Alternative ecological and social proposals for preventing the global threat of emerging infectious diseases.

Emerging infectious diseases are a major global health threat in the human, animal and plant worlds. Zoonoses and vector borne diseases are becoming prevalent worldwide. A large part of global health funding is dedicated to the fight against Dengue, Zika, or Ebola. Until now, public health strategies have been mainly based on vaccine development, medication testing or on proposals for “acceptable” cultural changes in local population practices to limit transmission risk, without thinking about the root causes. In this literature review, it will be argued that the current economic system, through its growth imperatives which ignore planetary limits, together with intensive agricultural practices, is related to infectious disease emergence. Monocultural practices, such as rubber/palm oil industrial plantations, through the ecological perturbation inflicted, act as a driver of vector borne and zoonotic diseases. Deforestation, loss of biodiversity, and human invasion of remote forested areas are followed by the emergence of zoonoses such as Ebola disease. Even if any emergence is always a multifactorial process, it is still fundamental to highlight the major influence of environmental drivers. The characteristics of specific ecological and social contexts within which emergence occurs should be explored. Alternative health and environmental paradigms could help impede the emergence of infectious diseases.  A true “One health” approach which takes care of ecosystems and preserves the diversity of living things and of relationships corresponds to an “EcoHealth” approach. Ecological options and environmental solutions could produce a real innovation in public health. Stopping deforestation and ecosystem destruction and fostering peasant agroecology and free evolution for certain forested areas could slowly lead to rebalanced ecosystems. Furthermore, ecological actions would be less stigmatizing than promotion of cultural changes. An alternative public health program based on “health within a healthy environment” would be more effective than a secondary struggle against emerging diseases. This suggests introducing public health as a fundamental land use issue, inaugurating peasant agroecology, land use and conservation as fundamental public health issues, and developing coherent policies.  Key words: emerging infectious diseases, EcoHealth, pathogenic environment, plantacionocene, ecological alternatives, Planetary Health, ecosystemic approach to public healt

Parameterization using Generative Adversarial Networks for Control Space Reduction in Data Assimilation.

This thesis examines the use of generative adversarial networks (GANs) as a parameterization tool for inverse problems solved with ensemble-based data assimilation methods. Ensemble methods often rely on the assumption of Gaussian distributed parameters in cases where this assumption is not valid, the parameter estimation can be invalid. Parameterization methods allow the transformation of these non-Gaussian parameters into a better suited distribution, and optimally reduce their dimension. Another limitation of ensemble methods is the injection of prior information of the physical relation as a constraint between parameters such as spatial coherence or physical balances. Optimal parameterization should encompass these different properties to facilitate the estimation. The novel approach presented in this work relies on GANs to achieve these objectives. Two application domains are tackled through the present work. In a first application, subsurface reservoir characterization, the objective is to determine geological properties of a numerical reservoir model from the observation of the reservoir dynamical response by the way of data assimilation. Rock facies, that describe the type of rock present in each cell of the numerical model, have to be determined due to their strong influence on the dynamical response. The rock facies spatial distribution is ruled by geological phenomena such as sedimentation and forms well known patterns, like channels, called heterogeneities. The noncontinuous property and their spatial coherence make their characterization by ensemble-based data assimilation algorithms difficult, and requires parameterization. Parameterization is a challenge for numerous heterogeneities, notably channels, due to the numerical cost or the statistical representation of their spatial distribution. A Second application domain is the atmospheric balance in the context of numerical weather prediction. When new observations are available, correction of the atmospheric state is done using ensemblebased data assimilation methods. This correction step can introduce imbalance in the physical state and cause numerical instability during the integration in time of the atmosphere, deteriorating the information brought by the previous observations. The importance of generating or correcting balanced climate, also called initialized atmospheric state, during data assimilation is then a key step in numerical weather prediction. This work aims at presenting the performance of GAN parameterization and its multi-disciplinary applicability to researchers who are not familiar with the domain of deep learning. GAN is an unsupervised deep learning method belonging to the deep generative network family, able to learn a dataset distribution and generate new samples from the learned distribution in an unsupervised way. These synthetic samples are encoded in a low-dimensional latent space that can be sampled from a Gaussian distribution that is suited to perform ensemble data assimilation. Their recent ability to generate complex images led us to consider them as a good candidate for parameterization method. The unsupervised property of this type of parameterization makes it applicable to several diverse domains such as learning the pattern of geological heterogeneities or learning the physical constraints that makes an atmospheric state balanced. This study shows how to train GANs for two different applications : subsurface reservoir and climate data. The use of the parameterization in an ensemble based data assimilation such as ensemble smoother with multiple data assimilation (ES-MDA) is demonstrated for subsurface reservoir. Finally, a posteriori conditioning of the GAN function is examined using derivative free optimization

