Flight traits of dengue-infected Aedes aegypti mosquitoes.

Understanding the flight behaviour of dengue-infected mosquitoes can play a vital role in various contexts, including modelling disease risks and developing effective interventions against dengue. Studies on the locomotor activity of dengue-infected mosquitoes have often faced challenges in terms of methodology. Some studies used small tubes, which impacted the natural movement of the mosquitoes, while others that used cages did not capture the three-dimensional flights, despite mosquitoes naturally flying in three dimensions. In this study, we utilised Mask RCNN (Region-based Convolutional Neural Network) along with cubic spline interpolation to comprehensively track the three-dimensional flight behaviour of dengue-infected Aedes aegypti mosquitoes. This analysis considered a number of parameters as characteristics of mosquito flight, including flight duration, number of flights, Euclidean distance, flight speed, and the volume (space) covered during flights. The accuracy achieved for mosquito detection and tracking was 98.34% for flying mosquitoes and 100% for resting mosquitoes. Notably, the interpolated data accounted for only 0.31%, underscoring the reliability of the results. Flight traits results revealed that exposure to the dengue virus significantly increases the flight duration (p-value 0.0135 × 10-3) and volume (space) covered during flights (p-value 0.029) whilst decreasing the total number of flights compared to uninfected mosquitoes. The study did not observe any evident impact on the Euclidean distance (p-value 0.064) and speed (p-value 0.064) of Aedes aegypti. These results highlight the intricate relationship between dengue infection and the flight behaviour of Aedes aegypti, providing valuable insights into the virus transmission dynamics. This study focused on dengue-infected Aedes aegypti mosquitoes; future research can explore the impact of other arboviruses on mosquito flight behaviour.

EggCountAI: a convolutional neural network-based software for counting of Aedes aegypti mosquito eggs.

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.

Flight behaviour monitoring and quantification of aedes aegypti using convolution neural network.

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.

Advances in Understanding Vector Behavioural Traits after Infection.

Vector behavioural traits, such as fitness, host-seeking, and host-feeding, are key determinants of vectorial capacity, pathogen transmission, and epidemiology of the vector-borne disease. Several studies have shown that infection with pathogens can alter these behavioural traits of the arthropod vector. Here, we review relevant publications to assess how pathogens modulate the behaviour of mosquitoes and ticks, major vectors for human diseases. The research has shown that infection with pathogens alter the mosquito's flight activity, mating, fecundity, host-seeking, blood-feeding, and adaptations to insecticide bed nets, and similarly modify the tick's locomotion, questing heights, vertical and horizontal walks, tendency to overcome obstacles, and host-seeking ability. Although some of these behavioural changes may theoretically increase transmission potential of the pathogens, their effect on the disease epidemiology remains to be verified. This study will not only help in understanding virus-vector interactions but will also benefit in establishing role of these behavioural changes in improved epidemiological models and in devising new vector management strategies.