Background Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens. Methods As a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study. Results Hotspots – areas most likely to contain the actual source – were identified at early outbreak stages, based on the earliest 2–10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300–1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large. Conclusions Our model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission.
References
[1]
Van Jaarsveld JA (2004) The Operational Priority Substances model. Description and validation of OPS-Pro 4.1 [Internet]. Bilthoven, The Netherlands: National Institute for Public Health and the Environment. Report No.: 500045001/2004. Available: http://www.rivm.nl/bibliotheek/rapporten？/500045001.pdf. Accessed 27 March 2012.
[2]
Gloster J, Jones A, Redington A, Burgin L, S？rensen JH, et al. (2010) Airborne spread of foot-and-mouth disease – Model intercomparison. Vet J 183: 278–286.
[3]
Nguyen TMN, Ilef D, Jarraud S, Rouil L, Campese C, et al. (2006) A Community-Wide Outbreak of Legionnaires Disease Linked to Industrial Cooling Towers — How Far Can Contaminated Aerosols Spread? J Infect Dis 193: 102–111.
[4]
Wallensten A, Moore P, Webster H, Johnson C, Van der Burgt G, et al. (2010) Q fever outbreak in Cheltenham, United Kingdom, in 2007 and the use of dispersion modelling to investigate the possibility of airborne spread. Euro Surveill 15: 1–7.
[5]
Dijkstra F, Van der Hoek W, Wijers N, Schimmer B, Rietveld A, et al. (2012) The 2007–2010 Q fever epidemic in The Netherlands: characteristics of notified acute Q fever patients and the association with dairy goat farming. FEMS Immunol Med Microbiol 64 (1) 3–12.
[6]
Angelakis E, Raoult D (2010) Q Fever. Vet Microbiol 140: 297–309.
[7]
Jones RM, Nicas M, Hubbard AE, Reingold AL (2006) The Infectious Dose of Coxiella burnetii ( Q Fever ). Appl Biosaf 11: 32–41.
[8]
Roest HIJ, Tilburg JJHC, Van der Hoek W, Vellema P, Van Zijderveld FG, et al. (2011) The Q fever epidemic in The Netherlands: history, onset, response and reflection. Epidemiol Infect 139: 1–12.
[9]
Van den Brom R, Vellema P (2009) Q fever outbreaks in small ruminants and people in the Netherlands. Small Ruminant Res 86: 74–79.
[10]
Van den Brom R, Van Engelen E, Luttikholt S, Moll L, Van Maanen K, et al. (2012) Coxiella burnetii in bulk tank milk samples from dairy goat and dairy sheep farms in The Netherlands in 2008. Vet Rec 170: 310.
[11]
De Bruin A, Van der Plaats RQJ, De Heer L, Paauwe R, Schimmer B, et al. (2012) Detection of Coxiella burnetii DNA on small-ruminant farms during a Q fever outbreak in the Netherlands. Appl Environ Microb 78: 1652–1657.
[12]
Schimmer B, Luttikholt S, Hautvast JLA, Graat EAM, Vellema P, et al. (2011) Seroprevalence and risk factors of Q fever in goats on commercial dairy goat farms in the Netherlands, 2009–2010. BMC Vet Res 7: 81.
[13]
Hogerwerf L, Van den Brom R, Roest HIJ, Bouma A, Vellema P, et al. (2011) Reduction of Coxiella burnetii prevalence by vaccination of goats and sheep, The Netherlands. Emerg Infect Dis 17: 379–386.
[14]
Q-koorts E (2010) Van verwerping tot verheffing; Q-koortsbeleid in Nederland 2005–2010. The Hague, The Netherlands: Evaluatiecommissie Q-koorts. Available: http://www.rijksoverheid.nl/documenten-e？n-publicaties/rapporten/2010/11/22/van-v？erwerping-tot-verheffing.html. Accessed 15 May 2012.
