Academic literature on the topic 'Epidemic event extraction'
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Journal articles on the topic "Epidemic event extraction"
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Lejeune, Gaël, Romain Brixtel, Antoine Doucet, and Nadine Lucas. "Multilingual event extraction for epidemic detection." Artificial Intelligence in Medicine 65, no. 2 (October 2015): 131–43. http://dx.doi.org/10.1016/j.artmed.2015.06.005.
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Han, Xuehua, Juanle Wang, Min Zhang, and Xiaojie Wang. "Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China." International Journal of Environmental Research and Public Health 17, no. 8 (April 17, 2020): 2788. http://dx.doi.org/10.3390/ijerph17082788.
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The outbreak of Corona Virus Disease 2019 (COVID-19) is a grave global public health emergency. Nowadays, social media has become the main channel through which the public can obtain information and express their opinions and feelings. This study explored public opinion in the early stages of COVID-19 in China by analyzing Sina-Weibo (a Twitter-like microblogging system in China) texts in terms of space, time, and content. Temporal changes within one-hour intervals and the spatial distribution of COVID-19-related Weibo texts were analyzed. Based on the latent Dirichlet allocation model and the random forest algorithm, a topic extraction and classification model was developed to hierarchically identify seven COVID-19-relevant topics and 13 sub-topics from Weibo texts. The results indicate that the number of Weibo texts varied over time for different topics and sub-topics corresponding with the different developmental stages of the event. The spatial distribution of COVID-19-relevant Weibo was mainly concentrated in Wuhan, Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Chengdu-Chongqing urban agglomeration. There is a synchronization between frequent daily discussions on Weibo and the trend of the COVID-19 outbreak in the real world. Public response is very sensitive to the epidemic and significant social events, especially in urban agglomerations with convenient transportation and a large population. The timely dissemination and updating of epidemic-related information and the popularization of such information by the government can contribute to stabilizing public sentiments. However, the surge of public demand and the hysteresis of social support demonstrated that the allocation of medical resources was under enormous pressure in the early stage of the epidemic. It is suggested that the government should strengthen the response in terms of public opinion and epidemic prevention and exert control in key epidemic areas, urban agglomerations, and transboundary areas at the province level. In controlling the crisis, accurate response countermeasures should be formulated following public help demands. The findings can help government and emergency agencies to better understand the public opinion and sentiments towards COVID-19, to accelerate emergency responses, and to support post-disaster management.
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Saba, Djamel, Abdelkader Hadidi, Omar Cheikhrouhou, Monia Hamdi, and Habib Hamam. "Development of an Ontology-Based Solution to Reduce the Spread of Viruses." Applied Sciences 12, no. 22 (November 21, 2022): 11839. http://dx.doi.org/10.3390/app122211839.
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With the sudden emergence of many dangerous viruses in recent years and with their rapid transmission and danger to individuals, most countries have adopted several strategies, such as closure and social distancing, to control the spread of the virus in the population. In parallel with all these precautions, scientific laboratories are working on developing the appropriate vaccine, which in many cases takes many years. Until then, it is necessary to resort to many solutions, including solutions that rely on information technologies and artificial intelligence (AI). In this context, this paper proposes a new solution based on the ontology and rules of intelligent reasoning. Initially, the virus environment is analyzed, followed by the extraction and editing of the main elements of the ontology using the “Protégé” software. In the last step, the proposed solution is tested, by choosing the city of Adrar in southwestern Algeria, which was particularly affected by COVID-19. Three scenarios were shown for different cases. The efficiency of the proposed solution was confirmed through the instructions it provides in the event of symptoms appearing in a person. In addition, this solution helps the competent authorities know the location and extent of the epidemic by informing the local communities.
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Abdelkoui, Feriel, and Mohamed-Khireddine Kholladi. "Extracting Criminal-Related Events from Arabic Tweets." Journal of Information Technology Research 10, no. 3 (July 2017): 34–47. http://dx.doi.org/10.4018/jitr.2017070103.
