Forecasting peaks of seasonal influenza epidemics

PLoS Currents

Volumn 5, Issue Outbreaks, 2013, Pages

  Nsoesie, Elaine O ^a   Marathe, Madhav ^b   Brownstein, John S ^c  

^a UNIVERSITY OF WASHINGTON   (United States)

^b VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DISEASE SIMULATION; DISEASE SURVEILLANCE; DISEASE TRANSMISSION; EPIDEMIC; FLU LIKE SYNDROME; FORECASTING; HUMAN; RETROSPECTIVE STUDY; SEASON; SEASONAL INFLUENZA; SPATIOTEMPORAL ANALYSIS;

EID: 84898028869     PISSN: None     EISSN: 21573999     Source Type: Journal    
DOI: 10.1371/currents.outbreaks.bb1e879a23137022ea79a8c508b030bc     Document Type: Article

References (36)

1. Longini IM, Fine PEM, Thacker SB (1986) Predicting the global spread of new infectious agents. American Journal of Epidemiology 123: 383-391.
2. Ong J, Mark I, Chen C, Cook A, Lee H, et al. (2010) Real-time epidemic monitoring and fore- casting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore. PloS one 5: e10036.
3. Towers S, Feng Z (2009) Pandemic h1n1 influenza: predicting the course of a pandemic and assessing the efficacy of the planned vaccination programme in the united states. Euro surveillance: bulletin europeen sur les maladies transmissibles = European communicable disease bulletin 14: 19358.
4. Chao DL, Matrajt L, Basta NE, Sugimoto JD, Dean B, et al. (2011) Planning for the control of pandemic influenza A (H1N1) in Los Angeles county and the United States. American Journal of Epidemiology 173: 1121-1130.
5. Tizzoni M, Bajardi P, Poletto C, Ramasco J, Balcan D, et al. (2012) Real-time numerical forecast of global epidemic spreading: case study of 2009 A/H1N1pdm. BMC Medicine 10: 165.
6. Dukic V, Lopes HF, Polson NG (2012) Tracking epidemics with Google flu trends data and a state-space SEIR model. Journal of the American Statistical Association 107: 1410-1426.
7. Sumi A, Ichi Kamo K, Ohtomo N, Mise K, Kobayashi N (2011) Time series analysis of incidence data of influenza in japan. Journal of Epidemiology 21: 21-29.
8. Moriña D, Puig P, Ríos J, Vilella A, Trilla A (2011) A statistical model for hospital admissions caused by seasonal diseases. Statistics in Medicine 30: 3125-3136.
9. Dugas AF, Jalalpour M, Gel Y, Levin S, Torcaso F, et al. (2013) Influenza forecasting with Google flu trends. PLoS ONE 8: e56176.
10. Shaman J, Karspeck A (2012) Forecasting seasonal outbreaks of influenza. Proceedings of the National Academy of Sciences 109: 20425-20430.
11. Shaman J, Pitzer VE, Viboud C, Grenfell BT, Lipsitch M (2010) Absolute humidity and the seasonal onset of influenza in the continental united states. PLoS Biol 8: e1000316.
12. Barrett C, Bisset K, Leidig J, Marathe A, Marathe M (2011) Economic and social impact of influenza mitigation strategies by demographic class. Epidemics 3: 19-31.
13. Halloran ME, Ferguson N, Eubank S, Longini I, Cummings D, et al. (2008) Modeling targeted layered containment of an influenza pandemic in the United States. Proceedings of the National Academy of Sciences 105: 4639-4644.
14. Nsoesie EO, Beckman RJ, Shashaani S, Nagaraj KS, Marathe MV (2013) A simulation optimization approach to epidemic forecasting. PLoS ONE (In Press).
15. Ginsberg J, Mohebbi M, Patel R, Brammer L, Smolinski M, et al. (2008) Detecting influenza epidemics using search engine query data. Nature 457: 1012-1014.
16. 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.
17. Beckman R, Baggerly K, Mckay M (1996) Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice 30: 415-429.
18. Speckman P, Vaughn K, Pas E (1997a) Generating household activity-travel patterns (HATPs) for synthetic populations. Transportation Research Board 1997 Annual Meeting.
19. 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.
20. Barrett C, Beckman R, Khan M, Kumar VSA, Marathe M, et al. (2009) Generation and analysis of large synthetic social contact networks. In: Winter Simulation Conference. WSC ’09, pp. 1003-1014.
21. TRBC (1995-2003) 5th-9th Biennial National Academies Transportation Research Board Conferences on Application Of Transportation Planning Methods.
22. 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.
23. Bailey N (1975) The Mathematical Theory of Infectious Diseases and its Applications. London: Griffin.
24. Elveback L, Fox J, Ackerman E, Langworthy A, Boyd M, et al. (1976) American Journal of Epidemiology 103: 152-165.
25. Longini I, Nizam A, Xu S, Ungchusak K, Hanshaworakul W, et al. (2005) Containing pandemic influenza at the source. Science 309: 1083-1087.
26. Hethcote HW (2000) The mathematics of infectious diseases. SIAM Review 42: 599-653.
27. Eubank S, Guclu H, Kumar VSA, Marathe M, Srinivasan A, et al. (2004) Modelling disease outbreaks in realistic urban social networks. Nature 429: 180-184.
28. Newman M (2003) The structure and function of complex networks. SIAM Review 45: 167-256.
29. 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.
30. Nsoesie EO, Beckman RJ, Marathe MV (2012) Sensitivity analysis of an individual-based model for simulation of influenza epidemics. PLoS ONE 7: e45414.
31. Ajelli M, Goncalves B, Balcan D, Colizza V, Hu H, et al. (2010) Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models. BMC Infectious Diseases 10: 190.
32. Wallinga J, Teunis P (2004) Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology 160: 509-516.
33. Robbins H, Monro S (1951) A stochastic approximation method. Annals of Mathematical Statistics 22: 400-407.
34. Truscott J, Fraser C, Cauchemez S, Meeyai A, Hinsley W, et al. (2012) Essential epidemiological mechanisms underpinning the transmission dynamics of seasonal influenza. Journal of The Royal Society Interface 9: 304-312.
35. Butler D (2013) When Google got flu wrong. Nature 494: 155-156.
36. Carrat F, Vergu E, Ferguson NM, Lemaitre M, Cauchemez S, et al. (2008) Time Lines of Infection and Disease in Human Influenza: A Review of Volunteer Challenge Studies. American Journal of Epidemiology 167 (7): 775-785.