Development and validation of the MosquitoWise survey to assess perceptions towards mosquitoes and mosquito-borne viruses in Europe.

Due to climate change and the expanding geographical ranges of key mosquito species, several mosquito-borne viruses (MBVs) have recently emerged in Europe. Understanding people's perceptions and behaviours towards these viruses and the mosquitoes capable of transmitting them is crucial for implementing effective prevention measures and targeted communication campaigns. However, there is currently no appropriate validated survey for European populations to assess this. This study developed and validated a standardized survey, based on the Health Belief Model (HBM), to assess perceptions of mosquitoes and MBVs among Europe's residents. The survey was distributed online to United Kingdom (UK), Dutch and Spanish participants through panel providers. Survey validity and reliability were tested using confirmatory factor analysis (CFA) and Cronbach's alpha. The optimised survey was completed by 336 UK, 438 Dutch and 475 Spanish residents, respectively, and the HBM items passed our validity and reliability testing in all three countries. The final survey has 57 questions, including 19 validated HBM items, and questions to assess demographic characteristics, knowledge, prevention measures and behavioural determinants. Our MosquitoWise survey bridges researchers' understandings of European residents' perceptions and knowledge as a first step to improve preventive behaviour towards mosquitoes and MBVs and guide prevention and communication initiatives.

A fast and inexpensive genotyping system for the simultaneous analysis of human and Aedes albopictus short tandem repeats.

BACKGROUND: Determination of the interactions between hematophagous mosquitoes and their human hosts is of great importance for better understanding the transmission dynamics of mosquito-borne arboviruses and developing effective strategies to mitigate risk. Genetic analysis of human and mosquito DNA can play a key role in this, but commercial kits for human short tandem repeat (STR) genotyping are expensive and do not allow for the simultaneous STR analysis of host and vector DNA. Here, we present an inexpensive and straightforward STR-loci multiplex system capable of simultaneously amplifying Aedes albopictus and human STRs from blood-fed mosquitoes. Additionally, we examine the effect of storage methods and post-feeding time on the integrity of host DNA. METHODS: Thirty-five STRs (16 human and 19 Ae. albopictus STRs) subdivided in three multiplexes were tested for amplification and scoring reliability. Under laboratory conditions we compared the efficacy of two preservation methods (absolute ethanol vs lysis buffer) on the integrity of host DNA in Ae. albopictus blood meals. We also evaluated the effect of post-feeding time by sacrificing blood-fed mosquitoes at different time intervals after feeding, and we assessed our ability to detect multiple feedings. To determine if the system can be employed successfully under field conditions, we carried out a preliminary study using field-collected Ae. albopictus. RESULTS: All 35 STRs amplified consistently in the laboratory. Lysis buffer performed better than absolute ethanol in terms of allele peak height and clarity of electropherograms. Complete human DNA profiles could be obtained up to 48 h following the blood meal. Analysis of multiple feedings confirmed that peak heights can be used as a proxy to determine post-feeding time and thus derive the number of different people bitten by a mosquito. In the field trial, amplification was successful for 32 STRs. We found human DNA signal in 38 of the 61 field-collected mosquitoes (62%), of which 34 (89%) had ingested a single blood meal, while four (11%) contained double meals. CONCLUSIONS: Our new genotyping system allows fast and reliable screening of both host and vector species, and can be further adapted to other mosquito species living in close contact with humans.

Drivers of longevity of wild-caught Aedes albopictus populations.

BACKGROUND: Age structure and longevity constitute fundamental determinants of mosquito populations' capacity to transmit pathogens. However, investigations on mosquito-borne diseases primarily focus on aspects such as abundance or dispersal rather than survival and demography. Here, we examine the post-capture longevity of wild-caught populations of the Asian tiger mosquito Aedes albopictus to investigate the influence of environmental factors and individual frailty on longevity. METHODS: We captured females of Ae. albopictus from June to November 2021 in a vegetated and an urban area by two methods of capture (BG traps and Human Landing catch). They were kept in semi-controlled conditions in the field, and survival was monitored daily across the 859 individuals captured. We studied the differences in longevity per capture method and location and the influence on longevity of seasonal, climatic and individual factors. RESULTS: Photoperiod, GDD, minimum and maximum temperature and relative humidity showed an effect on the risk of death of females in the field. Females captured in urban area with Human Landing catch methods had greater longevity than females captured in non-urban areas with BG traps. Individual variance, reflecting individual frailties, had an important effect on the risk of death: the greater the frailty, the shorter the post-capture longevity. Overall, longevity is affected not only by climate and seasonal drivers like temperature and photoperiod but also by the individual frailty of mosquitoes. CONCLUSION: This work unravels environmental drivers of key demographic parameters such as longevity, as modulated by individual frailty, in disease vectors with strong seasonal dynamics. Further demographic understanding of disease vectors in the wild is needed to adopt new surveillance and control strategies and improve our understanding of disease risk and spread.

