Health Sciences

Our team applies its expertise in modeling and simulation of massively interacting systems, network science, data science, and high-performance computing to problems in health sciences. We study how individual-level interactions – which might be host-to-host transmission of an infectious disease, peer influence on health-related behaviors, or interactions between a pathogen and cells in a host’s immune system – lead to population-level consequences. The models account for important heterogeneities in the population, constructing a synthetic population – of humans, cells, or other agents – from a variety of data sources that are often considered to be incommensurate. The resulting simulations support in silico estimates for the relative efficacy of control measures.




Project Contact: Bryan Lewis / Jiangzhuo Chen
Funding agency: AccuWeather, Inc. AccuWeather

AccuWeather provides superior accuracy in its weather forecasting, and through our collaboration we use our cutting-edge forecasting technology to provide them with highly detailed four-week influenza activity forecasts down to the county and state level. Additionally, our weekly executive summaries of current activity along with these forecasts are used to update AccuWeather corporate partners as they make business decisions surrounding influenza.



Bacterial and Viral Bioinformatics Resource Center (BV-BRC)

Project Contact: Rebecca Wattam / Ron Kenyon
Funding Agency: National Institute of Allergy and Infectious Diseases (NIAID)  National Institute of Allergy and Infectious Diseases

The Bacterial and Viral Bioinformatics Resource Center (BV-BRC) has an end-to-end analysis platform that allows researchers to take the direct data from the sequencer, assemble a genome, annotate it, and then use a suite of user-friendly tools to compare it to any public data that is available in the repository. With more than 228,000 bacterial, 3,000 archaeal, and 4,700 bacteriophage genomes, BV-BRC creates a unique research experience with “virtual integration” of private and public data, with many diverse tools and functionalities to explore both genome-scale and gene expression data.




Project Contact: Achla Marathe

Funding Agency: Unitaid UNITAID Logo

The collaborative project with Virginia Tech aims at assessing the effect of ivermectin mass drug administration (MDA) on malaria transmission in sub-Saharan African malaria-endemic countries. The project will seek the following broad objectives:

  • To determine the effect of MDA of ivermectin when given to humans and when given simultaneously to humans and livestock on malaria-related epidemiological and entomological outcomes;
  • To assess the safety and pharmacodynamics of the proposed ivermectin dose/regimen in MDA;
  • To assess the social acceptability, feasibility and practicability of ivermectin MDA;
  • To evaluate the indirect economic benefits to humans and direct health benefits to animal health when ivermectin is given in mass to humans and livestock;
  • To evaluate the environmental impact of ivermectin on non-target fauna and soil when administered in mass to livestock;
  • To inform and involve stakeholders at local, national and international levels with the aim of contributing to evidence-based policy.

We aim to provide solid and systematic evidence to contribute towards a World Health Organization policy recommendation by 2022 on the use of endectocides to reduce malaria transmission. Evidence on efficacy and safety will be supported by additional data on cost-effectiveness, acceptability and environmental impact to facilitate analysis leading to a policy recommendation. Close alignment with both National Malaria Control Programs (NMCP) and agricultural authorities will similarly facilitate early translation to country policy and uptake while upfront partnership with a generics industry producer will assure supply and affordability.



Detection and characterization of critical under-immunized hotspots

Project Contact: Achla Marathe/ Anil Vullikanti

Funding Agency: National Institutes of Health   NIH Logo

Emergence of undervaccinated geographical clusters for diseases like measles has become a national concern. A number of measles outbreaks have occurred in recent months, despite high MMR coverage in the United States (95%). Such undervaccinated clusters can act as reservoirs of infection that can transmit the disease to a wider population, magnifying their importance far beyond what their absolute numbers might indicate. The existence and growth of such undervaccinated clusters is often known to public health agencies and health provider networks, but they typically do not have enough resources to target people in each such cluster, to attempt to improve the vaccination rate. Preliminary results show that not all undervaccinated clusters are “equal” in terms of their potential for causing a big outbreak (referred to as its “criticality”), and the rate of undervaccination in a cluster does not necessarily correlate with its criticality. This project uses novel methods to identify these critical clusters.

