Factors influencing the spatial distribution of Anopheles larvae in Coimbatore District, Tamil Nadu, India

Arjunan, Naresh Kumar; Murugan, Kadarkarai; Kumar, Shobana; Madhiyazhagan, Pari; Nataraj, Thiyagarajan; Vincent, C. Thomas; Barnard, Donald R.

doi:10.1016/j.actatropica.2015.09.007

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…The 3S technology, including geographic information systems (GIS), remote sensing (RS), and global positioning systems (GPS), is widely used in vector-borne disease epidemiology research [ 16 , 17 ]. With the advantage of being a large-scale, real-time, and accurate 3S technology, researchers can discover the connection between vector-borne disease (JEV, for example) and geo-environmental features [ 18 ]. In this study, GPS was used to locate the pigsties, RS was used to identify and quantify the geo-environmental features, and GIS was used to construct the spatial datasets of the vector and geo-environmental features.…”

Section: Introductionmentioning

confidence: 99%

Spatial statistical and environmental correlation analyses on vector density, vector infection index and Japanese encephalitis cases at the village and pigsty levels in Liyi County, Shanxi Province, China

Liu

Cheng³

et al. 2022

Parasites Vectors

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Background In the eco-epidemiological context of Japanese encephalitis (JE), geo-environmental features influence the spatial spread of the vector (Culex tritaeniorhynchus, Giles 1901) density, vector infection, and JE cases. Methods In Liyi County, Shanxi Province, China, the spatial autocorrelation of mosquito vector density, vector infection indices, and JE cases were investigated at the pigsty and village scales. The map and Enhanced Thematic Mapper (ETM) remote sensing databases on township JE cases and geo-environmental features were combined in a Geographic Information System (GIS), and the connections among these variables were analyzed with regression and spatial analyses. Results At the pigsty level, the vector density but not the infection index of the vector was spatially autocorrelated. For the pigsty vector density, the cotton field area was positively related, whereas the road length and the distance between pigsties and gullies were negatively related. In addition, the vector infection index was correlated with the pigsty vector density (PVD) and the number of pigs. At the village level, the vector density, vector infection index, and number of JE cases were not spatially autocorrelated. In the study area, the geo-environmental features, vector density, vector infection index, and JE case number comprised the Geo-Environment-Vector-JE (GEVJ) intercorrelation net system. In this system, pig abundance and cotton area were positive factors influencing the vector density first. Second, the infection index was primarily influenced by the vector density. Lastly, the JE case number was determined by the vector infection index and the wheat area. Conclusions This study provided quantitative associations among geo-environmental features, vectors, and the incidence of JE in study sties, one typical northern Chinese JE epidemiological area without rice cultivation. The results highlighted the importance of using a diverse range of environmental management methods to control mosquito disease vectors and provided useful information for improving the control of vector mosquitoes and reducing the incidence of JE in the northern Chinese agricultural context. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Spatial statistical and environmental correlation analyses on vector density, vector infection index and Japanese encephalitis cases at the village and pigsty levels in Liyi County, Shanxi Province, China

Liu

Cheng³

et al. 2022

Parasites Vectors

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“…stephensi (Arjunan et al 2015, Sharma & Hamzakoya 2001, Surendran et al 2019) and An. culicifacies (Arjunan et al 2015, Kar et al 1999, Suguna et al 1983) are the main malaria vectors. Interestingly, few reports on natural Wolbachia endosymbionts in major malarial vector Anopheles species are reported including An.…”

Section: Introductionmentioning

confidence: 99%

“…stephensi [21-23] and An. culicifacies [22,24,25] are the main malaria vectors. Interestingly, few reports on natural Wolbachia endosymbionts in major malarial vector Anopheles species are reported including An.…”

Section: Introductionmentioning

confidence: 99%

Low prevalence of natural Wolbachia in major malarial vectors Anopheles culicifacies s.l., and Anopheles stephensi: a first report

Sankar

Sundari²,

Anand³

2020

Preprint

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Wolbachia is an alpha-proteobacteria present in several arthropods. The present study focussed on the identification of Wolbachia in wild malarial vector mosquitoes. This was achieved by molecular identification of Wolbachia from collected mosquitoes. A total of four hundred and eight seven mosquito samples were collected. Morphometric and molecular analysis revealed that they belong to Anopheles culicifacies s.l., (48.25%) and Anopheles stephensi (51.75%). The presence of Wolbachia was identified using 16S rRNA, wsp and FtsZ genes, where nested PCR of 16S rRNA alone was successful and then sequenced. Only seven mosquitoes (1.4%) were positive for Wolbachia. In silico and restriction digestion of 16S rRNA gene product using RsaI enzyme showed that the identified Wolbachia belongs to supergroup B. The prevalence rate of natural Wolbachia was lesser in native malarial vector An. culicifacies s.l. and An. stephensi was about 1.7% and 1.2%, respectively. This is the first report on the presence of Wolbachia in Anopheles culicifacies s.l. and Anopheles stephensi.

