We conducted the first search on 25 March 2020 and updated it on 20 April and 10 June 2020. We searched the COVID-19 living evidence database [10], which is generated using automated workflow processes [5] to (1) provide daily updates of searches of four electronic databases (Medline PubMed, Ovid Embase, bioRxiv, and medRxiv), using medical subject headings and free-text keywords for SARS-CoV-2 infection and COVID-19; (2) de-duplicate the records; (3) tag records that are preprints; and (4) allow searches of titles and abstracts using Boolean operators. We used the search function to identify studies of asymptomatic or presymptomatic SARS-CoV-2 infection using a search string of medical subject headings and free-text keywords (S1 Text). We also examined articles suggested by experts and the reference lists of retrieved mathematical modelling studies and systematic reviews. Reports from this living rapid systematic review will be updated at 3-monthly intervals, with continuously updated searches.

Reviewers worked in pairs to screen records using an application programming interface in the electronic data capture system (REDCap, Vanderbilt University, Nashville, TN, USA). One reviewer selected potentially eligible studies and a second reviewer verified all included and excluded studies. We reported the identification, exclusion, and inclusion of studies in a flowchart (S1 Fig). The reviewers determined which of the three review questions each study addressed, using the definitions in Box 1. One reviewer extracted data using a pre-piloted extraction form in REDCap, and a second reviewer verified the extracted data using the query system. A third reviewer adjudicated on disagreements that could not be resolved by discussion. We contacted study authors for clarification when the study description was insufficient to reach a decision on inclusion or if reported data in the manuscript were internally inconsistent. The extracted variables included, but were not limited to, study design, country and/or region, study setting, population, age, primary outcomes, and length of follow-up. From empirical studies, we extracted raw numbers of individuals with any outcome and its relevant denominator. From statistical and mathematical modelling studies, we extracted proportions and uncertainty intervals reported by the authors.

Two authors independently assessed the risk of bias. A third reviewer resolved disagreements. For observational epidemiological studies, we adapted the Joanna Briggs Institute Critical Appraisal Checklist for Case Series [11]. The adapted tool included items about inclusion criteria, measurement of asymptomatic status, follow-up of course of disease, and statistical analysis. We added items about selection biases affecting the study population from a tool for the assessment of risk of bias in prevalence studies [12]. For mathematical modelling studies, we used a checklist for assessing relevance and credibility [13].

We used the ‘metaprop’ and ‘metabin’ functions from the ‘meta’ package (version 4.11–0) [14] in R (version 3.5.1) to display the study findings in forest plots and synthesise their findings. The 95% confidence intervals (CIs) for each study are estimated using the Clopper-Pearson method [15]. We examined heterogeneity visually in forest plots. We stratified studies according to the methods used to identify people with asymptomatic SARS-CoV-2 infection and the study setting. To synthesise proportions from comparable studies, in terms of design and population, we used stratified random-effects meta-analysis. For the stratified and overall summary estimates, we calculated prediction intervals, to represent the likely range of proportions that would be obtained in subsequent studies conducted in similar settings [16]. We calculated the secondary attack rate as the number of cases among contacts as a proportion of all close contacts ascertained. We did not account for potential clustering of contacts because the included studies did not report the size of clusters. We compared the secondary attack rate from asymptomatic or presymptomatic index cases with that from symptomatic cases. If there were no events in a group, we added 0.5 to each cell in the 2 × 2 table. We used random-effects meta-analysis with the Mantel-Haenszel method to estimate a summary risk ratio (with 95% CI).

We included 79 studies that reported empirical data about 6,616 people with SARS-CoV-2 infection (1,287 defined as having asymptomatic infection) [17,18,21–23,26–28,31,32,34,36,39–45,47–50,52–54,56–62,64,66–68,70–77,79–90,92–112,114] and one statistical modelling study [24] (Table 1). The sex distribution of the people with asymptomatic infection was reported in 41/79 studies, and the median age was reported in 35/79 studies (Table 1). The results of the studies were heterogeneous (S2 Fig). We defined seven strata, according to the method of selection of asymptomatic status and study settings. Study findings within some of these strata were more consistent (Fig 1). We considered the statistical modelling study of passengers on the Diamond Princess cruise ship passengers [24] separately, because of the different method of analysis and overlap with the study population reported by Tabata and colleagues [27].

Fig 1. Forest plot of proportion (‘Prop.’) of people with asymptomatic SARS-CoV-2 infection, stratified by setting.

In the setting ‘Contact investigations’, in which more than one cluster was reported, clusters are annotated with ‘[cluster]’. The diamond shows the summary estimate and its 95% CI. The red bar and red text show the prediction interval. CI, confidence interval; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

https://doi.org/10.1371/journal.pmed.1003346.g001

The main risks of bias across all categories of empirical studies were in the selection and enrolment of people with asymptomatic infection and mismeasurement of asymptomatic status because of absent or incomplete definitions (S3 Fig). Sources of bias specific to studies in particular settings are discussed with the relevant results.

