Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

A simple method to describe the COVID-19 trajectory and dynamics in any country based on Johnson cumulative density function fitting.

A simple method is utilised to study and compare COVID-19 infection dynamics between countries based on curve fitting to publicly shared data of confirmed COVID-19 infections. The method was tested using data from 80 countries from 6 continents. We found that Johnson cumulative density functions (CDFs) were extremely well fitted to the data (R2 > 0.99) and that Johnson CDFs were much better fitted to the tails of the data than either the commonly used normal or lognormal CDFs. Fitted Johnson CDFs can be used to obtain basic parameters of the infection wave, such as the percentage of the population infected during an infection wave, the days of the start, peak and end of the infection wave, and the duration of the wave's increase and decrease. These parameters can be easily interpreted biologically and used both for describing infection wave dynamics and in further statistical analysis. The usefulness of the parameters obtained was analysed with respect to the relation between the gross domestic product (GDP) per capita, the population density, the percentage of the population infected during an infection wave, the starting day and the duration of the infection wave in the 80 countries. We found that all the above parameters were significantly associated with GDP per capita, but only the percentage of the population infected was significantly associated with population density. If used with caution, this method has a limited ability to predict the future trajectory and parameters of an ongoing infection wave.

Methodology for modelling the new COVID-19 pandemic spread and implementation to European countries.

After the outbreak of the new COVID-19 disease, the mitigation stage has been reached in most of the countries in the world. During this stage, a more accurate data analysis of the daily reported cases and other parameters became possible for the European countries and has been performed in this work. Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, investigating probable correlations between the country's characteristics and the parameters of the parametrization. We have also developed a methodology for coupling our model to the SIR-based models determining the basic and the effective reproductive number referring to the parameter space. The obtained results and conclusions could be useful in the case of a recurrence of this insidious disease in the future.

Associating the Change in New COVID-19 Cases to GDP per Capita in 38 European Countries in the First Wave of the Pandemic.

COVID-19 has affected all countries globally. We explore associations between the change in new COVID-19 registered cases per million population and various macroeconomic and well-being indicators in 38 European countries over a 2-month period (1st April-31st May 2020). A statistically significant (p = 0.002) negative association was estimated between the change in new COVID-19 cases and GDP per capita, after controlling for key health determinants including public expenditure on health, life expectancy, smoking tobacco and sanitation. The country with the highest GDP per capita in Europe (i.e., Luxemburg) was found to experience the lowest change in new COVID-19 cases within the time period whilst the opposite was found for countries with lower GDP per capita (i.e., Ukraine, Bulgaria, and Romania). The outcomes of this study indicate that, in the first wave of the pandemic in Europe, a country's GDP per capita might be associated with a lower rate of new COVID-19 cases. The study concludes by suggesting that in European regions a country's economic performance should be a critical health priority for policy makers.

Effects of Pandemics-Related Uncertainty on Household Consumption: Evidence From the Cross-Country Data.

The COVID-19 pandemic has affected various macroeconomic indicators. Given this backdrop, this research investigates the effects of the pandemics-related uncertainty on household consumption. For this purpose, we construct a simple theoretical model to study the effects of the pandemics-related uncertainty on household consumption. To estimate the theoretical model, we consider the panel dataset of 138 countries for the period from 1996 to 2017. We also use the Pandemic Uncertainty Index to measure the pandemics-related uncertainty. The theoretical model and the empirical findings from the Feasible Generalized Least Squares (FGLS) estimations indicate that the gross fixed capital formation, government consumption, balance of trade, and the Pandemic Uncertainty Index negatively affect household consumption. The results are also valid in the panel dataset of 42 high-income economies and the remaining 96 emerging economies.

Population risk factors for COVID-19 deaths in Nigeria at sub-national level.

