Application of GM(1,1) model modified with seasonal factors in prediction of PM2.5 concentration in Shanghai / 公共卫生与预防医学

Objective To explore PM2.5 concentration modeling and prediction based on the monthly average concentrations of PM2.5 in Shanghai since 2015, and to provide new ideas about PM2.5 prediction methods. Methods The seasonal factors were introduced into the Grey Model (GM). GM(1,1) model modified with seasonal factors was established and compared with seasonal autoregressive integrated moving average model (ARIMA) model. The data of 2015-2021 was used for modeling and prediction, and the data from January to October in 2022 was used as a validation set to evaluate the prediction effectiveness. The monthly average PM2.5 concentrations in Shanghai from November to December in 2022 were predicted. Results Seasonal ARIMA model showed RMSE=4.02 and MAPE=15.50% in the validation set, while GM(1,1) model modified with seasonal factors showed RMSE=3.30 and MAPE=11.59%. GM(1,1) model modified with seasonal factors predicted the monthly average PM2.5 concentrations in Shanghai from November to December in 2022 to be 24.99 and 34.83μg/m3, respectively. Conclusion The prediction effect of GM(1,1) model modified with seasonal factors has better predictive performance than seasonal ARIMA model. The grey prediction model modified with seasonal factors can be considered when predicting seasonal time series such as the concentration of PM2.5.

Death and prediction analysis of adult hematological malignancies in Shenzhen / 白血病·淋巴瘤

Objective:To investigate the characteristics of death, tendency and the prediction of Shenzhen residents with adult hematological malignancies from 2017 to 2020.Methods:The surveillance data of hematological malignancies from 2017 to 2020 and the demographic data in Shenzhen were collected from Shenzhen death cause monitoring system and Shenzhen Center for Disease Control and Prevention, respectively. The data of the 7th national demographic data in 2020 were set as the standardized population data. Crude mortality rate (CMR), standardized mortality rate (SMR) and annual percentage change (APC) of mortality were calculated by using Joinpoint software. The grey model GM(1,1) was built to predict the mortality of adult hematological malignancies in Shenzhen between 2021 and 2025.Results:From 2017 to 2022, the male CMR of hematological malignancies was 1.15/100 000 to 1.85/100 000, and the SMR was 2.24/100 000 to 2.44/100 000; the female CMR of hematological malignancies was 0.81/100 000 to 1.75/100 000, and the SMR was 1.67/100 000 to 1.90/100 000. There were no statistically significant differences in the annual CMR and SMR between male and female hematological malignancies (all P > 0.05), and the annual change trend of CMR and SMR was not significant. The APC of male and female CMR was 27.28% and 12.70%, respectively (χ 2 = 0.01, P = 0.939); the APC of male and female SMR was 1.12% and 4.77%, respectively (χ 2 = 0.91, P = 0.318). The death causes of hematological malignancies were successively acute myeloid leukemia (AML), lymphoma, multiple myeloma, acute lymphoblastic leukemia (ALL), myelodysplastic syndrome (MDS) plus chronic myelomonocytic leukemia (CMML), chronic lymphoblastic leukemia (CLL) plus chronic myelogenous leukemia (CML). The CMR of patients with hematological malignancies aged 18-40 years was low, the CMR began to rise in patients above 40 years, especially the rapid increase at the age of 60 years, reaching the peak at the age of 80 years or above. The shortest median time of all kinds of hematological malignancies from the onset of disease to the death was found in AML group (8 months, range 0.1-168 months), the longest time was in CLL+CML group (24 months, range 0.1-300 months). Infection was the most direct cause of death, followed by single organ failure. GM(1,1) model had the better predictive effects and the total SMR would increase from 2021 to 2025 (4.52/100 000, 4.76/100 000, 5.01/100 000, 5.28/100 000 and 5.57/100 000, respectively). Conclusions:The incidence of hematological malignancies in Shenzhen residents over 40 years old is on the increase. The trend of adult hematological malignancies in Shenzhen will rise predicted by GM (1,1) grey model.

