Skip to main content

Comparison of three time-series models for predicting campylobacteriosis risk

  • J. WEISENT (a1), W. SEAVER (a2), A. ODOI (a1) and B. ROHRBACH (a1)

Three time-series models (regression, decomposition, and Box–Jenkins autoregressive integrated moving averages) were applied to national surveillance data for campylobacteriosis with the goal of disease forecasting in three US states. Datasets spanned 1998–2007 for Minnesota and Oregon, and 1999–2007 for Georgia. Year 2008 was used to validate model results. Mean absolute percent error, mean square error and coefficient of determination (R2) were the main evaluation fit statistics. Results showed that decomposition best captured the temporal patterns in disease risk. Training dataset R2 values were 72·2%, 76·3% and 89·9% and validation year R2 values were 66·2%, 52·6% and 79·9% respectively for Georgia, Oregon and Minnesota. All three techniques could be utilized to predict monthly risk of infection for Campylobacter sp. However, the decomposition model provided the fastest, most accurate, user-friendly method. Use of this model can assist public health personnel in predicting epidemics and developing disease intervention strategies.

Corresponding author
*Author for correspondence: Dr J. Weisent, Department of Comparative Medicine, The University of Tennessee, College of Veterinary Medicine, 205A River Drive, Knoxville, TN37996, USA. (Email:
Hide All
1.Snelling, W, et al. Campylobacter jejuni. Letters in Applied Microbiology 2005; 41: 297302.
2.Humphrey, T, O'Brien, S, Madsen, M. Campylobacters as zoonotic pathogens: a food production perspective. International Journal of Food Microbiology 2007; 117: 237257.
3.Allos, BM, Taylor, DN. Campylobacter infections. In: Evans, AS, Brachman, PS, eds. Bacterial Infections of Humans, Epidemiology and Control, 3rd edn.New York: Plenum Medical Book Company, 1998, pp. 169190.
4.Altekruse, SF, Swerdlow, DL. Campylobacter jejuni and related organisms. In: Cliver, DO, Riemann, HP, eds. Foodborne Diseases, 2nd edn.Boston: Academic Press, 2002, pp. 103112.
5.Tauxe, RV. Incidence, trends and sources of campylobacteriosis in developed countries: an overview. In: WHO consultation on the increasing incidence of human campylobacteriosis. Report and Proceedings of a WHO Consultation of Experts, Copenhagen, Denmark, 21–25 November 2000. World Health Organization, 2001, pp. 4243.
6.Frost, J. Current epidemiological issues in human campylobacteriosis. Symposium Series (Society for Applied Microbiology) 2001; 30: 85S95S.
7.Vandamme, PAR. Methods for identification of Campylobacter. In: WHO consultation on the increasing incidence of human campylobacteriosis. Report and Proceedings of a WHO Consultation of Experts, Copenhagen, Denmark, 21–25 November 2000. World Health Organization, 2001, pp. 9499.
8.Newell, DG, Wassenaar, TM. Strengths and weaknesses of bacterial typing tools for the study of campylobacteriosis epidemiology. In: WHO consultation on the increasing incidence of human campylobacteriosis. Report and Proceedings of a WHO Consultation of Experts, Copenhagen, Denmark, 21–25 November 2000. World Health Organization, 2001, pp. 101104.
9.Jepsen, MR, Simonsen, J, Ethelberg, S. Spatio-temporal cluster analysis of the incidence of Campylobacter cases and patients with general diarrhea in a Danish county, 1995–2004. International Journal of Health Geographics 2009; 8: 11.
10.Hartnack, S, et al. Campylobacter monitoring in German broiler flocks: an explorative time series analysis. Zoonoses Public Health 2009; 56: 117128.
11.Hearnden, M, et al. The regionality of campylobacteriosis seasonality in New Zealand. International Journal of Environmental Health Research 2003; 13: 337348.
12.Kovats, R, et al. Climate variability and campylobacter infection: an international study. International Journal of Biometeorology 2005; 49: 207214.
13.Miller, G, et al. Human campylobacteriosis in Scotland: seasonality, regional trends and bursts of infection. Epidemiology and Infection 2004; 132: 585593.
14.Bi, P, et al. Weather and notified Campylobacter infections in temperate and sub-tropical regions of Australia: an ecological study. Journal of Infection 2008; 57: 317323.
15.Zhang, Y, Bi, P, Hiller, J. Climate variations and salmonellosis transmission in Adelaide, South Australia: a comparison between regression models. International Journal of Biometeorology 2008; 52: 179187.
16.Nylen, G, et al. The seasonal distribution of campylobacter infection in nine European countries and New Zealand. Epidemiology and Infection 2002; 128: 383390.
