[Application of ARIMA Model for Mid- and Long-term Forecasting of Ozone Concentration]

Relative humidity prediction with covariates and error correction based on SARIMA-EG-ECM model

RH is a physical quantity measuring atmospheric water vapor content. Predicting RH is of great importance in weather, climate, industrial production, crops, human health, and disease transmission, since it is helpful in making critical decisions. In this paper, the effects of covariates and error correction on relative humidity (RH) prediction have been studied, and a hybrid model based on seasonal autoregressive integrated moving average (SARIMA) model, cointegration (EG), and error correction model (ECM) named SARIMA-EG-ECM (SEE) has been proposed. The prediction model was performed in the meteorological observations of Hailun Agricultural Ecology Experimental Station, China. Based on the SARIMA model, the meteorological variables that interact with RH were used as covariates to perform EG tests. A cointegration model has been constructed. It revealed that RH had a cointegration relationship with air temperature (TEMP), dew point temperature (DEWP), precipitation (PRCP), atmospheric pressure (ATMO), sea-level pressure (SLP), and 40 cm soil temperature (40ST), which revealed the long-term equilibrium relationship between series. An ECM was established which indicated that the current fluctuations of DEWP, ATMO, and SLP have a significant impact on the current fluctuations of RH. The established ECM describes the short-term fluctuation relationship between the series. With the increase of the forecast horizon from 6 to 12 months, the prediction performance of the SEE model decreased slightly. A comparative study has also been introduced, indicating that the SEE performs superior to SARIMA and Long Short-Term Memory (LSTM) network.

Prediction of ground-level ozone by SOM-NARX hybrid neural network based on the correlation of predictors

Current approaches to ozone prediction using hybrid neural networks are numerous but not perfect. Decomposition algorithms ignore the correlation between predictors and ozone, and feature extraction methods rarely select appropriate predictors in terms of correlation, especially for VOCs. Therefore, this study proposes a hybrid neural network model SOM-NARX based on the correlation of predictors. The model is based on MIC to filter predictors, using SOM to make predictors as feature sequences and using NARX networks to make predictions. Data from the JCDZURI site were used for training, testing, and validation. The results show that the correlation of the predictors, classification numbers of SOM, neuron numbers, and delay steps can affect prediction accuracy. Model comparison shows that the SOM-NARX model has 13.82, 10.60, 6.58% and 12.05, 9.44, 68.14% RMSE, MAE, and MAEP in winter and summer, which is smaller than CNN-LSTM, CNN-BiLSTM, CNN-GRU, SOM-LSTM, SOM-BiLSTM, and SOM-GRU.

Bridging the Gap in End Tuberculosis Targets in the Elderly Population in Eastern China: Observational Study From 2015 to 2020

BACKGROUND

With a progressive increase in the aging process, the challenges posed by pulmonary tuberculosis (PTB) are also increasing for the elderly population.

OBJECTIVE

This study aimed to identify the epidemiological distribution of PTB among the elderly, forecast the achievement of the World Health Organization's 2025 goal in this specific group, and predict further advancement of PTB in the eastern area of China.

METHODS

All notified active PTB cases aged ≥65 years from Zhejiang Province were screened and analyzed. The general epidemiological characteristics were depicted and presented using the ArcGIS software. Further prediction of PTB was performed using R and SPSS software programs.

RESULTS

Altogether 41,431 cases aged ≥65 years were identified by the surveillance system from 2015 to 2020. After excluding extrapulmonary TB cases, we identified 39,832 PTB cases, including laboratory-confirmed (23,664, 59.41%) and clinically diagnosed (16,168, 40.59%) PTB. The notified PTB incidence indicated an evident downward trend with a reduction of 30%; however, the incidence of bacteriologically positive cases was steady at approximately 60/100,000. Based on the geographical distribution, Quzhou and Jinhua Cities had a higher PTB incidence among the elderly. The delay in PTB diagnosis was identified, and a significantly prolonged treatment course was observed in the elderly. Moreover, a 50% reduction of PTB incidence by the middle of 2024 was predicted using a linear regression model. It was found that using the exponential smoothing model would be better to predict the PTB trend in the elderly than a seasonal autoregressive integrated moving average model.

CONCLUSIONS

More comprehensive and effective interventions such as active PTB screening combined with physical checkup and succinct health education should be implemented and strengthened in the elderly. A more systematic assessment of the PTB epidemic trend in the elderly population should be considered to incorporate more predictive factors.