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Background & objectives: The affirmation about the prevalence of mosquito species at a particular place and time is very significant, not only to predict the danger of diseases or future outbreaks but also to control the vectors in time. Despite mosquitoes being medically important, the information about its faunal diversity is very scanty as far as Chandigarh in India and its nearby areas are concerned. So, this study was carried out to survey the mosquito fauna from areas in and around Chandigarh in northern India. Methods: Detailed mosquito surveys were carried out to explore the mosquito fauna from various habitats of developed urban areas, gardens, slums and surrounding villages of Chandigarh from June 2017-November 2019 using hand nets and oral aspirators. Results: A total of 34 mosquito species belonging to 8 genera viz; Anopheles, Aedes, Armigeres, Culex, Coquillet-tidia, Mansonia, Mimomyia and Verrallina were recorded, identified and preserved along with detailed collection data, of which eight are new records from Chandigarh. Interpretation & conclusion: The present checklist of mosquito fauna comprising 34 species provides information on the occurrence of mosquito vectors in Chandigarh and its adjoining areas which will be beneficial for the health authorities to adopt appropriate measures in time for the control of these vectors.

The need for a novel automated mosquito perception and classification method is becoming increasingly essential in recent years, with steeply increasing number of mosquito-borne diseases and associated casualties. There exist remote sensing and GIS-based methods for mapping potential mosquito inhabitants and locations that are prone to mosquito-borne diseases, but these methods generally do not account for species-wise identification of mosquitoes in closed-perimeter regions. Traditional methods for mosquito classification involve highly manual processes requiring tedious sample collection and supervised laboratory analysis. In this research work, we present the design and experimental validation of an automated vision-based mosquito classification module that can deploy in closed-perimeter mosquito inhabitants. The module is capable of identifying mosquitoes from other bugs such as bees and flies by extracting the morphological features, followed by support vector machine-based classification. In addition, this paper presents the results of three variants of support vector machine classifier in the context of mosquito classification problem. This vision-based approach to the mosquito classification problem presents an efficient alternative to the conventional methods for mosquito surveillance, mapping and sample image collection. Experimental results involving classification between mosquitoes and a predefined set of other bugs using multiple classification strategies demonstrate the efficacy and validity of the proposed approach with a maximum recall of 98%.

Background Current malaria surveillance methods are considered too expensive to scale-up within limited-resource settings; hence, new technologies and approaches are necessary to maximize the collection of data and ultimately design new malaria control tools. Effective mosquito surveillance can be enhanced through the utilization of digital technologies and the engagement of citizens in real-time data collection. This study used the HumBug acoustic sensor with the MozzWear app to detect and identify host-seeking mosquitoes based on their flight sounds, with citizens receiving airtime incentives for recording and uploading sounds. Methods A randomized controlled trial was used to assess the role of incentives to encourage the local community to record and upload mosquito sounds using the MozzWear application. Participants were randomized into two groups: (1) a control group, in which no incentive was provided; and (2) an incentive group, in which airtime credit was provided to participants. Both groups were provided with HumBug smartphones running the MozzWear app plus adapted mosquito bed nets (‘HumBug Nets’) to hold the phones during recording and were asked to record and upload mosquito flight tone data once per week for a period of four months. The intervention group was rewarded with an airtime incentive every week after the data were uploaded. At the end of the study, an experience survey was administered to participants in both groups to assess their experience participating in this study. Results The overall results indicate that the control group performed well in terms of the number of nights spent recording and uploading data compared to the incentive group. The level of intrinsic and extrinsic motivation differs between demographic variables. Their feedback suggested that fighting against malaria was more important and was everyone’s concern in rural Tanzania. In addition, the participants expressed their interest in being involved in future research related to mosquito surveillance and the fight against malaria. Conclusion Citizens can play a valuable role in scientific research; even without giving them incentives, they can still participate in the study. By participating in mosquito surveillance and malaria prevention studies, community members can make significant contributions to addressing mosquito-borne diseases and improving health outcomes.

Arbovirus epidemiology lacks efficient and timely surveillance systems with accurate outbreak alert signals. We devised a citywide integrated surveillance system combining entomologic, epidemiologic, and entomo-virologic data gathered during 2017-2020 in Foz do Iguaçu, Brazil. We installed 3,476 adult mosquito traps across the city and inspected traps every 2 months. We compared 5 entomologic indices: traditional house and Breteau indices for larval surveys and trap positivity, adult density, and mosquitoes per inhabitant indices for adult trapping. We screened for dengue, Zika, and chikungunya viruses in live adult Aedes aegypti mosquitoes collected from traps. Indices based on adult mosquito sampling had higher outbreak predictive values than larval indices, and we were able to build choropleth maps of infestation levels <36 h after each round of trap inspection. Locating naturally infected vectors provides a timely support tool for local public health managers to prioritize areas for intervention response to prevent virus outbreaks.

