Our hypothesis is that synthetic information systems built by integrating relevant mathematical models can provide timely, comprehensive situational awareness and course-of-action analysis that policymakers can and will use to inform their response to infectious disease outbreaks. By synthetic information systems we mean software tools that synthesize diverse, seemingly incommensurate data, models, and causal hypotheses into plausible and justifiable pictures of a specific population and locality that support analysis of demographically and/or geographically targeted interventions. By comprehensive, we mean the tools include constraints and consequences due to behavior, sociology, logistics, and economics as well as health sciences. By provide and inform we mean that, rather than define studies and publish prescriptive policy guidance ourselves, we will create tools that allow analysts and other end users to explore policy and implementation options themselves. We will evaluate this hypothesis by tailoring to epidemiology our synthetic information technologies developed in a variety of decision-informatics contexts. We will extend these methods to address specific lessons learned during our efforts to engage policymakers in the 2009 influenza pandemic.
Specific aims : 1. Create a synthetic information set tailored to infectious disease epidemiology that provides users distributional estimates of the health, social, and financial consequences of outbreaks and interventions in any target subpopulation. This includes designing and implementing a well-defined language for specifying outbreak and intervention scenarios flexibly, sophisticated models and simulations of disease spread, and methods for analyzing the resulting information. 2. Develop integrated dynamical models for individuals'behaviors relevant to the spread of disease and opinions (e.g. prevalence elasticity and sociological theories of complex contagion). 3. Compare the rankings of interventions given by compartmental and individual-based models. The comparison will trace differences in outcomes to specific differences between the models. 4. Conduct a comprehensive investigation of community-based, non-pharmaceutical interventions in an influenza outbreak. In the course of achieving these aims, we will introduce a formal mathematical treatment of multi- perspective, multi-theory, coupled network dynamical processes into epidemiology and epidemiological modeling.

Public Health Relevance

This proposal will develop tools that assist public health decision makers address issues related to surveillance and detection, dynamics of infectious diseases, response strategies, and behavior. They will bridge critical barriers that prevent epidemiological modeling from realizing its tremendous promise to support and coordinate the decision-making stakeholder communities, helping policy-makers save lives and preserve the Nation's economic and social stability in the almost certain event of an infectious disease pandemic. The tools will allow analysts to conduct the analogue of a Phase III study (large-scale randomized controlled study to assess efficacy and safety) of combinations of pharmaceutical and non- pharmaceutical infectious disease outbreak mitigation strategies.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01GM070694-09S1
Application #
8321110
Study Section
Special Emphasis Panel (ZGM1-CBCB-3 (MI))
Program Officer
Sheeley, Douglas
Project Start
2004-05-01
Project End
2016-08-31
Budget Start
2011-09-01
Budget End
2012-08-31
Support Year
9
Fiscal Year
2011
Total Cost
$120,097
Indirect Cost
Name
Virginia Polytechnic Institute and State University
Department
Type
Organized Research Units
DUNS #
003137015
City
Blacksburg
State
VA
Country
United States
Zip Code
24061
Venkatramanan, Srinivasan; Lewis, Bryan; Chen, Jiangzhuo et al. (2018) Using data-driven agent-based models for forecasting emerging infectious diseases. Epidemics 22:43-49
Nath, Madhurima; Ren, Yihui; Khorramzadeh, Yasamin et al. (2018) Determining whether a class of random graphs is consistent with an observed contact network. J Theor Biol 440:121-132
Adiga, Abhijin; Chu, Shuyu; Eubank, Stephen et al. (2018) Disparities in spread and control of influenza in slums of Delhi: findings from an agent-based modelling study. BMJ Open 8:e017353
Chen, Jiangzhuo; Marathe, Achla; Marathe, Madhav (2018) Feedback Between Behavioral Adaptations and Disease Dynamics. Sci Rep 8:12452
Tabataba, Farzaneh Sadat; Chakraborty, Prithwish; Ramakrishnan, Naren et al. (2017) A framework for evaluating epidemic forecasts. BMC Infect Dis 17:345
Medlock, Jan; Pandey, Abhishek; Parpia, Alyssa S et al. (2017) Effectiveness of UNAIDS targets and HIV vaccination across 127 countries. Proc Natl Acad Sci U S A 114:4017-4022
Bhuiyan, Hasanuzzaman; Khan, Maleq; Chen, Jiangzhuo et al. (2017) Parallel Algorithms for Switching Edges in Heterogeneous Graphs. J Parallel Distrib Comput 104:19-35
Pettey, W B P; Carter, M E; Toth, D J A et al. (2017) Constructing Ebola transmission chains from West Africa and estimating model parameters using internet sources. Epidemiol Infect 145:1993-2002
Akudibillah, G; Pandey, A; Medlock, J (2017) Maximizing the benefits of ART and PrEP in resource-limited settings. Epidemiol Infect 145:942-956
Ren, Yihui; Eubank, Stephen; Nath, Madhurima (2016) From network reliability to the Ising model: A parallel scheme for estimating the joint density of states. Phys Rev E 94:042125

Showing the most recent 10 out of 84 publications