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Spatial network based model forecasting transmission and control of COVID-19

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  • Sharma, Natasha
  • Verma, Atul Kumar
  • Gupta, Arvind Kumar

Abstract

The SARS-CoV-2 driven infectious novel coronavirus disease (COVID-19) has been declared a pandemic by its brutal impact on the world in terms of loss on human life, health, economy, and other crucial resources. To explore more about its aspects, we adopted the SEIRD (Susceptible–Exposed–Infected–Recovered–Death) pandemic spread with a time delay on the heterogeneous population and geography in this work. Focusing on the spatial heterogeneity, epidemic spread on the framework of modeling that incorporates population movement within and across the boundaries is studied. The entire population of interest in a region is divided into small distinct geographical sub regions, which interact using migration networks across boundaries. Utilizing the time delay differential equations based model estimations, we analyzed the spread dynamics of disease in India. The numerical outcomes from the model are validated using real time available data for COVID-19 cases. Based on the developed model in the framework of the recent data, we verified total infection cases in India considering the effect of nationwide lockdown at the onset of the pandemic and its unlocking by what seemed to be the end of the first wave. We have forecasted the total number of infection cases in two extreme situations of nationwide no lockdown and strict lockdown scenario. We expect that in future for any change in the key parameters, due to the regional differences, predictions will lie within the bounds of the above mentioned extreme plots. We computed the approximate peak infection in forwarding time and relative timespan when disease outspread halts. The most crucial parameter, the time-dependent generalization of the basic reproduction number, has been estimated. The impact of the social distancing and restricted movement measures that are crucial to contain the pandemic spread has been extensively studied by considering no lockdown scenario. Our model suggests that attaining a reduction in the contact rate between susceptible and infected individuals by practicing strict social distancing is one of the most effective control measures to manage COVID-19 spread in India. The cases can further decrease if social distancing is followed in conjunction with restricted movement.

Suggested Citation

  • Sharma, Natasha & Verma, Atul Kumar & Gupta, Arvind Kumar, 2021. "Spatial network based model forecasting transmission and control of COVID-19," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    • Handle: RePEc:eee:phsmap:v:581:y:2021:i:c:s0378437121004969
    DOI: 10.1016/j.physa.2021.126223
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    References listed on IDEAS

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    1. Sharma, Natasha & Gupta, Arvind Kumar, 2017. "Impact of time delay on the dynamics of SEIR epidemic model using cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 114-125.
    2. Pai, Chintamani & Bhaskar, Ankush & Rawoot, Vaibhav, 2020. "Investigating the dynamics of COVID-19 pandemic in India under lockdown," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    3. Sardar, Tridip & Nadim, Sk Shahid & Rana, Sourav & Chattopadhyay, Joydev, 2020. "Assessment of lockdown effect in some states and overall India: A predictive mathematical study on COVID-19 outbreak," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    4. Ofosuhene O Apenteng & Noor Azina Ismail, 2014. "The Impact of the Wavelet Propagation Distribution on SEIRS Modeling with Delay," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-9, June.
    5. Sarkar, Kankan & Khajanchi, Subhas & Nieto, Juan J., 2020. "Modeling and forecasting the COVID-19 pandemic in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    6. Samui, Piu & Mondal, Jayanta & Khajanchi, Subhas, 2020. "A mathematical model for COVID-19 transmission dynamics with a case study of India," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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    Cited by:

    1. Gabrick, Enrique C. & Sayari, Elaheh & Protachevicz, Paulo R. & Szezech, José D. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Viana, Ricardo L. & Caldas, Iberê L. & Batista, , 2023. "Unpredictability in seasonal infectious diseases spread," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    2. Gabrick, Enrique C. & Protachevicz, Paulo R. & Batista, Antonio M. & Iarosz, Kelly C. & de Souza, Silvio L.T. & Almeida, Alexandre C.L. & Szezech, José D. & Mugnaine, Michele & Caldas, Iberê L., 2022. "Effect of two vaccine doses in the SEIR epidemic model using a stochastic cellular automaton," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 597(C).
    3. Zheng, Qianqian & Shen, Jianwei & Xu, Yong & Pandey, Vikas & Guan, Linan, 2022. "Pattern mechanism in stochastic SIR networks with ER connectivity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).

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