Natural history and epidemiology of the monkeypox in Central African Republic

La variole du singe a une présentation clinique similaire à celle de la variole, avec un taux de létalité moindre entre 3 et 10 %. Suite à une transmission zoonotique initiale, le virus monkeypox se répand ensuite par transmission secondaire interhumaine. Si le réservoir animal n’est pas encore formellement identifié, les petits rongeurs de type écureuils arboricoles sont considérés comme les réservoirs les plus probables du virus. Préalablement à l’épidémie mondiale actuelle, la variole du singe était considérée comme une zoonose rare, confinée au continent africain. L’augmentation récente du nombre et de la fréquence des épidémies dans les pays endémiques, les changements épidémiologiques identifiés au Nigéria depuis 2017, ainsi que les exportations hors du continent Africain de cas animaux ou humains de plus en plus fréquentes, ont été des signaux d’alerte de la menace locale et globale représentée par cette zoonose. Depuis 2001, la République Centrafricaine a été un des rares pays à mettre en place une surveillance nationale de cette maladie endémique, avec une investigation systématique des cas confirmés réalisée par le laboratoire des fièvres hémorragiques virales, des zoonoses et virus émergents de l’Institut Pasteur de Bangui, dont les capacités de diagnostic biologique en font un centre de référence dans la sous-région. Depuis 2019, le projet AFRIPOX a développé une approche One Health pluridisciplinaire autour de la surveillance nationale afin de comprendre cette zoonose dans sa complexité et sous ses différents aspects : infection humaine, réservoir animal, écologie de la maladie, outils diagnostics. L’objectif de cette thèse est la description de l’histoire naturelle et de l’épidémiologie de la variole du singe en République Centrafricaine, à partir des investigations d’épidémies et à travers une approche One Health. L’analyse des données rétrospectives de la surveillance nationale de la variole du singe entre 2001 et 2021 permet de décrire les caractéristiques épidémiologiques et cliniques de 40 épidémies confirmées de variole du singe. La description d’une épidémie survenue en 2018 dans la région de la Lobaye, avec une présentation épidémiologique typique, donne un état de référence de l’épidémiologie de cette zoonose en Afrique centrale. Une épidémie survenue en 2021 caractérisée par une taille plus importante et sa situation en zone urbaine et dans une zone plus connectée, pourrait suggérer une évolution de l’épidémiologie de la maladie. Plusieurs épidémies survenues dans la Lobaye en 2019 au retour de camps de collecte de chenilles comestibles, soulèvent des hypothèses autour du rôle potentiel joué par ces camps forestiers saisonniers dans la survenue d’évènements zoonotiques, nécessitant des études complémentaires. Enfin, une analyse de l’hétérogénéité de la distribution spatiotemporelle des épidémies de variole du singe survenues en Afrique entre 1970 et 2021 a identifié une saisonnalité de la variole du singe dans les zones tropicales, appelant des recherches spécifiques ultérieures. L’ensemble de ces études est venu combler certaines zones d’ombre de la maladie dans son contexte d’émergence. En effet, bien que la RCA soit le quatrième pays le plus touché au monde par la variole du singe avant l’épidémie mondiale actuelle, les données épidémiologiques, cliniques et virologiques étaient préalablement rares. Cette thèse vise à produire des connaissances permettant aux autorités sanitaires d’adapter des mesures de prévention auprès des populations. Ces connaissances sont fondamentales pour circonscrire les épidémies localement et pour éviter une dissémination de la souche du clade 1 vers des zones non endémiques, dans la sous-région africaine et au-delà.Monkeypox has a similar clinical presentation to smallpox, with a lower Case Fatality Ratio of 3-10%. Following initial zoonotic transmission, monkeypox virus spreads by secondary human-to-human transmission. Although the animal reservoir has not yet been formally identified, small rodents such as tree squirrels are considered the most likely reservoirs of the virus. Prior to the current global epidemic, monkeypox was considered a rare zoonosis, confined to the African continent. The recent increase in the number and frequency of outbreaks in endemic countries, the epidemiological changes identified in Nigeria since 2017, as well as the increasingly frequent export of animal or human cases from the African continent, have been warning signals of the local and global threat posed by this zoonosis. Since 2001, the Central African Republic has been one of the few countries to set up national surveillance of this endemic disease, with systematic investigation of confirmed cases carried out by the laboratory of viral hemorrhagic fevers, zoonoses and emerging viruses of the Institut Pasteur de Bangui, whose biological diagnostic capabilities make it a reference center in the sub-region. Since 2019, the AFRIPOX project has developed a multidisciplinary One Health approach around national surveillance in order to understand this zoonosis in its complexity and under its different aspects: human infection, animal reservoir, disease ecology, and diagnostic tools. The objective of this thesis is to describe the natural history and epidemiology of monkeypox in the Central African Republic, based on epidemic investigations and through a One Health approach. Analysis of retrospective data from national monkeypox surveillance between 2001 and 2021 allows us to describe the epidemiological and clinical characteristics of 40 confirmed monkeypox outbreaks. The description of an epidemic that occurred in 2018 in the Lobaye region, with a typical epidemiological presentation, provides a baseline of the epidemiology of this zoonosis in Central Africa. An epidemic that occurred in 2021, characterized by a larger size and its location in an urban area and in a more connected zone, could suggest an evolution of the epidemiology of the disease. Several outbreaks that occurred in Lobaye in 2019 upon return from edible caterpillar collection camps raise hypotheses about the potential role of these seasonal forestry camps in the occurrence of zoonotic events, requiring further study. Finally, an analysis of the heterogeneity of the spatiotemporal distribution of monkeypox outbreaks in Africa between 1970 and 2021 identified a seasonality of monkeypox in tropical areas, calling for further specific research. Together, these studies have filled in some of the grey areas of the disease in its emergence context. Indeed, although the CAR was the fourth most affected country in the world by monkeypox before the current global epidemic, epidemiological, clinical and virological data were previously scarce. This thesis aims to generate knowledge that will allow health authorities to adapt prevention measures for the population. This knowledge is fundamental to contain the epidemics locally and to avoid dissemination of the clade 1 strain to non-endemic areas in the African sub-region and beyond

Paramétrisation avec réseaux générateurs antagonistes pour la réduction des espaces de contrôles en assimilation de données

