Les épidémies de méningite à méningocoque représentent un problème de santé publique majeur au Niger. L'objectif de la thèse est de contribuer à une meilleure compréhension de la dynamique spatio-temporelle des épidémies et des facteurs de risque, afin d'améliorer les stratégies de contrôle. Des méthodes statistiques d'épidémiologie spatiale sont appliquées aux données de surveillance de 2003 à 2010, à l'échelle des aires de santé.D'importantes caractéristiques de la distribution spatio-temporelle des cas sont d'abord mises en évidence par des méthodes d'autocorrélation spatiale et de scan spatial : faible étendue des agrégats spatio-temporels, hétérogénéité spatiale, variabilité inter-annuelle... L'analyse suggère que l'échelle des aires de santé pourrait être plus efficace pour la réponse aux épidémies.Un modèle explicatif hiérarchique bayésien est ensuite développé à l'échelle des aires de santé. Il suggère que la variabilité spatio-temporelle de l'incidence du méningocoque A résulte de variations dans l'intensité et la durée de facteurs climatiques, et est de plus impactée par des facteurs de contacts spatiaux. Enfin, un modèle prédictif est développé, basé sur les conditions climatiques, les interactions de voisinage et la précocité des cas, pour estimer le risque de survenue d'une épidémie localisée. Le système d'alerte ainsi élaboré pourrait améliorer la détection des épidémies et la vaccination réactive.Nos résultats offrent un nouvel éclairage sur les épidémies de méningite à méningocoque au Niger. Ils permettent de formuler des recommandations opérationnelles qui pourraient contribuer à l'élaboration de stratégies de contrôle et de prévention efficaces. / Epidemics of meningococcal meningitis are a major public health problem in Niger. The objective of the thesis is to contribute to a better understanding of the spatio-temporal dynamics of these epidemics and their risk factors, in order to improve control strategies. Statistical methods of spatial epidemiology are applied to surveillance data from 2003 to 2010, at the scale of health centre catchment areas (HCCAs).First, important features of the spatio-temporal distribution of cases are highlighted by methods of spatial autocorrelation and spatial scan: low extent of the spatio-temporal clusters, spatial heterogeneity, inter-annual variability… The analysis suggests that the HCCA scale could be more efficient for epidemic response. An explanatory Bayesian hierarchical model is then developed at the HCCA level. The model suggests that the spatio-temporal variability of meningococcal A incidence results from variations in the intensity or duration of climatic factors, and is further impacted by factors of spatial contacts.Finally, a predictive model is developed, based on climatic conditions, neighbourhood interactions and early cases, in order to estimate the risk of occurrence of a localized epidemic. The early warning system thus formulated could improve outbreak detection and reactive vaccination. Our results bring new insights into the meningococcal meningitis epidemics in Niger. They allow the formulation of operational recommendations that could contribute to the elaboration of more effective strategies for control and prevention of epidemics.

