Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data

Albinati, Julio; Meira, Wagner; Pappa, Gisele L.; Teixeira, Mauro Martins; Marques-Toledo, Cecilia

doi:10.1145/3079452.3079464

Cited by 12 publications

(7 citation statements)

References 12 publications

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“…Most of the included studies relied on machine learning methods, particularly supervised learning models, to assess traditional and also novel data streams. These models were useful also for the analysis of traditional data sources, and allowed scientists to harness non-structured data with NLP methods [40,43,48,49,[51][52][53]56,60,65,66,[69][70][71]73,76,77,[79][80][81]84,85,92,98,100,102,105,[110][111][112]114,115,126,127,130,[134][135][136][137][138][139]. Unsupervised learning models were not the method of choice in most studies, possibly because these studies wanted to identify relevant data sources and/or indicators for dengue monitoring and prediction.…”

Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

et al. 2022

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Background Traditionally, dengue surveillance is based on case reporting to a central health agency. However, the delay between a case and its notification can limit the system responsiveness. Machine learning methods have been developed to reduce the reporting delays and to predict outbreaks, based on non-traditional and non-clinical data sources. The aim of this systematic review was to identify studies that used real-world data, Big Data and/or machine learning methods to monitor and predict dengue-related outcomes. Methodology/Principal findings We performed a search in PubMed, Scopus, Web of Science and grey literature between January 1, 2000 and August 31, 2020. The review (ID: CRD42020172472) focused on data-driven studies. Reviews, randomized control trials and descriptive studies were not included. Among the 119 studies included, 67% were published between 2016 and 2020, and 39% used at least one novel data stream. The aim of the included studies was to predict a dengue-related outcome (55%), assess the validity of data sources for dengue surveillance (23%), or both (22%). Most studies (60%) used a machine learning approach. Studies on dengue prediction compared different prediction models, or identified significant predictors among several covariates in a model. The most significant predictors were rainfall (43%), temperature (41%), and humidity (25%). The two models with the highest performances were Neural Networks and Decision Trees (52%), followed by Support Vector Machine (17%). We cannot rule out a selection bias in our study because of our two main limitations: we did not include preprints and could not obtain the opinion of other international experts. Conclusions/Significance Combining real-world data and Big Data with machine learning methods is a promising approach to improve dengue prediction and monitoring. Future studies should focus on how to better integrate all available data sources and methods to improve the response and dengue management by stakeholders.

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Section: Plos Neglected Tropical Diseasesmentioning

confidence: 99%

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

et al. 2022

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“…The use of the epidemiology theory is not common as it can require data that are not available through the use of IBSs owing to its limits in terms of user information (eg, age and location); however, some studies have implemented various epidemiological models [96,121,140,170], as well as epidemiological parameters [34,119].…”

Section: Research Question 3: How Are Internet-based Sources Applied mentioning

confidence: 99%

The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review

2020

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Background Public health surveillance is based on the continuous and systematic collection, analysis, and interpretation of data. This informs the development of early warning systems to monitor epidemics and documents the impact of intervention measures. The introduction of digital data sources, and specifically sources available on the internet, has impacted the field of public health surveillance. New opportunities enabled by the underlying availability and scale of internet-based sources (IBSs) have paved the way for novel approaches for disease surveillance, exploration of health communities, and the study of epidemic dynamics. This field and approach is also known as infodemiology or infoveillance. Objective This review aimed to assess research findings regarding the application of IBSs for public health surveillance (infodemiology or infoveillance). To achieve this, we have presented a comprehensive systematic literature review with a focus on these sources and their limitations, the diseases targeted, and commonly applied methods. Methods A systematic literature review was conducted targeting publications between 2012 and 2018 that leveraged IBSs for public health surveillance, outbreak forecasting, disease characterization, diagnosis prediction, content analysis, and health-topic identification. The search results were filtered according to previously defined inclusion and exclusion criteria. Results Spanning a total of 162 publications, we determined infectious diseases to be the preferred case study (108/162, 66.7%). Of the eight categories of IBSs (search queries, social media, news, discussion forums, websites, web encyclopedia, and online obituaries), search queries and social media were applied in 95.1% (154/162) of the reviewed publications. We also identified limitations in representativeness and biased user age groups, as well as high susceptibility to media events by search queries, social media, and web encyclopedias. Conclusions IBSs are a valuable proxy to study illnesses affecting the general population; however, it is important to characterize which diseases are best suited for the available sources; the literature shows that the level of engagement among online platforms can be a potential indicator. There is a necessity to understand the population’s online behavior; in addition, the exploration of health information dissemination and its content is significantly unexplored. With this information, we can understand how the population communicates about illnesses online and, in the process, benefit public health.

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“…Existing studies have found that social media data are useful in identifying infectious disease outbreaks [12,13], analyzing sentimental reactions and responses [14,15], assessing risks [16], and understanding disease dynamics [17,18]. Before the COVID-19 pandemic, social media data have been used for multiple infectious diseases including Ebola [19,20], Zika [21,22], H1N1 [23,24], dengue [25][26][27], and seasonal influenza [28,29].…”

Section: Social Media For Human Mobility In Epidemiological Studiesmentioning

confidence: 99%

“…Geotagged social media data showed their potential in helping analyze and predict the spread of infectious diseases by deriving human mobility patterns from the data. For example, Albinati et al generated a prediction model for dengue using Twitter data [25]. Kraemer et al derived human mobility patterns from Twitter and analyzed spatiotemporal transmission variation of dengue in Lahore, Pakistan [27].…”

Section: Social Media For Human Mobility In Epidemiological Studiesmentioning

confidence: 99%

Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City

Jiang

Huang

2021

IJGI

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The novel coronavirus disease (COVID-19) pandemic has impacted every facet of society. One of the non-pharmacological measures to contain the COVID-19 infection is social distancing. Federal, state, and local governments have placed multiple executive orders for human mobility reduction to slow down the spread of COVID-19. This paper uses geotagged tweets data to reveal the spatiotemporal human mobility patterns during this COVID-19 pandemic in New York City. With New York City open data, human mobility pattern changes were detected by different categories of land use, including residential, parks, transportation facilities, and workplaces. This study further compares human mobility patterns by land use types based on an open social media platform (Twitter) and the human mobility patterns revealed by Google Community Mobility Report cell phone location, indicating that in some applications, open-access social media data can generate similar results to private data. The results of this study can be further used for human mobility analysis and the battle against COVID-19.

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Enhancement of Epidemiological Models for Dengue Fever Based on Twitter Data

Cited by 12 publications

References 12 publications

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

Data-driven methods for dengue prediction and surveillance using real-world and Big Data: A systematic review

The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review

Spatiotemporal Patterns of Human Mobility and Its Association with Land Use Types during COVID-19 in New York City

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