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 study validates Guinea's 2025 elimination of gambiense human African trypanosomiasis as a public health problem by quantifying a 97% transmission reduction since 2000, attributing the success to vector control and improved diagnostics while highlighting the significant disease burden caused by Ebola-related interruptions.
This study reveals that maternity ward surfaces in Yaounde, Cameroon, serve as critical reservoirs for multidrug-resistant, high-risk ESBL- and carbapenemase-producing *E. coli* and *Klebsiella* clones, underscoring the urgent need for enhanced infection control and genomic surveillance to protect vulnerable maternal and neonatal populations.
This study utilizes a One Health genomic approach to demonstrate the circulation and cross-reservoir persistence of diverse ESBL-producing *E. coli* and *K. pneumoniae* lineages, particularly those carrying the *bla*CTX-M-15 gene, among humans, pigs, and the environment in peri-urban pig farms in Yaounde, Cameroon.
This study employs a mechanistic model incorporating time-varying transmission, waning immunity, and vaccination to investigate and reproduce the dynamics of large-scale H1N1 resurgences, revealing how the interplay of environmental factors, population immunity, and viral evolution drives post-pandemic outbreak patterns.
This study employs a triangulation framework combining empirical analyses and simulations to demonstrate that plausible selection bias mechanisms are unlikely to fully explain the observed inverse causal effect of early-life adiposity on breast cancer risk, thereby supporting the validity of the protective association.
This study reveals that in 2023–24, Uganda experienced sustained high prevalence and geographic heterogeneity of multiple antimalarial drug resistance markers, including specific *K13* mutations associated with artemisinin partial resistance and widespread antifolate resistance, underscoring the urgent need for continued nationwide molecular surveillance to guide treatment policies.
The paper introduces TrialScout, a large language model-based tool that efficiently and reliably links registered clinical trials to their published results, achieving high accuracy compared to human coders and successfully identifying result publications for 63.6% of a large sample of trials.
This population-based study utilizing prospective analyses and Mendelian randomization demonstrates a bidirectional relationship between urinary incontinence and depression or anxiety in women, with evidence suggesting that depression and neuroticism causally increase the risk of developing urinary incontinence.
Using 2022 national survey data, this study reveals that intimate partner violence affects nearly 28% of pregnant women in Tanzania, with emotional abuse being the most common form and partner alcohol use, polygamy, and longer union duration identified as key risk factors.
This paper presents a machine learning framework that constructs scalable, heterogeneous contact networks from survey data to more accurately simulate epidemic dynamics, demonstrating that incorporating both age structure and contact heterogeneity significantly reduces projected outbreak sizes and improves the targeting of public health interventions.