Designing effective mitigation strategies against influenza outbreak requires an accurate prediction of a disease’s future course of spreading. Real time information such as syndromic surveillance data and influenza-like-illness (ILI) reports by clinicians can be used to generate estimates of the current state of spreading of a disease. Syndromic surveillance data are immediately available, in contrast to ILI reports that require data collection and processing. On the other hand, they are less credible than ILI data because they are essentially behavioral responses from a community. In this paper, we present a method to combine immediately-available-but-less-reliable syndromic surveillance data with reliable-but-time-delayed ILI data. This problem is formulated as a non-linear stochastic filtering problem, and solved by a particle filtering method. Our experimental results from hypothetical pandemic scenarios show that state estimation is improved by utilizing both sets of data compared to when using only one set. However, the amount of improvement depends on the relative credibility and length of delay in ILI data. An analysis for a linear, Gaussian case is presented to support the results observed in the experiments.
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We also tested other values for \(\sigma _q\) and \(\sigma _\gamma\) and find the results were qualitatively similar. Result data for the additional tests will be provided upon request.
In the context of epidemic state estimation, this assumption may not be required since storing the entire history of particles is most likely feasible: (1) measurement sampling frequency is in the order of day or week, and thus the size of measurement data is not huge, and (2) epidemic state estimation does not require real-time computation.
Slight variations visible in the figures are due to non-systematic causes.
Bisset KR, Chen J, Feng X, Kumar VSA, Marathe MV (2009) EpiFast: A Fast Algorithm for Large Scale Realistic Epidemic Simulations on Distributed Memory Systems. Proceedings of the 23rd international conference on Supercomputing. 430–439.
Chao DL, Halloran ME, Obenchain VJ, Longini IM Jr (2010) FluTE, a publicly available stochastic influenza epidemic simulation model. PLoS Comput Biol 6(1): doi:10.1371/journal.pcbi.1000656
Chen L, Achrekar H, Liu B, Lazarus R (2010) Vision: Towards Real Time Epidemic Vigilance through Online Social Networks. ACM Workshop Mobile Cloud Comput Serv, San Francisco, USA
Chew C, Eysenbach G (2010) Pandemics in the age of twitter: content analysis of tweets during the 2009 H1N1 outbreak. PLoS ONE 5(11): doi:10.1371/journal.pone.0014118
Dailey L, Watkins RE, Plant AJ (2007) Timeliness of data sources used for influenza surveillance. J Am Med Inform Assoc 14(5):626–631. doi:10.1197/jamia.M2328
Ducet A, Johansen AM (2013) A tutorial on particle filtering and smoothing: Fifteen years later. http://www.cs.ubc.ca/~arnaud/doucet_johansen_tutorialPF. Accessed 27 December 2013
Dukic V, Lopes HF, Polson NG (2012) Tracking epidemics with google flu trends data and a state-space SEIR model. J Am Stat Assoc 107(500):1410–1426
Eubank S, Guclu H, Kumar VSA, Marathe MV, Srinivasan A, Toroczkai Z, Wang N (2004) Modelling disease outbreaks in realistic urban social networks. Nature 429:180–184. doi:10.1038/nature02541
Ferguson NM, Cummings DA, Cauchemez S, Fraser C, Riley S, Meeyai A, Iamsirithaworn S, Burke DS (2005) Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature 437:209–214
FluView: A weekly influenza surveillance report. Centers for Disease Control and Prevention. http://www.cdc.gov/flu/weekly/. Accessed 5 February 2013
Gensheimer KF, Fukuda K, Brammer L, Cox N, Patriarca PA, Strikas RA (1999) Preparing for pandemic influenza: the need for enhanced surveillance. Emerg Infect Dis 5:297–299