Decision-support tools to build climate resilience against emerging infectious diseases in Europe and beyond.

Climate change is one of several drivers of recurrent outbreaks and geographical range expansion of infectious diseases in Europe. We propose a framework for the co-production of policy-relevant indicators and decision-support tools that track past, present, and future climate-induced disease risks across hazard, exposure, and vulnerability domains at the animal, human, and environmental interface. This entails the co-development of early warning and response systems and tools to assess the costs and benefits of climate change adaptation and mitigation measures across sectors, to increase health system resilience at regional and local levels and reveal novel policy entry points and opportunities. Our approach involves multi-level engagement, innovative methodologies, and novel data streams. We take advantage of intelligence generated locally and empirically to quantify effects in areas experiencing rapid urban transformation and heterogeneous climate-induced disease threats. Our goal is to reduce the knowledge-to-action gap by developing an integrated One Health-Climate Risk framework.

Demographic and motivational differences between participants in analog and digital citizen science projects for monitoring mosquitoes.

Worldwide, citizen scientists help to map the distribution of native and introduced mosquito species in a variety of programs, contributing to environmental research and management decisions. Participant background, behavior, and engagement may vary depending on the project design, especially between those using digital apps and those using physical samples, which in turn may impact the quality and representativeness of the data collected. During the analysis of the responses to a survey directed at citizen scientists participating in  a digital and an analog program, we found significant differences in the respondents' demographic backgrounds. Diverse participant motivations and varying sentiments towards mosquitoes were observed, indicating differing susceptibility in response to the program messages. The results can be translated into recommendations to further strengthen the appeal of citizen science projects and to promote and sustain public engagement in environmental research.

Citizen Science as an Approach for Responding to the Threat of Anopheles stephensi in Africa.

Even as novel technologies emerge and medicines advance, pathogen-transmitting mosquitoes pose a deadly and accelerating public health threat. Detecting and mitigating the spread of Anopheles stephensi in Africa is now critical to the fight against malaria, as this invasive mosquito poses urgent and unprecedented risks to the continent. Unlike typical African vectors of malaria, An. stephensi breeds in both natural and artificial water reservoirs, and flourishes in urban environments. With An. stephensi beginning to take hold in heavily populated settings, citizen science surveillance supported by novel artificial intelligence (AI) technologies may offer impactful opportunities to guide public health decisions and community-based interventions. Coalitions like the Global Mosquito Alert Consortium (GMAC) and our freely available digital products can be incorporated into enhanced surveillance of An. stephensi and other vector-borne public health threats. By connecting local citizen science networks with global databases that are findable, accessible, interoperable, and reusable (FAIR), we are leveraging a powerful suite of tools and infrastructure for the early detection of, and rapid response to, (re)emerging vectors and diseases.

Publishing data to support the fight against human vector-borne diseases.

Vector-borne diseases are responsible for more than 17% of human cases of infectious diseases. In most situations, effective control of debilitating and deadly vector-bone diseases (VBDs), such as malaria, dengue, chikungunya, yellow fever, Zika and Chagas requires up-to-date, robust and comprehensive information on the presence, diversity, ecology, bionomics and geographic spread of the organisms that carry and transmit the infectious agents. Huge gaps exist in the information related to these vectors, creating an essential need for campaigns to mobilise and share data. The publication of data papers is an effective tool for overcoming this challenge. These peer-reviewed articles provide scholarly credit for researchers whose vital work of assembling and publishing well-described, properly-formatted datasets often fails to receive appropriate recognition. To address this, GigaScience's sister journal GigaByte partnered with the Global Biodiversity Information Facility (GBIF) to publish a series of data papers, with support from the Special Programme for Research and Training in Tropical Diseases (TDR), hosted by the World Health Organisation (WHO). Here we outline the initial results of this targeted approach to sharing data and describe its importance for controlling VBDs and improving public health.

