Mapping the spatial distribution of Aedes aegypti and Aedes albopictus

Ding, Fang; Fu, Jingying; Jiang, Dong; Hao, Mengmeng; Lin, Gang

doi:10.1016/j.actatropica.2017.11.020

Cited by 90 publications

(76 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The uncontrolled ASF epidemic in Eastern Europe poses a serious threat to the pig industry throughout Europe, Central Asia and even China. Some studies have shown that using machine learning algorithms can help to better understand the factors associated with disease transmission and establish appropriate predictive models (Ding, Fu, Jiang, Hao, & Lin, ; Jiang et al, ; Machado et al, ; Safarishahrbijari & Osgood, ). A better understanding of the disease‐associated factors is a crucial step towards the definition of strategies for control and eradication.…”

Section: Discussionmentioning

confidence: 99%

“…China. Some studies have shown that using machine learning algorithms can help to better understand the factors associated with disease transmission and establish appropriate predictive models (Ding, Fu, Jiang, Hao, & Lin, 2018;Jiang et al, 2018;Machado et al, 2019;Safarishahrbijari & Osgood, 2019 with BF search method was used to search for the optimal feature subset. By comparing the calculation times of the model before and after feature selection, it can be found that the model calculation speed can be accelerated after using feature selection.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Liang

et al. 2019

Transbound Emerg Dis

View full text Add to dashboard Cite

African swine fever (ASF) is a virulent infectious disease of pigs. As there is no effective vaccine and treatment method at present, it poses a great threat to the pig industry once it breaks out. In this paper, we used ASF outbreak data and the WorldClim database meteorological data and selected the CfsSubset Evaluator‐Best First feature selection method combined with the random forest algorithms to construct an African swine fever outbreak prediction model. Subsequently, we also established a test set for data other than modelling, and the accuracy accuracy value range of the model on the independent test set was 76.02%–84.64%, which indicated that the modelling effect was better and the prediction accuracy was higher than previous estimates. In addition, logistic regression analysis was conducted on 12 features used for modelling and the ROC curves were drawn. The results showed that the bio14 features (precipitation of driest month) had the largest contribution to the outbreak of ASF, and it was speculated that the outbreak of the epidemic was significantly related to precipitation. Finally, we used this qualitative prediction model to build a global online prediction system for ASF outbreaks, in the hope that this study will help to decision‐makers who can then take the relevant prevention and control measures in order to prevent the further spread of future epidemics of the disease.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Liang

et al. 2019

Transbound Emerg Dis

View full text Add to dashboard Cite

show abstract

“…Correlative models are based on investigating the correlation existing between one or more variables and the current occurrence of the species. The main outputs of these models refers to species habitat suitability or potential spatial distribution (Benedict et al, 2007;Ding et al, 2018;Kraemer et al, 2019;Proestos et al, 2015;Santos and Mesenes, 2017;Sanz-Aguilar et al, 2018;Trájer et al, 2014). Despite their wide use, correlative approaches might have some limitations in predicting the potential distribution of the investigated species.…”

Section: Introductionmentioning

confidence: 99%

Development and calibration of a model for the potential establishment and impact of Aedes albopictus in Europe

et al. 2020

View full text Add to dashboard Cite

“…Mosquitoes in the family Culicidae, order Diptera, occur in virtually all regions of the planet. This family is divided into two subfamilies (Anophelinae and Culicinae) in which some species are considered vectors of pathogens of medical importance [1], such as Aedes Although A. aegypti is of African origin, its incidence is currently higher in the Americas, Southeast Asia, and the Western Pacific [4,6,7]. In Brazil, it is the main vector of the four dengue serotypes (DENV-1, DENV-2, DENV-3, DENV-4) and the urban yellow fever virus, which occurs throughout the Brazilian territory [2,3,8].…”

Section: Introductionmentioning

confidence: 99%

New traps for the capture of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) (Diptera: Culicidae) eggs and adults

Silva

et al. 2020

Preprint

View full text Add to dashboard Cite

The control of arboviruses carried by Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) can be performed with tools that monitor and reduce the circulation of these vectors. Therefore, the efficiency of four types of traps in capturing A. aegypti and A. albopictus eggs and adults, with the biological product Vectobac WG®, was evaluated in the field. For this, 20 traps were installed in two locations, which were in the South (Londrina, Paraná) and North (Manaus, Amazonas) Regions of Brazil, from March to April 2017 and January to February 2018, respectively. The UELtrap-E and UELtrap-EA traps captured A. aegypti and A. albopictus eggs: 1703/1866 eggs in Londrina, and 10268/2149 eggs in Manaus, respectively, and presented high ovitraps positivity index (OPI) values (averages: 100%/100% in Londrina, and 100%/96% in Manaus, respectively); and high egg density index (EDI) values (averages: 68/75 in Londrina, and 411/89 in Manaus, respectively), so they had statistically superior efficiency to that of the CRtrap-E and CRtrap-EA traps in both regions, that captured less eggs and adults: 96/69 eggs in Londrina, and 1091/510 eggs in Manaus, respectively. Also presented lower OPI values (averages: 28%/4% in Londrina, and 88%/60% in Manaus, respectively); and lower EDI values (averages: 10.5/9 in Londrina, and 47/30 in Manaus, respectively). The capture ratios of Aedes adults in the UELtrap-EA and CRtrap-EA traps in Londrina and Manaus were 53.3%/29.5% and 0%/9.8%, respectively. UELtrap-E and UELtrap-EA can be adopted as efficient tools for Aedes monitoring due to their high sensitivity, low cost and ease of use.Author summaryAedes aegypti and Aedes albopictus are species of mosquitoes responsible for the transmission of several arboviruses that cause infections worldwide. However, there are still no effective and safe vaccines or medications to prevent or treat arboviruses transmitted by these vectors, except for yellow fever. Moreover, current methodologies for monitoring and controlling A. aegypti and A. albopictus are not fully effective, as evidenced by the increasing cases of the arbovirus transmitted by these mosquitoes or have incompatible costs with the socioeconomic conditions of a large number of people. Thus, the traps tested in this study can be used as more effective and economical tools for monitoring and controlling A. aegypti and A. albopictus, since they are made with low cost material and they showed high efficiency in the capture of eggs, evidenced by the high values of ovitraps positive index and eggs density index, besides that one of the models captured Aedes spp. adults in both regions where they were tested. Therefore, the traps have potential for reducing Aedes spp. eggs and adults in the environment and sensibility for determining the local infestation index, which can be reconciled with official government strategies for more accurate vector monitoring and control actions.

show abstract

Mapping the spatial distribution of Aedes aegypti and Aedes albopictus

Cited by 90 publications

References 39 publications

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Prediction for global African swine fever outbreaks based on a combination of random forest algorithms and meteorological data

Development and calibration of a model for the potential establishment and impact of Aedes albopictus in Europe

New traps for the capture of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) (Diptera: Culicidae) eggs and adults

Contact Info

Product

Resources

About