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Recently, Twitter as one of social networks has been considered as a rich source of spatio-temporal information and significant revenue for mining data. Event detection from tweets can help to predict more serious real-world events. Such as: criminal events, natural hazards, and the spread of epidemics. Etc. This paper deals with event-based extraction for criminal incidents from Arabic tweets. It presents a framework that supports automated extraction of spatial and temporal information from tweets. The proposed approach is based on combining various indicators, including the names of places and temporal expressions that appear in the tweet message, related tweeting time, and additional locations from the user's profile. The effectiveness of the system was evaluated in term of recall, precision and f-measure.
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Libin, Pieter J. K., Lander Willem, Timothy Verstraeten, Andrea Torneri, Joris Vanderlocht, and Niel Hens. "Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics." PLOS Computational Biology 17, no. 3 (March 9, 2021): e1008688. http://dx.doi.org/10.1371/journal.pcbi.1008688.
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Outbreaks of SARS-CoV-2 are threatening the health care systems of several countries around the world. The initial control of SARS-CoV-2 epidemics relied on non-pharmaceutical interventions, such as social distancing, teleworking, mouth masks and contact tracing. However, as pre-symptomatic transmission remains an important driver of the epidemic, contact tracing efforts struggle to fully control SARS-CoV-2 epidemics. Therefore, in this work, we investigate to what extent the use of universal testing, i.e., an approach in which we screen the entire population, can be utilized to mitigate this epidemic. To this end, we rely on PCR test pooling of individuals that belong to the same households, to allow for a universal testing procedure that is feasible with the limited testing capacity. We evaluate two isolation strategies: on the one handpool isolation, where we isolate all individuals that belong to a positive PCR test pool, and on the other handindividual isolation, where we determine which of the individuals that belong to the positive PCR pool are positive, through an additional testing step. We evaluate this universal testing approach in theSTRIDEindividual-based epidemiological model in the context of the Belgian COVID-19 epidemic. As the organisation of universal testing will be challenging, we discuss the different aspects related to sample extraction and PCR testing, to demonstrate the feasibility of universal testing when a decentralized testing approach is used. We show through simulation, that weekly universal testing is able to control the epidemic, even when many of the contact reductions are relieved. Finally, our model shows that the use of universal testing in combination with stringent contact reductions could be considered as a strategy to eradicate the virus.
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Pisano, Carlo. "Strategies for Post-COVID Cities: An Insight to Paris En Commun and Milano 2020." Sustainability 12, no. 15 (July 22, 2020): 5883. http://dx.doi.org/10.3390/su12155883.
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In recent times, many infectious diseases have been spreading at an increasing scale and frequency. There is a common agreement in the literature that our cities should be prepared in the future to react promptly to epidemics, but the way in which this preparedness should be shaped is still an open question. This study aims to introduce a series of factors that should be taken into consideration in building a working framework to define and evaluate strategies for post-COVID cities. Through the use of the mutual learning methodology, this contribution draws on the concept of the epidemic prevention area (EPA) proposed by a research team at the School of Architecture, Southeast University (SEU) in China together with the UNESCO Chair in Cultural Resource Management, as an urban responding system to the COVID-19 epidemic, extracting its main factors and comparing them with two European post-COVID urban strategies: The Paris en Commun and Milano 2020. Research findings highlight that three factors—decentralization of facilities, hierarchization of the transport system and public services, and redundancy of public and semipublic functions—appeared to be particularly relevant in post-COVID cities, to promptly face future epidemic events, while improving their quality, equity, and resilience.
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Adamson, Rebecca. "Vulnerabilities of Women in Extractive Industries." ANTYAJAA: Indian Journal of Women and Social Change 2, no. 1 (June 2017): 24–31. http://dx.doi.org/10.1177/2455632717714134.
