Infodemiology for Syndromic Surveillance of Dengue and Typhoid Fever in the Philippines

Espina, Kennedy E.; Estuar, Ma. Regina E.

doi:10.1016/j.procs.2017.11.073

Cited by 23 publications

(18 citation statements)

References 8 publications

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“…K. Espina et al [24] analyzed the 2017 dengue outbreak in the Philippines by using different ML approaches. Their work showed the use of real-time data from OSNSs particularly from microblog platform Twitter, to enhance the current initiatives and to monitor epidemic outbreaks.…”

Section: Ii) Early Detection Of Dengue Outbreak In Tweetsmentioning

confidence: 99%

“…To classify health-related tweets their work has shown a range of dengue rates and typhoid fever in the Philippine by utilizing SVM for classification and regression was for possible disease rate. Based on the Philippines Health Ministry these both outbreaks were highly correlated (e.g., correlation (R) > .75) between tweet messages and monitoring information [24]. According to a recent survey in China, the CDC presented the rate of dengue cases has been increased from 0.00089 to 3.5471 per 10,000 people.…”

Section: Ii) Early Detection Of Dengue Outbreak In Tweetsmentioning

confidence: 99%

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Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding

et al. 2020

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With the massive spike in the use of Online Social Network Sites (OSNSs) platforms such as Web 2.0, microblogs services and online blogs, etc., valuable information in the form of sentiment, thoughts, opinions, as well as epidemic outbreaks, etc. are transferred. With the OSNSs being widely accessible, this work aims at proposing a novel approach for disease (dengue or flu) detection based on social media posts. For this purpose, an automated approach is designed with the help of LSTM (Long Short Term Memory) and word embedding techniques. Then the performance of the proposed approach is validated using a set of standard evaluation matrices. In addition, the effectiveness of the selected models is evaluated with performance measurement techniques. The accuracy of the proposed research approach is evaluated using two word embedding techniques; Word2Vec with Skip-gram (SG) and Word2Vec with Continuous-bag-ofwords (CBOW). Based on the results conducted in this paper the LSTM Word2Vec with CBOW achieved better results compared to LSTM with Word2Vec SG features embedding technique. Our findings prove that the proposed method yields 94% accuracy compared to state-of-the-art approaches. Consequently, LSTM performed better than other leading methods in the detection of disease-infected people in tweets. In the end, spatial analysis is performed to identify the disease infected region.

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Section: Ii) Early Detection Of Dengue Outbreak In Tweetsmentioning

confidence: 99%

Section: Ii) Early Detection Of Dengue Outbreak In Tweetsmentioning

confidence: 99%

Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding

et al. 2020

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“…Their model has been evaluated on 1,000 tweets; machine learning models such as Naïve Bayes (NB) and LDA-based topic modelling were used for analysis. In recent, the 2017 Dengue outbreak has been analyzed in Philippines [33]. In order to classify health related tweets this work has shown a range of Dengue cases and typhoid fever in Philippine using SVM for classification and regression model for possible disease incidence.…”

Section: Related Workmentioning

confidence: 99%

Recurrent Neural Networks With TF-IDF Embedding Technique for Detection and Classification in Tweets of Dengue Disease

et al. 2020

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With the increased usage of Web 2.0 and data-affluent tools such as social media platforms and web blog services, the challenge of extracting public sentiment and disseminating personal health information has become more common than ever in the last decade. This paper proposes a novel model for Dengue disease detection based on social media posts alone. The model does not access any personal information of people or any medical record. The model extracts the presence of a Dengue disease based on tweets only and decides whether it is a general discussion about the disease, and no one is actually infected, or people are actually infected with that disease. This paper uses efficient machine/deep learning approaches to utilize tweets data for automatic and efficient disease detection. Experimental results demonstrate that the proposed model is able to achieve 92% accuracy compared to the current state-of-theart techniques in this domain.

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“…In this new world scenario, as established by Espina [7], it is necessary to find the determinants of disease outbreaks before they occur, to reduce their impact on populations, and one of the great advantages is to obtain information brought by automated systems.…”

Section: Introductionmentioning

confidence: 99%

How reacted USA with the first case of Bubonic Plague on 21 August?

Jec

2021

Preprint

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The present work studies the reactions of citizens from United States of America (USA) during the first cases of Bubonic Plague in the territory on August 2020, during pandemic generated by Covid-19. The interest of the study is analyze the posts of users from all the states of USA following a Text Mining approach. The collection of data is performed through Twitter Application Program Interface (API) of Twitter, considering keywords: bubonic plague and black death. The results show that the states with highest number of coronavirus have more publications/posts than others and the interests around treatments, political issues and public health.

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Infodemiology for Syndromic Surveillance of Dengue and Typhoid Fever in the Philippines

Cited by 23 publications

References 8 publications

Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding

Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding

Recurrent Neural Networks With TF-IDF Embedding Technique for Detection and Classification in Tweets of Dengue Disease

How reacted USA with the first case of Bubonic Plague on 21 August?

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