Use of Weather Data and Remote Sensing to Predict the Geographic and Seasonal Distribution of Phlebotomus papatasi in Southwest Asia

Cross, Eleanor R.; Newcomb, W. W.; Tucker, Compton J.

doi:10.4269/ajtmh.1996.54.530

Cited by 133 publications

(85 citation statements)

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“…Our findings, similar to other studies, showed that tropical regions in Iran like Khorasan, Fars, and Kerman provinces had the highest incidence of the disease consistently (18,19). Other studies expressed that the effect of temperature and climatic conditions is known as a risk factor for the life cycle of the carrier, the frequency in animal reservoirs, and the transmission pattern of the predominant type pathogen (20)(21)(22). It is somewhat difficult to compare the distribution of age groups according to different designs and classification in various studies (23).…”

Section: Discussionmentioning

confidence: 99%

Epidemiological Aspects of Cutaneous Leishmaniasis in Iran

Khazaei

Hafshejani

Saatchi

et al. 2015

Arch Clin Infect Dis

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Section: Discussionmentioning

confidence: 99%

Epidemiological Aspects of Cutaneous Leishmaniasis in Iran

Khazaei

Hafshejani

Saatchi

et al. 2015

Arch Clin Infect Dis

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“…The minimum NDVI value positively correlated with sandfly density. Therefore, the NDVI values are extremely valuable and effective in analysing the conditions of Kala-azar occurrence (Cross et al 1996, Bavia et al 2005. Non-endemic areas have shown that proximity to the dense forest is an important determinant of the risk of Kala-azar transmission and can increase vector densities.…”

Section: Discussionmentioning

confidence: 99%

A comparative evaluation of endemic and non-endemic region of visceral leishmaniasis (Kala-azar) in India with ground survey and space technology

Kesari

Bhunia

Kumar

et al. 2011

Mem. Inst. Oswaldo Cruz

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“…An alternative approach is to use landscape variables derived from remote sensing satellites as predictors, with or without incorporating the effects of spatial dependence. Pertinent examples include vectors of Eastern equine encephalomyelitis (Moncayo et al 2000), tick vectors of Lyme disease (Brownstein et al 2003, Guerra et al 2001, Kitron et al 1996, sand fly vectors of leishmaniasis (Cross et al 1996, Elnaiem et al 2003, Miranda et al 1998, Thomson et al 1999, tse-tse fly vectors of African trypanosomiasis (Kitron et al 1996, Rogers 2000, and mosquito vectors of malaria (Beck et al 1994, Diuk-Wasser et al 2004, Thomson et al 1996, Wood et al 1991a,b, 1992. Of these models, however, only a few have been validated with an independent dataset (Beck et al 1997, Brownstein et al 2004).…”

Section: Introduction Wmentioning

confidence: 99%

Modeling the Spatial Distribution of Mosquito Vectors for West Nile Virus in Connecticut, USA

Diuk‐Wasser

Brown

Andreadis

et al. 2006

Vector-Borne and Zoonotic Diseases

155

142

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The risk of transmission of West Nile virus (WNV) to humans is associated with the density of infected vector mosquitoes in a given area. Current technology for estimating vector distribution and abundance is primarily based on Centers for Disease Control and Prevention (CDC) light trap collections, which provide only point data. In order to estimate mosquito abundance in areas not sampled by traps, we developed logistic regression models for five mosquito species implicated as the most likely vectors of WNV in Connecticut. Using data from 32 traps in Fairfield County from 2001 to 2003, the models were developed to predict high and low abundance for every 30 ؋ 30 m pixel in the County. They were then tested with an independent dataset from 16 traps in adjacent New Haven County. Environmental predictors of abundance were extracted from remotely sensed data. The best predictive models included non-forested areas for Culex pipiens, surface water and distance to estuaries for Cx. salinarius, surface water and grasslands/agriculture for Aedes vexans and seasonal difference in the normalized difference vegetation index and distance to palustrine habitats for Culiseta melanura. No significant predictors were found for Cx. restuans. The sensitivity of the models ranged from 75% to 87.5% and the specificity from 75% to 93.8%. In New Haven County, the models correctly classified 81.3% of the traps for Cx. pipiens, 75.0% for Cx. salinarius, 62.5% for Ae. vexans, and 75.0% for Cs. melanura. Continuous surface maps of habitat suitability were generated for each species for both counties, which could contribute to future surveillance and intervention activities.

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Use of Weather Data and Remote Sensing to Predict the Geographic and Seasonal Distribution of Phlebotomus papatasi in Southwest Asia

Cited by 133 publications

References 0 publications

Epidemiological Aspects of Cutaneous Leishmaniasis in Iran

Epidemiological Aspects of Cutaneous Leishmaniasis in Iran

A comparative evaluation of endemic and non-endemic region of visceral leishmaniasis (Kala-azar) in India with ground survey and space technology

Modeling the Spatial Distribution of Mosquito Vectors for West Nile Virus in Connecticut, USA

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