Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave


Considerable uncertainty surrounds the timeline of introductions and onsets of local transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) globally1,2,3,4,5,6,7. Although a limited number of SARS-CoV-2 introductions were reported in January and February 2020 (refs.8,9), the narrowness of the initial testing criteria, combined with a slow growth in testing capacity and porous travel screening10, left many countries vulnerable to unmitigated, cryptic transmission. Here we use a global metapopulation epidemic model to provide a mechanistic understanding of the early dispersal of infections and the temporal windows of the introduction of SARS-CoV-2 and onset of local transmission in Europe and the USA. We find that community transmission of SARS-CoV-2 was likely to have been present in several areas of Europe and the USA by January 2020, and estimate that by early March, only 1 to 4 in 100 SARS-CoV-2 infections were detected by surveillance systems. The modelling results highlight international travel as the key driver of the introduction of SARS-CoV-2, with possible introductions and transmission events as early as December 2019 to January 2020. We find a heterogeneous geographic distribution of cumulative infection attack rates by 4 July 2020, ranging from 0.78% to 15.2% across US states and 0.19% to 13.2% in European countries. Our approach complements phylogenetic analyses and other surveillance approaches and provides insights that can be used to design innovative, model-driven surveillance systems that guide enhanced testing and response strategies.


A few weeks after the initial announcement of a cluster of atypical pneumonia cases in Wuhan, China, the first confirmed cases of coronavirus disease 2019 (COVID-19) in the USA and Europe were detected (on 21 January 2020 in WA, USA1 and on 24 January 2020 in France2). Although many more states and countries began to report initial introductions in the following weeks, only a few cases were detected daily during this time period (Fig. 1a), and most countries adopted a testing policy that targeted symptomatic individuals with a travel history linked to China. Several reports suggest that the introduction of SARS-CoV-2 occurred earlier than initially recognized3,4,5,6,7,8, raising questions about the effectiveness of the initial testing policies and travel-related restrictions, as well as the extent to which the SARS-CoV-2 virus spread through cryptic transmission in January and February 2020. To address these questions, we use the global epidemic and mobility (GLEAM) model, a data-driven, stochastic, spatial, age-structured metapopulation epidemic model11,12, to study the global dynamic underlying the evolution of the COVID-19 pandemic in Europe and the USA. Our model maps the plausible pathways of the pandemic using information available at the early stages of the outbreak and provides a global picture of the cryptic phase as well as the ensuing first wave of the COVID-19 pandemic.

Fig. 1: Early picture of the COVID-19 outbreak in Europe and the USA.
figure 1

a, Timelines of the daily reported and confirmed cases of COVID-19 in Europe (left) and the USA (right). BEL, Belgium; ESP, Spain; EU, European Union; FIN, Finland; FRA, France; GER, Germany; ITA, Italy; SWE; Sweden. b, Model-based estimates for the daily number of new infections in Europe (left) and the USA (right). The model estimates reported are the median values with the IQR obtained with an ABC calibration method using n = 200,000 independent model realizations. The inset plots compare the weekly incidence of reported cases with the median, weekly incidence of infections estimated by the model for the week of 8–14 March 2020 for the contiguous US states and European countries that reported at least one case (Europe, n = 30; USA, n = 48). Circle size corresponds to the population size of each state and country. The correlations were calculated using the Pearson correlation coefficient with a two-sided P value (Europe: ρ = 0.80, P < 0.001; USA: ρ = 0.79, P < 0.001). c, The probability that a city in Europe (left) and the USA (right) had generated at least 100 cumulative infections by 21 February 2020. Colour and circle size are proportional to the probability.

Source data

We consider data concerning the continental USA and 30 European countries (the full list is reported in Extended Data Table 1). The model integrates real-time human mobility and population data with a mechanistic epidemic model at a global scale, incorporating changes in contact patterns and mobility according to the non-pharmaceutical interventions (NPIs) implemented in each region. It is calibrated on international case introductions out of mainland China at the early stage of the pandemic using an approximate Bayesian computation (ABC) methodology13. The model returns an ensemble of stochastic realizations of the global epidemic spread including international and domestic infection importations, incidence of infections and deaths at a daily resolution (see Methods). In the following text, we provide a detailed discussion of the analyses and results concerning European countries and the US states; however, to further test and validate our approach, in the Supplementary Information, we report the modelling results for 24 additional countries that are globally representative, including countries of world regions such as Latin America, the Middle East, Africa, East Asia and Oceania.

