Madhav Marathe
University of Virginia · Computer Science
Active 1977–2024
About
Madhav Marathe is a Professor of Biocomplexity, a Professor of Public Health Sciences, and the Executive Director at the Biocomplexity Institute. His research focuses on biocomplexity, which involves understanding complex biological systems through computational and mathematical modeling. As a distinguished professor, he contributes to advancing knowledge in the field of biocomplexity and public health sciences, playing a key role in interdisciplinary research efforts at the institute.
Research topics
- Computer Science
- Medicine
- Virology
- Machine Learning
- Computer Security
- Political Science
- Data science
- Artificial Intelligence
- Economics
- Geography
- Environmental health
- Business
- Data Mining
- Internal medicine
- Marketing
- Mathematical optimization
- Law
- World Wide Web
- Nursing
- Development economics
- Economic growth
- Demography
- History
- Mathematics
Selected publications
Fundamental limitations on efficiently forecasting certain epidemic measures in network models
Proceedings of the National Academy of Sciences · 2022 · 20 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Mathematical optimization
The ongoing COVID-19 pandemic underscores the importance of developing reliable forecasts that would allow decision makers to devise appropriate response strategies. Despite much recent research on the topic, epidemic forecasting remains poorly understood. Researchers have attributed the difficulty of forecasting contagion dynamics to a multitude of factors, including complex behavioral responses, uncertainty in data, the stochastic nature of the underlying process, and the high sensitivity of the disease parameters to changes in the environment. We offer a rigorous explanation of the difficulty of short-term forecasting on networked populations using ideas from computational complexity. Specifically, we show that several forecasting problems (e.g., the probability that at least a given number of people will get infected at a given time and the probability that the number of infections will reach a peak at a given time) are computationally intractable. For instance, efficient solvability of such problems would imply that the number of satisfying assignments of an arbitrary Boolean formula in conjunctive normal form can be computed efficiently, violating a widely believed hypothesis in computational complexity. This intractability result holds even under the ideal situation, where all the disease parameters are known and are assumed to be insensitive to changes in the environment. From a computational complexity viewpoint, our results, which show that contagion dynamics become unpredictable for both macroscopic and individual properties, bring out some fundamental difficulties of predicting disease parameters. On the positive side, we develop efficient algorithms or approximation algorithms for restricted versions of forecasting problems.
The United States COVID-19 Forecast Hub dataset
Scientific Data · 2022 · 126 citations
- Computer Science
- Machine Learning
- Computer Science
Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.
MMWR Morbidity and Mortality Weekly Report · 2021 · 163 citations
- Medicine
- Environmental health
- Demography
After a period of rapidly declining U.S. COVID-19 incidence during January-March 2021, increases occurred in several jurisdictions (1,2) despite the rapid rollout of a large-scale vaccination program. This increase coincided with the spread of more transmissible variants of SARS-CoV-2, the virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation of COVID-19 prevention strategies such as those for businesses, large-scale gatherings, and educational activities. To provide long-term projections of potential trends in COVID-19 cases, hospitalizations, and deaths, COVID-19 Scenario Modeling Hub teams used a multiple-model approach comprising six models to assess the potential course of COVID-19 in the United States across four scenarios with different vaccination coverage rates and effectiveness estimates and strength and implementation of nonpharmaceutical interventions (NPIs) (public health policies, such as physical distancing and masking) over a 6-month period (April-September 2021) using data available through March 27, 2021 (4). Among the four scenarios, an accelerated decline in NPI adherence (which encapsulates NPI mandates and population behavior) was shown to undermine vaccination-related gains over the subsequent 2-3 months and, in combination with increased transmissibility of new variants, could lead to surges in cases, hospitalizations, and deaths. A sharp decline in cases was projected by July 2021, with a faster decline in the high-vaccination scenarios. High vaccination rates and compliance with public health prevention measures are essential to control the COVID-19 pandemic and to prevent surges in hospitalizations and deaths in the coming months.
medRxiv (Cold Spring Harbor Laboratory) · 2021 · 70 citations
Senior authorCorresponding- Virology
- Medicine
We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 to 24 months to vaccinate their citizens. These time estimates and the emergence of new viral strains urge us to find quick and effective ways to allocate the vaccines and contain the pandemic. While current approaches use combinations of age-based and occupation-based prioritizations, our strategy marks a departure from such largely aggregate vaccine allocation strategies. We propose a novel approach motivated by recent advances in (i) science of real-world networks that point to efficacy of certain vaccination strategies and (ii) digital technologies that improve our ability to estimate some of these structural properties. Using a realistic representation of a social contact network for the Commonwealth of Virginia, combined with accurate surveillance data on spatiotemporal cases and currently accepted models of within- and between-host disease dynamics, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the currently used age-based allocation strategy in terms of number of infections, hospitalizations and deaths. Our results suggest that in just two months, by March 31, 2021, compared to age-based allocation, the proposed degree-based strategy can result in reducing an additional 56-110k infections, 3.2- 5.4k hospitalizations, and 700-900 deaths just in the Commonwealth of Virginia. Extrapolating these results for the entire US, this strategy can lead to 3-6 million fewer infections, 181-306k fewer hospitalizations, and 51-62k fewer deaths compared to age-based allocation. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.
