Tracing and analysis of 288 early SARS-CoV-2 infections outside China: A modeling study.

BACKGROUND: In the early months of 2020, a novel coronavirus disease (COVID-19) spread rapidly from China across multiple countries worldwide. As of March 17, 2020, COVID-19 was officially declared a pandemic by the World Health Organization. We collected data on COVID-19 cases outside China during the early phase of the pandemic and used them to predict trends in importations and quantify the proportion of undetected imported cases. METHODS AND
FINDINGS: Two hundred and eighty-eight cases have been confirmed out of China from January 3 to February 13, 2020. We collected and synthesized all available information on these cases from official sources and media. We analyzed importations that were successfully isolated and those leading to onward transmission. We modeled their number over time, in relation to the origin of travel (Hubei province, other Chinese provinces, other countries) and interventions. We characterized the importation timeline to assess the rapidity of isolation and epidemiologically linked clusters to estimate the rate of detection. We found a rapid exponential growth of importations from Hubei, corresponding to a doubling time of 2.8 days, combined with a slower growth from the other areas. We predicted a rebound of importations from South East Asia in the successive weeks. Time from travel to detection has considerably decreased since first importation, from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020. However, we estimated 36% of detection of imported cases. This study is restricted to the early phase of the pandemic, when China was the only large epicenter and foreign countries had not discovered extensive local transmission yet. Missing information in case history was accounted for through modeling and imputation.
CONCLUSIONS: Our findings indicate that travel bans and containment strategies adopted in China were effective in reducing the exportation growth rate. However, the risk of importation was estimated to increase again from other sources in South East Asia. Surveillance and management of traveling cases represented a priority in the early phase of the epidemic. With the majority of imported cases going undetected (6 out of 10), countries experienced several undetected clusters of chains of local transmissions, fueling silent epidemics in the community. These findings become again critical to prevent second waves, now that countries have reduced their epidemic activity and progressively phase out lockdown.

Introduction

After being first identified in China in January 2020, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reached all countries worldwide within a few months [1]. Massive intervention measures [2] were implemented by Chinese authorities in late January to control the epidemic. Countries outside China promptly reinforced border controls and intensified active surveillance to rapidly detect and isolate importations, trace contacts, and isolate suspect cases [3,4]. Such a massive response did not prevent countries to face importations of cases and experience extended independent chains of local transmission [5-7], ultimately resulting in sustained local spread.

The effectiveness of measures aiming at preventing importations and local transmissions critically depends on disease epidemiology and natural history of the infection [8,9], as well as the volume of importations [3]. For the case of coronavirus disease 2019 (COVID-19), the presence of an incubation period, during which infected individuals carry on their usual activities (including travel), was a major challenge for screening controls at airports [8]. Moreover, mild non-specific symptoms and transmission before the onset of clinical symptoms [10-12] compromised infection control measures for importations and onward transmissions [9]. Imported cases went undetected and contributed to the global spread of the disease [13-18].

Here we systematically collected and analyzed data on the first 288 COVID-19 confirmed cases outside China, to characterize the international spread of COVID-19 pandemic during its early phase. We show that the detailed information carried by case histories provided early evidence of the COVID-19 specific features that enabled the virus to escape containment efforts and reach pandemic proportion. We analyzed importations that were successfully isolated and those leading to onward transmission, characterizing their case timeline. We developed a statistical model to describe trends in importations up to mid-February and quantified the proportion of undetected imported cases.

Methods

Data collection and synthesis

We collected all international cases confirmed by official public health sources in the period from January 3 to February 13, 2020. Case history was reconstructed by searching the scientific literature, official public health sources, and news [19-39]. Case history included dates of travel and symptom onset, date of COVID-19 confirmation, date of hospital admission, date of case isolation, travel history, epidemiological link with other cases, and hospitalization history. International cases included imported cases, secondary cases out of China, and repatriations. Cases from cruises were not considered here. Information was extracted by LDD and EO and checked by MM. Additional cases in the period from February 14 to February 27 were collected and used to validate the results.

The full database, along with the database describing clusters, was made publicly available [40].

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline. STROBE checklist can be found in the Supporting Information (S1 STROBE Checklist). The study did not have a protocol.

