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is a leading cause of food and waterborne illness. Monitoring and modelling at chicken broiler farms, combined with weather pattern surveillance, can aid nowcasting of human gastrointestinal (GI) illness outbreaks. Near real-time sharing of data and model results with health authorities can help increase potential outbreak responsiveness.


To leverage data on weather and on broiler farms to build a risk model for possible human outbreaks and to communicate risk assessments with health authorities.


We developed a spatio-temporal random effects model for weekly GI illness consultations in Norwegian municipalities with monitoring and weather data from week 30 2010 to 11 2022 to give 1-week nowcasts of GI illness outbreaks. The approach combined a municipality random effects baseline model for seasonally-adjusted GI illness with a second model for peak deviations from that baseline. Model results are communicated to national and local stakeholders through an interactive website: Sykdomspulsen One Health.


Lagged temperature and precipitation covariates, as well as 2-week-lagged positive sampling in broilers, were associated with higher levels of GI consultations. Significant inter-municipality variability in outbreak nowcasts were observed.


surveillance in broilers can be useful in GI illness outbreak nowcasting. Surveillance of along potential pathways from the environment to illness such as via water system monitoring may improve nowcasting. A One Health system that communicates near real-time surveillance data and nowcast changes in risk to health professionals facilitates the prevention of outbreaks and reduces impact on human health.


