A Time-Dependent SEIRD Model for Forecasting the COVID-19 Transmission Dynamics

  1. Taarak Rapolu
  2. Brahmani Nutakki
  3. T. Sobha Rani
  4. S. Durga Bhavani

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Abstract

The spread of a disease caused by a virus can happen through human to human contact or could be from the environment. A mathematical model could be used to capture the dynamics of the disease spread to estimate the infections, recoveries, and fatalities that may result from the disease. An estimation is crucial to make policy decisions and for the alerts for the medical emergencies that may arise. Many epidemiological models are being used to make such an estimation. One major factor that is important in the forecasts using the models is the dynamic nature of the disease spread. Unless we can come up with a way of estimating the parameters that guide this dynamic spread, the models may not give accurate forecasts. The main principle is to keep the model generic while making minimal assumptions. In this work, we have derived a data-driven model from SEIRD, where we attempt to forecast Infected, Recovered and Deceased rates of COVID-19 up to a week. A method of estimating the parameters of the model is also discussed thoroughly in this work. The model is tested for India at a district level along with the most affected foreign cities like Lombardia from Italy and Moscow from Russia.

The forecasts can help the governments in planning for emergencies such as ICU requirements, PPEs, hospitalizations, and so on as the infection is going to be prevalent for some time until a vaccine or cure is invented.

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    SciScore for 10.1101/2020.05.29.20113571: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

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  2. Version published to 10.1101/2020.05.29.20113571v3 on medRxiv
  3. Version published to 10.1101/2020.05.29.20113571v2 on medRxiv
  4. Version published to 10.1101/2020.05.29.20113571v1 on medRxiv