Evaluating the impact of COVID-19 outbreak on hepatitis B and forecasting the epidemiological trend in mainland China: a causal analysis.

BACKGROUND: It is uncertain how COVID-19 outbreak influences the hepatitis B epidemics. This study aims to evaluate the effects on hepatitis B owing to the COVID-19 outbreak and forecast the hepatitis B epidemiological trend in mainland China to speed up the course of the "End viral hepatitis Strategy". METHODS: We estimated the causal impacts and created a forecast through adopting monthly notifications of hepatitis B each year from 2005 to 2020 in mainland China using the Bayesian structural time series (BSTS) method. RESULTS: The hepatitis B epidemics fluctuates irregularly during the period 2005-2007(APC = 8.7, P = 0.246) and 2015-2020(APC = 1.7, P = 0.290), and there is a downturn (APC=-3.2, 95% CI -5.2 to -1.2, P = 0.006) from 2007 to 2015 in mainland China. The COVID-19 outbreak was found to have a monthly average reduction on the hepatitis B epidemics of 26% (95% CI 18-35%) within the first three months in 2020,17% (95% CI 7.7-26%) within the first six months in 2020, and 10% (95% CI19-22%) all year as a result of the COVID-19 outbreak, (probability of causal effect = 96.591%, P = 0.034) and the forecasts showed an upward trend from 2021 to 2025 (annual percentage change = 4.18, 95% CI 4.0 to 4.3, P < 0.001). CONCLUSION: The COVID-19 has a positive effect on the decline of hepatitis B cases. And the potential of BSTS model to forecast the epidemiological trend of the hepatitis B can be applied in automatic public health policymaking in mainland China.

Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model.

BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. METHODS: We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. RESULTS: There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0)12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. CONCLUSIONS: The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

Time series analysis of human brucellosis in mainland China by using Elman and Jordan recurrent neural networks.

BACKGROUND: Establishing epidemiological models and conducting predictions seems to be useful for the prevention and control of human brucellosis. Autoregressive integrated moving average (ARIMA) models can capture the long-term trends and the periodic variations in time series. However, these models cannot handle the nonlinear trends correctly. Recurrent neural networks can address problems that involve nonlinear time series data. In this study, we intended to build prediction models for human brucellosis in mainland China with Elman and Jordan neural networks. The fitting and forecasting accuracy of the neural networks were compared with a traditional seasonal ARIMA model. METHODS: The reported human brucellosis cases were obtained from the website of the National Health and Family Planning Commission of China. The human brucellosis cases from January 2004 to December 2017 were assembled as monthly counts. The training set observed from January 2004 to December 2016 was used to build the seasonal ARIMA model, Elman and Jordan neural networks. The test set from January 2017 to December 2017 was used to test the forecast results. The root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to assess the fitting and forecasting accuracy of the three models. RESULTS: There were 52,868 cases of human brucellosis in Mainland China from January 2004 to December 2017. We observed a long-term upward trend and seasonal variance in the original time series. In the training set, the RMSE and MAE of Elman and Jordan neural networks were lower than those in the ARIMA model, whereas the MAPE of Elman and Jordan neural networks was slightly higher than that in the ARIMA model. In the test set, the RMSE, MAE and MAPE of Elman and Jordan neural networks were far lower than those in the ARIMA model. CONCLUSIONS: The Elman and Jordan recurrent neural networks achieved much higher forecasting accuracy. These models are more suitable for forecasting nonlinear time series data, such as human brucellosis than the traditional ARIMA model.

A data-driven interpretable ensemble framework based on tree models for forecasting the occurrence of COVID-19 in the USA.

This prevalence of coronavirus disease 2019 (COVID-19) has become one of the most serious public health crises. Tree-based machine learning methods, with the advantages of high efficiency, and strong interpretability, have been widely used in predicting diseases. A data-driven interpretable ensemble framework based on tree models was designed to forecast daily new cases of COVID-19 in the USA and to determine the important factors related to COVID-19. Based on a hyperparametric optimization technique, we developed three machine learning algorithms based on decision trees, including random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), and three linear ensemble models were used to integrate these outcomes for better prediction accuracy. Finally, the SHapley Additive explanation (SHAP) value was used to obtain the feature importance ranking. Our outcomes demonstrated that, among the three basic machine learners, the prediction accuracy was the following in descending order: LightGBM, XGBoost, and RF. The optimized LAD ensemble was the most precise prediction model that reduced the prediction error of the best base learner (LightGBM) by approximately 3.111%, while vaccination, wearing masks, less mobility, and government interventions had positive effects on the control and prevention of COVID-19.

