A5020293284- COVID-19 epidemiological studies
- Complex Network Analysis Techniques
- Data-Driven Disease Surveillance
- Regional Socio-Economic Development Trends
- COVID-19, Geopolitics, Technology, Migration
- Impact of AI and Big Data on Business and Society
- Opinion Dynamics and Social Influence
- Complexity and Algorithms in Graphs
- Advanced Graph Theory Research
- Mobile Ad Hoc Networks
- Influenza Virus Research Studies
- Human Mobility and Location-Based Analysis
- Opportunistic and Delay-Tolerant Networks
- Cooperative Communication and Network Coding
- Scientific Computing and Data Management
- Optimization and Search Problems
- Vaccine Coverage and Hesitancy
- Simulation Techniques and Applications
- Graph Theory and Algorithms
- COVID-19 Pandemic Impacts
- Formal Methods in Verification
- SARS-CoV-2 and COVID-19 Research
- Computational Geometry and Mesh Generation
- Data Management and Algorithms
- Game Theory and Applications
University of Virginia
2018-2025
Biocom
2016-2025
University of Stuttgart
2025
Virginia Tech
2011-2024
University of Colorado System
2024
University of Colorado Boulder
2024
Johns Hopkins University
2023
Urology of Virginia
2023
University Vascular Associates
2022
University of Maryland, College Park
2022
Abstract A recent manuscript (Ferguson et al. in Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand, Imperial College 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 modelers examining ways mitigate control the spread has attracted much attention. In this paper, we will discuss a coarse taxonomy models explore context...
Abstract Unit disk graphs are intersection of circles unit radius in the plane. We present simple and provably good heuristics for a number classical NP‐hard optimization problems on graphs. The considered include maximum independent set, minimum vertex cover, coloring, dominating set. also an on‐line coloring heuristic which achieves competitive ratio 6 Our do not need geometric representation Geometric representations used only establishing performance guarantees heuristics. Several our...
3G networks are currently overloaded, due to the increasing popularity of various applications for smartphones. Offloading mobile data traffic through opportunistic communications is a promising solution partially solve this problem, because there almost no monetary cost it. We propose exploit facilitate information dissemination in emerging Mobile Social Networks (MoSoNets) and thus reduce amount traffic. As case study, we investigate target-set selection problem delivery. In particular,...
Due to the increasing popularity of various applications for smartphones, 3G networks are currently overloaded by mobile data traffic. Offloading cellular traffic through opportunistic communications is a promising solution partially solve this problem, because there no monetary cost it. As case study, we investigate target-set selection problem information delivery in emerging Mobile Social Networks (MoSoNets). We propose exploit facilitate dissemination and thus reduce amount In...
An Ebola outbreak of unparalleled size is currently affecting several countries in West Africa, and international efforts to control the are underway. However, efficacy these interventions, their likely impact on an epidemic this size, unknown. Forecasting simulation interventions may inform public health efforts.We use existing data from Liberia Sierra Leone parameterize a mathematical model forecast progression epidemic, as well including increased contact tracing, improved infection...
Producing timely, well-informed and reliable forecasts for an ongoing epidemic of emerging infectious disease is a huge challenge. Epidemiologists policy makers have to deal with poor data quality, limited understanding the dynamics, rapidly changing social environment uncertainty on effects various interventions in place. Under this setting, detailed computational models provide comprehensive framework integrating diverse sources into well-defined model dynamics behavior, potentially...
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 large-scale vaccination program. This increase coincided with spread more transmissible variants SARS-CoV-2, virus that causes COVID-19, including B.1.1.7 (1,3) and relaxation prevention strategies such as those for businesses, gatherings, educational activities. To provide long-term projections potential trends cases,...
Abstract Privacy protection is paramount in conducting health research. However, studies often rely on data stored a centralized repository, where analysis done with full access to the sensitive underlying content. Recent advances federated learning enable building complex machine-learned models that are trained distributed fashion. These techniques facilitate calculation of research study endpoints such private never leaves given device or healthcare system. We show—on diverse set single...
Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, United States Centers for Disease Control Prevention (CDC) partnered with academic research lab University of Massachusetts Amherst to create US Forecast Hub. Launched in April 2020, Hub is a dataset point probabilistic incident cases, hospitalizations, deaths, cumulative deaths due county, state,...
Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields recent insights epidemiology, one maximise the predictive performance such if multiple models are combined into an ensemble. Here, we report ensembles predicting COVID-19 cases deaths across Europe between 08 March 2021 07 2022.
Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. We describe EpiSimdemics - scalable parallel algorithm to simulate the spread contagion in large, realistic social contact networks using individual-based models. an interaction-based simulation certain class stochastic reaction-diffusion processes. Straightforward simulations process do not scale well, limiting use models very small populations. specifically designed with...
This paper considers two inter-related questions: (i) Given a wireless ad-hoc network and collection of source-destination pairs {(si,ti)}, what is the maximum throughput capacity network, i.e. rate at which data from sources to their corresponding destinations can be transferred in network? (ii) Can protocols designed that jointly route packets schedule transmissions rates close capacity? Much earlier work focused on random instances proved analytical lower upper bounds capacity. Here,...
Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on novel interpretation the stochastic disease propagation in contact network. implement it using master-slave computation model which allows scalability distributed memory systems.
Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. We describe EpiSimdemics - scalable parallel algorithm to simulate the spread contagion in large, realistic social contact networks using individual-based models. an interaction-based simulation certain class stochastic reaction-diffusion processes. Straightforward simulations process do not scale well, limiting use models very small populations. specifically designed with...
We describe ¿first principles¿ based methods for developing synthetic urban and national scale social contact networks. Unlike simple random graph techniques, these use real world data sources combine them with behavioral theories to synthesize develop a population the United States modeling every individual in including household structure, demographics 24-hour activity sequence. The process involves collecting manipulating public proprietary sets integrated into common architecture...
We describe first principles based methods for developing synthetic urban and national scale social contact networks. Unlike simple random graph techniques, these use real world data sources combine them with behavioral theories to synthesize develop a population the United States modeling every individual in including household structure, demographics 24-hour activity sequence. The process involves collecting manipulating public proprietary sets integrated into common architecture exchange...
Massive networks arising in numerous application areas poses significant challenges for network analysts as these grow to billions of nodes and are prohibitively large fit the main memory. Finding number triangles a is an important problem analysis complex networks. Several interesting graph mining applications depend on graph. In this paper, we present efficient MPI-based distributed memory parallel algorithm, called PATRIC, counting massive PATRIC scales well with can compute exact one...
The challenge of developing and using computer models to understand control the diffusion disease through populations.
The 2014 outbreak of Ebola in West Africa is unprecedented its size and geographic range, demands swift, effective action from the international community. Understanding dynamics spread critical for directing interventions extinguishing epidemic; however, observational studies local conditions have been incomplete limited by urgent need to direct resources patient care.