Call for Papers: Research Progress on Zoonotic Fungal Diseases
October 5, 2026
Dates: October 13, 2026
Location: online
MIDAS
MODELS OF INFECTIOUS DISEASE AGENT STUDY
MIDAS is a global network of scientists and practitioners who develop and use computational, statistical and mathematical models to improve the understanding of infectious disease dynamics. Through education, research, service, and sharing of ideas, the MIDAS network aims to advance science to improve global preparedness and response.
Latest News
Training & Events
MIDAS Network Annual Meeting: MIDAS 2023
October 29th-31st
Courtland Grand Hotel
Atlanta, Georgia
Registration Link:
Agenda:
Courtyard Grand Hotel Room Reservation Link:
Featured Software
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The MIDAS Network Visualization is an interactive view of collaborators within MIDAS. In the visualization, collaborators are assigned to one of five distinct research clusters and linked to one another in a force directed graph.
MIDAS members are assigned to a research cluster by a Non-negative Matrix Factorization (NMF) algorithm which classifies members based on their published works. Specifically, the visualization uses the abstract and MeSH keywords associated with the publication in the PubMed database.
Notable features:
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- Search for a member by name to find their location in the graph.
- View the details of a member, including their photo, institution, social media links, collaborators, and papers (this information is shown when a user clicks on a node).
- Show/hide research clusters.
- Link collaborators by either paper co-authorship, or grant co-membership.
Please explore the visualization and send any comments/bugs/suggestions to questions@midasnetwork.us.
Membership
MIDAS membership is open to any infectious disease scientist, practitioner, or student who supports the mission and vision of the network. MIDAS is defined by broad inclusivity and diversity of its members, fostering collaboration to advance the science and application of infectious disease modeling. MIDAS members enjoy many advantages of being part of the network, free of charge.
MIDAS Members
COVID-19 Researchers
MIDAS Members
Wayne Getz
Professor
University of California Berkeley
Matthew Biggerstaff
Epidemiologist/Team Lead
U.S. Department of Health and Human Services
Matthew Ferrari
Associate Professor
Penn State University
Laura White
Professor
Boston University
Brittany Bustamante
Postdoctoral Scholar
University of California Davis
COVID-19 Researchers
Neil Ferguson
Department Head
Imperial College London
Cody Dailey
Researcher
University of Georgia
Sarita Bugalia
Postdoctoral Research Associate
Central University of Rajasthan
Li Gao
Assistant Professor
Johns Hopkins University
Joshua Weitz
Patton Distinguished Professor of Biological Sciences
Georgia Institute of Technology
MIDAS at a Glance
Our Vision and Mission
MIDAS is a global network of scientists and practitioners who develop and use computational, statistical and mathematical models to improve the understanding of infectious disease dynamics.
Membership
MIDAS network membership is open to any infectious disease scientist, practitioner, or student who supports our mission. The MIDAS network is defined by its broad member inclusivity.
Coodination Center
MIDAS creates educational, training, mentoring and career development opportunities in computational and statistical modeling, and is dedicated to train a broad community of scientists and practitioners.
Training
MIDAS creates educational, training, mentoring and career development opportunities in computational and statistical modeling, and is dedicated to train a broad community of scientists and practitioners.
Computing
HPC Services are funded by the NIH National Institute of General Medical Sciences (NIGMS) and are free of charge for MIDAS members from non-commercial organizations.
Students
MIDAS enables students to create professional relationships across the network, foster future collaborations, share educational resources, and provide a relaxed environment to share research and progress.
MIDAS Member Papers
Infectious Disease Papers
The bacterial march toward symbiosis: on-ramps and off-ramps.
Trends in microbiology
Bruijning M, Moeller AH, Carver G, Donia MS, Koskella B, Singh P, Metcalf CJE
2026-06-05 Continue Reading The bacterial march toward symbiosis: on-ramps and off-ramps.
Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data.
PLoS computational biology
Wenzel AR, Haughey PM, Nguyen KC, Nardini JT, Haugh JM, Flores KB
2026-09-25 Continue Reading Topologically-based parameter inference for agent-based model selection from spatiotemporal cellular data.
Longitudinal Changes in Wearable-Captured Physiology Before Sudden Cardiac Death.
JACC. Case reports
Lautman Z, Gravellier L, Malik S, Hittle M, Haddad F, Ræder H, Snyder MP
2026-09-25 Continue Reading Longitudinal Changes in Wearable-Captured Physiology Before Sudden Cardiac Death.
Explaining variation in antibiotic prescribing for common infections: a three-way variance decomposition using UK primary care data.
The Journal of infection
Nguyen N, Robotham JV, Walker AS, Eyre DW, Hope R, Butler CC, Sharland M, Pouwels KB
2026-09-24 Continue Reading Explaining variation in antibiotic prescribing for common infections: a three-way variance decomposition using UK primary care data.
MIDAS Member Projects
Mechanistic modeling to determine demographic, behavioral, and environmental factors that facilitate or prevent influenza transmission in households
National Institutes of Health, National Institute of Allergy and Infectious Diseases
People: Joseph Eisenberg
2026 – 2031 Continue Reading Mechanistic modeling to determine demographic, behavioral, and environmental factors that facilitate or prevent influenza transmission in households
Clinical Trajectory-Based Modeling of C. difficile to Link Individual Risk Profiles to Healthcare Burden
National Institutes of Health, National Institute of Allergy and Infectious Diseases
People: Seth Blumberg
2026 – 2028 Continue Reading Clinical Trajectory-Based Modeling of C. difficile to Link Individual Risk Profiles to Healthcare Burden
Enhancing early outbreak response through generative AI-driven compliance modeling and context-specific policy design
National Institutes of Health, National Institute of Allergy and Infectious Diseases
People: Sen Pei
2026 – 2031 Continue Reading Enhancing early outbreak response through generative AI-driven compliance modeling and context-specific policy design
Knowledge Augmented AI for Precision Surveillance and Control of Hospital-Acquired Infections
National Institutes of Health, National Institute of Allergy and Infectious Diseases
People: Anil Vullikanti
2026 – 2031 Continue Reading Knowledge Augmented AI for Precision Surveillance and Control of Hospital-Acquired Infections
MIDAS Coordination Center
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