In Fig. 1b we show the model estimates of the median daily incidence of new infections up to 21 February 2020, for both the USA and Europe. These values are much larger than the number of officially reported cases (see Fig. 1a), highlighting the substantial number of potential transmission events that may have already occurred before many states and countries had implemented testing strategies independent of travel history. As validation we compare our model’s estimates of the number of infections during the week of 8 March 2020 to the number of cases reported during that week within each US state and European country with at least one reported case (shown in Fig. 1b inset). While we see a strong correlation between the reported cases and our model’s estimated number of infections (Pearson’s correlation coefficient on log values, USA: 0.79, P < 0.001; Europe: 0.80, P < 0.001), far fewer cases had actually been reported by that time. If we assume that the number of reported cases and simulated infections are related through a simple binomial sampling process, we find that on average 9 in 1,000 infections (90% confidence interval (CI) 1–35 per 1,000) and 35 in 1,000 infections (90% CI 4–90 per 1,000) were detected by 8 March 2020 in the USA and Europe, respectively. As testing capacity increased, the ascertainment rate grows and our estimates increase to detecting 17 in 1,000 infections (90% CI 2–55 per 1,000) by 14 March 2020 in the USA and 77 in 1,000 infections (90% CI 5–166 per 1,000) in Europe. The estimated ascertainment rates are in agreement with independent results based on different statistical methodologies14,15,16. In Fig. 1c we show the probability that a city in the USA or Europe had generated at least 100 infections by 21 February 2020. We see that the progression of the virus through the USA and Europe was both temporally and spatially heterogeneous. While many cities had not yet experienced much community transmission by late February, a few areas such as New York City and London are very likely to have already had local outbreaks.

Onset of local transmission

The model’s ensemble of realizations provides a statistical description of all the potential pandemic histories compatible with the initial evolution of the pandemic in China. Rather than describing a specific, causal chain of events, we can estimate possible time windows pertaining to the initial chains of transmission in different geographical regions. We define the onset of local transmission for a country or state as the earliest date when at least 10 new infections are generated per day. This number is chosen because at this threshold the likelihood of stochastic extinction is extremely small17,18. As detailed in the Supplementary Information, further calibration on the US states and European countries suggests posterior values of R0 ranging from 2.4 to 2.8. These values are consistent with many other (country-dependent) estimates19,20,21,22,23,24. At the same time, given the doubling time of the number of COVID-19 cases before the implementation of public health measures, any variation of a factor 2 around the 10 infections per day threshold corresponds to a small adjustment of 3−5 days to the presented timelines.

In Fig. 2, we show the posterior probability distribution, p(t), of the week, t, of the onset of local transmission for 15 US states (Fig. 2a) and European countries (Fig. 2b) (see Supplementary Information for all states and countries). We also calculate, for each country or state, the median date, T, that identifies the first week in which the cumulative distribution function is larger than 50%. Among the US states, CA and NY have the earliest dates, T, by the week of 19 January (CA) and 2 February (NY) 2020. In Europe, Italy, the UK, Germany and France are the first countries with T close to the end of January 2020. However, it is worth noting that each distribution, p(t), has a support spanning several weeks. In Italy, the 5th and 95th percentiles of the p(t) distribution are the week of 6 January and the week of 30 January 2020, respectively. These dates also suggest that it is not possible to rule out introductions and transmission events as early as December 2019, although the probability of this is very small.

Fig. 2: Timing of the onset of local transmission.
figure 2

a, b, Posterior distributions of the week in which each US state (a) or European country (b) first reached 10 locally generated SARS-CoV-2 transmission events per day. Countries and states are ordered by the median date of their posterior distribution. The week of this date corresponds to the dates reported on the vertical axis.

Source data

For each state in the USA and each country in Europe, we compared the order in which they surpassed 100 cumulative infections in the model and in the surveillance data (gathered from the John Hopkins University Coronavirus Resource Center25). In Extended Data Fig. 1a we plot the ordering for states and compute the Kendall rank correlation coefficient τ (see Supplementary Information). The correlation is positive (\({\tau }_{{\rm{EU}}}\)= 0.71, P < 0.001 and \({\tau }_{{\rm{US}}}\) = 0.68, P < 0.001) indicating that, despite the detection and testing issues, the expected patterns of epidemic diffusion are largely described by the model in both regions.

SARS-CoV-2 introductions

As the model allows the recording of the origin and destination of travellers carrying SARS-CoV-2 at the global scale, we can study the possible sources of SARS-CoV-2 introductions for each US state and European country. More specifically, we record the cumulative number of introductions in each stochastic realization of the model until 30 April 2020. In Fig. 3 we visualize the origin of the introductions considering some key geographical regions (for example, Europe and Asia) while keeping the USA and China separate and aggregating all of the other countries (Others). For both the USA and Europe, the contribution from mainland China is barely visible and the local share (that is, sources within Europe and the USA) becomes significantly higher across the board. Hence, while introduction events in the early phases of the outbreak were key to start local spreading (see details in the Supplementary Information), the cryptic transmission phase was sustained largely by internal flows. Domestic SARS-CoV-2 introductions to 30 April 2020 account for 69% (interquartile range (IQR) 60%−81%) of the introductions in CA, 78% (IQR 71%−87%) in TX and 69% (IQR 60%−80%) in MA, which is supported by phylogenetic analysis26. European origins account for 69% (IQR 60%−80%), 84% (IQR 79%−91%) and 58% (IQR 48%−68%) of the introductions in Italy, Spain and the UK, respectively. In the Supplementary Information, we report the full breakdown for all states and countries.

