Quantifying the Effects of Contact Tracing, Testing, and Containment

Contact tracing has the potential to help identify, characterize, and predict disease-spreading human interactions at an unprecedented resolution. However, to realize this potential, we need to utilize data-driven epidemic models that can operate at a high spatiotemporal resolution and make use of and benefit from contact tracing data of individuals. Such data-driven models are currently missing, and in this work we initiate their development using the framework of temporal point processes. Using an efficient sampling algorithm, we can use our model to quantify the effects that different testing and tracing strategies, social distancing measures, and business restrictions may have on the course of the disease. Building on this algorithm, we use Bayesian optimization to estimate the transmission rate due to infectious individuals at the sites they visit and at their households as well as the mobility reduction due to social distancing from longitudinal case data. Simulations using real COVID-19 case data and mobility patterns from several cities and regions in Germany and Switzerland with a wide range of infection levels until today demonstrate that our model may allow individuals and policy makers to make more effective decisions.Comment: Extensive results and additional analysis; refined parameter estimation

Tests d'hypothèses en dynamique des populations fragmentées : développement et applications de modèles d'occupation des sites

Les approches classiques de modèles spatiaux pour les processus binaires de distribution d'espèces (i.e. occupation des sites) présentent trois importantes carences. i) Elles ne prennent pas explicitement en compte l'incertitude dans le processus d'échantillonnage. ii) Il y a un manque de modèles spatio-temporels, notamment hiérarchique. iii) La plupart des modèles existants sont de type phénoménologique et ne considèrent pas explicitement les mécanismes écologiques sous-jacents. Cette thèse répond à ces limitations en présentant des modèles spatio-temporels d'occupation des sites pour des processus écologiques dynamiques. Ces modèles sont appliqués à des sujets essentiels en écologie, tels que la sélection de l'habitat, les espèces invasives et les changements climatiques. Comprendre la dynamique d'occupation des sites permet de prédire les changements d'occupation qui accompagneront des modifications de l'habitat et de prendre des décisions adaptées en gestion des populations.Classical approaches to the development of spatial models for binary processes of species distribution (i.e. occupancy processes) present three important deficiencies. i) They do not explicitly accommodate sampling uncertainty. ii) There is a lack of spatio-temporal occupancy models, especially in the framework of hierarchical modeling. iii) Most of existing models are phenomenological and do not explicitly consider underlying ecological mechanisms. This thesis develops spatio-temporal occupancy models for dynamical ecological processes in order to respond to these limitations while incorporating scientific knowledge in every modeling step. Those models are applied to critical ecological topics ranging from the spread of invasive species to habitat selection via climate changes. Understanding range and occupancy dynamics will permit prediction of occupancy changes that are likely to accompany future changes and hopefully will permit informed attempts to mediate changes in occupancy

Sensitivity of soil organic carbon to the change in climate on the Tibetan Plateau

Soil organic carbon, as the main terrestrial component in the Earth’s carbon cycle, has a profound effect on the accumulation of CO2 in atmosphere and consequently on global warming. In the alpine grasslands of the Tibetan Plateau, the decompo- sition rate of soil organic carbon is controlled by several biotic and abiotic factors, which mostly change simultaneously and often leads to freezing and thawing cycles. However, it is highly uncertain whether the temperature sensitivity of decomposition around the freezing point of water is similar as in higher temperature ranges. The main objective of this dissertation is to evaluate the effects of simultaneous changes in three climate factors on soil organic carbon decomposition rates using an incubation experiment and a biogeochemical model. Due to the large divergence between empirical and model-based approaches in predicting the effects of abiotic factors on soil carbon dynamics, this dissertation first provides an approach to un- cover some sources of uncertainty in estimated SOC processes in alpine grasslands. In this study, I evaluated the complexity of the model required to represent the dynamics of carbon observed in incubation studies. Information theory metrics including AIC and BIC, as well as carbon particle mean transit time, were used as criteria to select models that better predict the data without making additional assumptions about model structure. These analyses showed that during the limited course of an incubation experiment, the amount of transfer between the different SOC pools is negligible and adding these parameters to the model could lead to over-parameterization. These findings suggest that carbon models with less parameterized structures, such as the one-pool model or the two-pool model with parallel structure, that does not account for transfers between pools, indeed have better predictive power in describing the decomposition of carbon fractions while following the principle of parsimony. The aforementioned information was later used to evaluate the sensitivity of SOC degradation rates to changes in soil temperature, soil moisture, and oxygen availabil- ity, especially at low temperatures. Functions from the Dual Arrhenius-Michaelis- Menten model (DAMM) were implemented in a one-pool model of SOC represented as first-order differential equation with time-dependent coefficients. A manipulative freeze-thaw cycle was imposed on a soil from Tibetan grasslands, in addition to soil moisture treatments that ranged from extremely dry to fully saturated, under both oxic and anoxic conditions. The intrinsic sensitivities indicated that temperature (energy) is the main factor limiting decomposition in cold environments, provided moisture and oxygen are sufficiently available. However, the intrinsic sensitivities related to soil moisture and oxygen concentration are only relevant in very narrow ranges when soils are nearly dry or partially anoxic, and small changes within these narrow ranges can result in large changes in decomposition rates

