scholarly journals Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study (Preprint)

2020 ◽  
Author(s):  
Wenjun Wang ◽  
Yikai Wang ◽  
Xin Zhang ◽  
Xiaoli Jia ◽  
Yaping Li ◽  
...  
Keyword(s):  
Social Media ◽  
Early Detection ◽  
New Technologies ◽  
Disease Outbreaks ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Early Detection Of Disease ◽  
New Approach ◽  
Novel Coronavirus ◽  
Chinese Social Media

BACKGROUND A novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future. OBJECTIVE This study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media. METHODS WeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020. RESULTS WeChat Index hits for “Feidian” (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority’s outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords “SARS,” “coronavirus,” “novel coronavirus,” “shortness of breath,” “dyspnea,” and “diarrhea,” but these terms were not as meaningful for the early detection of the outbreak as the term “Feidian”. CONCLUSIONS By using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks.

scholarly journals Using WeChat, a Chinese Social Media App, for Early Detection of the COVID-19 Outbreak in December 2019: Retrospective Study

10.2196/19589 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e19589
Author(s):  
Wenjun Wang ◽  
Yikai Wang ◽  
Xin Zhang ◽  
Xiaoli Jia ◽  
Yaping Li ◽  
...  
Keyword(s):  
Social Media ◽  
Early Detection ◽  
New Technologies ◽  
Disease Outbreaks ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Early Detection Of Disease ◽  
New Approach ◽  
Novel Coronavirus ◽  
Chinese Social Media

Background A novel coronavirus, SARS-CoV-2, was identified in December 2019, when the first cases were reported in Wuhan, China. The once-localized outbreak has since been declared a pandemic. As of April 24, 2020, there have been 2.7 million confirmed cases and nearly 200,000 deaths. Early warning systems using new technologies should be established to prevent or mitigate such events in the future. Objective This study aimed to explore the possibility of detecting the SARS-CoV-2 outbreak in 2019 using social media. Methods WeChat Index is a data service that shows how frequently a specific keyword appears in posts, subscriptions, and search over the last 90 days on WeChat, the most popular Chinese social media app. We plotted daily WeChat Index results for keywords related to SARS-CoV-2 from November 17, 2019, to February 14, 2020. Results WeChat Index hits for “Feidian” (which means severe acute respiratory syndrome in Chinese) stayed at low levels until 16 days ahead of the local authority’s outbreak announcement on December 31, 2019, when the index increased significantly. The WeChat Index values persisted at relatively high levels from December 15 to 29, 2019, and rose rapidly on December 30, 2019, the day before the announcement. The WeChat Index hits also spiked for the keywords “SARS,” “coronavirus,” “novel coronavirus,” “shortness of breath,” “dyspnea,” and “diarrhea,” but these terms were not as meaningful for the early detection of the outbreak as the term “Feidian”. Conclusions By using retrospective infoveillance data from the WeChat Index, the SARS-CoV-2 outbreak in December 2019 could have been detected about two weeks before the outbreak announcement. WeChat may offer a new approach for the early detection of disease outbreaks.

scholarly journals WeChat, a Chinese social media, may early detect the SARS-CoV-2 outbreak in 2019

2020 ◽  
Author(s):  
Wenjun Wang ◽  
Yikai Wang ◽  
Xin Zhang ◽  
Yaping Li ◽  
Xiaoli Jia ◽  
...  
Keyword(s):  
Social Media ◽  
Severe Acute Respiratory Syndrome ◽  
Disease Outbreaks ◽  
New Approach ◽  
Daily Data ◽  
Chinese Social Media

AbstractWe plotted daily data on the frequencies of keywords related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from WeChat, a Chinese social media. Using “Feidian”, Chinese abbreviation for SARS, may detect the SARS-CoV-2 outbreak in 2019 two weeks earlier. WeChat offered a new approach to early detect disease outbreaks.

scholarly journals Analysis of Implementation of EWSS (Early Warning Score Systems) Against AvLos (Average Length of Stay) and the Trust of Patients in Inpatient Installation at Jombang General Hospital

2020 ◽  
Vol 3 (2) ◽  
pp. 348-356
Author(s):  
Sutikno Sutikno ◽  
Sandu Siyoto ◽  
Byba Melda Suhita
Keyword(s):  
Quality Of Service ◽  
Early Detection ◽  
Early Warning ◽  
Health Care Quality ◽  
Early Warning Systems ◽  
Care Quality ◽  
Early Warning Score ◽  
Warning Systems ◽  
General Hospitals