17.Tam, CC, et al. Temperature dependence of reported Campylobacter infection in England, 1989–1999. Epidemiology and Infection 2006; 134: 119125.
18.Nobre, FF, et al. Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology. Statistics in Medicine 2001; 20: 30513069.
19.De Greeff, SC, et al. Seasonal patterns in time series of pertussis. Epidemiology and Infection. Published online: 31 March 2009. doi: 10.1017/S0950268809002489.
20.Altizer, S, et al. Seasonality and the dynamics of infectious diseases. Ecology Letters 2006; 9: 467484.
21.Tokars, JI, et al. Enhancing time-series detection algorithms for automated biosurveillance. Emerging Infectious Diseases 2009; 15: 533539.
22.Burkom, HS, Murphy, SP, Shmueli, G. Automated time series forecasting for biosurveillance. Statistics in Medicine 2007; 26: 42024218.
23.Paul, M, Held, L, Toschke, AM. Multivariate modelling of infectious disease surveillance data. Statistics in Medicine 2008; 27: 62506267.
24.Medina, DC, et al. Forecasting non-stationary diarrhea, acute respiratory infection, and malaria time-series in Niono, Mali. PLoS ONE 2007; 2: e1181.
25.Fleury, M, et al. A time series analysis of the relationship of ambient temperature and common bacterial enteric infections in two Canadian provinces. International Journal of Biometeorology 2006; 50: 385391.
26.Rhodes, CJ, Hollingsworth, TD. Variational data assimilation with epidemic models. Journal of Theoretical Biology. Published online: 10 March 2009. doi: 10.1016/j.jtbi.2009.02.017.
27.Williamson, GD, Weatherby Hudson, G. A monitoring system for detecting aberrations in public health surveillance reports. Statistics in Medicine 1999; 18: 32833298.
28.Rolfhamre, P, Ekdahl, K. An evaluation and comparison of three commonly used statistical models for automatic detection of outbreaks in epidemiological data of communicable diseases. Epidemiology and Infection 2006; 134: 863871.
29.Naumova, EN, et al. Use of passive surveillance data to study temporal and spatial variation in the incidence of giardiasis and cryptosporidiosis. Public Health Reports 2000; 115: 436447.
30.Naumova, EN, MacNeil, IB. Time-distributed effect of exposure and infectious outbreaks. Econometrics 2009; 20: 235348.
31.Cardinal, M, Roy, R, Lambert, J. On the application of integer-valued time series models for the analysis of disease incidence. Statistics in Medicine 1999; 18: 20252039.
32.Myers, MF, et al. Forecasting disease risk for increased epidemic preparedness in public health. Advances in Parasitology 2000; 47: 309330.
33.Benschop, J, et al. Temporal and longitudinal analysis of Danish Swine Salmonellosis Control Programme data: implications for surveillance. Epidemiology and Infection 2008; 136: 15111520.
34.CDC. Preliminary FoodNet Data on the incidence of infection with pathogens transmitted commonly through food – 10 states, 2008. Morbidity and Mortality Weekly Reports 2009; 58: 333337.
35.Oregon Climate. (http://wwwcity-datacom/states/Oregon-Climatehtml). Accessed 8 September 2009.
36.Net State Geography. (http://wwwnetstatecom/states/geography/ga_geographyhtm). Accessed 15 October 2009.
37.Minnesota Department of Natural Resources (DNR). (http://wwwdnrstatemnus/climate/indexhtml). Accessed 9 September 2009.
38.DeLurgio, SA. Forecasting Principles and Applications, 1st edn.St Louis, MO: Irwin McGraw-Hill, 1997, pp. 802.
39.SAS Institute. Statistical analysis systems (SAS), version 9.2. Cary, North Carolina, USA: SAS Institute Inc., 2008.
41.Hintze, J. NCSS, PASS and GESS. In: NCSS. Kaysville, Utah, USA, 2006.
42.Alwan, LC. Time series modelling for statistical process control. Journal of Business and Economic Statistics 1988; 6: 8795.
43.Box, EP, Jenkins, GM. Time Series Analysis: Forecasting and Control, 3rd edn.Upper Saddle River, New Jersey: Prentice Hall, 1994, pp. 592.
44.Reichert, TA, et al. Influenza and the winter increase in mortality in the United States, 1959–1999. American Journal of Epidemiology 2004; 160: 492502.
45.Patrick, M, et al. Effects of climate on incidence of Campylobacter spp. in humans and prevalence in broiler flocks in Denmark. Applied and Environmental Microbiology 2004; 70: 74747480.
46.Bi, P, et al. Climate variability and Ross River virus infections in Riverland, South Australia, 1992-2004. Epidemiology and Infection. Published online: 20 March 2009. doi: 10.1017/S0950268809002441.
47.Singer, JD, Willett, JB. Applied Longitudinal Data Analysis. New York: Oxford University Press Inc., 2003, pp. 644.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Epidemiology & Infection
  • ISSN: 0950-2688
  • EISSN: 1469-4409
  • URL: /core/journals/epidemiology-and-infection
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed