Dataset Information

Application of Twitter and web news mining in infectious disease surveillance systems and prospects for public health.

ABSTRACT: Aims: With the advancements of communication technology and growing access to social networks, these networks now play an important role in the dissemination of information and news without going through the time-consuming channels of official news networks. Analysis of social networking data is a new, interesting branch of text mining science. This study aimed to develop a text mining technique for extracting information about infectious diseases from tweets and news on social media. Methods: A method called "Fuzzy Algorithm for Extraction, Monitoring, and Classification of Infectious Diseases" (FAEMC-ID) was developed by the use of fuzzy modeling of the Takagi-Sugeno-Kang type. In addition to the real-time classification, the method is able to update its vocabulary for new keywords and visualize the classified data on the world map to mark the high risk areas. Results: As an example, the monitoring was performed for measles-related news items over a 183-hour period from 01/03/2019 (01:00 am) to 08/03/2019 (12:00 pm), which were related to 2,870 tweets from 2,556 users. This monitoring showed that the number of tweets posted from each region ranged from 1 to 47, with the highest number, 47 tweets, belonging to Canada. The origins of most measles-related news were in the Americas and Europe, and they were mostly from the United States and Canada. Conclusion: The performance analysis of the developed method in comparison with other algorithms in the literature demonstrated the excellent precision of the method with a recall ratio of 88.41% and the high inter-correlation of data in each class. The proposed algorithm can also be used in the development of more effective monitoring and tracking systems for other human and even animal health hazards.

SUBMITTER: Jahanbin K

PROVIDER: S-EPMC6997800 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Application of Twitter and web news mining in infectious disease surveillance systems and prospects for public health.

Jahanbin Kia K Rahmanian Fereshte F Rahmanian Vahid V Jahromi Abdolreza Sotoodeh AS

GMS hygiene and infection control 20191202

<b>Aims:</b> With the advancements of communication technology and growing access to social networks, these networks now play an important role in the dissemination of information and news without going through the time-consuming channels of official news networks. Analysis of social networking data is a new, interesting branch of text mining science. This study aimed to develop a text mining technique for extracting information about infectious diseases from tweets and news on social media. <b> ...[more]

PMID: 32047718

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Project description:BackgroundTimeliness is a key performance measure of public health surveillance systems. Timeliness can vary by disease, intended use of the data, and public health system level. Studies were reviewed to describe methods used to evaluate timeliness and the reporting timeliness of National Notifiable Diseases Surveillance System (NNDSS) data was evaluated to determine if this system could support timely notification and state response to multistate outbreaks.MethodsPublished papers that quantitatively measured timeliness of infectious disease surveillance systems operating in the U.S. were reviewed. Median reporting timeliness lags were computed for selected nationally notifiable infectious diseases based on a state-assigned week number and various date types. The percentage of cases reported within the estimated incubation periods for each disease was also computed.ResultsFew studies have published quantitative measures of reporting timeliness; these studies do not evaluate timeliness in a standard manner. When timeliness of NNDSS data was evaluated, the median national reporting delay, based on date of disease onset, ranged from 12 days for meningococcal disease to 40 days for pertussis. Diseases with the longer incubation periods tended to have a higher percentage of cases reported within its incubation period. For acute hepatitis A virus infection, which had the longest incubation period of the diseases studied, more than 60% of cases were reported within one incubation period for each date type reported. For cryptosporidiosis, Escherichia coli O157:H7 infection, meningococcal disease, salmonellosis, and shigellosis, less than 40% of cases were reported within one incubation period for each reported date type.ConclusionPublished evaluations of infectious disease surveillance reporting timeliness are few in number and are not comparable. A more standardized approach for evaluating and describing surveillance system timeliness should be considered; a recommended methodology is presented. Our analysis of NNDSS reporting timeliness indicated that among the conditions evaluated (except for acute hepatitis A infection), the long reporting lag and the variability across states limits the usefulness of NNDSS data and aberration detection analysis of those data for identification of and timely response to multistate outbreaks. Further evaluation of the factors that contribute to NNDSS reporting timeliness is warranted.

Dataset Information

Application of Twitter and web news mining in infectious disease surveillance systems and prospects for public health.

Publications

Application of Twitter and web news mining in infectious disease surveillance systems and prospects for public health.

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