Fig. 3: Importation sources from the beginning of the outbreak until the end of April 2020.
figure 3

a, b, Each US state (a) and European country (b) is displayed in a clockwise order with respect to the start of the local outbreak (as seen in Fig. 2). Importation flows are directed and weighted. We normalize links considering the total in-flow for each state so that the sum of importation flows, for each state, is 1. In the Supplementary Information, we report the complete list of countries contributing as importation sources in each geographical region.

Source data

It is also necessary to distinguish between the full volume of SARS-CoV-2 introductions and the introduction events that could be relevant to the early onset of local transmission in each stochastic realization of the model. To this point, it is worth stressing that seeding introductions are different from the actual number of times the virus has been introduced to each location with subsequent onward transmission. Even after a local outbreak has started, future importation events may give rise to additional onward transmission forming independently introduced transmission lineages of the virus27. In the model, we can investigate seeding events by recording introduction events before the local transmission chains were established. We report the results of this analysis in the Supplementary Information, showing that importations from mainland China may be relevant in seeding the epidemic in January, but then play a comparatively small role in the expansion in the number of COVID-19 cases in the USA and Europe owing to the travel restrictions imposed to/from mainland China after 23 January 2020.

The early timing of the initial introductions and diffusion pattern of SARS-CoV-2 were driven by air travel. We find a positive correlation (\({\tau }_{{\rm{EU}}}\) = 0.66, P < 0.001 and \({\tau }_{{\rm{US}}}\) = 0.66, P < 0.001) comparing the ordering of states according to when they surpassed 100 cumulative, reported cases (referred to as the epidemic order) and their domestic and international air travel volume rank (Extended Data Fig. 1B). Similar observations have been reported in China, where the initial spreading of the virus outside Hubei was strongly correlated with the traffic to/from the province28. Other factors such as population size are also correlated with both the travel flows (\({\tau }_{{\rm{EU}}}\) = 0.59, P < 0.001 and \({\tau }_{{\rm{US}}}\) = 0.7, P < 0.001) and the epidemic order (\({\tau }_{{\rm{EU}}}\) = 0.46, P < 0.001 and \({\tau }_{{\rm{US}}}\) = 0.68, P < 0.001), which are discussed in detail in the Supplementary Information. In our model, it is not possible to exclude increased contacts in highly populated places before social distancing interventions and disentangle this effect from increased seeding due to the correlation between travel volume and population size.

COVID-19 burden

Starting in March 2020, the establishment and timing of NPIs as well as other epidemiological drivers (that is, population size and density, age structure and so on) determined the disease burden in the USA and Europe29,30,31,32. We account for these features by calibrating the model results, individually, for each US state and European country. More precisely, we estimate the posterior distribution of the infection fatality ratio (IFR) and infection attack rate in each US state and European country. To this end, we adopt the ABC approach using as evidence the number of new deaths reported from 22 March 2020 to 27 June 2020. We consider a uniform prior for the average IFR in the range from 0.4% to 2% that is age stratified proportional to the IFR values reported in ref. 33. We also consider a uniform prior for reporting delays between the date of death and reporting ranging from 2 to 22 days in both Europe and the USA34. Details are provided in the Supplementary Information.

In Fig. 4a–d, f–i, we report the model fit of the estimated weekly deaths of the first wave for selected states and countries. Additional model results for all investigated regions including a sensitivity analysis of different calibration methods can be found in the Supplementary Information. We find a strong correlation between the weekly model-estimated deaths and the reported values with a Pearson correlation coefficient of 0.99 (P < 0.001) for both Europe and the USA (see Supplementary Fig. 6). As the data suggest, many European countries and US states saw peaks in April and May with various decreasing trajectories that depend on the mitigation strategies in place. Additionally, we report the estimated posteriors for the cumulative infection attack rates and IFRs as of 4 July 2020 in European countries experiencing more than 100 total deaths and the top 20 states ranked by infection attack rate in the USA.

Fig. 4: The burden of the first wave in Europe and the USA.
figure 4

ad, Model fit of the estimated weekly deaths for selected countries in Europe (France, a; Italy, b; Sweden, c; UK, d). e, Posterior distributions of the infection attack rates and IFRs by 4 July 2020, for European countries where there were at least 100 reported deaths. fi, Model fit of the estimated weekly deaths for selected states in the USA (CA, f; IL, g; MA, h; NY, i). j, Posterior distributions of the estimated infection attack rates and IFRs by 4 July 2020 for the top 20 US states (ranked according to their infection attack rates). The curves in ad and fi show the median values and 90% CIs. For e and j, the outer, lighter boxes represent the 90% CI, the darker, inner boxes represent the IQR, and the vertical lines represent the median value. Posterior distributions in e and j are the result of the ABC analysis of 200,000 independent model realizations.

