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Epidemiologists increasingly use causal inference methods that rely on machine learning, as these approaches can relax unnecessary model specification assumptions. While deriving and studying asymptotic properties of such estimators is a task usually associated with statisticians, it is useful for epidemiologists to understand the steps involved, as epidemiologists are often at the forefront of defining important new research questions and translating them into new parameters to be estimated. In this paper, our goal was to provide a relatively accessible guide through the process of (i) deriving an estimator based on the so-called efficient influence function (which we define and explain), and (ii) showing such an estimator's ability to validly incorporate machine learning, by demonstrating the so-called rate double robustness property. The derivations in this paper rely mainly on algebra and some foundational results from statistical inference, which are explained.
Deep learning methods are increasingly being applied to problems in medicine and healthcare. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces to the fundamentals of deep learning from an epidemiological perspective. Specifically, this article reviews core concepts in machine learning (overfitting, regularization, hyperparameters), explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks), and summarizes training, evaluation, and deployment of models. We aim to enable the reader to engage with and critically evaluate medical applications of deep learning, facilitating a dialogue between computer scientists and epidemiologists that will improve the safety and efficacy of applications of this technology.
The Metropolis algorithm is a Markov chain Monte Carlo (MCMC) algorithm used to simulate from parameter distributions of interest, such as generalized linear model parameters. The "Metropolis step" is a keystone concept that underlies classical and modern MCMC methods and facilitates simple analysis of complex statistical models. Beyond Bayesian analysis, MCMC is useful for generating uncertainty intervals, even under the common scenario in causal inference in which the target parameter is not directly estimated by a single, fitted statistical model. We demonstrate, with a worked example, pseudo-code, and R code, the basic mechanics of the Metropolis algorithm. We use the Metropolis algorithm to estimate the odds ratio and risk difference contrasting the risk of childhood leukemia among those exposed to high versus low level magnetic fields. This approach can be used for inference from Bayesian and frequentist paradigms and, in small samples, offers advantages over large-sample methods like the bootstrap.
Michigan has been at the epicenter of the wildfire smoke and explosive diarrhea outbreaks。 The progressive Democrat talks to WIRED about how he would tackle the forces behind them if he’s elected
Discussion of "Instrumental Variables: An Econometrician's Perspective" by Guido W. Imbens [arXiv:1410.0163].
Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of alternative methods that utilise large samples of unrelated or related individuals. Yet, specific challenges persist in the estimation of heritability such as epistasis, assortative mating and indirect genetic effects. Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability i.e., the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals (LD score regression, GREML), and family-based genomic designs (Sibling regression, GREML-KIN, Trio-GCTA, MGCTA, RDR). For each method, we describe how heritability is estimated, the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared to samples of related in
PEACH Tree is an easy-to-use, online tool for displaying multiple sequence alignments and phylogenetic trees side-by-side. PEACH Tree is powerful for rapidly tracing evolutionary and transmission histories by filtering invariant sites out of the display, and allowing samples to readily be filtered out of the display. These features, coupled with the ability to display epidemiological metadata, make the tool suitable for infectious disease epidemiology. PEACH Tree further enables much needed communication between the fields of genomics and infectious disease epidemiology, as exemplified by the COVID-19 pandemic.
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.
In epidemiology, some have argued that multiple comparison corrections are not necessary as there is rarely interest in the universal null hypothesis. From a parameter estimation perspective, epidemiologists may still be interested in multiple parameters. In this context, standard confidence intervals are not guaranteed to provide simultaneous coverage of more than one parameter. In other words, use of confidence intervals in these cases will understate the uncertainty due to random error. To address this challenge, one can use confidence bands, an extension of confidence intervals to parameter vectors. We illustrate the use of confidence bands in three case studies: estimation of multiple causal effects, effect measure modification by a binary variable, and effect measure modification by a continuous variable. Each example uses publicly available data is accompanied by SAS, R, and Python code. The type of confidence region reported by epidemiologists should depend on whether scientific interest is in a single parameter or a set of parameters. For sets of parameters, like in cases where multiple actions or outcomes, effect measure modification, dose-response, or other functions are
The COVID-19 pandemic highlighted the need for new data infrastructure, as epidemiologists and public health workers raced to harness rapidly evolving data, analytics, and infrastructure in support of cross-sector investigations. To meet this need, we developed AERO, an automated research and data sharing platform for continuous, distributed, and multi-disciplinary collaboration. In this paper, we describe the AERO design and how it supports the automatic ingestion, validation, and transformation of monitored data into a form suitable for analysis; the automated execution of analyses on this data; and the sharing of data among different entities. We also describe how our AERO implementation leverages capabilities provided by the Globus platform and GitHub for automation, distributed execution, data sharing, and authentication. We present results obtained with an instance of AERO running two public health surveillance applications and demonstrate benchmarking results with a synthetic application, all of which are publicly available for testing.
Dashboards have arguably been the most used visualizations during the COVID-19 pandemic. They were used to communicate its evolution to national governments for disaster mitigation, to the public domain to inform about its status, and to epidemiologists to comprehend and predict the evolution of the disease. Each design had to be tailored for different tasks and to varying audiences - in many cases set up in a very short time due to the urgent need. In this paper, we collect notable examples of dashboards and reflect on their use and design during the pandemic from a user-oriented perspective: we interview a group of researchers with varying visualization expertise who actively used dashboards during the pandemic as part of their daily workflow. We discuss our findings and compile a list of lessons learned to support future visualization researchers and dashboard designers.
