A5103176355- COVID-19 epidemiological studies
- Complex Network Analysis Techniques
- Mobile Ad Hoc Networks
- Opinion Dynamics and Social Influence
- Simulation Techniques and Applications
- Transportation Planning and Optimization
- RNA and protein synthesis mechanisms
- Cellular Automata and Applications
- Traffic control and management
- Data-Driven Disease Surveillance
- SARS-CoV-2 and COVID-19 Research
- Opportunistic and Delay-Tolerant Networks
- Traffic Prediction and Management Techniques
- Cooperative Communication and Network Coding
- RNA Research and Splicing
- Complex Systems and Decision Making
- Data Visualization and Analytics
- Human Mobility and Location-Based Analysis
- Wireless Networks and Protocols
- Scientific Computing and Data Management
- Energy Efficient Wireless Sensor Networks
- Genomics and Phylogenetic Studies
- Mental Health Research Topics
- Misinformation and Its Impacts
- demographic modeling and climate adaptation
University of Virginia
2019-2024
Biocom
2016-2024
University of Glasgow
2021
Queen Elizabeth University Hospital
2021
Virginia Tech
2006-2018
Cornell University
2016
Molecular Sciences Software Institute
2014
Dallas County
2013
University of California, San Diego
2010
University of New Hampshire
2010
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...
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...
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...
Abstract We study allocation of COVID-19 vaccines to individuals based on the structural properties their underlying social contact network. Even optimistic estimates suggest that most countries will likely take 6 24 months vaccinate citizens. These time and emergence new viral strains urge us find quick effective ways allocate contain pandemic. While current approaches use combinations age-based occupation-based prioritizations, our strategy marks a departure from such largely aggregate...
Abstract Human mobility is a primary driver of infectious disease spread. However, existing data limited in availability, coverage, granularity, and timeliness. Data-driven forecasts dynamics are crucial for decision-making by health officials private citizens alike. In this work, we focus on machine-learned anonymized map (hereon referred to as AMM) aggregated over hundreds millions smartphones evaluate its utility forecasting epidemics. We factor AMM into metapopulation model...
We consider the problem of determining maximum capacity media access (MAC) layer in wireless ad hoc networks. Due to spatial contention for shared medium, not all nodes can concurrently transmit packets each other these The number possible concurrent transmissions is, therefore, an estimate network capacity, and depends on MAC protocol being used. show that a large class protocols based virtual carrier sensing using RTS/CTS messages, which includes popular IEEE 802.11 standard, this may be...
Motivated by realistic sensor network scenarios that have misinformed nodes and variable topologies, we propose a fundamentally different approach to routing combines the best features of limited-flooding information-sensitive path-finding protocols into reliable, low-power method can make delivery guarantees independent parameter values or information noise levels. We introduce Parametric Probabilistic Sensor Network Routing Protocols, family light-weight robust multi-path for networks in...
Abstract In this article we investigate the statistical properties of wholesale electricity spot and futures prices traded on New York Mercantile Exchange for delivery at California–Oregon Border. Using daily data years 1998 1999, find that many characteristics market can be viewed to broadly consistent with efficient markets. The risk premium 6‐month contracts is estimated 0.1328% per day or about 4% month. a GARCH specification, estimate minimum variance hedge ratios futures. Finally,...
We give efficient sequential and distributed approximation algorithms for strong edge coloring graphs modeling wireless networks. Strong is equivalent to computing a conflict-free assignment of channels or frequencies pairwise links between transceivers in the network
Knowledge of fundamental traffic flow characteristics simulation models is an essential requirement when using these for the planning, design, and operation transportation systems. In this paper we discuss following: a description how features relevant to are currently under implementation in TRANSIMS microsimulation, proposition standardized tests models, results two different versions microsimulation.