Source data

Within Europe, Belgium has the highest estimated infection attack rate of 13.2% (90% CI [8.5%28.3%]) by 4 July 2020, in agreement with the results in ref. 14. Furthermore, by that time Belgium reported the highest COVID-19 mortality rate out of the European countries investigated with 8.5 deaths per 10,000 individuals. However, Italy is estimated to have the highest median IFR of 1.4% (90% CI [0.6%–1.8%]), which aligns with other ranges reported in the literature35,36. The US states with the highest infection attack rates are located within the northeast and experienced a significant first wave during March–April 2020. NY and NJ are the top two states with infection attack rates of 13.4% (90% CI [9.1%−26.7%]) and 15.2% (90% CI [10.2%−31.3%]), respectively. These numbers are aligned with estimates from New York City reported in ref. 37. In the Supplementary Information, we report summary tables with estimated IFRs, infection attack rates and the reproductive number in the absence of mitigation measures for all calibrated US states and European countries. Additionally, we compare our attack rate estimates to the prevalence of individuals with SARS-CoV-2 antibodies from serological studies across the USA and Europe (Extended Data Fig. 1D). The seroprevalence estimates are compared to the model estimates during the same time window the studies were performed (details on the seroprevalence data from this figure can be found in Supplementary Table 8 and Supplementary Section 9.3).


The model presented here captures the spatial and temporal heterogeneity of the early stage of the pandemic, going beyond the single-country-level reconstruction. It provides a mechanistic understanding of the underlying dynamics of the pandemic’s interconnected evolution. Furthermore, rather than showing specific evidence for early infection in a few locations, our study aims at providing a statistical characterization and quantification of the initial transmission pathways at a global scale. Our results can be compared to and complement analyses based on gene sequencing and travel volumes. We find that 72% of the early introductions to Italy, before the local outbreak, are linked to China, which is in agreement with ref. 38 highlighting the key role of importations between these regions at the beginning of the pandemic. Additionally, similar to our findings, ref. 27 estimates that the majority of importation events to April 2020, associated with onward transmission in the UK, came from Europe. The contributions from China are quantified below 1% and limited to the very early phase. Furthermore, seeding events from the USA are estimated to be below 3%, which aligns with our estimate (8%; IQR 3%−9%). However, their results point to a larger share from Europe (90%) compared to ours (58%; IQR 48%−68%), and conversely, we estimate a larger contribution from Asia (27%; IQR 19%−35%). As our analysis is a statistical description of the possible introduction pathways, differences could arise due to our model design, and also from genomic sampling biases39.

The sources of introduction of SARS-CoV-2 infections in Europe and the USA changed substantially and rapidly through time. This caused reactive response strategies, such as issuing travel restrictions targeting countries only after local transmission is confirmed, ineffective at preventing local outbreaks. Our results suggest that many regions in the USA and Europe experienced an onset of local transmission in January and February 2020, during the time when testing capacity was limited. If testing had been more widespread and not restricted to individuals with a travel history from China, there would have been more opportunities for earlier detection and interventions. In the Supplementary Information, we report a counterfactual scenario where we assume broader testing specifications not based on the individual travel history and find that the epidemic progression is considerably delayed (see Supplementary Section 8).

As testing capacity increased and more cases were detected, many governments began to issue social distancing guidelines to mitigate the spread of SARS-CoV-2. The first European country to implement a cordon sanitaire was Italy on 23 February 2020, for a few northern cities40. Many other countries followed suit and implemented national lockdowns in March 2020 (refs.30,41); however, this was weeks after our model estimates that SARS-CoV-2 was introduced and locally spreading. We find a strong correlation between the number of cases reported by the date of a lockdown/social distancing measure and the cumulative infections projected by 4 July 2020 (Extended Data Fig. 1C), indicating that the earlier NPIs had been issued, the smaller the COVID-19 burden experienced during the first wave. This is in agreement with other analyses showing that the timing of NPIs is crucial in limiting the burden of COVID-19 (refs.19,29,42,43,44,45,46,47,48). Overall, our results strengthen the case for preparedness plans with broader indication for testing that are able to detect local transmission earlier.

As with all modelling analyses, results are subject to biases from the limitations and assumptions within the model as well as the data used in its calibration. The model’s parameters, such as generation time, incubation period and the proportion of asymptomatic infections, are chosen according to the current knowledge of SARS-CoV-2. Although the model is robust to variations in these parameters (see the Supplementary Information for the sensitivity analysis), more information on the key characteristics of the disease would considerably reduce uncertainties. The model calibration does not consider correlations among importations (that is, family travel) and assumes that travel probabilities are age specific across all individuals in the catchment area of each transportation hub.

In light of the assumptions and limitations inherent to this modelling approach, the results are able to complement the SARS-CoV-2 genome sequencing analyses used to reconstruct the early epidemic history of the COVID-19 pandemic38. The methods used in this analysis offer a blueprint to identify the most likely early spreading dynamics of emerging viruses, and they can be used as a real-time risk assessment tool. Anticipating the locations where a virus is most likely to spread to next could be instrumental in guiding enhanced testing and surveillance activities. The estimated SARS-CoV-2 importation patterns and the cryptic transmission phase dynamics are of potential use when planning and developing public health policies in relation to international travelling, and they could provide important insights into assessing the potential risk and impact of emerging SARS-CoV-2 variants in regions of the world with limited testing and genomic surveillance resources.


