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      A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States

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          Abstract

          Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

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          Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study

          Qifang Bi, Yongsheng Wu, Shujiang Mei … (2020)
          Summary Background Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures. Methods From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk. Findings Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20–22). Cases were isolated on average 4·6 days (95% CI 4·1–5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1–2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49–26·33] for household contacts and 7·06 [1·43–34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1–13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The observed reproductive number (R) was 0·4 (95% CI 0·3–0·5), with a mean serial interval of 6·3 days (95% CI 5·2–7·6). Interpretation Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the R. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control. Funding Emergency Response Program of Harbin Institute of Technology, Emergency Response Program of Peng Cheng Laboratory, US Centers for Disease Control and Prevention.
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            Real-time tracking of self-reported symptoms to predict potential COVID-19

            Cristina Menni, Ana M. Valdes, Maxim Freidin … (2020)
            A total of 2,618,862 participants reported their potential symptoms of COVID-19 on a smartphone-based app. Among the 18,401 who had undergone a SARS-CoV-2 test, the proportion of participants who reported loss of smell and taste was higher in those with a positive test result (4,668 of 7,178 individuals; 65.03%) than in those with a negative test result (2,436 of 11,223 participants; 21.71%) (odds ratio = 6.74; 95% confidence interval = 6.31–7.21). A model combining symptoms to predict probable infection was applied to the data from all app users who reported symptoms (805,753) and predicted that 140,312 (17.42%) participants are likely to have COVID-19.
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              Classification of the cutaneous manifestations of COVID‐19: a rapid prospective nationwide consensus study in Spain with 375 cases

              C. Galván Casas, A Català, G. Carretero Hernández … (2020)
              Summary Background Cutaneous manifestations of COVID‐19 disease are poorly characterized. Objectives To describe the cutaneous manifestations of COVID‐19 disease and to relate them to other clinical findings Methods Nationwide case collection survey of images and clinical data. Using a consensus, we described 5 clinical patterns. We later described the association of these patterns with patient demographics, timing in relation to symptoms of the disease, severity, and prognosis. Results Lesions may be classified as acral areas of erythema with vesicles or pustules (Pseudo‐chilblain) (19%), other vesicular eruptions (9%), urticarial lesions (19%), maculopapular eruptions (47%) and livedo or necrosis (6%). Vesicular eruptions appear early in the course of the disease (15% before other symptoms). The pseudo‐chilblain pattern frequently appears late in the evolution of the COVID‐19 disease (59% after other symptoms), while the rest tend to appear with other symptoms of COVID‐19. Severity of COVID‐19 shows a gradient from less severe disease in acral lesions to most severe in the latter groups. Results are similar for confirmed and suspected cases, both in terms of clinical and epidemiological findings. Alternative diagnoses are discussed but seem unlikely for the most specific patterns (pseudo‐chilblain and vesicular). Conclusions We provide a description of the cutaneous manifestations associated with COVID‐19 infection. These may help clinicians approach patients with the disease and recognize paucisymptomatic cases.
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                Author and article information

                Contributors
                Amparo Güemes: aguemes1@jhu.edu
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 25 February 2021
                Publication date PMC-release: 25 February 2021
                Publication date Collection: 2021
                Volume: 11
                Electronic Location Identifier: 4660
                Affiliations
                [1 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, , The Johns Hopkins University, ; 3400 N. Charles Street, 105 Barton Hall, Baltimore, MD 21218 USA
                [2 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Epidemiology, Spatial Science for Public Health Center, , Johns Hopkins Bloomberg School of Public Health, ; Baltimore, MD 21205 USA
                [3 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Anesthesiology and Critical Care Medicine, Neurology, Neurosurgery and Radiology, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21205 USA
                Article
                Publisher ID: 84145
                DOI: 10.1038/s41598-021-84145-5
                PMC ID: 7907397
                PubMed ID: 33633250
                SO-VID: 83df5cde-d812-4b30-94db-487aed454738
                Copyright © © The Author(s) 2021
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 13 August 2020
                Date accepted : 12 February 2021
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2021

                ScienceOpen disciplines: Uncategorized
                Keywords: electrical and electronic engineering,health care,public health
                Data availability:
                ScienceOpen disciplines: Uncategorized
                Keywords: electrical and electronic engineering, health care, public health

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