227 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.
This study of the LIFE-Adult cohort found no evidence that self-reported or objectively measured physical activity positively influences hippocampal volume or cognitive function, suggesting instead a potential reverse causation where poorer brain health predicts lower physical capacity and highlighting an age-related tendency to overestimate activity levels.
A secondary analysis of the INTEGRATION cluster randomized trial in rural Mali and Burkina Faso found that a four-month community-based intervention delivering intermittent preventive treatment for malaria during seasonal home visits did not significantly improve antenatal care coverage, reduce malaria incidence in pregnancy, or improve adverse pregnancy and birth outcomes compared to standard clinic-based care.
By analyzing three years of targeted hybrid capture sequencing data from 15 Texas cities, this study demonstrates that the human wastewater virome exhibits strong, predictable seasonal patterns and interconnected ecological dynamics that enable machine learning models to accurately forecast viral abundance and infer sampling time, thereby establishing a foundation for proactive, metagenomics-based infectious disease monitoring.
This study utilizes machine learning on historical outbreak data to establish standardized criteria for defining typhoid fever outbreaks and identifies key sanitation and demographic risk factors to predict and prepare for large-scale epidemics in high-risk regions like Africa and South Asia.
This study demonstrates that *Onchocerca volvulus* transmission in the Kakoi-Koda focus of the DRC has markedly declined due to a combination of community-directed ivermectin treatment and landscape-driven shifts in vector species, although targeted surveillance remains essential to confirm elimination thresholds.
A 2024 survey of nearly 39,000 participants in Central Denmark reveals that public willingness to share health data for AI development varies significantly by data type and is substantially higher when the data is managed by public institutions rather than private ones, highlighting the critical role of institutional trust and data sensitivity in shaping acceptance.
This retrospective cross-sectional study in rural KwaZulu-Natal, South Africa, reveals a high prevalence of Hepatitis B Virus infection (10.4%) and exposure (34.9%) alongside low vaccine-mediated immunity, underscoring an urgent need for scaled-up interventions to meet global elimination targets.
This study utilizing UK Biobank data reveals that while ex-servicewomen exhibit higher overall trauma exposure and distinct profiles compared to civilian women, the association between specific trauma classes and adverse mental health outcomes is less consistent in the military group than in the civilian population.
This paper introduces a generalized method for assessing meta-analytic fragility using the Ellipse of Insignificance and Region of Attainable Redaction approaches, demonstrating through an analysis of vitamin D and cancer mortality studies that even large-scale meta-analyses can be highly sensitive to minor data changes or missing literature, thereby challenging the robustness of conflicting clinical findings.
The paper introduces the SEVA framework, an externally driven model that successfully reproduces the temporal structure of early COVID-19 mortality waves across multiple regions using a single parameter for population-level exposure, thereby offering a parsimonious alternative to traditional transmission-based models by decoupling epidemic dynamics from infection feedback.