Related Experiment Video
Updated: Feb 3, 2026

Author Spotlight: Advancing Pathogen Detection and Disease Assessment in Real-Time Using M-ROSE
Published on: March 1, 2024
Spatial infectious disease epidemiology: on the cusp
1Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, GA, USA. gchowell@gsu.edu.
Abstract:
Infectious diseases continue to pose a significant public health burden despite the great progress achieved in their prevention and control over the last few decades. Our ability to disentangle the factors and mechanisms driving their propagation in space and time has dramatically advanced in recent years. The current era is rich in mathematical and computational tools and detailed geospatial information, including sociodemographic, geographic, and environmental data, which are essential to elucidate key drivers of infectious disease transmission from epidemiological and genetic data. Indeed, this paradigm shift was driven by dramatic advances in complex systems approaches along with substantial improvements in data availability and computational power. The burgeoning output of infectious disease spatial modeling suggests that we are close to a fully integrated approach for early epidemic detection and intervention. This special collection in BMC Medicine aims to bring together a broad range of quantitative investigations that improve our understanding of the spatiotemporal transmission dynamics of infectious diseases in order to mitigate their impact on the human population.
More Related Videos
14:34Biocontained Carcass Composting for Control of Infectious Disease Outbreak in Livestock
Published on: May 6, 2010
10:11Fundus Photography as a Convenient Tool to Study Microvascular Responses to Cardiovascular Disease Risk Factors in Epidemiological Studies
Published on: October 22, 2014
Related Concept Videos
Introduction to Epidemiology
Causality in Epidemiology
Study Designs in Epidemiology
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Confounding in Epidemiological Studies
Bias in Epidemiological Studies
Statistical Methods for Analyzing Epidemiological Data