An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Clemente, Leonardo; Liautaud, Parker; Kaashoek, Justin; Link, Nicholas; Nguyen, André T.; Lu, Fred; Huybers, Peter; Resch, Bernd; Havas, Clemens; Petutschnig, Andreas; Davis, Jessica G.; Chinazzi, Matteo; Hanage, William P.; Vespignani, Alessandro; Santillana, Mauricio

doi:10.48550/arxiv.2007.00756

2020

DOI: 10.48550/arxiv.2007.00756

|View full text |Cite

Preprint

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Leonardo Clemente¹,

Parker Liautaud²,

Justin Kaashoek³

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2021

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the interest of economic and social needs, several countries re-opened schools and businesses, and many experienced surges in cases and deaths 39 , often referred to as second and even third waves. At this point, and as vaccines start to be distributed mostly in developed countries, all tools that can help us in identifying outbreaks are of utmost importance and different countries are deploying different measures such as conditional movement and contact tracing apps.For all these reasons, improving fast, online surveillance is even more crucial now than it was in 2009, and there are already several studies on using online data to explain and forecast Covid-19 dynamics [40][41][42][43][44][45] . However, and despite its potential, separating what is media hype from reporting of actual disease cases (be it on Google, Facebook, or any other platform), and understanding their impact on collective attention, has been considered a huge challenge.…”

mentioning

confidence: 99%

“…For all these reasons, improving fast, online surveillance is even more crucial now than it was in 2009, and there are already several studies on using online data to explain and forecast Covid-19 dynamics [40][41][42][43][44][45] . However, and despite its potential, separating what is media hype from reporting of actual disease cases (be it on Google, Facebook, or any other platform), and understanding their impact on collective attention, has been considered a huge challenge.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Learning from pandemics: using extraordinary events can improve disease now-casting models

Mesquita¹,

Vieira²,

Perfeito³

et al. 2021

Preprint

View full text Add to dashboard Cite

Online searches have been used to study different health-related behaviours, including monitoring disease outbreaks. An obvious caveat is that several reasons can motivate individuals to seek online information and models that are blind to people's motivations are of limited use and can even mislead. This is particularly true during extraordinary public health crisis, such as the ongoing pandemic, when fear, curiosity and many other reasons can lead individuals to search for healthrelated information, masking the disease-driven searches. However, health crisis can also offer an opportunity to disentangle between different drivers and learn about human behavior. Here, we focus on the two pandemics of the 21st century (2009-H1N1 flu and Covid-19) and propose a methodology to discriminate between search patterns linked to general information seeking (media driven) and search patterns possibly more associated with actual infection (disease driven). We show that by learning from such pandemic periods, with high anxiety and media hype, it is possible to select online searches and improve model performance both in pandemic and seasonal settings. Moreover, and despite the common claim that more data is always better, our results indicate that lower volume of the right data can be better than including large volumes of apparently similar data, especially in the long run. Our work provides a general framework that can be applied beyond specific events and diseases, and argues that algorithms can be improved simply by using less (better) data. This has important consequences, for example, to solve the accuracy-explainability trade-off in machine-learning.However, if we could decouple searches mostly driven by media, anxiety, or curiosity, from the ones related with actual disease, we could not only improve disease monitoring, we could also deepen our understanding of online human behavior. In the case of Google search trends, identifying what terms are more correlated with media exposure and reducing their influence in the model is crucial to correct past errors.In this paper, we propose that the characteristics that make pandemics unique and hard to now-cast, such as media hype, can also be used as opportunities for two main reasons: 1) as pandemics tend to exacerbate behaviors, the noise (media) is of the same order of magnitude as the signal (cases), making it more visible, allowing us to discriminate between the two; and 2) because information seeking becomes less common as the pandemic progresses 18, 28 and these different dynamics can be used when selecting the search terms. In fact, instead of ignoring pandemic periods, studying what happens during the worst possible moment can help us understand which are the search-terms more associated with the disease and the ones that were prompted by media exposure. This solution might avoid over-fitting and enable the predictive model to be more robust over time, especially during seasonal events. Therefore, we focus on the only two XXI century WHO declared pandemics and aim ...

show abstract

mentioning

confidence: 99%

mentioning

confidence: 99%

Learning from pandemics: using extraordinary events can improve disease now-casting models

Mesquita¹,

Vieira²,

Perfeito³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time

Cited by 1 publication

References 0 publications

Learning from pandemics: using extraordinary events can improve disease now-casting models

Learning from pandemics: using extraordinary events can improve disease now-casting models

Contact Info

Product

Resources

About