It brings together a systems science approach, combines agent-based stochastic epidemic models, and techniques from machine learning, high performance computing, data mining, and spatial statistics, along with novel public and private datasets on immunization and incidence, to develop a novel methodology for identifying critical clusters, through the following tasks: (i) Identify spatial clusters with significantly low immunization rates; (ii) Develop an agent based model for the spread of measles that incorporates detailed immunization data, and is calibrated using a novel source of incidence data; (iii) Develop methods to find and characterize critical spatial clusters, with respect to different metrics, which capture both epidemic and economic burden, and rank order underimmunized clusters based on their criticality; and (iv) Use the methodology to evaluate interventions in terms of their effect on criticality. A highly interdisciplinary team involving two universities, a health care delivery organization and a state department of Health, will work together to develop this methodology. Characterization of such clusters will enable public health departments and policy makers in targeted surveillance of their regions and a more efficient allocation of resources.



DIBBS CIF21: Middleware and High-Performance Analytics Libraries for Scalable Data Science

Project Contact: Anil Vullikanti / Madhav Marathe
Funding Agency: National Science Foundation National Science Foundation

This collaborative project is supported by scientists from a variety of domains, including network science, epidemiology, spatial GIS, biomolecular simulations, pathology, computer vision, and remote sensing. This project addresses the need for high-performance data analytics with efficient parallel algorithms using a novel approach, centered on combining the breadth and productivity of best practice commodity Apache Big Data Stack (HPC-ABDS) and high-performance computing. The project will produce two types of key building blocks: Middleware for Data-Intensive Analytics and Science (MIDAS) and the Scalable Parallel Interoperable Data Analytics Library (SPIDAL). MIDAS and SPIDAL are motivated and tested by the applications in different domains.




Project Contact: Bryan Lewis
Funding Agency: National Science Foundation NSF

This project seeks to conduct both field and computational research on the impact of disease on the social group dynamics of social animals, like the banded mongoose. This research involves the development of both compartmental and agent-based models of social group dynamics and disease spread.



Friend or Foe: iSENTRY

Project Contact: Rebecca Wattam / Allan Dickerman
Funding Agency: Defense Advanced Research Projects Agency (DARPA) DARPA

The DARPA funded Friend or Foe project combines environmental microbiology, high-throughput droplet microfluidics, DNA barcoding, single-cell sequencing, machine learning, and computational biology to detect rapidly good versus bad bacteria in our environment. The target of iSENTRY is to bring about a revolutionary advance in the science, devices, and systems needed for the isolation and characterization of known and potentially unknown or unculturable bacterial pathogens. As a collaborator among several partners, our team provides the computational biology expertise.




Project Contact: Chunhong Mao
Funding agency: National Institutes of Health (NIH)  National Institutes of Health

Genomic and Proteomic Architecture of Atherosclerosis (GPAA) is a collaborative project which aims to comprehensively study the genomic and proteomic architecture of atherosclerosis and identify genes/proteins that are associated with the development of atherosclerosis. This work will provide novel insights into the pathogenesis of atherosclerosis and facilitate development of reliable methods for diagnostics and targeted therapeutic interventions.



Metabolomic Signatures

Project Contact: Chunhong Mao
Funding agency: National Institutes of Health (NIH)   National Institutes of Health

The Metabolomic Signatures of Coronary Artery Disease (CAD)-Associated Genotypes project is an international collaborative venture. The overall goal of the project is to use un-targeted metabolomics to identify metabolites that are associated with CAD genotypes. This work will provide novel insights into the mechanisms and pathways involved in the pathogenesis of CAD, and help identify novel therapeutic targets to CAD.



