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. 2020 Oct 19:8:e10113.
doi: 10.7717/peerj.10113. eCollection 2020.

Spatiotemporal expansion of human brucellosis in Shaanxi Province, Northwestern China and model for risk prediction

Zurong Yang #  1   2 , Miaomiao Pang #  3 , Qingyang Zhou  2 , Shuxuan Song  1 , Weifeng Liang  4 , Junjiang Chen  1 , Tianci Guo  1 , Zhongjun Shao  1 , Kun Liu  1
Affiliations

Spatiotemporal expansion of human brucellosis in Shaanxi Province, Northwestern China and model for risk prediction

Zurong Yang et al. PeerJ. .

Abstract

Background: Human brucellosis imposes a heavy burden on the health and economy of endemic regions. Since 2011, China has reported at least 35,000 human brucellosis cases annually, with more than 90% of these cases reported in the northern. Given the alarmingly high incidence and variation in the geographical distribution of human brucellosis cases, there is an urgent need to decipher the causes of such variation in geographical distribution.

Method: We conducted a retrospective epidemiological study in Shaanxi Province from January 1, 2005 to December 31, 2018 to investigate the association between meteorological factors and transmission of human brucellosis according to differences in geographical distribution and seasonal fluctuation in northwestern China for the first time.

Results: Human brucellosis cases were mainly distributed in the Shaanbei upland plateau before 2008 and then slowly extended towards the southern region with significant seasonal fluctuation. The results of quasi-Poisson generalized additive mixed model (GAMM) indicated that air temperature, sunshine duration, rainfall, relative humidity, and evaporation with maximum lag time within 7 months played crucial roles in the transmission of human brucellosis with seasonal fluctuation. Compared with the Shaanbei upland plateau, Guanzhong basin had more obvious fluctuations in the occurrence of human brucellosis due to changes in meteorological factors. Additionally, the established GAMM model showed high accuracy in predicting the occurrence of human brucellosis based on the meteorological factors.

Conclusion: These findings may be used to predict the seasonal fluctuations of human brucellosis and to develop reliable and cost-effective prevention strategies in Shaanxi Province and other areas with similar environmental conditions.

Keywords: Generalized additive mixed model; Human brucellosis; Meteorological factors; Regional differences; Spatiotemporal expansion.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Temporal distribution of human brucellosis in Shaanxi Province, China, 2005–2018.
The bar chart represents the monthly incidence of human brucellosis in Shaanxi, and the line represents the annual number of cases of human brucellosis in Shaanxi Province.
Figure 2
Figure 2. Temporal distribution of human brucellosis in Shaanbei upland plateau, Guanzhong basin, and Shaannan mountainous region, 2005–2018.
Figure 3
Figure 3. Spatial and temporal distribution of human brucellosis in Shaanxi Province.
(A) Annual incidence of human brucellosis in each county in Shaanxi Province, 2005–2018. The map was created by Zurong Yang in ArcGIS 10.1 Software, ESRI Inc., Redlands, CA, USA, (https://www.arcgis.com/index.html). (B) Spatial distribution of human brucellosis in Shaanxi Province from January to December, 2005–2018. The map was created by Zurong Yang in ArcGIS 10.1 Software, ESRI Inc., Redlands, CA, USA, (https://www.arcgis.com/index.html). (C) Seasonal distribution of human brucellosis in Shaanbei upland plateau. (D) Seasonal distribution of human brucellosis in Guanzhong basin.
Figure 4
Figure 4. Observed and predicted numbers of human brucellosis cases (A), and the scatterplot (B) in Shaanbei upland plateau, 2005–2018.
Figure 5
Figure 5. Observed and predicted numbers of human brucellosis cases (A), and the scatterplot (B) in Guanzhong basin, 2005–2018.
Figure 6
Figure 6. Relationship between number of human brucellosis cases and temperature (A, B), relative humidity (C, D), rainfall (E, F), sunshine duration (G, H), and evaporation (I, J) in different regions.
Blue lines represent data for Shaanbei upland plateau, and red lines represent data for Guanzhong basin.
See this image and copyright information in PMC

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