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Mosquito-borne diseases remain a major public-health threat, and the effective control of mosquito populations requires sustained household participation in removing breeding sites. While environmental drivers of mosquito oscillations have been extensively studied, the influence of spontaneous household decision-making on the dynamics of mosquito populations remains poorly understood. We introduce a game-theoretic model in which the fraction of households performing breeding site control evolves through imitation dynamics driven by perceived risks. Household behavior regulates the carrying capacity of the aquatic mosquito stage, creating a feedback between control actions and mosquito population growth. For a simplified model with constant payoffs, we characterize four locally stable equilibria, corresponding to full or no household control and the presence or absence of mosquito populations. When the perceived risk of not controlling breeding sites depends on mosquito prevalence, the system admits an additional equilibrium with partial household engagement. We derive conditions under which this equilibrium undergoes a Hopf bifurcation, yielding sustained oscillations arising solel

The risk of mosquito-borne disease outbreaks is tightly linked to the frequency at which mosquitoes feed on blood, also known as the biting rate. However, standard models of mosquito-borne disease transmission inherently assume that mosquitoes bite only once per reproductive cycle -- an assumption commonly violated in nature. Drivers of multiple biting also affect the mosquito gonotrophic cycle duration (GCD), the quantity customarily used to estimate biting rates. Here, we present a novel framework for incorporating more complex mosquito biting behaviors into transmission models, accounting for heterogeneity and linkages between mosquito biting rates and multiple biting. We provide general formulas for the basic offspring number, $\mathcal{N}_0$, and basic reproduction number, $\mathcal{R}_0$, threshold measures for mosquito population and pathogen transmission persistence, respectively. To exhibit its flexibility, we expand on specific models derived from the framework that arise from empirical, phenomenological, or mechanistic modeling perspectives. Using the gonotrophic cycle duration as a standard quantity to make comparisons among the models, we show that assumptions about th

Mosquito vector competence is usually represented as a process in which once virus is detected in saliva, mosquitoes are assumed to remain infectious for life, implying an irreversible transition to the transmitting state. However, some experiments report declines in the proportion of transmitting mosquitoes at late times post-exposure, suggesting transmission capacity may not be permanent. To investigate this hypothesis, we extended a previously developed stochastic intra-vector viral dynamics model by introducing transmission states allowing either permanent cessation or temporary interruption of transmission. We fitted three competing models to data from 52 vector competence conditions covering chikungunya, dengue, Zika, West Nile, and Rift Valley fever viruses, using Approximate Bayesian Computation with Sequential Monte Carlo inference. Among the 10 experimental conditions showing decline in transmitter proportions, models allowing exit from the transmitting state provided a better fit in 7 cases, with clear improvement in 5. In these cases, allowing interruption of transmission increased posterior estimates of the proportion of mosquitoes that crossed all intra-mosquito barri

Mosquito-borne diseases pose a serious global health threat, causing over 700,000 deaths annually. This work introduces a proof-of-concept Synthetic Swarm Mosquito Dataset for Acoustic Classification, created to simulate realistic multi-species and noisy swarm conditions. Unlike conventional datasets that require labor-intensive recording of individual mosquitoes, the synthetic approach enables scalable data generation while reducing human resource demands. Using log-mel spectrograms, we evaluated lightweight deep learning architectures for the classification of mosquito species. Experiments show that these models can effectively identify six major mosquito vectors and are suitable for deployment on embedded low-power devices. The study demonstrates the potential of synthetic swarm audio datasets to accelerate acoustic mosquito research and enable scalable real-time surveillance solutions.

A deterministic nonlinear ordinary differential equation model for mosquito dynamics in which the mosquitoes can quest for blood either within a human population or within non-human/vertebrate populations is derived and studied. The model captures both the mosquito's aquatic and terrestrial forms and includes a mechanism to investigate the impact of mating on mosquito dynamics. The model uses a restricted form of homogeneous mixing based on the idea that the mosquito has a blood-feeding habit by accounting for the mosquitoes' blood-feeding preferences as well as its gonotrophic cycle. This characterization allows us to compartmentalise the total mosquito population into distinct compartments according to the spatial location of the mosquito (breeding site, resting places and questing places) as well as blood-fed status. Issues of overcrowding and intraspecific competition both within the aquatic and the terrestrial stages of the mosquito's life forms are addressed and considered in the model. Results show that the inclusion of mating induces bi-stability; a phenomenon whereby locally stable trivial and non-trivial equilibria co-exist with an unstable non-zero equilibrium. The local