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Perhaps it should not be surprising that sex crimes, the sex trade and anti-woman violence, have become major and predictable by-products of oil, gas and mining extraction operations. After all, mining and drilling camps attract hundreds, even thousands of mostly male workers, typically housed in makeshift ‘man camps’. It is a global epidemic. This article looks at the market trends among investors who look at social performance as well as financial performance. It includes a case study on the difference in financial performance between the oil, gas and mining companies that uphold Indigenous peoples’ rights and those companies that do not. The results indicate that for the extractive industry and its investors, doing what is right and doing what pays are one and the same when it comes to Indigenous peoples’ rights. This article proposes that it would be the same for women’s rights and that as governments increasingly prove incapable or unwilling to protect women, we need to turn to the market and make our voices heard. What is needed are the metrics and analytical tools for assessing the impact and financial risks a company can incur, when it fails to recognize women’s rights.
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Omoniwa, D. O., C. N. Chinyere, E. R. Agusi, N. Mkpuma2, J. S. Oyetunde, O. E. Igah, J. Adole, A. M. Adidu-Omoniwa, and C. A. Meseko. "Serological and molecular investigation of canine influenza virus in Plateau State, Nigeria." Sokoto Journal of Veterinary Sciences 20, no. 3 (October 17, 2022): 212–15. http://dx.doi.org/10.4314/sokjvs.v20i3.8.
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Canine influenza is a highly contagious respiratory infection of dogs caused by the Influenza A Virus (IAV), characterized by cough, sneeze, nasal secretions, and inappetence. Infections can be mild, severe or fatal. Aquatic birds constitute a natural reservoir for IAV, which is transmitted to terrestrial birds, including poultry. IAV has also emerged in other mammalian species, including humans, swine, horses, and dogs. IAV epidemics in dogs are a recent development. Commonly detected Canine Influenza Virus (CIV) strains are A/H3N2 and A/H3N8 from avian and equine influenza, respectively. Nigeria’s agro-ecology witnessed widespread circulation of avian influenza since 2006, and recent outbreaks of equine influenza in 2018/2019 raise the possibility of inter-species transmission to dogs. To investigate canine Influenza in Plateau State, we collected 113 nasal swabs and 270 sera samples from dogs in clinics, live dog markets, and during dog vaccination campaigns. After extracting nucleic acid with the Qiagen kit, RT-PCR analysed swabs for the Influenza A matrix gene. Sera samples were screened by Enzyme-Linked Immunosorbent Assay before subtyping a cross-section for H3 antibody by Hemagglutination Inhibition. No matrix gene was amplified from extracted nucleic acid from the nasal swabs. Though few sera were reactive to influenza A nucleoprotein, none was positive for influenza A/H3. The H3N8 strain of equine influenza virus first caused an epidemic in dogs in 1999 in the United States. Subsequently, avian-origin H3N2 CIV emerged in dogs in China and South Korea in 2005. Past CIV epidemics arose from a single cross-species transmission of H3N8 subtype from a mammalian intermediate host and the H3N2 subtype from an avian reservoir. Even though this limited investigation did not detect CIV in Plateau State, the potential remains because of the persistent circulation of avian, swine, and equine Influenza in Nigeria, which requires more extensive virological and serological surveillance.
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Park, Chulmin, Won-Bok Kim, Sung-Yeon Cho, Eun-Jee Oh, Hyeyoung Lee, Kyungjoon Kang, Yoonsuk Lee, and Dong-Gun Lee. "A Simple Method for the Design and Development of Flavivirus NS1 Recombinant Proteins Using an In Silico Approach." BioMed Research International 2020 (February 13, 2020): 1–13. http://dx.doi.org/10.1155/2020/3865707.
