357 papers
Epidemiology is the study of how diseases spread through populations and what factors influence their patterns. Rather than focusing on individual patients, this field examines broader trends to identify outbreaks, track transmission, and guide public health decisions. By analyzing data on infection rates and risk factors, researchers work to prevent future health crises and protect communities worldwide.
On Gist.Science, we process every new preprint in this category directly from medRxiv to make these critical findings instantly accessible. For each study, we provide both a plain-language explanation for general readers and a detailed technical summary for specialists. This dual approach ensures that vital insights into disease dynamics are understood clearly and quickly by everyone who needs them.
Explore the latest research below to see how scientists are currently mapping disease trends and developing strategies to safeguard global health.
The paper introduces BUDS, a framework for selecting two-stage single-arm Phase II trial designs that accounts for historical benchmark uncertainty by optimizing efficiency across a plausible range of outcomes, thereby preventing Type I error inflation while offering explicit trade-offs between robustness and sample size efficiency for both binary and time-to-event endpoints.
This study of 435 commercial bus drivers in Ebonyi State, Nigeria, reveals a critical lack of health insurance and high rates of undiagnosed cardio-metabolic and renal conditions alongside near-universal self-medication practices, underscoring the urgent need for targeted occupational health programs that integrate routine screening with education on the dangers of unsupervised medicine use.
Based on data from 31 low- and middle-income countries between 2017 and 2022, the Global Pediatric Diarrhea Surveillance network found that while rotavirus remains the leading cause of hospitalized diarrhea in children under five despite declining prevalence likely due to vaccination, Shigella, norovirus, and adenoviruses also contribute substantially to the global disease burden with significant regional and temporal variations.
This study applies active adaptive management and mathematical modeling to demonstrate that twice-yearly entomological monitoring can improve the cost-effectiveness of vector control for gambiense sleeping sickness in specific contexts, though its value depends on local uncertainty regarding intervention effectiveness.
This study found that influenza vaccine effectiveness estimates derived from retrospective electronic health record data were generally consistent with those from prospective test-negative enrollment in the United States during the 2024–2025 season, although notable discrepancies emerged specifically for adults aged 65 and older.
This study protocol outlines a population-based target trial emulation using linked administrative data from British Columbia to evaluate the comparative effectiveness of different methadone and buprenorphine/naloxone dose titration schedules on treatment induction completion and mortality, aiming to generate real-world evidence for updating opioid use disorder guidelines in the context of widespread fentanyl use.
This population-based study of 2,809 adults reveals that the composition of 24-hour physical behaviors, particularly during workdays, varies significantly across occupational groups, with low-skill blue-collar workers generally engaging in more physical activity during work hours compared to other occupational categories.
This study developed and validated three flexible models for measuring intrinsic capacity using Australian men aged 70 and older, demonstrating their strong convergent and predictive validity for assessing functional ability and mortality risk in both research and clinical settings.
This study addresses the lack of Alpha-gal syndrome case data in Illinois by developing a stable, county-level risk map using a Bayesian spatial model that integrates tick abundance, ehrlichiosis rates, and establishment status to guide clinical screening and resource allocation.
This paper introduces a spectral framework demonstrating that epidemiological delay distributions impose a frequency-dependent limit on the identifiability of rapid epidemic variations, rendering the reconstruction of upstream infection trajectories from delayed and noisy surveillance data an ill-posed problem where distinct event profiles may be indistinguishable.