The GLEAM model

The GLEAM model is a stochastic, spatial, age-structured metapopulation model. Previously this model was used to characterize the early stage of the COVID-19 epidemic in mainland China to estimate the effectiveness of travel bans and restrictions49. The GLEAM model divides the global population into more than 3,200 subpopulations in roughly 200 different countries and territories interconnected by realistic air-travel and commuting mobility networks. A subpopulation is defined as the catchment area around major transportation hubs. The airline transportation data encompass daily travel data in the origin–destination format from the Official Aviation Guide database50 reflecting actual traffic changes that occurred during the pandemic. Ground mobility and commuting flows are derived from the analysis and modelling of data collected from the statistics offices of 30 countries on 5 continents11,12. The international travel data account for travel restrictions and government-issued policies. Furthermore, the model accounts for the reduction of internal, country-wide mobility and changes in contact patterns in each country and state in 2020. Specific model details are reported in the Supplementary Information.

SARS-CoV-2 transmission dynamics

The transmission dynamics take place within each subpopulation and assume a classic compartmentalization scheme for disease progression similar to those used in several large-scale models of SARS-CoV-2 transmission15,51,52,53,54,55. Each individual, at any given point in time, is assigned to a compartment corresponding to their particular disease-related state (specifically, one could be susceptible, latent, infectious or removed)49. This state also controls the individual’s ability to travel (details in the Supplementary Information). Individuals transition between compartments through stochastic chain binomial processes. Susceptible individuals can acquire the virus through contact with individuals in the infectious category and can subsequently become latent (that is, infected but not yet able to transmit the infection). The process of infection is modelled using age-stratified contact patterns at the state and country level56,57. Latent individuals progress to the infectious stage at a rate inversely proportional to the latent period, and infectious individuals progress to the removed stage at a rate inversely proportional to the infectious period. The sum of the mean latent and infectious periods defines the generation time. Removed individuals are those who can no longer infect others. To estimate the number of deaths, we consider a uniformly distributed prior of the IFRs (ranging from 0.4% to 2%) that is age stratified proportional to the values estimated by ref. 33 and incorporates reporting delays. The transmission model does not assume heterogeneities due to age differences in susceptibility to the SARS-CoV-2 infection for younger children (1–10 years old). This is an intense area of discussion58,59,60. The transmission dynamic and the offspring distribution of infectious individuals in the model will depend on the specific details of each population, local and global mobility, NPIs and so on. While overdispersion in transmission varies by location in our model, we find that overall, it is consistent with 25% of primary infections causing 75% of transmission in our simulations (Supplementary Fig. 9). Additional simulations considering a fixed level of dispersion, informed by past studies, result in differences of less than 3 days in onset times (Supplementary Fig. 10; see also the Supplementary Information for further discussion).

Model calibration

We assume a start date of the epidemic in Wuhan, China, that falls between 15 November 2019 and 1 December 2019, with 20 initial infections49,51,61,62,63. This considers that our model has a posterior distribution for the emergence of the outbreak in China that includes the possibility of transmission starting in October, 2019 (refs. 64,65). The model generates an ensemble of possible epidemic realizations and is initially calibrated using an approximate Bayesian computation (ABC) rejection approach13 based on the observed international importations from mainland China up to 21 January 2020 (ref. 49). Only a fraction of imported cases is generally detected at the destination10,66. According to the estimates proposed in ref. 67, we stratify the detection capacity of countries into three groups: high, medium, and low surveillance capacity according to the Global Health Security Index68, and assume that asymptomatic infections are never detected. The model calibration does not consider correlated importations (for example, family travel) and assumes that travel probabilities are homogeneous across all individuals in each subpopulation. We further calibrate our model using the temporal ordering of the onset of local transmission (as defined in the section ‘Onset of local transmission’) of the countries investigated. If we consider the epidemiological evidence38,69,70, Italy was the first European country to experience substantial community transmission. Therefore, throughout the paper, we constrain the ensemble of simulations focusing only on stochastic realizations in which Italy is the first country, in the group under examination, to experience sustained local transmission (see Supplementary Information for details and further analyses of unconstrained simulations). Furthermore, we perform for each state and country an additional ABC rejection analysis using as evidence the weekly reported deaths in the time window starting on 22 March 2020 and ending on 27 June 2020. A full description of the model calibration is provided in the Supplementary Information.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this paper.

Data availability

Epidemic surveillance data were collected from the Johns Hopkins Coronavirus Resource Center ( Proprietary airline data are commercially available from the Official Aviation Guide ( and International Air Transport Association ( databases. Other model intervention data include data from Google’s COVID-19 Community Mobility Reports available at and the Oxford COVID-19 Response Tracker available at data are provided with this paper.

Code availability

The GLEAM model is publicly available at All data analyses of model results were performed using Python v3.8.


  1. CDC First Travel-related Case of 2019 Novel Coronavirus Detected in United States. (21 January 2020).

  2. Spiteri, G. et al. First cases of coronavirus disease 2019 (COVID-19) in the WHO European Region, 24 January to 21 February 2020. Euro Surveill. 25, 2000178 (2020).

