Microsoft AI for Health Program to support an AI-facilitated Optimization Framework for Improving COVID-19 Testing

By | News, Research

With the recent resurgence of COVID-19 infections, testing has become central to an integrated, global response to the pandemic. Accurate, effective, and efficient testing can lead to early detection and prompt an agile response by public health authorities. Strategic testing systems are critical for providing data that will inform disease prevention, preparation, and intervention. MICDE Associate Director and Associate Professor of Industrial and Operations Engineering and of Civil and Environmental Engineering, Siqian Shen, has recently published an article pin-pointing a number of pivotal operations research and industrial engineering tools that can be brought to  the fight against COVID-19. One of the key lessons from her research is the importance of expanding the availability of COVID-19 testing and making the resulting data transparent to the public as anonymized, summary statistics. This enables informed decision making by individuals, public health officials, and governments.  

Based on these high-impact findings, Professor Shen is striding ahead to design a comprehensive COVID-19 testing framework to efficiently serve the urgent needs of diverse population groups . A grant from Microsoft’s AI for Health program, part of the AI for Good initiative, will provide credits to use Microsoft’s Azure service.  With this cyber resource, Professor Shen and her team will integrate and coordinate decision-making models and data analytics tools that they have developed for testing on a Cloud-based platform. In addition, their AI framework is dynamic, and collects daily infection data to improve testing-related decisions. Such a platform could have significant impacts on three major problems that exist with current testing design strategies:

1) Where to locate testing facilities and how to allocate test kits and other resources.
2) How to effectively triage different population groups through effective appointment scheduling.
3) How to visualize real-time testing capacities to better inform the public and serve ad-hoc needs of patients. 

Prof. Shen’s research will integrate AI techniques with optimization to dynamically refine existing testing design methods for gathering and analyzing data from unexplored populations and regions around the globe. The development and refinement of these new models with the support of Microsoft Azure will create a transparent, data-informed testing system that will allow public health and government authorities to make agile, data-driven decisions to aid in the prevention, preparation, intervention, and management of COVID-19 and other outbreaks of infectious diseases.

Siqian Shen is a  Professor of Industrial and Operations Engineering, and of Civil and Environmental Engineering at the University of Michigan, an Associate Director of the Michigan Institute for Computational Discovery & Engineering, and an affiliated faculty member in the Michigan Institute for Data Science. Her research group works on both theoretical and applied aspects of problems by combining stochastic programming, integer programming, network optimization,  machine learning and statistics.

What is the right model? Different MRIO models yield very different carbon footprints estimates in China

By | Research

Appropriate accounting of greenhouse gas emissions is the first step to assign mitigation responsibilities and develop effective mitigation strategies. Consistent methods are required to fairly assess a region’s impact on climate change. Two leading reasons for the existence of different accounting systems are the political pressures, and the actual costs of climate mitigation to local governments. At the international level there has been consensus, and global environmentally extended multi-regional input-output (EE-MRIO) models that capture the interdependence of and their environmental impacts have been constructed.  However in China, the largest greenhouse gas emitter, where accurate interregional trade-related emission accounts are critical to develop mitigation strategies and monitor progresses at the regional level, this information is sporadic and inconsistent. Prof. Ming Xu from the School of Environment and Sustainability, and his research group, analyzed the available data from China, which dates back to 2012. They showed that the results varied wildly depending on the MRIO model used. For example, they found two MRIO models differed as much as 208 metric tons in a single region, which is equivalent to the emissions of Argentina, United Arab Emirates, or the Netherlands. Their results show the need to prioritize future efforts to harmonize greenhouse gas emissions accounting within China.

Ming Xu is an Associate Professor in the School for Environment and Sustainability and in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. His research focuses on the broad fields of sustainable engineering and industrial ecology. 

Read the full article.

Modeling the transmission of infectious aerosols

By | Feature, Research

Inhalation of micron-sized droplets represents the dominant transmission mechanism for influenza and rhinovirus, and recent research shows that it is likely also the case for the novel coronavirus.  Increasing evidence suggests that the transmission of infectious aerosols is more complex than previously thought. Coughing, sneezing and even talking yield a gaseous flow field near the infected person that is dynamic and turbulent in nature. Existing models commonly employed in simulations of aerosol transmission attempt to represent the effect of turbulence using random walk models that are often phenomenological in nature, employing adjustable parameters and inherently assuming the turbulent fluctuations ‘felt’ by a droplet do not depend upon direction. To design physics-informed guidelines to minimize the spread of this virus, improved predictive modeling capabilities for effectively tracking the aerosol paths are needed. Dr. Aaron M. Lattanzi and Prof. Jesse Capecelatro, from Mechanical Engineering and MICDE are tackling this problem by focusing on mathematical modeling of aerosol dispersion. They derived analytical solutions for the mean-squared-displacement resulting from systems of stochastic differential equations. A key element of their methodology is that the solution connects stochastic theory inputs to statistics present in high-fidelity simulations or experiments, providing a framework for developing improved models.

Simple simulation of aerosol dispersion from a single-point source. The grey, cone-like surface is the approximation using Force Langevin (FL) theory and the colored particles are from integration of Newton’s equations with stochastic drag forces.