This paper presents a day-to-day re-routing relaxation approach for traffic simulations. Starting from an initial planset the routes, route- based microsimulation is executed. The result of fed into re-router, which re-routes certain percentage all trips. makes patterns in much more reasonable. Further, it shown that method described this can lead to strong oscillations solutions.
We study allocation of COVID-19 vaccines to individuals based on the structural properties their underlying social contact network. Using a realistic representation network for Commonwealth Virginia, we how limited number vaccine doses can be strategically distributed reduce overall burden pandemic. show that individuals' degree (number contacts) and total proximity time is significantly more effective than usually used age-based strategy in reducing infections, hospitalizations deaths. The...
A class of finite discrete dynamical systems, called <b>Sequential Dynamical Systems</b> (SDSs), was proposed in [BMR99,BR99] as an abstract model computer simulations. Here, we address some questions concerning two special types the SDS configurations, namely Garden Eden and Fixed Point configurations. configuration $C$ is a (GE) if it cannot be reached from any configuration. necessary sufficient condition for non-existence GE configurations SDSs whose state values are domain...
Abstract Non-pharmaceutical interventions (NPIs) constitute the front-line responses against epidemics. Yet, interdependence of control measures and individual microeconomics, beliefs, perceptions health incentives, is not well understood. Epidemics complex adaptive systems where behavioral decisions drive are driven by, among other things, risk infection. To study impact heterogeneous on epidemic burden, we formulate a two risk-groups mathematical model that incorporates by perceptions. Our...
This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning response. The consists of ( i) automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems; ii) a data collect, integrate organize county-level disaggregated for initialization post-simulation analysis; iii) digital twin social contact networks made up 288 Million individuals...
Abstract The ongoing Russian aggression against Ukraine has forced over eight million people to migrate out of Ukraine. Understanding the dynamics migration is essential for policy-making and delivering humanitarian assistance. Existing work hindered by a reliance on observational data which only available well after fact. In this work, we study efficacy data-driven agent-based framework motivated social behavioral theory in predicting outflow migrants as result conflict events during...
In this paper we describe a multiagent simulation model of human behavior in the aftermath hypothetical, large-scale, human-initiated crisis center Washington D.C. Prior studies scenario have focused on modeling physical effects attack, such as thermal and blast effects, prompt radiation, fallout. Casualty mortality estimates been obtained by assuming spatially static population, ignoring behavioral response to event.We build behaving population its interaction with various interdependent...
A synthetic population is a simplified microscopic representation of an actual population. Statistically representative at the level, it provides valuable inputs to simulation models (especially agent-based models) in research areas such as transportation, land use, economics, and epidemiology. This article describes datasets from Synthetic Sweden Mobility (SySMo) model using state-of-art methodology, including machine learning (ML), iterative proportional fitting (IPF), probabilistic...
A class of finite discrete dynamical systems, called Sequential Dynamical Systems (SDSs), was introduced in [BR99] as a formal model for analyzing simulation systems. Here, we address the complexity two basic problems and their generalizations SDSs.Given an SDS $\mathcal{S}$ configuration $\mathcal{C}$, PREDECESSOR EXISTENCE (or PRE) problem is to determine whether there $\mathcal{C}'$ such that has transition from $\mathcal{C}$. Our results provide separations between efficiently solvable...
We study the interaction between communication protocols, network topology and packet traffic in wireless static radio networks. A particular interest is to empirically characterize effect of routing layer MAC on overall system performance. Three well-known protocols: 802.11, CSMA MACA are considered. Similarly three recently proposed AODV, DSR LAR scheme 1 The performance protocols measured with regard important parameters: (i) number packets received, (ii) average latency each (iii) long...
We present a synthetic information and modeling environment that can allow policy makers to study various counter-factual experiments in the event of large human-initiated crisis. The specific scenario we consider is ground detonation caused by an improvised nuclear device urban region. In contrast earlier work this area focuses largely on prompt effects human health injury, focus co-evolution individual collective behavior its interaction with differentially damaged infrastructure. This...