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Modelling tick bite risk by combining random forests and count data regression models. PLoS One 2019; 14:e0216511. [PMID: 31821325 PMCID: PMC6903726 DOI: 10.1371/journal.pone.0216511] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Accepted: 10/30/2019] [Indexed: 12/05/2022] Open
Abstract
The socio-economic and demographic changes that occurred over the past 50 years have dramatically expanded urban areas around the globe, thus bringing urban settlers in closer contact with nature. Ticks have trespassed the limits of forests and grasslands to start inhabiting green spaces within metropolitan areas. Hence, the transmission of pathogens causing tick-borne diseases is an important threat to public health. Using volunteered tick bite reports collected by two Dutch initiatives, here we present a method to model tick bite risk using human exposure and tick hazard predictors. Our method represents a step forward in risk modelling, since we combine a well-known ensemble learning method, Random Forest, with four count data models of the (zero-inflated) Poisson family. This combination allows us to better model the disproportions inherent in the volunteered tick bite reports. Unlike canonical machine learning models, our method can capture the overdispersion or zero-inflation inherent in data, thus yielding tick bite risk predictions that resemble the original signal captured by volunteers. Mapping model predictions enables a visual inspection of the spatial patterns of tick bite risk in the Netherlands. The Veluwe national park and the Utrechtse Heuvelrug forest, which are large forest-urban interfaces with several cities, are areas with high tick bite risk. This is expected, since these are popular places for recreation and tick activity is high in forests. However, our model can also predict high risk in less-intensively visited recreational areas, such as the patchy forests in the northeast of the country, the natural areas along the coastline, or some of the Frisian Islands. Our model could help public health specialists to design mitigation strategies for tick-borne diseases, and to target risky areas with awareness and prevention campaigns.
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Abstract
Diseases spread by ticks are complex and typically come under the One Health approach because the implications for human, animal and environmental health are so intricately interconnected. In Europe and North America, these diseases, particularly the emblematic case of Lyme disease, are constantly on the rise. They are associated with a very strong emotional element in Western societies, where citizens are preoccupied by this upsurge and call on governments and health services to act. There is no vaccine against Lyme disease. This is the backdrop against which scientists are looking for alternative solutions based on the identification of ecological factors that are liable to better control tick populations and the movements of pathogens within ecosystems. This article describes the main knowledge already acquired about the ecology of Lyme disease and then provides a list of a number of instruments that can be leveraged to limit the risks and improve control.
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Bouchard C, Aenishaenslin C, Rees EE, Koffi JK, Pelcat Y, Ripoche M, Milord F, Lindsay LR, Ogden NH, Leighton PA. Integrated Social-Behavioral and Ecological Risk Maps to Prioritize Local Public Health Responses to Lyme Disease. ENVIRONMENTAL HEALTH PERSPECTIVES 2018; 126:047008. [PMID: 29671475 PMCID: PMC6071748 DOI: 10.1289/ehp1943] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Revised: 03/15/2018] [Accepted: 03/19/2018] [Indexed: 05/31/2023]
Abstract
BACKGROUND The risk of contracting Lyme disease (LD) can vary spatially because of spatial heterogeneity in risk factors such as social-behavior and exposure to ecological risk factors. Integrating these risk factors to inform decision-making should therefore increase the effectiveness of mitigation interventions. OBJECTIVES The objective of this study was to develop an integrated social-behavioral and ecological risk-mapping approach to identify priority areas for LD interventions. METHODS The study was conducted in the Montérégie region of Southern Quebec, Canada, where LD is a newly endemic disease. Spatial variation in LD knowledge, risk perceptions, and behaviors in the population were measured using web survey data collected in 2012. These data were used as a proxy for the social-behavioral component of risk. Tick vector population densities were measured in the environment during field surveillance from 2007 to 2012 to provide an index of the ecological component of risk. Social-behavioral and ecological components of risk were combined with human population density to create integrated risk maps. Map predictions were validated by testing the association between high-risk areas and the current spatial distribution of human LD cases. RESULTS Social-behavioral and ecological components of LD risk had markedly different distributions within the study region, suggesting that both factors should be considered for locally adapted interventions. The occurrence of human LD cases in a municipality was positively associated with tick density (p<0.01) but was not significantly associated with social-behavioral risk. CONCLUSION This study is an applied demonstration of how integrated social-behavioral and ecological risk maps can be created to assist decision-making. Social survey data are a valuable but underutilized source of information for understanding regional variation in LD exposure, and integrating this information into risk maps provides a novel approach for prioritizing and adapting interventions to the local characteristics of target populations. https://doi.org/10.1289/EHP1943.
