Incorporating social contact data in spatio-temporal models for infectious disease spread

Biostatistics

Volumn 18, Issue 2, 2017, Pages 338-351

  Meyer, Sebastian ^a   Held, Leonhard ^b  

^a UNIVERSITY OF ERLANGEN NUREMBERG   (Germany)

^b UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

Age structured contact matrix; Areal count time series; Endemic epidemic modeling; Infectious disease epidemiology; Norovirus gastroenteritis; Norwalk virus; Spatio temporal surveillance data

Indexed keywords

COMMUNICABLE DISEASE; EPIDEMIC; EPIDEMIOLOGICAL MONITORING; GASTROENTERITIS; GERMANY; HUMAN; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TRANSMISSION;

BERLIN; COMMUNICABLE DISEASES; EPIDEMICS; EPIDEMIOLOGICAL MONITORING; GASTROENTERITIS; HUMANS; MODELS, STATISTICAL;

EID: 85018433434     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxw051     Document Type: Article

References (40)

1. BAGUELIN, M., FLASCHE, S., CAMACHO, A., DEMIRIS, N., MILLER, E. AND EDMUNDS, W. J. (2013). Assessing optimal target populations for influenza vaccination programmes: an evidence synthesis and modelling study. PLoS Medicine 10, e1001527.
2. BERNARD, H., FABER, M., WILKING, H., HALLER, S., HÖHLE, M., SCHIELKE, A., DUCOMBLE, T., SIFFCZYK, C., MERBECKS, S. S., FRICKE, G., HAMOUDA, O., STARK, K., WERBER, D. and others. (2014a). Large multistate outbreak of norovirus gastroenteritis associated with frozen strawberries, Germany, 2012. Eurosurveillance 19, pii=20719.
3. BERNARD, H., HÖHNE, M., NIENDORF, S., ALTMANN, D. AND STARK, K. (2014b). Epidemiology of norovirus gastroenteritis in Germany 2001-2009: eight seasons of routine surveillance. Epidemiology & Infection 142, 63-74.
4. BERNARD, H., WERBER, D. AND HÖHLE, M. (2014c). Estimating the under-reporting of norovirus illness in Germany utilizing enhanced awareness of diarrhoea during a large outbreak of Shiga toxin-producing E. coli O104:H4 in 2011-a time series analysis. BMC Infectious Diseases 14, 116.
5. BIRRELL, P. J., KETSETZIS, G., GAY, N. J., COOPER, B. S., PRESANIS, A. M., HARRIS, R. J., CHARLETT, A., ZHANG, X. S., WHITE, P. J., PEBODY, R. G. and others. (2011). Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in London. Proceedings of the National Academy of Sciences of the United States of America 108, 18238-18243.
6. BROCKMANN, D., HUFNAGEL, L. AND GEISEL, T. (2006). The scaling laws of human travel. Nature 439, 462-465.
7. FARRINGTON, C. P., KANAAN, M. N. AND GAY, N. J. (2003). Branching process models for surveillance of infectious diseases controlled by mass vaccination. Biostatistics 4, 279-295.
8. GEILHUFE, M., HELD, L., SKRØVSETH, S.O., SIMONSEN, G. S.AND GODTLIEBSEN, F.(2014). Power law approximations of movement network data for modeling infectious disease spread. Biometrical Journal 56, 363-382.
9. GIBBONS, C. L., MANGEN, M.-J., PLASS, D., HAVELAAR, A. H., BROOKE, R. J., KRAMARZ, P., PETERSON, K. L., STUURMAN, A. L., CASSINI, A., FÈVRE, E. M. and others. (2014). Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods. BMC Public Health 14, 1-17.
10. GOEYVAERTS, N., HENS, N., OGUNJIMI, B., AERTS, M., SHKEDY, Z., VA N DAMME, P. AND BEUTELS, P. (2010). Estimating infectious disease parameters from data on social contacts and serological status. Journal of the Royal Statistical Society, Series C 59, 255-277.
11. GOEYVAERTS, N., WILLEM, L., VAN KERCKHOVE, K., VANDENDIJCK, Y., HANQUET, G., BEUTELS, P. AND HENS, N. (2015). Estimating dynamic transmission model parameters for seasonal influenza by fitting to age and season-specific influenza-like illness incidence. Epidemics 13, 1-9.
12. GÖTZ, H., EKDAHL, K., LINDBÄCK, J., DE JONG, B., HEDLUND, K. O. AND GIESECKE, J. (2001). Clinical spectrum and transmission characteristics of infection with Norwalk-like virus: Findings from a large community outbreak in Sweden. Clinical Infectious Diseases 33, 622-628.
13. HELD, L. AND PAUL, M. (2012). Modeling seasonality in space-time infectious disease surveillance data. Biometrical Journal 54, 824-843.
14. HELD, L. AND SABANÉS BOVÉ, D. (2014). Applied Statistical Inference: Likelihood and Bayes. Berlin: Springer.
15. HELD, L., HÖHLE, M. AND HOFMANN, M. (2005). A statistical framework for the analysis of multivariate infectious disease surveillance counts. Statistical Modelling 5, 187-199.
16. HENS, N., AYELE, G., GOEYVAERTS, N., AERTS, M., MOSSONG, J., EDMUNDS, J. AND BEUTELS, P. (2009). Estimating the impact of school closure on social mixing behaviour and the transmission of close contact infections in eight European countries. BMC Infectious Diseases 9, 187.
