A Dynamic Network with Individual Mobility for Designing Vaccination Strategies

Mao, Liang; Bian, Ling

doi:10.1111/j.1467-9671.2010.01201.x

Cited by 16 publications

(28 citation statements)

References 47 publications

(53 reference statements)

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“…Gao and Bian [53] found that human interaction network within a metropolitan community is spatially clustered. Thus, the effectiveness of our vaccination strategies based on the school data with the high density of CPIs implies that those strategies are very likely to be effective to control disease spread with larger scale spatial-social network data with a large number of individuals move within and between communities on a daily basis such as urban areas [22]. We expect that the strategies would be more effective to control infectious disease transmissions with larger scale spatial-social network with weak connections between sparse population distributions such as rural areas [22].…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the effectiveness of our vaccination strategies based on the school data with the high density of CPIs implies that those strategies are very likely to be effective to control disease spread with larger scale spatial-social network data with a large number of individuals move within and between communities on a daily basis such as urban areas [22]. We expect that the strategies would be more effective to control infectious disease transmissions with larger scale spatial-social network with weak connections between sparse population distributions such as rural areas [22]. In terms of network size, the space complexity in GS-EpiViz is O(m + n + k 2 ), in which m represents the total number of edges, n represents the number of nodes, and k represents the number of communities.…”

Section: Discussionmentioning

confidence: 99%

“…Given the limited supply of vaccines, vaccinations aim to achieve the highest efficacy through immunizing a fraction of the population [20,21]. Current vaccination strategies identify the targeted population with the combinations of different network relationships among individuals [7,[22][23][24][25], and can be called as contact-based strategies. Some research has suggested that individuals who are socially close to an infection should have a high priority to be vaccinated, such as family members or office mates of those individuals [26,27].…”

Section: Agent-based Epidemic Models and Vaccination Strategiesmentioning

confidence: 99%

“…In terms of control strategies, GS-EpiViz allows users to design four vaccination strategies: random-based, degree-based, betweenness-based, and strength-based vaccination strategies; these are selected because they are the most typical ones used to compare effectiveness of vaccination strategies based on different network structures [7,22,29]. The random-based vaccination strategy randomly identifies a fraction of the population to vaccinate.…”

Section: Geo-social Epidemic Visual Analytics (Gs-epiviz)mentioning

confidence: 99%

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Visual analytics of geo-social interaction patterns for epidemic control

Luo

2016

Int J Health Geogr

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BackgroundHuman interaction and population mobility determine the spatio-temporal course of the spread of an airborne disease. This research views such spreads as geo-social interaction problems, because population mobility connects different groups of people over geographical locations via which the viruses transmit. Previous research argued that geo-social interaction patterns identified from population movement data can provide great potential in designing effective pandemic mitigation. However, little work has been done to examine the effectiveness of designing control strategies taking into account geo-social interaction patterns.MethodsTo address this gap, this research proposes a new framework for effective disease control; specifically this framework proposes that disease control strategies should start from identifying geo-social interaction patterns, designing effective control measures accordingly, and evaluating the efficacy of different control measures. This framework is used to structure design of a new visual analytic tool that consists of three components: a reorderable matrix for geo-social mixing patterns, agent-based epidemic models, and combined visualization methods.ResultsWith real world human interaction data in a French primary school as a proof of concept, this research compares the efficacy of vaccination strategies between the spatial–social interaction patterns and the whole areas. The simulation results show that locally targeted vaccination has the potential to keep infection to a small number and prevent spread to other regions. At some small probability, the local control strategies will fail; in these cases other control strategies will be needed. This research further explores the impact of varying spatial–social scales on the success of local vaccination strategies. The results show that a proper spatial–social scale can help achieve the best control efficacy with a limited number of vaccines.ConclusionsThe case study shows how GS-EpiViz does support the design and testing of advanced control scenarios in airborne disease (e.g., influenza). The geo-social patterns identified through exploring human interaction data can help target critical individuals, locations, and clusters of locations for disease control purposes. The varying spatial–social scales can help geographically and socially prioritize limited resources (e.g., vaccines).

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Agent-based Epidemic Models and Vaccination Strategiesmentioning

confidence: 99%

Section: Geo-social Epidemic Visual Analytics (Gs-epiviz)mentioning

confidence: 99%

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Visual analytics of geo-social interaction patterns for epidemic control

Luo

2016

Int J Health Geogr

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“…2005, Colizza et al. 2007, Mao and Bian 2010). Disease transmission between cells is modeled using probability‐driven or rule‐based approaches.…”

Section: Sub‐population Modelsmentioning

confidence: 99%

Spatial Approaches to Modeling Dispersion of Communicable Diseases – A Review

Bian

2012

Transactions in GIS

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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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Modeling the Spread of Infectious Diseases: A Review

Chen¹

2014

Wiley Series in Probability and Statistics

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A Dynamic Network with Individual Mobility for Designing Vaccination Strategies

Cited by 16 publications

References 47 publications

Visual analytics of geo-social interaction patterns for epidemic control

Visual analytics of geo-social interaction patterns for epidemic control

Spatial Approaches to Modeling Dispersion of Communicable Diseases – A Review

Modeling the Spread of Infectious Diseases: A Review

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