Computational Science Research

  • Sciris: Simplifying scientific software in Python

    Sciris aims to streamline the development of scientific software by making it easier to perform common tasks. Sciris provides classes and functions that simplify access to frequently used low-level functionality in the core libraries of the scientific Python ecosystem (such as numpy for math and matplotlib for plotting), as well as in libraries of broader…

  • Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning

    Understanding the complex interplay between human behavior, disease transmission and non-pharmaceutical interventions during the COVID-19 pandemic could provide valuable insights with which to focus future public health efforts. Cell phone mobility data offer a modern measurement instrument to investigate human mobility and behavior at an unprecedented scale. We investigate aggregated and anonymized mobility data, which…

  • Augmented state feedback for improving observability of linear systems with nonlinear measurements

    This paper is concerned with the design of an augmented state feedback controller for finite-dimensional linear systems with nonlinear observation dynamics. Most of the theoretical results in the area of (optimal) feedback design are based on the assumption that the state is available for measurement. In this paper, we focus on finding a feedback control…

  • Data-Driven Method for Efficient Characterization of Rare Event Probabilities in Biochemical Systems

    As mathematical models and computational tools become more sophisticated and powerful to accurately depict system dynamics, numerical methods that were previously considered computationally impractical started being utilized for large-scale simulations. Methods that characterize a rare event in biochemical systems are part of such phenomenon, as many of them are computationally expensive and require high-performance computing.…

  • Linear Model Regression on Time-series Data: Non-asymptotic Error Bounds and Applications

    Data-driven methods for modeling dynamic systems have recently received considerable attention as they provide a mechanism for control synthesis directly from the observed time-series data. In the absence of prior assumptions on how the time-series had been generated, regression on the system model has been particularly popular. In the linear case, the resulting least squares…

  • Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting

    In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers,Rc, describe the state of transmission at a point in time and differ…

  • Identifying spatiotemporal dynamics of Ebola in Sierra Leone using virus genomes

    Containing the recent West African outbreak of Ebola virus (EBOV) required the deployment of substantial global resources. Operationally, health workers and surveillance teams treated cases, collected genetic samples, and tracked case contacts. Despite the substantial progress in analyzing and modeling EBOV epidemiological data, a complete characterization of the spatiotemporal spread of Ebola cases remains a…

  • Application of a Second-order Stochastic Optimization Algorithm for Fitting Stochastic Epidemiological Models

    Introduction In public health, it is critical to have a reasonable understanding of an epidemic disease in order to set pragmatic goals and design highly-impactful and cost-effective interventions. Mathematical models of these epidemiological processes can support decision making by forecasting disease spread in space and time, and by evaluating intervention outcomes many times in-silico before…

  • Mean-field models for non-Markovian epidemics on networks

    This paper introduces a novel extension of the edge-based compartmental model to epidemics where the transmission and recovery processes are driven by general independent probability distributions. Edge-based compartmental modelling is just one of many different approaches used to model the spread of an infectious disease on a network; the major result of this paper is…

  • Fractional Diffusion Emulates a Human Mobility Network during a Simulated Disease Outbreak

    Mobility networks facilitate the growth of populations, the success of invasive species, and the spread of communicable diseases among social animals, including humans.Disease control and elimination efforts, especially during an outbreak, can be optimized by numerical modeling of disease dynamics on transport networks. This is especially true when incidence data from an emerging epidemic is…