Background Aedes albopictus is catalogued as one of the 100 most dangerous species worldwide. Native to Asia, the species has drastically increased its distribution range, reaching all continents except Antarctica. The presence of Ae . albopictus in Spain was first reported in 2004 in Cataluña (NE Spain), and it is spreading in the country. Methods We conducted an extensive mosquito monitoring study in the natural protected area of the Doñana National Park (SW Spain) in 2023. After identifying the presence of Ae . albopictus , a mosquito control strategy was developed and implemented to eradicate the species in the area. Results Overall, 12,652 mosquito females of 14 different species were captured at nine sites within the park. For the first time, the presence of Ae . albopictus was recorded in the area, despite intensive trapping performed at some localities since 2003. The presence of this invasive species in the park is most likely linked to human activities, potentially facilitated by daily car trips of personnel. Although larvae of Culex , Anopheles , and Culiseta mosquitoes were identified in these containers, the presence of Ae . albopictus larvae was not recorded in those locations. In spite of that, the biological larvicide Bacillus thuringiensis israelensis (Bti) was applied to artificial containers potentially used by Ae . albopictus as breeding sites. Conclusions This work evidences the high capacity of Ae . albopictus to reach highly conserved natural areas far from urban foci. We discuss the implications of the presence of Ae . albopictus in this endangered ecosystem and the potential control measures necessary to prevent its reintroduction. Graphical Abstract

The citizen science project ‘Mueckenatlas' (mosquito atlas) was implemented in early 2012 to improve mosquito surveillance in Germany. Citizens are asked to support the spatiotemporal mapping of culicids by submitting mosquito specimens collected in their private surroundings. The Mueckenatlas has developed into an efficient tool for data collection with close to 30,000 mosquitoes submitted by the end of 2015. While the vast majority of submissions included native mosquito species, a small percentage represented invasive species. The discovery of Aedes albopictus (Skuse) (Diptera: Culicidae), Aedes japonicus japonicus (Theobald) (Diptera: Culicidae) and Aedes koreicus (Edwards) (Diptera: Culicidae) specimens via the Mueckenatlas project prompted targeted monitoring activities in the field which produced additional information on the distribution of these species in Germany. Among others, Mueckenatlas submissions led to the detection of three populations of Ae. j. japonicus in West, North and Southeast Germany in 2012, 2013, and 2015, respectively. As demonstrated by on-site monitoring, the origins of Ae. j. japonicus specimens submitted to the Mueckenatlas mirror the distribution areas of the four presently known German populations as found by active field sampling (the fourth population already reported prior to the launch of the Mueckenatlas). The data suggest that a citizen science project such as the Mueckenatlas may aid in detecting changes in the mosquito fauna and can therefore be used to guide the design of more targeted field surveillance activities.

We compared the number of Aedes aegypti females per trap and the number of detections of this mosquito species per week during 8 wk in 3 types of autocidal gravid traps, the Centers for Disease Control and Prevention (CDC) Autocidal Gravid Ovitrap (AGO), Biogents Gravid Aedes Trap (GAT), and Singapore Gravitrap (SGT), in central Puerto Rico. These traps use the same principles for attracting gravid Ae. aegypti females as traditional ovitraps, such as dark colors, standing water, and decomposing plant materials. The traps differ in size, AGO being the biggest and SGT the smallest. Average captures of female Ae. aegypti per trap per week were 11.1, 7.2, and 1.7 in AGO, GAT, and SGT traps, respectively, a pattern consistent with the sizes of the traps. These results indicated that GAT traps and SGT traps captured 35.5% and 84.7% fewer females of Ae. aegypti , respectively, than AGO traps. Although Ae. aegypti was present in all 20 sites during the 8 wk of observations, AGO, GAT, and SGT traps did not catch specimens in 1, 9, and 58 out of 160 observations per trap type (trap-wk), respectively. Trap failures were 1, 6, and 1 for the AGO, GAT, and SGT traps, respectively. Despite the absence of females of Ae. aegypti at some sites and weeks in each of the traps, all 3 traps were able to detect the presence of this mosquito at each of the 20 sites during the 8 wk of observations and could be used for Ae . aegypti surveillance.

