Fine-scale modelling finds that breeding site fragmentation can reduce mosquito population persistence

Spatio-temporal characterization of phenotypic resistance in malaria vector species

BACKGROUND

Malaria, a deadly disease caused by Plasmodium protozoa parasite and transmitted through bites of infected female Anopheles mosquitoes, remains a significant public health challenge in sub-Saharan Africa. Efforts to eliminate malaria have increasingly focused on vector control using insecticides. However, the emergence of insecticide resistance (IR) in malaria vectors pose a formidable obstacle, and the current IR mapping models remain static, relying on fixed coefficients. This study introduces a dynamic spatio-temporal approach to characterize phenotypic resistance in Anopheles gambiae complex and Anopheles arabiensis. We developed a cellular automata (CA) model and applied it to data collected from Ethiopia, Nigeria, Cameroon, Chad, and Burkina Faso. The data encompasses georeferenced records detailing IR levels in mosquito vector populations across various classes of insecticides. In characterizing the dynamic patterns of confirmed resistance, we identified key driving factors through correlation analysis, chi-square tests, and extensive literature review.

RESULTS

The CA model demonstrated robustness in capturing the spatio-temporal dynamics of confirmed IR states in the vector populations. In our model, the key driving factors included insecticide usage, agricultural activities, human population density, Land Use and Land Cover (LULC) characteristics, and environmental variables.

CONCLUSIONS

The CA model developed offers a robust tool for countries that have limited data on confirmed IR in malaria vectors. The embrace of a dynamical modeling approach and accounting for evolving conditions and influences, contribute to deeper understanding of IR dynamics, and can inform effective strategies for malaria vector control, and prevention in regions facing this critical health challenge.

Non-household environments make a major contribution to dengue transmission: Implications for vector control

Aedes-borne pathogens have been increasing in incidence in recent decades despite vector control activities implemented in endemic settings. Vector control for Aedes-transmitted arboviruses typically focuses on households because vectors breed in household containers and bite indoors. Yet, our recent work shows a high abundance of Aedes spp. vectors in public spaces. To investigate the impact of non-household environments on dengue transmission and control, we used field data on the number of water containers and abundance of Aedes mosquitoes in Household (HH) and Non-Household (NH) environments in two Kenyan cities, Kisumu and Ukunda, from 2019-2022. Incorporating information on human activity space, we developed an agent-based model to simulate city-wide conditions considering HH and five types of NH environments in which people move and interact with other humans and vectors during peak biting times. We additionally evaluated the outcome of vector control activities implemented in different environments in preventive (before an epidemic) and reactive (after an epidemic commences) scenarios. We estimated that over half of infections take place in NH environments, where the main spaces for transmission are workplaces, markets, and recreational locations. Accordingly, results highlight the important role of vector control activities at NH locations to reduce dengue. A greater reduction of cases is expected as control activities are implemented earlier, at higher levels of coverage, with greater effectiveness when targeting only NH as opposed to when targeting only HH. Further, local ecological factors such as the differential abundance of water containers within cities are also influential factors to consider for control. This work provides insight into the importance of vector control in both household and non-household environments in endemic settings. It highlights a specific approach to inform evidence-based decision making to target limited vector control resources for optimal control.

Integrating vector control within an emerging agricultural system in a region of climate vulnerability in southern Malawi: A focus on malaria, schistosomiasis, and arboviral diseases

Infectious diseases are emerging at an unprecedented rate while food production intensifies to keep pace with population growth. Large-scale irrigation schemes have the potential to permanently transform the landscape with health, nutritional and socio-economic benefits; yet, this also leads to a shift in land-use patterns that can promote endemic and invasive insect vectors and pathogens. The balance between ensuring food security and preventing emerging infectious disease is a necessity; yet the impact of irrigation on vector-borne diseases at the epidemiological, entomological and economic level is uncertain and depends on the geographical and climatological context. Here, we highlight the risk factors and challenges facing vector-borne disease surveillance and control in an emerging agricultural ecosystem in the lower Shire Valley region of southern Malawi. A phased large scale irrigation programme (The Shire Valley Transformation Project, SVTP) promises to transform over 40,000 ha into viable and resilient farmland, yet the valley is endemic for malaria and schistosomiasis and experiences frequent extreme flooding events following tropical cyclones. The latter exacerbate vector-borne disease risk while simultaneously making any empirical assessment of that risk a significant hurdle. We propose that the SVTP provides a unique opportunity to take a One Health approach at mitigating vector-borne disease risk while maintaining agricultural output. A long-term and multi-disciplinary approach with buy-in from multiple stakeholders will be needed to achieve this goal.