Application of a data-driven XGBoost model for the prediction of COVID-19 in the USA: a time-series study.

OBJECTIVE: The COVID-19 outbreak was first reported in Wuhan, China, and has been acknowledged as a pandemic due to its rapid spread worldwide. Predicting the trend of COVID-19 is of great significance for its prevention. A comparison between the autoregressive integrated moving average (ARIMA) model and the eXtreme Gradient Boosting (XGBoost) model was conducted to determine which was more accurate for anticipating the occurrence of COVID-19 in the USA. DESIGN: Time-series study. SETTING: The USA was the setting for this study. MAIN OUTCOME MEASURES: Three accuracy metrics, mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE), were applied to evaluate the performance of the two models. RESULTS: In our study, for the training set and the validation set, the MAE, RMSE and MAPE of the XGBoost model were less than those of the ARIMA model. CONCLUSIONS: The XGBoost model can help improve prediction of COVID-19 cases in the USA over the ARIMA model.

The correlation between temperature and the incidence of COVID-19 in four first-tier cities of China: a time series study.

The COVID-19 outbreak emerged in Wuhan, China, and was declared a global pandemic in March 2020. This study aimed to explore the association of daily mean temperature with the daily counts of COVID-19 cases in Beijing, Shanghai, Guangzhou, and Shenzhen, China. Data on daily confirmed cases of COVID-19 and daily mean temperatures were retrieved from the 4 first-tier cities in China. Distributed lag nonlinear models (DLNMs) were used to assess the association between daily mean temperature and the daily cases of COVID-19 during the study period. After controlling for the imported risk index and long-term trends, the distributed lag nonlinear model showed that there were nonlinear and lag relationships. The daily cumulative relative risk decreased for every 1.0 °C change in temperature in Shanghai, Guangzhou, and Shenzhen. However, the cumulative relative risk increased with a daily mean temperature below - 3 °C in Beijing and then decreased. Moreover, the delayed effects of lower temperatures mostly occurred within 6-7 days of exposure. There was a negative correlation between the cumulative relative risk of COVID-19 incidence and temperature, especially when the temperature was higher than - 3 °C. The conclusions from this paper will help government and health regulators in these cities take prevention and protection measures to address the COVID-19 crisis and the possible collapse of the health system in the future.

Spatiotemporal cluster analysis of COVID-19 and its relationship with environmental factors at the city level in mainland China.

This study sought to identify the spatial, temporal, and spatiotemporal clusters of COVID-19 cases in 366 cities in mainland China with the highest risks and to explore the possible influencing factors of imported risks and environmental factors on the spatiotemporal aggregation, which would be useful to the design and implementation of critical preventative measures. The retrospective analysis of temporal, spatial, and spatiotemporal clustering of COVID-19 during the period (January 15 to February 25, 2020) was based on Kulldorff's time-space scanning statistics using the discrete Poisson probability model, and then the logistic regression model was used to evaluate the impact of imported risk and environmental factors on spatiotemporal aggregation. We found that the spatial distribution of COVID-19 cases was nonrandom; the Moran's I value ranged from 0.017 to 0.453 (P < 0.001). One most likely cluster and three secondary likely clusters were discovered in spatial cluster analysis. The period from February 2 to February 9, 2020, was identified as the most likely cluster in the temporal cluster analysis. One most likely cluster and seven secondary likely clusters were discovered in spatiotemporal cluster analysis. Imported risk, humidity, and inhalable particulate matter PM2.5 had a significant impact on temporal and spatial accumulation, and temperature and PM10 had a low correlation with the spatiotemporal aggregation of COVID-19. The information is useful for health departments to develop a better prevention strategy and potentially increase the effectiveness of public health interventions.

Effects of climate variables on the transmission of COVID-19: a systematic review of 62 ecological studies.

