Limits...
Inferring disease transmission networks at a metapopulation level.

Yang X, Liu J, Zhou XN, Cheung WK - Health Inf Sci Syst (2014)

Bottom Line: Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person.However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.In addition, it also discloses some hidden phenomenon.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China.

ABSTRACT

Background: To investigate transmission patterns of an infectious disease, e.g., malaria, it is desirable to use the observed surveillance data to discover the underlying (often hidden) disease transmission networks. Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person. However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.

Results: A network inference method called NetEpi (Network Epidemic) is developed and evaluated using both synthetic and real-world datasets. The experimental results show that NetEpi can not only recover most of the ground-truth disease transmission networks using only surveillance data, but also find a malaria transmission network based on a real-world dataset. The inferred malaria network can characterize the real-world observations to a certain extent. In addition, it also discloses some hidden phenomenon.

Conclusions: This research addresses the problem of inferring disease transmission networks at a metapopulation level. Such networks can be useful in several ways: (i) to investigate hidden impact factors that influence epidemic dynamics, (ii) to reveal possible sources of epidemic outbreaks, and (iii) to practically develop and/or improve strategies for controlling the spread of infectious diseases.

No MeSH data available.


Related in: MedlinePlus

Sensitivity analysis for the choice of observation or surveillance data with different size.(A) - (C) show the results of networks with different topologies but the same size of 256 nodes and 350 edges. Network in (A) is a core-periphery network. Network in (B) is a hierarchical community network. Network in (C) is a random graph. The curve with the size of a quarter of the number of network nodes is displayed as a blue solid line. The curve with the size of a half the number of network nodes is displayed as a green dashed line. The curve with the size of the same number of network nodes is displayed as a red dotted line. The curve with the size of two times the number of network nodes is displayed as a black dash-dot line.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4375841&req=5

Fig15: Sensitivity analysis for the choice of observation or surveillance data with different size.(A) - (C) show the results of networks with different topologies but the same size of 256 nodes and 350 edges. Network in (A) is a core-periphery network. Network in (B) is a hierarchical community network. Network in (C) is a random graph. The curve with the size of a quarter of the number of network nodes is displayed as a blue solid line. The curve with the size of a half the number of network nodes is displayed as a green dashed line. The curve with the size of the same number of network nodes is displayed as a red dotted line. The curve with the size of two times the number of network nodes is displayed as a black dash-dot line.

Mentions: Figures 14 and 15 show the results of experiments for six networks with different topologies (core-periphery networks, hierarchical community networks, and random graphs) and sizes (128 nodes with 200 edges and 256 nodes with 350 edges). For each network, different sizes of surveillance dataset are tested independently. All of them are tested under the time window of 35. The scale parameter φ is set to equal to 4, 2, 1 and 0.5, as shown in the precision-recall curves with the legends 0.25, 0.5, 1 and 2 times, respectively.


Inferring disease transmission networks at a metapopulation level.

Yang X, Liu J, Zhou XN, Cheung WK - Health Inf Sci Syst (2014)

Sensitivity analysis for the choice of observation or surveillance data with different size.(A) - (C) show the results of networks with different topologies but the same size of 256 nodes and 350 edges. Network in (A) is a core-periphery network. Network in (B) is a hierarchical community network. Network in (C) is a random graph. The curve with the size of a quarter of the number of network nodes is displayed as a blue solid line. The curve with the size of a half the number of network nodes is displayed as a green dashed line. The curve with the size of the same number of network nodes is displayed as a red dotted line. The curve with the size of two times the number of network nodes is displayed as a black dash-dot line.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4375841&req=5

Fig15: Sensitivity analysis for the choice of observation or surveillance data with different size.(A) - (C) show the results of networks with different topologies but the same size of 256 nodes and 350 edges. Network in (A) is a core-periphery network. Network in (B) is a hierarchical community network. Network in (C) is a random graph. The curve with the size of a quarter of the number of network nodes is displayed as a blue solid line. The curve with the size of a half the number of network nodes is displayed as a green dashed line. The curve with the size of the same number of network nodes is displayed as a red dotted line. The curve with the size of two times the number of network nodes is displayed as a black dash-dot line.
Mentions: Figures 14 and 15 show the results of experiments for six networks with different topologies (core-periphery networks, hierarchical community networks, and random graphs) and sizes (128 nodes with 200 edges and 256 nodes with 350 edges). For each network, different sizes of surveillance dataset are tested independently. All of them are tested under the time window of 35. The scale parameter φ is set to equal to 4, 2, 1 and 0.5, as shown in the precision-recall curves with the legends 0.25, 0.5, 1 and 2 times, respectively.

Bottom Line: Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person.However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.In addition, it also discloses some hidden phenomenon.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR, China.

ABSTRACT

Background: To investigate transmission patterns of an infectious disease, e.g., malaria, it is desirable to use the observed surveillance data to discover the underlying (often hidden) disease transmission networks. Previous studies have provided methods for inferring information diffusion networks in which each node corresponds to an individual person. However, in the case of disease transmission, to effectively propose and implement intervention strategies, it is more realistic and reasonable for policy makers to study the diffusion patterns at a metapopulation level when the disease transmission is affected by mobile population, that is, to consider disease transmission networks in which nodes represent subpopulations, and links indicate their interrelationships.

Results: A network inference method called NetEpi (Network Epidemic) is developed and evaluated using both synthetic and real-world datasets. The experimental results show that NetEpi can not only recover most of the ground-truth disease transmission networks using only surveillance data, but also find a malaria transmission network based on a real-world dataset. The inferred malaria network can characterize the real-world observations to a certain extent. In addition, it also discloses some hidden phenomenon.

Conclusions: This research addresses the problem of inferring disease transmission networks at a metapopulation level. Such networks can be useful in several ways: (i) to investigate hidden impact factors that influence epidemic dynamics, (ii) to reveal possible sources of epidemic outbreaks, and (iii) to practically develop and/or improve strategies for controlling the spread of infectious diseases.

No MeSH data available.


Related in: MedlinePlus