Predicting the future SARS-COV-2 reproductive rate

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 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. Results From 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 Pearson's coefficient of correlation for log10R0 ranged from 0.529 to 0.984 and for cumulative mortality from 0.980 to 1.000. 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.

The course of the UK COVID 19 pandemic; no measurable impact of new variants

“…Even in the absence of governmental instructions, in disease resurgence or publicity surrounding a more dangerous variant, a proportion of individuals are likely to implement their own measures such as avoiding busy locations and enhanced hygiene practices. [10] This element of ‘personal lockdown’ may also account for the discrepancy between transmissibility of the Kent variant and the results here, though overall our data demonstrates a very strong correlation with the lockdown stringency score.…”

“…The model generated here is similar to a previous proposed model,[10] the previous model could not be extrapolated due to a small change in the generation of the lockdown stringency score. The newer model is generated over a longer period of time (9 months versus 4 months) but also extrapolated over a longer period (4 months versus 1.5 months).…”

“…It is important to note that positive correlation has not been universally reported, including in a previous application of this model to multiple countries. [10] Some studies show a bell-shaped curve conferring lower virus survival at moderate levels of humidity,[25] potentially explained by the behaviour of salts dissolved within the respiratory droplets. [26]…”

“…[7] Humidity may be an important factor, with a negative correlation to Sars-CoV-2 transmission and deaths shown in a number of studies. [8], [9] We have applied the methodology described in a previous study[10], which demonstrated the means of predicting the viral reproductive rate based on lockdown and weather parameters.…”

“…The aim of this study is to determine the effect of the emergence of the Kent variant on the overall progression of the pandemic in the UK, through the application of a predictive model based on lockdown, and temperature and humidity in keeping with the methodology of a previous study[10]…”