scholarly journals Serological biomarker for assessing human exposure to Aedes mosquito bites during a randomized vector control intervention trial in northeastern Thailand

2021 ◽  
Vol 15 (5) ◽  
pp. e0009440
Author(s):  
Benedicte Fustec ◽  
Thipruethai Phanitchat ◽  
Sirinart Aromseree ◽  
Chamsai Pientong ◽  
Kesorn Thaewnongiew ◽  
...  
Keyword(s):  
Vector Control ◽  
Human Exposure ◽  
Individual Characteristics ◽  
Mean Difference ◽  
Transmission Risk ◽  
Health Concern ◽  
Mosquito Density ◽  
Control Intervention ◽  
Vector Control Intervention ◽  
Northeastern Thailand

Background Aedes mosquitoes are vectors for several major arboviruses of public health concern including dengue viruses. The relationships between Aedes infestation and disease transmission are complex wherein the epidemiological dynamics can be difficult to discern because of a lack of robust and sensitive indicators for predicting transmission risk. This study investigates the use of anti-Aedes saliva antibodies as a serological biomarker for Aedes mosquito bites to assess small scale variations in adult Aedes density and dengue virus (DENV) transmission risk in northeastern Thailand. Individual characteristics, behaviors/occupation and socio-demographics, climatic and epidemiological risk factors associated with human-mosquito exposure are also addressed. Methods The study was conducted within a randomized clustered control trial in Roi Et and Khon Kaen provinces over a consecutive 19 months period. Thirty-six (36) clusters were selected, each of ten houses. Serological and entomological surveys were conducted in all houses every four months and monthly in three sentinel households per cluster between September 2017 and April 2019 for blood spot collections and recording concurrent immature and adult Aedes indices. Additionally, the human exposure to Aedes mosquito bites (i.e., Mosquito Exposure Index or MEI) was estimated by ELISA measuring levels of human antibody response to the specific Nterm-34 kDa salivary antigen. The relationships between the MEI, vector infestation indices (adult and immature stages) and vector DENV infection were evaluated using a two-level (house and individual levels) mixed model analysis with one-month lag autoregressive correlation. Results There was a strong positive relationship between the MEI and adult Aedes (indoor and outdoor) density. Individuals from households with a medium mosquito density (mean difference: 0.091, p<0.001) and households with a high mosquito density (mean difference: 0.131, p<0.001) had higher MEI’s compared to individuals from households without Aedes. On a similar trend, individuals from households with a low, medium or high indoor Aedes densities (mean difference: 0.021, p<0.007, 0.053, p<0.0001 and 0.037, p<0.0001 for low, medium and high levels of infestation, respectively) had higher MEI than individuals from houses without indoor Aedes., The MEI was driven by individual characteristics, such as gender, age and occupation/behaviors, and varied according to climatic, seasonal factors and vector control intervention (p<0.05). Nevertheless, the study did not demonstrate a clear correlation between MEI and the presence of DENV-infected Aedes. Conclusion This study represents an important step toward the validation of the specific IgG response to the Aedes salivary peptide Nterm-34kDa as a proxy measure for Aedes infestation levels and human-mosquito exposure risk in a dengue endemic setting. The use of the IgG response to the Nterm-34 kDa peptide as a viable diagnostic tool for estimating dengue transmission requires further investigations and validation in other geographical and transmission settings.

scholarly journals Vector control intervention towards interruption of transmission of Chagas disease by Rhodnius prolixus, main vector in Guatemala

2012 ◽  
Vol 107 (7) ◽  
pp. 877-887 ◽  
Author(s):  
Ken Hashimoto ◽  
Hugo Álvarez ◽  
Jun Nakagawa ◽  
Jaime Juarez ◽  
Carlota Monroy ◽  
...  
Keyword(s):  
Vector Control ◽  
Chagas Disease ◽  
Rhodnius Prolixus ◽  
Control Intervention ◽  
Vector Control Intervention

scholarly journals A significant increase inkdrinAnopheles gambiaeis associated with an intensive vector control intervention in Burundi highlands

