MBI Publications

MBI Publications for Marisa Eisenberg (12)

  • N. Meshkat, M. Eisenberg and J. Distefano
    Algorithm for finding globally identifiable parameter combinations and reparameterizations of nonlinear ODE models using Groebner Bases
    Math BiosciencesVol. 222 (2009) pp. 61-72

    Abstract

  • M. Eisenberg and J. Distefano
    TSH Testing, Tablet Instability & Absorption Effects on L-T4 Bioequivalence Protocols
    ThyroidVol. 19 No. 2 (2009) pp. 103-110

    Abstract

  • M. Eisenberg, F. Santini, A. Marsili, A. Pinchera and J. Distefano
    TSH Regulation Dynamics In Central & Extreme Primary Hypothyroidism
    ThyroidVol. 22 No. 11 (2010) pp. 1215-1228

    Abstract

  • A. Tuite, A. Tuite, J. Tien, M. Eisenberg, D. Earn, J. Ma, J. Ma and D. Fisman
    Cholera epidemic in Haiti, 2010 - using a transmission model to explain spatial spread of disease and identify optimal control interventions
    Annals of Internal MedicineVol. 154 No. 9 (2011) pp. 593-601

    Abstract

  • M. Eisenberg, J. Ash and D. Siegal-Gaskins
    In silico synchronization of cellular populations through expression data deconvolution
    Proceedings of the 48th ACM/EDAC/IEEE Design Automation Conference (2011) pp. 812-817

    Abstract

  • M. Eisenberg, Y. Kim, R. Li, W. Ackerman, D. Kniss and A. Friedman
    Modeling the effects of myoferlin on tumor cell invasion
    Proc Natl Acad Sci USAVol. 108 No. 50 (2011) pp. 20078-20083

    Abstract

  • M. Eisenberg, J. Ash and D. Siegal-Gaskins
    In silico synchronization of cellular populations through expression data deconvolution
    Proceedings of the 48th ACM/IEEE Design Automation Conference (2011) pp. 812-817 (Submitted)

    Abstract

  • M. Eisenberg, S. Robertson and J. Tien
    Identifiability and estimation of multiple transmission pathways in waterborne disease
    (2011) (Under Revision)

    Abstract

  • M. Eisenberg, Y. Kim, R. Li, W. Ackerman, D. Kniss and A. Friedman
    Mechanistic modeling of a novel cancer protein: myoferlin effects on tumor cell invasion
    Proc Natl Acad SciVol. 108 No. 50 (2011) pp. 20078-20083

    Abstract

  • R. Ben-Shachar, M. Eisenberg, S. Huang and J. Distefano
    Simulation of post thyroidectomy treatment alternatives for T3 or T4 replacement in pediatric thyroid cancer patients
    Thyroid (2011) (In Press)

    Abstract

  • M. Eisenberg, S. Robertson and J. Tien
    Identifiability and estimation of multiple transmission pathways in cholera and waterborne disease.
    Journal of theoretical biologyVol. 324 (2013) pp. 84-102

    Abstract

    Cholera and many waterborne diseases exhibit multiple characteristic timescales or pathways of infection, which can be modeled as direct and indirect transmission. A major public health issue for waterborne diseases involves understanding the modes of transmission in order to improve control and prevention strategies. An important epidemiological question is: given data for an outbreak, can we determine the role and relative importance of direct vs. environmental/waterborne routes of transmission? We examine whether parameters for a differential equation model of waterborne disease transmission dynamics can be identified, both in the ideal setting of noise-free data (structural identifiability) and in the more realistic setting in the presence of noise (practical identifiability). We used a differential algebra approach together with several numerical approaches, with a particular emphasis on identifiability of the transmission rates. To examine these issues in a practical public health context, we apply the model to a recent cholera outbreak in Angola (2006). Our results show that the model parameters-including both water and person-to-person transmission routes-are globally structurally identifiable, although they become unidentifiable when the environmental transmission timescale is fast. Even for water dynamics within the identifiable range, when noisy data are considered, only a combination of the water transmission parameters can practically be estimated. This makes the waterborne transmission parameters difficult to estimate, leading to inaccurate estimates of important epidemiological parameters such as the basic reproduction number (R0). However, measurements of pathogen persistence time in environmental water sources or measurements of pathogen concentration in the water can improve model identifiability and allow for more accurate estimation of waterborne transmission pathway parameters as well as R0. Parameter estimates for the Angola outbreak suggest that both transmission pathways are needed to explain the observed cholera dynamics. These results highlight the importance of incorporating environmental data when examining waterborne disease.
  • M. Eisenberg, G. Kujbida, A. Tuite, D. Fisman and J. Tien
    Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches.
    EpidemicsVol. 5 No. 4 (2013) pp. 197-207

    Abstract

    Haiti has been in the midst of a cholera epidemic since October 2010. Rainfall is thought to be associated with cholera here, but this relationship has only begun to be quantitatively examined. In this paper, we quantitatively examine the link between rainfall and cholera in Haiti for several different settings (including urban, rural, and displaced person camps) and spatial scales, using a combination of statistical and dynamic models. Statistical analysis of the lagged relationship between rainfall and cholera incidence was conducted using case crossover analysis and distributed lag nonlinear models. Dynamic models consisted of compartmental differential equation models including direct (fast) and indirect (delayed) disease transmission, where indirect transmission was forced by empirical rainfall data. Data sources include cholera case and hospitalization time series from the Haitian Ministry of Public Health, the United Nations Water, Sanitation and Health Cluster, International Organization for Migration, and Hôpital Albert Schweitzer. Rainfall data was obtained from rain gauges from the U.S. Geological Survey and Haiti Regeneration Initiative, and remote sensing rainfall data from the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission. A strong relationship between rainfall and cholera was found for all spatial scales and locations examined. Increased rainfall was significantly correlated with increased cholera incidence 4-7 days later. Forcing the dynamic models with rainfall data resulted in good fits to the cholera case data, and rainfall-based predictions from the dynamic models closely matched observed cholera cases. These models provide a tool for planning and managing the epidemic as it continues.

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