搜索结果：Epidemiology (Cambridge, Mass.)

In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for successfully applying machine learning in epidemiology. It covers the principles of supervised and unsupervised learning and discusses the most important machine learning methods. Strategies for model evaluation and hyperparameter optimization are developed and interpretable machine learning is introduced. All these theoretical parts are accompanied by code examples in R, where an example dataset on heart disease is used throughout the chapter.

Recent advances in artificial intelligence (AI) - particularly generative AI - present new opportunities to accelerate, or even automate, epidemiological research. Unlike disciplines based on physical experimentation, a sizable fraction of Epidemiology relies on secondary data analysis and thus is well-suited for such augmentation. Yet, it remains unclear which specific tasks can benefit from AI interventions or where roadblocks exist. Awareness of current AI capabilities is also mixed. Here, we map the landscape of epidemiological tasks using existing datasets - from literature review to data access, analysis, writing up, and dissemination - and identify where existing AI tools offer efficiency gains. While AI can increase productivity in some areas such as coding and administrative tasks, its utility is constrained by limitations of existing AI models (e.g. hallucinations in literature reviews) and human systems (e.g. barriers to accessing datasets). Through examples of AI-generated epidemiological outputs, including fully AI-generated papers, we demonstrate that recently developed agentic systems can now design and execute epidemiological analysis, albeit to varied quality (see

The science of networks has revolutionised research into the dynamics of interacting elements. It could be argued that epidemiology in particular has embraced the potential of network theory more than any other discipline. Here we review the growing body of research concerning the spread of infectious diseases on networks, focusing on the interplay between network theory and epidemiology. The review is split into four main sections, which examine: the types of network relevant to epidemiology; the multitude of ways these networks can be characterised; the statistical methods that can be applied to infer the epidemiological parameters on a realised network; and finally simulation and analytical methods to determine epidemic dynamics on a given network. Given the breadth of areas covered and the ever-expanding number of publications, a comprehensive review of all work is impossible. Instead, we provide a personalised overview into the areas of network epidemiology that have seen the greatest progress in recent years or have the greatest potential to provide novel insights. As such, considerable importance is placed on analytical approaches and statistical methods which are both rapid

Epidemiology models are central in understanding and controlling large scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inference is highly amenable to efficient hardware acceleration. In this work, we develop parallel ABC inference of a stochastic epidemiology model for COVID-19. The statistical inference framework is implemented and compared on Intel Xeon CPU, NVIDIA Tesla V100 GPU and the Graphcore Mk1 IPU, and the results are discussed in the context of their computational architectures. Results show that GPUs are 4x and IPUs are 30x faster than Xeon CPUs. Extensive performance analysis indicates that the difference between IPU and GPU can be attributed to higher communication bandwidth, closeness of memory to compute, and higher compute power in the IPU. The proposed framework scales across 16 IPUs, with scaling overhead not exceeding 8% for the experiments performed. We present an example of our framework in practice, performing inference on the epidemiology model across three countries, and giving a brief overview of the results.

Defining the effect of exposure of interest and selecting an appropriate estimation method are prerequisite for causal inference. Understanding the ways in which association between heatwaves (i.e., consecutive days of extreme high temperature) and an outcome depends on whether adjustment was made for temperature and how such adjustment was conducted, is limited. This paper aims to investigate this dependency, demonstrate that temperature is a confounder in heatwave-outcome associations, and introduce a new modeling approach to estimate a new heatwave-outcome relation: E[R(Y)|HW=1, Z]/E[R(Y)|T=OT, Z], where HW is a daily binary variable to indicate the presence of a heatwave; R(Y) is the risk of an outcome, Y; T is a temperature variable; OT is optimal temperature; and Z is a set of confounders including typical confounders but also some types of T as a confounder. We recommend characterization of heatwave-outcome relations and careful selection of modeling approaches to understand the impacts of heatwaves under climate change. We demonstrate our approach using real-world data for Seoul, which suggests that the total effect of heatwaves may be larger than what may be inferred from

The epidemiology has recently witnessed great advances based on computational models. Its scope and impact are getting wider thanks to the new data sources feeding analytical frameworks and models. Besides traditional variables considered in epidemiology, large-scale social patterns can be now integrated in real time with multi-source data bridging the gap between different scales. In a hyper-connected world, models and analysis of interactions and social behaviors are key to understand and stop outbreaks. Big Data along with apps are enabling for validating and refining models with real world data at scale, as well as new applications and frameworks to map and track diseases in real time or optimize the necessary resources and interventions such as testing and vaccination strategies. Digital epidemiology is positioning as a discipline necessary to control epidemics and implement actionable protocols and policies. In this review we address the research areas configuring current digital epidemiology: transmission and propagation models and descriptions based on human networks and contact tracing, mobility analysis and spatio-temporal propagation of infectious diseases and infodemics