Descriptive analysis

For imported cases with full information on the timeline of events, we computed the average duration from travel to onset, from travel to hospitalization, and from hospitalization to reporting. We used analysis of variance to compare groups of imported cases that generated or did not generate local transmissions. We extended the analysis to all imported cases combining cases with full and partial information on the timeline. We used the analysis of variance and multiple imputation for missing data. Results were combined using Rubin’s approach [41].

Modeling and predicting importations

We modeled the total number of imported cases out of China over time accounting for date of travel, delay in reporting, and source areas.

We distinguished between 3 different sources: Hubei province (H), the rest of China (C), other countries (O). We modeled imported cases over time as a piecewise exponential function depending on the source and on travel restrictions in place. We assumed a different situation in Hubei province and the rest of the world because of the level of awareness in the different phases of the outbreak. The exponential functions are defined as follows: where is the growth rate of cases coming from Hubei, and , with , the growth rates of cases coming from the rest of China and other countries, respectively. Travel restrictions were modeled by assuming a discontinuity in the growth rate. For Hubei, we assumed the growth rate to change from to after the travel ban of January 23, 2020 (indicated with T); for the rest of China, we assumed an analogous change from to after January 29, 2020 (T), date of first flight cancellations [42]. No change was considered for the other countries (r constant over time), as no restrictions of travel were established toward these countries. The scale parameters of the exponential functions () were assumed to be different among the 3 sources, to account for different traveling volumes and dates of beginning of importations.

We modeled the time τ from importation to detection of a case with a gamma distribution, g(τ), conditioned to the date of case importation, t. The distribution g(τ) was assumed to have constant coefficient of variation (SD/mean) achieved by a constant shape parameter and a rate parameter varying smoothly in time to capture change in surveillance efficiency.

We used a Bayesian framework to fit the model to imported cases by origin, travel date, and confirmation date. Cases with partial information (e.g., missing date and/or origin of travel) were included by defining latent variables marginalized out during inference. The model was then used to predict imported cases 2 weeks in the future. All details of the analysis are reported in the Supporting Information (S1 Text).

Estimation of underdetection of imported cases

We analyzed clusters of transmission generated by imported cases (index case(s) in each cluster) to estimate undetected importations. A cluster can be seeded by more than 1 index case, e.g., by infected family members who traveled together. The number of clusters of local transmission was modeled with a multinomial distribution according to the number of index cases and whether these were imported cases. As detailed in the Supporting Information, the likelihood function was a function of 4 observable quantities: the number of observed clusters with 1 imported index case (x1); the number of observed clusters with more than 1 imported index case (x2); the number of known imported cases causing no onward transmission (); the number of clusters for which an index case was not identified (z); and a fifth and unobserved quantity, the number of undetected imported cases who did not start onward transmission (w). Maximization of the likelihood allowed to estimate w from the records of x1, x2, , z and to compute the expected number of unobserved imported cases. Additional details are described in the Supporting Information (S1 Text).

Results

Timeline of travel-related cases

We collected 288 cases, including 163 imported cases, 109 cases involved in local transmissions, 30 repatriations, and 1 case of unknown origin. Fifteen cases were classified as both imported and local transmissions, since they contracted the infection outside China and traveled to a different country once infected (ES01, ES02, GB03, GB04, GB05, GB06, GB07, GB08, KR12, KR16, KR17, KR19, MY09, TH20, and TH21 in our database [40]).

Fig 1 summarizes the timeline of imported cases. Symptom onset occurred after the travel to the destination country for almost all cases for which date of travel and of onset are available (68 out of 73, 93%). Complete information was available for 51 (31%) imported cases, with quality of information decreasing over time (S2 Text, Fig A).

Fig 1

Timeline of importation for all imported cases.

For each imported case, available information on travel date, onset date, hospitalization date, and confirmation date are displayed in the grid. Travel origin is color-coded (orange, red, brown for Hubei, China, outside China, respectively). Cases who generated a cluster upon arrival are highlighted in yellow.

