Since the middle of the 20th century, insecticides have been massively used to control the vectors of infectious diseases and thus limit their impact on public health. This drastic modification of their environment has selected different adaptations in these vectors, collectively referred to as insecticide resistance. The genomic architecture of these adaptations can be very diverse, ranging from simple nucleotide substitutions to large-scale mutations such as gene duplication. The effects of these different mutations on the vectors phenotype and fitness can differ and are difficult to anticipate, particularly in an environment that is itself variable. Using interdisciplinary approaches, the ArchR project aims 1) to understand how these different genomic architectures impact the phenotype of their carriers, and to measure the evolutionary dynamics of multi-copy alleles under various intensities of insecticidal pressure in natura, 2) to study how variations in environmental conditions and the architecture of these adaptive mutations influence the dynamics of genome polymorphism, via the natural selection of resistance alleles and the demographic effects of insecticide treatments, and 3) to measure the effect of mutations on vectorial competence and mosquito metabolism, in order to anticipate their impact on public health. To achieve these objectives, the ArchR project will rely on the wide diversity of resistance alleles with copy number variations found in the Culex pipiens mosquito, and on a unique collection of natural population samples collected over 30 years, combined with quantitative data on insecticide treatment variations. The ArchR project will also rely on a recognized international consortium, which has worked or is already working together on other projects, and which combines the complementary skills (population genetics, genomics and bioinformatics, molecular biology, vector competence and experimental infections, computer modeling, ...) and resources and infrastructure (insectariums level 2 and 3, molecular platforms and computer platforms) necessary to carry out this project. During this project, two PhD and several Master students will also be trained in high-level research with the different partners of this consortium. The ArchR project will thus allow crucial developments for fundamental research in evolutionary biology: early evolution of duplications, impacts of adaptive dynamics on genome evolution, impact of environmental variations on genome polymorphism and population adaptability, evolutionary trade-offs between adaptation and transmission. By linking the treatment practices with their demographic impacts and the dynamics of resistance alleles in natural populations, and by assessing the impact of resistance alleles on mosquito vectorial capacity, it will also provide useful information for professionals in charge of vector control or crop protection, and help design sustainable control strategies.

Until the early 2000s, detection of infectious disease emergence (in animals and humans) relied on classical reporting of cases for known pathogens (called indicator-based surveillance [IBS]). Despite having standardized procedures for verification and confirmation of cases by field practitioners, laboratories and health officials, the IBS lacks sensitivity, mainly due to non-reporting and delayed reporting of cases. In a context of a continuously changing environment due to climate change, animal and human mobility, population growth and urbanization, there is an increased risk of emergence of new and exotic pathogens, which may pass undetected with the IBS. Hence, the need to detect signals of infectious disease emergence using informal sources (event-based surveillance [EBS]). The current EBS mainly relies on data from one type of source (e.g., electronic media, laboratory data or health records); thus, decision makers are confounded with the interpretation of data from multiple sources and systems. To overcome this, the project MOOD (MOnitoring Outbreak events for Disease surveillance in a data science context) aims to harness the state-of-the-art data mining and analytical techniques to big data originating from multiple sources to improve monitoring of the (re-)emergence of zoonotic infectious diseases in Europe, including antimicrobial resistance (AMR). Indeed, zoonotic diseases present the additional difficulty of needing a common framework to address the surveillance issues both in animals and humans. To this purpose, MOOD will establish a “one serves all” framework and visualisation platform that will allow real-time analysis and interpretation of epidemiological and gene sequence data in combination with climate, environmental and socio-economic covariates in an integrated and interdisciplinary “One health” approach. The MOOD framework will link research, national and international animal and public health organizations in Europe and beyond, to develop: 1) Data mining methods for collecting and combining heterogeneous and multi-source big data, 2) A network of disease experts to interpret the (possibly) weak signals and identify the drivers of infectious disease emergence, 3) Data analysis methods applied to Big data, including, but not limited to, spatial-temporal analysis, social network analysis, and fuzzy logic, to model infectious disease (re-)emergence and spread, 4) Ready-to-use online platform destined to a community of animal and public health users, including the public, tailored to their needs, and including capacity building and a network of disease experts to facilitate risk assessment of detected signals. The outcomes from MOOD will be designed in collaboration with national and regional European stakeholders, to assure their routine use during and beyond the project duration. These users will be implicated in the project to identify and adapt the project according to their needs. MOOD will complement and link to existing surveillance systems and other related projects or initiatives global and European level. The functionalities of MOOD will be tested and adapted through continuous assessment and evaluation using case studies on air-borne, vector-borne, water-borne and food-borne diseases, as well as AMR. Throughout the project, extensive consultations with potential users, studies into the barriers to open data sharing, dissemination and training activities, and studies on the cost-effectiveness of the project will support future sustainable user uptake.

The project located in Southeast Asia aims at developing scenarios of future health embodied in the One Health approach at the human-animal-environment interface. By investigating the impacts of the intensification of the circulation along the economic corridor (Thailand-Laos) on the evolution of infectious diseases (IDs) of public health interests it intends to: a) integrate ecology and environmental sciences with health sciences and policies; b) foster scientific knowledge integration at various decision levels (regional to local); c) analyze retrospectively and comparatively IDs' dynamics associated to policies, land use and biodiversity changes; d) combine predictive process-based scenarios and policy-driven scenarios of health incorporating disease ecology, biodiversity erosion, future land use and climate changes. Important role will be devoted to ICT Sciences based on a strong partnership between France and Thailand. An integrative data / knowledge base will be created that will include three types of information: a) quantitative data (socio-economics at the village level; infectious diseases including zoonoses; domestic animal populations; resources from wildlife; distributions of precipitation, temperature and impacts of climate change on these variables; soils; agricultural inputs such as pesticides, herbicides and antibiotics); b) a large textual corpus on the strategies and targets to implement international law regionally and nationally and results of text mining to be generated, as well as information on local policy measures and customary law; c) qualitative data obtained from interviews and surveys conducted with decision-makers and community leaders about their representations and perceptions of agriculture, land planning / infrastructure, health and conservation policies. Given their importance in health-environment dynamics, LULC changes will be restored over nearly three decades and projected for the coming decades at various scales (region, habitats, landscapes) by combining various data sources (including satellite products). The interactions between variables and information will be identified and modeled by relying on a variety of tools combining simple correlations (linear or nonlinear), modeling reticular causalities (Bayesian networks), process-based epidemiological models and knowledge representation. The data / knowledge base and models will finally be exploited to produce scenarios combining predictions and story-telling about the likely impacts of strategies and legal and policy measures on health, biodiversity and resource uses (agriculture, LULC, living resources). Scientific evidences gained from the project results will be disseminated among decision-makers and community leaders through workshops and conferences. Indeed those evidences will play a pivotal role in helping decision-makers at different levels to associate socio-ecosystems dynamics and IDs' dynamics, at various spatial scales (villages to districts to transboundary provinces), in their public action.