The sustainability of the cities of today and tomorrow is joined to coherent revegetation policies of the urban space and intelligent planning via decision support tools. The Waqatali laboratory takes this problematic by combining the Internet of Things, artificial intelligence, participatory decision support and innovative expertise services to increase the environmental and societal co-benefits of plants by city. Cities and societies are undergoing heavy changes in order to face the climatic, environmental and societal challenges which are observed in Northern and Southern countries. Vegetation, usually identified as an ornament, is now becoming an infrastructure that provides services to inhabitants as roads, bus network or internet. Thinking of green spaces as an urban infrastructure is a new idea that we are defending in the Waqatali laboratory. Thus, the plant infrastructure is qualified by its benefits (temperature, air quality, etc.) and its costs (economic, spatial and water). Its design and deployment are the result of concerted actions between expert, decision-makers and city managers, within sustainable urban planner framework. Waqatali is based on an established partnership between Urbasense and the IRD's UMMISCO International research Unit. It will develop applied services based on fundamental cross-disciplinary researches. The laboratory will structure its activities around 6 axes of research and innovation (R&I): Axis 0 - Coordination and promotion of the project; Axis 1 - Irrigation deficit strategy; Axis 2 - Multi-criteria classification of local green spaces; Axis 3 - Construction of a Global Green Infrastructure Effectiveness Indicator; Axis 4 - Development and production of scientific equipment dedicated to measurements; Axis 5 - Participatory modeling for decision support. These R&I will allow us to produce environmental data, develop a coherent suite of instrumentation, data processing tools, and let us imagine indicators and methodologies to support decision. For example, we will develop fundamental researches to design a decision support and participatory planning service dedicated to design and deploy green facilities by stakeholders. This service will rely on the Qameleo and WAOU environmental stations which produce a qualified measurement of environmental parameters (temperatures, air quality, surface humidity, etc.). Thanks to the data acquired, a model based on artificial intelligence will be able to carry out a cost / benefit assessment of the strategies devised by stakeholders during consultation workshops. The simulations will report on the efficiency of the green infrastructure, through various indicators including cooling and air quality. We will contribute to fill a lack of the literature by promoting a new Global Indicator of Efficiency of the Green Infrastructure that will synthesize the cost / benefit ratio of a green infrastructure at the city scale and will measure Green Infrastructure efficiency. All scientific and technological productions will be developed inside the joint group Urbasense and the IRD Campus in Bondy (France center of UMMISCO and CoFAB in Bondy). They will tested and evaluated in two case study, to different agglomerations: Dijon in France and Dakar in Senegal.

Automatic control is very little used in epidemiology, whereas central questions of estimation, prediction and supervision naturally arise in this domain in terms of input-output systems. These questions are made difficult by the non-linearity of the models. The NOCIME project aims at studying new problems related to identification, observation and optimal control, posed by mathematical epidemiology. The most critical issues that will be examined relate to estimating the short- and long-term dynamics of a novel pathogen after its detection; and the control of the epidemic by minimizing "crisis" situations. Several types of compartmental models from the literature (based on ODE systems) will be considered: models with direct or vector transmission, intra-hosts and/or groups, with acquired or temporary immunity. The consortium has identified two major challenges to be conducted, which will be considered for a general class of models: 1. State and parameter estimation for non-globally identifiable/observable dynamics. Typically, at the onset of an epidemic, the state of the system is still close to disease-free equilibria, which are points of non-identifiability and non-observability. This renders the conventional estimators and observers inefficient. This type of situation has been studied very little in the literature. We will first analyze identification and observability according to various possible measurements, including incidence, number of reinfections and seroprevalence. For the design and tuning of estimators/observers, we will study several approaches: local (nonlinear) transformation and approximation; integral observers extending those obtained for “batch” processes; and interval observers. 2. Optimal control for unconventional criteria. The classical theory considers integral and/or terminal costs. However, minimizing the epidemic peak or the prevalence duration above a certain threshold (related to hospital capacity) turn out to be more relevant criteria. However, they cannot be expressed under classic form, or present a lack of regularity. These difficulties have recently been tackled by different approaches: approximation techniques leading to numerical procedures; and equivalent reformulations in higher dimension. We will apply and generalize these results to a class of problems rich enough to include epidemiological models. We will focus on the synthesis of optimal or sub-optimal state feedbacks with guaranteed value, by combining analytical and numerical approaches. Observers coupled with these control laws will also be tested. An originality of the project is to consider random models of population and measurement noise, and their deterministic approximations, for which observers will be established. The performances of the observers (and of the control laws) will then be tested in a more realistic framework of noisy data. The consortium brings together researchers familiar with automatic control, optimal control and epidemiological models. All of them contributed on at least one of the three themes considered, and carried out together several collaborations around mathematical epidemiology. The funding requested mainly corresponds to the recruitment of two post-docs dealing with the two challenges above, missions and the organization of an international workshop, organized with the aim of popularizing the tools of Automation in Epidemiology.

