Chinese Social Media Reaction to Information about 42 Notifiable Infectious Diseases.
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ABSTRACT:
SUBMITTER: Fung IC
PROVIDER: S-EPMC4422708 | biostudies-literature | 2015
REPOSITORIES: biostudies-literature
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Project description:Clinicians, researchers, and the public frequently turn to digital channels and social media for up-to-the-minute information on novel therapeutics and vaccines. The value of credible infectious diseases drug information is more apparent in the setting of the coronavirus disease 2019 (COVID-19) pandemic. This viewpoint by the Society of Infectious Diseases Pharmacists (SIDP) provides guidance on utilizing social media platforms to optimize infectious diseases pharmacotherapy. It includes tips for all levels of users but primarily serves a guide for the infectious diseases clinician who has not yet joined social media. It compares various social media platforms and suggests which to begin with based on user needs, recommends efficient curation of social media content, and outlines a stepwise approach (shown below) to increasing engagement over time. This summary will hopefully spur further quality content and engagement regarding drug information from the infectious diseases social media network.
| S-EPMC9384058 | biostudies-literature
Project description:As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases.
| S-EPMC11373815 | biostudies-literature
Project description:Nowadays, tuberculosis, scarlet fever, measles, influenza, and mumps are five major notifiable respiratory infectious diseases (RIDs) in China. The objective of this study was to describe, visualize, and compare the spatial-temporal distributions of these five RIDs from 2006 to 2016. In addition to descriptive epidemiology analysis, seasonality and spatial autocorrelation analysis were also applied to explore the epidemiologic trends and spatial changing patterns of the five RIDs, respectively. The results indicated that the incidence of tuberculosis, measles, and mumps presented a downtrend trend, while those of scarlet fever and influenza was in a strong uptrend across the research period. The incidences of the five diseases all peaked in spring. There were significant spatial disparities in the distribution of tuberculosis, scarlet fever, and measles cases, with the hotspots mainly located in the western plateau region, northern plain region, and southern mountainous region. To conclude, notable epidemiological differences were observed across regions, indicating that some provincial units should pay more attention to prevent and control respiratory infectious diseases.
| S-EPMC7177391 | biostudies-literature
Project description:BackgroundWithin the frame of National Epidemiological Surveillance System, family physicians have an obligation to report infections and suspicions cases. The aim of this study was to evaluate the knowledge, attitudes, daily practice and the reporting activities of Hungarian family physicians regarding to infectious diseases.MethodsA self-administered survey was developed, validated and used. The survey was completed by family physicians who had taken part in continuous medical educational programmes of all Hungarian medical faculties. The questionnaire, consisting demographic questions and 10 statements about their reporting habits were completed by 347 doctors, 8% of the total number of family physicians. The data were processed in a cross-sectional design with general linear model.ResultsAccording to the majority of responders, the current reporting system works efficiently. Rural physicians were mainly agreed, that reporting is not a simply obligation, it is a professional task as well. They were less hindered in daily work by reporting activities, waited less for laboratory confirmation before reporting, reported suspicious cases more frequently. Practitioner's based in urban settlements preferred to await laboratory tests before reporting and were hindered less by failures of the electronic reporting system. Older physicians trusted more in the recent system and they wished to increase the number of reports. Female physicians have higher consciousness in epidemiology. They were mostly in agreement that even severe infectious diseases can be diagnosed at primary care level and their daily practices were less burdened by reporting duties.ConclusionsBoth the epidemiological knowledge of general practitioners' and the electronic surveillance systems should be improved. There is a need to develope the electronic infrastructure of primary care. More and regular control is also expected by the health care authorities, beside the synthesis of professional and governmental expectations and regulations.
