Patterns of SARS-CoV-2 exposure and mortality suggest endemic infections, in addition to space and population factors, shape dynamics across countries
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Some countries have been crippled by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic while others have emerged with few infections and fatalities; the factors underscoring this macro-epidemiological variation is one of the mysteries of this global catastrophe. Variation in immune responses influence SARS-CoV-2 transmission and mortality, and factors shaping this variation at the country level, in addition to other socio-ecological drivers, may be important. Here, we construct spatially explicit Bayesian models that combine data on prevalence of endemic diseases and other socio-ecological characteristics to quantify patterns of confirmed deaths and cases across the globe before mass vaccination. We find that the prevalence of parasitic worms, human immunodeficiency virus and malaria play a surprisingly important role in predicting country-level SARS-CoV-2 patterns. When combined with factors such as population density, our models predict 63% (56-67) and 76% (69-81) of confirmed cases and deaths among countries, respectively. While our findings at this macro-scale are necessarily associative, they highlight a need for studies to consider factors, such as infection by other pathogens, on global SARS-CoV-2 dynamics. These relationships are vital for developing countries that already have the highest burden of endemic disease and are becoming the most affected by the SARS-CoV-2 pandemic.
Article activity feed
-
SciScore for 10.1101/2021.07.12.21260394: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Additional socioecological variables were extracted for each country from the World Bank dataset (https://data.worldbank.org/) including 5-year averages (2015-2019) for per capita GDP (in current USD), percent of the population living in urban areas (hereafter ‘percent urban’), and per-capita health care expenditure (hereafter ‘health spending’). https://data.worldbank.org/suggested: (Data World Bank, RRID:SCR_012767)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:As …
SciScore for 10.1101/2021.07.12.21260394: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Additional socioecological variables were extracted for each country from the World Bank dataset (https://data.worldbank.org/) including 5-year averages (2015-2019) for per capita GDP (in current USD), percent of the population living in urban areas (hereafter ‘percent urban’), and per-capita health care expenditure (hereafter ‘health spending’). https://data.worldbank.org/suggested: (Data World Bank, RRID:SCR_012767)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:As with any model at a global scale, there are important limitations. For example, our models cannot capture important regional differences and cannot directly infer individual-level mechanisms. Moreover, our models utilise WHO confirmed case and death data that are likely considerable underestimates of the true scale of this global emergency. Nonetheless, the overall strong relationship between cases and deaths supports the idea that modelling the case data is of utility as COVID-19 deaths are generally better reported than cases [58]. The weak relationship between cases and tests is not surprising as it has been demonstrated that regional differences can vary by country [1]. Moreover, while we assembled a diverse set of predictors, this dataset is not comprehensive. We aimed to maximise the number of countries we included in the analysis without introducing large amounts of missing data. However, the predictive performance of our models was surprisingly high, and few model estimates of cases and deaths not including the observed values for each country (Fig. 4). Countries such as Tanzania, for which our model overpredicted cases and deaths, may warrant increased attention and surveillance. Our study shows that for any global analysis of SARS-CoV-2 dynamics, the patterns of other pathogens can provide important insights. At a broad spatial scale, variations in climate are likely to be less critical than variations in contemporary and historical exposure to a diverse array of...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
-
