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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

Townships in this figure are located relatively far from each other, except 49 and 50. Their connections may result from sharing the same source of the imported cases.
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Fig10: Townships in this figure are located relatively far from each other, except 49 and 50. Their connections may result from sharing the same source of the imported cases.

Mentions: Small Communities: Figures 9 and 10 show that there are two communities in the whole malaria transmission network. The larger one (Figure 9) includes the nodes with the most severe epidemic situations. The severest township, 6, has connections to all the other second-level severity townships (green nodes), indicating that their disease transmission interactions may be the dominant reason for the local malaria endemics in the region. It is obvious that most nodes are connected by highways (e.g., S231, S233, S317 and S318) and rivers. The highways allow infectious patients to move among subpopulations, thus increasing the exposure risk of susceptible populations. The river usually plays a significant role in malaria endemics. It provides a suitable environment for the vector of malaria to reproduce and its flow moves the larva of vector downstream. Therefore, it is possible that the endemics within townships are affected by internal malaria transmission dynamics.Figure 9


Inferring disease transmission networks at a metapopulation level.

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

Townships in this figure are located relatively far from each other, except 49 and 50. Their connections may result from sharing the same source of the imported cases.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig10: Townships in this figure are located relatively far from each other, except 49 and 50. Their connections may result from sharing the same source of the imported cases.
Mentions: Small Communities: Figures 9 and 10 show that there are two communities in the whole malaria transmission network. The larger one (Figure 9) includes the nodes with the most severe epidemic situations. The severest township, 6, has connections to all the other second-level severity townships (green nodes), indicating that their disease transmission interactions may be the dominant reason for the local malaria endemics in the region. It is obvious that most nodes are connected by highways (e.g., S231, S233, S317 and S318) and rivers. The highways allow infectious patients to move among subpopulations, thus increasing the exposure risk of susceptible populations. The river usually plays a significant role in malaria endemics. It provides a suitable environment for the vector of malaria to reproduce and its flow moves the larva of vector downstream. Therefore, it is possible that the endemics within townships are affected by internal malaria transmission dynamics.Figure 9

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