Ecological Niche Modeling of Potential West Nile Virus Vector Mosquito Species in Iowa

Larson, Scott R.; DeGroote, John; Bartholomay, Lyric C.; Sugumaran, Ramanathan

doi:10.1673/031.010.11001

Cited by 56 publications

(45 citation statements)

References 49 publications

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“…This model consisted of a set of hierarchical rules based on, in order, the proximity of the fields to surface water, temperature, the proximity of fields to impervious cover, available water storage (AWS), and the proximity of fields to pasture (25). Studies in other disease systems (e.g., Lyme disease and West Nile virus) have not only developed (28)(29)(30)(31)(32)(33)(34) but have also validated (35)(36)(37)(38)(39)(40) geospatial predictive risk models. These validation studies (35)(36)(37)(38)(39)(40) demonstrate the utility of geospatial risk models, like the model developed by Strawn et al (25), to accurately and prospectively predict pathogen prevalence.…”

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confidence: 99%

“…Studies in other disease systems (e.g., Lyme disease and West Nile virus) have not only developed (28)(29)(30)(31)(32)(33)(34) but have also validated (35)(36)(37)(38)(39)(40) geospatial predictive risk models. These validation studies (35)(36)(37)(38)(39)(40) demonstrate the utility of geospatial risk models, like the model developed by Strawn et al (25), to accurately and prospectively predict pathogen prevalence. Additionally, these studies (37,39,40) used the output of their models to prioritize and identify risk management strategies, suggesting that geospatial models can also be integrated with on-farm food safety plans to develop targeted approaches to disease prevention.…”

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confidence: 99%

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Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Weller

Shiwakoti

Bergholz

et al. 2016

Appl Environ Microbiol

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dTechnological advancements, particularly in the field of geographic information systems (GIS), have made it possible to predict the likelihood of foodborne pathogen contamination in produce production environments using geospatial models. Yet, few studies have examined the validity and robustness of such models. This study was performed to test and refine the rules associated with a previously developed geospatial model that predicts the prevalence of Listeria monocytogenes in produce farms in New York State (NYS). Produce fields for each of four enrolled produce farms were categorized into areas of high or low predicted L. monocytogenes prevalence using rules based on a field's available water storage (AWS) and its proximity to water, impervious cover, and pastures. Drag swabs (n ‫؍‬ 1,056) were collected from plots assigned to each risk category. Logistic regression, which tested the ability of each rule to accurately predict the prevalence of L. monocytogenes, validated the rules based on water and pasture. Samples collected near water (odds ratio [OR], 3.0) and pasture (OR, 2.9) showed a significantly increased likelihood of L. monocytogenes isolation compared to that for samples collected far from water and pasture. Generalized linear mixed models identified additional land cover factors associated with an increased likelihood of L. monocytogenes isolation, such as proximity to wetlands. These findings validated a subset of previously developed rules that predict L. monocytogenes prevalence in produce production environments. This suggests that GIS and geospatial models can be used to accurately predict L. monocytogenes prevalence on farms and can be used prospectively to minimize the risk of preharvest contamination of produce.F resh produce presents a unique food safety challenge due to the absence of a kill step between harvest and consumption. An increase in recalls and reported outbreaks linked to fresh produce over the past decade (1-3) have been associated with consumer avoidance of products linked to outbreaks (4, 5). This trend can negatively affect growers and the produce industry (4-6). For example, following a 2011 listeriosis outbreak in the United States associated with fresh cantaloupe (7), cantaloupe consumption dropped 53% nationwide (6). The prevention of produce contamination in production environments is therefore a concern for growers, the produce industry, and public health professionals. To develop effective prevention strategies, it is important to understand the ecological processes and environmental factors that affect foodborne pathogen prevalence in produce production environments. Technological advancements, such as geographic information systems (GIS), have the potential to drastically improve our ability to examine these processes and to develop novel tools for ensuring the safety of fresh produce.Numerous studies (8-21) have examined the ecology of foodborne pathogens in agricultural environments, and several (22-27) have used GIS and geospatial analysis. For example, Chapin...

