Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019:A modelling and comparative study

Background Influenza is an acute respiratory infectious disease with a significant global disease burden.Additionally,the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions(NPIs)have introduced uncertainty to the spread of influenza.However,comparative studies on the performance of innovative models and approaches used for influenza prediction are limited.Therefore,this study aimed to predict the trend of influenza-like illness(ILI)in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance.Methods The generalized additive model(GAM),deep learning hybrid model based on Gate Recurrent Unit(GRU),and autoregressive moving average-generalized autoregressive conditional heteroscedasticity(ARMA—GARCH)model were established to predict the trends of ILI 1-,2-,3-,and 4-week-ahead in Beijing,Tianjin,Shanxi,Hubei,Chongqing,Guangdong,Hainan,and the Hong Kong Special Administrative Region in China,based on sentinel surveillance data from 2011 to 2019.Three relevant metrics,namely,Mean Absolute Percentage Error(MAPE),Root Mean Squared Error(RMSE),and R squared,were calculated to evaluate and compare the goodness of fit and robustness of the three models.Results Considering the MAPE,RMSE,and R squared values,the ARMA—GARCH model performed best,while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China.Additionally,the models’predictive performance declined as the weeks ahead increased.Furthermore,blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting.Conclusions Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model.Therefore,in the future,the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones,thereby contributing to influenza control and prevention efforts.

Spatiotemporal heterogeneity of schistosomiasis in China's Mainland:Evidence from a multi-stage continuous downscaling sentinel monitoring

Objective:To determine the spatiotemporal distribution of Schistosoma(S.)japonicum infections in humans,livestock,and Oncomelania(O.)hupensis across the endemic foci of China.Methods:Based on multi-stage continuous downscaling of sentinel monitoring,county-based schistosomiasis surveillance data were captured from the national schistosomiasis surveillance sites of China from 2005 to 2019.The data included S.japonicum infections in humans,livestock,and O.hupensis.The spatiotemporal trends for schistosomiasis were detected using a Joinpoint regression model,with a standard deviational ellipse(SDE)tool,which determined the central tendency and dispersion in the spatial distribution of schistosomiasis.Further,more spatiotemporal clusters of S.japonicum infections in humans,livestock,and O.hupensis were evaluated by the Poisson model.Results:The prevalence of S.japonicum human infections decreased from 2.06%to zero based on data of the national schistosomiasis surveillance sites of China from 2005 to 2019,with a reduction from 9.42%to zero for the prevalence of S.japonicum infections in livestock,and from 0.26%to zero for the prevalence of S.japonicum infections in O.hupensis.Analysis using an SDE tool showed that schistosomiasis-affected regions were reduced yearly from 2005 to 2014 in the endemic provinces of Hunan,Hubei,Jiangxi,and Anhui,as well as in the Poyang and Dongting Lake regions.Poisson model revealed 11 clusters of S.japonicum human infections,six clusters of S.japonicum infections in livestock,and nine clusters of S.japonicum infections in O.hupensis.The clusters of human infection were highly consistent with clusters of S.japonicum infections in livestock and O.hupensis.They were in the 5 provinces of Hunan,Hubei,Jiangxi,Anhui,and Jiangsu,as well as along the middle and lower reaches of the Yangtze River.Humans,livestock,and O.hupensis infections with S.japonicum were mainly concentrated in the north of the Hunan Province,south of the Hubei Province,north of the Jiangxi Province,and southwestern portion of Anhui Province.In the 2 mountainous provinces of Sichuan and Yunnan,human,livestock,and O.hupensis infections with S.japonicum were mainly concentrated in the northwestern portion of the Yunnan Province,the Daliangshan area in the south of Sichuan Province,and the hilly regions in the middle of Sichuan Province.Conclusions:A remarkable decline in the disease prevalence of S.japonicum infection was observed in endemic schistosomiasis in China between 2005 and 2019.However,there remains a long-term risk of transmission in local areas,with the highest-risk areas primarily in Poyang Lake and Dongting Lake regions,requiring to focus on vigilance against the rebound of the epidemic.Development of high-sensitivity detection methods and integrating the transmission links such as human and livestock infection,wild animal infection,and O.hupensis into the surveillance-response system will ensure the elimination of schistosomiasis in China by 2030.

Epidemiological, Clinical and Virological Profiles of Influenza Infection in the Democratic Republic of the Congo, from 2009 to 2018

Background: To better understand the epidemiological characteristic of influenza infection, the Democratic Republic of Congo set up the sentinel influenza surveillance system in 2007 with eleven health facilities. Oral and nasopharyngeal specimens were sampled from outpatients with influenza-like illness (ILI) and inpatients with severe acute respiratory illness (SARI) using case definitions. Those specimens were shipped to the Influenza National Laboratory for testing with the real-time reverse-transcription polymerase chain reaction. This study aimed to describe ILI and SARI patients’ epidemiological, clinical, and virological profiles. Material and Method: We conducted a cross-sectional study based on a documentary review of suspected notified influenza cases from January 2009 to December 2018. As variables, we exploited sex, age, symptoms, sentinel site of provenance, patient category, viral type and subtype identified, and period of health facility visit. Results: Of 18,461 notified cases, 1795 (9.7%) were positive for the Influenza virus, among them;53.1% of patients under five years old;68% of type A virus, 31.5% of type B;21% of SARI positive vs. 79% for ILI positive cases. The majority of cases occurred during the rainy season. Conclusion: The results of this study contribute to a better understanding of the influenza infection in the Democratic Republic of Congo.

The Epidemiology of Influenza in Children Hospitalized at the Hospital of Sikasso, Mali 2015-2019

Introduction: Influenza is an acute respiratory infectious disease, highly contagious due to influenza viruses. The objective of this work was to identify, understand the epidemiology of circulating strains and estimate disease transmission. Patients and Methods: The study was carried out in the pediatric department of the Sikasso Hospital. This was a prospective, longitudinal descriptive study over a five-year period (January 1, 2015 to December 31, 2019). She was interested in severe acute respiratory infections (SARI) for hospitalized patients in the pediatric department. Results: During the study period the prevalence of severe acute respiratory infections among hospitalized children was 21.85‰. The majority of cases were observed in 2019 with 58 cases, the sex ratio was 1.8. The age group from 0 to 1 was the most represented with 100 cases (48.30%) followed by 2 to 4 years 73 cases (35.24%) and 5 to 15 years 34 cases (16.46%). More than half of the patients lived in rural areas 129 (62.31%). Fever and cough were present in the majority of patients. No children had received influenza vaccination. In study 36 (17.39%) cases were positive for influenza A and B.