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

  1. Bachir Nail
  2. Abdelaziz Rabehi
  3. Belkacem Bekhiti
  4. Taha Arbaoui

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Abstract

Background

Mathematical infectious disease models available in literature, mostly take in their design that the parameters of basic reproduction number R 0 and interval serial S I 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.

Methods

The parameters R 0 and S I are adapted by adding three new weighting factors α, β and γ and two free parameters σ 1 and σ 2 in function of time t , thus the HH-COVID-19 become time-variant model. The parameters R 0 , S I , α, β, γ, σ 1 and σ 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.

Results

Parameters estimated optimally: R 0 = 1.341, S I = 5.991, α = 2.987, β = 1.566, γ = 4.998, σ 1 = − 0.133 and σ 2 = 0.0324. R 0 starts on 1.341 and ends to 2.677, and S I 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.

Conclusion

HH-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.

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    SciScore for 10.1101/2020.04.23.20077677: (What is this?)

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    Table 2: Resources

    Software and Algorithms
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    The results of this research have been obtained using MATLAB software, (The programs codes are in supplementary files).
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    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2020.04.23.20077677v1 on medRxiv