Predicting the future SARS-COV-2 reproductive rate

  1. DSJ Ellis
  2. DI Papadopoulos
  3. I Donkov
  4. S Bishara

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Abstract

Aims

To generate a predictive model for the SARS-COV-2 viral reproductive rate, based on government policy and weather parameters.

Methods

A multivariate model for the log 10 of viral reproductive rate was constructed for each country using lockdown stringency (Oxford University tracker), temperature and humidity, for the 1 st 110 days of 2020. This was validated by extrapolating to the following 51 days, and comparing the predicted viral rate and cumulative mortality with WHO data.

The country models was extrapolated to July 2021 using projected weather forecast for four scenarios; continuing with the 11/6/2020 lockdown policy, 100% lockdown, 20% lockdown and no lockdown.

Results

From pooled data (40 countries), lockdown stringency had a strong negative correlation with log 10 viral reproductive rate (−0.648 at 21 days later). Maximum temperature correlated at -0.14, 14 days later and humidity correlated at +0.25, 22 days later. Predictive Models were generated for 11 countries using multivariate regression of these parameters. The R 2 correlation for log 10 R 0 ranged from 0.817 to 0.987 for the model generation period. For the validation period, the Pearson’s coefficient of correlation ranged from 0.344 to 0.984 for log 10 R 0 and from 0.980 to 1.000 for cumulative mortality.

Forward extrapolation of these models for 5 nations, demonstrate, that removing the lockdown will result in rapid spread of the disease ranging from as soon as July 2020 for Russia, UK, Italy and India to January 2021 for the USA. The current (11/6/20) lockdown in the USA, Spain, UK, France, Germany, Turkey can control the disease but other nations will need to intensify their lockdowns to prevent future resurgence. Most nations will require more stringent lockdowns in January than in July.

Conclusion

The viral reproductive rate is highly predicted by a combination of lockdown stringency, temperature and humidity. Country specific predictive models can provide useful forecast of policy requirements.

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  1. Version published to 10.1101/2020.08.09.20170845v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.08.09.20170845: (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

    Software and Algorithms
    SentencesResources
    All statistics were calculated using IBM SPSS.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    This is due to the effect of cumulative errors over time, changes in government policies and behaviour in response to viral transmission and the case fatality rate, which is likely to decrease in response to improved treatment and novel therapies, such as the use of Dexamethasone emerging from the RECOVERY trial.[25] There is evidence that the ITU mortality rate has decreased over time,[26] and new trialled therapies such as nebulised Interferon Beta may confer significant survival benefits.[27] Even in the absence of governmental instructions, in the case of a resurgence of the disease, a large proportion of individuals are likely to implement their own measures such as avoiding busy locations and enhanced hygiene practices, akin to a degree of lockdown.[28] Weaknesses of this study include use of temperature from the capital city which may vary widely across regions, the relatively small number of data points for multivariate analysis, the limitations of recorded Sars-CoV-2-19 data, and the heterogeneity between interpretation and application of lockdown policies between countries. In conclusion: The viral reproductive rate for Sars-CoV-2-19 can be accurately predicted using a measure of Lockdown stringency and weather parameters.

    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.

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  3. Version published to 10.1101/2020.08.09.20170845v1 on medRxiv