Ce travail porte sur l’utilisation des réseaux de neurones génératifs et plus particulièrement les GANs (generative adversarial networks) pour la paramétrisation dans le cadre des méthodes d’ensemble pour l’assimilation de données. L’assimilation de données permet d’estimer les paramètres initiaux ou l’état d’un modèle physique à l’aide d’observations en prenant en compte les incertitudes associées à ces dernières. Le filtre de Kalman donne une solution analytique lorsque le modèle physique est linéaire et les différentes sources d’erreurs suivent une distribution Gaussienne. Les méthodes d’ensemble permettent d’appliquer cette méthode à des systèmes physiques non-linéaires représentés par des modèles numériques. L’estimation de paramètres ne suivant pas une distribution Gaussienne reste un challenge dans beaucoup de domaines. Des méthodes de paramétrisation sont alors mises en place afin de transformer ces paramètres en de nouveaux, plus adaptés aux hypothèses des méthodes ensemblistes. Une autre limitation est la cohérence des paramètres estimés et l’utilisation d’information a priori comme les contraintes physiques que les paramètres doivent respecter. En effet, les paramètres estimés peuvent ne pas avoir de sens physique comme une température négative par exemple. Les méthodes de paramétrisation sont également utilisées afin de limiter ce phénomène. Enfin un dernier avantage de ces méthodes est qu’elles permettent de limiter le nombre de paramètres en réduisant leur dimension après transformation. Dans cette étude une nouvelle méthode de paramétrisation utilisant les GANs, appliqués à la caractérisation de réservoirs souterrains est présentée. Lors de l’estimation à l’aide de méthodes d’ensemble de la disposition des différents types de roches au sein d’un réservoir, il est courant d’obtenir des formes d’hétérogénéités géologiques irréalistes. Ces hétérogénéités sont caractérisées par des formes et des motifs particuliers issus de phénomènes physiques connus. De plus, le type de roche n’est pas un paramètre continu respectant l’hypothèse de distribution Gaussienne et est de grande dimension pour des applications industrielles. L’utilisation d’une méthode de paramétrisation est alors requise, mais la conservation du réalisme géologique par ces dernières reste soit trop peu réaliste, soit trop coûteuse numériquement. Le GAN étant une technique issue des méthodes d’apprentissage automatique et ayant récemment gagné en notoriété pour sa capacité à pouvoir apprendre et générer des images complexes. Il constitue un choix prometteur pour son application dans le domaine de la caractérisation des réservoirs souterrains. Cette étude présente les résultats obtenus sur un cas de réservoir simplifié comportant des hétérogénéités en forme de chenaux, particulièrement difficile à paramétriser par les méthodes actuelles. Une seconde application est abordée lors de cette étude portant sur la prédiction des champs atmosphériques à l’aide des méthodes d’assimilation de données. Lors de l’estimation de l’état de l’atmosphère, pour la prédiction météorologique par exemple, il est important de corriger l’état atmosphérique avec de nouvelles observations de manière que les nouveaux champs respectent les équilibres physiques qui régissent la circulation atmosphérique. Quand cela n’est pas le cas, des instabilités numériques peuvent apparaitre lors de la simulation de l’état futur de l’atmosphère, détériorant l’information apportée par les observations. L’utilisation d’un GAN capable d’apprendre les contraintes physiques qui caractérise un champ atmosphérique à l’état d’équilibre peut s’avérer utile. C’est dans ce contexte que la seconde application de cette étude s’inscrit. Ce travail vise à présenter les performances de la paramétrisation du GAN et son applicabilité multidisciplinaire aux lecteurs