Seroprevalence of Antibodies to SARS-CoV-2 in Six Sites in the United States, March 23-May 3, 2020

Havers, Fiona; Reed, Carrie; Lim, Travis; Montgomery, Joel M.; Klena, John D.; Hall, Aron J.; Fry, Alicia M.; Cannon, Deborah; Chiang, Cheng‐Feng; Gibbons, Aridth; Krapiunaya, Inna; Morales-Betoulle, Maria; Roguski, Katherine; Rasheed, Mohammad Ata Ur; Freeman, Brandi; Lester, Sandra; Mills, Lisa; Carroll, Darin S.; Owen, S. Michele; Johnson, Jeffrey A.; Semenova, Vera; Schiffer, Jarad; Thornburg, Natalie J.; Blackmore, Carina; Blog, Debra; Dunn, Angela; Lindquist, Scott; Pritchard, Scott; Sosa, Lynn; Turabelidze, George; Wiesman, John; Williams, Randall

doi:10.1101/2020.06.25.20140384

Cited by 134 publications

(142 citation statements)

References 18 publications

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“…Other modeling work ( 16 ) suggests that the feasibility of containing SARS-CoV-2 is highly sensitive to the number of infections that occur prior to initiation of containment efforts. Our estimate that fewer than 10% of local symptomatic infections were detected by surveillance for around a month is consistent with estimates from a serological study ( 36 ) and suggests that a crucial opportunity to limit the impact of SARS-CoV-2 on the United States may have been missed. Although the number of tests administered increased in March ( 9 ), so too did the number of infections and consequently, the demand for testing.…”

Section: Discussionsupporting

confidence: 86%

“…Although other approaches—namely, serological surveys—could have provided more direct answers to the question of how many unobserved infections there were in the weeks following the arrival of SARS-CoV-2 in the United States, serological assays were only beginning to be developed at that time ( 35 ). Even now that results from serological surveys are beginning to emerge ( 36 ), they still do not address the extent of unobserved infections during the specific time frame of our analysis, are not representative of the United States as a whole, and can be sensitive to even small inaccuracies in assay performance ( 37 , 38 ). Relative to other approaches, our approach offers the ability to quickly obtain provisional estimates of the number of unobserved infections early in an epidemic, when there still might be time to act on that information with testing and case isolation.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating unobserved SARS-CoV-2 infections in the United States

Perkins

Cavany

Moore

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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By March 2020, COVID-19 led to thousands of deaths and disrupted economic activity worldwide. As a result of narrow case definitions and limited capacity for testing, the number of unobserved severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections during its initial invasion of the United States remains unknown. We developed an approach for estimating the number of unobserved infections based on data that are commonly available shortly after the emergence of a new infectious disease. The logic of our approach is, in essence, that there are bounds on the amount of exponential growth of new infections that can occur during the first few weeks after imported cases start appearing. Applying that logic to data on imported cases and local deaths in the United States through 12 March, we estimated that 108,689 (95% posterior predictive interval [95% PPI]: 1,023 to 14,182,310) infections occurred in the United States by this date. By comparing the model’s predictions of symptomatic infections with local cases reported over time, we obtained daily estimates of the proportion of symptomatic infections detected by surveillance. This revealed that detection of symptomatic infections decreased throughout February as exponential growth of infections outpaced increases in testing. Between 24 February and 12 March, we estimated an increase in detection of symptomatic infections, which was strongly correlated (median: 0.98; 95% PPI: 0.66 to 0.98) with increases in testing. These results suggest that testing was a major limiting factor in assessing the extent of SARS-CoV-2 transmission during its initial invasion of the United States.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Estimating unobserved SARS-CoV-2 infections in the United States

Perkins

Cavany

Moore

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…Are these low levels of case detection reasonable? For countries where per capita testing is over 100-fold higher than in Madagascar (79.1/100,000 population in July 2020), it is estimated that less than 10% of COVID-19 cases have been detected [ 29 ]. Though the precise case detection rates for Madagascar cannot be discerned from available data, there are a number of indicators suggesting that these are lower than the already low rates of Europe or the US.…”

