Background: China is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic autoregressive integrated moving average (ARIMA) model with the objective of monitoring and short-term forecasting HFRS incidence in China. Methods: Chinese HFRS data from 1975 to 2008 were used to fit ARIMA model. Akaike Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. Subsequently, the fitted ARIMA model was applied to obtain the fitted HFRS incidence from 1978 to 2008 and contrast with corresponding observed values. To assess the validity of the proposed model, the mean absolute percentage error (MAPE) between the observed and fitted HFRS incidence (1978-2008) was calculated. Finally, the fitted ARIMA model was used to forecast the incidence of HFRS of the years 2009 to 2011. All analyses were performed using SAS9.1 with a significant level of p < 0.05. Results: The goodness-of-fit test of the optimum ARIMA (0,3,1) model showed non-significant autocorrelations in the residuals of the model (Ljung-Box Q statistic = 5.95,P = 0.3113). The fitted values made by ARIMA (0,3,1) model for years 1978-2008 closely followed the observed values for the same years, with a mean absolute percentage error (MAPE) of 12.20%. The forecast values from 2009 to 2011 were 0.69, 0.86, and 1.21per 100,000 population, respectively. Conclusion: ARIMA models applied to historical HFRS incidence data are an important tool for HFRS surveillance in China. This study shows that accurate forecasting of the HFRS incidence is possible using an ARIMA model. If predicted values from this study are accurate, China can expect a rise in HFRS incidence.

To determine the epidemiology of 2019 novel coronavirus disease (COVID-19) in a remote region of China, far from Wuhan, we analyzed the epidemiology of COVID-19 in Gansu Province. From January 23 through February 3, 2020, a total of 35 (64.8%) of 54 reported cases were imported from COVID-19–epidemic areas. Characteristics that differed significantly during the first and second waves of illness in Gansu Province were mean patient age, occupation, having visited epidemic areas, and mode of transportation. Time from infection to illness onset for family clusters was shorter in Gansu Province than in Wuhan, consistent with shortened durations from onset to first medical visit or hospitalization. Spatial distribution pattern analysis indicated hot spots and spatial outliers in Gansu Province. As a result of adequate interventions, transmission of the COVID-19 virus in Gansu Province is decreasing.

Hemorrhagic fever with renal syndrome (HFRS) is an important infectious disease caused by different species of hantaviruses. As a rodent-borne disease with a seasonal distribution, external environmental factors including climate factors may play a significant role in its transmission. The city of Shenyang is one of the most seriously endemic areas for HFRS. Here, we characterized the dynamic temporal trend of HFRS, and identified climate-related risk factors and their roles in HFRS transmission in Shenyang, China. The annual and monthly cumulative numbers of HFRS cases from 2004 to 2009 were calculated and plotted to show the annual and seasonal fluctuation in Shenyang. Cross-correlation and autocorrelation analyses were performed to detect the lagged effect of climate factors on HFRS transmission and the autocorrelation of monthly HFRS cases. Principal component analysis was constructed by using climate data from 2004 to 2009 to extract principal components of climate factors to reduce co-linearity. The extracted principal components and autocorrelation terms of monthly HFRS cases were added into a multiple regression model called principal components regression model (PCR) to quantify the relationship between climate factors, autocorrelation terms and transmission of HFRS. The PCR model was compared to a general multiple regression model conducted only with climate factors as independent variables. A distinctly declining temporal trend of annual HFRS incidence was identified. HFRS cases were reported every month, and the two peak periods occurred in spring (March to May) and winter (November to January), during which, nearly 75% of the HFRS cases were reported. Three principal components were extracted with a cumulative contribution rate of 86.06%. Component 1 represented MinRH₀, MT₁, RH₁, and MWV₁; component 2 represented RH₂, MaxT₃, and MAP₃; and component 3 represented MaxT₂, MAP₂, and MWV₂. The PCR model was composed of three principal components and two autocorrelation terms. The association between HFRS epidemics and climate factors was better explained in the PCR model (F = 446.452, P < 0.001, adjusted R ² = 0.75) than in the general multiple regression model (F = 223.670, P < 0.000, adjusted R ² = 0.51). The temporal distribution of HFRS in Shenyang varied in different years with a distinctly declining trend. The monthly trends of HFRS were significantly associated with local temperature, relative humidity, precipitation, air pressure, and wind velocity of the different previous months. The model conducted in this study will make HFRS surveillance simpler and the control of HFRS more targeted in Shenyang.

SUMMARY: The monthly and annual incidence of haemorrhagic fever with renal syndrome (HFRS) in China for 2004–2009 was analysed in conjunction with associated geographical and demographic data. We applied the seasonal autoregressive integrated moving average (SARIMA) model to fit and forecast monthly HFRS incidence in China. HFRS was endemic in most regions of China except Hainan Province. There was a high risk of infection for male farmers aged 30–50 years. The fitted SARIMA(0,1,1)(0,1,1)₁₂ model had a root-mean-square-error criterion of 0·0133 that indicated accurate forecasts were possible. These findings have practical applications for more effective HFRS control and prevention. The conducted SARIMA model may have applications as a decision support tool in HFRS control and risk-management planning programmes.