Understanding factors that affect the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial for mitigating the impacts of COVID-19. Hamada Badr and colleagues 1 found a strong correlation between phone mobility data and decreased COVID-19 case growth rates, making the explicit assumption that phone mobility data serves as a proxy for social distancing. Thus, if true, concomitant increases in mobility will be correlated with an increased number of cases. We did a similar analysis using three social distancing metrics created from phone mobility data provided by the Unacast Social Distancing Scorecard. 2 The first metric-the daily distance difference-is analogous to the mobility ratio metric calculated by Badr and colleagues. The mobility ratio metric quantifies changes in behaviour relative to a baseline period before widespread transmission of COVID-19. The other two Unacast metrics measure changes in visits to nonessential places and encounter density, which were noted as limitations in the study by Badr and colleagues.Using the daily distance difference metric, we identified a strong correlation between decreased mobility and reduced COVID-19 case growth between March 27
Artificial intelligence (AI) refers to the performance of tasks by machines ordinarily associated with human intelligence. Machine learning (ML) is a subtype of AI; it refers to the ability of computers to draw conclusions (ie, learn) from data without being directly programmed. ML builds from traditional statistical methods and has drawn significant interest in healthcare epidemiology due to its potential for improving disease prediction and patient care. This review provides an overview of ML in healthcare epidemiology and practical examples of ML tools used to support healthcare decision making at 4 stages of hospital-based care: triage, diagnosis, treatment, and discharge. Examples include model-building efforts to assist emergency department triage, predicting time before septic shock onset, detecting community-acquired pneumonia, and classifying COVID-19 disposition risk level. Increasing availability and quality of electronic health record (EHR) data as well as computing power provides opportunities for ML to increase patient safety, improve the efficiency of clinical management, and reduce healthcare costs.
Mounting evidence suggests the primary mode of SARS-CoV-2 transmission is aerosolized transmission from close contact with infected individuals. While transmission is a direct result of human encounters, falling humidity may enhance aerosolized transmission risks similar to other respiratory viruses (e.g., influenza). Using Google COVID-19 Community Mobility Reports, we assessed the relative effects of absolute humidity and changes in individual movement patterns on daily cases while accounting for regional differences in climatological regimes. Our results indicate that increasing humidity was associated with declining cases in the spring and summer of 2020, while decreasing humidity and increase in residential mobility during winter months likely caused increases in COVID-19 cases. The effects of humidity were generally greater in regions with lower humidity levels. Given the possibility that COVID-19 will be endemic, understanding the behavioral and environmental drivers of COVID-19 seasonality in the United States will be paramount as policymakers, healthcare systems, and researchers forecast and plan accordingly.
Background Declines in outpatient antibiotic prescribing were reported during the beginning of the COVID-19 pandemic in the United States; however, the overall impact of COVID-19 cases on antibiotic prescribing remains unclear. Methods Ecological study using random effects panel regression of monthly reported COVID-19 county case and antibiotic prescription data, controlling for seasonality, urbanicity, healthcare access, non-pharmaceutical interventions (NPIs), and sociodemographic factors. Results Antibiotic prescribing fell 26.8% in 2020 compared to prior years. Each 1% increase in county-level monthly COVID-19 cases was associated with a 0.009% (95% CI 0.007%, 0.012%; p < 0.01) increase in prescriptions per 100,000 population dispensed to all ages and a 0.012% (95% CI -0.017%, -0.008%; p < 0.01) decrease in prescriptions per 100,000 children. Counties with schools open for in-person instruction were associated with a 0.044% (95% CI 0.024%, 0.065%; p < 0.01) increase in prescriptions per 100,000 children compared to counties that closed schools. Internal movement restrictions and requiring facemasks were also associated with lower prescribing among children. Conclusions The positive association of COVID-19 cases with prescribing for all ages and the negative association for children indicates increases in prescribing occurred primarily among adults. The rarity of bacterial co-infection in COVID-19 patients suggests a fraction of these prescriptions may have been inappropriate. Facemasks and school closures were correlated with reductions in prescribing among children, possibly due to the prevention of other upper respiratory infections. The strongest predictors of prescribing were prior years’ prescribing trends, suggesting the possibility that behavioral norms are an important driver of prescribing practices.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.