Short-Term Prediction of COVID-19 Using Novel Hybrid Ensemble Empirical Mode Decomposition and Error Trend Seasonal Model.

In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic. The proposed hybrid model decomposes the complex, nonlinear, and nonstationary data into different intrinsic mode functions (IMFs) from low to high frequencies, and a single monotone residue by applying EEMD. The stationarity of each IMF component is checked with the help of the augmented Dicky-Fuller (ADF) test and is then used to build up the EEMD-ETS model, and finally, future predictions have been obtained from the proposed hybrid model. For illustration purposes and to check the performance of the proposed model, four datasets of daily confirmed cases and deaths from COVID-19 in Italy, Germany, the United Kingdom (UK), and France have been used. Similarly, four different statistical metrics, i.e., root mean square error (RMSE), symmetric mean absolute parentage error (sMAPE), mean absolute error (MAE), and mean absolute percentage error (MAPE) have been used for a comparison of different time series models. It is evident from the results that the proposed hybrid EEMD-ETS model outperforms the other time series and machine learning models. Hence, it is worthy to be used as an effective model for the prediction of COVID-19.

Development of season-dependent land use regression models to estimate BC and PM1 exposure.

Reliable estimation of exposure to black carbon (BC) and sub-micrometer particles (PM1) within a city is challenging because of limited monitoring data as well as the lack of models suitable for assessing the intra-urban environment. In this study, to estimate exposure levels in the inner-city area, we developed land use regression (LUR) models for BC and PM1 based on specially designed mobile monitoring surveys conducted in 2019 and 2020 for three seasons. The daytime and nighttime LUR models were developed separately to capture additional details on the variation in pollutants. The results of mobile monitoring indicated similar temporal variation characteristics of BC and PM1. The mean concentrations of pollutants were higher in winter (BC: 4.72 µg/m3; PM1: 56.97 µg/m3) than in fall (BC: 3.74 µg/m3; PM1: 33.29 µg/m3) and summer (BC: 2.77 µg/m3; PM1: 27.04 µg/m3). For both BC and PM1, higher nighttime concentrations were found in winter and fall, whereas higher daytime concentrations were observed in the summer. A supervised forward stepwise regression method was used to select the predictors for the LUR models. The adjusted R2 of the LUR models for BC and PM1 ranged from 0.39 to 0.66 and 0.45 to 0.80, respectively. Traffic-related predictors were incorporated into all the models for BC. In contrast, more meteorology-related predictors were incorporated into the PM1 models. The concentration surface based on the LUR models was mapped at a spatial resolution of 100 m, and significant seasonal and diurnal trends were observed. PM1 was dominated by seasonal variations, whereas BC showed more spatial variation. In conclusion, the development of season-dependent diurnal LUR models based on mobile monitoring could provide a methodology for the estimation of exposure and screening of influencing factors of BC and PM1 in typical inner-city environments, and support pollution management.

Prediction and analysis of death of children under 5 years old in Lanzhou based on time series model / 中华疾病控制杂志

Objective To analyze the death trend of children under 5 years old in Lanzhou and establish the time series model to predict the mortality and incidence of children under 5 years old in Lanzhou in 2019. Methods Descriptive epidemiological method was used to analyze the mortality of children under 5 years old in Lanzhou from January 2010 to December 2018. SPSS 21.0 software was used to construct time series analysis model, selecting the best model and predict the mortality of children under 5 years old in Lanzhou in 2019. Results A total of 1 650 deaths of children under 5 years old were reported in Lanzhou from 2010 to 2018. The number of deaths reported by boys and girls was 871 and 774 respectively, with an average annual mortality rate of 6.23‰. In recent years, the overall mortality rate of children under 5 years old in Lanzhou had declined. The majority of deaths among children under 5 years old were neonates, accounting for 65.27%. Simple seasonal model was the best model by comparing different models. The model could well fit the monthly death cases of children under 5 years old in Lanzhou from 2010 to 2018. It is predicted that the total number of deaths of children under 5 years old in Lanzhou will be 140 in 2019, which is similar to the number of deaths in 2018. Conclusions The mortality rate of children under 5 years old in Lanzhou is decreasing year by year. Simple seasonal model can better reflect the mortality trend of children under 5 years old in Lanzhou and make short-term prediction.