Modeling the Potential Distribution of the Malaria Vector Anopheles (Ano.) pseudopunctipennis Theobald (Diptera: Culicidae) in Arid Regions of Northern Chile

Valderrama, Lara; Ayala, Salvador; Reyes, Carolina; González, Christian

doi:10.3389/fpubh.2021.611152

Cited by 11 publications

(11 citation statements)

References 33 publications

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“…For a single model, the out-of-sample performance of RF and FDA was best, and SRE and MAXENT were worst. Although MAXENT has been used in some studies to predict malaria distribution, 18,39,73 its out-of-sample performance was less ideal in our study. Further, studies have shown that a single model did not perform well under various conditions, 35,46,74,75 which influences the effectiveness of model predictions.…”

Section: Discussionmentioning

confidence: 89%

“…3 Species distribution models are pivotal tools for forecasting and comprehending species distributions and have successfully identified the risk distribution of a disease in an area by changing environmental variables, including a dozen algorithms. [39][40][41] Messina et al used the boosted regression tree to predict the distribution of dengue and population at risk. 42 Manyangadzer et al used the negative binomial generalized linear mixed model to predict the spatial distribution of schistosomiasis infections.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the Risk Distributions for Anopheles sinensis, a Vector for Malaria in Shanghai, China

Tong

Xia

Wang

et al. 2023

The American Journal of Tropical Medicine and Hygiene

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Malaria is a parasitic disease caused by Plasmodium, and Anopheles sinensis is a vector of malaria. Although malaria is no longer indigenous to China, a high risk remains for local transmission of imported malaria. This study aimed to identify the risk distribution of vector An. sinensis and malaria transmission. Using data collected from routine monitoring in Shanghai from 2010 to 2020, online databases for An. sinensis and malaria, and environmental variables including climate, geography, vegetation, and hosts, we constructed 10 algorithms and developed ensemble models. The ensemble models combining multiple algorithms (An. sinensis: area under the curve [AUC] = 0.981, kappa = 0.920; malaria: AUC = 0.959, kappa = 0.800), with the best out-of-sample performance, were used to identify important environmental predictors for the risk distributions of An. sinensis and malaria transmission. For An. sinensis, the most important predictor in the ensemble model was moisture index, which reflected degree of wetness; the risk of An. sinensis decreased with higher degrees of wetness. For malaria transmission, the most important predictor in the ensemble model was the normalized differential vegetation index, which reflected vegetation cover; the risk of malaria transmission decreased with more vegetation cover. Risk levels for An. sinensis and malaria transmission for each district of Shanghai were presented; however, there was a mismatch between the risk classification maps of An. sinensis and malaria transmission. Facing the challenge of malaria transmission in Shanghai, in addition to precise An. sinensis monitoring in risk areas of malaria transmission, malaria surveillance should occur even in low-risk areas for An. sinensis.

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Section: Discussionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Prediction of the Risk Distributions for Anopheles sinensis, a Vector for Malaria in Shanghai, China

Tong

Xia

Wang

et al. 2023

The American Journal of Tropical Medicine and Hygiene

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“…Several studies have used an ecological niche modelling approach to model the species distribution of Anopheles mosquitoes. Studies where the maximum entropy tool was used to determine the spatial distribution of the local Anopheles mosquito species have been undertaken in regions all over the world, including Asia and the Middle East [ 9 ], South America [ 10 , 11 ], and Africa [ 12 , 13 ]. MaxEnt has also been used to model the effects of climate change on malaria transmission and Anopheles species density in China [ 14 ], Iran [ 15 ], and parts of Africa [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling