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Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Weller

Shiwakoti

Bergholz

et al. 2016

Appl Environ Microbiol

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“…Spatial risk maps have been successfully developed for many diseases including Japanese encephalitis (24), leishmaniasis (25) West Nile virus (26), RVF (27, 28), and mosquito vectors in general (29–31). This study contributes to the growing success of using spatial distribution maps in the prediction of disease risk that may assist in prioritization of vector and disease control.…”

Section: Discussionmentioning

confidence: 99%

Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya

Ochieng

Nanyingi

Kipruto

et al. 2016

Infection Ecology & Epidemiology

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BackgroundRift Valley fever (RVF) is a vector-borne zoonotic disease that has an impact on human health and animal productivity. Here, we explore the use of vector presence modelling to predict the distribution of RVF vector species under climate change scenario to demonstrate the potential for geographic spread of Rift Valley fever virus (RVFV).ObjectivesTo evaluate the effect of climate change on RVF vector distribution in Baringo County, Kenya, with an aim of developing a risk map for spatial prediction of RVF outbreaks.MethodologyThe study used data on vector presence and ecological niche modelling (MaxEnt) algorithm to predict the effect of climatic change on habitat suitability and the spatial distribution of RVF vectors in Baringo County. Data on species occurrence were obtained from longitudinal sampling of adult mosquitoes and larvae in the study area. We used present (2000) and future (2050) Bioclim climate databases to model the vector distribution.ResultsModel results predicted potential suitable areas with high success rates for Culex quinquefasciatus, Culex univitattus, Mansonia africana, and Mansonia uniformis. Under the present climatic conditions, the lowlands were found to be highly suitable for all the species. Future climatic conditions indicate an increase in the spatial distribution of Cx. quinquefasciatus and M. africana. Model performance was statistically significant.ConclusionSoil types, precipitation in the driest quarter, precipitation seasonality, and isothermality showed the highest predictive potential for the four species.

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“…When there is little confidence in the data on the absence of a disease or vector, ecological niche modelling can be used with presence-only data to map risk areas for vector or disease occurrence. This has been used for several VBDs, such as plague in California ground squirrels (54), RVF vectors in Saudi Arabia (55), vectors and reservoirs of leishmaniosis in North America (56) and WNV vectors in Iowa (57).…”

Section: Participatory Surveillancementioning

confidence: 99%

Epidemiological surveillance methods for vector-borne diseases

Thompson¹,

Etter²

2015

Rev. Sci. Tech. OIE

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SummaryCompared with many other diseases, the ever-increasing threat of vectorborne diseases (VBDs) represents a great challenge to public and animal health managers. Complex life cycles, changing distribution ranges, a variety of potential vectors and hosts, and the possible role of reservoirs make surveillance for VBDs a grave concern in a changing environment with increasing economic constraints. Surveillance activities may have various specific objectives and may focus on clinical disease, pathogens, vectors, hosts and/or reservoirs, but ultimately such activities should improve our ability to predict, prevent and/or control the diseases concerned. This paper briefly reviews existing and newly developed tools for the surveillance of VBDs. A range of examples, by no means exhaustive, illustrates that VBD surveillance usually involves a combination of methods to achieve its aims, and is best accomplished when these techniques are adapted to the specific environment and constraints of the region. More so than any other diseases, VBDs respect no administrative boundaries; in addition, animal, human and commodity movements are increasing dramatically, with illegal or unknown movements difficult to quantify. Vector-borne disease surveillance therefore becomes a serious issue for local and national organisations and is being conducted more and more at the regional and international level through multidisciplinary networks. With economic and logistical constraints, tools for optimising and evaluating the performance of surveillance systems are essential and examples of recent developments in this area are included. The continuous development of mapping, analytical and modelling tools provides us with an enhanced ability to interpret, visualise and communicate surveillance results. This review also demonstrates the importance of the link between surveillance and research, with interactions and benefits in both directions.

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Ecological Niche Modeling of Potential West Nile Virus Vector Mosquito Species in Iowa

Cited by 56 publications

References 49 publications

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Validation of a Previously Developed Geospatial Model That Predicts the Prevalence of Listeria monocytogenes in New York State Produce Fields

Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya

Epidemiological surveillance methods for vector-borne diseases

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