Funded by the National Institute of General Medical Sciences at the NIH, MIDAS is a collaborative network of research scientists who use computational, statistical, and mathematical models to understand infectious disease dynamics and thereby assist the nation to prepare for, detect, and respond to infectious disease threats. Please explore our website to learn more.
MIDAS creates awareness, provides hands-on experience, and builds bridges for public health decision support tools at Preparedness Summit
A full day workshop, “Hands On Experience with MIDAS Decision Support Tools” was presented to public health practitioners and others at the 2014 Public Health Preparedness Summit.
For 10 years, the NIGMS Modeling of Infectious Disease Agent Study (MIDAS) has brought together the world’s leading computational modeling experts to help prepare our nation for a wide array of public health issues. Over this time MIDAS teams have worked with local, state, and national stakeholders to create a number of sophisticated, user-focused tools that are ready to use in decision support. Computational modeling has become a crucial component of public health and healthcare preparedness, and in the spirit of the summit’s theme, creating awareness and preliminary training in these tools will do much to continuing to build bridges between the modeling and public health communities, indeed making us “Stronger Together”.
The workshop starts with introductory talks to create awareness of the cadre of tools that are being developed in MIDAS, allowing in-depth presentations about specific tools. There will then be an hour of hands-on interactive demonstrations where participants will be encouraged to use the tools and speak with the developers. The workshop concludes with in-depth one-on-one sessions with the developers. This last component will allow the participants to deep-dive into the tools that are of most interest to them and begin the process of building a relationship with the developer that will continue beyond the summit.
Public Health Dynamics Seminar Series
Sandro Galea, MD, MPH, DrPH
Anna Cheskis Gelman and Murray Charles Gelman Professor
and Chair of the Department of Epidemiology
Columbia University Mailman School of Public Health
Tuesday, April 1, 2014
3:00 - 4:00 PM
109 Parran Hall
The foundational determinants of population health are well understood to influence a broad range of health conditions. However, a substantial proportion of efforts to improve health focus on individual behaviors and proximal health determinants. But, can work on only proximal determinants improve the health of populations? Is work that aims to improve foundational determinants necessary to improve population health? We shall explore this question through simulation modeling, illustrating how, absent work to improve foundational determinants there is a limit to the potential improvement to the health of populations.
PLOS Revised Data Policy
PLOS is releasing a revised Data Policy that will come into effect on March 1, 2014. Authors will be required to include a data availability statement in all research articles published by PLOS journals.
To see the policy, click here: https://www.plos.org/data-access-for-the-open-access-literature-ploss-data-policy/
Universal Paid Sick Leave Reduces Spread of Flu
Allowing all employees access to paid sick days would reduce influenza infections in the workplace, according to a first-of-its-kind analysis by Pitt Public Health modeling experts. The researchers simulated an influenza epidemic in Pittsburgh and surrounding Allegheny County and found that universal access to paid sick days would reduce flu cases in the workplace by nearly six percent. They estimated it to be more effective for small, compared to large, workplaces.
“The Centers for Disease Control and Prevention recommends that people with flu stay home for 24 hours after their fever breaks,” said lead author Supriya Kumar, a postdoctoral associate in the Department of Epidemiology. “However, not everyone is able to follow these guidelines. Many more workers in small workplaces than in large ones lack access to paid sick days and hence find it difficult to stay home when ill. Our simulations show that allowing all workers access to paid sick days would reduce illness because fewer workers get the flu over the course of the season if employees are able to stay home and keep the virus from being transmitted to their coworkers.”