Asymptomatic and Presymptomatic SARS-CoV-2 Infections in Residents of a Long-Term Care Skilled Nursing Facility — King County, Washington, March 2020

Kimball, Anne; Hatfield, Kelly M; Arons, Melissa; James, Allison; Taylor, Joanne; Spicer, Kevin B.; Bardossy, Ana C.; Oakley, Lisa; Tanwar, Sukarma; Chisty, Zeshan; Bell, Jeneita M.; Methner, Mark M.; Harney, Josh; Jacobs, Jesica R.; Carlson, Christina M.; McLaughlin, Heather P.; Stone, Nimalie D.; Clark, Shauna; Brostrom-Smith, Claire; Page, Libby C.; Kay, Meagan; Lewis, James; Russell, Denny; Hiatt, Brian; Gant, Jessica; Duchin, Jeffrey S.; Clark, Thomas A.; Honein, Margaret A.; Reddy, Sujan; Jernigan, John A.; Baer, Atar; Barnard, Leslie M.; Benoliel, Eileen; Fagalde, Meaghan; Ferro, Jessica; Smith, H; Gonzales, Elysia; Hatley, Noël; Hatt, Grace; Hope, Michaela; Huntington-Frazier, Melinda; Kawakami, Vance; Lenahan, Jennifer L.; Lukoff, Margaret D.; Maier, Emily; McKeirnan, Shelly; Montgomery, Patricia; Morgan, Jennifer L.; Mummert, Laura A.; Pogosjans, Sargis; Riedo, Francis X.; Schwarcz, Leilani; Smith, Daniel; Stearns, Steve; Sykes, Kaitlyn J.; Whitney, Holly; Ali, Hammad; Banks, Michelle; Balajee, Arun; Chow, Eric J; Cooper, Barbara M.; Currie, Dustin W.; Dyal, Jonathan; Healy, Jessica M.; Hughes, Michael S.; McMichael, Temet M.; Nolen, Leisha D.; Olson, Christine K.; Rao, Agam K.; Schmit, Kristine M; Schwartz, Noah G.; Tobolowsky, Farrell; Zacks, Rachael; Zane, Suzanne

doi:10.15585/mmwr.mm6913e1

Cited by 1,043 publications

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“…For example, at another facility (Facility B) that had a subsequent Covid-19 outbreak epidemiologically linked to Facility A, swabbing of all residents revealed that infections with low cycle threshold values (indicating a large quantity of viral RNA) occurred among some residents who did not have symptoms. 19 There was not a complete roster of visitors to Facility A, and it is possible that some infections among visitors were also missed by these surveillance and investigation efforts. Because symptom onset dates were not available in many cases, the epidemic curve is presented by date of report; however, this does not adequately represent the timing of disease onset in the facility, given that the median time from symptom onset to diagnosis in this cohort was 8 days but had considerable variability.…”

Section: Discussionmentioning

confidence: 99%

“…T h e ne w e ngl a nd jou r na l o f m e dicine O n December 31, 2019, China reported a cluster of pneumonia cases of unknown cause that would later be identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). [1][2][3] Patients with the illness, called coronavirus disease 2019 (Covid- 19), frequently present with fever, cough, and shortness of breath within 2 to 14 days after exposure. 4 As of March 23, 2020, there had been 332,930 confirmed cases of Covid-19 reported globally, and 14,510 deaths had been reported.…”

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confidence: 99%

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Epidemiology of Covid-19 in a Long-Term Care Facility in King County, Washington

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Epidemiology of Covid-19 in a Long-Term Care Facility in King County, Washington

et al. 2020

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“…Our model could easily be extended to account for re-infections and predictions might significantly change. There is also a growing body of evidence for pre-symptomatic transmission [40][41][42][43][44] . There is the potential to incorporate this in our models by varying the incubation rate parameter.…”

Section: Implications For Policy Limitations Of the Models And Conclmentioning

confidence: 99%

Importance of untested infectious individuals for the suppression of COVID-19 epidemics

Forbes

Njc.

