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Objective To compare the prediction effect of combined model and single model in HFRS incidence fitting and prediction, and to provide a reference for optimizing HFRS prediction model. Methods The province with the highest incidence in China (Heilongjiang Province) in recent years was selected as the research site. The monthly incidence data of HFRS in Heilongjiang Province from 2004 to 2017 were collected. The data from 2004 to 2016 was used as training data, and the data from January to December 2017 was used as test data. The training data was used to train SARIMA , ETS and NNAR models, respectively. The reciprocal variance method and particle swarm optimization algorithm (PSO) were used to calculate the model coefficients of SARIMA, ETS and NNAR, respectively, to construct combined model A and combined model B. The established models were used to predict the incidence of HFRS from January to December 2017. The fitted and predicted values of the five models were compared with the training data and test data, respectively. Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Standard Deviation (RMSE), and Mean Error Rate (MER) were used to evaluate the model fitting and prediction effects. Results The optimal SARIMA model was SARIMA(1,0,2)(2,1,1)12. The optimal ETS model was ETS(M, N, M), and the smoothing parameter =0.738，=1*10. The optimal NNAR model was NNAR(13,1,7)12. The residuals of the three single models were white noise (P>0.05). The expression of combined model A was ŷ=0.134*ySARIMA+0.162*yETS+0.704*yNNAR; the expression of combined model B was ŷ=0.246*ySARIMA+0.435*yETS+0.319*yNNAR. The MAPE, MAE, RMSE, and MER fitted by SARIMA, ETS, NNAR, combined model A and combined model B were 24.10%, 0.11, 0.17, 23.29%; 17.14%, 0.08, 0.14, 17.96%; 6.33%, 0.02, 0.03, 4.25%; 9.03%, 0.03, 0.05, 7.51%; 13.16%, 0.06, 0.09, 12.33%, respectively. The MAPE, MAE, RMSE, and MER predicted by the five models were 18.70%, 0.05, 0.06, 19.62%; 23.83%, 0.06, 0.07, 24.49%; 28.30%, 0.07, 0.10, 29.21%; 21.69%, 0.06, 0.08, 22.63%; 17.39%, 0.05, 0.07, 18.76%, respectively. Conclusion The fitting and prediction effects of the combined models are better than the single models. The combined model based on PSO to calculate the weight of the single model is the optimal model.
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Objective To explore the applicability of the TBATS in predicting the incidence of mumps. Methods The incidence of mumps of Jiangxi Province from 2004 to 2017 was used as the demonstration data. The incidence of mumps in Jiangxi Province from July to December 2017 was used as test data. The training data from January 2004 to June 2017 were used to train the TBATS and the SARIMA, and predict the value from July to December 2017. The fitted and predicted values were compared with the test data. The MAPE, RMSE, MAE and MER were used to evaluate model fitting and prediction effects. Results SARIMA (1,0,0)(1,1,0)12 with drift was the optimal SARIMA. The MAPE, MAE, RMSE and MER fitted by the TBATS and the SARIMA were 15.06%, 0.21, 0.29, 13.57% and 21.93%, 0.29, 0.41, 18.73%, respectively. The MAPE, MAE, RMSE and MER predicted by the TBATS and the SARIMA were 7.95%, 0.08, 0.11, 7.12% and 15.33%, 0.17, 0.18, 14.93%. Conclusion The TBATS has high accuracy in predicting the incidence of mumps and is worthy of popularization and application.
