Network-based Modeling of COVID-19 Dynamics: Early Pandemic Spread in India

  1. Rupam Bhattachayya
  2. Sayantan Banerjee
  3. Shariq Mohammed
  4. Veera Baladandayuthapani

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Abstract

Modeling the dynamics of COVID-19 pandemic spread is a challenging and relevant problem. Established models for the epidemic spread such as compartmental epidemiological models e.g. Susceptible-Infected-Recovered (SIR) models and its variants, have been discussed extensively in the literature and utilized to forecast the growth of the pandemic across different hot-spots in the world. The standard formulations of SIR models rely upon summary-level data, which may not be able to fully capture the complete dynamics of the pandemic growth. Since the disease spreads from carriers to susceptible individuals via some form of contact, it inherently relies upon a network of individuals for its growth, with edges established via direct interaction, such as shared physical proximity. Using individual-level COVID-19 data from the early days (January 30 to April 15, 2020) of the pandemic in India, and under a network-based SIR model framework, we performed state-specific forecasting under multiple scenarios characterized by the basic reproduction number of COVID-19 across 34 Indian states and union territories. We validated our short-term projections using observed case counts and the long-term projections using national sero-survey findings. Based on healthcare availability data, we also performed projections to assess the burdens on the infrastructure along the spectrum of the pandemic growth. We have developed an interactive dashboard summarizing our results. Our predictions successfully identified the initial hot-spots of India such as Maharashtra and Delhi, and those that emerged later, such as Madhya Pradesh and Kerala. These models have the potential to inform appropriate policies for isolation and mitigation strategies to contain the pandemic, through a phased approach by appropriate resource prioritization and allocation.

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  1. ScreenIT

    SciScore for 10.1101/2021.03.16.21253772: (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.

    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).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Given the limitations of our current dataset, we did not explore these avenues but these could provide valuable insights, prospectively or retrospectively, for learning the dynamics of the COVID-19 spread. In terms of methodology, there are several refinements possible. We only focused on simulation based on assumed parameter choices such as R0, and not on any formal inference or hypothesis testing. One interesting avenue could be to construct a unified framework, through likelihood based constructions for network-based models and employ a frequentist or Bayesian estimation procedure and obtain uncertainty quantifications. Furthermore, one could explore potential stochastic formulations of the deterministic SIR-type models that can accommodate flexible noise scenarios such as nonlinearity or non-stationarity (Bhadra et al., 2011). When we started this work in March 2020, given the lack of tests and the large number of asymptomatic carriers, the strategy for slowing the spread of the COVID-19 pandemic had changed from containment to mitigation (Parodi and Liu, 2020). In essence, the focus was on slowing the further spread of the virus, reducing the anticipated surge in health care use, providing patients with the right level of care to maximize the likelihood that the majority of patients will only require time-limited home isolation, expanding testing capability to increase available hospital capacity, and tailoring isolation to minimize transmission of SARS-CoV-2. In a count...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2021.03.16.21253772v1 on medRxiv