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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.

Training & Events

Member Spotlight

Charles Nunn
Professor
Duke University

MIDAS Network Visualization (Beta Release)

The MIDAS Network Visualization is a view of the MIDAS-related collaborations in graph form with two types of nodes: member nodes and institution nodes.  Member nodes represent MIDAS members and edges link to collaborators on an infectious disease paper published by the member.  Institution nodes represent institutions with members in the MIDAS network and edges link to all MIDAS members of the institution.

 

This is a beta release. Please explore the graph and send any comments/bugs/suggestions to questions@midasnetwork.us.  

 

Visit the MIDAS Network Visualization.

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 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

MIDAS Member Projects

MIDAS Coordination Center

Director

Harry Hochheiser

Associate Professor
University of Pittsburgh

Project Coordinator

Kristin Kropf

Project Coordinator
University of Pittsburgh

Outreach Coordinator

Stephanie Shadbolt

Associate Director
Fred Hutch

Lead Data Curator

Lucie Contamin

Data Curator
University of Pittsburgh

Follow @MIDAS_Network

very happy to see this published:
@ElizabethRBrow2 @fredhutch
can #COVID19 #antivirals help to curb the pandemic and reduce the burden of disease?
Yes, especially if they are targeted to high risk groups!

https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-022-07639-1

I'm excited to announce that I'll be starting a lab @CUBoulder in the Department of Computer Science in fall 2023! Our aim is to use mathematical models to characterize the cross-species and cross-scale dynamics of respiratory pathogens.

Home

Welcome to the Kissler Lab

kisslerlab.github.io

First day at the new office @PublicHealthUGA! If you’re interested grad school or post-doc in disease modeling, stay tuned!

Thrilled to receive a @NIH/@NIAIDNews New Innovator (DP2) Award, which is a 5-year #NIHHighRisk grant that will support my research group to develop predictive models for public health departments to combat infectious diseases. I am thankful to so many! #IDtwitter #Epitwitter 🧵

L👀king for data scientist in San Francisco! https://bit.ly/3JbGgon
@NathanLo3579 https://profiles.ucsf.edu/nathan.lo

@IDDjobs @WBioinformatics @UCLAQCBio @KroganLab @rafalab @Bioinfo4women @dmaccannell @WOBionetworks @QIAGENBiox @SFBNJobs @BioinformaticsJ
Forward to amazing candidates

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