 and interval serial SI as constant values during tracking the outbreak cases. In this report a new intelligent model called HH-COVID-19 is proposed, with simple design and adaptive parameters. MethodsThe parameters R0 and SI are adapted by adding three new weighting factors , {beta} and{gamma} and two free parameters{sigma} 1 and{sigma} 2 in function of time t, thus the HH-COVID-19 become time-variant model. The parameters R0, SI, , {beta}, {gamma}, {sigma}1 and{sigma} 2 are estimated optimally based on a recent algorithm of artificial intelligence (AI), inspired from nature called Harris Hawks Optimizer (HHO), using the data of the confirmed infected cases in Algeria country in the first t = 55 days. ResultsParameters estimated optimally: R0 = 1.341, SI = 5.991, = 2.987, {beta} = 1.566,{gamma} = 4.998,{sigma} 1 = -0.133 and{sigma} 2 = 0.0324. R0 starts on 1.341 and ends to 2.677, and SI starts on 5.991 and ends to 6.692. The estimated results are identically to the actual infected incidence in Algeria, HH-COVID-19 proved its superiority in comparison study. HH-COVID-19 predicts that in 1 May, the infected cases exceed 50 000, during May, to reach quickly the herd immunity stage at beginning of July. ConclusionHH-COVID-19 can be used for tracking any COVID-19 outbreak cases around the world, just should updating its new parameters to fitting the area to be studied, especially when the population is directly vulnerable to COVID-19 infection.