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 demonstrates that diagnostic delays in dense urban settings create a significant transmission externality during the waiting period for test results, which can be effectively mitigated by implementing decentralized, near-patient, or home-based testing workflows that eliminate this delay.
By integrating human mobility data with *P. falciparum* genomics, this study developed a novel Bayesian approach to quantify malaria importation in southern Mozambique, revealing that nearly half of infections in targeted elimination districts are imported—primarily from Inhambane province—and highlighting the need for fine-scale, source-targeted strategies to achieve malaria elimination.
A network-based mathematical model of the 2021–2022 US epidemic reveals that while moderate adherence to isolation and masking guidance in office workplaces helps reduce transmission, increasing adherence yields limited additional benefits due to low contact intensity and hybrid work, highlighting that community-wide adoption of these non-pharmaceutical interventions is more effective for reducing infections and deaths.
By integrating 22 years of national surveillance data with seroprevalence and antibody kinetics through latent process modelling, this study reveals that scrub typhus transmission in Thailand extends significantly beyond its traditional northern focus into the northeast and south, while the at-risk demographic has progressively shifted toward older adults who face the highest mortality, necessitating regionally tailored surveillance and age-targeted clinical strategies.
By integrating data from a human challenge study into a multi-scale mathematical model, this study quantifies key biological parameters of human-to-mosquito malaria transmission and identifies that parasite multiplication and gametocyte maturation drive the onset of infectiousness, while circulating gametocyte availability and fertilization efficiency determine the infectiousness of established infections.
Drawing on longitudinal data from 4,547 participants in the UK, this study utilizes Bayesian growth curve modelling to demonstrate that social prescribing significantly reduces health service utilization across primary and secondary care settings, with substantial decreases observed in GP visits, A&E attendances, and hospital admissions.
Episia is an open-source Python library developed in Burkina Faso to address resource constraints in global health by integrating compartmental epidemic modeling, Monte Carlo sensitivity analysis, validated biostatistics, and automated DHIS2 data ingestion into a single, accessible framework for epidemiological surveillance.
This study analyzes US county-level pulmonary embolism mortality from 2005 to 2022, revealing significant spatial-temporal clustering in the South and Midwest, identifying age as a key predictor, and advocating for geographically tailored prevention policies based on granular local patterns.
This study developed and validated an accuracy-weighted ensemble model that effectively forecasts the frequency of non-vaccine pneumococcal serotypes following vaccine introduction or switching, thereby providing a valuable tool for guiding future vaccine impact assessments and formulation updates.
This paper outlines the protocol for a descriptive cohort study using the ORCHID-e database to investigate the diagnostic accuracy of unscheduled bleeding for endometrial cancer in postmenopausal women taking hormone replacement therapy compared to matched controls, aiming to inform guidelines on unnecessary referrals and invasive testing.