Background Dengue fever has been a significant public health challenge in China. This will be particularly important in the context of global warming, frequent international travels, and urbanization with increasing city size and population movement. In order to design relevant prevention and control strategies and allocate health resources reasonably, this study evaluated the economic burden of dengue fever in China in 2019. Methods The economic burden of dengue fever patients was calculated from both family and the organisation perspectives. A survey was conducted among 1,027 dengue fever patients in Zhejiang, Chongqing, and Yunnan Provinces. Treatment expenses, lost working days, and insurance reimbursement expenses information were collected to estimate the total economic burden of dengue fever patients in 2019. The expenditures related to dengue fever prevention and control from government, Center for Disease Control and Prevention (CDC), communities and subdistrict offices of 30 counties (or districts) in Zhejiang Province and Chongqing City were also collected. Results The direct, indirect and total economic burden for dengue fever patients in 2019 in the three Provinces were about 36,927,380.00 Chinese Yuan (CNY), 10,579,572.00 CNY and 46,805,064.00 CNY, respectively. The costs for prevention and control of dengue fever for the counties (or districts) without cases, counties (or districts) with imported cases, and counties (or districts) with local cases are 205,800.00 CNY, 731,180.00 CNY and 6,934,378.00 CNY, respectively. The total investment of dengue fever prevention and control in the 30 counties in China in 2019 was approximately 3,166,660,240.00 CNY. Conclusion The economic burden of dengue fever patients is relatively high, and medical insurance coverage should be increased to lighten patients’ direct medical economic burden. At the same time, the results suggests that China should increase funding for primary health service institutions to prevent dengue fever transmission.

The recent development of mapping technologies, such as Hi-C, that probes the 3D genome organization reveals that a chromosome is divided into topologically associating domains (TADs). TADs are genomic regions where chromatin loci are more frequently interacting with chromatin loci from the same TADs rather than from other TADs. TADs are functional units for transcriptional regulation, such as constraining interactions between enhancers and promoters.

What is already known about this topic? Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron subvariant has a stronger transmission capacity and faster transmission speed than the previous strain. What is added by this report? The first coronavirus disease 2019 (COVID-19) case infected with the SARS-CoV-2 Omicron subvariant BA.2.76 who caused local transmission was reported in Chongqing Municipality on August 16, 2022. For 35 minutes, the Patient Zero jogged along a lake at a local park without wearing a mask. Among the 2,836 people potentially exposed at the time, 39 tested positive. Overall, 38 out of 39 cases did not wear a mask on the morning of August 16. All 39 cases lacked any previous exposure to the variant before testing positive on their nucleic acid test. What are the implications for public health practice? It is essential to maintain personal wellbeing by ensuring one maintains personal protection and follows regulated guidelines such as maintaining safe distances from others both indoors and outdoors.

What is already known about this topic? The key epidemiological parameters including serial interval, basic reproductive number (R-0), and effective reproductive number (Rt) are crucial for coronavirus disease 2019 (COVID-19) control and prevention. Previous studies provided different estimations but were often flawed by some limitations such as insufficient sample size and selection bias. What is added by this report? In this study, a total of 116 infector-infectee pairs meeting strict inclusion criteria were selected for analysis. The mean serial interval of COVID-19 was 5.81 days (standard deviation: 3.24). The estimated mean with 95% confidence interval of R0 was 3.39 (3.07-3.75) and 2.98 (2.62-3.38) using exponential growth (EG) and maximum likelihood (ML) methods, respectively. The Rt in the early phase of the epidemic was above 1 with the peak of 4.43 occurring on January 8, and then showing subsequent declines and approaching 1 on January 24. What are the implications for public health practices? This study supports previous findings that COVID-19 has high transmissibility and that implementing comprehensive measures is effective in controlling the COVID-19 outbreak.

The study of competition pressure of athletes has been in the science circle for many years. However, computer science research as a coping strategy has not been involved in the past. Based on this, data mining was applied to the survey data of sports competition stress in this article. The basic theory of content-based recommendation algorithm was studied, including the idea of algorithm, algorithm description and algorithm implementation. Combining with the characteristics of the stressor data of sports competition, the algorithm was further improved from the perspective of similarity calculation and potential semantic analysis, and then the word frequency of the data was calculated to get the most similar suggestions, and the results obtained were analyzed.

Background: Early detection of influenza activity followed by timely response is a critical component of preparedness for seasonal influenza epidemic and influenza pandemic. However, most relevant studies were conducted at the regional or national level with regular seasonal influenza trends. There are few feasible strategies to forecast influenza activity at the local level with irregular trends. Methods: Multi-source electronic data, including historical percentage of influenza-like illness (ILI%), weather data, Baidu search index and Sina Weibo data of Chongqing, China, were collected and integrated into an innovative Self-adaptive AI Model (SAAIM), which was constructed by integrating Seasonal Autoregressive Integrated Moving Average model and XGBoost model using a self-adaptive weight adjustment mechanism. SAAIM was applied to ILI% forecast in Chongqing from 2017 to 2018, of which the performance was compared with three previously available models on forecasting. Findings: ILI% showed an irregular seasonal trend from 2012 to 2018 in Chongqing. Compared with three reference models, SAAIM achieved the best performance on forecasting ILI% of Chongqing with the mean absolute percentage error (MAPE) of 11.9%, 7.5%, and 11.9% during the periods of the year 2014-2016, 2017, and 2018 respectively. Among the three categories of source data, historical influenza activity contributed the most to the forecast accuracy by decreasing the MAPE by 19.6%, 43.1%, and 11.1%, followed by weather information (MAPE reduced by 3.3%, 17.1%, and 2.2%), and Internet-related public sentiment data (MAPE reduced by 1.1%, 0.9%, and 1.3%). Interpretation: Accurate influenza forecast in areas with irregular seasonal influenza trends can be made by SAAIM with multi-source electronic data. (c) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Seasonal influenza epidemics which annually cause substantial diseases and deaths in high-risk population groups are a major public health concern around the world. Considering the hysteresis of traditional flu surveillance systems, this work aims to present a methodology capable of forecasting influenza activity of a city in China precisely 1 week ahead of the official publication. To that end, exogenous information collected from different sources were separately tested with historical influenza-like illness reports for the ability of detecting influenza activity, including climate surveillance, Internet users’ search activity, twitter and health inquiry on an online health consultation platform. Moreover, an ensemble model combining a time series analysis model and a tree boosting model based on those multisource data was applied to improve the accuracy and generalizability of influenza forecasting, in which a model fusion method based on the Kalman Filter was proposed. The validation experiments in this work were performed on the influenza-like illness reports collected from Chongqing city over 4 influenza seasons within 2014–2017. The results show that the proposed model outperformed other tested models by not only taking the periodic law of influenza into consideration but also incorporating information from diverse data sources. The mean absolute percentage error of the validation data set decreased to about 10%. This work provides a viable suggestion for improving the influenza activity forecasting of a city at its early stage.