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Even in countries that are currently not facing a flavivirus epidemic, the spread of mosquito-borne flaviviruses presents an increasing public threat, owing to climate change, international travel, and other factors. Many of these countries lack the resources (viral strains, clinical specimens, etc.) needed for the research that could help cope with the threat imposed by flaviviruses, and therefore, an alternative approach is needed. Using an in silico approach to global databases, we aimed to design and develop flavivirus NS1 recombinant proteins with due consideration towards antigenic variation. NS1 genes analyzed in this study included a total of 6,823 sequences, from Dengue virus (DENV), Japanese encephalitis virus (JEV), West Nile virus (WNV), Zika virus (ZIKV), and Yellow fever virus (YKV). We extracted and analyzed 316 DENV NS1 sequence types (STs), 59 JEV STs, 75 WNV STs, 30 YFV STs, and 43 ZIKV STs using a simple algorithm based on phylogenetic analysis. STs were reclassified according to the variation of the major epitope by MHC II binding. 78 DENV epitope type (EpT), 29 JEV EpTs, 29 WNV EpTs, 12 YFV EpTs, and 5 ZIKV EpTs were extracted according to their major epitopes. Also, frequency results showed that there were dominant EpTs in all flavivirus. Fifteen STs were selected and purified for the expression of recombinant antigen in Escherichia coli by sodium dodecyl sulfate extraction. Our study details a novel in silico approach for the development of flavivirus diagnostics, including a simple way to screen the important peptide regions.
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Gagarina, G. Yu, L. S. Arkhipova, and D. A. Sizova. "Labour Productivity as Indicator of Regional Economy Efficiency." Vestnik of the Plekhanov Russian University of Economics, no. 6 (December 22, 2021): 83–92. http://dx.doi.org/10.21686/2413-2829-2021-6-83-92.
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The interrelation of labour productivity and economic situation in regions of the Russian Federation is really acute in current conditions, when such destructive factors as sanctions, inflation, instability on global markets due to coronavirus epidemic take place. Therefore, the key goal of the research is to estimate labour productivity in regions as a factor of economy efficiency. Among methods of research it is possible to mention comparative analysis of the indicator, typology of regions by a number of parameters in order to identify regional differentiation. It is assumed that the highest labour productivity is typical of raw-materials regions, which specialize on extraction and export of mineral resources. However, we should take into account that natural and climatic factors in this group of regions increase labour costs and therefore, cut labour productivity. The authors show that in regions with conventional specialization on manufacturing industry (when the raw-materials sector retains a certain importance) the said indicator demonstrates a high value, even in spite of some unfavorable trends on labour market. The authors used the following materials and research findings for preparing the article: results of deep interviews of employment service workers in the Ryazan region conducted in 2021 within the frames of the research ‘Digitalization of Labour Market and Employment in Russia: Trends and Mechanisms of Development’; materials of expert discussions and surveys of employment service heads in regions of the Central Federal District within the frames of the research ‘Organizational and Finance Mechanisms to Support Population Employment in 2021-2023, which Aims at Unemployment Reduction’
Dissertations / Theses on the topic "Epidemic event extraction"
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Mutuvi, Stephen. "Epidemic Event Extraction in Multilingual and Low-resource Settings." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS044.