MicroTrace is an open-source R tool that performs SNP-based hierarchical clustering to detect potential transmission clusters from pathogen whole-genome sequencing (WGS) data. Designed for epidemiologists, microbiologists, and genomic surveillance teams, it processes SNP distance matrices and outputs dendrograms and cluster tables with optional metadata integration. MicroTrace enables reproducible outbreak detection workflows with minimal setup.
Static maps and animations remain popular in spatial epidemiology of dengue, limiting the analytical depth and scope of visualisations. Over half of the global population live in dengue endemic regions. Understanding the spatiotemporal dynamics of the four closely related dengue serotypes, and their immunological interactions, remains a challenge at a global scale. To facilitate this understanding, we worked with dengue epidemiologists in a user-centered design framework to create GeoDEN, an exploratory visualisation tool that empowers experts to investigate spatiotemporal patterns in dengue serotype reports. The tool has several linked visualisations and filtering mechanisms, enabling analysis at a range of spatial and temporal scales. To identify successes and failures, we present both insight-based and value-driven evaluations. Our domain experts found GeoDEN valuable, verifying existing hypotheses and uncovering novel insights that warrant further investigation by the epidemiology community. The developed visual exploration approach can be adapted for exploring other epidemiology and disease incident datasets.
Environmental epidemiologists are often interested in estimating the effect of time-varying functions of the exposure history on health outcomes. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually subject to measurement errors. To obtain unbiased estimates of the effects of such mismeasured functions in longitudinal studies with discrete outcomes, a method applicable to the main study/validation study design is developed. Various estimation procedures are explored. Simulation studies were conducted to assess its performance compared to standard analysis, and we found that the proposed method had good performance in terms of finite sample bias reduction and nominal coverage probability improvement. As an illustrative example, we applied the new method to a study of long-term exposure to PM2.5, in relation to the occurrence of anxiety disorders in the Nurses Health Study II. Failing to correct the error-prone exposure can lead to an underestimation of the chronic exposure effect of PM2.5.
G-computation is a useful estimation method that can be adapted to address various biases in epidemiology. However, these adaptations may not be obvious for some complex causal structures. This challenge is an example of the much wider issue of translating a causal diagram into a novel estimation strategy. To highlight these challenges, we consider two recent cases from the selection bias literature: treatment-induced selection and co-occurrence of biases that lack a joint adjustment set. For each case study, we show how g-computation can be adapted, describe how to implement that adaptation, show some general statistical properties, and illustrate the estimator using simulation. To simplify both the theoretical study and practical application of our estimators, we express the proposed g-computation estimators as stacked estimating equations. These examples illustrate how epidemiologists can translate identification results into a g-computation estimator and study the theoretical and finite-sample properties of a novel estimator.
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
We study algorithms to analyze a particular class of Markov population processes that is often used in epidemiology. More specifically, Markov binomial chains are the model that arises from stochastic time-discretizations of classical compartmental models. In this work we formalize this class of Markov population processes and focus on the problem of computing the expected time to termination in a given such model. Our theoretical contributions include proving that Markov binomial chains whose flow of individuals through compartments is acyclic almost surely terminate. We give a PSPACE algorithm for the problem of approximating the time to termination and a direct algorithm for the exact problem in the Blum-Shub-Smale model of computation. Finally, we provide a natural encoding of Markov binomial chains into a common input language for probabilistic model checkers. We implemented the latter encoding and present some initial empirical results showcasing what formal methods can do for practicing epidemiologists.
We analyze the management of the Italian pandemic during the five identified waves. We considered the following problems: (i) The composition of the CTS ("Scientific Technical Committee"), which was composed entirely of doctors, mainly virologists, without mathematical epidemiologists, statisticians, physicists, etc. In fact, a pandemic has a behavior described by mathematical, stochastic and probabilistic criteria; (ii) Political interference in security measures and media propaganda; (iii) The initial stages of the vaccination campaign, ignoring the age factor, and (iv) The persistence of the pandemic due to the population unvaccinated (anti-vax or "no-vax"), which amounts to about six to seven million people, including 10% of anti-vax doctors.
Epidemiologists have a growing interest in employing computational approaches to solve analytic problems, with simulation being arguably the most accessible among all approaches. While previous literature discussed the utility of simulation and demonstrated how to carry out them, few have focused on connecting underlying statistical concepts to these simulation approaches, creating gaps between theory and application. Based on the recent series of discussions on the balancing intercept, we explain the growing complexity when generalizing the balancing intercept to a wider class of simulations and revise the closed-form equation for the balancing intercept under assumptions. The discussion can broadly inform the future design of more complex simulations and emphasize the importance of applying statistical thinking in the new era of computational science.
Inferring how an epidemic will progress and what actions to take when presented with limited information is of critical importance for epidemiologists and health professionals. In real world settings, epidemiology data can be scarce or subject to reporting errors. In this project there are different epidemic scenarios simulated and, using hidden Markov Chains, it is attempted to mimic the imperfect data an epidemiologist will encounter. Furthermore, different kinds of compartmental models are modelled using the particle Markov Chain Monte Carlo algorithm with a variation of the adaptive Metropolis-Hastings algorithm to estimate the posterior density of the parameters underlying the models. Moreover, the sensitivity of these algorithms is investigated when subjected with changes in the dataset. This is accomplished by limiting the information provided, while using an adaptive approach on the posterior covariance of the parameters.