Many technological innovations based on Artificial Intelligence contribute to the achievement of the sustainable development goal dedicated to industry, innovation and infrastructure (SDG 9). Among them, agent-based modeling and IoT-based crowdsourcing methods appear as promising approaches for two main reasons: (1) on the one hand, enable scientists and stakeholders to virtually explore the sustainability of different pathways in the management of complex socio-environmental systems and, (2) on the other hand, empower stakeholders so that they themselves can monitor the progress of the chosen pathways. The objective of PREMISS is to go further and demonstrate the extent to which a combination of these two technologies can effectively support transdisciplinary approaches, which are based on the integration of knowledge between disciplines and actors in society, which promote the creative and collaborative design of new problem-solving methods, and which are gradually becoming an essential part of the sustainability science paradigm to address complex socio-environmental problems. PREMISS, handled by an international consortium gathering South-African, French, Turkish, Vietnamese and Taiwanese academic and non-academic partners, will (1) deliver a systematic review of the literature that addresses these subjects, with the goal of producing operational and policy conclusions alongside at least two scientific review papers; (2) implement and manage three case studies that represent the three nexuses formed by SDG 9 and, respectively, SDG 6 (about the sustainable management of irrigation systems, through participatory modeling, in Vietnam), SDG 13 (about citizen science and participatory environmental mapping in Taiwan) and SDG 15 (about the adoption and impact of sensor networks on precision agriculture in Turkey); (3) deliver a scholarly book and a methodological guide providing new perspectives and sets of recommendations to support transdisciplinary approaches in sustainability science projects.

As agent-based social simulation is gaining ground as a candidate of choice for building decision-support tools in the management of complex socio-environmental systems, and as the resulting models are therefore being driven to produce more realistic outcomes, the issue of integrating large data corpuses (demographical, environmental, geographical…) as an input to these models becomes more critical. In such models, the evolution of simulations is partly being driven by social agents (which may represent individuals, households or institutions), whose behaviors are strongly determined by their attributes, connections with other agents, but also location in the artificial worlds they populate. Nowadays data becomes available (partly thanks to the Big Data and Open Data initiatives), generating synthetic populations of agents that conform to the data available on real populations becomes a necessity and a concern for most social modelers, and although several approaches have been undertaken in recent works, it constitutes a significant scientific and methodological challenge that we plan to address in this proposal. The first challenge concerns the conceptual and operational handling of scales, both the scale at which populations need to be generated and the scales at which data is available (or not). The ratio between these two scales will determine the use of robust and innovative up-scaling and down-scaling methods. Moreover, generating millions of agents or just a few hundreds is likely to involve completely different operational methods. The second challenge is a consequence of the previous one and requires building an adaptive tool. The necessity to couple several conceptual and operational methods has to rely on a complete understanding, description (and documentation) of their aims and means, so that users can choose the most adapted with respect to their needs, but should also be left to decide what is the most appropriate, given a scenario of generation (objectives, scales, data available, computational power available, time required, etc.). The Gen* project aims at combining applied mathematics and computer science approaches in order to incorporate arbitrary data and to generate statistically valid populations of artificial agents. Generic methods applicable to different use cases (e.g. urban/rural populations, downscaling needs…) will be provided and implemented in R and Java in order to be integrated as open-source libraries in existing agent-based simulation platforms. This offer will of course be completed with a methodological guide and several tutorials in order for end-users to master the methods and their combinations. Finally, a standalone, industrial-grade, application will also be developed independently from existing platforms, which will support a dedicated graphical user interface that will enable modelers to design, save and reuse workflows linking the different data sources, methods from the library and data converters (akin to what the Kepler platform offers) so as to generate artificial populations. All these developments will be undertaken by the consortium of the project, which has already a solid experience in managing large-scale open-source methodological projects dedicated to agent-based simulation. They will be primarily validated on several case studies provided by the partners, sufficiently diverse to enable the design of generic methods. In addition, our goal is to build a strong community of users and developers during the 42 months of the project. Outcomes (libraries, tools, documentation) will be delivered at regular intervals under an open source license; feedback from users (and validation on their case studies) will be sought during dedicated workshops; and we will use all means of dissemination to provide incentives to the international agent-based social simulation community to adopt, develop and improve the Gen* libraries.

