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 T.; Chinazzi, Matteo; Mustafa, Backtosch; Hanage, William P.; Vespignani, Alessandro; Santillana, Mauricio

doi:10.1126/sciadv.abd6989

Cited by 132 publications

(126 citation statements)

References 69 publications

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“…We recognize that both approaches can be impacted by limitations in data collection. Several publications have noted reporting lags although these are most problematic with respect to death reports rather than daily reported case counts [ 35 – 38 ]. There is clearly the potential for inaccuracies in data collection covering many different jurisdictions.…”

Section: Discussionmentioning

confidence: 99%

A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance

Beard

2021

PLoS ONE

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The outbreak of the COVID-19 disease was first reported in Wuhan, China, in December 2019. Cases in the United States began appearing in late January. On March 11, the World Health Organization (WHO) declared a pandemic. By mid-March COVID-19 cases were spreading across the US with several hotspots appearing by April. Health officials point to the importance of surveillance of COVID-19 to better inform decision makers at various levels and efficiently manage distribution of human and technical resources to areas of need. The prospective space-time scan statistic has been used to help identify emerging COVID-19 disease clusters, but results from this approach can encounter strategic limitations imposed by constraints of the scanning window. This paper presents a different approach to COVID-19 surveillance based on a spatiotemporal event sequence (STES) similarity. In this STES based approach, adapted for this pandemic context we compute the similarity of evolving daily COVID-19 incidence rates by county and then cluster these sequences to identify counties with similarly trending COVID-19 case loads. We analyze four study periods and compare the sequence similarity-based clusters to prospective space-time scan statistic-based clusters. The sequence similarity-based clusters provide an alternate surveillance perspective by identifying locations that may not be spatially proximate but share a similar disease progression pattern. Results of the two approaches taken together can aid in tracking the progression of the pandemic to aid local or regional public health responses and policy actions taken to control or moderate the disease spread.

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Section: Discussionmentioning

confidence: 99%

A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance

Beard

2021

PLoS ONE

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“…Further work has been done to improve the PCR analyses via rapid kit testing (Jorgensen et al, 2020) or use aerosol samples to further detect SARS-CoV-2 presence to prevent direct contact and have analyses for low viral presence [65] . The most recent efforts were done by using extensive databases to predict COVID-19 cases ( [60] and use machine learning tool such as Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network methods to model the COVID-19 outbreak [43] .…”

Section: Societal Issues and Risks Of Covid-19 On Rural / Impoverished Populationsmentioning

confidence: 99%

Challenges to detect SARS-CoV-2 on environmental media, the need and strategies to implement the detection methodologies in wastewaters

Sanchez-Galán

Ureña

Escovar³

et al. 2021

Journal of Environmental Chemical Engineering

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Understanding risks, putting in place preventative methods to seamlessly continue daily activities are essential tools to fight a pandemic. All social, commercial and leisure human activities have an impact on the environmental media. Therefore, to accurately predict the fate and behavior of viruses in the environment, it is necessary to well understand and analyze available detection methods, possible transmission pathways and preventative techniques. The aim of this review is to critically analyze and summarize the research done regarding SARS-COV-2 virus detection, focusing on sampling and laboratory detection methods in environmental media. A special attention will be given to wastewater and sewage sludge. This review has summarized the survival of the virus on surfaces to estimate the risk carried by different environmental media (water, wastewater, air and soil) in order to explain which communities are under higher risk. The critical analysis concludes that the detection of SARS-CoV-2 with current technologies and sampling strategies would reveal the presence of the virus. This information could be used to design systematic sampling points throughout the sewage systems when available, taking into account peak flows and more importantly economic factors on when to sample. Such approaches will be able to provide clues for potential future viral outbreak, saving financial resources by reducing testing necessities for viral detection, hence contributing for more appropriate confinement policies by governments and could be further used to define more precisely post-pandemic or second wave measures if/ when needed.

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“…Several studies attempted to track the volume of health-related online content and associated it with official cases or deaths (3,4). In a recent work by Mackey et al, English Twitter conversations were collected and used in an unsupervised machine learning approach to assess users' self-reports of COVID-19 symptoms, testing, and recovery from disease.…”

Section: Introductionmentioning

confidence: 99%

“…Search engines have been analyzed to monitor COVID-19 activities too (8,9). A study utilized multiple digital data sources, including Google Trends to calculate the probability of exponential growth/decay in COVID-19 activities as early signals of the epidemic in Massachusetts, New York, and California states (4). Another study in the United States found a high correlation between search trends and the number of cases with a 7-day lag (10).…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada

Yousefinaghani

Dara

Mubareka

et al. 2021

Front. Public Health

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The ongoing COVID-19 pandemic has posed a severe threat to public health worldwide. In this study, we aimed to evaluate several digital data streams as early warning signals of COVID-19 outbreaks in Canada, the US and their provinces and states. Two types of terms including symptoms and preventive measures were used to filter Twitter and Google Trends data. We visualized and correlated the trends for each source of data against confirmed cases for all provinces and states. Subsequently, we attempted to find anomalies in indicator time-series to understand the lag between the warning signals and real-word outbreak waves. For Canada, we were able to detect a maximum of 83% of initial waves 1 week earlier using Google searches on symptoms. We divided states in the US into two categories: category I if they experienced an initial wave and category II if the states have not experienced the initial wave of the outbreak. For the first category, we found that tweets related to symptoms showed the best prediction performance by predicting 100% of first waves about 2–6 days earlier than other data streams. We were able to only detect up to 6% of second waves in category I. On the other hand, 78% of second waves in states of category II were predictable 1–2 weeks in advance. In addition, we discovered that the most important symptoms in providing early warnings are fever and cough in the US. As the COVID-19 pandemic continues to spread around the world, the work presented here is an initial effort for future COVID-19 outbreaks.

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An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time

Cited by 132 publications

References 69 publications

A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance

A comparison of prospective space-time scan statistics and spatiotemporal event sequence based clustering for COVID-19 surveillance

Challenges to detect SARS-CoV-2 on environmental media, the need and strategies to implement the detection methodologies in wastewaters

Prediction of COVID-19 Waves Using Social Media and Google Search: A Case Study of the US and Canada

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