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  1. Faverjon C, Carmo LP, Berezowski J. Multivariate syndromic surveillance for cattle diseases: Epidemic simulation and algorithm performance evaluation. Prev Vet Med. 2019;172:104778.  https://doi.org/10.1016/j.prevetmed.2019.104778  PMID: 31586719 
  2. Fefferman N, Naumova E. Innovation in observation: a vision for early outbreak detection. Emerg Health Threats J. 2010;3(1):e6.  https://doi.org/10.3402/ehtj.v3i0.7103  PMID: 22460396 
  3. Berger M, Shiau R, Weintraub JM. Review of syndromic surveillance: implications for waterborne disease detection. J Epidemiol Community Health. 2006;60(6):543-50.  https://doi.org/10.1136/jech.2005.038539  PMID: 16698988 
  4. Taylor LH, Latham SM, Woolhouse ME. Risk factors for human disease emergence. Philos Trans R Soc Lond B Biol Sci. 2001;356(1411):983-9.  https://doi.org/10.1098/rstb.2001.0888  PMID: 11516376 
  5. Battersby T, Whyte P, Bolton DJ. The pattern of Campylobacter contamination on broiler farms; external and internal sources. J Appl Microbiol. 2016;120(4):1108-18.  https://doi.org/10.1111/jam.13066  PMID: 26788933 
  6. Jonsson ME, Heier BT, Norström M, Hofshagen M. Analysis of simultaneous space-time clusters of Campylobacter spp. in humans and in broiler flocks using a multiple dataset approach. Int J Health Geogr. 2010;9(1):48.  https://doi.org/10.1186/1476-072X-9-48  PMID: 20860801 
  7. Norwegian Institute of Public Health (NIPH). Meldingssystem for smittsomme sykdommer (MSIS). [Communicable Disease Reporting System (MSIS)]. Oslo: NIPH; 2021. Norwegian. Available from: https://www.fhi.no/hn/helseregistre-og-registre/msis
  8. Norwegian Institute of Public Health (NIPH). Utbrudd av campylobacteriose i Norge: Kronologisk oversikt over større utbrudd av campylobacteriose som har vært i Norge siden 1999. [Outbreak of campylobacteriosis in Norway. Chronological overview of major outbreaks of campylobacteriosis that have occurred in Norway since 1999]. Oslo: NIPH; 2019. Available from: https://www.fhi.no/sv/utbrudd/oversikt-over-storre-utbrudd/utbrudd-av-campylobacteriose-i-norg
  9. European Food Safety Authority (EFSA). Campylobacter. Parma: EFSA. [Accessed: 12 Oct 2022]. Available from: https://www.efsa.europa.eu/en/topics/topic/campylobacter
  10. Hutchison ML, Taylor MJ, Tchòrzewska MA, Ford G, Madden RH, Knowles TG. Modelling-based identification of factors influencing campylobacters in chicken broiler houses and on carcasses sampled after processing and chilling. J Appl Microbiol. 2017;122(5):1389-401.  https://doi.org/10.1111/jam.13434  PMID: 28258625 
  11. Sibanda N, McKenna A, Richmond A, Ricke SC, Callaway T, Stratakos AC, et al. A Review of the Effect of Management Practices on Campylobacter Prevalence in Poultry Farms. Front Microbiol. 2018;9:2002.  https://doi.org/10.3389/fmicb.2018.02002  PMID: 30197638 
  12. Sommer HM, Høg BB, Larsen LS, Sørensen AIV, Williams N, Merga JY, et al. Analysis of farm specific risk factors for Campylobacter colonization of broilers in six European countries. Microb Risk Anal. 2016;2-3:16-26.  https://doi.org/10.1016/j.mran.2016.06.002 
  13. Hansson I, Sandberg M, Habib I, Lowman R, Engvall EO. Knowledge gaps in control of Campylobacter for prevention of campylobacteriosis. Transbound Emerg Dis. 2018;65(Suppl 1):30-48.  https://doi.org/10.1111/tbed.12870  PMID: 29663680 
  14. Jore S, Viljugrein H, Brun E, Heier BT, Borck B, Ethelberg S, et al. Trends in Campylobacter incidence in broilers and humans in six European countries, 1997-2007. Prev Vet Med. 2010;93(1):33-41.  https://doi.org/10.1016/j.prevetmed.2009.09.015  PMID: 19837471 
  15. Kovaļenko K. The effect of air temperature on the occurrence of thermophilic Campylobacter Spp. in Latvian broiler chicken production on day of sampling. 2012;5. Conference abstract. Available from: https://www.cabdirect.org/cabdirect/abstract/20133132109
  16. Jonsson ME, Chriél M, Norström M, Hofshagen M. Effect of climate and farm environment on Campylobacter spp. colonisation in Norwegian broiler flocks. Prev Vet Med. 2012;107(1-2):95-104.  https://doi.org/10.1016/j.prevetmed.2012.05.002  PMID: 22673580 
  17. Prachantasena S, Charununtakorn P, Muangnoicharoen S, Hankla L, Techawal N, Chaveerach P, et al. Climatic factors and prevalence of Campylobacter in commercial broiler flocks in Thailand. Poult Sci. 2017;96(4):980-5.  https://doi.org/10.3382/ps/pew364  PMID: 28339543 
  18. Simpson RB, Zhou B, Alarcon Falconi TM, Naumova EN. An analecta of visualizations for foodborne illness trends and seasonality. Sci Data. 2020;7(1):346.  https://doi.org/10.1038/s41597-020-00677-x  PMID: 33051470 
  19. Castronovo DA, Chui KK, Naumova EN. Dynamic maps: a visual-analytic methodology for exploring spatio-temporal disease patterns. Environ Health. 2009;8(1):61.  https://doi.org/10.1186/1476-069X-8-61  PMID: 20042115 
  20. Norwegian Institute of Public Health (NIPH). Sykdomspulsen. [Disease pulse]. Oslo: NIPH;. [Accessed: 12 Oct 2022]. Available from: https://www.fhi.no/en/hn/statistics/NorSySS
  21. Norwegian Institute of Public Health (NIPH). Sykdomspulsen. [Disease pulse]. Oslo: NIPH. [Accessed: 12 Oct 2022]. Available from: https://docs.sykdomspulsen.no
  22. World Health Organization (WHO). International Classification of Primary Care, 2nd edition (ICPC-2). Geneva: WHO; 2003. Available from: https://www.who.int/standards/classifications/other-classifications/international-classification-of-primary-care
  23. Helsedata. Norwegian Control and Payment of Health Reimbursements Database (KUHR). Oslo: Norwegian Directorate of Health. [Accessed: 21 Sep 2022]. Available from: https://helsedata.no/en/forvaltere/norwegian-directorate-of-health/norwegian-control-and-payment-of-health-reimbursements-database-kuhr
