Abstract

Hemorrhagic fever with renal syndrome (HFRS), one of the main public health concerns in mainland China, is a group of clinically similar diseases caused by hantaviruses. Statistical approaches have always been leveraged to forecast the future incidence rates of certain infectious diseases to effectively control their prevalence and outbreak potential. Compared to the use of one base model, model stacking can often produce better forecasting results. In this study, we fitted the monthly reported cases of HFRS in mainland China with a model stacking approach and compared its forecasting performance with those of five base models. We fitted the monthly reported cases of HFRS ranging from January 2004 to June 2019 in mainland China with an autoregressive integrated moving average (ARIMA) model; the Holt-Winter (HW) method, seasonal decomposition of the time series by LOESS (STL); a neural network autoregressive (NNAR) model; and an exponential smoothing state space model with a Box-Cox transformation; ARMA errors; and trend and seasonal components (TBATS), and we combined the forecasting results with the inverse rank approach. The forecasting performance was estimated based on several accuracy criteria for model prediction, including the mean absolute percentage error (MAPE), root-mean-squared error (RMSE) and mean absolute error (MAE). There was a slight downward trend and obvious seasonal periodicity inherent in the time series data for HFRS in mainland China. The model stacking method was selected as the best approach with the best performance in terms of both fitting (RMSE 128.19, MAE 85.63, MAPE 8.18) and prediction (RMSE 151.86, MAE 118.28, MAPE 13.16). The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future. This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS.

Highlights

  • Hemorrhagic fever with renal syndrome (HFRS) is an infectious rodent-borne disease caused by hantavirus, and it is a significant public health problem in China, comprising approximately 90% of HFRS cases reported globally [1]

  • The results showed that model stacking by using the optimal mean forecasting weight of the five abovementioned models achieved the best performance in terms of predicting HFRS one year into the future

  • This study has corroborated the conclusion that model stacking is an easy way to enhance prediction accuracy when modeling HFRS

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Summary

Introduction

Hemorrhagic fever with renal syndrome (HFRS) is an infectious rodent-borne disease caused by hantavirus, and it is a significant public health problem in China, comprising approximately 90% of HFRS cases reported globally [1]. Humans become infected with hantavirus mainly through exposure to aerosols with contaminated urine and feces, ingestion of contaminated food and rodent bites [3,4,5]. It has been conclusively proven that the incidence of HFRS contagion is contingent upon the population densities of rodents, the level of exposure, and other ambient factors, such as temperature, humidity and the level of urbanization [6]. In a countermeasure designed to stop the spread of HFRS in China, the Chinese Center for Disease Control and Prevention (CDC) contrived a comprehensive mandatory surveillance system to collect data related to HFRS and publicize this data on its official website. A statistical approach for handling data pertaining to HFRS has been routine practice to describe its epidemiological characteristics and predict its occurrence characteristics in the future by modeling the data

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