Among imported cases with full information, the delay from travel to hospitalization was longer in cases that generated secondary transmissions (mean of 10 ± 0.97 days compared with 5.5 ± 0.67 days, p = 0.003). Overall, the duration from travel to first event (whether symptom onset, or hospitalization for asymptomatic) was also longer, although the difference was not statistically significant (5.0 ± 0.9 days versus 3.7 ± 0.5 days, p = 0.08). Durations of hospitalization were instead comparable among the 2 groups of cases (1.5 ± 0.7 days versus 2.6 ± 0.4 days for cases that generated or did not generate secondary transmissions, respectively). Including imported cases with missing information through imputation, we found the same trend though smaller in magnitude and not statistically significant (delay from travel to hospitalization 9.8 ± 1.2 versus 8.3 ± 0.5 days p = 0.3; delay from travel to onset 5.8 ± 1.1 versus 4.2 ± 0.5 p = 0.16, for cases that generated or did not generate secondary transmissions, respectively). This suggests that importations with missing information may be closer in characteristics to index cases leading to onward transmission.

The statistical model predicted a decrease in the average time from travel to detection from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020 (Fig 2).

Fig 2

Delay from travel to detection as a function of the date of travel: data points, mean, and model estimate.

Predicting travel-related cases

The model predicted a rapid exponential growth of importations from Hubei, with a growth rate (95% CI 0.21–0.31), corresponding to a doubling time of 2.8 days. In comparison, the exponential growth from other territories (rest of China and countries other than China) was slow, (95% CI 0.00–0.08). After the implementation of travel restrictions, a negative growth rate was estimated, signaling a decline in imported cases. The decline was sharp for Hubei ( [95% CI −0.85 to −0.48]) and more gradual for the rest of China ( [95% CI −0.54, 0.00]). All estimated parameters and their confidence intervals are reported in the Supporting Information (S2 Text, Table B).

The predicted trend of all imported cases over time is shown in Fig 3, compared with the observed data. Reported importations were predicted to remain stationary in the second and third week of February and to rise again because of the effect of transmission clusters outside China. Imported cases after February 13, 2020, were in agreement with model predictions (Fig 3) up to February 24. Data in the successive 3 days diverged from our predictions as they were linked with early detection of multiple imported cases in the Middle East and Europe from sustained local transmission in Iran and Italy.

Fig 3

Number of imported cases by date of travel and of reporting: data points and model predictions.

Stars correspond to model predictions in the successive 2 weeks; void stars refer to importations from Iran and Italy that could not be captured by the model because of its assumptions.

Transmission clusters outside China

Forty-two transmission clusters were identified out of China in the timeframe under study. Table 1 summarizes the size and country of each cluster. Clusters were grouped according to whether the index case (1) was a traveling case identified prior to cluster detection, (2) a traveling case not identified or identified retrospectively once the cluster was observed, or (3) completely unknown. Assuming that clusters of unknown origin were linked to one of the already observed imported cases—or, in other words, not linked to an undetected imported case—led to an estimate of 76 (95% CI 49–118) undetected imported cases. In this scenario, detected cases would amount to 65% of all imported cases. Assuming instead that all clusters of unknown origin were due to undetected imported cases increased the number of undetected cases to 225 (95% CI 186–369), i.e., detected cases would correspond to only 36% of the total.

Table 1

Summary of transmission clusters according to the type of index case.

Index Case	Number of Clusters	Clusters (size)
Traveler(s) identified prior to cluster detectiona	15	cDE01 (16), cFR02 (12), cVN02 (7), cKR01 (5), cSG04b (5), cKR04 (3), cMY01 (3), cSG11b (3), cVN01 (3), cGB01 (2), cKR02 (2), cKR03 (2), cKR05 (2), cUS01 (2), cUS02 (2)
Traveler(s) not identified or retrospectively identifiedc	8	cSG01 (10), cSG02 (8), cJP01 (4), cCA01 (3), cKR06 (3), cTH04 (3), cFR01 (2), cJP02 (2)
Unknownd	19	cSG13 (8), cSG09 (5), cJP03 (3), cJP06 (3), cSG14 (3), cJP04 (2), cJP05 (2), cJP07 (2), cSG03 (2), cSG05 (2), cSG06 (2), cSG07 (2), cSG08 (2), cSG10 (2), cSG12 (2), cTH01 (2), cTH02 (2), cTH03 (2), cAE01e

aThe index case was identified independently from secondary transmissions.

bCluster associated with 2 traveling cases.

cThe index case was either identified retrospectively following case investigation prompted by the detection of secondary cases or the identity was not identified; however, the cluster was linked to a specific location/circumstance visited by Chinese travelers (shop, conference, bus tour).

dNo connection with other case or source of infection has been identified yet.

eInsufficient information on the size of the cluster.