Modeling the impact of surveillance activities combined with physical distancing interventions on COVID-19 epidemics at a local level.

Physical distancing and contact tracing are two key components in controlling the COVID-19 epidemics. Understanding their interaction at local level is important for policymakers. We propose a flexible modeling framework to assess the effect of combining contact tracing with different physical distancing strategies. Using scenario tree analyses, we compute the probability of COVID-19 detection using passive surveillance, with and without contact tracing, in metropolitan Barcelona. The estimates of detection probability and the frequency of daily social contacts are fitted into an age-structured susceptible-exposed-infectious-recovered compartmental model to simulate the epidemics considering different physical distancing scenarios over a period of 26 weeks. With the original Wuhan strain, the probability of detecting an infected individual without implementing physical distancing would have been 0.465, 0.515, 0.617, and 0.665 in designated age groups (0-14, 15-49, 50-64, and >65), respectively. As the physical distancing measures were reinforced and the disease circulation decreased, the interaction between the two interventions resulted in a reduction of the detection probabilities; however, despite this reduction, active contact tracing and isolation remained an effective supplement to physical distancing. If we relied solely on passive surveillance for diagnosing COVID-19, the model required a minimal 50% (95% credible interval, 39-69%) reduction of daily social contacts to keep the infected population under 5%, as compared to the 36% (95% credible interval, 22-56%) reduction with contact tracing systems. The simulation with the B.1.1.7 and B.1.167.2 strains shows similar results. Our simulations showed that a functioning contact tracing program would reduce the need for physical distancing and mitigate the COVID-19 epidemics.

Multiple invasions, Wolbachia and human-aided transport drive the genetic variability of Aedes albopictus in the Iberian Peninsula.

The Asian tiger mosquito, Aedes albopictus, is one of the most invasive species in the world. Native to the tropical forests of Southeast Asia, over the past 30 years it has rapidly spread throughout tropical and temperate regions of the world. Its dramatic expansion has resulted in public health concerns as a consequence of its vector competence for at least 16 viruses. Previous studies showed that Ae. albopictus spread has been facilitated by human-mediated transportation, but much remains unknown about how this has affected its genetic attributes. Here we examined the factors that contributed to shaping the current genetic constitution of Ae. albopictus in the Iberian Peninsula, where the species was first found in 2004, by combining population genetics and Bayesian modelling. We found that both mitochondrial and nuclear DNA markers showed a lack of genetic structure and the presence of worldwide dominant haplotypes, suggesting regular introductions from abroad. Mitochondrial DNA showed little genetic diversity compared to nuclear DNA, likely explained by infection with maternally transmitted bacteria of the genus Wolbachia. Multilevel models revealed that greater mosquito fluxes (estimated from commuting patterns and tiger mosquito population distribution) and spatial proximity between sampling sites were associated with lower nuclear genetic distance, suggesting that rapid short- and medium-distance dispersal is facilitated by humans through vehicular traffic. This study highlights the significant role of human transportation in shaping the genetic attributes of Ae. albopictus and promoting regional gene flow, and underscores the need for a territorially integrated surveillance across scales of this disease-carrying mosquito.

Integrating Global Citizen Science Platforms to Enable Next-Generation Surveillance of Invasive and Vector Mosquitoes.

Mosquito-borne diseases continue to ravage humankind with >700 million infections and nearly one million deaths every year. Yet only a small percentage of the >3500 mosquito species transmit diseases, necessitating both extensive surveillance and precise identification. Unfortunately, such efforts are costly, time-consuming, and require entomological expertise. As envisioned by the Global Mosquito Alert Consortium, citizen science can provide a scalable solution. However, disparate data standards across existing platforms have thus far precluded truly global integration. Here, utilizing Open Geospatial Consortium standards, we harmonized four data streams from three established mobile apps­Mosquito Alert, iNaturalist, and GLOBE Observer's Mosquito Habitat Mapper and Land Cover­to facilitate interoperability and utility for researchers, mosquito control personnel, and policymakers. We also launched coordinated media campaigns that generated unprecedented numbers and types of observations, including successfully capturing the first images of targeted invasive and vector species. Additionally, we leveraged pooled image data to develop a toolset of artificial intelligence algorithms for future deployment in taxonomic and anatomical identification. Ultimately, by harnessing the combined powers of citizen science and artificial intelligence, we establish a next-generation surveillance framework to serve as a united front to combat the ongoing threat of mosquito-borne diseases worldwide.