Section: Case Detectionmentioning

confidence: 99%

Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar

Evans

Garchitorena

Rakotonanahary

et al. 2020

Global Health Action

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COVID-19 has wreaked havoc globally with particular concerns for sub-Saharan Africa (SSA), where models suggest that the majority of the population will become infected. Conventional wisdom suggests that the continent will bear a higher burden of COVID-19 for the same reasons it suffers from other infectious diseases: ecology, socio-economic conditions, lack of water and sanitation infrastructure, and weak health systems. However, so far SSA has reported lower incidence and fatalities compared to the predictions of standard models and the experience of other regions of the world. There are three leading explanations, each with different implications for the final epidemic burden: (1) low case detection, (2) differences in epidemiology (e.g. low R 0 ), and (3) policy interventions. The low number of cases have led some SSA governments to relaxing these policy interventions. Will this result in a resurgence of cases? To understand how to interpret the lower-than-expected COVID-19 case data in Madagascar, we use a simple age-structured model to explore each of these explanations and predict the epidemic impact associated with them. We show that the incidence of COVID-19 cases as of July 2020 can be explained by any combination of the late introduction of first imported cases, early implementation of non-pharmaceutical interventions (NPIs), and low case detection rates. We then re-evaluate these findings in the context of the COVID-19 epidemic in Madagascar through August 2020. This analysis reinforces that Madagascar, along with other countries in SSA, remains at risk of a growing health crisis. If NPIs remain enforced, up to 50,000 lives may be saved. Even with NPIs, without vaccines and new therapies, COVID-19 could infect up to 30% of the population, making it the largest public health threat in Madagascar for the coming year, hence the importance of clinical trials and continually improving access to healthcare.

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“…For different countries around the world, several researchers [1][2][3][4][5][6][7] have concluded that the number of people actually infected, or affected, by COVID-19 is far greater than the number of cases actually reported, or detected officially. Recent serological surveys for COVID-19 also indicate that the infected people outnumber detected people by about 12 times in Spain 8 and 6 to 24 times in the USA 9 . Other serological surveys suggest that about 18% of people in London 10 and 23% of the people in New Delhi 11 were already infected by mid-April and early July, respectively, far outnumbering the reported cases.…”

Section: Introductionmentioning

confidence: 99%

Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model

Tiwari

Vyasarayani

Chatterjee

2020

Preprint

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People in many countries are now infected with COVID-19. By now, it is clear that the number of people infected is much more than the number of reported cases. To estimate the infected but undetected/unreported cases using a mathematical model, we can use a parameter called the probability of quarantining an infected individual. This parameter exists in the time-delayed SEIQR model (Scientific Reports, article number: 3505). Two limiting cases of a network of such models are used to estimate the undetected population. The first limit corresponds to the network collapsing onto a single node and is referred to as the mean-$\beta$ model. In the second case, the number of nodes in the network is infinite and results in a continuum model, treating the infectivity as statistically distributed. We use a shifted Pareto distribution to model the infectivity. This distribution has a long tail and incorporates the presence of super-spreaders that contribute to the disease progression. While both the models capture the {\em detected} numbers equally well, the predictions of {\em affected} numbers from the continuum model are more realistic. Results suggest that affected people outnumber detected people by one to two orders of magnitude in Spain, UK, Italy, and Germany.

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Seroprevalence of Antibodies to SARS-CoV-2 in Six Sites in the United States, March 23-May 3, 2020

Cited by 134 publications

References 18 publications

Estimating unobserved SARS-CoV-2 infections in the United States

Estimating unobserved SARS-CoV-2 infections in the United States

Reconciling model predictions with low reported cases of COVID-19 in Sub-Saharan Africa: insights from Madagascar

Data suggest COVID-19 affected numbers greatly exceeded detected numbers, in four European countries, as per a delayed SEIQR model

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