    Article  PubMed Central  Google Scholar 

  3. Althoff, K. N. et al. Antibodies to SARS-CoV-2 in All of Us Research Program Participants, January 2-March 18, 2020. Clin. Infect. Dis. (2021).

  4. La Rosa, G. et al. SARS-CoV-2 has been circulating in northern Italy since December 2019: evidence from environmental monitoring. Sci. Total Environ. 750, 141711 (2021).

    ADS  PubMed  Article  CAS  Google Scholar 

  5. Deslandes, A. et al. SARS-CoV-2 was already spreading in France in late December 2019. Int. J. Antimicrob. Agents 55, 106006 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. Bedford, T. et al. Cryptic transmission of SARS-CoV-2 in Washington state. Science 370, 571–575 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. Basavaraju, S. V. et al. Serologic testing of US blood donations to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)–reactive antibodies: December 2019–January 2020. Clin. Infect. Dis. 72, e1004–e1009 (2020).

    Article  CAS  Google Scholar 

  8. CDC COVID-19 Response Team et al. Evidence for limited early spread of COVID-19 within the United States, January–February 2020. MMWR Morb. Mortal. Wkly Rep. 69, 680–684 (2020).

    Article  PubMed Central  Google Scholar 

  9. Lescure, F.-X. et al. Clinical and virological data of the first cases of COVID-19 in Europe: a case series. Lancet Infect. Dis. 20, 697–706 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Gostic, K., Gomez, A. C., Mummah, R. O., Kucharski, A. J. & Lloyd-Smith, J. O. Estimated effectiveness of symptom and risk screening to prevent the spread of COVID-19. eLife 9, e55570 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  11. Balcan, D. et al. Multiscale mobility networks and the spatial spreading of infectious diseases. Proc. Natl Acad. Sci. USA 106, 21484–21489 (2009).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Balcan, D. et al. Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model. J. Comput. Sci. 1, 132–145 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  13. Sunnåker, M. et al. Approximate Bayesian computation. PLoS Comput. Biol. 9, e1002803 (2013).

  14. Russell, T. W. et al. Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections. BMC Med. 18, 332 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  15. Gatto, M. et al. Spread and dynamics of the COVID-19 epidemic in Italy: effects of emergency containment measures. Proc. Natl Acad. Sci. USA 117, 10484–10491 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Havers, F. P. et al. Seroprevalence of antibodies to SARS-CoV-2 in 10 sites in the United States, March 23-May 12, 2020. JAMA Intern. Med. 180, 1576–1586 (2020).

    CAS  Article  Google Scholar 

  17. Bailey, N. T. et al. The Mathematical Theory of Infectious Diseases and Its Applications (Charles Griffin, 1975).

  18. Colizza, V. & Vespignani, A. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. J. Theor. Biol. 251, 450–467 (2008).

    ADS  MathSciNet  PubMed  MATH  Article  Google Scholar 

  19. Perra, N. Non-pharmaceutical interventions during the COVID-19 pandemic: a review. Phys. Rep. (2021).

  20. Salje, H. et al. Estimating the burden of SARS-CoV-2 in France. Science 369, 208–211 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. Domenico, L. D., Pullano, G., Sabbatini, C. E., Boëlle, P.-Y. & Colizza, V. Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies. BMC Med. (2020).

  22. Chang, S. et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature (2020).

  23. Pei, S., Kandula, S. & Shaman, J. Differential effects of intervention timing on COVID-19 spread in the United States. Sci. Adv. 6, eabd6370 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Lau, M. S. Y. et al. Characterizing superspreading events and age-specific infectiousness of SARS-CoV-2 transmission in Georgia, USA. Proc. Natl Acad. Sci. USA 117, 22430–22435 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Dong, E., Du, H. & Gardner, L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20, 533–534 (2020).

  26. Fauver, J. R. et al. Coast-to-coast spread of SARS-CoV-2 during the early epidemic in the United States. Cell 181, 990–996 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. du Plessis, L. et al. Establishment and lineage dynamics of the SARS-CoV-2 epidemic in the UK. Science 371, 708–712 (2021).

    ADS  PubMed  Article  CAS  Google Scholar 

  28. Kraemer, M. U. et al. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368, 493–497 (2020).

    ADS  CAS  PubMed  Article  Google Scholar 

  29. White, E. R. & Hébert-Dufresne, L. State-level variation of initial COVID-19 dynamics in the United States. PLoS ONE 15, e0240648 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Desvars-Larrive, A. et al. A structured open dataset of government interventions in response to COVID-19. Sci. Data 7, 285 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. Althouse, B. M. et al. The unintended consequences of inconsistent pandemic control policies. Preprint at medRxiv (2020).

  32. Rader, B. et al. Crowding and the shape of COVID-19 epidemics. Nat. Med. 26, 1829–1834 (2020).

    CAS  PubMed  Article  Google Scholar 

  33. Verity, R. et al. Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect. Dis. 20, 669–677 (2020).

    MathSciNet  CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. CDC & ASPR. COVID-19 pandemic planning scenarios. CDC (2021).

