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Background: Mosquito-borne diseases exert a huge impact on both animal and human populations, posing substantial health risks. The behavioural and fitness traits of mosquitoes, such as locomotion and fecundity, are crucial factors that influence the spread of diseases. In existing egg-counting tools, each image requires separate processing with adjustments to various parameters such as intensity threshold and egg area size. Furthermore, accuracy decreases significantly when dealing with clustered or overlapping eggs. To overcome these issues, we have developed EggCountAI, a Mask Region-based Convolutional Neural Network (RCNN)-based free automatic egg-counting tool for Aedes aegypti mosquitoes. Methods: The study design involves developing EggCountAI for counting mosquito eggs and comparing its performance with two commonly employed tools—ICount and MECVision—using 10 microscopic and 10 macroscopic images of eggs laid by females on a paper strip. The results were validated through manual egg counting on the strips using ImageJ software. Two different models were trained on macroscopic and microscopic images to enhance egg detection accuracy, achieving mean average precision, mean average recall, and F1-scores of 0.92, 0.90, and 0.91 for the microscopic model, and 0.91, 0.90, and 0.90 for the macroscopic model, respectively. EggCountAI automatically counts eggs in a folder containing egg strip images, offering adaptable filtration for handling impurities of varying sizes. Results: The results obtained from EggCountAI highlight its remarkable performance, achieving overall accuracy of 98.88% for micro images and 96.06% for macro images. EggCountAI significantly outperformed ICount and MECVision, with ICount achieving 81.71% accuracy for micro images and 82.22% for macro images, while MECVision achieved 68.01% accuracy for micro images and 51.71% for macro images. EggCountAI also excelled in other statistical parameters, with mean absolute error of 1.90 eggs for micro, 74.30 eggs for macro, and a strong correlation and R-squared value (0.99) for both micro and macro. The superior performance of EggCountAI was most evident when handling overlapping or clustered eggs. Conclusion: Accurate detection and counting of mosquito eggs enables the identification of preferred egg-laying sites and facilitates optimal placement of oviposition traps, enhancing targeted vector control efforts and disease transmission prevention. In future research, the tool holds the potential to extend its application to monitor mosquito feeding preferences. [ABSTRACT FROM AUTHOR]

Mosquito-borne diseases cause a huge burden on public health worldwide. The viruses that cause these diseases impact the behavioural traits of mosquitoes, including locomotion and feeding. Understanding these traits can help in improving existing epidemiological models and developing effective mosquito traps. However, it is difficult to understand the flight behaviour of mosquitoes due to their small sizes, complicated poses, and seemingly random moving patterns. Currently, no open-source tool is available that can detect and track resting or flying mosquitoes. Our work presented in this paper provides a detection and trajectory estimation method using the Mask RCNN algorithm and spline interpolation, which can efficiently detect mosquitoes and track their trajectories with higher accuracy. The method does not require special equipment and works excellently even with low-resolution videos. Considering the mosquito size, the proposed method's detection performance is validated using a tracker error and a custom metric that considers the mean distance between positions (estimated and ground truth), pooled standard deviation, and average accuracy. The results showed that the proposed method could successfully detect and track the flying (≈ 96% accuracy) as well as resting (100% accuracy) mosquitoes. The performance can be impacted in the case of occlusions and background clutters. Overall, this research serves as an efficient open-source tool to facilitate further examination of mosquito behavioural traits. [ABSTRACT FROM AUTHOR]

Aedes aegypti mosquitoes carrying self-spreading, virus-blocking Wolbachia bacteria are being deployed to suppress dengue transmission. However, there are challenges in applying this technology in extreme environments. We introduced two Wolbachia strains into Ae. aegypti from Saudi Arabia for a release program in the hot coastal city of Jeddah. Wolbachia reduced infection and dissemination of dengue virus (DENV2) in Saudi Arabian mosquitoes and showed complete maternal transmission and cytoplasmic incompatibility. Wolbachia reduced egg hatch under a range of environmental conditions, with the Wolbachia strains showing differential thermal stability. Wolbachia effects were similar across mosquito genetic backgrounds but we found evidence of local adaptation, with Saudi Arabian mosquitoes having lower egg viability but higher adult desiccation tolerance than Australian mosquitoes. Genetic background effects will influence Wolbachia invasion dynamics, reinforcing the need to use local genotypes for mosquito release programs, particularly in extreme environments like Jeddah. Our comprehensive characterization of Wolbachia strains provides a foundation for Wolbachia-based disease interventions in harsh climates. Author summary: Wolbachia bacteria live inside the cells of many insects including mosquitoes. Wolbachia strains can block the transmission of dengue and other arboviruses transmitted by Aedes mosquitoes and are being introduced around the world for disease control. The success of this strategy relies on the ability of Wolbachia to spread locally by causing cytoplasmic incompatibility (embryo death when infected males mate with uninfected females), on Wolbachia having limited fitness costs in local mosquito hosts, and on Wolbachia persisting in the local mosquito background in the environmental conditions that mosquitoes encounter. Here we comprehensively assess the feasibility of using two Wolbachia strains in releases with Aedes aegypti mosquitoes in the challenging environment of Jeddah, Saudi Arabia. We show effective dengue virus blocking and incompatibility in both strains in the local mosquito genetic background but higher fitness costs for one strain which may offset its ability to maintain higher infection levels under the fluctuating temperatures expected in Jeddah. We find that the Jeddah Ae. aegypti are more resistant to desiccation, highlighting the importance of establishing infections in the local genetic background. These findings directly inform the employment of Wolbachia strategies in Jeddah and emphasize the value of testing the local context. [ABSTRACT FROM AUTHOR]