A large-scale simulation model of pandemic influenza outbreaks for development of dynamic mitigation strategies

IIE Transactions (Institute of Industrial Engineers)

Volumn 40, Issue 9, 2008, Pages 893-905

  Das, Tapas ^a   Savachkin, Alex ^a   Zhu, Yiliang ^b  

^a University of South Florida   (United States)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

Dynamic mitigation strategy; Epidemeology; Pandemic influenza; Resource allocation strategy; Simulation

Indexed keywords

CHANNEL CAPACITY; CHEMOTHERAPY; DISEASE CONTROL; DRUG DELIVERY; DRUG DOSAGE; FORESTRY; HOSPITALS; IMMUNIZATION; PLANNING; QUALITY ASSURANCE; REINFORCEMENT; REINFORCEMENT LEARNING; RELIABILITY; STOCHASTIC MODELS; STRATEGIC PLANNING;

ANTI-VIRAL DRUGS; CENTER FOR DISEASE CONTROL (CDC); DECISION MAKER (DM); HEALTH CARE DELIVERY; HUMAN ACTIVITIES; HUMAN SERVICES; INFECTIOUS DISEASES; INFLUENZA PANDEMICS; LARGE-SCALE SIMULATIONS; LEARNING FRAMEWORKS; LIMITED RESOURCES; MARKOV DECISION PROCESS (MDP); MITIGATION STRATEGIES; MODEL PARAMETERS; PANDEMIC INFLUENZA; SIMULATION MODELLING; SIMULATION-BASED; STATISTICAL SIGNIFICANCE; STOCHASTIC PROPAGATION;

HEALTH;

EID: 48249104032     PISSN: 0740817X     EISSN: 15458830     Source Type: Journal    
DOI: 10.1080/07408170802165856     Document Type: Article

References (28)

1. Aunins, J. G., Lee, A. L. and Volkin, D. B. (2000) CRC Press LLC, Boca Raton, FL
2. Das, T., Gosavi, A., Mahadevan, S. and Marchalleck, N. (1999) Solving semi-Markov decision problems using average reward reinforcement learning. 45:4, pp. 560-574.
3. Fakih, S. J. and Das, T. K. (2006) LEAD: A methodology for learning efficient approaches for medical diagnosis. 10:2, pp. 220-228.
4. Fedson, D. S. and Hant, S. (2003) Pandemic influenza and the global vaccine supply. 36, pp. 1552-1561.
5. Fraser, C., Riley, S., Anderson, R. M. and Ferguson, N. (2004) Factors that make an infectious desease outbreak controllable. 101:16, pp. 6146-6151.
6. Gani, R. and Leach, S. (2004) Epidemiologic determinants for modeling pneumonic plague outbreaks. 10:4
7. Gosavi, A. (2004) Reinforcement learning for long-run average cost. 155, pp. 654-674.
8. Halfhill, T. (2006)
9. Halloran, M. E., Longini, I. M., Cowart, D. M. and Nizam, A. (2002) Community interventions and the epidemics prevention potential. 20, pp. 3254-3262.
10. Halloran, M. E., Longini, I. M., Nizam, A. and Yang, Y. (2002) 298
11. IOM (2004)
12. Johns Hopkins Bloomberg School of Public Health (2005)
13. Kaelbling, L., Littman, M. and Moore, A. (1996) Reinforcement learning: a survey. 4, pp. 237-285.
14. Kaplan, E. H., Craft, D. L. and Wein, L. M. (2002)
15. Longini, I. M., Halloran, M. E., Nizam, A. and Yang, Y. (2004) Containing pandemic influenza with antiviral agents. 159, pp. 623-633.
16. Longini, I. M. and Hill, A. N. (2003) The critical vaccination fraction for heterogeneous epidemic models. 181, pp. 85-106.
17. Mahadevan, S. (1996) Average reward reinforcement learning: Foundations, algorithms, and empirical results. 22, pp. 159-196.
18. Meltzer, M. I., Cox, N. J. and Fukuda, K. (1999) The economic impact of pandemic influenza in the United States: Priorities for intervention. 5:5
19. Monto, A. S. (2005)
20. NAE (2005)
21. NIMGS (2004)
22. Patel, R., Longini, I. M. and Halloran, M. E. (2005) Finding optimal vaccination strategies for pandemic influenza using genetic algorithms. 234, pp. 201-212.
23. Puterman, M. (1994) Wiley, New York, NY
24. Ravulapati, K. K., Rao, J. and Das, T. K. (2002) A reinforcement learning approach to stochastic business games. 36, pp. 373-385.
25. USC DC (2006)
26. US HHS (2006)
27. US HHS (2007)
28. WHO (2004)