Prediction of maternal and child health care indicators in China based on GM(1,1) model / 中华疾病控制杂志

Objective To study the predictive effect of model ［GM(1,1)］ in China’s maternal and child health indicators, and to predict the future maternal and child health indicators in a short-term, and provide a scientific basis for the gradual improvement of maternal and child health care services in China. Methods The maternal mortality rate (MMR), neonatal mortality rate (NMR), infant mortality rate (IMR) and under-five mortality rate (U5MR) were collected from 2008 to 2017 in China. Models were established and MATLAB 2018b software was used for predictive analysis. Results The prediction models of maternal mortality rate, neonatal mortality rate, infant mortality rate and under-five mortality rate were as follows: x

Application of the gray model GM (1, 1) in predicting birth defects / 西安交通大学学报(医学版)

Objective: To investigate the application of gray model GM(1, 1) in predicting the incidence of birth defects at different levels and the effect of data volatility on the prediction outcome. Methods: Based on the monitoring data of birth defects in Xi'an from October 2009 to September 2016, the GM(1, 1) was used to predict the overall incidence of birth defects and incidence of five main birth defects at three levels (month, quarter, and year). We compared the fitting accuracy of different level prediction models. Results: The average relative error for yearly prediction of overall birth defect was 4.6%, and the mean square deviation was 0.259, which might suggest better prediction. Quarterly forecasting results were almost qualified and the average relative error was 10.2%. Monthly prediction was poor with an average relative error of 17.5%. With the extension of the forecast period, the grey model prediction results of the top five birth defects (congenital heart disease, cleft lip and palate, neural tube defects, multiple fingers, and congenital hydrocephalus) in Xi'an all increased, and the fitting accuracy gradually improved. The gray scale of the year was the best. Conclusion: The prediction results of the gray model may be related to the volatility of the data. It may be suitable for predicting the incidence of birth defects by the year.

Temporal Trend and Prediction of Mortality of Life Lost due to Esophageal Cancer in Residents in Tieling / 中国卫生统计

Objective To explore the trend of mortality and years of life lost due to Esophageal Cancer in residents in Tieling,so as to provide the basis data on preventing Esophageal cancer in Tieling.Methods The data of residents in Tieling dying of Esophageal cancer from 2007 to 2015 was collected and cleared up to calculate the evaluation indexes including the mortality rate,the average percentage change of mortality rate.GM(1,1) model was used to predict the future mortality.Results From 2007 to 2015,the Average Esophageal cancer Mortality Rate of in residents in Tieling was 5.26 per 100000 persons,and especially 1.95％ raised a year.The Mortality Rate would increase from 2016 to 2019.Conclusion Tieling Esophageal Cancer mortality rate is on the rise,especially for elder men more than 60.So that the proper prevention measures should be car ried and strengthened.

Forecast of Clinical Blood Collection Volume with Dynamic Grey GM(1,1)Mathematical Model / 现代检验医学杂志

Objective To explore the application of dynamic grey GM (1,1)modeling,analyse and forecast varieties of blood collection volume for central blood bank of Chengde in Hebei Province,under normal dynamic development trends,and make quantitative predictions according to the model’s application.Methods According to the blood collection data of whole blood,single white blood platelet (person-portion)in the blood Bank of Chengde city fom January 2004 to December 2013 (400 ml/people),and in order to test predictive ability of the model by comparing the forecast value with the actual value in 2013.At the same time,analysed blood collection value in 2014 to 2016.Results The above two types of blood collection number grey GM (1,1)model Y (t)posterior difference (standard deviation)C<0.35,small error probability P was 1.Ac-curacy was excellent,good for prediction of blood collection.Conclusion These two categories of blood collecting species in Central Blood Bank of Chengde increased gradually.Grey model GM (1,1)as a new prediction model can forecast reasonably clinical blood collection volume under normal lynamic development trends.

Study on the application of information renewal GM (1,1)—linear regression coupling model in the prediction of health personnel resources: taking nurses relative number prediction for example / 中华医学科研管理杂志

Objective Taking nurses relative number prediction for example,this paper discussed the application of Information Renewal GM (1,1)—Linear Regression Coupling Model in the prediction of health personnel resources,so as to provide methodology reference for forecasting health personnel.Methods The information renewal GM(1,1) and linear regression coupling model was built and explored to predict and fit analyzing.Results The error between the predictive value that calculated by information renewal GM(1,1) and the actual value was small,and the prediction accuracy of coupling model was high.Conclusion The coupling model not only remedied the defect that grey system model did not including linear factors,but also improved the fact that linear forecasting model could not express exponential growth.Therefore the coupling model was reasonable and feasible.

The transdermal absorption data' GM(1,1) fitting of sinomenine liposome patch and comparison with related model / 中成药

AIM: The transdermal absorption data of sinomenine patch was fitted by grey model GM(1,1) and compared with other models. METHODS: The transdermal absorption experiment was carried out with Franz diffusion cell and the GM(1,1) was established by accumulation generation operator (AGO). RESULTS: The fitting precision was compared by using mean relative error and grey absolute correlation degree as evaluation criterion. CONCLUSION: The fitting precision of GM(1,1) is as high as acceptable.