INTRODUCTION: Nigeria is the most populous country in the African continent. The aim of this study was to analyze risk factors for COVID-19 prevalence and deaths in all 6 geopolitical regions and 37 States in Nigeria. METHODS: we analyzed the data retrieved from various sources, including Nigeria CDC, Nigeria National Bureau of Statistics, Unicef-Nigeria multiple indicator cluster survey and the Institute of Health Metrics and Evaluation, University of Washington. We examined 4 clinical risk factors (prevalence of TB, HIV, smoking and BCG vaccination coverage) and 5 sociodemographic factors (age ≥65, population density, literacy rate, unemployment and GDP per capita). Multivariate modeling was conducted using generalized linear model. RESULTS: our analysis showed that the incidence of confirmed COVID-19 cases differed widely across the 37 States, from 0.09 per 100,000 in Kogi to 83.7 in Lagos. However, more than 70% of confirmed cases were concentrated in just 7 States: Lagos, Abuja, Oyo, Kano, Edo, Rivers and Delta. Case mortality rate (CMR) also varied considerably, with Lagos, Abuja and Edo having CMR above 9 per million population. On bivariate analysis, higher CMR correlated positively with GDP (r=0.53) and to a lesser extent with TB (r=0.36) and population density (r=0.38). On multivariate analysis, which is more definitive, States with higher HIV prevalence and BCG coverage had lower CMR, while high GDP States had a greater CMR. CONCLUSION: this study indicates that COVID-19 has disproportionately affected certain States in Nigeria. Population susceptibility factors include higher economic development but not literacy or unemployment. Death rates were mildly lower in States with higher HIV prevalence and BCG vaccination coverage.

Estimating the global economic benefits of physically active populations over 30 years (2020-2050).

OBJECTIVES: We assess the potential benefits of increased physical activity for the global economy for 23 countries and the rest of the world from 2020 to 2050. The main factors taken into account in the economic assessment are excess mortality and lower productivity. METHODS: This study links three methodologies. First, we estimate the association between physical inactivity and workplace productivity using multivariable regression models with proprietary data on 120 143 individuals in the UK and six Asian countries (Australia, Malaysia, Hong Kong, Thailand, Singapore and Sri Lanka). Second, we analyse the association between physical activity and mortality risk through a meta-regression analysis with data from 74 prior studies with global coverage. Finally, the estimated effects are combined in a computable general equilibrium macroeconomic model to project the economic benefits of physical activity over time. RESULTS: Doing at least 150 min of moderate-intensity physical activity per week, as per lower limit of the range recommended by the 2020 WHO guidelines, would lead to an increase in global gross domestic product (GDP) of 0.15%-0.24% per year by 2050, worth up to US$314-446 billion per year and US$6.0-8.6 trillion cumulatively over the 30-year projection horizon (in 2019 prices). The results vary by country due to differences in baseline levels of physical activity and GDP per capita. CONCLUSIONS: Increasing physical activity in the population would lead to reduction in working-age mortality and morbidity and an increase in productivity, particularly through lower presenteeism, leading to substantial economic gains for the global economy.

COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data.

OBJECTIVE: To investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally. DESIGN: Publicly available register-based ecological study. SETTING: Two hundred and nine countries/territories in the world. PARTICIPANTS: Aggregated data including 10 445 656 confirmed COVID-19 cases. PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 CFR and crude cause-specific death rate were calculated using country-level data from the Our World in Data website. RESULTS: The average of country/territory-specific COVID-19 CFR is about 2%-3% worldwide and higher than previously reported at 0.7%-1.3%. A doubling in size of a population is associated with a 0.48% (95% CI 0.25% to 0.70%) increase in COVID-19 CFR, and a doubling in the proportion of female smokers is associated with a 0.55% (95% CI 0.09% to 1.02%) increase in COVID-19 CFR. The open testing policies are associated with a 2.23% (95% CI 0.21% to 4.25%) decrease in CFR. The strictness of anti-COVID-19 measures was not statistically significantly associated with CFR overall, but the higher Stringency Index was associated with higher CFR in higher-income countries with active testing policies (regression coefficient beta=0.14, 95% CI 0.01 to 0.27). Inverse associations were found between cardiovascular disease death rate and diabetes prevalence and CFR. CONCLUSION: The association between population size and COVID-19 CFR may imply the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in women and COVID-19 CFR might be due to the finding that the proportion of female smokers reflected broadly the income level of a country. When testing is warranted and healthcare resources are sufficient, strict quarantine and/or lockdown measures might result in excess deaths in underprivileged populations. Spatial dependence and temporal trends in the data should be taken into account in global joint strategy and/or policy making against the COVID-19 pandemic.