Journal of Epidemiology
Online ISSN : 1349-9092
Print ISSN : 0917-5040
ISSN-L : 0917-5040
Original Article
Transmission Network of Measles During the Yamagata Outbreak in Japan, 2017
Tetsuro KobayashiHiroshi Nishiura
著者情報
ジャーナル オープンアクセス
電子付録

2022 年 32 巻 2 号 p. 96-104

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抄録

Background: A measles outbreak involving 60 cases occurred in Yamagata, Japan in 2017. Using two different mathematical models for different datasets, we aimed to estimate measles transmissibility over time and explore any heterogeneous transmission patterns.

Methods: The first model relied on the temporal distribution for date of illness onset for cases, and a generation-dependent model was applied to the data. Another model focused on the transmission network. Using the illness-onset date along with the serial interval and geographical location of exposure, we reconstructed a transmission network with 19 unknown links. We then compared the number of secondary transmissions with and without clinical symptoms or laboratory findings.

Results: Using a generation-dependent model (assuming three generations other than the index case), the reproduction number (R) over generations 0, 1, and 2 were 25.3, 1.3, and <0.1, respectively, explicitly yielding the transmissibility over each generation. The network data enabled us to demonstrate that both the mean and the variance for the number of secondary transmissions per primary case declined over time. Comparing primary cases with and without secondary transmission, high viral shedding was the only significant determinant (P < 0.01).

Conclusions: The R declined abruptly over subsequent generations. Use of network data revealed the distribution of the number of secondary transmissions per primary case and also allowed us to identify possible secondary transmission risk factors. High viral shedding from the throat mucosa was identified as a potential predictor of secondary transmission.

著者関連情報
© 2020 Tetsuro Kobayashi et al. This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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