Sur les dernières décennies, progresse sur la compréhension de la sismicité de clustering dans le temps, taille et l'espace ont été chassés par deux approches parallèles. De l'on étudie la main sur la mécanique des failles dans un milieu élastique soutiennent pour la contrainte statique de déclenchement à dominent dans le champ proche, c'est à dire à une distance de moins de 10 longueur de faute. De l'autre part, les propriétés de champ moyen du déclenchement sont reproduits en utilisant des effets en cascade dans modèles de processus point. Dans cette étude, nous essayons de concilier ces approches en soulignant l'importance de la faille de style sur les propriétés moyennes de la sismicité. En commençant par l'étude du taux de sismicité déclenchée par la Ville Muzaffarabad, au Cachemire, 2005 Mw = 7.6, Ms = 7,7 tremblement de terre, qui apparaît comme supérieure à la moyenne dans l'analyse des séquences répliques dans la ceinture de collision Inde-Asie, nous décidons de la productivité évènement grève de glissement sont en moyenne 4 fois plus petit que la poussée des failles de productivité. En utilisant le catalogue sismique global, nous étendre ce résultat comme tous les paramètres de la loi d'Omori (P, K, α, N (t)) étant dépendant failles styles. Dans le K ETAS modèle solide, N et de faibles valeurs de α sont entraînés par branchement ratio élevé (n). Comme conséquences de la relative n haute - La valeur la poussée des événements, il prédit aussi une faible p - La valeur pour l'événement de la poussée que comparer à bordereau de grève et des manifestations normales de la PN> PS S> PT que nous observons. Dans l'état de taux et cadre de la friction cela implique un changement dans les habitudes hétérogénéité stress. Nous ne résolvent pas tout changement dans le taux robustes foreshocks, p ' - La valeur, alors que notre analyse nous permettra d'étendre B ˚ droit aths dans le temps, d'espace et mécanisme focal. Pour des failles inverses, l'ampleur différence et la distance entre le choc principal de la plus grande réplique sont un peu moins que pour les défauts de glissement de grève. La distribution des intervalles de temps entre mainshocks et leurs le plus grand répliques est conforme au droit d'Omori, mais avec un taux un peu plus rapide de la carie que pour les répliques en général. Ceci implique que la plus grande réplique est plus susceptible de survient plus tôt que plus tard dans une séquence donnée de répliques. Par ailleurs, cette constatation plaide en faveur allant au-delà du modèle de point de branchement, avec des implications pour les prévisions à court terme. Aussi nous résoudre la dépendance univoque de p - La valeur du droit à l'ampleur Omori choc principal pour la réplique dans les 10 jours après la survenance choc principal, cette dépendance étant perdus lors de l'utilisation des séquences en cascade complète. Nous trouvons ce seuil correspond également le temps à un changement dans les schémas de diffusion, tous ces changements se synchroniser avec l'apparition de la plus grande réplique. En conséquence, nos résultats convergent vers le rôle clé du secondaire répliques sur la mécanique de la taille, le temps et l'espace modèle de processus en cascade. / On the last decades, progresses on the understanding of clustering seismicity in time, size and space have been driven by two parallel approaches. From the one hand studies on the mechanics of faulting in an elastic medium argue for the static stress triggering to dominate in the near field, i.e within distance less than 10 fault length. From the other hand, mean field properties of the triggering are reproduced using cascading effects in point process models. In this study we try to reconcile these approaches by emphasizing the importance of faulting style on average properties of seismicity. Starting with the study of the seismicity rate triggered by the Muzaffarabad, Kashmir, 2005 Mw = 7.6, Ms = 7.7 earthquake, which appears as above the average when analysing the aftershocks sequences in the India-Asia collision belt, we resolve the strike slip event productivity to be on average 4 times smaller than the thrust faulting productivity. Using global earthquake catalog, we further extend this result as all the parameters of the Omori law (p, K, α, N (t)) being dependent on faulting styles. Within the ETAS model strong K, N and low α values are driven by high branching ratio (n). As consequences of the relative high n − value of the thrust events, it also predicts a lower p − value for thrust event as compare to strike slip and normal events as the pN > pS S > pT we observe. Within rate and state friction framework it implies a change in stress heterogeneity patterns. We do not resolve any robust changes in foreshocks rate, p′ − value, whereas our analysis allow us to extend B˚aths law in time, space and focal mechanism. For reverse faults, both the magnitude difference and the distance from the mainshock to the largest aftershock are somewhat less than for strike slip faults. The distribution of time intervals between mainshocks and their largest aftershocks is consistent with Omori's law but with a somewhat faster rate of decay than for aftershocks in general. This implies that the largest aftershock is more likely to occurs earlier than later in a given sequence of aftershocks. Moreover, this finding argues for going beyond the branching point model, with implications for short term forecasts. Also we resolve unambiguous dependency of p − value of Omori law to mainshock magnitude for the aftershock within 10 days after the mainshock occurrence, this dependency being lost when using complete cascade sequences. We find this time threshold also corresponds to a change in diffusion patterns, all these changes synchronize with the occurrence of the largest aftershock. Accordingly, our results converge toward the key role of the secondary aftershocks on the mechanics of size, time and space pattern of cascading processes.