The size is a key property of a nucleus. Accurate nuclear radii are extracted from elastic electron scattering, laser spectroscopy, and muonic atom spectroscopy. The results are not always compatible, as the proton-radius puzzle has shown most dramatically. Beyond helium, precision data from muonic and electronic sources are scarce in the light-mass region. The stable isotopes of carbon are an exception. We present a laser spectroscopic measurement of the root-mean-square (rms) charge radius of $^{13}\mathrm{C}$ and compare this with ab initio nuclear structure calculations. Measuring all hyperfine components of the $2\,^3\mathrm{S} \rightarrow 2\,^3\mathrm{P}$ fine-structure triplet in $^{13}\mathrm{C}^{4+}$ ions referenced to a frequency comb allows us to determine its center-of-gravity with accuracy better than $2\,\mathrm{MHz}$ although second-order hyperfine-structure effects shift individual lines by several $\mathrm{GHz}$. We improved the uncertainty of $R_\mathrm{c}(^{13}\mathrm{C})$ determined with electrons by a factor of $6$ and found a $3σ$ discrepancy with the muonic atom result of similar accuracy.

This work provides a geometric version of the next-generation matrix method for obtaining the basic reproduction number of an epidemiological model. We exhibit a certain correspondence between any system of ODEs and Petri nets. We observe that any epidemiological model has the basic structures found in the SIR model of Kermack-McKendrick. This means that the basic reproduction number depends only on three substructures inside the Petri net, which are also given by three Petri nets inside, representing the susceptible population, the infection process, and the infected population. The five assumptions of the next-generation matrix method given by van den Driessche-Watmough can be described geometrically using Petri nets. Thus, the next-generation matrix results in a matrix of flows between the infection compartments with a dominant eigenvalue given by the basic reproduction number.

Discrete and Continuous Dynamics is the first in a series of articles on Network Models for Epidemiology. This project began in the Fall quarter of 2014 in my continuous modeling course. Since then, it has taken off and turned into a series of articles, which I hope to compile into a single report. The purpose of the report is to explore mathematical epidemiology. In this article, we discuss the historical approach to disease modeling with compartmental models. We discuss the issues and benefits of using network models. We build a discrete dynamical system to describe infection and recovery of individuals in the population. Lastly, we detail the computational scheme for iterating this model.

The approach of causality based on physical laws and systems is revisited. The issue of "levels", the relevance to epidemiology and the definition of effects are particularly developed. Moreover it is argued that this approach that we call the stochastic system approach is particularly well fitted to study lifecourse epidemiology. A hierarchy of factors is described that could be modeled using a suitable multivariate stochastic process. To illustrate this approach, a conceptual model for coronary heart disease mixing continuous and discrete state-space processes is proposed.

Network epidemiology has become a core framework for investigating the role of human contact patterns in the spreading of infectious diseases. In network epidemiology represents the contact structure as a network of nodes (individuals) connected by links (sometimes as a temporal network where the links are not continuously active) and the disease as a compartmental model (where individuals are assigned states with respect to the disease and follow certain transition rules between the states). In this paper, we discuss fast algorithms for such simulations and also compare two commonly used versions - one where there is a constant recovery rate (the number of individuals that stop being infectious per time is proportional to the number of such people), the other where the duration of the disease is constant. We find that, for most practical purposes, these versions are qualitatively the same.

In this paper we establish a relation between the spread of infectious diseases and the dynamics of so called M/G/1 queues with processor sharing. The in epidemiology well known relation between the spread of epidemics and branching processes and the in queueing theory well known relation between M/G/1 queues and birth death processes will be combined to provide a framework in which results from queueing theory can be used in epidemiology and vice versa. In particular, we consider the number of infectious individuals in a standard SIR epidemic model at the moment of the first detection of the epidemic, where infectious individuals are detected at a constant per capita rate. We use a result from the literature on queueing processes to show that this number of infectious individuals is geometrically distributed.

Malaria is a major contributor to health burdens throughout the regions where it is endemic. Historically, it was believed that there was limited morbidity and essentially no mortality associated with Plasmodium vivax; however, evidence from diverse settings now suggests that infections with P. vivax can be both severe and fatal. This awareness has highlighted a critical gap: the vast majority of research has been directed towards P. falciparum, leading to a decades-long neglect of epidemiological and clinical studies of P. vivax. There exists a large body of historical data on human experimental infections with P. vivax; these studies in controlled settings provided a wealth of wide-ranging statements based on expert opinion, which form the basis for much of what is currently known about P. vivax. In this thesis, portions of this evidence-base have been re-examined using modern epidemiological analyses with two aims: to critically examine this accumulated knowledge base, and to inform current research agendas towards global malaria elimination for all species of Plasmodium. Chapter 2 examines geographic variation in the epidemiology of P. vivax, especially the timing of incubation

In this review, we recall the concepts of Identifiability and Observability of dynamical systems, and analyse them in the framework of Mathematical Epidemiology. We show that, even for simple and well known models of the literature, these properties are not always fulfilled. We then consider the problem of practical identifiability and observability, which are connected to sensitivity and numerical condition numbers. We also recall the concept of observers to reconstruct state variable of the model which are not observed, and show how it can used with epidemiological models.