[15]
Schimmer B, Ter Schegget R, Wegdam M, Züchner L, De Bruin A, et al. (2010) The use of a geographic information system to identify a dairy goat farm as the most likely source of an urban Q-fever outbreak. BMC Infect Dis 10.
[16]
Hackert VH, Van der Hoek W, Dukers-Muijrers N, De Bruin A, Al Dahouk S, et al. (2012) Q Fever: Single-point source outbreak with high attack rates and massive number of undetected infections across an entire region. Clin Infect Dis 55: 1591–9.
[17]
Berri M, Rousset E, Champion JL, Russo P, Rodolakis A (2007) Goats may experience reproductive failures and shed Coxiella burnetii at two successive parturitions after a Q fever infection. Res Vet Sci 83: 47–52.
[18]
Hoek W Van Der, Kassteele J Van De, Bom B, Bruin A De, Dijkstra F (2012) Smooth incidence maps give valuable insight into Q fever outbreaks in The Netherlands. Geospat Health 7 (1) 127–134.
[19]
Van der Hoek W, Meekelenkamp JCE, Dijkstra F, Notermans DW, Bom B, et al. (2011) Proximity to Goat Farms and Seroprevalence among Pregnant Women. Emerg Infect Dis 17: 2360–2363.
[20]
Maule MM, Magnani C, Dalmasso P, Mirabelli D, Merletti F, et al. (2007) Modeling mesothelioma risk associated with environmental asbestos exposure. Environ Health Perspect 115: 1066–1071.
[21]
Vose D (2008) Risk Analysis - A quantitative Guide. 3rd ed. West Sussex, UK: John Wiley & Sons, Ltd.
[22]
CRAN (2013) The Comprehensive R Archive Network. Available: http://cran.r-project.org/. Accessed 1 April 2013.
[23]
Ypma RJF, Bataille AMA, Stegeman A, Koch G, Wallinga J, et al. (2012) Unravelling transmission trees of infectious diseases by combining genetic and epidemiological data. Proc R Soc 279: 444–450.
[24]
Ssematimba A, Hagenaars TJ, De Jong MCM (2012) Modelling the Wind-Borne Spread of Highly Pathogenic Avian Influenza Virus between Farms. PloS ONE 7 (2) 1–9.
[25]
Boender GJ, Hagenaars TJ, Bouma A, Nodelijk G, Elbers ARW, et al. (2007) Risk maps for the spread of highly pathogenic avian influenza in poultry. PLoS Comp Biol 3 (4) 704–12.
[26]
Clo？n M, Kamphuis C, Schols M, Tiessen-Raaphorst A, Verbeek D (2011) Nederland in een dag; Tijdsbesteding in Nederland vergeleken met die in vijftien andere Europese landen [Internet]. The Hague, The Netherlands: Netherlands Institute for Social Research; 2011. Report No.: 2011–30. Available: http://www.scp.nl/dsresource?objectid=29？049&type=org. Accessed 16 July 2012.
[27]
Hawker JI, Ayres JG, Blair I, Evans MR, Smith DL, et al. (1998) A large outbreak of Q fever in the West Midlands: windborne spread into a metropolitan area? Commun Dis Public Health 1: 180–187.
[28]
Tissot-Dupont H, Amadei M, Nezri M, Raoult D (2004) Wind in November , Q fever in December. Emerg Infect Dis 10: 1264–1269.
[29]
Bamberg WM, Pape WJ, Beebe JL, Nevin-Woods C, Ray W, et al. (2007) Outbreak of Q fever associated with a horse-boarding ranch, Colorado, 2005. Vector Borne Zoonotic Dis 7: 394–402.
[30]
Elliott P, Wartenberg D (2004) Spatial epidemiology: current approaches and future challenges. Environ Health Perspect 112: 998–1005.
[31]
Porten K, Rissland J, Tigges A, Broll S, Hopp W, et al. (2006) A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany. BMC Infect Dis 6: 147.