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“…The vector population is closed correlated to the environmental factor [9,10], which providing the breeding for larva and resting for adults [11][12][13]. So, studying the correlation of environmental factor with mosquito population could provide corresponded measures for the vector prevention and control.…”

Section: Introductionmentioning

confidence: 99%

“…With the recent application advancement of 3S technology, especially RS-remote sensing and GIS-Geographic Information System, researchers are able to use these technologies for quantitative extraction of environmental features [11], conducting analyses of features with mosquito population indexes [18], and prevention of mosquito-borne disease [19]. That, the 3 s system had been used to study the correlation between environmental factor with malaria [20], dengue [21], WNV [22], and so on.…”

Section: Introductionmentioning

confidence: 99%

Study on spatial-temporal distribution and environmental ties of Japanese B Encephalitis’s mosquito vector as well as landing rate in The Olympic forest park

Liu

Zhang

Tong

et al. 2020

Preprint

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Background: Mosquito surveillance is crucial for understanding the epidemic potential, planning early-warning, and effective control strategies. As a key public place in Beijing, the Beijing Olympic Forest Park (BOFP) makes serious epidemiological means for the Beijing, and the survey on the Japanese B Encephalitis (JBE) vector of Culex tritaeniorhynchus and mosquito landing in BOFP is crucial for the people living as well as JBE surveillance and control. It was hypothesized that the Cx. tritaeniorhynchus could be trapped in a well-urbanization area of Beijing, and the distribution of Cx. tritaeniorhynchus and mosquito landing rate are determined by the environmental factors. Methods: Thus, this investigation was performed to understand the spatial-temporal distribution of Cx. Tritaeniorhynchus and landing rate in BOFP, and the relationship between environmental features and densities of Cx. tritaeniorhynchus as well as mosquito landing rate, considering the vector competent of Cx. tritaeniorhynchus and important epidemiological means of BOFP in Beijing. From July to September, the Cx. tritaeniorhynchus was trapped with CDC-lights together with CO2 and the landing rate on the same sites as light traps were also investigated. What’s more, the ties of environmental factors to the density of Cx. tritaeniorhynchus and landing rate was performed with SPSS, after the environmental factors datasets had been extracted with the 3S procedures. Results: As result showed, the highest density of Cx. tritaeniorhynchus could be detected in September in the wetland environment, and the density was related to the grass-water in 400meters buffer (GW_400) and lower-trees in 100meter buffer distance (LT_100). For the landing rate investigation, the highest landing rates was observed in the wetland environment during September. In addition, there were two environmental features could be identified to correlate to landing rates; that is, grass-water in 400meters buffer (GW_400) and high-trees in 100meter buffer distance (HT_100). It was concluded that the Cx. tritaeniorhynchus, the vector of JBE, could be detected in BOFP (well-urbanization area), and the environmental factor could affect not only the spatial distribution of JBE vector but also the mosquito landing rates in BOFP. Conclusions: The information achieved from this study could offer advice to people escaping from mosquito biting and provide useful information to prevent mosquito-borne diseases in Beijing, which means a lot to the public health of Beijing.

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Factors influencing the spatial distribution of Anopheles larvae in Coimbatore District, Tamil Nadu, India

Cited by 5 publications

References 14 publications

Spatial statistical and environmental correlation analyses on vector density, vector infection index and Japanese encephalitis cases at the village and pigsty levels in Liyi County, Shanxi Province, China

Spatial statistical and environmental correlation analyses on vector density, vector infection index and Japanese encephalitis cases at the village and pigsty levels in Liyi County, Shanxi Province, China

Low prevalence of natural Wolbachia in major malarial vectors Anopheles culicifacies s.l., and Anopheles stephensi: a first report

Study on spatial-temporal distribution and environmental ties of Japanese B Encephalitis’s mosquito vector as well as landing rate in The Olympic forest park

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