Mosquito alert: leveraging citizen science to create a GBIF mosquito occurrence dataset.

The Mosquito Alert dataset includes occurrence records of adult mosquitoes collected worldwide in 2014-2020 through Mosquito Alert, a citizen science system for investigating and managing disease-carrying mosquitoes. Records are linked to citizen science-submitted photographs and validated by entomologists to determine the presence of five targeted European mosquito vectors: Aedes albopictus, Ae. aegypti, Ae. japonicus, Ae. koreicus, and Culex pipiens. Most records are from Spain, reflecting Spanish national and regional funding, but since autumn 2020 substantial records from other European countries are included, thanks to volunteer entomologists coordinated by the AIM-COST Action, and to technological developments to increase scalability. Among other applications, the Mosquito Alert dataset will help develop citizen science-based early warning systems for mosquito-borne disease risk. It can also be reused for modelling vector exposure risk, or to train machine-learning detection and classification routines on the linked images, to assist with data validation and establishing automated alert systems.

Deep learning identification for citizen science surveillance of tiger mosquitoes.

Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Traditional surveillance of mosquitoes, vectors of many diseases, relies on catches, which requires regular manual inspection and reporting, and dedicated personnel, making large-scale monitoring difficult and expensive. New approaches are solving the problem of scalability by relying on smartphones and the Internet to enable novel community-based and digital observatories, where people can upload pictures of mosquitoes whenever they encounter them. An example is the Mosquito Alert citizen science system, which includes a dedicated mobile phone app through which geotagged images are collected. This system provides a viable option for monitoring the spread of various mosquito species across the globe, although it is partly limited by the quality of the citizen scientists' photos. To make the system useful for public health agencies, and to give feedback to the volunteering citizens, the submitted images are inspected and labeled by entomology experts. Although citizen-based data collection can greatly broaden disease-vector monitoring scales, manual inspection of each image is not an easily scalable option in the long run, and the system could be improved through automation. Based on Mosquito Alert's curated database of expert-validated mosquito photos, we trained a deep learning model to find tiger mosquitoes (Aedes albopictus), a species that is responsible for spreading chikungunya, dengue, and Zika among other diseases. The highly accurate 0.96 area under the receiver operating characteristic curve score promises not only a helpful pre-selector for the expert validation process but also an automated classifier giving quick feedback to the app participants, which may help to keep them motivated. In the paper, we also explored the possibilities of using the model to improve future data collection quality as a feedback loop.

Citizen Science: A Gateway for Innovation in Disease-Carrying Mosquito Management?

Traditional methods for tracking disease-carrying mosquitoes are hitting budget constraints as the scales over which they must be implemented grow exponentially. Citizen science offers a novel solution to this problem but requires new models of innovation in the public health sector.

Direct Evidence of Adult Aedes albopictus Dispersal by Car.

Whereas the Asian tiger mosquito (Aedes albopictus) has low active dispersal capabilities, its worldwide colonization has been rapid. Indirect evidence and informal reports have long implicated passive transportation in cars, but this has not previously been studied systematically given the difficulties of real-time roadside surveys. Here we report the first sampling study confirming that adult tiger mosquitoes travel with humans in cars and enabling us to estimate the frequency of these events. We combine the results with citizen science data to model the car-facilitated dispersal of Aedes albopictus at a nationwide level. During the summer of 2015, we sampled 770 cars in north-eastern Spain, discovering 4 adult female tiger mosquitoes that had entered cars prior to sampling. Our Bayesian model suggests that of the 6.5 million daily car trips in the Barcelona metropolitan area, between 13,000 and 71,500 facilitate tiger mosquito movement, and that Barcelona is the largest source of inter-province tiger mosquito transfers in Spain. Our results are supported by expert-validated citizen science data, and will contribute to better understanding the tiger mosquito's invasion process and ultimately lead to more effective vector control strategies.