Memarsadeghi

Stewart

et al. 2023

Malar J

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Background Estimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria exposure risk by analysing the spatial pattern of malaria cases (primarily Plasmodium vivax) in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution of P. vivax malaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes. Methods A maximum entropy model was trained to estimate the distribution of P. vivax malaria for a period between January 2019 and April 2020, capturing estimated malaria occurrence for these provinces. A random simulation workflow was developed to make region-based case data usable for the machine learning approach. This workflow was used to generate a probability surface for the ecological niche regions. The resulting niche regions were analysed by occupation type, home and work locations, and work-related travel routes to determine the relationship between these variables and malaria occurrence. A one-way analysis of variance (ANOVA) test was used to understand the relationship between predicted malaria occurrence and occupation type. Results The MaxEnt (full name) model indicated a higher occurrence of P. vivax malaria in forested areas especially along the Thailand–Cambodia border. The ANOVA results showed a statistically significant difference between average malaria risk values predicted from the ecological niche model for rubber plantation workers and farmers, the two main occupation groups in the study. The rubber plantation workers were found to be at higher risk of exposure to malaria than farmers in Ubon Ratchathani and Sisaket provinces of Thailand. Conclusion The results from this study point to occupation-related factors such as work location and the routes travelled to work, being risk factors in malaria occurrence and possible contributors to transmission among local populations.

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“…darlingi requires high levels of humidity to develop its life cycle [ 15 ]; nevertheless, both species are considered dominant malaria vectors [ 7 , 8 ]. Climate change is predicted to have a direct effect on the distribution and dynamics of human malaria vectors [ 16 , 17 ]. Consequently, continuous monitoring of changes in the distribution of vector species and their bionomic characteristics is relevant for decision makers in each country to efficiently channel resources for malaria control.…”

Section: Introductionmentioning

confidence: 99%

A Follow-Up to the Geographical Distribution of Anopheles Species in Malaria-Endemic and Non-Endemic Areas of Honduras

Escobar

Archaga

Reyes

et al. 2022

Insects

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Anopheles species are the vectors of malaria, one of the diseases with the greatest impact on the health of the inhabitants of the tropics. Due to their epidemiological relevance and biological complexity, monitoring of anopheline populations in current and former malaria-endemic areas is critical for malaria risk assessment. Recent efforts have described the anopheline species present in the main malaria foci in Honduras. This study updates and expands knowledge about Anopheles species composition, geographical distribution, and genetic diversity in the continental territory of Honduras as in the Bay Islands. Outdoor insect collections were carried out at 25 sites in eight municipalities in five departments of Honduras between 2018 and 2021. Specimens were identified using taxonomic keys. Partial COI gene sequences were used for molecular species identification and phylogenetic analyses. In addition, detection of Plasmodium DNA was carried out in 255 female mosquitoes. Overall, 288 Anopheles mosquitoes were collected from 8 municipalities. Eight species were morphologically identified. Anopheles albimanus was the most abundant and widely distributed species (79.5%). A subset of 175 partial COI gene sequences from 8 species was obtained. Taxonomic identifications were confirmed via sequence analysis. Anopheles albimanus and An. apicimacula showed the highest haplotype diversity and nucleotide variation, respectively. Phylogenetic clustering was found for An. argyritarsis and An. neomaculipalpus when compared with mosquitoes from other Neotropical countries. Plasmodium DNA was not detected in any of the mosquitoes tested. This report builds upon recent records of the distribution and diversity of Anopheles species in malaria-endemic and non-endemic areas of Honduras. New COI sequences are reported for three anopheline species. This is also the first report of COI sequences of An. albimanus collected on the island of Roatán with apparent gene flow relative to mainland populations.

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Modeling the Potential Distribution of the Malaria Vector Anopheles (Ano.) pseudopunctipennis Theobald (Diptera: Culicidae) in Arid Regions of Northern Chile

Cited by 11 publications

References 33 publications

Prediction of the Risk Distributions for Anopheles sinensis, a Vector for Malaria in Shanghai, China

Prediction of the Risk Distributions for Anopheles sinensis, a Vector for Malaria in Shanghai, China

Understanding work-related travel and its relation to malaria occurrence in Thailand using geospatial maximum entropy modelling

A Follow-Up to the Geographical Distribution of Anopheles Species in Malaria-Endemic and Non-Endemic Areas of Honduras

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