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L'extraction d'événements épidémiques a pour but d'extraire de textes des incidents d'importance pour la santé publique, tels que des épidémies. Alors que l'extraction d'événements a fait l'objet de recherches approfondies pour les langues à fortes ressources comme l'anglais, les systèmes existants d'extraction d'événements épidémiques ne sont pas optimaux pour les contextes multilingues à faibles ressources en raison de la rareté des données d'entraînement. Tout d'abord, nous nous attaquons au problème de la rareté des données en transformant et en annotant un ensemble de données multilingues existantes au niveau des documents en un ensemble de données annotées au niveau des jetons, adapté à l'apprentissage supervisé des séquences. Ensuite, nous formulons la tâche d'extraction d'événements comme une tâche d'étiquetage de séquences et nous utilisons l'ensemble de données annotées au niveau des jetons pour entraîner des modèles supervisés d'apprentissage automatique et profond pour l'extraction d'événements épidémiques. Les résultats montrent que les modèles linguistiques pré-entraînés ont produit la meilleure performance globale dans toutes les langues évaluées. Troisièmement, nous proposons une technique d'adaptation au domaine en incluant des entités épidémiologiques (noms de maladies et lieux) dans le vocabulaire des modèles pré-entraînés. L'incorporation de ces entités a eu un impact positif sur la qualité de la tokénisation, contribuant ainsi à l'amélioration des performances du modèle. Enfin, nous évaluons l'auto-formation et observons que l'approche est légèrement plus performante que les modèles formés par apprentissage superviséEpidemic event extraction aims to extract incidents of public health importance from text, such as disease outbreaks. While event extraction has been extensively researched for high-resource languages such as English, existing systems for epidemic event extraction are sub-optimal for low-resource, multilingual settings due to training data scarcity. First, we tackle the data scarcity challenge by transforming and annotating an existing document-level multilingual dataset into a token-level annotated dataset suitable for supervised sequence learning. Second, we formulate the event extraction task as a sequence labeling task and utilize the token-level annotated dataset to train supervised machine and deep learning models for epidemic event extraction. The results show that pre-trained language models produced the best overall performance across all the evaluated languages. Third, we propose a domain adaptation technique by including epidemiological entities (disease names and locations) in the vocabulary of pre-trained models. Incorporating the entities positively impacted the tokenization quality, contributing to model performance improvement. Finally, we evaluate self-training and observe that the approach performs marginally better than models trained using supervised learning
Books on the topic "Epidemic event extraction"
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Bianconi, Ginestra. Multilayer Networks. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198753919.001.0001.
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Multilayer networks are formed by several networks that interact with each other and co-evolve. Multilayer networks include social networks, financial markets, transportation systems, infrastructures and molecular networks and the brain. The multilayer structure of these networks strongly affects the properties of dynamical and stochastic processes defined on them, which can display unexpected characteristics. For example, interdependencies between different networks of a multilayer structure can cause cascades of failure events that can dramatically increase the fragility of these systems; spreading of diseases, opinions and ideas might take advantage of multilayer network topology and spread even when its single layers cannot sustain an epidemic when taken in isolation; diffusion on multilayer transportation networks can significantly speed up with respect to diffusion on single layers; finally, the interplay between multiplexity and controllability of multilayer networks is a problem with major consequences in financial, transportation, molecular biology and brain networks. This field is one of the most prosperous recent developments of Network Science and Data Science. Multilayer networks include multiplex networks, multi-slice temporal networks, networks of networks, interdependent networks. Multilayer networks are characterized by having a highly correlated multilayer network structure, providing a significant advantage for extracting information from them using multilayer network measures and centralities and community detection methods. The multilayer network dynamics (including percolation, epidemic spreading, diffusion, synchronization, game theory and control) is strongly affected by the multilayer network topology. This book will present a comprehensive account of this emerging field.
Book chapters on the topic "Epidemic event extraction"
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Mutuvi, Stephen, Emanuela Boros, Antoine Doucet, Gaël Lejeune, Adam Jatowt, and Moses Odeo. "Multilingual Epidemic Event Extraction." In Lecture Notes in Computer Science, 139–56. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91669-5_12.
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Mutuvi, Stephen, Emanuela Boros, Antoine Doucet, Gaël Lejeune, Adam Jatowt, and Moses Odeo. "Token-Level Multilingual Epidemic Dataset for Event Extraction." In Linking Theory and Practice of Digital Libraries, 55–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86324-1_6.
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Li, Jingwei, Wayne Huang, and Ping Chen. "LDA Based Event Extraction: Detecting Influenza Epidemics Using Microblog." In Data Science, 30–33. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24474-7_5.
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Soussan, Tariq, and Marcello Trovati. "Information Extraction From Social Media for Epidemic Models." In Advances in Data Mining and Database Management, 125–39. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6736-4.ch007.
