Ensemble forecast of COVID-19 in Karnataka for vulnerability assessment and policy interventions

  1. Sashikumaar Ganesan
  2. Deepak Subramani
  3. Thivin Anandh
  4. Divij Ghose
  5. Giridhara R Babu

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Abstract

We present an ensemble forecast for Wave-3 of COVID-19 in the state of Karnataka, India, using the IISc Population Balance Model for infectious disease spread. The reported data of confirmed, recovered, and deceased cases in Karnataka from 1 July 2020 to 4 July 2021 is utilized to tune the model’s parameters, and an ensemble forecast is done from 5 July 2021 to 30 June 2022. The ensemble is built with 972 members by varying seven critical parameters that quantify the uncertainty in the spread dynamics (antibody waning, viral mutation) and interventions (pharmaceutical, non-pharmaceutical). The probability of Wave-3, the peak date distribution, and the peak caseload distribution are estimated from the ensemble forecast. Our analysis shows that the most significant causal factors are compliance to Covid-appropriate behavior, daily vaccination rate, and the immune escape new variant emergence-time. These causal factors determine when and how severe the Wave-3 of COVID-19 would be in Karnataka. We observe that when compliance to Covid-Appropriate Behavior is good (i.e., lockdown-like compliance), the emergence of new immune-escape variants beyond Sep ‘21 is unlikely to induce a new wave. A new wave is inevitable when compliance to Covid-Appropriate Behavior is only partial. Increasing the daily vaccination rates reduces the peak active caseload at Wave-3. Consequently, the hospitalization, ICU, and Oxygen requirements also decrease. Compared to Wave-2, the ensemble forecast indicates that the number of daily confirmed cases of children (0-17 years) at Wave-3’s peak could be seven times more on average. Our results provide insights to plan science-informed policy interventions and public health response.

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    SciScore for 10.1101/2021.08.31.21262943: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Furthermore, we consider the transmission of immune-escape new viral variants in 33% (IENV-33P), 66% (IENV-66P), and 100% (IENV-100P) of the recovered population. 2.2.3 Antibody Waning (ABW): The antibody waning could be a factor for reinfections and consequently induce new waves.
    IENV-66P
    suggested: None

    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:
    One limitation of the ensemble forecast is that we have not considered the possibility of new variants infecting the vaccinated population, but we have considered a fixed efficacy of 70% for the vaccinated population that can partially offset the limitation. Further, the recovery rate is fitted to the data from July 2020 to May 2021; however, it could be more due to vaccination effect or less due to new variants.

    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.
    • Thank you for including a protocol registration statement.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.08.31.21262943v1 on medRxiv