The project AIRQALI 4 ASMAFRI (hence A4A) will examine the air pollution-asthma relationship in urban West Africa, where different types of air pollution sources (anthropogenic and natural) are combined. A4A will focus on asthma in children in sub-Saharan Africa because: i) This complex chronic inflammatory disease of the respiratory tract is an excellent model for studying the health impacts of air pollution; ii) Sub-Saharan Africa is the low- and middle-income region of the world where asthma prevalence among adolescents (13-14 years) is highest (15.3%); iii) Asthma is under-recognised, under-diagnosed, under-treated, and insufficiently prevented in this region. It is a major noncommunicable disease which remains little studied and not sufficiently considered in public policy priorities in Africa, including Benin. A4A will be based in Cotonou, the economic capital of Benin, which is particularly affected by air pollution. Traffic-related pollution is one of the main contributors to air pollution, especially near some schools in the inner city where NO2 levels are high. Annual average concentrations of PM2.5 are also well above the WHO guideline, as are VOCs (benzene), ultrafine particles associated with polycyclic aromatic hydrocarbons (PAHs). A4A aims to amplify evidence-based decision-making of policy actors on health and air quality issues. The project's research efforts will aim to demonstrate that a less polluted air both indoors and outdoors can help alleviate the burden of asthma in children by reducing asthma exacerbations and improving lung capacity. Because the project is solution oriented, A4A will show that health literacy in children and their families on asthma and the provision of real-time air pollution information promote more protective behaviours, more effective care, and better children’s quality of life. A4A will also provide policy support and technical guidance - locally adapted and meeting country-specific needs – to manage air pollution health risks and set up an air quality warning system. To achieve these goals, we set the following objectives: Implement: • A long-term follow-up (3 years) of a schoolchildren cohort (aged 13-14 years) with diagnosed asthma; and the collection for the first time in Africa of environmental, social, and medical data to better understand the links between air pollution and asthma in children. • A cohort-targeted air pollution warning system, with sensors in schools and living areas. Produce: • Time series at local scale of air pollution exposure to PMs. • Measurements of personal exposure to pollutants as a continuum, in indoors (household, classroom, etc.) and outdoors (during outdoor activities and school-home commuting, etc.). • Unbiased and repeated measurements of lung function, oxidative stress, and airway inflammation in children with asthma. Analyse: • Characterise air pollutants and children’s exposure (indoor and outdoor). Analyse the nature and toxicity of primary contaminants, and measure the external exposure dose, personal exposure (school/commute/home) and their biological effects (biomarkers). • Determine pollutant thresholds in relation to asthma risks. Model the relationship between pollution, weather factors and exacerbation episodes. • Identify the modifiable risk factors by controlling confounding variables. Study the role of environmental factors in asthma and of socio-spatial vulnerability factors on air pollution exposure related to child asthma. Provide: • A demonstrator to model future urban air pollution scenarios and their health impacts. • The development of a self-management care package, including personalized health education and counselling using the project’s datasets. • Training materials to support capacity building for college teachers and health staff to raise awareness about exposure to air pollution and adverse health effects among schoolchildren. • A project for a pre-operational air pollution warning system in Cotonou.