The new severe acute respiratory syndrome coronavirus 2 was initially discovered at the end of 2019 in Wuhan City in China and has caused one of the most serious global public health crises. A collection and analysis of studies related to the association between COVID-19 (coronavirus disease 2019) transmission and meteorological factors, such as humidity, is vital and indispensable for disease prevention and control. A comprehensive literature search using various databases, including Web of Science, PubMed, and Chinese National Knowledge Infrastructure, was systematically performed to identify eligible studies from Dec 2019 to Feb 1, 2021. We also established six criteria to screen the literature to obtain high-quality literature with consistent research purposes. This systematic review included a total of 62 publications. The study period ranged from 1 to 8 months, with 6 papers considering incubation, and the lag effect of climate factors on COVID-19 activity being taken into account in 22 studies. After quality assessment, no study was found to have a high risk of bias, 30 studies were scored as having moderate risks of bias, and 32 studies were classified as having low risks of bias. The certainty of evidence was also graded as being low. When considering the existing scientific evidence, higher temperatures may slow the progression of the COVID-19 epidemic. However, during the course of the epidemic, these climate variables alone could not account for most of the variability. Therefore, countries should focus more on health policies while also taking into account the influence of weather.

[Isolation of the Culex flavivirus from mosquitoes in Liaoning Province, China].

A flavivirus, Culex flavivirus, was first isolated from Chinese mosquitoes with high sequences similarities to those of flaviviruses found in America and Japan. In this study, a total of 48 pools of field-collected mosquitoes were sampled from Dandong of Liaoning Province, China during July to September of 2011. Six isolated viruses showing cytopathic effect (CPE) in C6/C36 cells were tested by reverse transcription polymerase chain reaction(RT-PCR)using Flavivirus genus--specific primers and Culex flavivirus-specific primers and the positive PCR-product was sequenced and compared with the sequences of 10 isolates from GenBank. Phylogenetic tree of NS5 and enevelop genes of flavivirus were constructed. The GenBank accession numbers of NS5 gene were JQ409188, JQ409186, JQ409187, JQ409191, JQ409189 and JQ409190. The GenBank accession numbers of envelope gene were JQ065883, JQ065882, JQ065881, JQ065879,JQ065877 and JQ065878.

[Detection of bacterial meningitis by multiplex polymerase chain reaction].

OBJECTIVE: To develop a rapid method for detecting 8 pathogens which were highly related to bacterial meningitis by multiplex polymerase chain reaction. METHOD: By optimizing the reaction condition and amplification program of single pair polymerase chain reaction, the multiplex pairs polymerase chain reactions (M-PCR) was developed to identify eight pathogens simultaneously including Neisseria Meningitis, Haemophilus Influenzae, Streptococcus Pneumoniae, Cryptococcus Neoformans, Staphylococcus Aureus, Listerisa Monocytogene, Streptococcus Suis and Mycobacterium Tuberculosis. Meanwhile, The sensitivity of M-PCR assay was also studied. RESULTS: M-PCR methods for detecting 8 pathogens which could cause bacterial meningitis have been established. M-PCR showed specific, sensitive and more rapid than conventional culturing method. CONCLUSION: This multiplex polymerase chain reaction method can be used for diagnosis and scanning of suspicious bacterial meningitis cases in order to improve the diagnostic positive rate of bacterial meningitis cases.

[Testing of antibodies against serogroup C Neisseria meningitidis by serum bactericidal assay in healthy population, Liaoning province].

OBJECTIVE: To test the serum antibodies from healthy population by Serum Bactericidal Assay (SBA), in order to evaluate the level of protective antibodies against serogroup C Neisseria meningitidis in Liaoning province. METHODS: 240 serum samples were selected from eight age-group randomly. Serogroup C vaccine candidate strain (C11) and the prevail serogroup C strain (053442) were used for SBA. RESULTS: 48.33% of 240 serum samples were positive (titer > or = 1:2) to C11 vaccine strain. Protective rate of SBA was 35.83% (titer > or = 1:8), in which, > or = 6 years old were 13.33%, 7-19 years old was 61.67%, 20-39 years old were 46.67% and > or = 40 years old were 63.33%. Rate of SBA to 053442 was lower than that to C11 in the group over 15 years old by statistic analysis. CONCLUSION: Population under 6 years old showed lower SBA capacities. With the implemention of Expanded Program on Immunization, children under 3 years old should be considered how to give them meningococcal vaccine in order to improve the titer of antibodies against Neisseria meningitidis serogroup C.