285 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 paper introduces an adaptive multi-model ensemble approach that dynamically selects individual model trajectories based on observed data to improve the accuracy of long-term epidemic projections and short-term forecasting while offering a resource-efficient alternative to traditional coordinated modeling efforts.
This cross-sectional study utilized latent class analysis on 1,117 patient surveys to identify three distinct symptom burden subtypes in pernicious anaemia, revealing that high-burden patients experience significant neurological and mental health challenges with longer diagnostic delays and low treatment satisfaction, thereby supporting a shift toward tailored, symptom-guided management over current standardized protocols.
This retrospective cross-sectional study in rural Tchaourou, Northern Benin, reveals a high incidence of snakebite envenoming (399 per 100,000 person-years) primarily affecting young farmers during the rainy season, with significantly higher complication rates linked to delayed or traditional treatment and low health center utilization.
This causal AI study utilizing All of Us and BRFSS data reveals that while short-term environmental exposures show initial associations with subjective cognitive difficulties, these links largely attenuate when controlling for temporal variation, whereas psychosocial factors such as mental health burden, loneliness, and impaired social functioning demonstrate consistently stronger and more robust associations across multiple analytical frameworks.
This paper proposes an amortized Bayesian inference framework to accelerate the detection of spatial disease boundaries and quantify required disparity reductions, successfully scaling areal wombling to large-scale public health surveillance while maintaining accuracy comparable to traditional MCMC methods.
This three-season study in Connecticut demonstrates that while nirsevimab remains highly effective against RSV-associated hospitalization for up to 12 months post-immunization, its overall protection against medically attended infections declines in subsequent seasons and is significantly influenced by the timing of local RSV circulation.
A 2021 serological study in Kinshasa, DRC, reveals substantial and age-dependent transmission of dengue and chikungunya viruses, particularly in peri-urban areas, highlighting an under-appreciated arboviral risk and the urgent need for enhanced surveillance in the region.
This study of over 222,000 Nordic twins reveals that cancer risk is significantly driven by both heritable (28%) and shared environmental (40%) factors, with extensive genetic and environmental overlaps across different cancer sites underscoring the need to integrate familial exposures into risk prediction and prevention strategies.
UKBAnalytica is a comprehensive, extensible R package designed for the UK Biobank Research Analysis Platform that integrates scalable multi-source disease phenotyping, survival-ready cohort construction, and diverse downstream analytical modules to streamline reproducible epidemiological research.
EpiLink is a novel, threshold-free, simulation-based method that estimates the compatibility of recent transmission between pathogen cases by modeling genetic and temporal uncertainties, offering an interpretable alternative to fixed-distance thresholds and supervised models when labeled transmission data are unavailable.