Explainable features responsible for the high or low spread of SARS-CoV-2: Africa in view.

The low spread of the global pandemic in Africa has raised concerns. Consequently, many commentators have misconstrued concerns suspecting weather, and immunity to be prime reasons. This study investigates the factors associated with the high and low spread of the SARS-CoV-2 (also known as COVID-19) and employs graphical Bayesian models to investigate feature interactions and causality. Through experimentation with the Bayesian framework, we propose that: (i) the proportion of people within the country population who test positive for SARS-CoV-2 and a country's test capacity cause the rate of spread of the virus [i.e., P(S|P) and P(S|T)] (ii) poverty gaps, welfare and freedom of the press directly cause the spread of the virus [i.e., P(S|E), P(S|W), and P(S|R)] (iii) Government effectiveness serves as a parent to poverty gaps and welfare [ i.e., P(E|G) and P(W|G)] and voice and accountability serve as a parent to freedom of the press [i.e., P(R|V)]. For the output, we "dichotomized" regions based on the "share of global infection rate" metric (SGIR) that implicitly accounts for a given region's population, and we find that - out of two hundred and nineteen countries investigated, one hundred and twenty-seven have SGIR ≥ 1%, and the majority (44 out 58 - 75.86%) of Africa countries (as of 12th February 2021) have SGIR < 1%. With Africa in the mirror, the study shows that only 2.2% of the Africa population has been tested for SARS-CoV-2 and finds that the low proportion of population tested [i.e., P(S|P)] for SARS-CoV-2 is the cause of the low spread (i.e., cases reported) of SARS-CoV-2 in Africa. Similarly, the fragmented socioeconomic statuses [i.e., P(S|E)] among citizens leads to socioeconomic distancing, causing socio-class gaps between the rich and poor/average citizens, ensuring low interaction in social space, thus limiting the spread.

An information‐based index of uncertainty and the predictability of energy prices

We develop an index of uncertainty, the COVID‐19 induced uncertainty (CIU) index, and employ it to empirically examine the vulnerability of energy prices amidst the COVID‐19 pandemic using a distributed lag model that jointly accounts for conditional heteroscedasticity, autocorrelation, persistence, and structural breaks, as well as day‐of‐the‐week effect. The nexus between energy returns and uncertainty index is analyzed, using daily price returns of eight energy sources (Brent oil, diesel, gasoline, heating oil, kerosene, natural gas, propane, and WTI oil) and four news/information‐based uncertainty proxies [CIU, EPU, Global Fear Index (GFI) and VIX]. The CIU and alternative indexes are used, respectively for the main estimation and sensitivity analysis. We show the outperformance of CIU over alternative news uncertainty proxies in the prediction of energy prices. News (aggregate) and bad news are found to negatively and significantly impact energy returns, while good news has a significantly positive impact. Imperatively, energy variables lack hedging potentials against the uncertainty occasioned by the COVID‐19 pandemic, while we find no strong evidence of asymmetry. Our results are robust to the choice of news variables, forecast horizons employed, with likely sensitivity to energy prices.