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Social media platforms are widely used to share opinions, facts, and real-time general information on specific events. This chapter will focus on discussing and presenting data analytics approaches which combine a variety of techniques based on text mining, machine learning, network analysis, and mathematical modelling to assess real-time data extracted from social media and other suitable data related to pandemic outbreaks. The use of real-time insights regarding pandemic outbreaks provides a valuable tool to inform and validate existing modelling techniques and methods. Furthermore, this would also support the discovering process of actionable information to facilitate the decision-making process by enhancing the most informed and appropriate decision, based on the available data. The chapter will also focus on the visualisation and usability of the insight identified during the process to address a non-technical audience.
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Patel, Haris, Riyâz Patel, Djamel Zitouni, Benjamin Guinhouya, Mathilde Fruchart, and Antoine Lamer. "Automated Twitter Extraction and Visual Analytics with Dashboards: Development and First Experimentations." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220562.
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Information found in the social media may help to set up infoveillance and track epidemics, identify high-risk behaviours, or assess trends or feelings about a subject or event. We developed a dashboard to enable novice users to easily and autonomously extract and analyze data from Twitter. Eleven users tested the dashboard and considered the tool to be highly usable and useful. They were able to conduct the research they wanted and appreciated being able to use this tool without having to program.
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Nawaz, M. Saqib, Raza Ul Mustafa, and M. Ikram Ullah Lali. "Role of Online Data from Search Engine and Social Media in Healthcare Informatics." In Advances in Bioinformatics and Biomedical Engineering, 272–93. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-2607-0.ch011.
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Search engines and social media are two different online data sources where search engines can provide health related queries logs and Internet users' discuss their diseases, symptoms, causes, preventions and even suggest treatment by sharing their views, experiences and opinions on social media. This chapter hypothesizes that online data from Google and Twitter can provide vital first-hand healthcare information. An approach is provided for collecting twitter data by exploring contextual information gleaned from Google search queries logs. Furthermore, it is investigated that whether it is possible to use tweets to track, monitor and predict diseases, especially Influenza epidemics. Obtained results show that healthcare institutes and professional's uses social media to provide up-to date health related information and interact with public. Moreover, proposed approach is beneficial for extracting useful information regarding disease symptoms, side effects, medications and to track geographical location of epidemics affected area.
Conference papers on the topic "Epidemic event extraction"
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Sahnoun, Sihem, and Gaël Lejeune. "Multilingual Epidemic Event Extraction : From simple Classification methods to Open Information Extraction (OIE) and Ontology." In International Conference Recent Advances in Natural Language Processing. INCOMA Ltd. Shoumen, BULGARIA, 2021. http://dx.doi.org/10.26615/978-954-452-072-4_138.
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Jarvis, David, Angela Edwards, and Narayan Bhattarai. "Extraction and Production of Keratin-Based Nanofibers for Biomedical Applications." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64501.
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Keratin, a natural biomaterial found within the hair, nails, and epidermis of humans, has shown promise of being a useful material for tissue engineering scaffolds and drug delivery systems, due in part to its favorable biological qualities. The scaffolds generated by electrospinning are useful in proliferating cells, and can even biodegrade over time, reducing the impact on the body and not invoking any adverse tissue response. This research details the extraction process of keratin from human hair, and using electrospinning to weave the keratin into nanofibrous polymers. Using a synthetic polymer solution, for example, polycaprolactone (PCL) in trifluoroethanol (TFE), keratin was easily mixed and successfully electrospun into nanofibers. The fiber formation characteristics and nanofiber morphology was studied under a scanning electron microscope (SEM).
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Najeeb, Abdul, Abhishek Sachan, Ashutosh Tomer, and Ayushi Prakash. "Face Mask Detection Using OpenCV." In International Research Conference on IOT, Cloud and Data Science. Switzerland: Trans Tech Publications Ltd, 2023. http://dx.doi.org/10.4028/p-2ffx83.