qui ne sont pas familiers avec le domaine de l’apprentissage profond.This thesis examines the use of generative adversarial networks (GANs) as a parameterization tool for inverse problems solved with ensemble-based data assimilation methods. Ensemble methods often rely on the assumption of Gaussian distributed parameters in cases where this assumption is not valid, the parameter estimation can be invalid. Parameterization methods allow the transformation of these non-Gaussian parameters into a better suited distribution, and optimally reduce their dimension. Another limitation of ensemble methods is the injection of prior information of the physical relation as a constraint between parameters such as spatial coherence or physical balances. Optimal parameterization should encompass these different properties to facilitate the estimation. The novel approach presented in this work relies on GANs to achieve these objectives. Two application domains are tackled through the present work. In a first application, subsurface reservoir characterization, the objective is to determine geological properties of a numerical reservoir model from the observation of the reservoir dynamical response by the way of data assimilation. Rock facies, that describe the type of rock present in each cell of the numerical model, have to be determined due to their strong influence on the dynamical response. The rock facies spatial distribution is ruled by geological phenomena such as sedimentation and forms well known patterns, like channels, called heterogeneities. The noncontinuous property and their spatial coherence make their characterization by ensemble-based data assimilation algorithms difficult, and requires parameterization. Parameterization is a challenge for numerous heterogeneities, notably channels, due to the numerical cost or the statistical representation of their spatial distribution. A Second application domain is the atmospheric balance in the context of numerical weather prediction. When new observations are available, correction of the atmospheric state is done using ensemblebased data assimilation methods. This correction step can introduce imbalance in the physical state and cause numerical instability during the integration in time of the atmosphere, deteriorating the information brought by the previous observations. The importance of generating or correcting balanced climate, also called initialized atmospheric state, during data assimilation is then a key step in numerical weather prediction. This work aims at presenting the performance of GAN parameterization and its multi-disciplinary applicability to researchers who are not familiar with the domain of deep learning. GAN is an unsupervised deep learning method belonging to the deep generative network family, able to learn a dataset distribution and generate new samples from the learned distribution in an unsupervised way. These synthetic samples are encoded in a low-dimensional latent space that can be sampled from a Gaussian distribution that is suited to perform ensemble data assimilation. Their recent ability to generate complex images led us to consider them as a good candidate for parameterization method. The unsupervised property of this type of parameterization makes it applicable to several diverse domains such as learning the pattern of geological heterogeneities or learning the physical constraints that makes an atmospheric state balanced. This study shows how to train GANs for two different applications : subsurface reservoir and climate data. The use of the parameterization in an ensemble based data assimilation such as ensemble smoother with multiple data assimilation (ES-MDA) is demonstrated for subsurface reservoir. Finally, a posteriori conditioning of the GAN function is examined using derivative free optimization