Hospitals are required to always improve the quality of service provided to patients. These challenges have forced the hospital to develop its ability to manifest in various aspects of health care quality responsible. One of them by applying the assessment and early detection in patients kegawatan as well as the critical state of activation becomes very important. Quick and proper response to a nurse against the worsening conditions of patients giving a great impact to the quality of the quality of service provided. The purpose of this research is to analyze the implementation of Early Warning systems (EWSS) Score against AvLOS and trust patients in Inpatient installation at Jombang General Hospitals. The research design was analytic observational with a quantitative approach. Research variables i.e. implementation of EWSS as independent variables. AvLos and trust patients as the dependent variable. The population of this entire research nurses in Inpatient installation at Jombang General Hospitals as much as 135 nurses, patients and families of patients who are being treated in Inpatient installation at Jombang General Hospitals Jombang. Samples taken with the cluster random sampling technique as much as 101 respondents. Data is collected with instruments ceklist and processed in coding, editing, tabulating and scoring as well as tested with logistics regression test. Logistic regression results indicate that partially and simultaneously show that the value of p values < 0.05 so that there were the implementation of Early Warning systems (EWSS) Score against AvLOS and trust of the patient, and the simultaneous influence of 83.2%. The existence of implementation of EWSS in patients with good then early detection and response officers can be done in a proper and effective against the condition and the healing of patients and can shorten the day care patients, so that it can affect the confidence and trust family and patient in receiving health services in the hospital

scholarly journals Using Social Media Trends to Provide Disaster Early Warning Systems and Disaster Assessment

2019 ◽  
Vol 8 (12) ◽  
pp. 4510-4516
Keyword(s):  
Social Media ◽  
Early Warning Systems ◽  
Tropical Storm ◽  
Warning Systems ◽  
Huge Number ◽  
Web Based ◽  
Disaster Assessment ◽  
Information Assets ◽  
Informal Communities ◽  
Area Data

Tremors, floods, dry season, and other normal perils cause billions of dollars in monetary misfortunes every year around the globe. A huge number of dollars in philanthropic help, crisis credits, and advancement help are consumed every year. However endeavors to lessen the dangers of normal perils remain generally ungraceful crosswise over various risk types and don't really concentrate on regions at most astounding danger of debacle. Informal communities are assuming an undeniably significant job as early cautioning frameworks, supporting with quick debacle appraisal and post-fiasco recuperation. There is a requirement for both the general population and fiasco help offices to all the more likely see how web based life can be used to survey and react to catastrophic events. This work directs a various leveled multistage investigation dependent on numerous information assets, consolidating internet based life information and monetary misfortunes. This work attracts regard for the way that during a catastrophe, residents go to internet based life and most of tweets contain data about the tropical storm as well as its contact with negative estimation. This paper researches whether the mix of web based life and geo-area data can add to an increasingly proficient early cautioning framework and help with calamity evaluation.

Assessment of Flood Early Warning Systems with Social Media

2021 ◽  
Author(s):  
Thierry Hohmann ◽  
Judit Lienert ◽  
Jafet Andersson ◽  
Darcy Molnar ◽  
Peter Molnar ◽  
...  
Keyword(s):  
Social Media ◽  
Performance Assessment ◽  
Early Warning ◽  
Early Warning Systems ◽  
Probability Of Detection ◽  
Economic Losses ◽  
Warning Systems ◽  
Flood Events ◽  
Spatial Coverage ◽  
De Bruijn

