Volume 27, Number 7—July 2021
Research
Transmission Dynamics of African Swine Fever Virus, South Korea, 2019
Table
Posterior parameter estimates and posterior predictive length of time between infection and reporting of African swine fever, South Korea, 2019*
Parameters | Model output |
||
---|---|---|---|
Median (95% HDI) | G-R | DIC | |
Full model | |||
Potentially contaminating vehicle movement (Pv) | 53.9 (7.4–113.4) × 10−4 | 1.00 | 275.8 (null model: 284.6) |
Wild boar cluster (Pw) | 8.2 (0–19.0) × 10−4 | 1.00 | |
Background (country, ) | 0.03 (0–0.1) × 10−4 | 1.00 | |
Background (epidemic region, ) | 5.4 (1.1–11.2) × 10−4 | 1.00 | |
Mean of the gamma distribution (α) | 3.7 (1.0–8.8) | 1.00 | |
Variance of the gamma distribution (β) | 44.6 (5.2–113.5) | 1.00 | |
Length of time between infection and reporting (D)† | 4.3 (1.0–15.8) |
*DIC, deviance information criteria; G-R, Gelman-Rubin convergence diagnostic; HDI, highest density interval; Pv, risk for infection resulting from 1 potentially contaminating vehicle movement; Pw, daily risk for infection resulting from being located in an African swine fever virus–positive wild boar cluster; PB1, daily background risk (country); PB2, daily background risk (epidemic region). †The distribution was obtained by simulating values from the gamma distribution, based on parameters α and β randomly sampled from their joint distribution.
1These authors contributed equally to this article.