Effective control measures considering spatial heterogeneity to mitigate the 2016-2017 avian influenza epidemic in the Republic of Korea

Abstract

During the winter of 2016-2017, an epidemic of highly pathogenic avian influenza (HPAI) led to high mortality in poultry and put a serious burden on the poultry industry of the Republic of Korea. Effective control measures considering spatial heterogeneity to mitigate the HPAI epidemic is still a challenging issue. Here we develop a spatial-temporal compartmental model that incorporates the culling rate as a function of the reported farms and farm density in each town. The epidemiological and geographical data of two species, chickens and ducks, from the farms in the sixteen towns in Eumseong-gun and Jincheon-gun are used to find the best-fitted parameters of the metapopulation model. The best culling radius to maximize the final size of the susceptible farms and minimize the total number of culled farms is calculated from the model. The local reproductive number using the next generation method is calculated as an indicator of virus transmission in a given area. Simulation results indicate that this parameter is strongly influenced not only by epidemiological factors such as transmissibility and/or susceptibility of poultry species but also by geographical and demographical factors such as the distribution of poultry farms (or density) and connectivity (or distance) between farms. Based on this result, we suggest the best culling radius with respect to the local reproductive number in a targeted area.

Fig 1. Map of Eumseong-gun and Jincheon-gun, which are the municipal-level divisions located in the central part of the Republic of Korea, including the density of poultry farms with white indicating low density and red indicating high density.

The index of the town is written below its name. There are 16 towns; 9 towns in Eumseong-gun and 7 in Jincheon-gun. Reprinted from [27] under a CC BY license, with permission from National Geographic Information Institute, original copyright 2017.

Fig 2. Estimation of IP culling rate.

The stars and the solid curve denote the approximated IP culling rate with respect to the number of AI-reported farms and the corresponding calibrated curve, respectively. The dashed line represents constant culling rate averaged over data. The data for IP culling rate approximation is presented in S4 Table.

Fig 3. In each panel, circles represent the AI-reported farm data and black solid curves represent the corresponding simulation results.

Fig 4

(A) Local reproductive numbers in each town. The darker red color represents the larger reproductive number. (B) Local reproductive number with respect to farm density in each town. The size of circle shows the size of duck farms. Reprinted from [27] under a CC BY license, with permission from National Geographic Information Institute, original copyright 2017.

Fig 5. Impact of culling radius on the AI outbreak using the metapopulation model (Eq (2)) with all other parameters in Table 2.

The left panel depicts the final size of susceptible farms with respect to culling radius. The right panel displays the total number of PE culling (dot-dashed), IP culling (dashed) and both (solid) with respect to culling radius. Note that when culling radius is 2.24 km, the final size of susceptible farms is maximized and the total number of culling is minimized.

Fig 6. Impact of local-dependent culling radii on the final size of susceptible farms.

The final size of susceptible farms with respect to culling radii is maximized as 459 when r_{c high} and r_{c low} are 2.65 and 0, respectively.