Background Urbanization and land-use changes profoundly affect mosquito ecology, potentially altering species’ abundance, seasonal dynamics, and pathogen transmission risk. The invasive mosquito Aedes albopictus has rapidly expanded from Southeast Asia to temperate regions worldwide, including Europe, where it now coexists with native species such as Culex pipiens . Both are competent vectors of zoonotic pathogens and may respond differently to urban environmental gradients. Methods We assessed the impact of urbanization on mosquito populations by comparing the abundance and seasonality of Ae. albopictus and Cx. pipiens in urban and periurban areas of Granada, southern Spain, over two consecutive years (2023–2024). A total of 19 mosquito trapping sessions were conducted using BG-Sentinel traps baited with CO 2 and BG-Lure, covering the main seasonal activity period. Additionally, 260 mosquito pools were screened for Dirofilaria spp. DNA using PCR. Results A total of 450 Ae. albopictus and 641 Cx. pipiens females were captured. Ae. albopictus showed a unimodal seasonal pattern, peaking from late July to late August, with a more pronounced increase in urban sites. Cx. pipiens was more abundant in periurban areas, especially during its late July peak. Generalized linear mixed models revealed significant interactions between habitat and year for Cx. pipiens : abundance declined in periurban habitats from 2023 to 2024 but remained stable in urban areas. Seasonal peaks were also more extended in periurban sites. All mosquito pools tested negative for Dirofilaria spp. Conclusions Our findings highlight species-specific responses to urbanization, with Ae. albopictus favoring urban environments and Cx. pipiens thriving in periurban zones. These patterns underscore the need for habitat- and species-specific vector surveillance and control strategies. Urban control efforts should target early summer Ae. albopictus peaks, particularly around artificial breeding sites, while periurban interventions should focus on Cx. pipiens populations that persist later in the season. The absence of Dirofilaria DNA suggests low current transmission, but continued molecular surveillance is warranted, particularly in periurban areas where high densities of Cx. pipiens vectors and animal reservoirs may overlap under changing environmental conditions. Graphical Abstract

Mosquito-borne diseases can pose serious risks to human health. Therefore, mosquito surveillance and control programs are essential for the wellbeing of the community. Further, human-assisted mosquito surveillance and population mapping methods are time-consuming, labor-intensive, and require skilled manpower. This work presents an AI-enabled mosquito surveillance and population mapping framework using our in-house-developed robot, named ‘Dragonfly’, which uses the You Only Look Once (YOLO) V4 Deep Neural Network (DNN) algorithm and a two-dimensional (2D) environment map generated by the robot. The Dragonfly robot was designed with a differential drive mechanism and a mosquito trapping module to attract mosquitoes in the environment. The YOLO V4 was trained with three mosquito classes, namely Aedes aegypti, Aedes albopictus, and Culex, to detect and classify the mosquito breeds from the mosquito glue trap. The efficiency of the mosquito surveillance framework was determined in terms of mosquito classification accuracy and detection confidence level on offline and real-time field tests in a garden, drain perimeter area, and covered car parking area. The experimental results show that the trained YOLO V4 DNN model detects and classifies the mosquito classes with an 88% confidence level on offline mosquito test image datasets and scores an average of an 82% confidence level on the real-time field trial. Further, to generate the mosquito population map, the detection results are fused in the robot’s 2D map, which will help to understand mosquito population dynamics and species distribution.

Malaria was once endemic in the United States prior to its elimination in 1951. However, due to consistent introductions of travel-associated malaria cases and the presence of several native Anopheles species (Diptera: Culicidae) that are competent vectors of malaria, the potential for local (autochthonous) malaria transmission remains a persistent threat in the United States. While several intermittent cases of local malaria transmission have occurred in the United States in the decades since elimination, the emergence of autochthonous transmission in 4 states in 2023 demonstrates the continued risk for future outbreaks. Moreover, these recent examples also highlight significant gaps in current mosquito surveillance efforts that have predominantly focused on threats of arboviral disease, such that our understanding of Anopheles distributions relies only on historical records and offers limited insight into the ecological factors that influence their abundance. Herein, we summarize mosquito surveillance data collected over the last 20 years (2004–2023) across 59 Iowa counties to provide essential information into the spatial distribution, temporal abundance, and trap preferences of Anopheles species in the state. Further analyses of the 2 most abundant species, Anopheles punctipennis Say and Anopheles quadrimaculatus Say, reveal the additional influence of precipitation and forested habitats in defining An. punctipennis abundance. Together, we believe these results provide an increased understanding of previously neglected Anopheles species that have the potential for autochthonous malaria transmission in Iowa and that can be extended to other regions of the United States to enhance preparedness for future malaria outbreaks.