Many physical quantities around us vary across space or space-time. An example of a spatial quantity is provided by the temperature across Sweden on a given day and as an example of a spatio-temporal quantity we observe the counts of the corona virus cases across the globe. Spatial and spatio-temporal data enable opportunities to answer many important questions. For example, what the weather would be like tomorrow or where the highest risk for occurrence of a disease is in the next few days? Answering questions such as these requires formulating and learning statistical models. One of the challenges with spatial and spatio-temporal data is that the size of data can be extremely large which makes learning a model computationally costly. There are several means of overcoming this problem by means of matrix manipulations and approximations. In paper I, we propose a solution to this problem where the model islearned in a streaming fashion, i.e., as the data arrives point by point. This also allows for efficient updating of the learned model based on newly arriving data which is very pertinent to spatio-temporal data. Another interesting problem in the spatial context is to study the causal effect that an exposure variable has on a response variable. For instance, policy makers might be interested in knowing whether increasing the number of police in a district has the desired effect of reducing crimes there. The challenge here is that of spatial confounding. A spatial map of the number of police against the spatial map of the number of crimes in different districts might show a clear association between these two quantities. However, there might be a third unobserved confounding variable that makes both quantities small and large together. In paper II, we propose a solution for estimating causal effects in the presence of such a confounding variable. Another common type of spatial data is point or event data, i.e., the occurrence of events across space. The event could for example be a reported disease or crime and one may be interested in predicting the counts of the event in a given region. A fundamental challenge here is to quantify the uncertainty in the predicted counts in a model in a robust manner. In paper III, we propose a regularized criterion for learning a predictive model of counts of events across spatial regions.The regularization ensures tighter prediction intervals around the predicted counts and have valid coverage irrespective of the degree of model misspecification.

The presence of acoustic noise in audio recordings is an ongoing issue that plagues many applications. This ambient background noise is difficult to reduce due to its unpredictable nature. Many single channel noise reduction techniques exist but are limited in that they may distort the desired speech signal due to overlapping spectral content of the speech and noise. It is therefore of interest to investigate the use of multichannel noise reduction algorithms to further attenuate noise while attempting to preserve the speech signal of interest. Specifically, this thesis looks to investigate the use of microphone arrays in conjunction with multichannel noise reduction algorithms to aid aiding in speaker identification. Recording a speaker in the presence of acoustic background noise ultimately limits the performance and confidence of speaker identification algorithms. In situations where it is impossible to control the noise environment where the speech sample is taken, noise reduction algorithms must be developed and applied to clean the speech signal in order to give speaker identification software a chance at a positive identification. Due to the limitations of single channel techniques, it is of interest to see if spatial information provided by microphone arrays can be exploited to aid in speaker identification. This thesis provides an exploration of several time domain multichannel noise reduction techniques including delay sum beamforming, multi-channel Wiener filtering, and Spatial-Temporal Prediction filtering. Each algorithm is prototyped and filter performance is evaluated using various simulations and experiments. A three-dimensional noise model is developed to simulate and compare the performance of the above methods and experimental results of three data collections are presented and analyzed. The algorithms are compared and recommendations are given for the use of each technique. Finally, ideas for future work are discussed to improve performance and implementation of these multichannel algorithms. Possible applications for this technology include audio surveillance, identity verification, video chatting, conference calling and sound source localization.

As a core problem in video analysis, action recognition is of great significance for many higher-level tasks, both in research and industrial applications. With more and more video data being produced and shared daily, effective automatic action recognition methods are needed. Although, many deep-learning methods have been proposed to solve the problem, recent research reveals that single-stream, RGB-based networks are always outperformed by two-stream networks using both RGB and optical flow as inputs. This dependence on optical flow, which indicates a deficiency in learning motion, is present not only in 2D networks but also in 3D networks. This is somewhat surprising since 3D networks are explicitly designed for spatio-temporal learning. In this thesis, we assume that this deficiency is caused by difficulties associated with learning from videos exhibiting strong temporal variations, such as sudden motion, occlusions, acceleration, or deceleration. Temporal variations occur commonly in real-world videos and force a neural network to account for them, but often are not useful for recognizing actions at coarse granularity. We propose a Dynamic Equilibrium Module (DEM) for spatio-temporal learning through adaptive Eulerian motion manipulation. The proposed module can be inserted into existing networks with separate spatial and temporal convolutions, like the R(2+1)D model, to effectively handle temporal video variations and learn more robust spatio-temporal features. We demonstrate performance gains due to the use of DEM in the R(2+1)D model on miniKinetics, UCF-101, and HMDB-51 datasets.

