Unified real-time environmental-epidemiological data for multiscale modeling of the COVID-19 pandemic.

An impressive number of COVID-19 data catalogs exist. However, none are fully optimized for data science applications. Inconsistent naming and data conventions, uneven quality control, and lack of alignment between disease data and potential predictors pose barriers to robust modeling and analysis. To address this gap, we generated a unified dataset that integrates and implements quality checks of the data from numerous leading sources of COVID-19 epidemiological and environmental data. We use a globally consistent hierarchy of administrative units to facilitate analysis within and across countries. The dataset applies this unified hierarchy to align COVID-19 epidemiological data with a number of other data types relevant to understanding and predicting COVID-19 risk, including hydrometeorological data, air quality, information on COVID-19 control policies, vaccine data, and key demographic characteristics.

Effects of hydrometeorological and other factors on SARS-CoV-2 reproduction number in three contiguous countries of tropical Andean South America: a spatiotemporally disaggregated time series analysis.

Background: The COVID-19 pandemic has caused societal disruption globally, and South America has been hit harder than other lower-income regions. This study modeled the effects of six weather variables on district-level SARS-CoV-2 reproduction numbers (Rt ) in three contiguous countries of tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors. Methods: Daily time-series data on SARS-CoV-2 infections were sourced from the health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a unified COVID-19 dataset and other publicly available sources for May-December, 2020. Generalized additive models were fitted. Findings: Relative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt . Days with radiation above 1000 kJ/m2 saw a 1.3% reduction in Rt , and those with humidity above 50% recorded a 0.9% reduction in Rt . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with lowest population mobility. Wind speed, temperature, region, aggregate government policy response, and population age structure had little impact. The fully adjusted model explained 4.3% of Rt variance. Interpretation: Dry atmospheric conditions of low humidity increase district-level SARS-CoV-2 reproduction numbers, while higher levels of solar radiation decrease district-level SARS-CoV-2 reproduction numbers - effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures. Funding: NASA's Group on Earth Observations Work Programme (16-GEO16-0047).

Enteropathy Markers in Early Life Were Associated with Adipokine, Apolipoprotein, and Cytokine Profiles Consistent with an Adverse Cardiometabolic Disease Risk Profile Later in Childhood in a Peruvian Birth Cohort.

Metabolic syndrome is a cluster of risk factors for cardiovascular disease afflicting more than 1 billion people worldwide and is increasingly being identified in younger age groups and in socioeconomically disadvantaged settings in the global south. Enteropathogen exposure and environmental enteropathy in infancy may contribute to metabolic syndrome by disrupting the metabolic profile in a way that is detectable in cardiometabolic markers later in childhood. A total of 217 subjects previously enrolled in a birth cohort in Amazonian Peru were monitored annually from ages 2 to 5 years. A total of 197 blood samples collected in later childhood were analyzed for 37 cardiometabolic biomarkers, including adipokines, apolipoproteins, cytokines, which were matched to extant early-life markers of enteropathy ascertained between birth and 2 years. Multivariate and multivariable regression models were fitted to test for associations, adjusting for confounders. Fecal and urinary markers of intestinal permeability and inflammation (myeloperoxidase, lactulose, and mannitol) measured in infancy were associated with later serum concentrations of soluble CD40-ligand, a proinflammatory cytokine correlated with adverse metabolic outcomes. Fecal myeloperoxidase was also associated with later levels of omentin-1. Enteric protozoa exposure showed stronger associations with later cardiometabolic markers than viruses, bacteria, and overall diarrheal episodes. Early-life enteropathy markers were associated with altered adipokine, apolipoprotein, and cytokine profiles later in childhood consistent with an adverse cardiometabolic disease risk profile in this cohort. Markers of intestinal permeability and inflammation measured in urine (lactulose, mannitol) and stool (myeloperoxidase, protozoal infections) during infancy may predict metabolic syndrome in adulthood.