Mosquito-borne diseases affect more than one billion people each year and cause close to one million deaths. Traditional surveillance methods rely on traps and manual identification that are slow, labor-intensive, and difficult to scale. Audio-based mosquito monitoring offers a non-destructive, lower-cost, and more scalable complement to trap-based surveillance, but reliable species classification remains difficult under real-world recording conditions. Mosquito flight tones are narrow-band, often low in signal-to-noise ratio, and easily masked by background noise, and recordings for several epidemiologically relevant species remain limited, creating pronounced class imbalance. Variation across devices, environments, and collection protocols further increases the difficulty of robust classification. Such variation can cause models to rely on domain-specific recording artefacts rather than species-relevant acoustic cues, which makes transfer to new acquisition settings difficult. The BioDCASE 2026 Cross-Domain Mosquito Species Classification (CD-MSC) challenge is designed around this deployment problem by evaluating performance on both seen and unseen domains. This paper presents th

This paper is devoted to the analysis of a reaction-diffusion system with strong competition and spatial heterogeneities modelling the interaction between two species of mosquitoes. In particular, we propose a mathematical model that accounts for the spatial segregation observed between two species of mosquito vectors of numerous viruses. Indeed, it has been observed that, in tropical regions, Aedes aegypti mosquitoes are well established in urban areas whereas Aedes albopictus mosquitoes spread widely in forest regions. Moreover, these species of mosquitoes compete with each other in the larval stage. Based on these observations, we introduce a simple mathematical model to account for this phenomenon. This model consists of a system of reaction-diffusion equations describing the dynamics of the aquatic and aerial phases of each species in a spatially heterogeneous environment. The competition takes place at the aquatic phase and is assumed to be strong which allows us to reduce the dimensionality of the system. We first establish a sufficient condition on the parameters to prevent one species from invading another in a homogeneous environment. Next, using this sufficient condition

Wolbachia is a naturally occurring bacterium that can infect Aedes mosquitoes and reduce the transmission of mosquito-borne diseases, including dengue fever, Zika, and chikungunya. Field trials have been conducted worldwide to suppress local epidemics. We introduce a novel partial differential equation model to simulate the spread of Wolbachia infection in mosquito populations. Our model incorporates the intricate Wolbachia maternal transmission cycle and detailed mosquito life stages, while also accounting for the spatial heterogeneity induced by mosquito dispersion across a two-dimensional domain. Prior modeling studies and field data indicate that a critical threshold of Wolbachia-infected mosquitoes is necessary for infection to persist among the mosquito population. Through our spatial model, we identify a threshold condition, termed the ``critical bubble'', for having a self-sustainable Wolbachia infection in the field. When releasing beyond this threshold, the model predicts a spatial wave of Wolbachia infection. We further quantify how this threshold and infection wave velocity depend on the diffusion process and other parameters. We numerically study various intervention s

The rapid spread of West Nile Virus (WNV) is a growing concern. With no vaccines or specific medications available, prevention through mosquito control is the only solution to curb the spread. Mosquito traps, used to detect viral presence in mosquito populations, are essential tools for WNV surveillance. But how do we decide where to place a mosquito trap? And what makes a good trap location, anyway? We present a robust statistical approach to determine a mosquito trap's ability to predict human WNV cases in the Chicago metropolitan area and its suburbs. We then use this value to detect the landscape, demographic, and socioeconomic factors associated with a mosquito trap's predictive ability. This approach enables resource-limited mosquito control programs to identify better trap locations while reducing trap numbers to increase trap-based surveillance efficiency. The approach can also be applied to a wide range of different environmental surveillance programs.