2008 ◽  
Vol 13 (12) ◽  
pp. 1479-1487 ◽  
Author(s):  
Natacha Protopopoff ◽  
Katrijn Verhaeghen ◽  
Wim Van Bortel ◽  
Patricia Roelants ◽  
Tanguy Marcotty ◽  
...  
Keyword(s):  
Vector Control ◽  
Control Intervention ◽  
Vector Control Intervention

scholarly journals Impact of vector control on effective population sizes; empirical evidence for a control-based genetic bottleneck in the tsetse fly Glossina fuscipes

2020 ◽  
Author(s):  
Allan Muhwezi ◽  
Lucas J. Cunningham ◽  
Johan Esterhuizen ◽  
Inaki Tirados ◽  
Enock Matovu ◽  
...  
Keyword(s):  
Population Genetics ◽  
Vector Control ◽  
Genetic Bottleneck ◽  
Tsetse Fly ◽  
Effective Population ◽  
Intervention Area ◽  
Control Intervention ◽  
Short Period ◽  
Vector Control Intervention ◽  
The Impact

AbstractWe investigated genetic variation at 37 newly-developed microsatellite loci in populations of the tsetse fly Glossina fuscipes fuscipes captured from the upper and lower reaches of a single hydrographical network within an endemic Human African Trypanosomiasis focus. Our primary aim was to assess the impact of vector control using insecticide-treated baits (Tiny Targets) on genetic structure. We initially used STRUCTURE to delineate geographical boundaries of two stable ‘ancestral’ reference populations without any history of vector control but marked for either vector control (‘intervention’) or no control (‘non-intervention’). We then used the ADMIXTURE model to assess genetic divergence in temporal populations collected after vector control implementation. We applied the Linkage Disequilibrium method to explicitly measure spatial and temporal changes in effective population size (Ne). We observed a significant reduction in Ne coincident with vector control, whereas Ne remained stable in the non-intervention area. Our empirical findings show how classical population genetics approaches detected within a short period of time, a significant genetic bottleneck associated with vector control, and opens up the possibility of using routine genomic surveillance. We have also generated a resource of new genetic markers for studies on the population genetics of tsetse at finer-scale resolution.FundingThis work was funded through a Wellcome Trust Master’s Fellowship in Public Health and Tropical Medicine awarded to Allan Muhwezi (103268/Z/13/Z).

scholarly journals Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Paul Taconet ◽  
Angélique Porciani ◽  
Dieudonné Diloma Soma ◽  
Karine Mouline ◽  
Frédéric Simard ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Vector Control ◽  
Environmental Variables ◽  
Data Driven ◽  
Malaria Vectors ◽  
Environmental Determinants ◽  
Interpretable Machine Learning ◽  
Control Intervention ◽  
Vector Control Intervention

Abstract Background Improving the knowledge and understanding of the environmental determinants of malaria vector abundance at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work is aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors (Anopheles gambiae s.s., Anopheles coluzzii and Anopheles funestus) in the health district of Diébougou, rural Burkina Faso. Methods Anopheles human-biting activity was monitored in 27 villages during 15 months (in 2017–2018), and environmental variables (meteorological and landscape) were extracted from high-resolution satellite imagery. A two-step data-driven modeling study was then carried out. Correlation coefficients between the biting rates of each vector species and the environmental variables taken at various temporal lags and spatial distances from the biting events were first calculated. Then, multivariate machine-learning models were generated and interpreted to (i) pinpoint primary and secondary environmental drivers of variation in the biting rates of each species and (ii) identify complex associations between the environmental conditions and the biting rates. Results Meteorological and landscape variables were often significantly correlated with the vectors’ biting rates. Many nonlinear associations and thresholds were unveiled by the multivariate models, for both meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were identified or hypothesized for the Diébougou area, including breeding site typologies, development and survival rates in relation to weather, flight ranges from breeding sites and dispersal related to landscape openness. Conclusions Using high-resolution data in an interpretable machine-learning modeling framework proved to be an efficient way to enhance the knowledge of the complex links between the environment and the malaria vectors at a local scale. More broadly, the emerging field of interpretable machine learning has significant potential to help improve our understanding of the complex processes leading to malaria transmission, and to aid in developing operational tools to support the fight against the disease (e.g. vector control intervention plans, seasonal maps of predicted biting rates, early warning systems). Graphical abstract

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