RNA interference to combat the Asian tiger mosquito in Europe: A pathway from design of an innovative vector control tool to its application

The Asian tiger mosquito Aedes albopictus is currently spreading across Europe, facilitated by climate change and global transportation. It is a vector of arboviruses causing human diseases such as chikungunya, dengue hemorrhagic fever and Zika fever. For the majority of these diseases, no vaccines or therapeutics are available. Options for the control of Ae. albopictus are limited by European regulations introduced to protect biodiversity by restricting or phasing out the use of pesticides, genetically modified organisms (GMOs) or products of genome editing. Alternative solutions are thus urgently needed to avoid a future scenario in which Europe faces a choice between prioritizing human health or biodiversity when it comes to Aedes-vectored pathogens. To ensure regulatory compliance and public acceptance, these solutions should preferably not be based on chemicals or GMOs and must be cost-efficient and specific. The present review aims to synthesize available evidence on RNAi-based mosquito vector control and its potential for application in the European Union. The recent literature has identified some potential target sites in Ae. albopictus and formulations for delivery. However, we found little information concerning non-target effects on the environment or human health, on social aspects, regulatory frameworks, or on management perspectives. We propose optimal designs for RNAi-based vector control tools against Ae. albopictus (target product profiles), discuss their efficacy and reflect on potential risks to environmental health and the importance of societal aspects. The roadmap from design to application will provide readers with a comprehensive perspective on the application of emerging RNAi-based vector control tools for the suppression of Ae. albopictus populations with special focus on Europe.

Habitat loss estimation for assessing terrestrial mammalian species extinction risk: an open data framework

Terrestrial mammals face a severe crisis of habitat loss worldwide. Therefore, assessing information on habitat loss throughout different time periods is crucial for assessing species' conservation statuses based on the IUCN Red List system. To support the national extinction risk assessment in Brazil (2016-2022), we developed a script that uses the MapBiomas Project 6.0 data source of land cover and land use (annual maps at 30 m scale) within the Google Earth Engine (GEE) platform to calculate habitat loss. We defined suitable habitats from the MapBiomas Project land cover classification for 190 mammalian taxa, according to each species range map and ecological characteristics. We considered a period of three generation lengths to assess habitat loss in accordance with the Red List assessment criteria. We used the script to estimate changes in available habitat throughout the analyzed period within the species' known ranges. The results indicated that habitat loss occurred within 94.3% of the analyzed taxa range, with the Carnivora order suffering the greatest habitat loss, followed by the Cingulata order. These analyses may be decisive for applying criteria, defining categories during the assessment of at least 17 species (9%), enriching discussions, and raising new questions for several other species. We considered the outcome of estimating habitat loss for various taxa when applying criterion A, which refers to population reduction, thus supporting more accurate inferences about past population declines.

Modeling the impact of genetically modified male mosquitoes in the spatial population dynamics of Aedes aegypti

The mosquito Aedes aegypti is the primary vector of diseases such as dengue, Zika, chikungunya, and yellow fever. Improving control techniques requires a better understanding of the mosquito’s life cycle, including spatial population dynamics in endemic regions. One of the most promising techniques consists of introducing genetically modified male mosquitoes. Several models proposed to describe this technique present mathematical issues or rely on numerous parameters, making their application challenging to real-world situations. We propose a model describing the spatial population dynamics of the Aedes aegypti in the presence of genetically modified males. This model presents some mathematical improvements compared to the literature allowing deeper mathematical analysis. Moreover, this model relies on few parameters, which we show how to obtain or estimate from the literature. Through numerical simulations, we investigate the impacts of environmental heterogeneity, the periodicity of genetically modified male releases, and released genetically modified males quantity on the population dynamics of Aedes aegypti. The main results point to that the successful application of this vector control technique relies on releasing more than a critical amount of modified males with a frequency exceeding a specific critical value.

