COVID 19 Pandemic: A Real-time Forecasts & Prediction of Confirmed Cases, Active Cases using the ARIMA model & Public Health in West Bengal, India

  1. Dibash Sarkar
  2. Moinak Biswas

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Abstract

Background

COVID-19 is an emerging infectious disease which has been declared a Pandemic by the World Health Organization (WHO) on March 11 2020. This pandemic has spread over the world in more than 200 countries. India is also adversely affected by this pandemic, and there are no signs of slowing down of the virus in coming time. The absence of a vaccine for COVID-19 is making the situation worse for the already overstretched Indian public health care system. As economic burden makes it increasingly difficult for our country to continue imposing control measures, it is vital for states like West Bengal to make predictions using time series forecasting for the upcoming cases, test kits, health care and estimated the requirement of Quarantine centers, isolation beds, ICU beds and ventilators for COVID-19 patients.

Objective

This study is forecasting the confirmed and active cases for COVID-19 until August, using time series ARIMA model & Public Health in West Bengal, India.

Methods

We used ARIMA model, and Auto ARIMA model for forecasting confirmed and active cases till the end of August month using time series data of COVID-19 cases in West Bengal, India from March 1, 2020, to June 4, 2020.

Results

We are expecting that West Bengal will have around 62279 ± 5000 Cases by the end of August based on our forecasts. Meanwhile Maharashtra, Punjab, Gujarat and Delhi (UT) will be the most affected states, having the highest number of active and confirmed cases at the end of August.

Discussion and Conclusion

This forecasts show a very crucial situation for West Bengal in coming days and, the actual numbers can go higher than our estimates of confirmed case as Lockdown 5.0 & Unlock 1.0 will be implemented from 1 st June 2020 in India, West Bengal will be observing a partial lift of the lockdown and in that case, there will be a surge in the number of daily confirmed and active cases. The requirement of Health care sector needs to be further improved isolation beds, ICUs and ventilators will also be needed to increase in that scenario. Inter State & Intra State Movement restrictions are lifted. Hence, Migrants returning to their homes due to loss of livelihood and income in the lockdown period may lead to a rise in the number of cases, which could not be accounted for in our projections. We suggest more of Public-Private Partnership (PPP) model in the health sector to accommodate COVID-19 patients adequately and reduce the burden of the already overstretched Indian public health care system, which will directly or indirectly affect the States in the time of crisis.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.06.20124180: (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

    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:
    Limitations or Errors that may occur: The forecasting of COVID-19 cases is done based on the data under the lockdown duration and few in Unlock 1.0. So, the forecasted cases in future will be showing the same trend as India would have observed, had it been observing complete lockdown. Since May 4, India is observing Unlock 1.0, and for that actual cases will/can/may be more than the forecasted cases. For our state it is showing hike in COVID-19 infection and increased trend in future, but the situation may not occur in future because of the nature of the previous trend-pattern is different from now. Forecasted cases based on ARIMA model in our study for some states having lower bound for the 95% CI comes negative values which we have considered zero cases in that situation. In our study, the seasonality factor was considered but it may vary now due to Unlock 1.0, and it may affect our Forecast, Therefore the is a Plus – Minus in the forecasted cases to avoid any Big Error, & make the Data more Reliable.

    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 10.1101/2020.06.06.20124180v1 on medRxiv