Discussion

We reviewed here all confirmed cases out of China from January 3 to February 13, 2020, and gathered detailed information on case history and epidemiological links to (1) identify salient epidemiological features, (2) assess the impact of travel restrictions in China on the importations of cases worldwide, and (3) evaluate the effectiveness of control measures against importations. We found a rapid exponential growth of importations from Hubei, up to the closure of Wuhan airport preventing further travel of cases, combined with a slower growth from other countries in South East Asia. The estimated growth of importation before interventions is compatible with a doubling time of 2.8 days. Time from travel to detection considerably decreased across time, from 14.5 ± 5.5 days on January 5, 2020, to 6 ± 3.5 days on February 1, 2020. However, our estimates indicate that only 36% of imported cases were detected. This study is restricted to the early phase of the pandemic, when China was the only large epicenter and extensive silent transmission in other countries, such as Iran and Italy, was not discovered yet.

The substantially larger growth of importations from China compared with other territories is related to a stronger epidemic activity in the Hubei province, origin of the outbreak, with respect to other affected areas [5]. This difference is likely an outcome of the containment measures in China [2,43,44] and of the increased awareness following their implementation [45-49], leading to more efficient control. Identification, rapid management of cases, and contact tracing were indeed proposed by the World Health Organization as key to contain the epidemic globally.

We found that the travel ban in Wuhan produced a sharp decline in importations from the region. Combined with local containment measures in the rest of China and other countries in Southeast Asia, this resulted in a substantial overall reduction of exported cases worldwide. Indeed, after peaking at the end of January, registered traveling cases declined to plateau at very low levels. On one side, this shows that strict travel bans may be beneficial by reducing importations to manageable levels and by giving countries the time to prepare and strengthen their surveillance systems in the short term, as signaled by a reduction of the interval from travel date to detection over time. On the other hand, however, the decline likely occurred too late when local spreading was already established in many countries outside the epicenter of the epidemic [50,51]. At the end of February, sustained community transmission in Iran and Italy led to exportations to several other countries in the world in a timeframe of few days [52,53]. This was facilitated by the fact that no local transmission was reported in these countries, preventing the alert to other countries for them to deploy targeted surveillance and control. At that time, indeed, case definition for the importation of a COVID-19 suspect case was based exclusively on China as the origin of exposure or travel [54,55], with few exceptions including East Asian countries [56,57].

Monitoring imported cases is critical in territories with sporadic epidemic activity where the attention is concentrated on preventing the introduction of cases. At the time of writing, countries such as China and Australia show limited local transmission and are concentrating strong efforts in border controls to avoid a second epidemic wave, that could be fueled by the large proportion of the population still susceptible to SARS-CoV-2. Strict measures, such as massive testing and quarantine of incoming travelers, are being implemented to block silent introductions of cases as it occurred at the beginning of the pandemic.

We found that during the first half of February, countries outside China witnessed an increasing reporting of clusters with no known epidemiological link [5,16]. Our estimates indicate a detection ability of 36% to ascertain imported cases in countries outside China. This means that approximately 6 imported cases out of 10 have gone undetected. Previous detection rates estimates range from 27% [15] to 38% [15,17], with variations across countries [13,15]. Ascertainment was estimated to be even lower (approximately 10%) when assessed on repatriations [58]. Here, we excluded from this analysis all repatriation events and cruises with outbreaks, as conditions for detection and identification may be different.

Underdetection may have been due to several different factors including asymptomatic infections, infections with mild clinical symptoms, health-seeking behavior and declaration of travel history, case definition, and underdiagnosis. A relative long prodrome phase preceding symptom onset (approximately 2 days [59]) may have limited the tracing and isolation of contacts. We found, indeed, that traveling cases generating transmissions following importation were, in general, spending more time in the community prior to hospitalization—although this result was diluted upon imputation of missing values—signaling that the period of mild or no symptoms during which the individual carries on a normal life is important for the generation of secondary cases. The epidemic emergency starting with one single critical case on February 21, 2020, in Italy and accumulating hundreds of cases in few days [16] showed that clusters had gone undetected and epidemiological links with the index case were not found. Subsequent developments of the pandemic showed that underdetection of importations was a clear pattern in fueling large-scale epidemics that spread unseen in several countries before the first critical cases were finally detected [52,60].

