Factors associated with resistance to SARS-CoV-2 infection discovered using large-scale medical record data and machine learning.

There have been over 621 million cases of COVID-19 worldwide with over 6.5 million deaths. Despite the high secondary attack rate of COVID-19 in shared households, some exposed individuals do not contract the virus. In addition, little is known about whether the occurrence of COVID-19 resistance differs among people by health characteristics as stored in the electronic health records (EHR). In this retrospective analysis, we develop a statistical model to predict COVID-19 resistance in 8,536 individuals with prior COVID-19 exposure using demographics, diagnostic codes, outpatient medication orders, and count of Elixhauser comorbidities in EHR data from the COVID-19 Precision Medicine Platform Registry. Cluster analyses identified 5 patterns of diagnostic codes that distinguished resistant from non-resistant patients in our study population. In addition, our models showed modest performance in predicting COVID-19 resistance (best performing model AUROC = 0.61). Monte Carlo simulations conducted indicated that the AUROC results are statistically significant (p < 0.001) for the testing set. We hope to validate the features found to be associated with resistance/non-resistance through more advanced association studies.

Rapid drinking water safety estimation in cities: Piloting a globally scalable method in Cochabamba, Bolivia.

BACKGROUND: Systematically collected and comparable data on drinking water safety at city-scale is currently unavailable, despite the stated importance of water safety monitoring at scale under the United Nations Sustainable Development Goals (SDGs). We developed a rapid drinking water quality assessment methodology intended to be replicable across all cities and useful for monitoring towards achieving SDG 6 (Clean Water and Sanitation). METHODS: We collected drinking water samples at the point-of-consumption for basic microbial, physical and chemical water quality analysis and conducted household surveys on drinking water, sanitation, and hygiene access from 80 households in the city of Cochabamba over 1 week. We categorized the household's water service level according to the SDG 6 framework. RESULTS: We estimated an average time requirement of 6.4 person-hours and a consumable cost of US $51 per household (n = 80). In this cross-sectional study, 71% of drinking water samples met World Health Organization (WHO) microbiological safety criteria, 96% met WHO chemical quality criteria, and all met WHO aesthetic quality criteria. However, only 18% of the households were categorized as having safely managed drinking water services. None met the criteria for having safely managed sanitation services; nonetheless, 81% had basic sanitation services and 78% had basic hygiene facilities. CONCLUSIONS: This method can generate basic water safety data for a city at a relatively low cost in terms of person-time and materials, yielding useful information for inter-city analyses. Because 29% of samples did not meet microbiological safety criteria, 22% of the households did not have access to handwashing facilities and none had safe sanitation services, we concluded that Cochabamba did not meet normative SDG 6 targets when surveyed. Our study further suggests that water quality at point-of-use more accurately characterizes drinking water safety than infrastructure type.