Socio-economic and corporate factors and COVID-19 pandemic: a wake-up call

Araníbar J, Munayco Escate CV. High altitudes, population density, and poverty: Unraveling the complexities of COVID-19 in Peru during the years 2020-2022

Background

Several factors related to hospitalizations, morbidity, and mortality from COVID-19 have been identified. However, limited exploration has been done on geographic and socioeconomic factors that could significantly impact these outcomes.

Objectives

This study aimed to determine whether altitude, population density, and percentage of population in total poverty are associated with COVID-19 incidence per 1000 inhabitants and COVID-19 case-fatality rate in Peru, from 2020 to 2022.

Methods

This study utilized a multiple group ecological design and relied on secondary databases containing daily records of COVID-19 positive cases and deaths due to COVID-19. An epidemiological analysis was performed, subsequently processed using a random effects model.

Results

As of August 2022, Peru had recorded a total of 3,838,028 COVID-19 positive cases and 215,023 deaths due to COVID-19. Our analysis revealed a statistically significant negative association between altitude and COVID-19 incidence (aBETA: -0.004; Standard Error: 0.001; p < 0.05). Moreover, we observed a positive association between population density and incidence (aBETA: 0.006; Standard Error: 0.001; p < 0.05). However, we found no significant association between the percentage of population in total poverty and COVID-19 incidence.

Conclusion

Our study found that an increase in altitude was associated with a decrease in COVID-19 incidence, while an increase in population density was associated with an increase in COVID-19 incidence. High altitude, population density and percentage of population in total poverty does not change case-fatality rate due to COVID-19.

Does money buy health? evaluation of stock market performance and economic growth in the wake of the COVID-19 pandemic

The novel coronavirus pandemic of 2019 (COVID-19) has significantly harmed numerous social and economic activities worldwide, drawing researchers and governments’ attention to the problem and tackling it via persistent healthcare measures. The study’s primary purpose is to examine the effects of total coronavirus cases, total fatalities, total recovered cases, unemployment, and trade openness on stock prices and economic growth in the world’s top 39 affected nations. An analysis of the coronavirus outbreaks found that wealthier countries had a well-established healthcare infrastructure, but they were disproportionately affected by the virus. Conversely, the less wealthy nations had inadequate healthcare infrastructures, but they were not as affected as the wealthier countries. Is it possible to buy health with money? That was the question at the heart of the study’s money-and-health curve. The robust least square regression results indicate that an increase in coronavirus cases influences economic growth and stock market performance due to massive healthcare funding distributed globally, sustaining economic and financial activities for a shorter period. However, a continuous increase in coronavirus fatalities depresses the stock market, resulting in financial depression worldwide. Additionally, a rise in overall coronavirus recovered cases has a negative effect on the country’s economic development and stock market performance because of greater uncertainty in economic and financial activities. Case fatality ratios influence economic growth, whereas case recovery ratios decrease economic and financial performance due to greater healthcare concerns across countries. Finally, trade openness is critical in sustaining the country’s economic development and stock market performance in the wake of the COVID-19 epidemic.

When might host heterogeneity drive the evolution of asymptomatic, pandemic coronaviruses?

Controlling many infectious diseases, including SARS-Coronavirus-2 (SARS-CoV-2), requires surveillance followed by isolation, contact-tracing and quarantining. These interventions often begin by identifying symptomatic individuals. However, actively removing pathogen strains causing symptomatic infections may inadvertently select for strains less likely to cause symptomatic infections. Moreover, a pathogen's fitness landscape is structured around a heterogeneous host pool; uneven surveillance efforts and distinct transmission risks across host classes can meaningfully alter selection pressures. Here, we explore this interplay between evolution caused by disease control efforts and the evolutionary consequences of host heterogeneity. Using an evolutionary epidemiology model parameterized for coronaviruses, we show that intense symptoms-driven disease control selects for asymptomatic strains, particularly when these efforts are applied unevenly across host groups. Under these conditions, increasing quarantine efforts have diverging effects. If isolation alone cannot eradicate, intensive quarantine efforts combined with uneven detections of asymptomatic infections (e.g., via neglect of some host classes) can favor the evolution of asymptomatic strains. We further show how, when intervention intensity depends on the prevalence of symptomatic infections, higher removal efforts (and isolating symptomatic cases in particular) more readily select for asymptomatic strains than when these efforts do not depend on prevalence. The selection pressures on pathogens caused by isolation and quarantining likely lie between the extremes of no intervention and thoroughly successful eradication. Thus, analyzing how different public health responses can select for asymptomatic pathogen strains is critical for identifying disease suppression efforts that can effectively manage emerging infectious diseases.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11071-022-07548-7.

Do environmental pollutants carrier to COVID-19 pandemic? A cross-sectional analysis

The coronavirus disease (COVID-19) is a highly transmitted disease that spreads all over the globe in a short period. Environmental pollutants are considered one of the carriers to spread the COVID-19 pandemic through health damages. Carbon emissions, PM2.5 emissions, nitrous oxide emissions, GHG, and other GHG emissions are mainly judged separately in the earlier studies in different economic settings. The study hypothesizes that environmental pollutants adversely affect healthcare outcomes, likely to infected people by contagious diseases, including coronavirus cases. The subject matter is vital to analyze the preventive healthcare theory by using different environmental pollutants on the COVID-19 factors: total infected cases, total death cases, and case fatality ratio, in a large cross-section of 119 countries. The study employed the generalized least square (GLS) method for robust inferences. The results show that GHG and CO2 emissions are critical factors likely to increase total coronavirus cases and death rates. On the other hand, nitrous oxide, carbon, and transport emissions increase the case fatality ratio through healthcare damages. The study concludes that stringent environmental policies and improving healthcare infrastructure can control coronavirus cases across countries.