As cities grow, efficient public transport systems are becoming increasingly important. To offer a more efficient service, public transport providers use systems that predict arrival times of buses, trains and similar vehicles, and present this information to the general public. The accuracy and reliability of these predictions are paramount, since many people depend on them, and erroneous predictions reflect badly on the public transport provider. When public transport vehicles move throughout the cities, they create motion patterns, which describe how their positions change over time. This thesis proposes a way of modeling their motion patterns using Gaussian processes, and investigates whether it is possible to predict the arrival times of public transport buses in Linköping based on their motion patterns. The results are evaluated by comparing the accuracy of the model with a simple baseline model and a recurrent neural network (RNN), and the results show that the proposed model achieves superior performance to that of an RNN trained on the same amounts of data, with excellent explainability and quantifiable uncertainty. However, an RNN is capable of training on much more data than the proposed model in the same amount of time, so in a scenario with large amounts of data the RNN outperforms the proposed model.

Congestion is a major problem in most metropolitan areas and given the increasingrate of urbanization it is likely to be an even more serious problem in the rapidlyexpanding mega cities. One possible method to combat congestion is to provide in-telligent traffic management systems that can in a timely manner inform drivers aboutcurrent or predicted traffic congestions that are relevant to them on their journeys. Thedetection of traffic congestion and the determination of whom to send in advance no-tifications about the detected congestions is the objective of the present research. Byadopting a grid based discretization of space, the proposed system extracts and main-tains traffic flow statistics and mobility statistics from the grid based recent trajectoriesof moving objects, and captures periodical spatio-temporal changes in the traffic flowsand movements by managing statistics for relevant temporal domain projections, i.e.,hour-of-day and day-of-week. Then, the proposed system identifies a directional con-gestion as a cell and its immediate neighbor, where the speed and flow of the objectsthat have moved from the neighbor to the cell significantly deviates from the histori-cal speed and flow statistics. Subsequently, based on one of two notification criteria,namely, Mobility Statistic Criterion (MSC) and Linear Movement Criterion (LMC),the system decides which objects are likely to be affected by the identified conges-tions and sends out notifications to the corresponding objects such that the numberof false negative (missed) and false positive (unnecessary) notifications is minimized.The thesis discusses the design and DBMS-based implementation of the proposedsystem. Empirical evaluations on realistically simulated trajectory data assess the ac-curacy of the methods and test the scalability of the system for varying input sizes andparameter settings. The accuracy assessment results show that the MSC based systemachieves an optimal performance with a true positive notification rate of 0.67 and afalse positive notification rate of 0.05 when min prob equals to 0.35, which is superiorto the performance of the LMC based system. The execution time of- and the spaceused by the system scales linearly with the input size (number of concurrently movingvehicles) and the methods mutually dependent parameters (grid resolution r and RTlength l) that jointly define a spatio-temporal resolution. Within the area of a large  city (40km by 40km), assuming a 60km/h average vehicle speed, the system, runningon a commodity personal computer, can manage the described congestion detectionand three-minute-ahead notification tasks within real-time requirements for 2000 and20000 concurrently moving vehicles for spatio-temporal resolutions (r=100m, l=19)and (r=2km, l=3), respectively.

