Dataset Information

Determining whether a class of random graphs is consistent with an observed contact network.

ABSTRACT: We demonstrate a general method to analyze the sensitivity of attack rate in a network model of infectious disease epidemiology to the structure of the network. We use Moore and Shannon's "network reliability" statistic to measure the epidemic potential of a network. A number of networks are generated using exponential random graph models based on the properties of the contact network structure of one of the Add Health surveys. The expected number of infections on the original Add Health network is significantly different from that on any of the models derived from it. Because individual-level transmissibility and network structure are not separately identifiable parameters given population-level attack rate data it is possible to re-calibrate the transmissibility to fix this difference. However, the temporal behavior of the outbreak remains significantly different. Hence any estimates of the effectiveness of time dependent interventions on one network are unlikely to generalize to the other. Moreover, we show that in one case even a small perturbation to the network spoils the re-calibration. Unfortunately, the set of sufficient statistics for specifying a contact network model is not yet known. Until it is, estimates of the outcome of a dynamical process on a particular network obtained from simulations on a different network are not reliable.

SUBMITTER: Nath M

PROVIDER: S-EPMC6026086 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Determining whether a class of random graphs is consistent with an observed contact network.

Nath Madhurima M Ren Yihui Y Khorramzadeh Yasamin Y Eubank Stephen S

Journal of theoretical biology 20171229

We demonstrate a general method to analyze the sensitivity of attack rate in a network model of infectious disease epidemiology to the structure of the network. We use Moore and Shannon's "network reliability" statistic to measure the epidemic potential of a network. A number of networks are generated using exponential random graph models based on the properties of the contact network structure of one of the Add Health surveys. The expected number of infections on the original Add Health network ...[more]

PMID: 29289606

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Project description:BackgroundCommunity Health Workers (CHWs) provide basic health screening and advice to members of their own communities. Although CHWs are trained, no CHW programmes have used a formal method to identify the level of achievement on post-training assessments that distinguishes "safe" from "unsafe". Objectives: The aim of this study was to use Ebel method of standard setting for a post-training written knowledge assessment for CHWs in Neno, Malawi.Methods12 participants agreed the definitions of a "just-deployment ready" and an "ideal" CHW. Participants rated the importance and difficulty of each question on a three-point scale and also indicated the proportion of "just-deployment ready" CHWs expected to answer each of the nine question types correctly. Mean scores were used to determine the passing standard, which was reduced by one standard error of measurement (SEM) as this was the first time any passing standard had been employed.The level of agreement across participants' ratings of importance and difficulty was calculated using Krippendorf's alpha. The assessment results from the first cohort of CHW trainees were analysed using classical test theory.FindingsThere was poor agreement between participants on item ratings of both importance and difficulty (Krippendorf's alphas of 0.064 and 0.074 respectively). The pass mark applied to the assessment, following adjustment using the SEM, was 53.3%. Based on this pass mark, 68% of 129 CHW trainees were 'clear passes', 11% 'borderline passes', 9% 'borderline fails' and 12% 'clear fails'.ConclusionsDetermining whether a CHW is deployment-ready is an important, but difficult exercise, as evidenced by a lack of agreement regarding question importance and difficulty. Future exercises should allow more time for training, discussion and modification of ratings. Based on the assessment, most CHWs trained could be considered deployment-ready, but following-up their performance in the field will be vital to validate the pass mark set.

Dataset Information

Determining whether a class of random graphs is consistent with an observed contact network.

Publications

Determining whether a class of random graphs is consistent with an observed contact network.

Similar Datasets

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