Time series analysis of temporal trends in hemorrhagic fever with renal syndrome morbidity rate in China from 2005 to 2019

Yongbin Wang,Yuchun Li,Jingchao Ren,Sanqiao Yao,Chunjie Xu,Weidong Wu,Lihui Gui

doi:10.1038/s41598-020-66758-4

Abstract

Hemorrhagic fever with renal syndrome (HFRS) is seriously endemic in China with 70%~90% of the notified cases worldwide and showing an epidemic tendency of upturn in recent years. Early detection for its future epidemic trends plays a pivotal role in combating this threat. In this scenario, our study investigates the suitability for application in analyzing and forecasting the epidemic tendencies based on the monthly HFRS morbidity data from 2005 through 2019 using the nonlinear model-based self-exciting threshold autoregressive (SETAR) and logistic smooth transition autoregressive (LSTAR) methods. The experimental results manifested that the SETAR and LSTAR approaches presented smaller values among the performance measures in both two forecasting subsamples, when compared with the most extensively used seasonal autoregressive integrated moving average (SARIMA) method, and the former slightly outperformed the latter. Descriptive statistics showed an epidemic tendency of downturn with average annual percent change (AAPC) of −5.640% in overall HFRS, however, an upward trend with an AAPC = 1.213% was observed since 2016 and according to the forecasts using the SETAR, it would seemingly experience an outbreak of HFRS in China in December 2019. Remarkably, there were dual-peak patterns in HFRS incidence with a strong one occurring in November until January of the following year, additionally, a weak one in May and June annually. Therefore, the SETAR and LSTAR approaches may be a potential useful tool in analyzing the temporal behaviors of HFRS in China.

Highlights

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne contagious disease caused by several distinct families of Hantaviruses, which can lead to various degrees of fever, shock, congestion, bleeding, and acute renal failure[1]
The HFRS incidence series was strongly seasonal with a cycle of 12 months, where a semi-annual seasonal pattern was observed, with a strong peak occurring from November to January of the following year and a weak one in May and June annually, while a trough was observed in August and September per year (Fig. 2 and Supplementary Fig. S2)
Before modeling the training samples from January 1, 2005 through December 31, 2018, the augmented Dickey-Fuller (ADF) test was applied to the data (ADF = − 3.621, p < 0.001), being indicative of a stationary series, which met the requirement of the seasonal autoregressive integrated moving average (SARIMA) method establishment

Summary

Introduction

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne contagious disease caused by several distinct families of Hantaviruses, which can lead to various degrees of fever, shock, congestion, bleeding, and acute renal failure[1]. What is most often encountered in practice is that the data-generating process is highly nonlinear, especially for the morbidity series of infectious diseases because such data often include complicated traits of seasonality, secular trend, cyclicity, and stochastic fluctuation[15,18] At this time, the linear methods simulated to such complicated nonlinear data frequently fail to obtain satisfactory forecasting performance, whereas the nonlinear methods may do better in that they can better capture the underlying dynamic mechanism of the target series[18,19]. In the setting of the epidemic status of HFRS in China, the aim is to investigate their forecasting abilities of the SETAR and LSTAR approaches to the HFRS incidence data

Methods

Results

Conclusion