  35. O’Driscoll, M. et al. Age-specific mortality and immunity patterns of SARS-CoV-2. Nature 590, 140–145 (2021).

    ADS  PubMed  Article  CAS  Google Scholar 

  36. Poletti, P. et al. Infection fatality ratio of SARS-CoV-2 in Italy. Euro Surveill. 25, 2001381 (2020).

    Google Scholar 

  37. Kissler, S. et al. Reductions in commuting mobility correlate with geographic differences in SARS-CoV-2 prevalence in New York City. Nat. Commun. 11, 4674 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. Lemey, P. et al. Accommodating individual travel history and unsampled diversity in Bayesian phylogeographic inference of SARS-CoV-2. Nat. Commun. 11, 51120 (2020).

    Article  CAS  Google Scholar 

  39. Martin, M. A., Van Insberghe, D. & Koelle, K. Insights from SARS-CoV-2 sequences. Science. 371, 466–467 (2021).

    ADS  CAS  PubMed  Article  Google Scholar 

  40. Lazzerini, M. & Putoto, G. COVID-19 in Italy: momentous decisions and many uncertainties. Lancet Glob. Health 8, e641–e642 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  41. Cheng, C., Barceló, J., Hartnett, A. S., Kubinec, R. & Messerschmidt, L. Covid-19 government response event dataset (coronanet v.1.0). Nat. Hum. Behav. 4, 756–768 (2020).

    PubMed  Article  Google Scholar 

  42. Yang, W., Shaff, J. & Shaman, J. Effectiveness of non-pharmaceutical interventions to contain COVID- 19: a case study of the 2020 spring pandemic wave in New York City. J. R. Soc. Interface 18, 2020082.

  43. Ali, S. T. et al. Serial interval of SARS-CoV-2 was shortened over time by nonpharmaceutical interventions. Science 369, 1106–1109 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Pan, A. et al. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA 323, 1915–1923 (2020).

    CAS  PubMed  Article  Google Scholar 

  45. Jefferies, S. et al. COVID-19 in New Zealand and the impact of the national response: a descriptive epidemiological study. Lancet Public Health 5, e612–e623 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  46. Auger, K. A. et al. Association between statewide school closure and COVID-19 incidence and mortality in the US. JAMA 324, 859–870 (2020).

    CAS  PubMed  Article  Google Scholar 

  47. Islam, N. et al. Physical distancing interventions and incidence of coronavirus disease 2019: natural experiment in 149 countries. BMJ (2020).

  48. Haug, N. et al. Ranking the effectiveness of worldwide covid-19 government interventions. Nat. Hum. Behav. 4, 1303–1312 (2020).

    PubMed  Article  Google Scholar 

  49. Chinazzi, M. et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science 368, 395–400 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. Official Aviation Guide (OAG); (2020).

  51. Imai, N. et al. Report 3: Transmissibility of 2019-nCoV. (Imperial College London, 2020).

  52. Kissler, S. M., Tedijanto, C., Goldstein, E., Grad, Y. H. & Lipsitch, M. Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period. Science 368, 860–868 (2020).

    ADS  CAS  PubMed  Article  Google Scholar 

  53. Li, R. et al. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 368, 489–493 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Wu, J. T., Leung, K. & Leung, G. M. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 395, 10225 (2020).

    Google Scholar 

  55. Lai, S. et al. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature. 585, 410–413 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Mistry, D. et al. Inferring high-resolution human mixing patterns for disease modeling. Nature Commun. 12, 323 (2021).

  57. Prem, K., Cook, A. R. & Jit, M. Projecting social contact matrices in 152 countries using contact surveys and demographic data. PLoS Comput. Biol. 13, e1005697 (2017).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  58. Zhang, J. et al. Changes in contact patterns shape the dynamics of the COVID-19 outbreak in China. Science 368, 1481–1486 (2020).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  59. Davies, N. G. et al. Age-dependent effects in the transmission and control of COVID-19 epidemics. Nat. Med. 26, 1205–1211 (2020).

    CAS  PubMed  Article  Google Scholar 

  60. Bi, Q. et al. Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study. Lancet Infect. Dis. 20, 911–919 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. Rambaut, A. Preliminary phylogenetic analysis of 11 nCoV2019 genomes, 2020-01-19. Virological (2020).

  62. Anderson, K. Clock and TMRCA based on 27 genomes. Virological (2020).

  63. Bedford, T. et al. Genomic analysis of nCoV spread. Situation report 2020-01-23. Nextstrain (2020).

  64. Pekar, J., Worobey, M., Moshiri, N., Scheffler, K. & Wertheim, J. O. Timing the SARS-Cov-2 index case in Hubei province. Science. 372, 412–417 (2021).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  65. van Dorp, L. et al. Emergence of genomic diversity and recurrent mutations in SARS-CoV-2. Infect. Genet. Evol. 83, 104351 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  66. De Salazar, P. M., Niehus, R., Taylor, A., Buckee, C. & Lipsitch, M. Identifying locations with possible undetected imported severe acute respiratory syndrome coronavirus 2 cases by using importation predictions. Emerg. Infect. Dis. 26, 1465–1469 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  67. Niehus, R., De Salazar, P. M., Taylor, A. R. & Lipsitch, M. Using observational data to quantify bias of traveller-derived COVID-19 prevalence estimates in Wuhan, China. Lancet Infect. Dis. 20, 803– 808 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  68. Global Health Security Index (GHS); (2020).