Bayesian hierarchical modeling and analysis for physical activity trajectories using actigraph data

Rapid developments in streaming data technologies are continuing to generate increased interest in monitoring human activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraphy), have become prevalent. An actigraph unit continually records the activity level of an individual, producing a very large amount of data at a high-resolution that can be immediately downloaded and analyzed. While this kind of \textit{big data} includes both spatial and temporal information, the variation in such data seems to be more appropriately modeled by considering stochastic evolution through time while accounting for spatial information separately. We propose a comprehensive Bayesian hierarchical modeling and inferential framework for actigraphy data reckoning with the massive sizes of such databases while attempting to offer full inference. Building upon recent developments in this field, we construct Nearest Neighbour Gaussian Processes (NNGPs) for actigraphy data to compute at large temporal scales. More specifically, we construct a temporal NNGP and we focus on the optimized implementation of the collapsed algorithm in this specific context. This approach permits improved model scaling while also offering full inference. We test and validate our methods on simulated data and subsequently apply and verify their predictive ability on an original dataset concerning a health study conducted by the Fielding School of Public Health of the University of California, Los Angeles

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

Modelling intense rainfall in a changing climate

Anthropogenic climate change is unequivocal, with serious implications for society. Decades of atmospheric pollution have precipitated rapid non-stationarity in the hydrosphere, changing the frequency and intensity of storms in space and time. Utilities and civil infrastructure span generations, requiring practitioners to assess the local impacts of hydro-meteorological change. Rainfall simulation and extreme value theory are necessary for water resources planning and hazard mitigation. However, purely statistical techniques lack physical realism and the estimation of larger extremes can be highly uncertain. This thesis presents a new approach for estimating short duration rainfall extremes in a changing climate with mechanistic stochastic rainfall models. Mechanistic stochastic models simulate rainfall with rectangular pulses which conceptualise the phenomenology of rainfall generation in storms. But, since their inception over 30 years ago, they have tended to under estimate rainfall extremes at fine temporal scales. Motivated by industry to improve the physical realism of extreme rainfall estimation at sub-hourly scales, a censored modelling approach is presented with Bartlett-Lewis rectangular pulse models to simulate the intense rainfall profile. With censored rainfall simulation, intense storm profiles are constructed from the superposition of cells, from which extremes are sampled. The approach is applied to two test sites in the UK and Germany and used to estimate rainfall extremes in the present and hypothesised future climates at the end of this century. A new downscaling methodology is developed in which the rainfall models are conditioned on an ensemble of CMIP5 climate model outputs for moderate and severe climate forcing. Using K-nearest neighbour sampling to identify the training data for calibration, model parameter estimators are approximated using multivariate linear regression to enable estimation outside the covariate range. The approach is introduced with conditioning on mean monthly near surface air temperature and verified with further conditioning on relative humidity.Open Acces

A non-parametric Hawkes process model of primary and secondary accidents on a UK smart motorway

A self-exciting spatio-temporal point process is fitted to incident data from the UK National Traffic Information Service to model the rates of primary and secondary ac- cidents on the M25 motorway in a 12-month period during 2017-18. This process uses a background component to represent primary accidents, and a self-exciting component to represent secondary accidents. The background consists of periodic daily and weekly components, a spatial component and a long-term trend. The self-exciting components are decaying, unidirectional functions of space and time. These components are de- termined via kernel smoothing and likelihood estimation. Temporally, the background is stable across seasons with a daily double peak structure reflecting commuting patterns. Spatially, there are two peaks in intensity, one of which becomes more pronounced dur- ing the study period. Self-excitation accounts for 6-7% of the data with associated time and length scales around 100 minutes and 1 kilometre respectively. In-sample and out- of-sample validation are performed to assess the model fit. When we restrict the data to incidents that resulted in large speed drops on the network, the results remain coherent