Strategies to Mitigate Establishment Using the Wolbachia Incompatible Insect Technique

The Incompatible Insect Technique (IIT) strategy involves the release of male mosquitoes infected with the bacterium Wolbachia. Regular releases of male Wolbachia-infected mosquitoes can lead to the suppression of mosquito populations, thereby reducing the risk of transmission of vector-borne diseases such as dengue. However, due to imperfect sex-sorting under IIT, fertile Wolbachia-infected female mosquitoes may potentially be unintentionally released into the environment, which may result in replacement and failure to suppress the mosquito populations. As such, mitigating Wolbachia establishment requires a combination of IIT with other strategies. We introduced a simple compartmental model to simulate ex-ante mosquito population dynamics subjected to a Wolbachia-IIT programme. In silico, we explored the risk of replacement, and strategies that could mitigate the establishment of the released Wolbachia strain in the mosquito population. Our results suggest that mitigation may be achieved through the application of a sterile insect technique. Our simulations indicate that these interventions do not override the intended wild type suppression of the IIT approach. These findings will inform policy makers of possible ways to mitigate the potential establishment of Wolbachia using the IIT population control strategy.

Modeling and simulation of the spatial population dynamics of the Aedes aegypti mosquito with an insecticide application

Background

The Aedes aegypti mosquito is the primary vector for several diseases. Its control requires a better understanding of the mosquitoes’ live cycle, including the spatial dynamics. Several models address this issue. However, they rely on many hard to measure parameters. This work presents a model describing the spatial population dynamics of Aedes aegypti mosquitoes using partial differential equations (PDEs) relying on a few parameters.

Methods

We show how to estimate model parameter values from the experimental data found in the literature using concepts from dynamical systems, genetic algorithm optimization and partial differential equations. We show that our model reproduces some analytical formulas relating the carrying capacity coefficient to experimentally measurable quantities as the maximum number of mobile female mosquitoes, the maximum number of eggs, or the maximum number of larvae. As an application of the presented methodology, we replicate one field experiment numerically and investigate the effect of different frequencies in the insecticide application in the urban environment.

Results

The numerical results suggest that the insecticide application has a limited impact on the mosquitoes population and that the optimal application frequency is close to one week.

Conclusions

Models based on partial differential equations provide an efficient tool for simulating mosquitoes’ spatial population dynamics. The reduced model can reproduce such dynamics on a sufficiently large scale.

Using spatial genetics to quantify mosquito dispersal for control programs

Background

Hundreds of millions of people get a mosquito-borne disease every year and nearly one million die. Transmission of these infections is primarily tackled through the control of mosquito vectors. The accurate quantification of mosquito dispersal is critical for the design and optimization of vector control programs, yet the measurement of dispersal using traditional mark-release-recapture (MRR) methods is logistically challenging and often unrepresentative of an insect’s true behavior. Using Aedes aegypti (a major arboviral vector) as a model and two study sites in Singapore, we show how mosquito dispersal can be characterized by the spatial analyses of genetic relatedness among individuals sampled over a short time span without interruption of their natural behaviors.

Results

Using simple oviposition traps, we captured adult female Ae. aegypti across high-rise apartment blocks and genotyped them using genome-wide SNP markers. We developed a methodology that produces a dispersal kernel for distance which results from one generation of successful breeding (effective dispersal), using the distance separating full siblings and 2nd- and 3rd-degree relatives (close kin). The estimated dispersal distance kernel was exponential (Laplacian), with a mean dispersal distance (and dispersal kernel spread σ) of 45.2 m (95% CI 39.7–51.3 m), and 10% probability of a dispersal > 100 m (95% CI 92–117 m). Our genetically derived estimates matched the parametrized dispersal kernels from previous MRR experiments. If few close kin are captured, a conventional genetic isolation-by-distance analysis can be used, as it can produce σ estimates congruent with the close-kin method if effective population density is accurately estimated. Genetic patch size, estimated by spatial autocorrelation analysis, reflects the spatial extent of the dispersal kernel “tail” that influences, for example, the critical radii of release zones and the speed of Wolbachia spread in mosquito replacement programs.

Conclusions

We demonstrate that spatial genetics can provide a robust characterization of mosquito dispersal. With the decreasing cost of next-generation sequencing, the production of spatial genetic data is increasingly accessible. Given the challenges of conventional MRR methods, and the importance of quantified dispersal in operational vector control decisions, we recommend genetic-based dispersal characterization as the more desirable means of parameterization.