Mosquito-borne diseases pose a major global health risk, requiring early detection and proactive control of breeding sites to prevent outbreaks. In this paper, we present VisText-Mosquito, a multimodal dataset that integrates visual and textual data to support automated detection, segmentation, and explanation for mosquito breeding site analysis. The dataset includes 1,828 annotated images for object detection, 142 images for water surface segmentation, and natural language explanation texts linked to each image. The YOLOv9s model achieves the highest precision of 0.92926 and mAP@50 of 0.92891 for object detection, while YOLOv11n-Seg reaches a segmentation precision of 0.91587 and mAP@50 of 0.79795. For textual explanation generation, we tested a range of large vision-language models (LVLMs) in both zero-shot and few-shot settings. Our fine-tuned Mosquito-LLaMA3-8B model achieved the best results, with a final loss of 0.0028, a BLEU score of 54.7, BERTScore of 0.91, and ROUGE-L of 0.85. This dataset and model framework emphasize the theme "Prevention is Better than Cure", showcasing how AI-based detection can proactively address mosquito-borne disease risks. The dataset and impleme

Vector-Borne Disease (VBD) is an infectious disease transmitted through the pathogenic female Aedes mosquito to humans and animals. It is important to control dengue disease by reducing the spread of Aedes mosquito vectors. Community awareness plays acrucial role to ensure Aedes control programmes and encourages the communities to involve active participation. Identifying the species of mosquito will help to recognize the mosquito density in the locality and intensifying mosquito control efforts in particular areas. This willhelp in avoiding Aedes breeding sites around residential areas and reduce adult mosquitoes. To serve this purpose, an android application are developed to identify Aedes species that help the community to contribute in mosquito control events. Several Android applications have been developed to identify species like birds, plant species, and Anopheles mosquito species. In this work, a user-friendly mobile application mAedesID is developed for identifying the Aedes mosquito species using a deep learning Convolutional Neural Network (CNN) algorithm which is best suited for species image classification and achieves better accuracy for voluminous images. The mobile

Mosquito Species Classification (MSC) is crucial for vector surveillance and disease control. The collection of mosquito bioacoustic data is often limited by mosquito activity seasons and fieldwork. Mosquito recordings across regions, habitats, and laboratories often show non-biological variations from the recording environment, which we refer to as domain features. This study finds that models directly trained on audio recordings with domain features tend to rely on domain information rather than the species' acoustic cues for identification, resulting in illusory good performance while actually performing poor cross-domain generalization. To this end, we propose a Domain-Robust Bioacoustic Learning (DR-BioL) framework that combines contrastive learning with distribution alignment. Contrastive learning aims to promote cohesion within the same species and mitigate inter-domain discrepancies, and species-conditional distribution alignment further enhances cross-domain species representation. Experiments on a multi-domain mosquito bioacoustic dataset from diverse environments show that the DR-BioL improves the accuracy and robustness of baselines, highlighting its potential for relia

In this paper, we present a novel approach to the development and deployment of an autonomous mosquito breeding place detector rover with the object and obstacle detection capabilities to control mosquitoes. Mosquito-borne diseases continue to pose significant health threats globally, with conventional control methods proving slow and inefficient. Amidst rising concerns over the rapid spread of these diseases, there is an urgent need for innovative and efficient strategies to manage mosquito populations and prevent disease transmission. To mitigate the limitations of manual labor and traditional methods, our rover employs autonomous control strategies. Leveraging our own custom dataset, the rover can autonomously navigate along a pre-defined path, identifying and mitigating potential breeding grounds with precision. It then proceeds to eliminate these breeding grounds by spraying a chemical agent, effectively eradicating mosquito habitats. Our project demonstrates the effectiveness that is absent in traditional ways of controlling and safeguarding public health. The code for this project is available on GitHub at - https://github.com/faiyazabdullah/MosquitoMiner

An ordinary differential model is proposed to understand the role of egg quiescence on the efficacy of releasing Wolbachia-infected mosquitoes to control arbovirus transmission. The model admits up to five equilibrium points and four biologically meaningful scenarios: extinction of both populations; persistence of the uninfected population with extinction of the infected one; persistence of the infected population with extinction of the uninfected one; and coexistence of both populations. This occurs because the coexistence scenario allows for bistability in the system. A sensitivity analysis shows that mosquitoes optimize their fitness by adjusting the quiescence rate. Because Wolbachia-infected eggs do not survive quiescence, or the adults that emerge are infertile, quiescence negatively impacts the fitness of infected mosquitoes, thereby reducing the prevalence of infection in the mosquito population. This increases the chance of encountering an uninfected mosquito, which is more likely to carry the dengue virus compared to a Wolbachia-infected one, potentially increasing the risk of dengue transmission during or after environmental stress that triggers quiescence. More importan

