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

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 selfexciting 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. 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. Currently, this disease globally occurs in more than seventy countries, and an approximate 70%~90% notification was reported in China 2 , where HFRS is still considered a serious public health problem due to its highly endemic in 28 of 31 provinces, municipal districts and autonomous regions with about 20,000-50,000 incident cases per year 3-5 , leading to a fatality rate of around 3%~10% 6 , despite many efforts, such as effective rodent control, vaccination, and environmental management, in reducing HFRS-related incidence over the past decades 7. In China, the pathogenic agents of HFRS predominantly include Hantaan virus (HTNV) and Seoul virus (SEOV), though other viruses can be involved 4,8,9. Since 1990s, under the intervention-driven strategies, the notified HFRS cases have begun to decline 2 , but its epidemic trends of HFRS seemingly show a recurring sign in recent years 9-13. Therefore, to facilitate to offer a quantitative and explicit direction for the prevention and control of HFRS, a forecasting model with strong robustness and high accuracy to understand its epidemic trajectories is required. At present, many forecasting methods that act as effective policy-supportive tools have widely been adopted to assess and analyze the te...

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

“…As a result, it is necessary to construct the mathematical and statistical models with strong robustness and good reliability to estimate the duration and extent of the COVID-19 outbreak in the most affected countries. Time-series analysis is considerably helpful in forming hypotheses to analyse the epidemiological trends of different diseases and to forecast the epidemic dynamics of the target disease, and subsequently developing a quality control system based on the modelling results [4, 6, 35, 43]. As far as we are aware, this is the only study to perform time-series forecasting for the ongoing trend and extent of the COVID-19 outbreak in the world, Brazil, Peru, Canada and Chile using the advanced α -Sutte Indicator, and its predictive performances on the different prevalence and mortality datasets were compared with the ARIMA model which was recommended as the most frequent and powerful tool in the domain of time-series prediction [37, 38].…”

Forecasting the Tuberculosis Incidence Using a Novel Ensemble Empirical Mode Decomposition-Based Data-Driven Hybrid Model in Tibet, China

“…The Lagrangian Multiplier (LM) method was used to test the conditional heteroskedastic behavior and volatility (ARCH effect) of the residual series from the selected four models. 32 The root-mean-square error (RMSE), mean absolute deviation (MAD), mean error rate (MER), mean absolute percentage error (MAPE), and root-mean-square percentage error (RMSPE) were chosen to assess the predictive accuracy levels among models. The lower the indices’ values are, the better the models are.…”

The effect of the TF-IDF algorithm in times series in forecasting word on social media

“…This is what distinguishes the cycle, the cycle also shows an up and down pattern, but over a long period of time. The last component is random, that is, other variables that cannot be explained by the previous three components are random data [33].…”