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A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies

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  • Farooq, Junaid
  • Bazaz, Mohammad Abid

Abstract

We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. Modeling and simulation of such problems pose an additional challenge of continuously evolving training data in which the model parameters change over time depending upon external factors. Our main contribution is that in a scenario of continuously evolving training data, unlike typical deep learning techniques, this non-intrusive algorithm eliminates the need to retrain or rebuild the model from scratch every time a new training data set is received. After validating the model, we use it to study the impact of different strategies for epidemic control. Finally, we propose and simulate a strategy of controlled natural immunization through risk-based population compartmentalization (PC) wherein the population is divided in Low Risk (LR) and High Risk (HR) compartments based on risk factors (like comorbidities and age) and subjected to different disease transmission dynamics by isolating the HR compartment while allowing the LR compartment to develop natural immunity. Upon release from the preventive isolation, the HR compartment finds itself surrounded by enough number of immunized individuals to prevent the spread of infection and thus most of the deaths occurring in this group are avoided.

Suggested Citation

  • Farooq, Junaid & Bazaz, Mohammad Abid, 2020. "A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
  • Handle: RePEc:eee:chsofr:v:138:y:2020:i:c:s0960077920305440
    DOI: 10.1016/j.chaos.2020.110148
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    References listed on IDEAS

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    1. Bombardini, Matilde & Li, Bingjing, 2020. "Trade, pollution and mortality in China," Journal of International Economics, Elsevier, vol. 125(C).
    2. Oecd, 2019. "Population and Morphology of Border Cities," West African Papers 21, OECD Publishing.
    3. Ocde, 2019. "Population et morphologies des villes frontalières," Notes ouest-africaines 21, OECD Publishing.
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    Cited by:

    1. Rasheed, Jawad & Jamil, Akhtar & Hameed, Alaa Ali & Aftab, Usman & Aftab, Javaria & Shah, Syed Attique & Draheim, Dirk, 2020. "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    2. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    3. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    4. Tianqi Song & Yishi Wang & Xi Gu & Sijia Qiao, 2023. "Modeling the Within-Host Dynamics of SARS-CoV-2 Infection Based on Antiviral Treatment," Mathematics, MDPI, vol. 11(16), pages 1-19, August.
    5. Fehaid Salem Alshammari & Fahir Talay Akyildiz, 2023. "Epidemic Waves in a Stochastic SIRVI Epidemic Model Incorporating the Ornstein–Uhlenbeck Process," Mathematics, MDPI, vol. 11(18), pages 1-15, September.

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