Prof. Capecelatro’s research group develops physics-based models and numerical algorithms to leverage supercomputers for prediction and optimization of the complex flows relevant to energy and the environment. The main focus of their research involves developing robust and scalable numerical tools to investigate the multiphysics and multiscale phenomena under various flow conditions, like those that they study here. They recently submitted their findings to the Journal of Fluid Mechanics, and are continuing to work on this problem hoping it will help understand the transmission of COVID-19 and therefore help optimize current guidelines.

U-M draws global attention for MOOC: Problem Solving using Computational Thinking

By | Educational, Feature, Research

Problem Solving using Computational Thinking, a Massive Open Online Course (MOOC) launched by the University of Michigan in November of 2019, has already drawn more than 1,200 learners from around the globe. The Michigan Institute for Computational Discovery & Engineering (MICDE) and the University of Michigan Center for Academic Innovation partnered to create this course. The idea for this MOOC arose from the team’s recognition of the ubiquity of computation. However, the developers were equally keen to distinguish this offering from MOOCs on programming and to instead highlight how broader computational thinking also makes its presence felt in somewhat unexpected domains.

Using computational thinking, the MOOC challenges learners with a series of real-world examples, including how it is possible to help plan and prepare for a flu season–a subject that has gained particular relevance in the months following the launch of this MOOC, track human rights violations or monitor the safety of crowds.

While enrollment numbers are encouraging, the work being done by learners within the MOOC is most inspiring. For their final project, learners have applied the computational thinking strategies discussed throughout the MOOC to a wide array of noble social problems in hopes of finding cogent solutions.

Not surprisingly, there have been several projects that seek to address challenges related to COVID 19.

The MOOC’s Epidemiology Case Study walks the student through the process of building a communicable disease transmission model.

One learner wrote: “For the final project, I am assuming the role of a member of the team responsible to combat COVID-19 from India and I have to decide on what should be our strategy to fight coronavirus in India, be it the extension of a lockdown or any other important decision related to this pandemic.”

In another project, a learner assuming the role of a Wuhan pathologist wrote that they must “decide what the Chinese government’s strategy against coronaviruses” should be.

Learners addressing today’s most pressing societal concerns, such as COVID-19, exemplifies the transformative potential of open-access, digital, and distance education made possible by a MOOC.

Across the board, the MOOC has received tremendously positive reviews, with an overall course rating of 5 out of 5 stars. One learner, in particular, wrote in their course review: “I really enjoyed this course! It got me prepared to study for an entry into a career working with computers!!” Another learner simply stated: “Fantastic, loved it!”

The developers of this MOOC are drawn from the School of Public Health, the College of Engineering, the School of Education and MICDE. Problem Solving using Computational Thinking is available in Coursera through Michigan Online. To learn more please visit online.umich.edu/courses/problem-solving-using-computational-thinking/.

U-M modeling epidemiologists helping navigate the COVID-19 pandemic

By | Feature, News, Research

[top] Screenshoot of the Michigan COVID-19 Modeling Dashboard (epimath.github.io/covid-19-modeling/); [bottom left] Marisa Eisenberg (Epidemiology, Complex Systems and Mathematics); [bottom right] Jonathan Zelner (Epidemiology).

The COVID-19 pandemic is producing massive amounts of information that more often than not lead to different interpretations. The accurate analysis of this daily input of data is crucial to predict possible outcomes and design solutions rapidly. These can only be achieved with expertise in modeling infectious diseases, and with the power of computational science theory and infrastructure. U-M’s Epidemiology Department, in the School of Public Health, has a very strong cohort of researchers who work on mathematically modeling the dynamics of infectious diseases, the analysis of these models, and large scale computer simulations — all to understand the spread and mitigation of pandemics. They are applying their long experience and expertise to the current COVID-19 outbreak, aiding the government make informed decisions, and helping media outlets produce accurate reports for the general public. Marisa Eisenberg, Associate Professor of Epidemiology, of Complex Systems, and of Mathematics, and her colleagues are using a differential equation transmission modeling approach to analyze scenarios and generate short-term forecasts for the COVID-19 epidemic in State of Michigan. They are communicating directly with the Michigan Department of Health and Human Services and providing critical tools, like the Michigan COVID-19 Modeling Dashboard, to inform decision-making. Prof. Eisenberg’s team is helping to forecast the numbers of laboratory-confirmed cases, fatalities, hospitalized patients, and hospital capacity issues (such as ICU beds needed), and examining how social distancing can impact the spread of the epidemic. Prof. Jonathan Zelner, whose research is focused on using spatial and social network analysis and dynamic modeling to prevent infectious diseases, is part of a group helping map the outbreak in Michigan. He also has provided valuable insights to journalists contributing to a better understanding of the situation, including what made New York City so vulnerable to the coronavirus (NYT), the role of wealth inequality during epidemics (CNBC) and what professions and communities are particularly vulnerable to infection (NG). 

Professors Eisenberg and Zelner are not alone in this fight. Many more researchers from U-M’s School of Public Health and throughout campus have risen to the challenges posed by this pandemic.