The overall estimate of the proportion of people who become infected with SARS-CoV-2 and remain asymptomatic throughout the course of infection was 20% (95% CI 17%–25%, 79 studies), with a prediction interval of 3%–67% (Fig 1). One statistical modelling study was based on data from all 634 passengers from the Diamond Princess cruise ship with RT-PCR positive test results [24]. The authors adjusted for the proportion of people who would develop symptoms (right censoring) in a Bayesian framework to estimate that, if all were followed up until the end of the incubation period, the probability of asymptomatic infections would be 17.9% (95% credibility interval [CrI] 15.5%–20.2%).

The summary estimates of the proportion of people with asymptomatic SARS-CoV-2 infection differed according to study setting, although prediction intervals for all groups overlapped. The first three strata in Fig 1 involve studies that reported on different types of contact investigation, which start with an identified COVID-19 case. The studies reporting on single-family clusters (21 estimates from 16 studies in China, n = 102 people with SARS-CoV-2) all included at least one asymptomatic person [17,18,21–23,26,44,49,50,70,73–76,85,110]. The summary estimate was 34% (95% CI 26%–44%, prediction interval 25%–45%). In nine studies that reported on close contacts of infected individuals and aggregated data from clusters of both asymptomatic and symptomatic people with SARS-CoV-2 the summary estimate was 14% (95% CI 8%–23%, prediction interval 2%–53%) [36,47,60,62,66,72,105,108,111]. We included 12 studies (n = 675 people) that reported on outbreak investigations arising from a single symptomatic person or from the country’s first imported cases of people with COVID-19 [32,43,48,58,61,68,71,90,94,95,97,100]. Four of the outbreaks involved nursing homes [58,68,71,94] and four involved occupational settings [43,61,90,95]. The summary estimate of the proportion of asymptomatic SARS-CoV-2 infections was 18% (95% CI 10%–28%, prediction interval 2%–64%).

In seven studies, people with SARS-CoV-2 infection were detected through screening of all people in defined populations who were potentially exposed (303 infected people amongst 10,090 screened) [28,31,34,81,82,93,101]. The screened populations included healthcare workers [82,93,101]; people evacuated from a setting where SARS-CoV-2 transmission was confirmed, irrespective of symptom status [28,34]; the whole population of one village in Italy [81]; and blood donors [31]. In these studies, the summary estimate of the proportion asymptomatic was 31% (95% CI 26%–37%, prediction interval 24%–38%). There is a risk of selection bias in studies of certain groups, such as healthcare workers and blood donors, because people with symptoms are excluded [31,82,93,101], or from nonresponders in population-based screening [81]. Retrospective symptom ascertainment could also increase the proportion determined asymptomatic [81,82,101].

The remaining studies, in hospital settings, included adult patients only (15 studies, n = 3,228) [27,39,45,52,53,56,57,64,80,83,89,92,103,107,114], children only (10 studies, n = 285) [40–42,59,84,87,98,99,104,106], or adults and children (10 studies, n = 1,518) [54,67,77,79,86,88,96,102,109,112] (Table 1, Fig 1). The types of hospital and clinical severity of patients differed, including settings in which anyone with SARS-CoV-2 infection was admitted for isolation and traditional hospitals.

We included 31 studies in which the people with no symptoms of COVID-19 at enrolment were followed up, and the proportion that develops symptoms is defined as presymptomatic (Table 2, Fig 2) [21,27,28,31,34,37,38,41,45,46,49,52,55,56,58,67,68,71,73,76,77,79,81,90,93,95,103,110,111,113,114]. Four studies addressed only this review question [37,38,55,113]. The findings from the 31 studies were heterogeneous (S4 Fig), even when categorised according to the method of selection of asymptomatic participants, and we did not estimate a summary measure (Fig 2).

Fig 2. Forest plot of proportion (‘Prop.’) of people with presymptomatic SARS-CoV-2 infection, stratified by setting.

CI, confidence interval; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

https://doi.org/10.1371/journal.pmed.1003346.g002

We investigated heterogeneity in the estimates of the proportion of asymptomatic SARS-CoV-2 infections in subgroup analyses that were not specified in the original protocol. In studies of hospitalised children, the point estimate was higher (27%, 95% CI 22%–32%, 10 studies) than in adults (11%, 95% CI 6%–19%, 15 studies) (Fig 1). The proportion of asymptomatic SARS-CoV-2 infection estimated in studies of hospitalised patients (35 studies, 19%, 95% CI 14%–25%) was similar to that in all other settings (44 studies, 22%, 95% CI 17%–29%, S5 Fig). To examine publication status, we conducted a sensitivity analysis, omitting studies that were identified as preprints at the time of data extraction (S6 Fig). The estimate of the proportion of asymptomatic infection in all settings (18%, 95% CI 14%–22%) and setting-specific estimates were very similar to the main analysis.