The sterile insect technique has emerged recently as a biologically secure and effective tool for suppressing wild mosquito pests. To improve the performance of this strategy, understanding the interaction between wild and sterile mosquitoes is critical. Although the common models for this biological problem are scalar equations, they are remarkably resistant to the mathematical analysis. In a series of papers, Dueñas, Nuñez, and Obaya have developed a powerful approach to describe the dynamical behavior of scalar equations with d-concave nonlinearities, a property typically related to the sign of the third derivative. In this paper, we show that, for periodic equations coming from population dynamics, this condition is typically associated with the positive sign of the third derivative of the inverse of the Poincaré map. This remark allows us to simplify some arguments in the periodic case and obtain a deep geometrical understanding of the global bifurcation patterns. Consequently, the dynamical behavior of the models is analyzed in terms of simple and testable conditions. Our methodology allows us to describe precisely the dynamical behavior of the common mosquito population supp

Mosquito-related diseases pose a significant threat to global public health, necessitating efficient and accurate mosquito classification for effective surveillance and control. This work presents an innovative approach to mosquito classification by leveraging state-of-the-art vision transformers and open-set learning techniques. A novel framework has been introduced that integrates Transformer-based deep learning models with comprehensive data augmentation and preprocessing methods, enabling robust and precise identification of ten mosquito species. The Swin Transformer model achieves the best performance for traditional closed-set learning with 99.80% accuracy and 0.998 F1 score. The lightweight MobileViT technique attains an almost similar accuracy of 98.90% with significantly reduced parameters and model complexities. Next, the applied deep learning models' adaptability and generalizability in a static environment have been enhanced by using new classes of data samples during the inference stage that have not been included in the training set. The proposed framework's ability to handle unseen classes like insects similar to mosquitoes, even humans, through open-set learning fur

Mosquitoes are vectors of diseases like malaria, dengue fever, yellow fever, chikungunya and Zika. For mosquito control it is crucial to understand their hearing system, as mosquitoes' courting behavior is mostly auditory. Many nonlinear characteristics of the mosquito hearing organ have been observed through behavioral studies and neural measurements. These enable mosquitoes to detect and synchronize to other mosquitoes. Many hypotheses concerning the role of the flagellum and the fibrillae of the antenna in mosquito hearing have been made, and neural processes have been considered as the origin of the nonlinearities. In this study we introduce a geometric model based on the morphology of the mosquito antenna base. The model produces many of the observed nonlinear characteristics, providing evidence that the base of the antenna plays a crucial role in mosquito hearing. Even without neural processing, the antenna response to sound produces behaviorally relevant cues that can inform about the presence, location and sex of other mosquitoes.

Mosquito-borne diseases cause significant public health burden, mostly in tropical and sub-tropical regions, and are widely emerging or re-emerging in areas where previously absent. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modeling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for the movement of individuals through mosquito habitats and responds to environmental factors such as rainfall and temperature. Our approach extends the capabilities of existing agent-based models for individual movement developed to predict the spread of directly transmitted pathogens in populations. To extend to mosquito-borne disease, agent-based models are coupled with differential equations representing `clouds' of mosquitoes in geographic patches that account for mosquito ecology, including heterogeneity in mosquito density, emergence rates, and extrinsic incubation period. We illustrate the method by adapting an agent-based model for human movement across a network to mosquito-borne disease. We investigated the importance of heterogeneity in mosquito

Releasing sterile Wolbachia-infected mosquitoes to invade wild mosquito population is a method of mosquito control. In this paper, a stochastic mosquito population model with Wolbachia invasion perturbed by environmental fluctuation is studied. Firstly, well-posedness, positivity and Markov-Feller property of solution for this model are proved. Then a group of sharp threshold-type conditions is provided to characterize the long-term behavior of the model, which pinpoints the almost necessary and sufficient conditions for persistence and extinction of Wolbachia-infected and uninfected mosquito populations. Especially, our results indicates that even the initial Wolbachia infection frequency is low, the Wolbachia invasion into wild mosquito population can be promoted by stochastic environmental fluctuations. Finally, some numerical experiments are carried out to support our theoretical results.