+1 Recommend
1 collections
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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


      Read this article at

          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.


          In early 2020, delays in availability of diagnostic testing for COVID-19 prompted questions about the extent of unobserved community transmission in the United States. We quantified unobserved infections in the United States during this time using a stochastic transmission model. Although precision of our estimates is limited, we conclude that many more thousands of people were infected than were reported as cases by the time a national emergency was declared and that fewer than 10% of locally acquired, symptomatic infections in the United States may have been detected over a period of a month. This gap in surveillance during a critical phase of the epidemic resulted in a large, unobserved reservoir of infection in the United States by early March.


          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.

          Related collections

          Most cited references37

          • Record: found
          • Abstract: found
          • Article: not found

          Clinical Characteristics of Coronavirus Disease 2019 in China

          Abstract Background Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected patients. Methods We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death. Results The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission. Conclusions During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.)
            • Record: found
            • Abstract: found
            • Article: not found

            Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

            Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/mL (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
              • Record: found
              • Abstract: found
              • Article: found

              Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases From the Chinese Center for Disease Control and Prevention


                Author and article information

                Proc Natl Acad Sci U S A
                Proc. Natl. Acad. Sci. U.S.A
                Proceedings of the National Academy of Sciences of the United States of America
                National Academy of Sciences
                8 September 2020
                21 August 2020
                21 August 2020
                : 117
                : 36
                : 22597-22602
                [1] aDepartment of Biological Sciences, University of Notre Dame , Notre Dame, IN 46556;
                [2] bEck Institute for Global Health, University of Notre Dame , Notre Dame, IN 46556
                Author notes
                2To whom correspondence may be addressed. Email: taperkins@ 123456nd.edu .

                Edited by Nils Chr. Stenseth, University of Oslo, Oslo, Norway, and approved July 21, 2020 (received for review March 24, 2020)

                Author contributions: T.A.P. designed research; T.A.P., S.M.C., S.M.M., and A.L. performed research; S.M.C., S.M.M., and R.J.O. analyzed data; and T.A.P., S.M.C., S.M.M., R.J.O., and M.P. wrote the paper.

                1T.A.P., S.M.C., and S.M.M. contributed equally to this work.

                Author information
                Copyright © 2020 the Author(s). Published by PNAS.

                This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).

                Page count
                Pages: 6
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: DEB 2027718
                Award Recipient : T. Alex Perkins Award Recipient : Sean M Cavany Award Recipient : Sean M Moore Award Recipient : Rachel J Oidtman Award Recipient : Anita Lerch Award Recipient : Marya Poterek
                Funded by: National Science Foundation (NSF) 100000001
                Award ID: CBET 2027752
                Award Recipient : T. Alex Perkins Award Recipient : Sean M Cavany Award Recipient : Sean M Moore Award Recipient : Rachel J Oidtman Award Recipient : Anita Lerch Award Recipient : Marya Poterek
                Biological Sciences
                Population Biology
                Custom metadata

                coronavirus,emerging infectious disease,mathematical modeling,public health surveillance,silent transmission


                Comment on this article