Literature DB >> 16377860

Infection transmission science and models.

James S Koopman1.   

Abstract

Infection transmission systems circulate infection through complex contact patterns related to both contact patterns and patterns of factors that affect the risk of transmission given contact. The nonlinear dynamics of infection transmission cause these patterns to make big differences in population infection levels. A science of infection transmission system analysis is needed to focus on those details that affect the control of infection transmission. This science must have a strong theoretical base because there is little chance that a dominantly data based approach not using mechanistic models of transmission will have any predictive value. The theoretical base should be built on linked transmission system models that are focused on making needed inferences for both building the theoretical base and making infection control decisions. The linking of different models is needed for a strategy of inference robustness assessment that is designed to find the model that is simple enough to effectively analyze the transmission system but not so simple that realistic violation of simplifying assumptions will change an inference. Types of models that should be used in such linked analyses include deterministic and stochastic compartmental models, discrete individual models with individual event histories but structured mass action mixing, network models that provide more detail as to who has contact with whom, and intermediate model forms such as correlation models that address some aspects of contact details while preserving the flexibility of deterministic compartmental models to structure mixing and analyze the system. While transmission system science is currently weak in regards both to its data base and its theory base, many things are now coming together that could make this science flourish. On the data side these include greater ability to detect infectious agent sequences in the environment and greater ability to sequence and genetically relate agents identified at different sites in the transmission system. On the theory sides, new model construction and model analysis methods are providing new potential to use the new sources of data. Also new parameter estimation methods provide new potential to combine models and data in effective analytic strategies.

Mesh:

Year:  2005        PMID: 16377860

Source DB:  PubMed          Journal:  Jpn J Infect Dis        ISSN: 1344-6304            Impact factor:   1.362


  16 in total

1.  Interactive agent based modeling of public health decision-making.

Authors:  Amanda L Parks; Brett Walker; Warren Pettey; Jose Benuzillo; Per Gesteland; Juliana Grant; James Koopman; Frank Drews; Matthew Samore
Journal:  AMIA Annu Symp Proc       Date:  2009-11-14

2.  What can mathematical models tell us about the relationship between circular migrations and HIV transmission dynamics?

Authors:  Aditya S Khanna; Dobromir T Dimitrov; Steven M Goodreau
Journal:  Math Biosci Eng       Date:  2014-10       Impact factor: 2.080

3.  Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection.

Authors:  Xinyu Zhang; Lin Zhong; Ethan Romero-Severson; Shah Jamal Alam; Christopher J Henry; Erik M Volz; James S Koopman
Journal:  Stat Commun Infect Dis       Date:  2012-11-01

Review 4.  Causal inference in public health.

Authors:  Thomas A Glass; Steven N Goodman; Miguel A Hernán; Jonathan M Samet
Journal:  Annu Rev Public Health       Date:  2013-01-07       Impact factor: 21.981

5.  Social network analysis of patient sharing among hospitals in Orange County, California.

Authors:  Bruce Y Lee; Sarah M McGlone; Yeohan Song; Taliser R Avery; Stephen Eubank; Chung-Chou Chang; Rachel R Bailey; Diane K Wagener; Donald S Burke; Richard Platt; Susan S Huang
Journal:  Am J Public Health       Date:  2011-02-17       Impact factor: 9.308

6.  Detectable signals of episodic risk effects on acute HIV transmission: strategies for analyzing transmission systems using genetic data.

Authors:  Shah Jamal Alam; Xinyu Zhang; Ethan Obie Romero-Severson; Christopher Henry; Lin Zhong; Erik M Volz; Bluma G Brenner; James S Koopman
Journal:  Epidemics       Date:  2012-11-23       Impact factor: 4.396

7.  Models of epidemics: when contact repetition and clustering should be included.

Authors:  Timo Smieszek; Lena Fiebig; Roland W Scholz
Journal:  Theor Biol Med Model       Date:  2009-06-29       Impact factor: 2.432

8.  HIV, Sexually Transmitted Infection, and Substance Use Continuum of Care Interventions Among Criminal Justice-Involved Black Men Who Have Sex With Men: A Systematic Review.

Authors:  Nina T Harawa; Russell Brewer; Victoria Buckman; Santhoshini Ramani; Aditya Khanna; Kayo Fujimoto; John A Schneider
Journal:  Am J Public Health       Date:  2018-11       Impact factor: 9.308

9.  Analysis of CDC social control measures using an agent-based simulation of an influenza epidemic in a city.

Authors:  Yong Yang; Peter M Atkinson; Dick Ettema
Journal:  BMC Infect Dis       Date:  2011-07-18       Impact factor: 3.090

10.  Predicting the Effectiveness of Endemic Infectious Disease Control Interventions: The Impact of Mass Action versus Network Model Structure.

Authors:  Giovanni S P Malloy; Jeremy D Goldhaber-Fiebert; Eva A Enns; Margaret L Brandeau
Journal:  Med Decis Making       Date:  2021-04-24       Impact factor: 2.749

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