Spatio-Temporal Distribution of Vector-Host Contact (VHC) Ratios and Ecological Niche Modeling of the West Nile Virus Mosquito Vector, Culex quinquefasciatus, in the City of New Orleans, LA, USA

Sallam, Mohamed F.; Michaels, Sarah; Riegel, Claudia; Pereira, Roberto M.; Zipperer, Wayne C.; Lockaby, B. Graeme; koehler, Philip G.

doi:10.3390/ijerph14080892

Cited by 32 publications

(22 citation statements)

References 60 publications

(97 reference statements)

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“…Robert et al [ 34 ] adopted a different approach in their model. Usually abundance of mosquito vectors and disease incidence data do not reflect actual risk to human populations, unless this abundance is coupled with biting rates or VHC [ 50 ]. Therefore, Robert et al [ 34 ] utilized VHC ratios as a precursor for biting and infection rates, which eventually could help in determining the actual transmission risk.…”

Section: Resultsmentioning

confidence: 99%

Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping

Sallam¹,

Fizer

Pilant

et al. 2017

IJERPH

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Asian tiger and yellow fever mosquitoes (Aedes albopictus and Ae. aegypti) are global nuisances and are competent vectors for viruses such as Chikungunya (CHIKV), Dengue (DV), and Zika (ZIKV). This review aims to analyze available spatiotemporal distribution models of Aedes mosquitoes and their influential factors. A combination of five sets of 3–5 keywords were used to retrieve all relevant published models. Five electronic search databases were used: PubMed, MEDLINE, EMBASE, Scopus, and Google Scholar through 17 May 2017. We generated a hierarchical decision tree for article selection. We identified 21 relevant published studies that highlight different combinations of methodologies, models and influential factors. Only a few studies adopted a comprehensive approach highlighting the interaction between environmental, socioeconomic, meteorological and topographic systems. The selected articles showed inconsistent findings in terms of number and type of influential factors affecting the distribution of Aedes vectors, which is most likely attributed to: (i) limited availability of high-resolution data for physical variables, (ii) variation in sampling methods; Aedes feeding and oviposition behavior; (iii) data collinearity and statistical distribution of observed data. This review highlights the need and sets the stage for a rigorous multi-system modeling approach to improve our knowledge about Aedes presence/abundance within their flight range in response to the interaction between environmental, socioeconomic, and meteorological systems.

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Section: Resultsmentioning

confidence: 99%

Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping

Sallam¹,

Fizer

Pilant

et al. 2017

IJERPH

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“…ENM explores geographic and ecological patterns of vectors, host and pathogens to inform about its potential distribution (Peterson, ). This approach has shown effectiveness under diverse applications to fundamental ecological questions such as areas at risk of disease infection (Machado, Weiblen, & Escobar, ), likely pathogen spillover to humans (Peterson, Martínez‐Campos, Nakazawa, & Martínez‐Meyer, ; Samy, Thomas, Wahed, Cohoon, & Peterson, ) and environmental factors linked to infectious diseases (Jia & Joyner, ; Sallam et al, ). Thus, ENM has proven to be a powerful approach to reconstruct the likely factors shaping infectious diseases distributions.…”

Section: Introductionmentioning

confidence: 99%

Spatial distribution and spread potential of sixteenLeptospiraserovars in a subtropical region of Brazil

Jara

Escobar

Rodriges³

et al. 2019

Transbound Emerg Dis

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Leptospirosis is a bacterial disease that represents a major problem in animal and public health due to its high prevalence and widespread distribution. This zoonotic disease is most prevalent in tropical environments where conditions favour pathogen survival. The ecological preferences of Leptospira serovars are poorly understood, limiting our knowledge of where and when outbreaks can occur, which may result in misinformed prevention and control plans. While the disease can occur consistently in time and space in tropical regions, research on the ecology of leptospirosis remains limited in subtropical regions. This research gap regarding Leptospira ecology brings public and veterinary health problems, impacting local economies. To fill this gap of knowledge, we suggest to assess geographic and ecological features among Leptospira serovars in a subtropical area of Brazil where leptospirosis is endemic to (a) highlight environmental conditions that facilitate or limit Leptospira spread and survival and (b) reconstruct its geographic distribution. An ecological niche modelling framework was used to characterize and compare Leptospira serovars in both geographic and environmental space. Our results show that despite the geographic overlap exhibited by the different serovars assessed, we found ecological divergence among their occupied ecological niches. Ecological divergences were expressed as ranges of potential distributions and environmental conditions found suitably by serovar, Sejroe being the most asymmetric (<0.15). Most important predictors for the potential distribution of most serovars were soil pH (31.7%) and landscape temperature (24.2%). Identification of environmental preferences will allow epidemiologists to better infer the presence of a serovar based on the environmental characteristics of regions rather than inferences based solely on historical epidemiological records. Including geographic and ecological ranges of serovars also may help to forecast transmission potential of Leptospira in public health and the food animal practice.

