Proposed conceptual framework of Dengue Active Surveillance System (DASS) in Malaysia

Othman, Mohd Khalit Bin; Danuri, Mohd Shahrul Nizam Mohd

doi:10.1109/icictm.2016.7890783

Cited by 13 publications

(8 citation statements)

References 18 publications

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“…Em [Othman and Danuri 2017] foi proposto um Framework para alerta precoce de surto de dengue na Malásia. Seu objetivo é prever epidemias e em seguida apresentar as informac ¸ões aos usuários via aplicac ¸ão Web ou aplicativo móvel.…”

Section: Trabalhos Relacionadosunclassified

Predição de Casos de Dengue na Cidade de Fortaleza-CE Utilizando Internet das Coisas e Aprendizado de Máquina

Freitas

Rodrigues

2022

Anais Do VI Workshop De Computação Urbana (CoUrb 2022)

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Este trabalho apresenta uma contribuição na área de e-health e utiliza, para isso, ferramentas de Internet das Coisas (IoT) e aplicação de aprendizado de máquina para prever casos de dengue em semanas no futuro. Ele faz uma análise de dados meteorológicos, populacionais e de dengue para identificar valores discrepantes, seleção de variáveis a partir de níveis de correlação de Spearman e Pearson, bem como faz comparação de modelos de aprendizado de máquina utilizando como métricas o erro médio absoluto (MAE) e o coeficiente de determinação R². Este trabalho propõe e implementa uma arquitetura composta por um simulador de estação meteorológica e uma aplicação backend que faz a integração com uma plataforma de IoT chamada dojot que recebe como notificação previsões de casos de dengue.

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Section: Trabalhos Relacionadosunclassified

Predição de Casos de Dengue na Cidade de Fortaleza-CE Utilizando Internet das Coisas e Aprendizado de Máquina

Freitas

Rodrigues

2022

Anais Do VI Workshop De Computação Urbana (CoUrb 2022)

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“…These tweets were classified as "reassuring" or "alarming" based on contextually similar keywords provided by word2vec, an embedding technique based on shallow neural networks. A dengue active surveillance system framework was proposed in [21] as a prediction system aiming to improve upon the passive surveillance systems available in Malaysia. This work used data collected from both weather and flood information and social media, and aggregated them in order to be processed and filtered using keywords.…”

Section: A Epidemic Surveillance and Forecastingmentioning

confidence: 99%

“…Twitter [19,20] Dengue Fever Twitter [21,22] Ebola Twitter [23,85] Weibo [24] H1N1/Swine flu Twitter [25,26] Influenza/Flu Twitter [28][29][30][31][32][33][34]37,38] Sina Weibo, Tancent Weibo [35,39] Zika Twitter [27,36] Reddit [40] MERS Twitter [43] Facebook [43] Multiple Epidemics Twitter [41][42][43] Facebook [43] ML Classification Dengue Fever Twitter [44,45] Influenza/Flu Twitter [47][48][49][50][51][52][53][54][55][56] Facebook [54] Sina Weibo, Tancent Weibo [39,51,56] H1N1/Swine Flu Twitter [46] MERS Twitter [57][58]…”

Section: Covid-19mentioning

confidence: 99%

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

Asaad¹,

Khaouja²,

Ghogho³

et al. 2022

Preprint

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BACKGROUND Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media sites offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. OBJECTIVE The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. METHODS Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. RESULTS Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. CONCLUSIONS Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment.

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“…One analysis of the project revealed that GFT reduced errors in Centers for Disease Control (CDC) predictive models by up to 52.7% compared to CDC data alone (Preis & Moat, 2014). More recent efforts combine sentiment analysis with various supervised and unsupervised Machine Learning (ML) algorithms to predict influenza (Broniatowski et al., 2013), dengue (Othman & Danuri, 2016) and other vector borne diseases (Jain & Kumar, 2018).…”

Section: Introductionmentioning

confidence: 99%

Web crawling of social media and related web platforms to analyze backyard poultry owners responses to the 2018–2020 Newcastle Disease (ND) outbreak in Southern California

Gendreau

Ramsubeik²,

Pitesky

2022

Transbounding Emerging Dis

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As social media becomes an ever‐increasing staple of everyday life and a growing percentage of people turn to community driven platforms as a primary source of information, the data created from these posts can provide a new source of information from which to better understand an event in near real time. The 2018–2020 outbreak of Newcastle Disease (ND) in Southern California is the third outbreak of ND in Southern California within a 50‐year time span. These outbreaks are thought to be primarily driven by non‐commercial poultry (i.e. backyard and game fowl) in the region. Here we employed a commercial ‘web crawling’ tool between June of 2018 and July of 2020 which encompassed the majority of the outbreak in order to collect all available online mentions of ‘virulent Newcastle Disease’ (vND), the terminology commonly used by the California Department of Food and Agriculture (CDFA), United States Department of Agriculture (USDA), and the general public, in relation to the outbreak. A total of 2498 posts in English and Spanish were returned using a Boolean logic‐based string search. While the number of posts was relatively small, their impact as measured by the number of visitors to the website and the number of people viewing the post (where provided) was much larger. Posts with negative sentiment were found to have a larger audience relative to posts with a positive sentiment. In addition, posts with negative sentiment peaked in May of 2019 which preceded the formation of the anti‐depopulation group Save Our Birds (SOB). As the usage and impact of social media grows, the ability to utilize tools to analyze social media may improve both response and outreach‐based strategies for various disease outbreaks including vND in Southern California which has a large non‐commercial poultry population.

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Proposed conceptual framework of Dengue Active Surveillance System (DASS) in Malaysia

Cited by 13 publications

References 18 publications

Predição de Casos de Dengue na Cidade de Fortaleza-CE Utilizando Internet das Coisas e Aprendizado de Máquina

Predição de Casos de Dengue na Cidade de Fortaleza-CE Utilizando Internet das Coisas e Aprendizado de Máquina

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

Web crawling of social media and related web platforms to analyze backyard poultry owners responses to the 2018–2020 Newcastle Disease (ND) outbreak in Southern California

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