Our study is affected by limitations. Underdetection of imported cases during the early phase of the pandemic might have been higher than what estimated here, as our analysis is conditional to the identification of clusters of cases. Underdetection may also proceed from the imperfect characteristics of RT-PCR (reverse transcription-polymerase chain reaction) tests used to identify infected cases. Some cases tested for SARS-CoV-2 could have been falsely negative, and this would affect both analyses presented in the manuscript. This is in line with our conclusion that a large part of imported cases may have been undetected. As the number of detected imported cases grew considerably, the quality of the information on case history degraded over time. We used modeling and imputation to account for the missing information regarding the most recent imported cases. Extension of this study beyond the early phase of the pandemic is limited by the strong spatiotemporal heterogeneities currently characterizing the pandemic. Our assumptions are based on a context of localized epicenter and border controls targeted against importations from China and countries in South East Asia who experienced local transmission during the early phase. As the pandemic regime shifted toward a multiple delocalized epicenter in late February, additional travel bans were implemented worldwide to prevent importations from Europe and North America. This led to heavy disruptions of the flight network and a change of focus toward within-country transmission.

Our findings provide critical epidemiological understanding on the rate of importation and detection of cases during the early phase of the pandemic. Though effective in reducing international spread, the travel ban in Wuhan did not prevent the seeding of the pandemic in other countries, later becoming new epicenters of the pandemic. The epidemiological features of COVID-19 facilitated the silent spread of the epidemic in seeded countries. The lessons learnt during the early phase of the pandemic become critical again to prevent second waves now that countries have reduced their epidemic activity and progressively phase out lockdown.

Statistical methods.

Modeling traveling cases, delay from arrival to detection and index case detection probability.

(PDF)

Click here for additional data file.

Additional results.

Dataset of international cases, results of likelihood estimation, sensitivity analyses, and analysis of imported clusters.

(PDF)

Click here for additional data file.

Official sources for international cases.

(PDF)

Click here for additional data file.

(PDF)

Click here for additional data file.

28 Feb 2020

Dear Dr Colizza,

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14 Apr 2020

Dear Dr. Colizza,

Thank you very much for submitting your manuscript "Lessons learnt from 288 COVID-19 international cases: importations over time, effect of interventions, underdetection of imported cases" (PMEDICINE-D-20-00657R1) for consideration at PLOS Medicine.

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Discussion-please revise the language in this section esp lines 184-190, line 206-209

You may wish to expand on local transmission, underdetection and undiagnosis of cases worldwide here

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Comments from the reviewers:

Reviewer #1: This is a review for "Lessons learnt from 288 COVID-19 international cases: importations over time, effect of interventions, underdetection of imported cases" by Pinotti et al. This paper presents results from statistical inference on a model of case importation along with responsive intervention in regards to the early stages of the COVID-19 outbreak. The research is novel and of interest to a potentially large audience but I believe that the paper needs revisions before it is ready for publication.

1) The language in this paper needs to be reframed as analysis of the early stages of an outbreak rather than a description of an ongoing and changing situation. Current sections read somewhat like a news article rather than journal article. Many of these problems can be fixed by changing tense.

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23-24 "With the majority of imported cases going undetected (6 out of 10), countries should be prepared for the possible emergence of several undetected clusters of chains of local transmissions."

184  "The reduced volume of exported cases worldwide following the travel ban may have given countries the time to prepare and strengthen their surveillance systems, as signaled by a reduction of the interval from travel date to detection over time."

192  "...from East Asian countries [29]. ECDC and WHO currently base their case definition on travel from China only [30,31], but this may rapidly change in the next days."

2) There are not enough references from primary sources - this paper should be framed around existing scientific knowledge from previous outbreaks. About a third of the references are from primary sources (published articles or books), another third are from organisational reports or medRxiv and the last third are from online sources (e.g. Twitter, online news). Additionally, a couple citations appear to be incomplete.

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S. Bhatia et al., "Report 6: Relative sensitivity of international surveillance," p. 6.

3) Several sections were difficult to read or had grammatical errors.

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13 "We characterized importations timeline..."

76 The definition in this section is confusing. Here S = H, C while in the supplementary information (which is clearer) S = H, C, O. Perhaps include S on the second line of the piecewise equation where S = O?

