Predicting the future SARS-COV-2 reproductive rate.

AimsTo generate a predictive model for the SARS-COV-2 viral reproductive rate, based on government policy and weather parameters. MethodsA multivariate model for the log10 of viral reproductive rate was constructed for each country using lockdown stringency (Oxford University tracker), temperature and humidity, for the 1st 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. ResultsFrom pooled data (40 countries), lockdown stringency had a strong negative correlation with log10viral 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 R2 correlation for log10R0 ranged from 0.817 to 0.987 for the model generation period. For the validation period, the Pearsons coefficient of correlation ranged from 0.344 to 0.984 for log10R0 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. ConclusionThe 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.