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Madhav V. Marathe

Researcher at University of Virginia

Publications -  356
Citations -  15017

Madhav V. Marathe is an academic researcher from University of Virginia. The author has contributed to research in topics: Approximation algorithm & Computer science. The author has an hindex of 53, co-authored 315 publications receiving 13493 citations. Previous affiliations of Madhav V. Marathe include University at Albany, SUNY & Los Alamos National Laboratory.

Papers
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Book ChapterDOI

Epidemiology and Wireless Communication: Tight Analogy or Loose Metaphor?

TL;DR: It is argued that while epidemiology as a metaphor may hold insights into communication networks, the relationship is not concrete enough to permit us to adapt solutions from one domain to another.
Posted ContentDOI

Projected resurgence of COVID-19 in the United States in July-December 2021 resulting from the increased transmissibility of the Delta variant and faltering vaccination

Shaun A. Truelove, +63 more
- 02 Sep 2021 - 
TL;DR: In this article, data from nine mechanistic models project substantial resurgences of COVID-19 across the US resulting from the more transmissible SARS-CoV-2 Delta variant, which is expected to increase the risk of pandemic resurgence in the US in July-December 2021.
Journal ArticleDOI

Efficient approximation algorithms for domatic partition and on-line coloring of circular arc graphs

TL;DR: A heuristic for the domatic partition problem with a performance ratio of 4.5 and on-line minimum vertex coloring, which guarantees a solution which is within a factor of 2 of the optimal (off-line) value.
Posted ContentDOI

Medical Costs of Keeping the US Economy Open During COVID-19

TL;DR: An individual based model and national level epidemic simulations are used to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order.
Proceedings ArticleDOI

All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting

TL;DR: In this paper, the authors present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States.