286 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 demonstrates how hash-based matching and pseudo-random number generation methods, specifically the Bernoulli hashing approach, enable efficient and reproducible comparisons of stochastic simulation strategies across varying model complexities while effectively addressing counterfactual scenarios.
This cross-sectional study of 1,316 Ghanaian pregnant women reveals a high and geographically variable burden of syphilis (10.5%) and frequent co-infections with HIV and HBV, highlighting critical gaps in the implementation of national integrated antenatal screening policies and the urgent need for targeted interventions to prevent adverse maternal and neonatal outcomes.
This paper demonstrates that Mendelian randomization studies using genetic variants with small effect sizes are susceptible to heritable confounding that biases causal estimates in a consistent direction across methods, but shows that this bias can be mitigated through pre-estimation filtering or multivariable MR when potential confounders are identified.
Using a temperature-dependent, multi-serotype model fitted to Thai surveillance data, this study demonstrates that interventions directly disrupting adult mosquito-human contact, such as personal protection and adulticide application, are significantly more effective at reducing dengue transmission than further intensifying larval control efforts.
This retrospective cohort study utilizing the TriNetX real-world data platform found that a positive Group A Streptococcus test was associated with a modestly increased risk of incident ADHD but failed to detect most established poststreptococcal autoimmune conditions, highlighting both the potential and limitations of large health care databases in evaluating postinfectious neuropsychiatric risks.
The Iga City Cohort Study is a population-based genomic study involving 1,516 participants recruited between 2013 and 2014 to collect lifestyle, socioeconomic, and biological data to establish evidence for personalized disease prevention.
This paper introduces a robust decisional framework based on finite mixture models that overcomes the limitations of conventional cutoff-based serological analysis by integrating flexible distributional assumptions, rigorous model selection, and biologically guided clustering to accurately characterize antibody responses and improve infectious disease surveillance across diverse pathogens and epidemiological settings.
This study utilizing the FinnGen cohort demonstrates that narcolepsy is significantly associated with an increased burden of cardiometabolic diseases, including stroke, dyslipidemia, and diabetes, highlighting the need for enhanced cardiovascular risk management in affected patients.
This pooled cohort study of Swedish adults demonstrates that weight gain trajectories from adolescence to mid-adulthood, particularly when combined with early obesity onset, are significantly associated with an increased risk of various site-specific cancers, underscoring the need for life-course weight management and tailored prevention strategies.
This study of over 59,000 pneumococcal disease cases in South Africa reveals that while air pollution (PM2.5, PM10, SO2) and low humidity increase invasive disease risk, the magnitude and timing of these environmental effects are significantly modulated by the specific serotypes and genomic lineages of the circulating bacteria.