The peculiar emergence of monkeypox/mpox: A modeling perspective

The first epidemiological accounts of monkeypox date since the early 80's, yet monkeypox emerged as an epidemic threat more than 40 years later. This scenario appears very different from the epidemiologies of other emerging diseases such as HIV and SARS, which immediately spread globally, in a fully susceptible population, starting from respective patients zero. In this letter, we use mathematical modeling to explain the peculiar emergence of monkeypox. We employ stochastic models to describe the dynamics of monkeypox herd immunity. We argue that monkeypox first emerged in small rural communities in touch with the monkeypox' sylvatic reservoir and then spread globally. The relative isolation of these communities and their herd-immunity dynamics against monkeypox worked to delay the introduction of monkeypox in larger urban communities. Our modeling considerations lead to new strategies for monkeypox vaccination in the endemic areas, and new clues for the search of the monkeypox reservoir

Chemical characterization of SOA from diesel and gasoline vehicles with untargeted approaches

Recent studies have shown that the PM emissions of modern cars equipped with the newest after-treatment technologies are mainly related to the production of secondary organic aerosols (SOA) especially in the case of gasoline vehicles. The objective of this work is to identify specific molecular markers or chemical patterns that can be further used in PM source apportionment studies to discriminate SOA from diesel and gasoline vehicles. Experiments were performed on a chassis dynamometer with a gasoline and diesel Euro 5 vehicles. Exhaust emissions were diluted before introduction into a potential aerosol mass (PAM) oxidation flow reactor (OFR) for aging and SOA formation. About 50 filter samples were collected upstream and downstream the PAM-OFR and analyzed using innovative non-targeted approaches with high-resolution mass spectrometry coupled to gas or liquid chromatography. The data treatment and statistical analysis will allow to highlight features or chemical patterns specific of gasoline and diesel SOA