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BackgroundThe coronavirus disease 2019 (Covid-19) spreads rapidly around the world. ObjectiveTo evaluate the association between comorbidities and the risk of death in patients with COVID-19, and to further explore potential sex-specific differences. MethodsWe analyzed the data from 18,465 laboratory-confirmed cases that completed an epidemiological investigation in Hubei Province as of February 27, 2020. Information on death was obtained from the Infectious Disease Information System. The Cox proportional hazards model was used to estimate the association between comorbidities and the risk of death in patients with COVID-19. ResultsThe median age for COVID-19 patients was 50.5 years. 8828(47.81%) patients were females. Severe cases accounted for 20.11% of the study population. As of March 7, 2020, a total of 919 cases deceased from COVID-19 for a fatality rate of 4.98%. Hypertension (13.87%), diabetes (5.53%), and cardiovascular and cerebrovascular diseases (CBVDs) (4.45%) were the most prevalent comorbidities, and 27.37% of patients with COVID-19 reported having at least one comorbidity. After adjustment for age, gender, address, and clinical severity, patients with hypertension (HR 1.55, 95%CI 1.35-1.78), diabetes (HR 1.35, 95%CI 1.13-1.62), CBVDs (HR 1.70, 95%CI 1.43-2.02), chronic kidney diseases (HR 2.09, 95%CI 1.47-2.98), and at least two comorbidities (HR 1.84, 95%CI 1.55-2.18) had significant increased risks of death. And the association between diabetes and the risk of death from COVID-19 was prominent in women (HR 1.69, 95%CI 1.27-2.25) than in men (HR 1.16, 95%CI 0.91-1.46) (P for interaction = 0.036). ConclusionAmong laboratory-confirmed cases of COVID-19 in Hubei province, China, patients with hypertension, diabetes, CBVDs, chronic kidney diseases were significantly associated with increased risk of death. The association between diabetes and the risk of death tended to be stronger in women than in men. Clinicians should increase their awareness of the increased risk of death in COVID-19 patients with comorbidities.
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Since the outbreak of the COVID-19 at the end of December 2019, Hubei province has actively adopted a series of prevention and control measures such as “quarantine, treatment, testing, and containment”, and have basically blocked the spread of COVID-19. However, with the development of overseas epidemics and the occurrence of case clusters in local areas, we not only face the threat of imported cases, but also face the urgent need to resume normal work and daily life. This puts forward higher requirements for regular prevention and control of COVID-19. Therefore, we should more deeply understand the significance of regular prevention and control as well as the epidemic situation in our province, summarize experience and lessons, and adhere to the prevention and control strategy of “government-led, group-specialist combination, and specialized-oriented”. Meanwhile, it is necessary to implement the working requirement that combines regular prevention and control surveillance with rapid emergency response to local COVID-19 outbreaks. Furthermore, we should establish a regular multi-point trigger early warning mechanism for COVID-19, strengthen the reserve of emergency supplies and carry out training and drills on epidemic prevention and control across the province to make full preparations for the coming autumn and winter epidemics. The most important is to reform the system of disease prevention and control and public health, comprehensively improve the ability of prevention and treatment, and promote the modernization of public health governance.
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Objective To understand the epidemiological characteristics of the novel coronavirus diseases 2019 (COVID-19), and to scientifically guide the prevention and control of COVID-19 in Hubei Province. Methods All COVID-19 cases reported online in Hubei Province as of March 31, 2020 were extracted from Hubei's Infectious Disease Information System. The epidemic curve, age and sex characteristics, and spatiotemporal distribution characteristics of the COVID-19 cases were analyzed. Results As of March 31, 2020, a total of 70 764 cases were reported in Hubei Province, including 49 195 confirmed cases. A total of 4 579 deaths occurred among the confirmed cases, and the reported case fatality rate was 6.47%. The peak of the onset of symptoms occurred from January 20 to February 14, 2020. The sex ratio of male to female of the confirmed cases was 0.99: 1, and most were 30-69 years old. The cases diagnosed before January 5 were mainly reported by Wuhan City. From January 6 to January 31, all counties and districts in the province reported that the incidence of confirmed COVID-19 cases began to rise, and about 50% counties reported that the morbidity rate of confirmed COVID-19 cases was over 10 cases per 100 000. The morbidity rate of COVID-19 cases rose rapidly between February 1-15, and then gradually reached its peak after February 16. Conclusion Wuhan City of Hubei Province first discovered and reported the COVID-19 outbreak. The onset of symptoms peaked in January 20 to February 14, and the 30-69 years old group was the key population. Many measures such as restricting personnel movement, reducing contact, and strengthening health education played an important role in controlling the outbreak of COVID-19 in Hubei.