  69. Nadeau, S. A., Vaughan, T. G., Scire, J., Huisman, J. S. & Stadler, T. The origin and early spread of SARS- CoV-2 in Europe. Proc. Natl Acad. Sci. USA 118, e2012008118 (2021).

  70. Worobey, M. et al. The emergence of SARS-CoV-2 in Europe and North America. Science 370, 6516 (2020).

    Article  CAS  Google Scholar 

  71. Kontis, V. et al. Magnitude, demographics, and dynamics of the effect of the first wave of the COVID-19 pandemic on all-cause mortality in 21 industrialized countries. Nat. Med. 26, 1919–1928 (2020).

    CAS  PubMed  Article  Google Scholar 

  72. Managan, D. Trump issues ‘Coronavirus Guidelines’ for next 15 days to slow pandemic. CNBC (16 March 2020).

Download references


A.V., M.E.H., N.E.D. and I.M.L. acknowledge support from the award NIH-R56AI148284. S.M. acknowledges support from the EU H2020 MOOD project. C.G. and L.R. acknowledge support from the EU H2020 Icarus project. M.A., M.C. and A.V. acknowledge support from the COVID Supplement CDC-HHS-6U01IP001137-01. M.C. and A.V. acknowledge support from the Google Cloud Research Credits programme to fund this project. A.V. acknowledges support from the McGovern Foundation and the Chleck Foundation. The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the funding agencies, the National Institutes of Health or the US Department of Health and Human Services.

Author information

Authors and Affiliations



J.T.D., M.C., N.P. and A.V. designed research; M.C., J.T.D., N.P., M.A., C.G., M.L., S.M., A.P.P., K.M., L.R., K.S., C.V., X.X., M.E.H., I.M.L. and A.V. performed research; M.C., J.T.D., N.P., A.P.P., K.M. and A.V. analysed data; and M.C., J.T.D., N.P., M.A., C.G., M.L., S.M., A.P.P., K.M., N.E.D., L.R., K.S., C.V., X.X., M.E.H., I.M.L. and A.V. wrote and edited the paper.

Corresponding author

Correspondence to Alessandro Vespignani.

Ethics declarations

Competing interests

M.A. reports research funding from Seqirus, not related to COVID-19. A.V., M.C. and A.P.P. report grants from Metabiota Inc., outside the submitted work. The authors declare no other relationships or activities that could appear to have influenced the submitted work.

Additional information

Peer review information Nature thanks Steven Riley and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Correlation Analysis for European countries and US states.

(a) The correlation between the ordering of each country/state to reach 100 infections in the model-estimates and to reach 100 reported cases in the surveillance data (Europe: n = 23, US: n = 49). (b) The correlation between the ordering of each country/state considering the time needed to reach 100 reported cases in the surveillance data and the ranking of the combined international and domestic air traffic (Europe n = 23, US n = 49). Correlations in (a, b) are computed considering the Kendall rank correlation coefficient reported with a two-sided p-value, we consider European countries that reached at least 100 reported deaths by July 4, 2020 and countries in Scandinavia (c) Left: the correlation between the number of cases reported by the date of lockdown for European countries (from Table 4 in Ref. 71) and the estimated total number of infections by July 4, 2020 (median values, n = 15). Right: the correlation between the number of cases reported by March 16, 2020 (the date the “15 days to slow the spread” guidelines were released in the US Ref. 72) for each US state and the estimated total infections by July 4, 2020 (median values, n = 36). We consider states that reached at least 100 reported deaths by July 4, 2020. The circle sizes in (ac) correspond to the population sizes of each country/state. (d) The correlation between the model-estimated infection attack rate and the serological prevalence collected from studies, n = 20. Estimated attack rates are the posterior distributions that are the result of the ABC analysis of 200,000 independent model realizations. Data points refer to different dates and the locations for which serological surveys were available (see table S8 in SI for study descriptions). The model-estimated attack rates use the median value, and the error bars represent the 90%CI. The correlations are calculated using the Pearson correlation coefficient in (c, d) reported with a two-sided p-value.

Extended Data Table 1 Regions under investigation

Supplementary information

Supplementary Information

This file contains Supplementary Sections 1–9, Figs. 1–20, Tables 1–8 and References.

Reporting Summary

Peer Review File

Source data

Rights and permissions

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Davis, J.T., Chinazzi, M., Perra, N. et al. Cryptic transmission of SARS-CoV-2 and the first COVID-19 wave. Nature 600, 127–132 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing