Practical Guidelines and Reflections on Building Public Health Software: A Measles Case Study (Preprint)
JMIR Public Health and Surveillance · 2026
Work in progress2026
Abstract
Academic–public health partnerships are essential for strengthening outbreak preparedness and response, yet translating modeling tools into routine public health practice remains challenging. Structural, technical, and workforce constraints often limit the capacity for modeling tools during emergencies. Here, we describe the development a software ecosystem designed to support real-time infectious disease response through sustained collaboration between an academic research team and public health agencies, with particular focus on the recent measles outbreak. The resulting software enabled real-time scenario modeling, visualization of transmission dynamics, and iterative updates as new data became available. Beyond immediate outbreak response, the initiative strengthened cross-sector collaboration, expanded modeling capacity, and highlighted ongoing gaps in technical infrastructure and workforce readiness at the state and local levels. This case study demonstrates how sustained academic–government partnerships combined with streamlined development practices can accelerate the translation of modeling tools into operational public health settings. Establishing and maintaining analytic infrastructure and agile processes between emergencies may be critical for ensuring timely, data-informed decision-making during future outbreaks.
Cite
@misc{vegayonPracticalGuidelinesReflections2026,
title = {Practical Guidelines and Reflections on Building Public Health Software: A Measles Case Study (Preprint)},
author = {{Vega Yon}, {George G.} and {Thornton}, {Delaney} and {Redd}, {Andrew} and {Pulsipher}, {Andrew} and {Toth}, {Damon} and {Dorsan}, {Egenia} and {Rennert}, {Lior} and {Johnson}, {Kaitlyn} and {White}, {Laura F} and {Gruninger}, {Randon} and {Nolen}, {Leisha D} and {Samore}, {Matthew H}},
year = {2026},
month = {feb},
publisher = {JMIR Public Health and Surveillance},
doi = {10.2196/preprints.94240},
}