Privacy-first health research with federated learning
npj Digital Medicine · 2021 · 177 citations
- Computer Science
- Computer Science
- Data science
Privacy protection is paramount in conducting health research. However, studies often rely on data stored in a centralized repository, where analysis is done with full access to the sensitive underlying content. Recent advances in federated learning enable building complex machine-learned models that are trained in a distributed fashion. These techniques facilitate the calculation of research study endpoints such that private data never leaves a given device or healthcare system. We show-on a diverse set of single and multi-site health studies-that federated models can achieve similar accuracy, precision, and generalizability, and lead to the same interpretation as standard centralized statistical models while achieving considerably stronger privacy protections and without significantly raising computational costs. This work is the first to apply modern and general federated learning methods that explicitly incorporate differential privacy to clinical and epidemiological research-across a spectrum of units of federation, model architectures, complexity of learning tasks and diseases. As a result, it enables health research participants to remain in control of their data and still contribute to advancing science-aspects that used to be at odds with each other.
Evaluating the impact of international airline suspensions on the early global spread of COVID-19
medRxiv (Cold Spring Harbor Laboratory) · 2020 · 47 citations
- Computer Science
- Computer Security
- Business
Global airline networks play a key role in the global importation of emerging infectious diseases. Detailed information on air traffic between international airports has been demonstrated to be useful in retrospectively validating and prospectively predicting case emergence in other countries. In this paper, we use a well-established metric known as effective distance on the global air traffic data from IATA to quantify risk of emergence for different countries as a consequence of direct importation from China, and compare it against arrival times for the first 24 countries. Using this model trained on official first reports from WHO, we estimate time of arrival (ToA) for all other countries. We then incorporate data on airline suspensions to recompute the effective distance and assess the effect of such cancellations in delaying the estimated arrival time for all other countries. Finally we use the infectious disease vulnerability indices to explain some of the estimated reporting delays.
Mathematical Models for COVID-19 Pandemic: A Comparative Analysis
Journal of the Indian Institute of Science · 2020 · 211 citations
- Political Science
- Computer Science
- Political Science
Bulletin of Mathematical Biology · 2020 · 945 citations
- Computer Science
- Political Science
- Computer Science
A recent manuscript (Ferguson et al. in Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, Imperial College COVID-19 Response Team, London, 2020. https://www.imperial.ac.uk/media/imperial-college/medicine/sph/ide/gida-fellowships/Imperial-College-COVID19-NPI-modelling-16-03-2020.pdf) from Imperial College modelers examining ways to mitigate and control the spread of COVID-19 has attracted much attention. In this paper, we will discuss a coarse taxonomy of models and explore the context and significance of the Imperial College and other models in contributing to the analysis of COVID-19.
Recent grants
NSF · $2.9M · 2018–2024
RAPID: Collaborative: Transfer Learning Techniques for Better Response to COVID-19 in the US
NSF · $25k · 2020–2021
EAGER: SSDIM: Ensembles of Interdependent Critical Infrastructure Networks
NSF · $200k · 2017–2019
NSF · $540k · 2007–2012
NIH · $6.7M · 2018
Frequent coauthors
- 343 shared
Bryan Lewis
Biocom
- 274 shared
Srinivasan Venkatramanan
Biocom
- 251 shared
Anil Vullikanti
University of Virginia
- 218 shared
Henning Mortveit
University of Virginia
- 200 shared
Przemyslaw Porebski
University of Virginia
- 200 shared
Jiangzhuo Chen
University of Virginia
- 198 shared
Aniruddha Adiga
- 196 shared
Samarth Swarup
University of Virginia
Labs
Awards & honors
- Distinguished Researcher Award, University of Virginia (2023…
- Honorary Doctoral Degree conferred by Chalmers University (2…
- Distinguished Alumni Award, Indian Institute of Technology,…
- Best paper award, SIGKDD 2021, Applied Data Science Category
- Finalist, Trinity Challenge (2021)
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