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 demonstrates that in global COVID-19 incidence forecasting under structural change, simple baseline models like drift and seasonal naive often outperform complex statistical methods, with optimal model performance being strongly dependent on the forecast horizon.
This US cohort study reveals that frequent adolescent alcohol use, particularly when combined with smoking, leads to a significantly higher risk of adult cardiovascular disease and hypertension among individuals from financially disadvantaged families compared to their peers, highlighting the unequal impact of health behaviors across socioeconomic backgrounds.
A mathematical model projecting dengue control across 1,634 cities to 2050 reveals that while new interventions offer short-term benefits, the most effective and durable strategy for achieving over 90% disease reduction combines these innovations with long-term urban development to eliminate mosquito habitats.
This study integrates proteomic, metabolic, and anthropometric data from UK and Chinese biobanks to identify five distinct protein clusters—including two novel groups related to adiposity and kidney function—and pinpoints specific causal proteins and pathways that elucidate the heterogeneous molecular architecture of type 2 diabetes, offering new targets for precision medicine.
By analyzing serology data from 656 English children using a mathematical model of multi-variant antibody kinetics, this study estimates high annual norovirus GII.4 infection rates, reveals age-specific patterns and moderate evidence for immune imprinting, and highlights the prevalence of asymptomatic infections to better inform epidemic planning.
This study demonstrates that the diversity of mammalian reservoirs is the strongest predictor of coccidioidomycosis (Valley fever) endemicity in the western United States, outperforming environmental variables and providing a framework to identify underreported disease risk areas by integrating wildlife distribution data with human case surveillance.
This study of 69,314 adults reveals that higher educational attainment significantly attenuates the effect of genetic liability on BMI in females throughout adulthood, whereas this protective socioeconomic moderation is absent in males.
This study systematically benchmarks proteomic organ ages against established multimodal ageing biomarkers in the Lothian Birth Cohort 1936, revealing that while accelerated liver, immune, and heart ageing predict mortality, traditional markers like brain volume and cognitive function remain stronger predictors, and further identifies specific plasma proteins, notably GDF15, CST3, and COL18A1, as robust signatures of all-cause mortality.
This study proposes and demonstrates a five-phase integrated analytical framework combining predictive machine learning and causal inference methods on synthetic Kenya Demographic and Health Survey data to identify key GBV predictors and test mediation pathways, serving as a proof-of-concept for future application to real-world longitudinal datasets.
This study introduces a conservative statistical method that adjusts for shared testing propensity in surveillance data, revealing that apparent RSV-COVID viral interference signals are not robust to this adjustment, though the method's inherent bias toward null results means biological interference cannot be definitively ruled out.