Assessment of changes of vector borne diseases with wetland characteristics using multivariate analysis

Sheela, A. M.; Sarun, S.; Justus, J.; Vineetha, P.; Sheeja, R. V.

doi:10.1007/s10653-014-9655-y

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…In addition, hierarchical cluster analysis was employed to group and classify wetland characteristics using JMP software from SAS (version 13). Cluster analysis can divide objects (e.g., wetlands) into different subgroups in such a way that objects in the same group are more similar with each other than with those in other groups (Richarme, 2002;Sheela et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Multi-element fingerprinting of waters to evaluate connectivity among depressional wetlands

Yuan

Zhu

Mushet

et al. 2019

Ecological Indicators

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Establishing the connectivity among depressional wetlands is important for their proper management, conservation and restoration. In this study, the concentrations of 38 elements in surface water and porewater of depressional wetlands were investigated to determine chemical and hydrological connectivity of three hydrological types: recharge, flow-through, and discharge, in the Prairie Pothole Region of North America. Most element concentrations of porewater varied significantly by wetland hydrologic type (p < 0.05), and increased along a recharge to discharge hydrologic gradient. Significant spatial variation of element concentrations in surface water was observed in discharge wetlands. Generally, higher element concentrations occurred in natural wetlands compared to wetlands with known disturbances (previous drainage and grazing). Electrical conductivity explained 42.3% and 30.5% of the variation of all element concentrations in porewater and surface water. Non-metric multidimensional scaling analysis showed that the similarity decreased from recharge to flowthrough to discharge wetland in each sampling site. Cluster analysis confirmed that element compositions in porewater of interconnected wetlands were more similar to each other than to those of wetlands located farther away. Porewater and surface water in a restored wetland showed similar multi-element characteristics to natural wetlands. In contrast, depressional wetlands connected by seeps along a deactivated drain-tile path and a grazed wetland showed distinctly different multi-element characteristics compared to other wetlands sampled. Our findings confirm that the multi-element fingerprinting method can be useful for assessing hydro-chemical connectivity across the landscape, and indicate that element concentrations are not only affected by land use, but also by hydrological characteristics.

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Section: Discussionmentioning

confidence: 99%

Multi-element fingerprinting of waters to evaluate connectivity among depressional wetlands

Yuan

Zhu

Mushet

et al. 2019

Ecological Indicators

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“…Such variations have been reported by other studies that used MaxEnt (36, 37). This is because although the mosquito species share similar habitats, their ecological requirements may show significant variation in terms of water quality (38, 39) and vegetation type (40). Other studies have also indicated that temperature and rainfall derivatives are the most influential predictors of mosquito distribution (24, 27, 41).…”

Section: Discussionmentioning

confidence: 99%

Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya

Ochieng

Nanyingi

Kipruto

et al. 2016

Infection Ecology & Epidemiology

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BackgroundRift Valley fever (RVF) is a vector-borne zoonotic disease that has an impact on human health and animal productivity. Here, we explore the use of vector presence modelling to predict the distribution of RVF vector species under climate change scenario to demonstrate the potential for geographic spread of Rift Valley fever virus (RVFV).ObjectivesTo evaluate the effect of climate change on RVF vector distribution in Baringo County, Kenya, with an aim of developing a risk map for spatial prediction of RVF outbreaks.MethodologyThe study used data on vector presence and ecological niche modelling (MaxEnt) algorithm to predict the effect of climatic change on habitat suitability and the spatial distribution of RVF vectors in Baringo County. Data on species occurrence were obtained from longitudinal sampling of adult mosquitoes and larvae in the study area. We used present (2000) and future (2050) Bioclim climate databases to model the vector distribution.ResultsModel results predicted potential suitable areas with high success rates for Culex quinquefasciatus, Culex univitattus, Mansonia africana, and Mansonia uniformis. Under the present climatic conditions, the lowlands were found to be highly suitable for all the species. Future climatic conditions indicate an increase in the spatial distribution of Cx. quinquefasciatus and M. africana. Model performance was statistically significant.ConclusionSoil types, precipitation in the driest quarter, precipitation seasonality, and isothermality showed the highest predictive potential for the four species.

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“…Previous studies have explored the relationship between environmental variables and disease outbreaks (Franklinos et al, 2019; Hess et al, 2018; Semenza & Suk, 2018; Sheela et al, 2015). These investigations highlight that factors such as vegetation, hydrology, and physiography are strongly linked to vector habitat and disease outbreaks.…”

Section: Methodsmentioning

confidence: 99%

Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning

P¹,

Sadasivuni²,

Nóbrega

et al. 2021

Multi Criteria Decision Anal

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Modelling infectious diseases is a complex and multi‐disciplinary problem that necessitates the combined use of multicriteria decision analysis (MCDA) and machine learning (ML) in a spatial framework. This research attempts to demonstrate the extensive applications of MCDA in the field of public health and to illustrate its utility with the combined use of spatial models and machine learning. The study investigates the risk factors for communicable diseases with a focus on vector‐borne infectious diseases, such as West Nile Virus (WNV), malaria, dengue, etc. It aims to quantify vector‐borne disease risk by examining the geographic contextual effects of socio‐economic, climatic, and environmental factors using the objective‐weighting technique adopted from MCDA and machine learning in a geographic information systems (GIS) framework. The authors attempted to minimize subjective bias from the decision space by utilizing an objective‐weighted technique to quantify the risk. The study adopted Shannon's entropy to derive weights for each factor and its classes. The derived weighted layers are fed to an artificial neural network to obtain a final map of risk susceptibility. This final risk map allows policymakers to examine vulnerable areas and identify the factors pivotal to the contribution of risk. Findings show the traffic volume as the most influential variable, and terrain slope as the least one in the disease spread for the study area. The risk appears to be concentrated and distributed along vegetation, wetlands, and around water bodies. The results produced by ensemble learning show great promise with more than 94% accuracy. The accuracy of the results was determined by the confusion matrix and the kappa index of agreement (KIA). The vector control programmes need to adapt to better manage the dynamic changes in patterns involving vector‐borne infectious diseases.

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Assessment of changes of vector borne diseases with wetland characteristics using multivariate analysis

Cited by 11 publications

References 7 publications

Multi-element fingerprinting of waters to evaluate connectivity among depressional wetlands

Multi-element fingerprinting of waters to evaluate connectivity among depressional wetlands

Ecological niche modelling of Rift Valley fever virus vectors in Baringo, Kenya

Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning

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