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.

📊 epidemiology

A gut microbiome-derived risk score is associated with future cancer development

This study demonstrates that a gut microbiome-derived risk score, based on 28 microbial species, can predict the future development of cancer up to a decade before diagnosis, highlighting the microbiome's potential as a novel biomarker for early cancer risk stratification.

Oosterhout, E., Leijdesdorff, R. S., Kambur, O., van der Vegt, B., Gacesa, R., Bolte, L. A., Klaassen, M. A., Salomaa, V (…)2026-09-14
📊 epidemiology

Assessing and refining a metabolite-based distress score for use in different populations within three US cohorts

This study demonstrates that while a previously developed metabolite-based distress score (MDS) effectively predicts depression in White populations, its utility is enhanced for Black individuals and generalizability across diverse groups by deriving a multi-ethnic version (MDS-ME) that incorporates additional metabolites identified specifically within Black cohorts.

Huang, T., Zhu, Y., Balasubramanian, R., Roberts, A., Clish, C. B., Pacheco, J. A., Rotter, J., Guo, X., Yao, J., Chen (…)2026-09-13
📊 epidemiology

Decision Analysis Modeling Favors Optimal Influenza Vaccination in Late November or Early December

A simulation-based decision analysis modeling US influenza surveillance data from 2010 to 2026 suggests that vaccinating in late November or early December (MMWR weeks 47–48) optimally balances waning immunity and epidemic timing to reduce outpatient influenza burden more effectively than current September–October guidance.

Meyer, A. G., Rosales, B. N., Yang, S., Santillana, M.2026-09-13
📊 epidemiology

Development of Longitudinal, Linked Maternal-Infant Cohorts using the Epic Cosmos Electronic Health Record Dataset

This study establishes a rigorous, reproducible framework for creating longitudinal, linked maternal-infant cohorts within the Epic Cosmos dataset, demonstrating that the resulting data is highly complete, representative of the US birth population, and suitable for epidemiologic research despite significant attrition when enforcing strict longitudinal follow-up criteria.

Leonard, S. A., Dysart, K., Callahan, A., Siadat, S., Zhang, J., Handley, S. C., Huybrechts, K. F., Igbinosa, I., Batema (…)2026-09-12
📊 epidemiology

Connecting diet and disease: Using Mendelian randomisation to bridge the gap

This paper proposes and validates a two-step framework that integrates short-term dietary randomised controlled trials with Mendelian randomisation to identify molecular mediators and infer the long-term causal effects of dietary interventions on disease risk, demonstrating its utility through the DiRECT diabetes remission study.

Deslandes, B., Corbin, L. J., Goudswaard, L. J., Sandu, M. R., Lee, M. A., Beynon, R. A., McGeagh, L., Smith, G. D., Sat (…)2026-09-10
📊 epidemiology

Wastewater Metagenomic Virome Analysis of the 2026 World Cup in Texas

An analysis of over 5,000 wastewater metagenomic samples from 17 Texas cities spanning the 2026 World Cup period found no evidence that the mass gathering significantly altered viral community composition, respiratory virus trends, or the prevalence of rare taxa compared to historical baselines, suggesting that continuous surveillance is more effective than event-triggered monitoring.

Perez, R. K., Prakash, H., Jaynes, D., Huang, P., Deegan, J., Brooks, B., Hopkins, L., Ross, M., Minor, M., Payne, K., A (…)2026-09-10