Use of meteorological parameters for forecasting scarlet fever morbidity in Tianjin, Northern China

Long- and short-run asymmetric impacts of climate variation on tuberculosis based on a time series study

Distinguishing between long-term and short-term effects allows for the identification of different response mechanisms. This study investigated the long- and short-run asymmetric impacts of climate variation on tuberculosis (TB) and constructed forecasting models using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL). TB showed a downward trend, peaking in March-May per year. A 1 h increment or decrement in aggregate sunshine hours resulted in an increase of 32 TB cases. A 1 m/s increment and decrement in average wind velocity contributed to a decrement of 3600 and 5021 TB cases, respectively (Wald long-run asymmetry test [WLR] = 13.275, P < 0.001). A 1% increment and decrement in average relative humidity contributed to an increase of 115 and 153 TB cases, respectively. A 1 hPa increment and decrement in average air pressure contributed to a decrease of 318 and 91 TB cases, respectively (WLR = 7.966, P = 0.005). ∆temperature(-), ∆(sunshine hours)( -), ∆(wind velocity)(+) and ∆(wind velocity)(-) at different lags had a meaningful short-run effect on TB. The NARDL outperformed the ARDL in forecasting. Climate variation has significant long- and short-run asymmetric impacts on TB. By incorporating both dimensions of effects into the NARDL, the accuracy of the forecasts and policy recommendations for TB can be enhanced.

Long- and Short-Run Asymmetric Effects of Meteorological Parameters on Hemorrhagic Fever with Renal Syndrome in Heilongjiang: A Population-Based Retrospective Study

Examining both long-term and short-term effects can enhance the precision and reliability of time series analysis. This study aimed to delve into the asymmetric effects of weather conditions on hemorrhagic fever with renal syndrome (HFRS) in the long and short terms and build a forecasting system. Data comprising monthly HFRS incidents and weather factors in Heilongjiang from January 2004 to December 2019 were extracted. Subsequently, the long- and short-term asymmetric impacts were examined using the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Next, the samples were partitioned into training and testing subsets to evaluate the predictive potential of both models. From 2004 to 2019, HFRS exhibited a declining trend (average annual percentage change = -6.744%, 95% CI: -13.52%-0.563%) and a dual seasonal pattern, with a prominent peak in June and a secondary one in October-December. This study identified long-term asymmetric effects of rainfall (Wald long-run asymmetry (WLR) = 3.292, p=0.001), wind velocity (WLR = -3.271, p=0.001), and air pressure (WLR = -6.453, p < 0.001) on HFRS. Additionally, this study observed short-term asymmetric impacts of relative humidity (Wald short-run symmetry (WSR) = -1.547, p=0.001), rainfall (WSR = -1.984, p=0.049), and air pressure (WSR = -2.33, p=0.021) on HFRS. A unit increase in relative humidity, sunshine hours, and air pressure resulted in about 10.9%, 1.9%, and 13.6% decreases in HFRS, respectively; a unit decrease in relative humidity, rainfall, and sunshine hours led to about 6.7%, 1.8%, and 2% decreases in HFRS, respectively. When temperature increased and decreased by one unit, the HFRS incidence increased by 11.6% and 22.5%, respectively. HFRS also varied significantly with the positive and negative changes in differenced (D) temperature, D (relative humidity), D (wind velocity), D (rainfall), D (air pressure), and D (sunshine hours) at 0-3-month delays over the short term. The NARDL model exhibited notably lower error rates in forecasting compared to the ARDL model. Meteorological parameters affect HFRS in both the long and short term, often showing asymmetric effects. The NARDL model, capable of incorporating various weather parameters, proves to be valuable in predicting HFRS epidemic and guiding strategies for prevention and control.

Asymmetric impact of climatic parameters on hemorrhagic fever with renal syndrome in Shandong using a nonlinear autoregressive distributed lag model

Hemorrhagic fever with renal syndrome (HFRS) poses a major threat in Shandong. This study aimed to investigate the long- and short-term asymmetric effects of meteorological factors on HFRS and establish an early forecasting system using autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) models. Between 2004 and 2019, HFRS exhibited a declining trend (average annual percentage change = - 9.568%, 95% CI - 16.165 to - 2.451%) with a bimodal seasonality. A long-term asymmetric influence of aggregate precipitation (AP) (Wald long-run asymmetry [WLR] = - 2.697, P = 0.008) and aggregate sunshine hours (ASH) (WLR = 2.561, P = 0.011) on HFRS was observed. Additionally, a short-term asymmetric impact of AP (Wald short-run symmetry [WSR] = - 2.419, P = 0.017), ASH (WSR = 2.075, P = 0.04), mean wind velocity (MWV) (WSR = - 4.594, P < 0.001), and mean relative humidity (MRH) (WSR = - 2.515, P = 0.013) on HFRS was identified. Also, HFRS demonstrated notable variations in response to positive and negative changes in ∆MRH(-), ∆AP(+), ∆MWV(+), and ∆ASH(-) at 0-2 month delays over the short term. In terms of forecasting, the NARDL model demonstrated lower error rates compared to ARDL. Meteorological parameters have substantial long- and short-term asymmetric and/or symmetric impacts on HFRS. Merging NARDL model with meteorological factors can enhance early warning systems and support proactive measures to mitigate the disease's impact.

