Evaluating temperature and humidity gradients of COVID-19 infection rates in light of Non-Pharmaceutical Interventions

We evaluate potential temperature and humidity impact on the infection rate of COVID-19 with a data up to June 10th 2020, which comprises a large geographical footprint. It is critical to analyse data from different countries or regions at similar stages of the pandemic in order to avoid picking up false gradients. The degree of severity of NPIs is found to be a good gauge of the stage of the pandemic for individual countries. Data points are classified according to the stringency index of the NPIs in order to ensure that comparisons between countries are made on equal footing. We find that temperature and relative humidity gradients dont significantly deviate from the zero-gradient hypothesis. Upper limits on the absolute value of the gradients are set. The procedure chosen here yields 6 10-3 {degrees}C-1 and 3.3 10-3 (%)-1 upper limits on the absolute values of the temperature and relative humidity gradients, respectively, with a 95% Confidence Level. These findings do not preclude existence of seasonal effects and are indicative that these are likely to be nuanced.

Risk Adjusted Non-Pharmaceutical Interventions for the Management of COVID-19 in South Africa

A global analysis of the impact of non-pharmaceutical interventions (NPIs) on the dynamics of the spread of the COVID-19 indicates that these can be classified using the stringency index proposed by the Oxford COVID-19 Government Response Tracker (OxCGRT) team. The world average for the coefficient that linearises the level of transmission with respect to the OxCGRT stringency index is s = 0.01{+/-}0.0017 (95% C.I.). The corresponding South African coefficient is s = 0.0078 {+/-} 0.00036 (95% C.I.), compatible with the world average. Here, we implement the stringency index for the recently announced 5-tier regulatory alert system. Predictions are made for the spread of the virus for each alert level. Assuming constant rates of recovery and mortality, it is essential to increase s. For the system to remain sub-critical, the rate with which s increases should outpace that of the decrease of the stringency index. Monitoring of s becomes essential to controlling the post-lockdown phase. Data from the Gauteng province obtained in May 2020 has been used to re-calibrate the model, where s was found increase by 20% with respect to the period before lockdown. Predictions for the province are made in this light.