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Affiliation(s)
- Catherine Bouchard
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire (FMV), Université de Montréal, Saint-Hyacinthe, Québec, Canada
| | - Cécile Aenishaenslin
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire (FMV), Université de Montréal, Saint-Hyacinthe, Québec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Erin E Rees
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire (FMV), Université de Montréal, Saint-Hyacinthe, Québec, Canada
| | - Jules K Koffi
- Policy Integration and Zoonoses Division, Centre for Foodborne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada
| | - Yann Pelcat
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire (FMV), Université de Montréal, Saint-Hyacinthe, Québec, Canada
| | - Marion Ripoche
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire (FMV), Université de Montréal, Saint-Hyacinthe, Québec, Canada
| | - François Milord
- Direction de santé publique de la Montérégie, Centre intégré de santé et de services sociaux Montérégie-Centre, Québec, Canada
| | - L Robbin Lindsay
- Zoonotic Diseases and Special Pathogens, National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, Manitoba, Canada
| | - Nicholas H Ogden
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire (FMV), Université de Montréal, Saint-Hyacinthe, Québec, Canada
| | - Patrick A Leighton
- Groupe de recherche en épidémiologie des zoonoses et santé publique (GREZOSP), Faculté de médecine vétérinaire (FMV), Université de Montréal, Saint-Hyacinthe, Québec, Canada
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Khatchikian CE, Nadelman RB, Nowakowski J, Schwartz I, Wormser GP, Brisson D. The impact of strain-specific immunity on Lyme disease incidence is spatially heterogeneous. Diagn Microbiol Infect Dis 2017; 89:288-293. [PMID: 29021088 DOI: 10.1016/j.diagmicrobio.2017.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Revised: 08/16/2017] [Accepted: 08/20/2017] [Indexed: 10/19/2022]
Abstract
Lyme disease, caused by the bacterium Borrelia burgdorferi, is the most common tick-borne infection in the US. Recent studies have demonstrated that the incidence of human Lyme disease would have been even greater were it not for the presence of strain-specific immunity, which protects previously infected patients against subsequent infections by the same B. burgdorferi strain. Here, spatial heterogeneity is incorporated into epidemiological models to accurately estimate the impact of strain-specific immunity on human Lyme disease incidence. The estimated reduction in the number of Lyme disease cases is greater in epidemiologic models that explicitly include the spatial distribution of Lyme disease cases reported at the county level than those that utilize nationwide data. strain-specific immunity has the greatest epidemiologic impact in geographic areas with the highest Lyme disease incidence due to the greater proportion of people that have been previously infected and have developed strain-specific immunity.
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Affiliation(s)
- Camilo E Khatchikian
- Department of Biology, University of Pennsylvania, PA; Department of Biological Sciences, University of Texas at El Paso, TX.
| | - Robert B Nadelman
- Division of Infectious Diseases, Department of Medicine, New York Medical College, Valhalla, NY
| | - John Nowakowski
- Division of Infectious Diseases, Department of Medicine, New York Medical College, Valhalla, NY
| | - Ira Schwartz
- Department of Microbiology and Immunology, New York Medical College, Valhalla, NY
| | - Gary P Wormser
- Division of Infectious Diseases, Department of Medicine, New York Medical College, Valhalla, NY
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