A geographical information system-based web model of arbovirus transmission risk in the continental United States of America

Konrad, Sarah K.; Zou, Li; Miller, Scott N.

doi:10.4081/gh.2012.114

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Even if our experience suggests the usefulness of using Google Trends for predicting West Nile virus, this should be considered as a pilot study, calling for the need for making our model more accurate and reliable, and maybe incorporating other variables (eg, environmental, socioeconomic, and ecological ones). This is of fundamental importance when designing and implementing a digital system for West Nile virus surveillance, which could complement the classical one or those actually under experimentation [21,22]. The combination of Google Trends and other predictors could reach an adequate temporal concordance with the real-world epidemiological figures and, therefore, could enable nowcasting or forecasting of new West Nile virus cases.…”

Section: Discussionmentioning

confidence: 99%

Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams–Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior

Watad

Mahroum

et al. 2019

JMIR Public Health Surveill

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BackgroundWest Nile virus is an arbovirus responsible for an infection that tends to peak during the late summer and early fall. Tools monitoring Web searches are emerging as powerful sources of data, especially concerning infectious diseases such as West Nile virus.ObjectiveThis study aimed at exploring the potential predictive power of West Nile virus–related Web searches.MethodsDifferent novel data streams, including Google Trends, WikiTrends, YouTube, and Google News, were used to extract search trends. Data regarding West Nile virus cases were obtained from the Centers for Disease Control and Prevention. Data were analyzed using regression, times series analysis, structural equation modeling, and clustering analysis.ResultsIn the regression analysis, an association between Web searches and “real-world” epidemiological figures was found. The best seasonal autoregressive integrated moving average model with explicative variable (SARIMAX) was found to be (0,1,1)x(0,1,1)4. Using data from 2004 to 2015, we were able to predict data for 2016. From the structural equation modeling, the consumption of West Nile virus–related news fully mediated the relation between Google Trends and the consumption of YouTube videos, as well as the relation between the latter variable and the number of West Nile virus cases. Web searches fully mediated the relation between epidemiological figures and the consumption of YouTube videos, as well as the relation between epidemiological data and the number of accesses to the West Nile virus–related Wikipedia page. In the clustering analysis, the consumption of news was most similar to the Web searches pattern, which was less close to the consumption of YouTube videos and least similar to the behavior of accessing West Nile virus–related Wikipedia pages.ConclusionsOur study demonstrated an association between epidemiological data and search patterns related to the West Nile virus. Based on this correlation, further studies are needed to examine the practicality of these findings.

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Section: Discussionmentioning

confidence: 99%

Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams–Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior

Watad

Mahroum

et al. 2019

JMIR Public Health Surveill

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“…Several viral, bacterial, parasitic and protozoal diseases have been studied to identify their spatial distribution, characteristics, and risk factors such as temperature, soil type, elevation, slope and land use. For example, Aujeszky's disease in US, fascioliasis in Brazil, bovine tuberculosis in New Zealand and UK, FMD in France, UK, Brazil and New Zealand; Campylobacteriosis in Sweden; Rift valley fever in US (Sorensen et al, 2000;Nygard et al, 2004;Musella et al, 2011;Konrad et al, 2012;Martins et al, 2012) spread were mapped using GIS. In Ethiopia, Yilma and Malone (1998) applied GIS to forecast model for strategic control of fasciolosis.…”

Section: Depicting the Spread Of A Diseasementioning

confidence: 99%

“…Vet. J., 2021, 25 (1), 128-143 (Konrad et al, 2012). Spatial analysis involves three basic steps; the preparation of an appropriate model, its proper visualization, and an exploratory data analysis, which range from simple map overlay to statistical models (Law et al, 2004).…”

Section: Depicting the Spread Of A Diseasementioning

confidence: 99%

Application of Geographical Information System in Animal Disease Surveillance and Control: A Review

Tadesse¹,

Amare²

2021

Ethiop. Vet. J.

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Animal disease patterns are changing because of climate change and there is a continuous occurrence of re-emerging and emerging types of diseases. So, new and modern tools are essential for monitoring and surveillance of these diseases. This review was done to give an insight on the applications of GIS in animal disease surveillance, reporting and control. Geographic information systems (GIS) provide the easy access, utilization and manipulation of geospatial information. The advantage of GIS is mapping the many different locations of farms and other facilities with animals on a single map which helps in better monitoring and surveillance. GIS also provides detailed information on disease forecasting, prediction of outbreaks, identification of disease clusters or hotspot, creation of buffer zones and to evaluate different strategies to prevent the spread of infectious diseases. Moreover, it provides an ideal condition for the collection of disease related data and their analyses in relation to population distribution, surrounding social and health services and the natural environmental conditions. Keywords: Animal diseases; Control; GIS; Surveillance

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Sampling Design and Mosquito Trapping for Surveillance of Arboviral Activity

Paternina

Rodas

2017

Methods in Molecular Biology

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Mosquitoes are the most important vectors for arboviral human diseases across the world. Diseases such as Dengue Fever (DF), West Nile Virus (WNV), Yellow Fever (YF), Japanese Encephalitis (JE), Venezuelan Equine Encephalitis (VEE), and St. Louis Encephalitis (SLE), among others, have a deep impact in public health. Usually mosquitoes acquire the arboviral infection when they feed on viremic animals (birds or mammals), so their infection can be detected along the year or in short periods of time (seasons). All of this depends on the frequency and seasonality of the encounters between viremic animals and vectors.With the convergence of several phenomena like the increasing traveling of human populations, globalization of economy and more recently the global warming, the introduction of nonendemic arbovirus into new areas has become the current scenario. As examples of this new social and environmental frame we can mention the outbreak of West Nile Virus in North America in the late 1990s and more recently the outbreaks of chikungunya and Zika virus in the Americas. The present chapter deals with one of the first steps in the development of research studies and diagnosis programs, the surveillance of arboviruses in their vectors, the sampling design and mosquito trapping methods. The chapter also includes some important considerations and tips to be taken into account during the mosquito fieldwork.

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A geographical information system-based web model of arbovirus transmission risk in the continental United States of America

Cited by 5 publications

References 11 publications

Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams–Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior

Forecasting the West Nile Virus in the United States: An Extensive Novel Data Streams–Based Time Series Analysis and Structural Equation Modeling of Related Digital Searching Behavior

Application of Geographical Information System in Animal Disease Surveillance and Control: A Review

Sampling Design and Mosquito Trapping for Surveillance of Arboviral Activity

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