Asymmetric Effects of Weather-Integrated Human Brucellosis Forecasting System Using a New Nonlinear Autoregressive Distributed Lag Model

Human brucellosis (HB) remains a significant public health concern in China. This study aimed to investigate the long- and short-term asymmetric impacts of meteorological variables on HB and develop an early prediction system. Monthly data on HB incidence and meteorological variables were collected from 2005 to 2020. The study employed the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) to analyze the long- and short-term effects of climate variables on HB. Subsequently, the data were split into training (from January 2005 to December 2019) and testing parts (from January to December 2020) to develop and validate the forecasting accuracy of both models. During 2005-2020, there were 34,993 HB cases (2.03 per 100,000 persons) and there was an overall rising trend (average annual percentage change = 21.18%, 95%CI 18.36%-26.01%) in HB incidence, peaked in May and troughed in December per year. A 1 m/s increment and decrement in differenced (Δ) average wind velocity (AWV) contributed to 73.8% and 87.5% increases in ΔHB incidence, respectively (Wald long-run asymmetry test (WLR) = 1.17, P=0.25). A 1 hr increment and decrement in Δ(average relative humidity) contributed to both 3.1% increases in ΔHB incidence (Wald short-run asymmetry test = 3.01, P=0.003). Average temperature (AT) (P < 0.001) and average air pressure (P=0.012) played a long-run linear impact on HB. Δ(aggregate precipitation) (WLR = 1.76, P=0.08) and Δ(aggregate sunshine hours) (WLR = 0.07, P=0.94) did not have a significant long-term asymmetric impact on Δlog(HB). ΔΔAT(+) and ΔΔAWV(-) at a 1-month lag had a meaningful short-run effect on Δlog(HB). In the forecasting aspect, the NARDL produced significantly smaller error rates compared to the ARDL. Weather variability played significant long- and short-run asymmetric roles in HB incidence. The NARDL by integrating climatic variables could accurately capture the dynamic structure of HB epidemic, meaning that meteorological variables should be integrated into the public health intervention plan for HB.

Estimating the tuberculosis incidence using a SARIMAX-NNARX hybrid model by integrating meteorological factors in Qinghai Province, China

Tuberculosis (TB) is recognized as being a major public health concern owing to its increase in Qinghai, China. In this study, we aimed to estimate the long-term effects of meteorological variables on TB incidence and construct an advanced hybrid model with seasonal autoregressive integrated moving average (SARIMA) and a neural network nonlinear autoregression (SARIMAX-NNARX) by integrating meteorological factors and evaluating the model fitting and prediction effect. During 2005-2017, TB experienced an upward trend with obvious periodic and seasonal characteristics, peaking in spring and winter. The results showed that TB incidence was positively correlated with average relative humidity (ARH) with a 2-month lag (β = 1.889, p = 0.003), but negatively correlated with average atmospheric pressure (AAP) with a 1-month lag (β =  - 1.633, p = 0.012), average temperature (AT) with a 2-month lag (β =  - 0.093, p = 0.027), and average wind speed (AWS) with a 0-month lag (β =  - 13.221, p = 0.033), respectively. The SARIMA (3,1,0)(1,1,1)12, SARIMAX(3,1,0)(1,1,1)12, and SARIMAX(3,1,0)(1,1,1)12-NNARX(15,3) were considered preferred models based on the evaluation criteria. Of them, the SARIMAX-NNARX technique had smaller error values than the SARIMA and SARIMAX models in both fitting and forecasting aspects. The sensitivity analysis also revealed the robustness of the mixture forecasting model. Therefore, the SARIMAX-NNARX model by integrating meteorological variables can be used as an accurate method for forecasting the epidemic trends which would be great importance for TB prevention and control in the coming periods in Qinghai.

Statistical Analysis Dow Jones Stock Index—Cumulative Return Gap and Finite Difference Method

This study was motivated by the poor performance of the current models used in stock return forecasting and aimed to improve the accuracy of the existing models in forecasting future stock returns. The current literature largely assumes that the residual term used in the existing model is white noise and, as such, has no valuable information. We exploit the valuable information contained in the residuals of the models in the context of cumulative return and construct a new cumulative return gap (CRG) model to overcome the weaknesses of the traditional cumulative abnormal returns (CAR) and buy-and-hold abnormal returns (BHAR) models. To deal with the residual items of the prediction model and improving the prediction accuracy, we also lead the finite difference (FD) method into the autoregressive (AR) model and autoregressive distributed lag (ARDL) model. The empirical results of the study show that the cumulative return (CR) model is better than the simple return model for stock return prediction. We found that the CRG model can improve prediction accuracy, the term of the residuals from the autoregressive analysis is very important in stock return prediction, and the FD model can improve prediction accuracy.

Predictive analysis of the number of human brucellosis cases in Xinjiang, China

Brucellosis is one of the major public health problems in China, and human brucellosis represents a serious public health concern in Xinjiang and requires a prediction analysis to help making early planning and putting forward science preventive and control countermeasures. According to the characteristics of the time series of monthly reported cases of human brucellosis in Xinjiang from January 2008 to June 2020, we used seasonal autoregressive integrated moving average (SARIMA) method and nonlinear autoregressive regression neural network (NARNN) method, which are widely prevalent and have high prediction accuracy, to construct prediction models and make prediction analysis. Finally, we established the SARIMA((1,4,5,7),0,0)(0,1,2)12 model and the NARNN model with a time lag of 5 and a hidden layer neuron of 10. Both models have high fitting performance. After comparing the accuracies of two established models, we found that the SARIMA((1,4,5,7),0,0)(0,1,2)12 model was better than the NARNN model. We used the SARIMA((1,4,5,7),0,0)(0,1,2)12 model to predict the number of monthly reported cases of human brucellosis in Xinjiang from July 2020 to December 2021, and the results showed that the fluctuation of the time series from July 2020 to December 2021 was similar to that of the last year and a half while maintaining the current prevention and control ability. The methodology applied here and its prediction values of this study could be useful to give a scientific reference for prevention and control human brucellosis.