| S-EPMC5773032 | biostudies-literature
Project description:BackgroundThe prevalence of infectious diseases remains one of the major challenges faced by the Chinese health sector. Policymakers have a tremendous interest in investigating the spatiotemporal epidemiology of infectious diseases. We aimed to review the small-scale (city level, county level, or below) spatiotemporal epidemiology of notifiable infectious diseases in China through a systematic review, thus summarizing the evidence to facilitate more effective prevention and control of the diseases.MethodsWe searched four English language databases (PubMed, EMBASE, Cochrane Library, and Web of Science) and three Chinese databases (CNKI, WanFang, and SinoMed), for studies published between January 1, 2004 (the year in which China's Internet-based disease reporting system was established) and December 31, 2021. Eligible works were small-scale spatial or spatiotemporal studies focusing on at least one notifiable infectious disease, with the entire territory of mainland China as the study area. Two independent reviewers completed the review process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.ResultsA total of 18,195 articles were identified, with 71 eligible for inclusion, focusing on 22 diseases. Thirty-one studies (43.66%) were analyzed using city-level data, 34 (47.89%) were analyzed using county-level data, and six (8.45%) used community or individual data. Approximately four-fifths (80.28%) of the studies visualized incidence using rate maps. Of these, 76.06% employed various spatial clustering methods to explore the spatial variations in the burden, with Moran's I statistic being the most common. Of the studies, 40.85% explored risk factors, in which the geographically weighted regression model was the most commonly used method. Climate, socioeconomic factors, and population density were the three most considered factors.ConclusionsSmall-scale spatiotemporal epidemiology has been applied in studies on notifiable infectious diseases in China, involving spatiotemporal distribution and risk factors. Health authorities should improve prevention strategies and clarify the direction of future work in the field of infectious disease research in China.
| S-EPMC9442567 | biostudies-literature
Project description:We reviewed the first 21 years (1991-2011) of Australia's National Notifiable Diseases Surveillance System (NNDSS). All nationally notified diseases (except HIV/AIDS and Creutzfeldt-Jakob disease) were analysed by disease group (n = 8), jurisdiction (six states and two territories), Indigenous status, age group and notification year. In total, 2 421 134 cases were analysed. The 10 diseases with highest notification incidence (chlamydial infection, campylobacteriosis, varicella zoster, hepatitis C, influenza, pertussis, salmonellosis, hepatitis B, gonococcal infection, and Ross River virus infection) comprised 88% of all notifications. Annual notification incidence was 591 cases/100 000, highest in the Northern Territory (2598/100 000) and in children aged <5 years (698/100 000). A total of 8·4% of cases were Indigenous Australians. Notification incidence increased by 6·4% per year (12% for sexually transmissible infections and 15% for vaccine-preventable diseases). The number of notifiable diseases also increased from 37 to 65. The number and incidence of notifications increased throughout the study period, partly due to addition of diseases to the NNDSS and increasing availability of sensitive diagnostic tests. The most commonly notified diseases require a range of public health responses addressing high-risk sexual and drug-use behaviours, food safety and immunization. Our results highlight populations with higher notification incidence that might require tailored public health interventions.
| S-EPMC9150208 | biostudies-literature
Project description:BackgroundCertain bacterial infectious diseases are categorized as notifiable infectious diseases in China. Understanding the time-varying epidemiology of bacterial infections diseases can provide scientific evidence to inform prevention and control measures.MethodsYearly incidence data for all 17 major notifiable bacterial infectious diseases (BIDs) at the province level were obtained from the National Notifiable Infectious Disease Reporting Information System in China between 2004 and 2019. Of them 16 BIDs are divided into four categories, respiratory transmitted diseases (RTDs, 6 diseases), direct contact/fecal-oral transmitted diseases (DCFTDs, 3 diseases), blood-borne/sexually transmitted diseases (BSTDs, 2 diseases), and zoonotic and vector-borne diseases (ZVDs, 5 diseases), and neonatal tetanus is excluded in the analysis. We characterized the demographic, temporal, and geographical features of the BIDs and examined their changing trends using a joinpoint regression analysis.ResultsDuring 2004‒2019, 28 779 thousand cases of BIDs were reported, with an annualized incidence rate of 134.00 per 100 000. RTDs were the most commonly reported BIDs, accounting for 57.02% of the cases (16 410 639/28 779 000). Average annual percent changes (AAPC) in incidence were - 1.98% for RTDs, - 11.66% for DCFTDs, 4.74% for BSTDs, and 4.46% for ZVDs. Females had a higher incidence of syphilis than males, and other BIDs were more commonly reported in males. Among 0-5-year-olds, the diseases with the largest increases in incidence were pertussis (15.17% AAPC) and scarlet fever (12.05%). Children and students had the highest incidence rates of scarlet fever, pertussis, meningococcal meningitis, and bacillary dysentery. Northwest China had the highest incidence of RTDs, while South and East China had the highest incidences of BSTDs. Laboratory confirmation of BIDs increased from 43.80 to 64.04% during the study period.ConclusionsRTDs and DCFTDs decreased from 2004 to 2019 in China, while BSTDs and ZVDs increased during the same period. Great attention should be paid to BSTDs and ZVDs, active surveillance should be strengthened, and timely control measures should be adopted to reduce the incidence.