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As a biosafety precaution, the World Health Organization (WHO) introduced the wearing of face masks after the COVID-19 epidemic. This posed challenges to existing facial recognition systems, so this study was born. In this publication, we describe how to create a system that allows you to identify people from images, even when they wear a mask. The face detector in OpenCV is used in conjunction with Based on the Mobile NetV2 architecture, a classification model in this way, it is possible to determine whether the face is wearing a mask and where it is situated. To conduct face recognition, A Face Net model is used as a feature extractor and a multilayer feedforward perceptron is used for training facial recognition models using a collection of about 4000+ photographs. Of the images, 52.9 percent came with a face mask and 47.1 percent were without mask. The outcomes of the tests demonstration that determining whether or not someone is wearing a mask is 99.65% accurate. Face recognition accuracy for ten people wearing masks is 99.52 percent, whereas face recognition accuracy without masks is 99.96 percent.
Reports on the topic "Epidemic event extraction"
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Tang, Jiqin, Gong Zhang, Jinxiao Xing, Ying Yu, and Tao Han. Network Meta-analysis of Heat-clearing and Detoxifying Oral Liquid of Chinese Medicines in Treatment of Children’s Hand-foot-mouth Disease：a protocol for systematic review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2022. http://dx.doi.org/10.37766/inplasy2022.1.0032.
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Review question / Objective: The type of study was clinical randomized controlled trial (RCT). The object of study is the patients with HFMD. There is no limit to gender and race. In the case of clear diagnosis standard, curative effect judgment standard and consistent baseline treatment, the experimental group was treated with pure oral liquid of traditional Chinese medicine(A: Fuganlin oral liquid, B: huangzhihua oral liquid, C: Lanqin oral liquid, D: antiviral oral liquid, E: Huangqin oral liquid, F: Pudilan oral liquid, G: Shuanghuanglian oral liquid.)and the control group was treated with ribavirin or any oral liquid of traditional Chinese medicine. The data were extracted by two researchers independently, cross checked and reviewed according to the pre-determined tables. The data extraction content is (1) Basic information (including the first author, published journal and year, research topic). (2) Relevant information (including number of cases, total number of cases, gender, age, intervention measures, course of treatment of the experimental group and the control group in the literature). (3) Design type and quality evaluation information of the included literature. (4) Outcome measures (effective rate, healing time of oral ulcer, regression time of hand and foot rash, regression time of fever, adverse reactions.). The seven traditional Chinese medicine oral liquids are comparable in clinical practice, but their actual clinical efficacy is lack of evidence-based basis. Therefore, the purpose of this study is to use the network meta-analysis method to integrate the clinical relevant evidence of direct and indirect comparative relationship, to make quantitative comprehensive statistical analysis and sequencing of different oral liquid of traditional Chinese medicine with the same evidence body for the treatment of the disease, and then to explore the advantages and disadvantages of the efficacy and safety of different oral liquid of traditional Chinese medicine to get the best treatment plan, so as to provide reference value and evidence-based medicine evidence for clinical optimization of drug selection. Condition being studied: Hand foot mouth disease (HFMD) is a common infectious disease in pediatrics caused by a variety of enteroviruses. Its clinical manifestations are mainly characterized by persistent fever, hand foot rash, oral herpes, ulcers, etc. Because it is often found in preschool children, its immune system development is not perfect, so it is very vulnerable to infection by pathogens and epidemic diseases, resulting in rapid progress of the disease. A few patients will also have neurogenic pulmonary edema Meningitis, myocarditis and other serious complications even lead to death, so effectively improve the cure rate, shorten the course of disease, prevent the deterioration of the disease as the focus of the study. In recent years, traditional Chinese medicine has played an important role in the research of antiviral treatment. Many clinical practices have confirmed that oral liquid of traditional Chinese medicine can effectively play the role of antiviral and improve the body's immunity.