Driving role of climatic and socioenvironmental factors on human brucellosis in China: machine-learning-based predictive analyses

Abstract

Background: Brucellosis is a common zoonotic infectious disease in China. This study aimed to investigate the incidence trends of brucellosis in China, construct an optimal prediction model, and analyze the driving role of climatic factors for human brucellosis.

Methods: Using brucellosis incidence, and the socioeconomic and climatic data for 2014-2020 in China, we performed spatiotemporal analyses and calculated correlations with brucellosis incidence in China, developed and compared a series of regression and Seasonal Autoregressive Integrated Moving Average X (SARIMAX) models for brucellosis prediction based on socioeconomic and climatic data, and analyzed the relationship between extreme weather conditions and brucellosis incidence using copula models.

Results: In total, 327,456 brucellosis cases were reported in China in 2014-2020 (monthly average of 3898 cases). The incidence of brucellosis was distinctly seasonal, with a high incidence in spring and summer and an average annual peak in May. The incidence rate was highest in the northern regions' arid and continental climatic zones (1.88 and 0.47 per million people, respectively) and lowest in the tropics (0.003 per million people). The incidence of brucellosis showed opposite trends of decrease and increase in northern and southern China, respectively, with an overall severe epidemic in northern China. Most regression models using socioeconomic and climatic data cannot predict brucellosis incidence. The SARIMAX model was suitable for brucellosis prediction. There were significant negative correlations between the proportion of extreme weather values for both high sunshine and high humidity and the incidence of brucellosis as follows: high sunshine, [Formula: see text] = -0.59 and -0.69 in arid and temperate zones; high humidity, [Formula: see text] = -0.62, -0.64, and -0.65 in arid, temperate, and tropical zones.

Conclusions: Significant seasonal and climatic zone differences were observed for brucellosis incidence in China. Sunlight, humidity, and wind speed significantly influenced brucellosis. The SARIMAX model performed better for brucellosis prediction than did the regression model. Notably, high sunshine and humidity values in extreme weather conditions negatively affect brucellosis. Brucellosis should be managed according to the "One Health" concept.

Keywords: Climatic; Copula model; Extreme weather; Human brucellosis; Socioeconomics.

Fig. 1

Spatial distribution of brucellosis in China by a climatic and b economic zones. Incidence rates are calculated for 2014–2020 per 100,000 people. The purple line is the Qinling Mountains-Huaihe River line divided between northern and southern China

Fig. 2

Temporal distribution of brucellosis in a northern and b southern China, divided by the Qinling Mountains-Huaihe River line. Incidence rates are calculated for 2014–2020 in units per 1 million people. The black line is the trend line

Fig. 3

Correlation between climatic factors and incidence of brucellosis. Correlation coefficients and heat map matrices for climatic factors and incidence of brucellosis. a Spearman correlation, and b Kendall correlation. ^* In the heat map part of the figure represents $P<0.05,$ which indicates that the corresponding correlations are statistically significant. MAP refers to monthly average precipitation, MAS refers to monthly average sunshine, MAH refers to monthly average humidity, MAWS refers to monthly average wind speed, MAT refers to monthly average temperature and MAI refers to monthly average incidence

Fig. 4

Incidence of brucellosis in different geographical regions of China by season between 2014 and 2020. The top 10 prefecture-level cities in each of the 4 geographic regions using economic division criteria for the average incidence of brucellosis are presented in the Figure colored dots represent the quarterly average incidence of brucellosis between 2014 and 2020

Fig. 5

Predicted MAI of brucellosis in a Baicheng (in Northeast China), b Datong (in Central China), c Jinchang (in Western China), and d Zhangjiakou (in Eastern China) based on SARIMAX model. The four prefecture-level cities in the figure are the cities with the highest average incidence of brucellosis among the four major economic regions in China that are used as typical data for analysis. The data from 2015 to 2019 was used as the model training set, and the data from 2020 was the prediction set. The black line represents the data as a comparison in the prediction set. The colored lines represent the SARIMAX results after different climatic data are input as exogenous variables

Fig. 6

Copula a three-dimensional contours and b two- dimensional joint distribution of sunshine and humidity. The range of all axes is 0–1, representing probability values 0–100%. MAS refers to monthly average sunshine and MAH refers to monthly average humidity

Fig. 7

Trends in the sunshine and humidity extremes and incidence of brucellosis in a arid and continental, b temperate and tropical climatic zones after copula-processing. We normalized the differences due to order-of-magnitude gaps, which may thus lead to an unclear presentation in the figure; $\mathit{rs}$ represents the Pearson correlation coefficient for the year-to-year difference between sunshine and the incidence of brucellosis in the corresponding climate zone; $\mathit{rh}$ represents humidity. The rationale for selecting Pearson for the correlation coefficients is that the data all conform to a normal distribution (see Additional file 1) but have not been tested for statistical significance because the amount of data is too small (n = 6) to qualify for the test