| S-EPMC10152418 | biostudies-literature
Project description:In order to accurately grasp the timing for the prevention and control of diseases, we established an artificial neural network model to issue early warning signals. The real-time recurrent learning (RTRL) and extended Kalman filter (EKF) methods were performed to analyse four types of respiratory infectious diseases and four types of digestive tract infectious diseases in China to comprehensively determine the epidemic intensities and whether to issue early warning signals. The numbers of new confirmed cases per month between January 2004 and December 2017 were used as the training set; the data from 2018 were used as the test set. The results of RTRL showed that the number of new confirmed cases of respiratory infectious diseases in September 2018 increased abnormally. The results of the EKF showed that the number of new confirmed cases of respiratory infectious diseases increased abnormally in January and February of 2018. The results of these two algorithms showed that the number of new confirmed cases of digestive tract infectious diseases in the test set did not have any abnormal increases. The neural network and machine learning can further enrich and develop the early warning theory.
| S-EPMC7062078 | biostudies-literature
Project description:BackgroundChina is the largest developing country with a relatively developed public health system. To further prevent and eliminate the spread of infectious diseases, China has listed 39 notifiable infectious diseases characterized by wide prevalence or great harm, and classified them into classes A, B, and C, with severity decreasing across classes. Class A diseases have been almost eradicated in China, thus making class B diseases a priority in infectious disease prevention and control. In this retrospective study, we analyze the spatial distribution patterns of 12 class B notifiable infectious diseases that remain active all over China.MethodsGlobal and local Moran's I and corresponding graphic tools are adopted to explore and visualize the global and local spatial distribution of the incidence of the selected epidemics, respectively. Inter-correlations of clustering patterns of each pair of diseases and a cumulative summary of the high/low cluster frequency of the provincial units are also provided by means of figures and maps.ResultsOf the 12 most commonly notifiable class B infectious diseases, viral hepatitis and tuberculosis show high incidence rates and account for more than half of the reported cases. Almost all the diseases, except pertussis, exhibit positive spatial autocorrelation at the provincial level. All diseases feature varying spatial concentrations. Nevertheless, associations exist between spatial distribution patterns, with some provincial units displaying the same type of cluster features for two or more infectious diseases. Overall, high-low (unit with high incidence surrounded by units with high incidence, the same below) and high-high spatial cluster areas tend to be prevalent in the provincial units located in western and southwest China, whereas low-low and low-high spatial cluster areas abound in provincial units in north and east China.ConclusionDespite the various distribution patterns of 12 class B notifiable infectious diseases, certain similarities between their spatial distributions are present. Substantial evidence is available to support disease-specific, location-specific, and disease-combined interventions. Regarding provinces that show high-high/high-low patterns of multiple diseases, comprehensive interventions targeting different diseases should be established. As to the adjacent provincial units revealing similar patterns, coordinated actions need to be taken across borders.
| S-EPMC5886686 | biostudies-literature
Project description:BackgroundEpidemics of infectious diseases have a great negative impact on people's daily life. How it changes over time and what kind of laws it obeys are important questions that researchers are always interested in. Among the characteristics of infectious diseases, the phenomenon of recrudescence is undoubtedly of great concern. Understanding the mechanisms of the outbreak cycle of infectious diseases could be conducive for public health policies to the government.MethodIn this study, we collected time-series data for nine class C notifiable infectious diseases from 2009 to 2021 using public datasets from the National Health Commission of China. Oscillatory power of each infectious disease was captured using the method of the power spectrum analysis.ResultsWe found that all the nine class C diseases have strong oscillations, which could be divided into three categories according to their oscillatory frequencies each year. Then, we calculated the oscillation power and the average number of infected cases of all nine diseases in the first 6 years (2009-2015) and the next 6 years (2015-2021) since the update of the surveillance system. The change of oscillation power is positively correlated to the change in the number of infected cases. Moreover, the diseases that break out in summer are more selective than those in winter.ConclusionOur results enable us to better understand the oscillation characteristics of class C infectious diseases and provide guidance and suggestions for the government's prevention and control policies.
| S-EPMC9402928 | biostudies-literature