&lt;p&gt;&lt;strong&gt;Introduction&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;Flood early warning systems (FEWS) can reduce casualties and economic losses (UNEP, 2012). The EC Horizon 2020 project FANFAR (www.fanfar.eu) aims to co-develop a FEWS in West Africa together with stakeholders, predicting streamflow and return period threshold exceedance (Andersson et al., 2020). A Multi-Criteria Decision Analysis (MCDA) indicated, that stakeholders find information accuracy especially important, among a broad set of fundamental objectives (Lienert et al., 2020). Social media have the potential to support accuracy assessment by detecting flood events (Lorini et al., 2019; de Bruijn et al., 2019) due to their large spatial coverage (Restrepo-Estrada et al., 2018). We investigated the potential of social media to assess FANFAR forecast accuracy.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Research Approach&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;FANFAR forecasts are based on HYPE, which is a semi-distributed land-cover and sub-catchment based hydrological model (Arheimer et al., 2020). We lumped the forecasted flood risk (FFR) on a country scale and compared it to flood events detected on Twitter, using an algorithm (FEDA) developed by de Bruijn et al. (2019). FEDA detects flood-related tweet bursts based on regionally and temporally adjusted thresholds (de Bruijn et al., 2019). We compared FEDA detected events with floods from the disaster database EM-DAT (https://www.emdat.be/), to find if tweets indicate flooding. We also compared FEDA to the lumped FFR to identify false positives (FP), false negatives (FN), and true positives (TP), from which we deduced the probability of detection (POD) and false alarm rate (FAR). We further calculated the correlation of single flood-related tweets with the lumped FFR and investigated seasonality, lag, and the influence of rainfall.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;The detailed findings are described in Hohmann (2021). FEDA (i.e., tweets) and EM-DAT events (i.e., floods) mostly occurred in the same period. However, FEDA detected shorter and more frequent events than EM-DAT. In the Upper Niger, POD&lt;sub&gt;FEDA&lt;/sub&gt; and FAR&lt;sub&gt;FEDA&lt;/sub&gt; (deduced from FEDA) were of similar order of magnitude as the POD&lt;sub&gt;S&lt;/sub&gt; and FAR&lt;sub&gt;S&lt;/sub&gt; (deduced from streamflow) but were different in the Lower Niger region. This suggests that tweets can be employed additionally to e.g. streamflow timeseries as a complementary way to evaluate accuracy. Correlation analysis between single flood-related tweets and the lumped FFR showed no relationship. We also did not find a systematic influence of seasonality or a lagged response between tweets and FFR. The correlation coefficients between tweets and rainfall ranged from 0.1-0.9, but were mostly non-significant. This suggests that a performance assessment based on single tweets is not (yet) adequate. Also, since FEDA does not differentiate between pluvial and fluvial floods, it is less suited to assess the accuracy of FANFAR. Our findings suggest the need for inclusion of other factors into the performance assessment of FEWSs, such as regional thresholds to identify TP, FP, and FN. Also, rainfall causing pluvial flooding must be considered. Finally, our approach is limited to Twitter. Further research should assess the potential of e.g. Facebook to be included in FEWS performance assessment. The question whether social media, FEWSs, or EM-DAT are correct remains, and is in our opinion best addressed by employing multiple data sources.&lt;/p&gt;

scholarly journals Early Warning Systems (E-Wars) Design for Early Detection of Stroke Incidence

2018 ◽  
Vol 54 (2) ◽  
pp. 136
Author(s):  
Feby Erawantini ◽  
Rinda Nurul Karimah
Keyword(s):  
Risk Factors ◽  
Early Detection ◽  
Literature Review ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Stroke Risk Factor ◽  
Inference Mechanism ◽  
High Blood Glucose ◽  
Stroke Incidence

Stroke is a neurological disease whose occurrence increases from year to year and causes disability and death worldwide. Stroke is caused by many factors or multicausal. This was a qualitative study conducted for one year with system design using prototype method. The prototype method began with the identification of needs, mapping, and then inference mechanism. Identification of needs was based on the literature review and discussion. The literature review from 15 sources consisting of journal articles, books and proceedings was done by comparing, contrasting, criticizing, synthesizing and summarizing. Stroke risk factor discussion were carried out with neurologists. The results of the review and literature discussion found identification of factors that cause stroke, which consisted of hypertension, high blood glucose, cholesterol, heart disease, behavioral factors, such as smoking behavior and alcoholism, stress and other causes. The risk factors of stroke were then mapped in the form of mobile application prototype through inference mechanism. The output in this study was early warning systems (E-WARS) prototype for early detection of stroke occurrence. The prototype results were expected to be used in operations into mobile applications that were beneficial to the public, in particular for self-control and personal risk factors for stroke. It was intended for early screening and early detection of the risk of stroke.

scholarly journals A global database of historic and real-time flood events based on social media

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Jens A. de Bruijn ◽  
Hans de Moel ◽  
Brenden Jongman ◽  
Marleen C. de Ruiter ◽  
Jurjen Wagemaker ◽  
...  
Keyword(s):  
Social Media ◽  
Real Time ◽  
Early Warning Systems ◽  
Radar Data ◽  
Global Scale ◽  
Early Event ◽  
Warning Systems ◽  
Flood Events ◽  
Global Database ◽  
Use Of Social Media

AbstractEarly event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10,000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE were detected. All results are publicly available on www.globalfloodmonitor.org.

scholarly journals The Emerging Science of Very Early Detection of Disease Outbreaks

2001 ◽  
Vol 7 (6) ◽  
pp. 51-59 ◽  
Author(s):  
Michael M. Wagner ◽  
Fu-Chiang Tsui ◽  
Jeremy U. Espino ◽  
Virginia M. Dato ◽  
Dean F. Sitting ◽  
...  
Keyword(s):  
Early Detection ◽  
Disease Outbreaks ◽  
Early Detection Of Disease
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