Implementation of Modeling- and Surveillance-Informed Interventions for Infectious Disease Prevention and Control

Mathematical modeling and public health surveillance are complementary tools in epidemiology. Together, they can support timely and effective interventions for infectious diseases and other population health threats. Advances in data availability, computational capacity, and analytical methods now allow real-time monitoring of disease dynamics, risk forecasting, and evaluation of intervention scenarios across diverse settings. When effectively integrated, these approaches can strengthen evidence-informed decision-making, improve resource allocation, and enhance public health response.Despite these advances, translating modeling outputs and surveillance signals into actionable, context-specific interventions remains challenging. Analytical findings are often underused because of data limitations, uncertainty in model assumptions, communication gaps between analysts and decision-makers, and operational constraints within health systems. These challenges are especially important in resource-constrained and rapidly changing settings.This Research Topic focuses on the practical implementation of interventions informed by epidemiological modeling and public health surveillance. It aims to highlight applied evidence, methodological innovation, and implementation approaches that strengthen the use of analytical insights in public health action.This Research Topic welcomes contributions on:- integration of real-time surveillance data into public health decision-making- translation of modeling outputs into policy and operational responses- adaptive intervention design informed by surveillance and forecasting- early warning systems and outbreak forecasting for response planning- evaluation of interventions informed by modeling and surveillance- implementation challenges in applying analytical evidence in real-world settings- ethical considerations and data governance in surveillance- and model-informed interventions- stakeholder engagement and communication between analysts, policymakers, and practitioners- capacity building for the use of modeling and surveillance in public health, particularly in low- and middle-income settingsWe welcome original research, methodological papers, implementation studies, case studies, systematic or scoping reviews, and policy analyses. Interdisciplinary contributions bridging public health, data science, health systems, and policy are especially encouraged. Submissions should emphasize practical relevance, scalability, and lessons learned for strengthening evidence-informed public health action. Topic editors: Chiara Achangwa, Hiroshi Nishiura Source: https://www.frontiersin.org/research-topics/79917/implementation-of-modeling--and-surveillance-informed-interventions-for-infectious-disease-prevention-and-control