Ginsberg J, Mohebbi MH, Patel RS, Brammer L, Smolinski MS, Brilliant L (2009) Detecting influenza epidemics using search engine query data. Nature 457:1012–1014
Henning KJ (2004) Overview of syndromic surveillance: what is syndromic surveillance? Morb Mortal Wkly Rep 53(Supplement):5–11
Hethcote HW (2000) The mathematics of infectious diseases. SIAM Rev 42(4):599–653
Influenza weekly report. Korea Centers for Disease Control and Prevention. http://www.cdc.go.kr/CDC/info/CdcKrInfo0402.jsp?menuIds=HOME001-MNU0003-MNU0727-MNU0045. Accessed 27 October 2013
Influenza Surveillance. Korea Centers for Disease Control and Prevention. http://www.cdc.go.kr/CDC/contents/CdcKrContentView.jsp?cid=17936&menuIds=HOME001-MNU1132-MNU1138-MNU0741. Accessed 2 May 2014
Jegat C, Carrat F, Lajaunie C, Wackernagel H (2008) Early detection and assessment of epidemics by particle filtering. In: Soares A, Pereira M, Dimitrakopoulos R (eds) geoENV VI: geostatistics for environmental applications. Springer, Netherlands, pp 23–35
Kermack WO, McKendrick AG (1927) A contribution to the mathematical theory of epidemics. Proc R Soc Lond A 115(772):7000–7721. doi:10.1098/rspa.1927.0118
Lampos V, Bie T, Cristianini N (2010) Flu detector: tracking epidemics on twitter. In: Balcazar J, Bonchi F, Gionis A, Sebag M (eds) Machine learning and knowledge discovery in databases. Springer, Heidelberg, pp 599–602
Longini IM Jr, Nizam A, Xu S, Ungchusak K, Hanchaoworakul W, Cummings DA, Halloran ME (2005) Containing pandemic influenza at the source. Science 309:1083–1087
Ong JBS, Chen MI, Cook AR, Lee HC, Lee VJ (2010) Real-time epidemic monitoring and forecasting of H1N1-2009 using influenza-like illness from general practice and family doctor clinics in Singapore. PLoS ONE 5(4): doi:10.1371/journal.pone.0010036
Orton M, Marrs A (2005) Particle filters for tracking with out-of-sequence measurements. IEEE Trans Aerosp Electron Syst 41(2):693–702
Que J, Tsui F-C (2011) Rank-based spatial clustering: an algorithm for rapid outbreak detection. J Am Med Inform Assoc 18(3):218–224. doi:10.1136/amiajnl-2011-000137
Reis BY, Kohane IS, Mandl KD (2007) An epidemiological network model for disease outbreak detection. PLoS Med 4(6):e210. doi:10.1371/journal.pmed.0040210
Ristic B, Arulampalam S, Gordon N (2004) Beyond the kalman filter: particle filters for tracking applications. Artech House Publishers, Boston
Rahmandad H, Sterman J (2008) Heterogeneity and network structure in the dynamics of diffusion: comparing agent-based and differential equation models. Manag Sci 54(5):998–1014
Shaman J, Karspeck A (2012) Forecasting seasonal outbreaks of influenza. Proc Natl Acad Sci. doi:10.1073/pnas.1208772109
Singh BK, Savill NJ, Ferguson NM, Robertson C, Woolhouse ME (2010) Rapid detection of pandemic influenza in the presence of seasonal influenza. BMC Public Health 10(726): doi:10.1186/1471-2458-10-726
Skvortsov A, Ristic B (2012) Monitoring and prediction of an epidemic outbreak using syndromic observations. Math Biosci 240:12–19
Vidal Rodeiro CL, Lawson AB (2006) Online updating of space-time disease surveillance models via particle filters. Stat Methods Med Res 15:423–444
WHO checklist for influenza pandemic preparedness planning. Department of communicable disease surveillance and response global influenza programme. http://www.who.int/influenza/resources/documents/FluCheck6web. Accessed 27 December 2013
This research was supported by the Public Welfare & Safety Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning (No.2011-0029881) and by Basic Science Research Program through NRF funded by the Ministry of Education (NRF-2010-0025224).
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Lee, T., Shin, H. Combining syndromic surveillance and ILI data using particle filter for epidemic state estimation. Flex Serv Manuf J 28, 233–253 (2016). https://doi.org/10.1007/s10696-014-9204-0
- Syndromic surveillance
- Particle filter
- Data fusion