This paper critically evaluates the HESA (Higher Education Statistics Agency) Data Report for the Employer Justified Retirement Age (EJRA) Review Group at the University of Cambridge (\cite{CambridgeHESA2024}), identifying significant methodological flaws and misinterpretations. Our analysis reveals issues such as unclear application of data filters, inconsistent variable treatment, and erroneous statistical conclusions. The Report suggests that the EJRA increased job creation rates at Cambridge, but we show Cambridge consistently had lower job creation rates for Established Academic Careers compared to other Russell Group universities, both before and after EJRA implementation in 2011, with no evidence for a significant change in this deficit post implementation. This suggests that EJRA is not a significant factor driving job creation rates. Since other universities without an EJRA exhibit higher job creation rates, this suggests job creation can be sustained without such a policy. We conclude that the EJRA did not achieve its intended goal of increasing opportunities for young academics and may have exacerbated existing disparities compared to other leading universities. We recom

The paper proposes to analyze epidemiological data using regression models which enable subject-matter (epidemiological) interpretation of such data whether with uncorrelated or correlated predictors. To this end, response functions should include not only terms linear in predictors but also higher order ones (e.g. quadratic and cross terms). For epidemiological interpretation of a regression model, the suggestion is to construct conditional functions derived from the general regression function with the values of all predictor variables held fixed excepting one predictor. Unlike the conventional techniques based on linear-predictor models in which the coefficient at any variable is interpreted, our approach proposes to interpret this conditional function, which is multivariate for any predictor being dependent on the values of all the other predictors. It is such functions that can describe relationships between Y and a predictor that have different forms in different predictor domains. The paper discusses differences in the interpretation of the proposed conditional functions between cases involving correlated and uncorrelated predictor variables. The construction and analysis of

Maps have played an important role in epidemiology and public health since the beginnings of these disciplines. With the advent of geographical information systems and advanced information visualization techniques, interactive maps have become essential tools for the analysis of geographical patterns of disease incidence and prevalence, as well as communication of public health knowledge, as dramatically illustrated by the proliferation of web-based maps and disease surveillance ``dashboards'' during the COVID-19 pandemic. While such interactive maps are usually effective in supporting static spatial analysis, support for spatial epidemiological visualization and modelling involving distributed and dynamic data sources, and support for analysis of temporal aspects of disease spread have proved more challenging. Combining these two aspects can be crucial in applications of interactive maps in epidemiology and public health work. In this paper, we discuss these issues in the context of support for disease surveillance in remote regions, including tools for distributed data collection, simulation and analysis, and enabling multidisciplinary collaboration.

The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even-$Z$ nuclei with $8\le Z\le120$, extended from the previous work for even-even nuclei [Zhang $\it{et.~al.}$ (DRHBc Mass Table Collaboration), At. Data Nucl. Data Tables 144, 101488 (2022)]. The calculated binding energies, two-nucleon and one-neutron separation energies, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, quadrupole deformations, and neutron and proton Fermi surfaces are tabulated and compared with available experimental data. A total of 4829 even-$Z$ nuclei are predicted to be bound, with an rms deviation of 1.477 MeV from the 1244 mass data. Good agreement with the available experimental odd-even mass differences, $α$ decay energies, and charge radii is also achieved. The description accuracy for nuclear masses and nucleon separation energies as well as the prediction for drip lines is compared with the results obtained from other relativistic and nonrelativistic density functional. The comparison shows that the DRHBc theory with PC-PK1 provides an excellent microscopic descriptio

Single-file transport occurs in various scientific fields, including diffusion through nanopores, nanofluidic devices, and cellular processes. We here investigate the impact of polydispersity on particle currents for single-file Brownian motion of hard spheres, when they are driven through periodic potentials by a constant drag force. Through theoretical analysis and extensive Brownian dynamics simulations, we unveil the behavior of particle currents for random binary mixtures. The particle currents show a recurring pattern in dependence of the hard-sphere diameters and mixing ratio. We explain this recurrent behavior by showing that a basic unit cell exists in the space of the two hard-sphere diameters. Once the behavior of an observable inside the unit cell is determined, it can be inferred for any diameter. The overall variation of particle currents with the mixing ratio and hard-sphere diameters is reflected by their variation in the limit where the system is fully covered by hard spheres. In this limit, the currents can be predicted analytically. Our analysis explains the occurrence of pronounced maxima and minima of the currents by changes of an effective potential barrier fo