COVID-19 transmission risk factors

Abstract

We analyze risk factors correlated with the initial transmission growth rate of the recent COVID-19 pandemic in different countries. The number of cases follows in its early stages an almost exponential expansion; we chose as a starting point in each country the first day with 30 cases and we fitted for 12 days, capturing thus the early exponential growth. We looked then for linear correlations of the exponents with other variables, for a sample of 126 countries. We find a positive correlation, i.e. faster spread of COVID-19 , with high confidence level with the following variables, with respective -value: low Temperature ( ), high ratio of old vs. working-age people ( ), life expectancy ( ), number of international tourists ( ), earlier epidemic starting date ( ), high level of physical contact in greeting habits ( ), lung cancer prevalence ( ), obesity in males ( ), share of population in urban areas ( ), cancer prevalence ( ), alcohol consumption ( ), daily smoking prevalence ( ), and UV index ( , 73 countries). We also find a correlation with low Vitamin D serum levels ( ), but on a smaller sample, countries, to be confirmed on a larger sample. There is highly significant correlation also with blood types: positive correlation with types RH- ( ) and A+ ( ), negative correlation with B+ ( ). We also find positive correlation with moderate confidence level ( -value of ) with: CO 2 /SO emissions, type-1 diabetes in children, low vaccination coverage for Tuberculosis (BCG). Several of the above variables are correlated with each other, and so they are likely to have common interpretations. We thus performed a Principal Component Analysis, to find the significant independent linear combinations of such variables. The variables with loadings of at least 0.3 on the significant PCA are: greeting habits, urbanization, epidemic starting date, number of international tourists, temperature, lung cancer, smoking, and obesity in males. We also analyzed the possible existence of a bias: countries with low GDP-per capita might have less intense testing, and we discuss correlation with the above variables.

An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic

“…In a sample community with an ageing population and a higher incidence of comorbidities, 80% of the sample cases had a likelihood of the community being a COVID-19 hotspot greater than 0.5. According to Notari and Torrieri ( 2021 ), there is a positive correlation between a high ratio of people over 65 compared to people in working age, the number of international tourists, male obesity, and the proportion of the population living in urban areas, which results in a faster spread of COVID-19. In nations where obesity prevalence and median population age were higher, the overall incidence of COVID-19 per million was much higher.…”

Inferring the main drivers of SARS-CoV-2 transmissibility

“…Numerous studies tried to assess the correlations of different factors with the SARS-CoV-2 virus transmissibility (Li et al, 2020;Notari and Torrieri, 2020;Salom et al, 2021), but the next step should be predicting the environmental risk of the high spreadability in a certain population (Allel et al, 2020;Coccia, 2020;Gupta and Gharehgozli, 2020). Specifically, a relatively small number of the most influential meteorological and demographic factors should be selected for a predictive risk measure that is accurate enough and practical for use.…”

“…In particular, one may select different combinations of variables accounting together for a similar proportion of variance in the virus transmissibility, which seems to be a dead-end (Notari and Torrieri, 2020). There is consequently a challenge to narrow down the possibilities and illuminate important contributions of the seemingly small differences between highly correlated variables.…”

“…There is consequently a challenge to narrow down the possibilities and illuminate important contributions of the seemingly small differences between highly correlated variables. Noticeably, while numerous studies examined the correlations of several selected (Lin et al, 2020;Ran et al, 2020;Xie et al, 2020) or many different (Li et al, 2020;Hassan et al, 2021;Salom et al, 2021) sociodemographic and meteorological factors with the magnitude of the COVID-19 epidemic, only few studies tried to select a handful of key factors whose combination can explain a large portion of the variance between regions (Allel et al, 2020;Coccia, 2020;Gupta and Gharehgozli, 2020;Notari and Torrieri, 2020). Even a smaller number of studies included data from multiple countries (Allel et al, 2020;Notari and Torrieri, 2020).…”

What makes health systems resilient? An analytical framework drawing on European learnings from the COVID-19 pandemic based on a multitiered approach

“…91 92 Another example of a relevant cultural factor in the context of infectious diseases are social habits regarding physical contact such as greeting customs. 93 Household structure can also play an important role in this context: patterns of cohabitation vary across countries and cultural or socioeconomic groups, and multiperson (intergenerational) households have an increased transmission risk. 94 Prevailing beliefs-for example, conspiracy or health-related beliefs-and values-for example, solidarity, responsibility-have been identified as crucial determinants of compliance with infection control measures during the COVID-19 pandemic.…”