Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Kostkova, Patty; Saigí-Rubió, Francesc; Eguia, Hans; Borbolla, Damián; Verschuuren, Marieke; Hamilton, Clayton; Azzopardi-Muscat, Natasha; Novillo-Ortiz, David

doi:10.3389/fdgth.2021.707902

Cited by 48 publications

(21 citation statements)

References 112 publications

(154 reference statements)

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“…Beyond official surveillance relying on detected case numbers and medical statistics, alternative methods can monitor the unfolding of a pandemic 40 . Some methodologies rely on geo-localized web search and social media tracking to nowcast trends in epidemic-related topics 41,42 .…”

Section: Discussionmentioning

confidence: 99%

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Bokányi¹,

Vizi²,

Koltai³

et al. 2022

Preprint

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Near-real time estimations of the effective reproduction number are among the most important tools to track the progression of a pandemic and to inform policy makers and the general public. However, these estimations rely on reported case numbers, commonly recorded with significant biases. The epidemic outcome is strongly influenced by the dynamics of social contacts, which are neglected in conventional surveillance systems as their real-time observation is challenging. Here, we propose a concept using online and offline behavioral data, recording age-stratified contact matrices at a daily rate. Modeling the epidemic using the reconstructed matrices we dynamically estimate the effective reproduction number during the two first waves of the COVID-19 pandemic in Hungary. Our results demonstrate how behavioral data can be used to build alternative monitoring systems complementing the established public health surveillance. They can identify and provide better signals during periods when official estimates appear unreliable due to observational biases.

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Section: Discussionmentioning

confidence: 99%

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Bokányi¹,

Vizi²,

Koltai³

et al. 2022

Preprint

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“…Social media, such as Twitter, are perceived as an increasingly useful form of real-time data with a geolocation resolution that can be systematically mined, aggregated and analyzed by algorithmic applications to inform public health agencies [30,33,34]. The increasing number of smartphones worldwide and lower policy barriers in many lower-middle income countries (LMICs) also provide an opportunity for the usage of AI-enabled mobile applications in event-based surveillance [18,35].…”

Section: Table 1 Disease Surveillance Systemsmentioning

confidence: 99%

“…BlueDot analyzed data from news reports, airline ticketing and animal disease outbreaks, to predict areas that would be prone to the outbreak, expanding from regions in China [48]. Data sources and AI played an important role in various countries' responses to the pandemic [18].…”

Section: Ai and Public Health Disease Surveillancementioning

confidence: 99%

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Ethical Issues in AI-enabled Disease Surveillance: Perspectives from Global Health

Borda¹,

Molnar²,

Neesham³

et al. 2022

Preprint

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Infectious diseases, as COVID-19 is proving, pose a global health threat in an interconnected world. In the last 20 years, resistant infectious diseases such as SARS, MERS, H1N1, Ebola, Zika and now COVID-19 have been impacting global health defences, and aggressively flourishing within the rise of global travel, urbanization, climate change and ecological degradation. In parallel, this extraordinary episode in global human health highlights the potential for artificial intelligence (AI)-enabled disease surveillance to collect and analyse vast amounts of unstructured and real-time data to inform epidemiological and public health emergency responses. The uses of AI in these dynamic environments are increasingly complex, challenging the potential for human autonomous decisions. In this context, our study of qualitative perspectives will consider a responsible AI framework to explore its potential application to disease surveillance in a global health context. Thus far, there is a gap in the literature in considering these multiple and interconnected levels of disease surveillance and emergency health management through the lens of a responsible AI framework.

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“…The dynamic nature of pandemics, including the stop-and-go cadence of the related public health restrictions, provides a complex moment in time to examine people’s behaviour [ 11 , 12 ]. In understanding the activities of those in the UK during this moment, we gain insights into behaviour change, resilience, what remains unchanged during a pandemic, which would be valuable for policymakers and researchers to prepare for the future public measures and develop digital tools to support the public adapting to the pandemic [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

To Zoom or not to Zoom: A longitudinal study of UK population’s activities during the COVID-19 pandemic

Sullivan

Musah

et al. 2022

PLoS ONE

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This longitudinal study determines the frequency and way of people doing activities from Spring 2020 to Summer 2021 during different phases of the COVID-19 pandemic in the UK. Six online surveys were carried out between April 2020 and July 2021. 4,992 participants were engaged in the cross-sectional study and 203 participants who provided repeat responses were included in the subset sample of prospective cohort analysis. Primary outcomes measured were the frequency and the mode of doing the activities (online or in-person) across sixteen selected activity groups, as defined by the UK National Time Use Survey. The results show that cultural activities, spending time with others, and travelling, were the activities with the largest proportions of frequency and mode changes. The most significant changes occurred from March to June 2020, a period that included the first lockdown. Survey results from this period show a significant decrease among most of the sixteen measured activities. From March to October 2020, a period which spans the first lockdown and its subsequent ease of restrictions, showed the most significant shift from accessing activities in-person to online. Despite ‘Freedom Day’, the July 19th 2021 date in which all restrictions were abolished, it was found that people do cultural activities and group activities at a significantly lower frequency than before the pandemic. In addition, despite a lack of restrictions after this date, more than half of participants access many activities, such as spending time with others, shopping, work and studying, online or hybrid. This study provides an invaluable insight into understanding how people in the UK changed their lifestyle, including what activities they do, and how they accessed those activities in light of the COVID-19 pandemic and related public health policy implemented to address the pandemic. These results may serve as unique evidence for policymakers.

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Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic

Cited by 48 publications

References 112 publications

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Real-time estimation of the effective reproduction number of COVID-19 from behavioral data

Ethical Issues in AI-enabled Disease Surveillance: Perspectives from Global Health

To Zoom or not to Zoom: A longitudinal study of UK population’s activities during the COVID-19 pandemic

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