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“…Different spatial scales have been used in geographical analyses to identify the drivers of human risk from WNV infections. The most commonly used spatial scale in the United States is counties [17,22,31], census tracts or Zip Code Tabulation Areas (ZCTA) [12,32], census block groups [33], and buffers of varying sizes around trap locations or human cases [24]. Each of these spatial scales has its own inherent biases, as these political boundaries do not necessarily correspond to the ecological processes of the disease in question [34].…”

Section: Introductionmentioning

confidence: 99%

The drivers of West Nile virus human illness: fine scale dynamic effects of weather, mosquito infection, social, and biological conditions

Karki

Brown

Uelmen

et al. 2019

Preprint

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14West Nile virus (WNV) has consistently been reported to be associated with human cases of 15 illness in the region near Chicago, Illinois. However, the number of reported cases of human 16 illness varies across years, with intermittent outbreaks. Several dynamic factors, including 17 temperature, rainfall, and infection status of vector mosquito populations, are responsible for 18 much of these observed variations. However, local landscape structure and human 19 demographic characteristics also play a key role. The geographic and temporal scales used to 20 analyze such complex data affect the observed associations. Here, we used spatial and 21 statistical modeling approaches to investigate the factors that drive the outcome of WNV 22 human illness on fine temporal and spatial scales. Our approach included multi-level 23 modeling of long-term weekly data from 2005 to 2016, with weekly measures of mosquito 24 infection, human illness and weather combined with more stable landscape and demographic 25 factors on the geographical scale of 1000m hexagons. We found that hot weather conditions, 26 warm winters, and higher MIR in earlier weeks increased the probability of an area of having 27 a WNV human case. Higher population and the proportion of urban light intensity in an area 28 also increased the probability of observing a WNV human case. A higher proportion of open 29 water sources, percentage of grass land, deciduous forests, and housing built post 1990 30 decreased the probability of having a WNV case. Additionally, we found that cumulative 31 positive mosquito pools up to 31 weeks can strongly predict the total annual human WNV 32 cases in the Chicago region. This study helped us to improve our understanding of the fine-33 scale drivers of spatiotemporal variability of human WNV cases.34 35 Introduction 36 West Nile virus (WNV), a mosquito-borne zoonotic disease, was first identified in the 37 United States in the summer of 1999 in New York City [1].The mosquitoes of several Culex 38 species are the primary enzootic and bridge vectors for the transmission of WNV, and several 39 bird species are known to contribute in the amplification of the virus [2-4]. Since its first 40 successful invasion in New York, WNV quickly adapted to the local populations of Culex 41 vector mosquitoes and avian populations and rapidly spread throughout the conterminous 42 United States [5,6]. The first major WNV outbreak in the United States was observed in 43 2002, when more than 4,150 human cases and 284 deaths attributable to WNV infection were 44 reported to the CDC from 40 states compared to only 149 cases and 19 deaths from 10 states 45 cumulatively during the three years from 1999 to 2001 [7]. This stirred a prompt public 46 health response from federal, state, and local public health agencies and led to the 47 establishment of a more robust surveillance of mosquitoes and birds to monitor and control 48 the spread of WNV [8]. 49 Public health surveillance for West Nile virus (WNV) involves collection and testing 50 of Cul...

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Spatio-Temporal Distribution of Vector-Host Contact (VHC) Ratios and Ecological Niche Modeling of the West Nile Virus Mosquito Vector, Culex quinquefasciatus, in the City of New Orleans, LA, USA

Cited by 32 publications

References 60 publications

Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping

Systematic Review: Land Cover, Meteorological, and Socioeconomic Determinants of Aedes Mosquito Habitat for Risk Mapping

Spatial distribution and spread potential of sixteenLeptospiraserovars in a subtropical region of Brazil

The drivers of West Nile virus human illness: fine scale dynamic effects of weather, mosquito infection, social, and biological conditions

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