Inferring the potential risks of H7N9 infection by spatiotemporally characterizing bird migration and poultry distribution in eastern China

Abstract

Background: In view of the rapid geographic spread and the increasing number of confirmed cases of novel influenza A(H7N9) virus infections in eastern China, we developed a diffusion model to spatiotemporally characterize the impacts of bird migration and poultry distribution on the geographic spread of H7N9 infection.

Methods: Three types of infection risks were estimated for 12 weeks, from February 4 to April 28, 2013, including (i) the risk caused by bird migration, (ii) the risk caused by poultry distribution, and (iii) the integrated risk caused by both bird migration and poultry distribution. To achieve this, we first developed a method for estimating the likelihood of bird migration based on available environmental and meteorological data. Then, we adopted a computational mobility model to estimate poultry distribution based on annual poultry production and consumption of each province/municipality. Finally, the spatiotemporal risk maps were created based on the integrated impacts of both bird migration and poultry distribution.

Results: In the study of risk estimation caused by bird migration, the likelihood matrix was estimated based on the 7-day temperature, from February 4 to April 28, 2013. It was found the estimated migrant birds mainly appear in the southeastern provinces of Zhejiang, Shanghai and Jiangsu during Weeks 1 to 4, and Week 6, followed by appearing in central eastern provinces of Shandong, Hebei, Beijing, and Tianjin during Weeks 7 to 9, and finally in northeastern provinces of Liaoning, Jilin, and Heilongjiang during Weeks 10 to 12.In the study of risk caused by poultry distribution, poultry distribution matrix was created to show the probability of poultry distribution. In spite of the fact that the majority of the initial infections were reported in Shanghai and Jiangsu, the relative risk of H7N9 infection estimated based on the poultry distribution model predicted that Jiangsu may have a slightly higher likelihood of H7N9 infection than those in Zhejiang and Shanghai, if we only take the probability of poultry distribution into consideration.In the study of integrated risk caused by both bird migration and poultry distribution, the higher risk in southeastern provinces occurred during the first 8 weeks, and that in central eastern provinces appeared during Weeks 8 to 12, and that in northeastern provinces since Week 12. Therefore, it is necessary to regulate the poultry markets as long as the poultry-to-poultry transmission is not so well understood.

Conclusion: With reference to the reported infection cases, the demonstrated risk mapping results will provide guidance in active surveillance and control of human H7N9 infections by taking intensive intervention in poultry markets.

Figure 1

The likelihood of bird migration from one province or municipality to another. The estimated likelihood of bird migration from one province or municipality to another from February 4 to April 28, 2013 with 7 days as a time window. The likelihood matrix for Week 12 is calculated based on 7-day temperature prediction on April 22, 2013.

Figure 2

The estimated spatiotemporal patterns of infection risks based on the likelihood of bird migration. The estimated spatiotemporal patterns of infection risks based on the likelihood of bird migration from February 4 to April 28, 2013. It can be observed that after Week 7, the birds gradually migrate from southern provinces, to central and northeastern provinces.

Figure 3

The map of relative H7N9 infection risk in mainland China. The map of relative H7N9 infection risk in mainland China estimated by assuming that the source of infection originated from the southeastern regions of Zhejiang, Shanghai, and Jiangsu (Jiangsu has a slightly higher likelihood). We can find that if we only consider poultry distribution, Jiangsu have relatively larger impacts on H7N9 infection in Zhejiang and Shanghai, where most infection cases were reported. Meanwhile, we can also observe that Jiangsu can also affect the infection risks of other regions that are relatively far away from Jiangsu.

Figure 4

The estimated spatiotemporal patterns of integrated risk caused by both bird migration and poultry distribution in eastern China. The estimated integrated risk caused by both bird migration and poultry distribution in eastern China. It can be found that during the first 8 weeks, except for Jiangsu, Shanghai, and Zhejiang, other provinces such as Hebei, Anhui, Shangdong, and Beijing, also have the risk of H7N9 infection. After Week 8, different from the estimations in Figure 2, we can still observe sustained infection risks in Hebei, Tianjin, and Beijing until Week 12.