Organizing Decentralized Resilience in Critical Interdependent-infrastructure Systems and Processes

Project Contact: Chris Kuhlman / Anil Vullikanti
Funding Agency: National Science Foundation National Science Foundation

The goal of the ORDER-CRISP (Organizing Decentralized Resilience in Critical Interdependent-infrastructure Systems and Processes) project is to study damage-inducing mechanisms such as wind, rain, storm surge, and flooding. We model their effects on damage to both infrastructure (e.g., transportation, communications, electric power, potable water) and human (social/societal) networks. In particular, our focus is on understanding these interacting utility infrastructures and devising and assessing methods that make human populations more resilient before, during, and after natural disasters. Topics being studied include social isolation, social support structures, social vulnerability indices, and various well-being indices.



Systems analysis of social pathways of epidemics to reduce health disparities

Project Contact: Achla Marathe
Funding agency: National Institutes of General Medical Sciences (NIGMS), National Institutes of Health (NIH)   National Institutes of Health National Institute of General Medical Sciences logo

The objective of this R01 is to incorporate social behavior into mathematical models of infectious disease transmission dynamics, with a focus on influenza like illness. The inferences of this project will improve our understanding of the impact of different control and prevention strategies for infectious disease epidemics in general and Influenza epidemics in particular. Our hypothesis is that individual behavior, disease dynamics, and interventions coevolve across multiple scales to create statistically and epidemiologically significant differences in the efficacy and social equity of public health policies such as infectious disease control strategies.

This project extends well studied computational simulations to include people's behaviors relevant to infectious disease epidemics and will be used to determine the consequences of feedback between population-level effects and individual-level behavior. In particular, we will determine the sensitivity of outcomes to particular behaviors. A survey designed to focus on those particular behaviors will be used to estimate variability across communities and to calibrate the simulations. Published models and results on Influenza transmissibility and intervention efficacy will be revisited with the improved simulations. Initially, our analysis will describe the mean performance of interventions over the whole population. The analyses will then extend to scenarios reflecting the observed variability in behavior to reveal how health disparities could arise from behavioral differences at the community level. Altogether, the results of the new and comparative analyses will inform the design of optimal epidemic interventions with fewer unintended consequences.

Project Team

Achla Marathe (Contact PI), Professor, Department of Public Health Sciences, Biocomplexity Institute & Initiative, UVA.
Kaja Abbas (mPI), Assistant Professor, London School of Hygiene and Tropical Medicine, UK
Samarth Swarup, Associate Research Professor, Biocomplexity Institute & Initiative, UVA.
Jiangzhuo Chen, Associate Research Professor, Biocomplexity Institute & Initiative, UVA.
Stephen Eubank, Professor, Department of Public Health Sciences, Biocomplexity Institute & Initiative, UVA.
Bryan Lewis, Associate Research Professor, Biocomplexity Institute & Initiative, UVA.
Pamela Murray-Tuite, Associate Professor, Civil and Environmental Engineering, Clemson University

Past and Current Students Supported on the Grant

Lijing Wang, PhD Computer Science, University of Virginia
Gloria Kang, MPH/PhD, Biomedical and Veterinary Sciences, Virginia Tech
Narges Dorratoltaj, MPH (Infectious Disease) / PhD (Biomedical and Veterinary Sciences), Virginia Tech
Meghendra Singh, MS Computer Science, Virginia Tech
Kunal Singh, MS Civil and Environmental Engineering, Virginia Tech
Bowen Shi, PhD Economics, Virginia Tech
Arminder Deol, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine

Selected Publications

Please see this document for selected publications supported by this project.



Y Chromosomes in Mosquitoes

Project Contact: Chunhong Mao
Funding agency: National Institutes of Health (NIH)   National Institutes of Health

This collaborative project aims to identify Y chromosome genes across divergent Anopheles mosquito species, and to study the function and evolution of selected Y genes that are important in male development. This work will provide baseline information on genes that control male mosquito biology, facilitating the creation of novel methods to control mosquito-borne diseases.