Sensitivity Analysis of an Individual-Based Model for Simulation of Influenza Epidemics

PLoS ONE

Volumn 7, Issue 10, 2012, Pages

  Nsoesie, Elaine O ^a   Beckman, Richard J ^a   Marathe, Madhav V ^a,b  

^a VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

^b State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; ADULT; AGE DISTRIBUTION; AGED; ARTICLE; CHILD; COMPUTER MODEL; EPIDEMIC; GEOGRAPHY; HUMAN; INCUBATION TIME; INFECTION SENSITIVITY; INFLUENZA; MEASUREMENT; PEAK INFECTED PROPORTION; PREDICTION AND FORECASTING; PRESCHOOL CHILD; PUBLIC HEALTH; SCHOOL CHILD; SENSITIVITY ANALYSIS; SIMULATION; SOCIAL NETWORK; TIME TO PEAK; TOTAL ATTACK RATE; UNITED STATES; URBAN RURAL DIFFERENCE; VIRUS TRANSMISSION;

ALGORITHMS; COMPUTER SIMULATION; EPIDEMICS; HUMANS; INFLUENZA, HUMAN; MODELS, THEORETICAL; NEW YORK CITY; PUBLIC HEALTH; REPRODUCIBILITY OF RESULTS; RURAL HEALTH; STOCHASTIC PROCESSES; TIME FACTORS; URBAN HEALTH; VIRGINIA;

EID: 84868087361     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0045414     Document Type: Article

References (49)

1. Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, et al. (2010) Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index. Computer Physics Communications 181: 259-270.
2. Helton JC (2008) Uncertainty and Sensitivity Analysis for Models of Complex Systems. In: Graziani F, editor, Computational Methods in Transport: Verification and Validation, Springer Berlin Heidelberg, volume 62 of Lecture Notes in Computational Science and Engineering, chap-ter 9. pp. 207-228.
3. Cacuci DG, Ionescu-Bujor M, Navon IM (2005) Sensitivity And Uncertainty Analysis: Applications to Large-Scale Systems. New York: Chapman & Hall/CRC, volume ii edition.
4. Ginot V, Gaba S, Beaudouin R, Aries F, Monod H, (2006) Combined use of local and anova-based global sensitivity analyses for the investigation of a stochastic dynamic model: application to the case study of an individual-based model of a fish population. Ecological Modelling 193: 479491.
5. Frey HC, Patil SR, (2002) Identification and Review of Sensitivity Analysis Methods. Risk Analysis 22: 553-578.
6. Hamby DM, (1995) A comparison of sensitivity analysis techniques. Health Physics 68: 195-204.
7. Saltelli A, Tarantola S, Campolongo F, Ratto M (2004) Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. New York, NY, USA: Halsted Press.
8. Epstein J, (2009) Modelling to contain pandemics. Nature pp. 687.
9. Longini I, Nizam A, Xu S, Ungchusak K, Hanshaworakul W, et al. (2005) Containing pandemic inuenza at the source. Science 309: 1083-1087.
10. Chao DL, Halloran ME, Obenchain VJ, Longini IM, (2010) FluTE, a Publicly Available Stochastic Inuenza Epidemic Simulation Model. PLoS Computational Biology 6: e1000656+.
11. Barrett C, Bisset K, Leidig J, Marathe A, Marathe M, (2011) Economic and social impact of inuenza mitigation strategies by demographic class. Epidemics 3: 19-31.
12. Bisset K, Chen J, Feng X, Kumar VSA, Marathe M (2009) Epifast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems. In: Proceedings of the 23rd international conference on Supercomputing. ICS '09, pp. 430-439.
13. Heath B, Hill R, Ciarallo F, (2009) A survey of agent-based modeling practices (january 1998 to july 2008). Journal of Artificial Societies and Social Simulation 12: 9.
14. Burke D, Epstein J, Cummings D, Parker J, Cline K, et al. (2006) Individual-based Computational Modeling of Smallpox Epidemic Control Strategies. Academic Emergency Medicine 13: 1142-1149.
15. Halloran ME, Ferguson N, Eubank S, Longini I, Cummings D, et al. (2008) Modeling targeted layered containment of an inuenza pandemic in the United States. Proceedings of the National Academy of Sciences.
16. Germann TC, Kadau K, Longini IM, Macken CA, (2006) Mitigation strategies for pandemic in-uenza in the United States. Proceedings of the National Academy of Sciences 103: 5935-5940.
17. Beckman R, Baggerly K, Mckay M, (1996) Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice 30: 415-429.
18. Eubank S, Guclu H, Kumar VSA, Marathe M, Srinivasan A, et al. (2004) Modelling disease out-breaks in realistic urban social networks. Nature.
19. Eubank S, Barrett C, Beckman R, Bisset K, Durbeck L, et al. (2010) Detail in network models of epidemiology: are we there yet? Journal of Biological Dynamics 4: 446-455.
20. Clinical Signs and Symptoms of Inuenza. Available: http://www.cdc.gov/flu/professionals/acip/clinical.htm. Accessed 2012 Aug 27.
21. Nsoesie EO, Beckman R, Marathe M, Lewis B, (2011) Prediction of an epidemic curve: A supervised classification approach. Statistical Communications in Infectious Diseases 3.
22. Goldstein E, Apolloni A, Lewis B, Miller J, Macauley M, et al. (2010) Distribution of vac-cine/antivirals and the "least spread line" in a stratified population. Journal of the Royal Society Interface 7: 755-764.
23. Lipsitch M, Finelli L, Heffernan RT, Leung GM, Redd SC, (2011) Improving the evidence base for decision making during a pandemic: the example of 2009 inuenza A/H1N1. Biosecurity and bioterrorism biodefense strategy practice and science 9: 89-115.
24. Ong JBS, Chen MIC, Cook AR, Lee HC, Lee VJ, et al. (2010) Real-time epidemic monitoring and forecasting of H1N1-2009 using inuenza-like illness from general practice and family doctor clinics in singapore. PLoS ONE 5: e10036.
25. Chao DL, Matrajt L, Basta NE, Sugimoto JD, Dean B, et al. (2011) Planning for the control of pandemic inuenza A (H1N1) in los angeles county and the united states. American Journal of Epidemiology 173: 1121-1130.
26. Opatowski L, Fraser C, Griffin J, De Silva E, Van Kerkhove MD, et al. (2011) Transmission char-acteristics of the 2009 H1N1 inuenza pandemic: Comparison of 8 southern hemisphere countries. PLoS Pathogens 7.
27. Merler S, Ajelli M, (2010) The role of population heterogeneity and human mobility in the spread of pandemic inuenza. Proceedings of the Royal Society B: Biological Sciences 277: 557-565.
28. Monto AS, Davenport FM, Napier JA, Francis T Jr, (1969) Effect of vaccination of a school-age population upon the course of an A2/Hong Kong inuenza epidemic. Bull World Health Organ 41: 53742.
29. Reichert TA, Sugaya N, Fedson DS, Glezen WP, Simonsen L, et al. (2001) The Japanese Experience with Vaccinating Schoolchildren against Inuenza. New England Journal of Medicine 344: 889-896.
30. Salathé M, Kazandjieva M, Lee JW, Levis P, Feldman MW, et al. (2010) A high-resolution hu-man contact network for infectious disease transmission. Proceedings of the National Academy of Sciences.
31. Basta NE, Chao DL, Halloran ME, Matrajt L, Longini IM, (2009) Strategies for pandemic and seasonal inuenza vaccination of schoolchildren in the united states. American Journal of Epi-demiology 170: 679-686.
32. Nishiura H, (2011) Real-time forecasting of an epidemic using a discrete time stochastic model: a case study of pandemic inuenza (H1N1-2009). Bio Medical Engineering Online 10.
33. Ohkusa Y, Sugawara T, Taniguchi K, Okabe N, (2011) Real-time estimation and prediction for pandemic A/H1N1(2009) in Japan. Journal of infection and chemotherapy.
34. Barrett CL, Beckman R, Berkbigler K, Bisset K, Bush K, et al. (2001) TRANSIMS: Transportation analysis simulation system. Technical Report, LA-UR-00-1725, Los Alamos National Laboratory Unclassified Report 3.
35. Speckman P, Vaughn K, Pas E (1997a) Generating household activity-travel patterns (HATPs) for synthetic populations. Transportation Research Board 1997 Annual Meeting.
36. Speckman P, Vaughn K, Pas E (1997b) A continuous spatial interaction model: Application to home-work travel in Portland, Oregon. Transportation Research Board 1997 Annual Meeting.
37. Bowman J, (2001) Activity-based disaggregate travel demand model system with activity schedules. Transportation Research Part A: Policy and Practice 35: 1-28.
38. Bowman J, Bradley M, Shiftan Y, Lawton TK, Ben-Akiva M (1998) Demonstration of an activity based model system for Portland. In: Proceedings of the 8th World Conference on Transport Research.
39. Yang Y, Halloran ME, Daniels MJ, Longini IM, Burke DS, et al. (2010) Modeling competing infectious pathogens from a bayesian perspective: Application to inuenza studies with incomplete laboratory results. Journal of the American Statistical Association pp. 1310-1322.
40. McKay MD, Beckman RJ, Conover WJ, (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21: 239245.
41. Box GEP, Hunter WG, Hunter JS, Hunter WG (1978) Statistics for Experimenters: An Introduc-tion to Design, Data Analysis, and Model Building. John Wiley & Sons.
42. Beckman R, Chaturvedi G, Lewis B (2008) Clustering principal components to _nd groupings in a collection of curves. Technical Report 07-043, NDSSL, VBI, Virginia Tech, Blacksburg, Virginia.
43. Rahmandad H, Sterman J, (2008) Heterogeneity and network structure in the dynamics of diffusion: Comparing agent-based and differential equation models. Management Science 54: 998-1014.
44. Cowling BJ, Chan KH, Fang VJ, Lau LLH, So HC, et al. (2010) Comparative Epidemiology of Pandemic and Seasonal Inuenza A in Households. New England Journal of Medicine 362: 2175-2184.
45. Cauchemez S, Donnelly CA, Reed C, Ghani AC, Fraser C, et al. (2009) Household transmission of 2009 pandemic inuenza A (H1N1) virus in the United States. New England Journal of Medicine 361: 2619-2627.
46. Chao DL, Halloran ME, Longini IM, (2010) School opening dates predict pandemic inuenza a(h1n1) outbreaks in the united states. Journal of Infectious Diseases 202: 877-880.
47. Keeling MJ, Eames KT, (2005) Networks and epidemic models. Journal of the Royal Society, Interface/the Royal Society 2: 295-307.
48. Pautasso M, Jeger MJ, (2008) Epidemic threshold and network structure: The interplay of proba-bility of transmission and of persistence in small-size directed networks. Ecological Complexity 5: 1-8.
49. Barrett C, Eubank S, Kumar V, Marathe M (2004) Understanding large scale social and infras-tructure networks: A simulation based approach. SIAM news: The Mathematics of Networks.