Data, Dynamics, and Analytics

  • Modeling methodology

    Mathematical models are invaluable for exploring the dynamics of infectious diseases, health delivery systems, intervention strategies, and other public health concerns. With technological advancements and the progression of AI, models are more accessible than ever before. However, it can be difficult to balance model selection, performance, and usability. IDM’s modeling methodology teams are working to…

  • Using phylogenetic summary statistics for epidemiological inference

    Since the coining of the term phylodynamics, the use of phylogenies to understand infectious disease dynamics has steadily increased. As methods for phylodynamics and genomic epidemiology have proliferated and grown more computationally expensive, the epidemiological information they extract has also evolved to better complement what can be learned through traditional epidemiological data. However, for genomic…

  • A modular approach to integrating multiple data sources into real-time clinical prediction for pediatric diarrhea

    Traditional clinical prediction models focus on parameters of the individual patient. For infectious diseases, sources external to the patient, including characteristics of prior patients and seasonal factors, may improve predictive performance. We describe the development of a predictive model that integrates multiple sources of data in a principled statistical framework using a post-test odds formulation.…

  • Clinical management and mortality among COVID-19 cases in sub-Saharan Africa: A retrospective study from Burkina Faso and simulated case analysis

    Background Absolute numbers of COVID-19 cases and deaths reported to date in the sub-Saharan Africa (SSA) region have been significantly lower than those across the Americas, Asia and Europe. As a result, there has been limited information about the demographic and clinical characteristics of deceased cases in the region, as well as the impacts of…

  • Model selection for hybrid dynamical systems via sparse regression

    Hybrid systems are traditionally difficult to identify and analyse using classical dynamical systems theory. Moreover, recently developed model identification methodologies largely focus on identifying a single set of governing equations solely from measurement data. In this article, we develop a new methodology, Hybrid-Sparse Identification of Nonlinear Dynamics, which identifies separate nonlinear dynamical regimes, employs information…

  • Dynamic Mode Decomposition for Compressive System Identification

    Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data, benefiting from a strong connection to nonlinear dynamical systems via the Koopman operator. In this work, two recent innovations that extend dynamic mode decomposition to systems with actuation and systems with heavily subsampled measurements are integrated and unified.…

  • (Preprint) Compartmental Modeling Software: a fast, discrete stochastic framework for biochemical and epidemiological simulation

    The compartmental modeling software (CMS) is an open source computational framework that can simulate discrete, stochastic reaction models which are often utilized to describe complex systems from epidemiology and systems biology. In this article, we report the computational requirements, the novel input model language, the available numerical solvers, and the output file format for CMS.…

  • Tracking progress towards malaria elimination in China: estimates of reproduction numbers and their spatiotemporal variation

    China reported zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of…

  • Compartmental Modeling Software: A Fast, Discrete Stochastic Framework for Biochemical and Epidemiological Simulation

    The compartmental modeling software (CMS) is an open source computational framework that can simulate discrete, stochastic reaction models which are often utilized to describe complex systems from epidemiology and systems biology. In this article, we report the computational requirements, the novel input model language, the available numerical solvers, and the output file format for CMS.…

  • Generalizing Koopman Theory to allow for inputs and control

    We develop a new generalization of Koopman operator theory that incorporates the effects of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of nonlinear dynamical systems. Moreover, Koopman is intimately connected to Dynamic Mode Decomposition (DMD), a method that discovers spatial-temporal coherent modes from data, connects local-linear analysis to nonlinear…