  24. Pettersen K, Moldal T, Gjerset B, Bergsjø B. The surveillance programme for Campylobacter spp. in broiler flocks in Norway 2021. Surveillance program report. Oslo: Veterinærinstituttet. [Accessed: 21 Sep 2022]. Available from: https://www.vetinst.no/overvaking/campylobacter-fjorfe/_/attachment/download/c534aecf-905f-402d-a057-d65028a440e3:0b09efc61f0d7f1199f2166ff936346e1843ebff/2022_14_Campylobacter%20broiler%20in%20Norway%202021.pdf
  25. Lund M, Nordentoft S, Pedersen K, Madsen M. Detection of Campylobacter spp. in chicken fecal samples by real-time PCR. J Clin Microbiol. 2004;42(11):5125-32.  https://doi.org/10.1128/JCM.42.11.5125-5132.2004  PMID: 15528705 
  26. Norwegian Public Roads Administration. Værstasjoner og meteorologiske data. [Weather stations and meteorological data.] Oslo: Norwegian Public Roads Administration. [Accessed: 15 Mar 2022]. Norwegian. Available from: https://www.vegvesen.no/fag/teknologi/vegteknologi/tilstandsregistrering-pa-veg/varstasjoner-og-meteorologiske-data
  27. Varsom SeNorge. Nedbor og Temperaturkart. [Precipitation and temperature maps]. Oslo: senorge.no. [Accessed: 15 Mar 2022]. Norwegian. Available from: https://www.senorge.no/PrecTempMap
  28. Held L, Paul M. Modeling seasonality in space-time infectious disease surveillance data. Biom J. 2012;54(6):824-43.  https://doi.org/10.1002/bimj.201200037  PMID: 23034894 
  29. Meyer S, Held L. Power-law models for infectious disease spread. Ann Appl Stat. 2014;8(3):1612-39.  https://doi.org/10.1214/14-AOAS743 
  30. Paul M, Held L. Predictive assessment of a non-linear random effects model for multivariate time series of infectious disease counts. Stat Med. 2011;30(10):1118-36.  https://doi.org/10.1002/sim.4177  PMID: 21484849 
  31. Paul M, Held L, Toschke AM. Multivariate modelling of infectious disease surveillance data. Stat Med. 2008;27(29):6250-67.  https://doi.org/10.1002/sim.3440  PMID: 18800337 
  32. Meyer S, Held L, Höhle M. Spatio-temporal analysis of epidemic phenomena using the R package surveillance. J Stat Softw. 2017;77(11).  https://doi.org/10.18637/jss.v077.i11 
  33. R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2021. Available from: https://www.R-project.org
  34. Held L, Höhle M, Hofmann M. A statistical framework for the analysis of multivariate infectious disease surveillance counts. Stat Model. 2005;5(3):187-99.  https://doi.org/10.1191/1471082X05st098oa 
  35. Czado C, Gneiting T, Held L. Predictive model assessment for count data. Biometrics. 2009;65(4):1254-61.  https://doi.org/10.1111/j.1541-0420.2009.01191.x  PMID: 19432783 
  36. Oberheim J. Weather conditions and campylobacteriosis in Germany. Dissertation. Rheinische Friedrich-Wilhelms-Universität Bonn. 9 Dec 2020. Available from: https://hdl.handle.net/20.500.11811/8852
  37. Akaike H. Information theory and an extension of the maximum likelihood principle. In: Selected papers of Hirotugu Akaike. Springer; 1998. p. 199-213. Available from:  https://doi.org/10.1007/978-1-4612-1694-0_15  https://doi.org/10.1007/978-1-4612-1694-0_15 
  38. Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, et al. shiny: Web Application Framework for R. R package version 2022. Available from: https://shiny.rstudio.com/tutorial
  39. Long JS, Freese J. Scalar measures of fit for regression models. Stata Tech Bull. 2001;10(56):34-40. Available from: https://EconPapers.repec.org/RePEc:tsj:stbull:y:2001:v:10:i:56:sg145
  40. EFSA Panel on Biological Hazards (BIOHAZ). Scientific opinion on quantification of the risk posed by broiler meat to human campylobacteriosis in the EU. EFSA J. 2010;8(1):1437.  https://doi.org/10.2903/j.efsa.2010.1437 
  41. MacDonald E, White R, Mexia R, Bruun T, Kapperud G, Lange H, et al. Risk factors for sporadic domestically acquired Campylobacter infections in Norway 2010-2011: A national prospective case-control study. PLoS One. 2015;10(10):e0139636.  https://doi.org/10.1371/journal.pone.0139636  PMID: 26431341 
  42. Lyngstad TMVL, Krosness MM, Valcarcel SB, Lange H, Nygård K, Jore S, Kapperud G, MacDonald E, Brandal LT, Feruglio SL, Grøneng GM, Blystad H. Årsrapport 2018. Overvåkning av infeksjonssykdommer som smitter fra mat, vann og dyr, inkludert vektorbårne sykdommer. Årsrapp Mat-Og Vannbårne Infeksjoner. [Annual Report 2018. Surveillance of diseases transmitted from food, water and animals, including vector-borne diseases]. Oslo: NIPH; 2019. Norwegian. Available from: https://www.fhi.no/globalassets/dokumenterfiler/rapporter/2019/arsrapport-2018-overvakning-av-infeksjonssykdommer-som-smitter-fra-mat-vann-og-dyr-inkludert-vektorbarne-sykdommer.pdf
  43. Ranta J, Matjushin D, Virtanen T, Kuusi M, Viljugrein H, Hofshagen M, et al. Bayesian temporal source attribution of foodborne zoonoses: Campylobacter in Finland and Norway. Risk Anal. 2011;31(7):1156-71.  https://doi.org/10.1111/j.1539-6924.2010.01558.x  PMID: 21231942 
  44. Williams MS, Golden NJ, Ebel ED, Crarey ET, Tate HP. Temporal patterns of Campylobacter contamination on chicken and their relationship to campylobacteriosis cases in the United States. Int J Food Microbiol. 2015;208:114-21.  https://doi.org/10.1016/j.ijfoodmicro.2015.05.018  PMID: 26065728 
  45. Williams MS, Ebel ED, Nyirabahizi E. Comparative history of Campylobacter contamination on chicken meat and campylobacteriosis cases in the United States: 1994-2018. Int J Food Microbiol. 2021;342:109075.  https://doi.org/10.1016/j.ijfoodmicro.2021.109075  PMID: 33550153 

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