Saudi Arabia has been trying to change its long tradition of relying on fossil fuels and seek renewable energy sources such as wind power. In this thesis, I firstly provide a comprehensive assessment of wind energy resources and associated spatio-temporal patterns over Saudi Arabia in both current and future climate conditions, based on a Regional Climate Model output. A high wind energy potential exists and is likely to persist at least until 2050 over a vast area ofWestern Saudi Arabia, particularly in the region between Medina and the Red Sea coast and during Summer months. Since an accurate assessment of wind extremes is crucial for risk management purposes, I then present the first high-resolution risk assessment of wind extremes over Saudi Arabia. Under the Bayesian framework, I measure the uncertainty of return levels and produce risk maps of wind extremes, which show that locations in the South of Saudi Arabia and near the Red Sea and the Persian Gulf are at very high risk of disruption of wind turbine operations. In order to perform spatial predictions of the bivariate wind random field for efficient turbine control, I propose parametric variogram matrix (function) models for cokriging, which have the advantage of allowing for a smooth transition between a joint second-order and intrinsically stationary vector random field. Under Gaussianity, the covariance function is central to spatio-temporal modeling, which is useful to understand the dynamics of winds in space and time. I review the various space-time covariance structures and models, some of which are visualized with animations, and associated tests. I also discuss inference issues and a case study based on a high-resolution wind-speed dataset. The Gaussian assumption commonly made in statistics needs to be validated, and I show that tests for independently and identically distributed data cannot be used directly for spatial data. I then propose a new multivariate test for spatial data by accounting for the spatial dependence. The new test is easy to compute, has a chi-square null distribution, and has a good control of the type I error and a high empirical power.

L'objectif de cette thèse est de réunir les deux approches suivantes qui existent actuellement pour étudier une foule: ou bien à temps fixé on s'intéresse à la distribution spatiale des individus, ou bien on suit un seul individu à la fois au cours du temps. On se propose de construire des processus spatio-temporels, qui, comme leur nom l'indique, permettraient de rendre compte du caractère aléatoire des usages d'une foule dans un réseau de télécommunication, à la fois du point de vue spatial (modèles de route) et du point de vue temporel (déplacements sur ces routes, usages qui varient au cours de ces déplacements…). Une fois ces processus construits de manière rigoureuse, on étudie leur comportement d'une manière fine. Nous développons trois modèles différents qui chacun mènent à des formules analytiques fermées, ce qui permet de les utiliser d'une manière très confortable à des fins de dimensionnement. / This thesis consists in joining two approaches that currently exist when one wants to study crowds phenomena: either taking a snapshot by freezing time to study the spatial repartition of the individuals, or following one given individual over time.We build space-time processes that let us model random phenomena in a crowd, being on a spatial level (roads models) or a time level (movings on these roads, space-dependent behaviors…). Once we have built them in a rigorous manner, we study their properties, which let us obtain analytical closed formulas that can be widely used for dimensioning purposes.

Land degradation is one of the most important drivers of landscape change around the globe. This dissertation examines land use-land cover change within a mosaic landscape in Eastern Terai, India, and shows evidence of anthropogenic factors contributing to landscape change. Land use and land cover change were examined within the Alipurduar Subdivision, a representative of the Eastern Terai landscape and the Jaldapara Wildlife Sanctuary, a protected area nested within Alipurduar through the use of multi-temporal satellite data over the past 28 years (1978 – 2006). This study establishes the potential of remote sensing technology to identify the drivers of landscape change; it provides an assessment of how regional drivers of landscape change influence the change within smaller local study extents and provides a methodology to map different types of grassland and monitor their loss within the region. The Normalized Difference Vegetation Index (NDVI) and a Normalized Difference Dry Index (NDDI) were found instrumental in change detection and the classification of different grasslands found inside the park based on their location, structure, and composition. Successful spectral segregation of different types of grasslands and their direct association with different grassland specialist species (e.g., hispid hare, hog deer, Bengal florican) clearly showed the potential of remote sensing technology to efficiently monitor these grasslands and assist in species conservation. Temporal analysis provided evidence of the loss of dense forest and grasslands within both study areas with a considerably higher rate of loss outside the protected area than inside. Results show a decline of forest from 40% in 1978 to 25% in 2006 across Alipurduar. Future trends project forest cover and grassland within Alipurduar to reduce to 15% and 5%, respectively. Within the Alipurduar, deforestation due to growth of tea industry was the primary driver of change. Flooding changed the landscape, but more intensely inside the wildlife preserve. Change of the river course inside Jaldapara during the flood of 1968 significantly altered the distribution of grassland inside the park. Unless, the direction of landscape change is altered, future trends predict growth of the tea industry within the region, increased forest loss, and homogenization of the landscape.