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The effectiveness of contact tracing in mitigating COVID-19 outbreak: A model-based analysis in the context of India

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  • Kumar Das, Dhiraj
  • Khatua, Anupam
  • Kar, T.K.
  • Jana, Soovoojeet

Abstract

The ongoing pandemic situation due to COVID-19 originated from the Wuhan city, China affects the world in an unprecedented scale. Unavailability of totally effective vaccination and proper treatment regimen forces to employ a non-pharmaceutical way of disease mitigation. The world is in desperate demand of useful control intervention to combat the deadly virus. This manuscript introduces a new mathematical model that addresses two different diagnosis efforts and isolation of confirmed cases. The basic reproductive number, R0, is inspected, and the model’s dynamical characteristics are also studied. We found that with the condition R0<1, the disease can be eliminated from the system. Further, we fit our proposed model system with cumulative confirmed cases of six Indian states, namely, Maharashtra, Tamil Nadu, Andhra Pradesh, Karnataka, Delhi and West Bengal. Sensitivity analysis carried out to scale the impact of different parameters in determining the size of the epidemic threshold of R0. It reveals that unidentified symptomatic cases result in an underestimation of R0 whereas, diagnosis based on new contact made by confirmed cases can gradually reduce the size of R0 and hence helps to mitigate the ongoing disease. An optimal control problem is framed using a control variable u(t), projecting the effectiveness of diagnosis based on traced contacts made by a confirmed COVID patient. It is noticed that optimal contact tracing effort reduces R0 effectively over time.

Suggested Citation

  • Kumar Das, Dhiraj & Khatua, Anupam & Kar, T.K. & Jana, Soovoojeet, 2021. "The effectiveness of contact tracing in mitigating COVID-19 outbreak: A model-based analysis in the context of India," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    • Handle: RePEc:eee:apmaco:v:404:y:2021:i:c:s0096300321002976
    DOI: 10.1016/j.amc.2021.126207
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    References listed on IDEAS

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    1. Ndaïrou, Faïçal & Area, Iván & Nieto, Juan J. & Torres, Delfim F.M., 2020. "Mathematical modeling of COVID-19 transmission dynamics with a case study of Wuhan," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
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    Cited by:

    1. Zhou, Baoquan & Jiang, Daqing & Han, Bingtao & Hayat, Tasawar, 2022. "Threshold dynamics and density function of a stochastic epidemic model with media coverage and mean-reverting Ornstein–Uhlenbeck process," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 196(C), pages 15-44.
    2. Hong, Xiao & Han, Yuexing & Wang, Bing, 2023. "Impacts of detection and contact tracing on the epidemic spread in time-varying networks," Applied Mathematics and Computation, Elsevier, vol. 439(C).
    3. Jiang, Jiehui & Ma, Jie, 2023. "Dynamic analysis of pandemic cross-regional transmission considering quarantine strategies in the context of limited medical resources," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    4. Li, Tangjuan & Xiao, Yanni, 2023. "Optimal strategies for coordinating infection control and socio-economic activities," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 533-555.

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