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.
The paper introduces **serocalculator**, an open-source R package that utilizes a likelihood-based framework to estimate seroincidence rates from cross-sectional serological data by integrating antibody decay models, biological variability, and measurement noise.
This paper describes the successful integration of the National Alzheimer's Coordinating Center Uniform Data Set version 3 into the long-standing Diabetes Prevention Program Outcomes Study through data harmonization, electronic capture, and automated reporting, demonstrating a practical framework for adapting established cohorts to meet national Alzheimer's disease research standards.
This paper demonstrates that the basic reproduction number () is fundamentally non-identifiable from epidemic trajectories alone because it conflates pathogen biology with adaptive human behavior, yet empirical analysis of US COVID-19 mobility data reveals that while this behavioral component is structurally characterizable as state-dependent, its practical impact is modest due to a negative correlation between risk responsiveness and behavioral saturation across jurisdictions.
Using 11 years of US Medicaid and Medicare data and machine learning, this study analyzed 137,293 adults with Down syndrome to identify key mortality predictors such as dementia, pneumonia, cardiovascular disease, heart failure, and epilepsy, highlighting intervenable areas to reduce death rates in this population.
This systematic review and Bayesian analysis of 67 Nipah virus outbreaks in Bangladesh and India from 2001 to 2026 reveals that transmission is self-limiting with a median reproduction number below 1 but highly overdispersed, indicating that a small fraction of cases drive most onward spread and supporting targeted containment strategies like contact tracing and quarantine.
This paper proposes a deterministic model that predicts the efficacy of interventions, such as vaccination campaigns, for vector-borne infections like dengue by leveraging the observation that age-dependent case distributions remain consistent across varying outbreak intensities and geographic regions, thereby enabling the estimation of intervention impacts without relying on steady-state assumptions.
This study utilizes spatial machine learning and longitudinal analysis of nine Ghana Demographic and Health Survey waves (1988–2022) to demonstrate that while skilled antenatal care coverage has nearly universalized and inter-regional inequality has drastically declined, significant fertility-related spatial inequities persist in the Northern Belt, necessitating targeted health-system investments guided by new metrics like the Care Efficiency Index.
This study demonstrates the feasibility and high success rate of implementing a standardized, video-based asynchronous neurological examination (VANE) across 25 multi-center sites to efficiently screen for neurological conditions in a large cohort of patients with pre-diabetes and type 2 diabetes.
This study presents a Bayesian hierarchical model that integrates mixed surveillance data and seroprevalence surveys to accurately estimate dengue transmission dynamics and identify high-priority districts for vaccination in Indonesia, revealing that reliance on reported incidence alone often underestimates risk in areas with weak surveillance.
This nationwide Bayesian spatio-temporal analysis of Ghanaian snakebite data from 2020 to 2025 identifies persistent high-risk districts concentrated in specific northern and southern regions, links increased risk to temperature and humidity, and highlights critical inequities in geographic access to treatment to inform targeted prevention and resource allocation.