17. HERZOG, S.A., PAUL, M.AND HELD, L. (2011). Heterogeneity in vaccination coverage explains the size and occurrence of measles epidemics in German surveillance data. Epidemiology & Infection 139, 505-515.
18. HÖHLE, M. (2016). Infectious Disease Modelling. In: Lawson, A. B., Banerjee, S., Haining, R. P. and Ugarte, M. D. (editors), Handbook of Spatial Epidemiology, Chapman & Hall/CRC Handbooks of Modern Statistical Methods, Chapter 26. Boca Raton: Chapman and Hall/CRC, pp. 477-500.
19. KUCHARSKI, A. J., CONLAN, A. J. K. AND EAMES, K. T. D. (2015). School's out: seasonal variation in the movement patterns of school children. PLoS One 10, 1-10.
20. KÜCHENHOFF, H., MWALILI, S. M. AND LESAFFRE, E. (2006). A general method for dealing with misclassification in regression: the misclassification SIMEX. Biometrics 62, 85-96.
21. MARSHALL, J. A. AND BRUGGINK, L. D. (2011). The dynamics of norovirus outbreak epidemics: recent insights. International Journal of Environmental Research and Public Health 8, 1141-1149.
22. MEYER, S. AND HELD, L. (2014). Power-law models for infectious disease spread. Annals of Applied Statistics 8, 1612-1639.
23. MEYER, S., HELD, L. AND HÖHLE, M. (2016). Spatio-temporal analysis of epidemic phenomena using the R package surveillance. Journal of Statistical Software (in press). Preprint at arXiv:1411.0416.
24. MOSSONG, J., HENS, N., JIT, M., BEUTELS, P., AURANEN, K., MIKOLAJCZYK, R., MASSARI, M., SALMASO, S., TOMBA, G. S., WALLINGA, J., HEIJNE, J., SADKOWSKA-TODYS, M., ROSINSKA, M. and others. (2008). Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Medicine 5, e74.
25. O'DEA, E. B., PEPIN, K. M., LOPMAN, B. A. AND WILKE, C. O. (2014). Fitting outbreak models to data from many small norovirus outbreaks. Epidemics 6, 18-29.
26. PAUL, M. AND HELD, L. (2011). Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts. Statistics in Medicine 30, 1118-1136.
27. PAUL, M., HELD, L.AND TOSCHKE, A. (2008). Multivariate modelling of infectious disease surveillance data. Statistics in Medicine 27, 6250-6267.
28. PRINGLE, K., LOPMAN, B., VEGA, E., VINJE, J., PARASHAR, U. D. AND HALL, A. J. (2015). Noroviruses: Epidemiology, immunity and prospects for prevention. Future Microbiology 10, 53-67.
29. R CORE TEAM. (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
30. READ, J. M., EDMUNDS, W. J., RILEY, S., LESSLER, J. AND CUMMINGS, D. A. T. (2012). Close encounters of the infectious kind: Methods to measure social mixing behaviour. Epidemiology & Infection 140, 2117-2130.
31. READ, J. M., LESSLER, J., RILEY, S., WANG, S., TAN, L. J., KWOK, K. O., GUAN, Y., JIANG, C. Q. AND CUMMINGS, D. A. T. (2014). Social mixing patterns in rural and urban areas of southern China. Proceedings of the Royal Society of London, Series B 281(1785), 20140268.
32. ROHANI, P., ZHONG, X. AND KING, A. A. (2010). Contact network structure explains the changing epidemiology of pertussis. Science 330, 982-985.
33. SCHRÖDLE, B., HELD, L. AND RUE, H. (2012). Assessing the impact of a movement network on the spatiotemporal spread of infectious diseases. Biometrics 68, 736-744.
34. SMIESZEK, T., BARCLAY, V., SEENI, I., RAINEY, J., GAO, H., UZICANIN, A. AND SALATHÉ, M. (2014). How should social mixing be measured: comparing web-based survey and sensor-based methods. BMC Infectious Diseases 14, 136.
35. SUKHRIE, F. H. A., TEUNIS, P., VENNEMA, H., COPRA, C., THIJS BEERSMA, M. F. C., BOGERMAN, J. AND KOOPMANS, M. (2012). Nosocomial transmission of norovirus is mainly caused by symptomatic cases. Clinical Infectious Diseases 54, 931-937.
36. VAN ASTEN, L., VAN DEN WIJNGAARD, C., VAN PELT, W., VAN DE KASSTEELE, J., MEIJER, A., VAN DER HOEK, W., KRETZSCHMAR, M. AND KOOPMANS, M. (2012). Mortality attributable to 9 common infections: Significant effect of influenza A, respiratory syncytial virus, influenza B, norovirus, and parainfluenza in elderly persons. Journal of Infectious Diseases 206, 628-639.
37. VAN KERCKHOVE, K., HENS, N., EDMUNDS, W. J.AND EAMES, K.T.D.(2013). The impactof illnessonsocial networks: implications for transmission and control of influenza. American Journal of Epidemiology 178, 1655-1662.
38. WALLINGA, J., TEUNIS, P. AND KRETZSCHMAR, M. (2006). Using data on social contacts to estimate age-specific transmission parameters for respiratory-spread infectious agents.American Journal of Epidemiology 164, 936-944.
39. WORBY, C. J., CHAVES, S. S., WALLINGA, J., LIPSITCH, M., FINELLI, L. AND GOLDSTEIN, E. (2015). On the relative role of different age groups in influenza epidemics. Epidemics 13, 10-16.
40. XIA, Y., BJØRNSTAD, O. N. AND GRENFELL, B. T. (2004). Measles metapopulation dynamics: a gravity model for epidemiological coupling and dynamics. The American Naturalist 164, 267-281.