Temporal trends analysis of human brucellosis incidence in mainland China from 2004 to 2018

Yongbin Wang,Juxiang Yuan,Chunjie Xu,Shengkui Zhang,Zhende Wang,Ying Zhu

doi:10.1038/s41598-018-33165-9

Abstract

With the re-emergence of brucellosis in mainland China since the mid-1990s, an increasing threat to public health tends to become even more violent, advanced warning plays a pivotal role in the control of brucellosis. However, a model integrating the autoregressive integrated moving average (ARIMA) with Error-Trend-Seasonal (ETS) methods remains unexplored in the epidemiological prediction. The hybrid ARIMA-ETS model based on discrete wavelet transform was hence constructed to assess the epidemics of human brucellosis from January 2004 to February 2018 in mainland China. The preferred hybrid model including the best-performing ARIMA method for approximation-forecasting and the best-fitting ETS approach for detail-forecasting is evidently superior to the standard ARIMA and ETS techniques in both three in-sample simulating and out-of-sample forecasting horizons in terms of the minimum performance indices of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error. Whereafter, an ahead prediction from March to December in 2018 displays a dropping trend compared to the preceding years. But being still present, in various trends, in the present or future. This hybrid model can be highlighted in predicting the temporal trends of human brucellosis, which may act as the potential for far-reaching implications for prevention and control of this disease.

Highlights

Brucellosis is a globally infectious allergic zoonosis caused by bacteria of Brucella spp., the disease can predominantly be transmitted to humans, among whom some special occupational exposures remain to be at potential risk, for farmers, herdsmen, slaughterhouse workers and veterinary workers[1,2,3], through contact with the infected animals, especially like cattle, sheep, pigs, dogs, camels and deer, together with consumption of contaminated products, which further spurs the acute and chronic diseases in humans[4,5]
Reported here is an extension of the basic autoregressive integrated moving average (ARIMA) and ETS models to forecast the morbidity components included in infectious diseases, the constructed hybrid ARIMA-ETS approach based on the coif[1] method of one-dimensional discrete wavelet transform (DWT) was applied to grasp the temporal trends of human brucellosis incidence cases in mainland China
By analyzing different forecasting intervals, our results show that the predictive capacity and fitting efficiency of the combined ARIMA-ETS model can provide a notable improvement in the forecasting for the reported human brucellosis cases over the individual ARIMA and ETS approaches in the three forecasting horizons

Summary

Introduction

Brucellosis is a globally infectious allergic zoonosis caused by bacteria of Brucella spp., the disease can predominantly be transmitted to humans, among whom some special occupational exposures remain to be at potential risk, for farmers, herdsmen, slaughterhouse workers and veterinary workers[1,2,3], through contact with the infected animals, especially like cattle, sheep, pigs, dogs, camels and deer, together with consumption of contaminated products, which further spurs the acute and chronic diseases in humans[4,5]. With the emerging and reemerging foci occurrence of brucellosis, especially in the developing countries of Asia[8,11,12], China is one of the quintessential countries, where the morbidity of brucellosis has tardily been on the rise since the middle and late 1990s13, while such increasing epidemic has become more pronounced with the acceleration of approximately annual 10%14 over the past decade, ranking top 10 of the total cases in class A and B national notifiable infectious diseases reported in mainland China[4]. The combined ARIMA-ETS model can realize the goal of absorbing the essence and neglecting the drawbacks of single model for first forecasting the human brucellosis incidence on the Chinese mainland

Methods

Results

Conclusion