2020

Preprint

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A mathematical model which accounts for tested and untested infectious individuals is calibrated during the early stages of COVID-19 outbreaks in Germany, the Hubei province, Italy, Spain and the UK. The predicted percentage of untested infected individuals depends on the specific outbreak but we found that they typically represent 50% to 80% of the infected individuals. Even when unreported cases are taken into account, we estimate that less than 8% of the population would have been exposed to SARS-CoV-2 by 09/04/2020 in the analysed outbreaks. These levels are far from the 70-85% needed to ensure herd immunity and we predict a resurgence of infection if ongoing lockdowns in the analysed outbreaks are fully lifted. We propose that partially lifted lockdowns together with fast and thorough testing allowing for quick isolation of both symptomatic and asymptomatic cases could lead to suppression of secondary waves of COVID-19 epidemics.

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“…Because the implications of infection fatality rate and projected deaths are large, the extent of COVID-19 infection under-ascertainment (the multiplier used to arrive from cases to infections) has been a topic of great interest and provided estimates of the number of infections about 1-6-fold higher than the number of cases. [7][8][9][10] The extent of infection under-ascertainment has been difficult to assess because of three biasing processes: (i) cases have been diagnosed with PCR-based tests, which do not provide information about resolved infections; (ii) the majority of cases tested early in the course of the epidemic have been acutely ill and highly symptomatic, while most asymptomatic or mildly symptomatic individuals have not been tested; and (iii) PCR-based testing rates have been highly variable across contexts and over time, leading to noisy relationships between the number of cases and infections. If, in the absence of interventions, the epidemic's early doubling time is estimated to be four days 6,11,12 , then by February 27th, 2020, when the third case was identified in Santa Clara County, the county may have already had 256 infections.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Antibody Seroprevalence in Santa Clara County, California

Bendavid

Mulaney

Sood

et al. 2020

Preprint

448

529

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BackgroundAddressing COVID-19 is a pressing health and social concern. To date, many epidemic projections and policies addressing COVID-19 have been designed without seroprevalence data to inform epidemic parameters. We measured the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County. MethodsOn 4/3-4/4, 2020, we tested county residents for antibodies to SARS-CoV-2 using a lateral flow immunoassay. Participants were recruited using Facebook ads targeting a representative sample of the county by demographic and geographic characteristics. We report the prevalence of antibodies to SARS-CoV-2 in a sample of 3,330 people, adjusting for zip code, sex, and race/ethnicity. We also adjust for test performance characteristics using 3 different estimates: (i) the test manufacturer's data, (ii) a sample of 37 positive and 30 negative controls tested at Stanford, and (iii) a combination of both. ResultsThe unadjusted prevalence of antibodies to SARS-CoV-2 in Santa Clara County was 1.5% (exact binomial 95CI 1.11-1.97%), and the population-weighted prevalence was 2.81% (95CI 2.24-3.37%). Under the three scenarios for test performance characteristics, the population prevalence of COVID-19 in Santa Clara ranged from 2.49% (95CI 1.80-3.17%) to 4.16% (2.58-5.70%). These prevalence estimates represent a range between 48,000 and 81,000 people infected in Santa Clara County by early April, 50-85-fold more than the number of confirmed cases. ConclusionsThe population prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that the infection is much more widespread than indicated by the number of confirmed cases. Population prevalence estimates can now be used to calibrate epidemic and mortality projections.

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Asymptomatic and Presymptomatic SARS-CoV-2 Infections in Residents of a Long-Term Care Skilled Nursing Facility — King County, Washington, March 2020

Cited by 1,043 publications

References 6 publications

Epidemiology of Covid-19 in a Long-Term Care Facility in King County, Washington

Epidemiology of Covid-19 in a Long-Term Care Facility in King County, Washington

Importance of untested infectious individuals for the suppression of COVID-19 epidemics

COVID-19 Antibody Seroprevalence in Santa Clara County, California

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