Citizen science provides a reliable and scalable tool to track disease-carrying mosquitoes.

Recent outbreaks of Zika, chikungunya and dengue highlight the importance of better understanding the spread of disease-carrying mosquitoes across multiple spatio-temporal scales. Traditional surveillance tools are limited by jurisdictional boundaries and cost constraints. Here we show how a scalable citizen science system can solve this problem by combining citizen scientists' observations with expert validation and correcting for sampling effort. Our system provides accurate early warning information about the Asian tiger mosquito (Aedes albopictus) invasion in Spain, well beyond that available from traditional methods, and vital for public health services. It also provides estimates of tiger mosquito risk comparable to those from traditional methods but more directly related to the human-mosquito encounters that are relevant for epidemiological modelling and scalable enough to cover the entire country. These results illustrate how powerful public participation in science can be and suggest citizen science is positioned to revolutionize mosquito-borne disease surveillance worldwide.

Human population growth offsets climate-driven increase in woody vegetation in sub-Saharan Africa.

The rapidly growing human population in sub-Saharan Africa generates increasing demand for agricultural land and forest products, which presumably leads to deforestation. Conversely, a greening of African drylands has been reported, but this has been difficult to associate with changes in woody vegetation. There is thus an incomplete understanding of how woody vegetation responds to socio-economic and environmental change. Here we used a passive microwave Earth observation data set to document two different trends in land area with woody cover for 1992-2011: 36% of the land area (6,870,000 km2) had an increase in woody cover largely in drylands, and 11% had a decrease (2,150,000 km2), mostly in humid zones. Increases in woody cover were associated with low population growth, and were driven by increases in CO2 in the humid zones and by increases in precipitation in drylands, whereas decreases in woody cover were associated with high population growth. The spatially distinct pattern of these opposing trends reflects, first, the natural response of vegetation to precipitation and atmospheric CO2, and second, deforestation in humid areas, minor in size but important for ecosystem services, such as biodiversity and carbon stocks. This nuanced picture of changes in woody cover challenges widely held views of a general and ongoing reduction of the woody vegetation in Africa.

Expectation-Maximization Binary Clustering for Behavioural Annotation.

The growing capacity to process and store animal tracks has spurred the development of new methods to segment animal trajectories into elementary units of movement. Key challenges for movement trajectory segmentation are to (i) minimize the need of supervision, (ii) reduce computational costs, (iii) minimize the need of prior assumptions (e.g. simple parametrizations), and (iv) capture biologically meaningful semantics, useful across a broad range of species. We introduce the Expectation-Maximization binary Clustering (EMbC), a general purpose, unsupervised approach to multivariate data clustering. The EMbC is a variant of the Expectation-Maximization Clustering (EMC), a clustering algorithm based on the maximum likelihood estimation of a Gaussian mixture model. This is an iterative algorithm with a closed form step solution and hence a reasonable computational cost. The method looks for a good compromise between statistical soundness and ease and generality of use (by minimizing prior assumptions and favouring the semantic interpretation of the final clustering). Here we focus on the suitability of the EMbC algorithm for behavioural annotation of movement data. We show and discuss the EMbC outputs in both simulated trajectories and empirical movement trajectories including different species and different tracking methodologies. We use synthetic trajectories to assess the performance of EMbC compared to classic EMC and Hidden Markov Models. Empirical trajectories allow us to explore the robustness of the EMbC to data loss and data inaccuracies, and assess the relationship between EMbC output and expert label assignments. Additionally, we suggest a smoothing procedure to account for temporal correlations among labels, and a proper visualization of the output for movement trajectories. Our algorithm is available as an R-package with a set of complementary functions to ease the analysis.

New approaches to human mobility: using mobile phones for demographic research.

This article explores new methods for gathering and analyzing spatially rich demographic data using mobile phones. It describes a pilot study (the Human Mobility Project) in which volunteers around the world were successfully recruited to share GPS and cellular tower information on their trajectories and respond to dynamic, location-based surveys using an open-source Android application. The pilot study illustrates the great potential of mobile phone methodology for moving spatial measures beyond residential census units and investigating a range of important social phenomena, including the heterogeneity of activity spaces, the dynamic nature of spatial segregation, and the contextual dependence of subjective well-being.