Emerging Infections: What Have We Learned from SARS?

Galvani, Alison P.

doi:10.3201/eid1007.040166

Cited by 22 publications

(20 citation statements)

References 15 publications

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“…These population‐wide homogeneous parameters cannot help decipher how diseases spread and whether the decline of an epidemic is a result of intervention measures or heterogeneity in infection (Dye and Gay 2003). Many reports indicate that it is the heterogeneity in transmission that often leads to the sustaining or decline of an epidemic, instead of intervention measures (Arita et al 2003, Dye and Gay 2003, Francesconi et al 2003, Mbogo et al 2003, Meyers et al 2003, Galvani 2004, Kretzschmar et al 2004).…”

Section: Population‐based Modelsmentioning

confidence: 99%

“…These mathematics‐based studies provide insights into how network topology affects the performance of a network and how the topology of a network is characterized. Epidemiological studies, on the other hand, offer ample anecdotal evidence supporting a network model structure for describing the spread of diseases (Jernigan et al 2002, Traeger et al 2002, Arita et al 2003, Francesconi et al 2003, Griffith et al 2003, Meyers et al 2003, Galvani 2004, Hsueh et al 2004, Lau et al 2004, Zhuang et al 2004). Both fields of studies contribute to the establishment of this analytical framework.…”

Section: Analytical Frameworkmentioning

confidence: 99%

“…The observations of recent outbreaks of severe acute respiratory syndrome (SARS), anthrax, and ebola raised serious questions on how these parameters should be estimated. At the core of the issue is the heterogeneity in disease transmission (Arita et al 2003, Dye and Gay 2003, Francesconi et al 2003, Mbogo et al 2003, Meyers et al 2003, Galvani 2004, Kretzschmar et al 2004). It is argued that an averaged value over a population may not lead to a correct understanding of how diseases spread (Dye and Gay 2003).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Network Model for Dispersion of Communicable Diseases

Bian

Liebner

2007

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The spread of communicable diseases through a population is an intrinsic spatial and temporal process. This paper presents an individual‐based analytical framework for modeling the spatial and temporal heterogeneity in the disease transmission. The framework specifies a network model structure and six associated parameters. These parameters describe the properties of nodes, the properties of links, and the topology of the network. Through this model structure and associated parameters, this framework allows the representation of discrete individuals, individualized interactions, and interaction patterns in a network of human contact. The explicit representation of the spatial distribution and mobility of individuals in particular facilitates the modeling of spatial heterogeneity in the disease transmission.

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Section: Population‐based Modelsmentioning

confidence: 99%

Section: Analytical Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Network Model for Dispersion of Communicable Diseases

Bian

Liebner

2007

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“…better prepared [11][12][13][14][15][16][17][18][19][20][21]24,26]; here, we argue that preparedness should include development of a broad-use data collection system that can be easily and quickly adapted to any disease (in agreement with [11]) as well as the regular collection of population health data in centralized systems. Different infectious diseases may require different types of data [140]: a single approach is not applicable to all diseases.…”

Section: Discussionmentioning

confidence: 99%

Key data for outbreak evaluation: building on the Ebola experience

Cori

Donnelly

Dorigatti

et al. 2017

Phil. Trans. R. Soc. B

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Following the detection of an infectious disease outbreak, rapid epidemiological assessment is critical for guiding an effective public health response. To understand the transmission dynamics and potential impact of an outbreak, several types of data are necessary. Here we build on experience gained in the West African Ebola epidemic and prior emerging infectious disease outbreaks to set out a checklist of data needed to: (1) quantify severity and transmissibility; (2) characterize heterogeneities in transmission and their determinants; and (3) assess the effectiveness of different interventions. We differentiate data needs into individual-level data (e.g. a detailed list of reported cases), exposure data (e.g. identifying where/how cases may have been infected) and population-level data (e.g. size/demographics of the population(s) affected and when/where interventions were implemented). A remarkable amount of individual-level and exposure data was collected during the West African Ebola epidemic, which allowed the assessment of (1) and (2). However, gaps in population-level data (particularly around which interventions were applied when and where) posed challenges to the assessment of (3). Here we highlight recurrent data issues, give practical suggestions for addressing these issues and discuss priorities for improvements in data collection in future outbreaks.This article is part of the themed issue ‘The 2013–2016 West African Ebola epidemic: data, decision-making and disease control’.

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“…2003, Meyers et al. 2003, Galvani 2004, Koopman 2004, Kretzschmar et al. 2004, Galvani and May 2005, Watts et al.…”

Section: Classic Modelsmentioning

confidence: 99%

Spatial Approaches to Modeling Dispersion of Communicable Diseases – A Review

Bian

2012

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The dispersion of communicable diseases in a population is intrinsically spatial. In the last several decades, a range of spatial approaches has been devised to model epidemiological processes; and they differ significantly from each other. A review of spatially oriented epidemiological models is necessary to assess advances in spatial approaches to modeling disease dispersion and to help identify those most appropriate for specific research goals. The most notable difference in the design of these spatially oriented models is the scale and mobility of the modeling unit. Using two criteria, this review identifies six types of spatially oriented models. These include: (1) population‐based wave models, (2) sub‐population models, (3) individual‐based cellular automata models, (4) mobile sub‐population models, (5) individual‐based spatially implicit models, and (6) individual‐based mobile models. Each model type is evaluated in terms of its design principles, assumptions, and intended applications. For the evaluation of design, four aspects of design principles are discussed: the modeling unit, the interaction between the modeling units, the spatial process, and the temporal process utilized in a design. Insights gained from this review can be useful for devising much‐needed spatially and temporally oriented strategies to forecast, prevent, and control communicable diseases.

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Emerging Infections: What Have We Learned from SARS?

Cited by 22 publications

References 15 publications

A Network Model for Dispersion of Communicable Diseases

A Network Model for Dispersion of Communicable Diseases

Key data for outbreak evaluation: building on the Ebola experience

Spatial Approaches to Modeling Dispersion of Communicable Diseases – A Review

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