Syndromic surveillance of COVID-19 using crowdsourced data

Desjardins, Michael R.

doi:10.1016/j.lanwpc.2020.100024

Cited by 16 publications

(16 citation statements)

References 10 publications

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“…Additionally, possibility of using both loss of smell and taste as early indicators of emerging COVID-19 wave or a surge in Nigeria would be useful in improving COVID-19 response, such as allocation of already limited testing resources, risk communication and aid decision-making concerning lockdowns and quarantines. 37 The poor predictive capacity of cough or fever alone in the present study is congruent with that in a meta-analysis. 38 …”

Section: Discussionsupporting

confidence: 89%

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study

et al. 2021

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ObjectivesThis study aimed to develop and validate a symptom prediction tool for COVID-19 test positivity in Nigeria.DesignPredictive modelling study.SettingAll Nigeria States and the Federal Capital Territory.ParticipantsA cohort of 43 221 individuals within the national COVID-19 surveillance dataset from 27 February to 27 August 2020. Complete dataset was randomly split into two equal halves: derivation and validation datasets. Using the derivation dataset (n=21 477), backward multivariable logistic regression approach was used to identify symptoms positively associated with COVID-19 positivity (by real-time PCR) in children (≤17 years), adults (18–64 years) and elderly (≥65 years) patients separately.Outcome measuresWeighted statistical and clinical scores based on beta regression coefficients and clinicians’ judgements, respectively. Using the validation dataset (n=21 744), area under the receiver operating characteristic curve (AUROC) values were used to assess the predictive capacity of individual symptoms, unweighted score and the two weighted scores.ResultsOverall, 27.6% of children (4415/15 988), 34.6% of adults (9154/26 441) and 40.0% of elderly (317/792) that had been tested were positive for COVID-19. Best individual symptom predictor of COVID-19 positivity was loss of smell in children (AUROC 0.56, 95% CI 0.55 to 0.56), either fever or cough in adults (AUROC 0.57, 95% CI 0.56 to 0.58) and difficulty in breathing in the elderly (AUROC 0.53, 95% CI 0.48 to 0.58) patients. In children, adults and the elderly patients, all scoring approaches showed similar predictive performance.ConclusionsThe predictive capacity of various symptom scores for COVID-19 positivity was poor overall. However, the findings could serve as an advocacy tool for more investments in resources for capacity strengthening of molecular testing for COVID-19 in Nigeria.

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Section: Discussionsupporting

confidence: 89%

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study

et al. 2021

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“…Yoneoka et al and Nomura et al reported analyses of syndromic data collected through a large-scale (over 350,000 participants) digital surveillance system in Tokyo, Japan for one week. They observed a strong spatial correlation between symptoms and confirmed COVID-19 cases in Tokyo, Japan 12–14 . Here we show similar findings of self-reported symptoms tracking with COVID-19 confirmed cases for long-term data collected over several months in an entire Canadian province.…”

Section: Discussionmentioning

confidence: 89%

“…Participatory surveillance, a subtype of syndromic surveillance, allows individuals to self-report symptoms through phone or internet-based applications 11 . Where testing is incomplete, crowd-sourced symptom data for COVID-19 can be used as a proxy for confirmed case counts, help to estimate the true burden of disease, and forecast future epidemiological trends with strong spatial and temporal resolution [12][13][14] . There have not been any studies comparing trends in self-reported symptoms through participatory syndromic surveillance with laboratory-confirmed COVID-19 cases in Canada.…”

Section: Introductionmentioning

confidence: 99%

Anticipating the curve: can online symptom-based data reflect COVID-19 case activity in Ontario, Canada?

Maharaj

Parker

Hopkins

et al. 2021

Preprint

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BackgroundLimitations in laboratory diagnostic capacity and reporting delays have hampered efforts to mitigate and control the ongoing COVID-19 pandemic globally. Syndromic surveillance of COVID-19 is an important public health tool that can help detect outbreaks, mobilize a rapid response, and thereby reduce morbidity and mortality. The primary objective of this study was to determine whether syndromic surveillance through self-reported COVID-19 symptoms could be a timely proxy for laboratory-confirmed case trends in the Canadian province of Ontario.MethodsWe retrospectively analyzed self-reported symptoms data collected using an online tool – Outbreaks Near Me (ONM) – from April 20th to Oct 11th, 2020 in Ontario, Canada. We estimated the correlation coefficient between the weekly proportion of respondents reporting a COVID-like illness (CLI) to both the weekly number of PCR-confirmed COVID-19 cases and the percent positivity in the same period for the same week and with a one-week lag.ResultsThere were 314,686 responses from 188,783 unique respondents to the ONM symptom survey. Respondents were more likely to be female and be in the 40-59 age demographic compared to the Ontario general population. There was a strong positive correlation between the weekly number of reported cases in Ontario and the percent of respondents reporting CLI each week (r = 0.89, p <0.01) and with a one-week lag (r = 0.89, p <0.01).InterpretationWe demonstrate a strong positive and significant correlation (r = 0.89, p <0.01) between percent of self-reported COVID-like illness and the subsequent week’s COVID-19 cases reported, highlighting that a rise in CLI may precede official statistics by at least 1 week. This demonstrates the utility of syndromic surveillance in predicting near-future disease activity. Digital surveillance systems are low-cost tools that may help measure the burden of COVID-19 in a community if there is under-detection of cases through conventional laboratory diagnostic testing. This additional information can be used to guide a healthcare response and policy decisions.

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“…The implementation of PPGIS in Greece (Antoniou, Vassilakis, & Hatzaki, 2020 ) and India (Debnath & Bardhan, 2020 ) during the spring of 2020 was motivated by the need to rapidly acquire data based on location. These studies find that crowdsourcing applications are important tools for real‐time mapping and monitoring to allow health authorities to make decisions and design effective management approaches (Antoniou et al., 2020 ; Brito et al., 2020 ; Desjardins, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

Franch-Pardo

Desjardins

Barea-Navarro

et al. 2021

Transactions in GIS

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COVID‐19 has infected over 163 million people and has resulted in over 3.9 million deaths. Regarding the tools and strategies to research the ongoing pandemic, spatial analysis has been increasingly utilized to study the impacts of COVID‐19. This article provides a review of 221 scientific articles that used spatial science to study the pandemic published from June 2020 to December 2020. The main objectives are: to identify the tools and techniques used by the authors; to review the subjects addressed and their disciplines; and to classify the studies based on their applications. This contribution will facilitate comparisons with the body of work published during the first half of 2020, revealing the evolution of the COVID‐19 phenomenon through the lens of spatial analysis. Our results show that there was an increase in the use of both spatial statistical tools (e.g., geographically weighted regression, Bayesian models, spatial regression) applied to socioeconomic variables and analysis at finer spatial and temporal scales. We found an increase in remote sensing approaches, which are now widely applied in studies around the world. Lockdowns and associated changes in human mobility have been extensively examined using spatiotemporal techniques. Another dominant topic studied has been the relationship between pollution and COVID‐19 dynamics, which enhance the impact of human activities on the pandemic's evolution. This represents a shift from the first half of 2020, when the research focused on climatic and weather factors. Overall, we have seen a vast increase in spatial tools and techniques to study COVID‐19 transmission and the associated risk factors.

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Syndromic surveillance of COVID-19 using crowdsourced data

Cited by 16 publications

References 10 publications

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study

Anticipating the curve: can online symptom-based data reflect COVID-19 case activity in Ontario, Canada?

A review of GIS methodologies to analyze the dynamics of COVID‐19 in the second half of 2020

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