Five of the studies that conducted detailed contact investigations provided enough data to calculate a secondary attack rate according to the symptom status of the index cases (Fig 3) [36,65,66,90,111]. The summary risk ratio for asymptomatic compared with symptomatic was 0.35 (95% CI 0.1–1.27) and for presymptomatic compared with symptomatic people was 0.63 (95% CI 0.18–2.26) [66,90]. The risk of bias in ascertainment of contacts was judged to be low in all studies.

Fig 3. Forest plot of the RR and 95% CI of the SAR, comparing infections in contacts of asymptomatic and presymptomatic index cases with infections in contacts of symptomatic cases.

The RR is on a logarithmic scale. CI, confidence interval; E, number of secondary transmission events; N, number of close contacts; RR, risk ratio; SAR, secondary attack rate.

https://doi.org/10.1371/journal.pmed.1003346.g003

We included eight mathematical modelling studies (Fig 4) [19,20,33,51,63,69,78,91]. The models in five studies were informed by analysis of data from contact investigations in China, South Korea, Singapore, and the Diamond Princess cruise ship, using data to estimate the serial interval or generation time [19,20,33,69,78], and in three studies the authors used previously published estimates [51,63,91].

Fig 4. Forest plot of proportion (‘Prop.’) of SARS-CoV-2 infection resulting from asymptomatic or presymptomatic transmission.

For studies that report outcomes in multiple settings, these are annotated in brackets. CI, confidence interval; GI, generation interval; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; SI, serial interval.

https://doi.org/10.1371/journal.pmed.1003346.g004

Estimates of the contributions of both asymptomatic and presymptomatic infections SARS-CoV-2 transmission were very heterogeneous. In two studies, the contributions to SARS-CoV-2 transmission of asymptomatic infection were estimated to be 6% (95% CrI 0%–57%) [19] and 69% (95% CrI 20%–85%) [69] (Fig 4). The estimates have large uncertainty intervals and the disparate predictions result from differences in the proportion of asymptomatic infections and relative infectiousness of asymptomatic infection. Ferretti and colleagues provide an interactive web application [19] that shows how these parameters affect the model results.

Models of the contribution of presymptomatic transmission used different assumptions about the durations and distributions of infection parameters such as incubation period, generation time, and serial interval [19,20,33,51,63,78,91]. In models that accounted for uncertainty appropriately, most estimates of the proportion of transmission resulting from people with SARS-CoV-2 who are presymptomatic ranged from 20% to 70%. In one study that estimated a contribution of <1% [91], the model-fitted serial interval was longer than observed in empirical studies [115]. The credibility of most modelling studies was limited by the absence of external validation. The data to which the models were fitted were generally from small samples (S7 Fig).

A strength of this review is that we used clear definitions and separated review questions to distinguish between SARS-CoV-2 infections that remain asymptomatic throughout their course from those that become symptomatic and to separate proportions of people with infection from their contribution to transmission in a population. This living systematic review uses methods to minimise bias whilst increasing the speed of the review process [5,6] and will be updated regularly. We only included studies that provided information about follow-up through the course of infection, which allowed reliable assessment about the proportion of asymptomatic people in different settings. In the statistical synthesis of proportions, we used a method that accounts for the binary nature of the data and avoids the normality approximation (weighted logistic regression).

Limitations of the review are that most included studies were not designed to estimate the proportion of asymptomatic SARS-CoV-2 infection and definitions of asymptomatic status were often incomplete or absent. The risks of bias, particularly those affecting selection of participants, differed between studies and could result in both underestimation and overestimation of the true proportion of asymptomatic infections. Also, we did not consider the possible impact of false negative RT-PCR results, which might be more likely to occur in asymptomatic infections [116] and would underestimate the proportion of asymptomatic infections [117]. The four databases that we searched are not comprehensive, but they cover the majority of publications and we do not believe that we have missed studies that would change our conclusions.

