Big Data- and Artificial Intelligence-Based Hot-Spot Analysis of COVID-19: Gauteng, South Africa, as a case study (preprint)

“Coronavirus Disease 2019” (COVID-19) related data contain many complexities that must be taken into account when extracting information to guide public health decision- and policy-makers. In generalising the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. This statistically random spread of a virus through a population is the core of the majority of Susceptible-Infectious-Recovered-Deceased (SIRD) models and is dependent on factors such as number of infected cases, infection rate, level of social interactions, susceptible population and total population. However, the spread of COVID-19 and, therefore, the data representing the virus progression do not always conform to a stochastic model. In this paper, we have focused on the most influential non-stochastic dynamics of COVID-19, hot-spots, utilizing artificial intelligence (AI) based geo-localization and clustering analyses, taking Gauteng (South Africa) as a case study.

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

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 do not significantly deviate from the zero-gradient hypothesis. Upper limits on the absolute value of the gradients are set. The procedure chosen here yields 6{middle dot}10^-3{degrees}C^-1 and 3.3{middle dot}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 (preprint)

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.

Worldwide Effectiveness of Various Non-Pharmaceutical Intervention Control Strategies on the Global COVID-19 Pandemic: A Linearised Control Model (preprint)

Background COVID-19 is a virus which has lead to a global pandemic. Worldwide, more than 130 countries have imposed severe restrictions, which form part of a set of non-pharmaceutical interventions (NPI)s. We aimed to quantify the country-specific effects of these NPIs and compare them using the Oxford COVID-19 Government Response Tracker (OxCGRT) stringency index, p, as a measure of NPI stringency. Methods We developed a dual latent/observable Susceptible Infected Recovered Deaths (SIRD) model and applied it on each of 22 countries and 25 states in the US using publicly available data. The observable model parameters were extracted using kernel functions. The regression of the transmission rate, {beta}, as a function of p in each locale was modeled through the intervention leverage, s, an initial transmission rate, {beta}0 and a typical adjustment time, br-1. Results The world average for the intervention leverage, s=0.01 (95% CI 0.0102 - 0.0112) had an ensemble standard deviation of 0.0017 (95% C.I. 0.0014 - 0.0021), strongly indicating a universal behavior. Discussion Our study indicates that removing NPIs too swiftly will result in the resurgence of the spread within one to two months, in alignment with the current WHO recommendations. Moreover, we have quantified and are able to predict the effect of various combinations of NPIs. There is a minimum NPI level, below which leads to resurgence of the outbreak (in the absence of pharmaceutical and clinical advances). For the epidemic to remain sub-critical, the rate with which the intervention leverage s increases should outpace that of the relaxation of NPIs.