A comparison of three approaches to identify West Nile Virus mosquito space-time hotspots in the Houston Vicinity for the period 2002–2011

Curtis, Andrew; Ye, Xinyue; Heob, Elyse; Targhetta, Joseph; Salvato, Vence; Reyna, Martin; Bueno, Rudy; Holmes, Louisa M.

doi:10.1016/j.apgeog.2014.02.003

Cited by 11 publications

(8 citation statements)

References 51 publications

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“…As noted, the temperature range variables were based off three annual assessments, so the results do not necessarily document the actual temperature limits of the Aedes aegypti. Additionally, since the temperature range is identified in this study as indicative of DF, it supports the theory of vector habitat re-distribution is impacted by climate change [4,6,21,70].…”

Section: Discussionsupporting

confidence: 54%

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

2016

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Previous research on Dengue Fever have involved laboratory tests or study areas with less diverse temperature and elevation ranges than is found in Colombia; therefore, preliminary research was needed to identify location specific attributes of Dengue Fever transmission. Environmental variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) satellites were combined with population variables to be statistically compared against reported cases of Dengue Fever in the Río Magdalena watershed, Colombia. Three-factor analysis models were investigated to analyze variable patterns, including a population, population density, and empirical Bayesian estimation model. Results identified varying levels of Dengue Fever transmission risk, and environmental characteristics which support, and advance, the research literature. Multiple temperature metrics, elevation, and vegetation composition were among the more contributory variables found to identify future potential outbreak locations.

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Section: Discussionsupporting

confidence: 54%

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

2016

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“…Kernel density estimation (KDE) is a commonly applied method to create “heat maps” in crime, epidemiological research [ 14 , 16 , 37 ], and other studies of violence in the region [ 2 , 21 ]. All IEDs falling inside a kernel (a circular area extending away from a single location) become part of a density calculation.…”

Section: Methodsmentioning

confidence: 99%

“…For the purposes of this paper, only the top five classifications for each year (the top 50 %) are overlaid on a composite map of all IEDs. This approach to visualizing spatio-temporal data using a relatively simple analytical approach has previously been applied by the authors for space–time sequences in epidemic disease [ 14 ]. 6…”

Section: Methodsmentioning

confidence: 99%

“…KDE is a useful and easily applied technique to compare different time periods, though it is less useful at identifying temporal trends within the data. Alternatively SaTScan (v9.1.1) can reveal space and time patterns in locations collected across multiple years [ 13 , 14 , 31 ] as it explicitly considers spatial and temporal data point interconnections using a retrospective space–time permutation model with testing for statistical significance by a Monte Carlo simulation (with 999 simulation runs). Conceptually, a tube with a set kernel moves across the study space, with the body of the tube shrinking or expanding at every location as it identifies clusters of events (in this case IEDs).…”

Section: Methodsmentioning

confidence: 99%

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A space–time analysis of the WikiLeaks Afghan War Diary: a resource for analyzing the conflict-health nexus

Curtis

Hachey

et al. 2015

Int J Health Geogr

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BackgroundAlthough it is widely acknowledged that areas of conflict are associated with a high health burden, from a geospatial perspective it is difficult to establish these patterns at fine scales because of a lack of data. The release of the “WikiLeaks” Afghan War Diary (AWD) provides an interesting opportunity to advance analysis and theory into this interrelationship.MethodsThis paper will apply two different space time analyses to identify patterns of improvised explosive devices (IED) detonations for the period of 2004 to 2009 in Afghanistan.ResultsThere is considerable spatial and temporal heterogeneity in IED explosions, with concentrations often following transportation links. The results are framed in terms of a resource for subsequent analyses to other existing health research in Afghanistan. To facilitate this, in our discussion we present a Google Earth file of overlapping rates that can be distributed to any researcher interested in combining his/her fine scale health data with a similarly granular layer of violence.ConclusionThe release of the AWD presents a previously unavailable opportunity to consider how spatially detailed data about violence can be incorporated into understanding, and predicting, health related spillover effects. The AWD can enrich previous research conducted on Afghanistan, and provide a justification for future “official” data sharing at appropriately fine scales.

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“…food storage, waste disposal, building materials, presence of pets) are unevenly distributed in the environment, focal clusters or hotspots of disease transmission can occur, defined as areas of elevated transmission efficiency and spillover risk [3]. Examples of zoonotic diseases that cluster in the human environment are leptospirosis, rabies, West Nile fever and bubonic plague [4][5][6]. Hotspot detection is important to understand the drivers of disease transmission and has successfully increased the cost-effectiveness of control programs [7,8].…”

Section: Introductionmentioning

confidence: 99%

Households as hotspots of Lassa fever? Assessing the spatial distribution of Lassa virus-infected rodents in rural villages of Guinea

Mariën

Iacono

Rieger

et al. 2020

Emerging Microbes & Infections

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The Natal multimammate mouse (Mastomys natalensis) is the reservoir host of Lassa virus (LASV), an arenavirus that causes Lassa haemorrhagic fever in humans in West Africa. While previous studies suggest that spillover risk is focal within rural villages due to the spatial behaviour of the rodents, the level of clustering was never specifically assessed. Nevertheless, detailed information on the spatial distribution of infected rodents would be highly valuable to optimize LASV-control campaigns, which are limited to rodent control or interrupting human-rodent contact considering that a human vaccine is not available. Here, we analysed data from a four-year field experiment to investigate whether LASVinfected rodents cluster in households in six rural villages in Guinea. Our analyses were based on the infection status (antibody or PCR) and geolocation of rodents (n = 864), and complemented with a phylogenetic analysis of LASV sequences (n = 119). We observed that the majority of infected rodents were trapped in a few houses (20%) and most houses were rodent-free at a specific point in time (60%). We also found that LASV strains circulating in a specific village were polyphyletic with respect to neighbouring villages, although most strains grouped together at the subvillage level and persisted over time. In conclusion, our results suggest that: (i) LASV spillover risk is heterogeneously distributed within villages in Guinea; (ii) viral elimination in one particular village is unlikely if rodents are not controlled in neighbouring villages. Such spatial information should be incorporated into eco-epidemiological models that assess the cost-efficiency of LASV control strategies.

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A comparison of three approaches to identify West Nile Virus mosquito space-time hotspots in the Houston Vicinity for the period 2002–2011

Cited by 11 publications

References 51 publications

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

Exploratory Analysis of Dengue Fever Niche Variables within the Río Magdalena Watershed

A space–time analysis of the WikiLeaks Afghan War Diary: a resource for analyzing the conflict-health nexus

Households as hotspots of Lassa fever? Assessing the spatial distribution of Lassa virus-infected rodents in rural villages of Guinea

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