We found narrative reviews that reported wide ranges (5%–96%) of infections that might be asymptomatic [1,118]. These reviews presented cross-sectional studies alongside longitudinal studies and did not distinguish between asymptomatic and presymptomatic infection. We found three systematic reviews, which reported similar summary estimates from meta-analysis of studies published up to May [119–121]. In two reviews, authors applied inclusion criteria to reduce the risks of selection bias, with summary estimates of 11% (95% CI 4%–18%, 6 studies) [120] and 15% (95% CI 12%–18%, 9 studies) [121]. Our review includes all these studies, mostly in the categories of aggregated contact or outbreak investigations, with compatible summary estimates (Fig 1). We categorised one report [81] with other studies in which a defined population was screened. The summary estimate in the third systematic review (16%, 95% CI 10%–23%, 41 studies) [119] was similar to that of other systematic reviews, despite inclusion of studies with no information about follow-up. In comparison with other reviews, rather than restricting inclusion, we give a comprehensive overview of studies with adequate follow-up, with assessment of risks of bias and exploration of heterogeneity (S2–S7 Figs). The three versions of this review to date have shown how types of evidence change over time, from single-family investigations to large screening studies (S1 Table).

The findings from systematic reviews, including ours [119–121], do not support the claim that a large majority of SARS-CoV-2 infections are asymptomatic [122]. We estimated that, across all study settings, the proportion of SARS-CoV-2 infections that are asymptomatic throughout the course of infection is 20% (95% CI 17%–25%). The wider prediction interval reflects the heterogeneity between studies and indicates that future studies with similar study designs and in similar settings will estimate a proportion of asymptomatic infections from 3% to 67%. Studies that detect SARS-CoV-2 through screening of defined populations irrespective of infection status at enrolment should be less affected by selection biases. In this group of studies, the estimated proportion of asymptomatic infection was 31% (95% CI 26%–37%, prediction interval 24%–38%). This estimate suggests that other studies might have had an overrepresentation of participants diagnosed because of symptoms, but there were also potential selection biases in screening studies that might have overestimated the proportion of asymptomatic infections. Our knowledge to date is based on data collected during the acute phase of an international public health emergency, mostly for other purposes. To estimate the true proportion of asymptomatic SARS-CoV-2 infections, researchers need to design prospective longitudinal studies with clear definitions, methods that minimise selection and measurement biases, and transparent reporting. Serological tests, in combination with virological diagnostic methods, might improve ascertainment of SARS-CoV-2 infection in asymptomatic populations. Prospective documentation of symptom status would be required, and improvements in the performance of serological tests are still needed [123].

Our review adds to information about the relative contributions of asymptomatic and presymptomatic infection to overall SARS-CoV-2 transmission. Since all people infected with SARS-CoV-2 are initially asymptomatic, the proportion that will go on to develop symptoms can be derived by subtraction from the estimated proportion with true asymptomatic infections; from our review, we would estimate this fraction to be 80% (95% CI 75%–83%). Since SARS-CoV-2 can be transmitted a few days before the onset of symptoms [124], presymptomatic transmission likely contributes substantially to overall SARS-CoV-2 epidemics. The analysis of secondary attack rates provides some evidence of lower infectiousness of people with asymptomatic than symptomatic infection (Fig 3) [36,65,66,90,111], but more studies are needed to quantify this association more precisely. If both the proportion and transmissibility of asymptomatic infection are relatively low, people with asymptomatic SARS-CoV-2 infection should account for a smaller proportion of overall transmission than presymptomatic individuals. This is consistent with the findings of the only mathematical modelling study in our review that explored this question [19]. Uncertainties in estimates of the true proportion and the relative infectiousness of asymptomatic SARS-Cov-2 infection and other infection parameters contributed to heterogeneous predictions about the proportion of presymptomatic transmission [20,33,51,63,78,91].

Integration of evidence from epidemiological, clinical, and laboratory studies will help to clarify the relative infectiousness of asymptomatic SARS-CoV-2. Studies using viral culture as well as RNA detection are needed, since RT-PCR defined viral loads appear to be broadly similar in asymptomatic and symptomatic people [116,125]. Age might play a role as children appear more likely than adults to have an asymptomatic course of infection (Fig 1) [126]; age was poorly reported in studies included in this review (Table 1).

SARS-CoV-2 transmission from people who are either asymptomatic or presymptomatic has implications for prevention. Social distancing measures will need to be sustained at some level because droplet transmission from close contact with people with asymptomatic and presymptomatic infection occurs. Easing of restrictions will, however, only be possible with wide access to testing, contact tracing, and rapid isolation of infected individuals. Quarantine of close contacts is also essential to prevent onward transmission during asymptomatic or presymptomatic periods of those that have become infected. Digital, proximity tracing could supplement classical contact tracing to speed up detection of contacts to interrupt transmission during the presymptomatic phase if shown to be effective [19,127]. The findings of this systematic review of publications early in the pandemic suggests that most SARS-CoV-2 infections are not asymptomatic throughout the course of infection. The contribution of presymptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention measures, with enhanced hand and respiratory hygiene, testing tracing, and isolation strategies and social distancing, will continue to be needed.