Producing realistic climate data with generative adversarial networks

International audienceThis paper investigates the potential of a Wasserstein generative adversarial network to produce realistic weather situations when trained from the climate of a general circulation model (GCM). To do so, a convolutional neural network architecture is proposed for the generator and trained on a synthetic climate database, computed using a simple three dimensional climate model: PLASIM. The generator transforms a "latent space", defined by a 64-dimensional Gaussian distribution, into spatially defined anomalies on the same output grid as PLASIM. The analysis of the statistics in the leading empirical orthogonal functions shows that the generator is able to reproduce many aspects of the multivariate distribution of the synthetic climate. Moreover, generated states reproduce the leading geostrophic balance present in the atmosphere. The ability to represent the climate state in a compact, dense and potentially nonlinear latent space opens new perspectives in the analysis and handling of the climate. This contribution discusses the exploration of the extremes close to a given state and how to connect two realistic weather situations with this approach

Identifying the Most Probable Mammal Reservoir Hosts for Monkeypox Virus Based on Ecological Niche Comparisons

International audiencePrevious human cases or epidemics have suggested that Monkeypox virus (MPXV) can be transmitted through contact with animals of African rainforests. Although MPXV has been identified in many mammal species, most are likely secondary hosts, and the reservoir host has yet to be discovered. In this study, we provide the full list of African mammal genera (and species) in which MPXV was previously detected, and predict the geographic distributions of all species of these genera based on museum specimens and an ecological niche modelling (ENM) method. Then, we reconstruct the ecological niche of MPXV using georeferenced data on animal MPXV sequences and human index cases, and conduct overlap analyses with the ecological niches inferred for 99 mammal species, in order to identify the most probable animal reservoir. Our results show that the MPXV niche covers three African rainforests: the Congo Basin, and Upper and Lower Guinean forests. The four mammal species showing the best niche overlap with MPXV are all arboreal rodents, including three squirrels: Funisciurus anerythrus, Funisciurus pyrropus, Heliosciurus rufobrachium, and Graphiurus lorraineus. We conclude that the most probable MPXV reservoir is F. anerythrus based on two niche overlap metrics, the areas of higher probabilities of occurrence, and available data on MPXV detection

One Health or ‘One Health washing’? An alternative to overcome now more than ever

International audienceAbstract In March 2022, WHO, WOAH, FAO, and UNEP jointly advocated a rebalancing of the multiple components of One Health, explicitly including the notion of equity between sectors and disciplines, and a clearer ecological vision of the whole. Here we illustrate the vital need for this shift based on the multi-decadal experience of the authors in this field of research. We explain why One Health research still crucially requires the expansion of the current collaborations between disciplines and sectors to achieve its goals, and to release significant funding in each field for a successful transformational change. If not, ‘One Health’ will still stay as an aspiration and will not hit its promised targets. One Health Impact Statement In March 2022, WHO, WOAH, FAO, and UNEP jointly advocated a rebalancing of the multiple components of One Health, explicitly including the notion of equity between sectors and disciplines, and a clearer ecological vision of the whole. Here we illustrate the vital need for this shift based on the multi-decadal experience of the authors in this field of research. We explain why One Health research still crucially requires the expansion of the current collaborations between disciplines and sectors to achieve its goals, and to release significant funding in each field for a successful transformational change. If not, ‘One Health’ will still stay as an aspiration and will not hit its promised targets

Discrimination of biomass burning sources using innovative non-targeted approaches

To date, the evaluation of the contributions of wood burning, used for residential heating, and green waste burning on the PM concentrations is rarely achieved. The objective of the research project SODEMASS is to identify specific organic molecular markers or chemical patterns of both biomass burning sources that can be further used in PM source apportionment studies. Several experiments have been performed in a combustion chamber to simulate the ambient air dilution conditions. Different wood combustion appliances (residential wood stove, fireplace) were tested and green waste burning experiments were performed using different burning materials (leaves and branches). About 50 PM samples have been characterized using both targeted and non-targeted (high-resolution mass spectrometry analyses) approaches. As targeted results were not enough to discriminate between wood and green waste burning sources in ambient air, non-targeted strategies were applied. After statistical analysis, several features specific of each biomass burning source were finally highlighted