86 It is hard to read subsequent sentences where one ends and the other starts with a variable or function name.

98-101 In "Estimation of under-detection of imported cases" it is not clear what you are saying here or how the multinomial is defined. The next part of the section (where you define the likelihood) clarifies but the intro in lines 98-101 is confusing:

"We modelled the number of such 'cluster seeds', i.e. groups of index cases, with a multinomial distribution depending on the portion of cluster seeds of size 1 or greater than 1 (for simplicity, this was taken as 2), on the probability of detection of a seed, and on occurrence of secondary transmission."

197-198 What has an ability of 36%?

"Our estimates indicate an ability of 36% to detect imported cases in countries outside China."

199 This reads poorly (perhaps "Previous detection rate estimates..." ?)

"Previous estimates range from 27% [13] to 38% [13, 15] detection rates, with variations across

200 countries [13, 15]."

4) Supplementary Information and Methods

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There is a section with a sensitivity analysis where the ban date in Wuhan is a day later. It would be nice to have model comparison using LOO or WAIC between a few select models (piecewise vs single exponential, etc...) Your model should explain the data better than a naive model and it would be easy to show here.

Fig S2:

a) The MCMC chains appear to be thinned; this should be noted in the caption.

b) You should state if the mixing in STAN reported any divergence if you are showing convergence of the chains.

c) You state that both the black line and histogram show the posterior distribution, but the black lines look a bit more like priors to me.

5) Possible additional data for analysis

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I realise the current COVID-19 pandemic is ongoing and any additional analysis will not be comprehensive. But given that there is onging data, the paper would benefit from comparing model predictions to a more current set of data.

Reviewer #2: Pinotti and colleagues have developed a mathematical model, using a Bayesian inference framework, to forecast trends in imported cases in China and estimate the percentage of imported cases and changes rate of detection of these cases. They used available data on 288 COVID-19 cases from a variety of data sources. Two growth functions are used: one for China - split by hubei and rest of China and separate growth function for other countries, with a break in growth rate denoted by Ts when restrictions were put in place. A separate growth function is used for other countries but assumes no discontinuity in growth rate. The mathematics are fully documented in supplemental materials and will seem complex but fundamentally - this is largely modelling growth functions with a structured break due to travel bans over a period of time. Latent variables were used to incorporate missing data where cases had incomplete information, authors used an inference technique to marginalise ("summing out" the probability of a random variable given the joint probability distribution). The Bayesian framework means that there are quite a few distributions to fit around model parameters. A slight nit-pick of this would be that the supplemental could potentially better rationalise the authors assumption no the choice of distribution. It may seem familiar to those with mathematical modelling experience, but it would not likely to be obvious to most. For instance, gamma distribution skewed nature to reflect change in surveillance; also rationale needed to truncated the gamma distribution on the detection delay to 25 days. Apart from some minor text issues (Line 25 in Supplemental - I think the authors meant "tuples" here), I think the model is quite reflective of what is occurring currently. Empirically, the model fits the data very well. The rebound in imported cases is occurring in China (and other areas) is now occurring so it would be interesting to conduct some follow-up work given how fast paced new data is coming in. One final issue is potentially, which the authors do not address in the discussion is the impact of inaccuracies of testing. Some studies have characterised multiple shedding routes (https://www.ncbi.nlm.nih.gov/pubmed/32065057?dopt=Abstract). Accurate testing methods are work in progress but it's worth noting that when new studies do come out on these parameters, it would be worth incorporating potential false negative results which may have on the impact of rebounding infection rates from imported cases. Apart from these comments - I think this study is important and merits publications.

Any attachments provided with reviews can be seen via the following link:

[LINK]

12 May 2020

Submitted filename: point_by_point_reply.docx

Click here for additional data file.

27 May 2020

Dear Dr. Colizza,

Thank you very much for re-submitting your manuscript "Lessons learnt from the first 288 COVID-19 international cases: a modelling study" (PMEDICINE-D-20-00657R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by xxx reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

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We look forward to receiving the revised manuscript by Jun 03 2020 11:59PM.

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PLOS Medicine

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Requests from Editors:

Please adapt the title to better match journal style. We suggest: "Tracing and analysis of 288 early SARS-CoV-2 infections outside China: a modeling study".

Please add some additional quantitative details of interest to the "methods and findings" subsection of your abstract. For example, the observation noted at lines 177-178 might be worth quoting; likewise the doubling time mentioned at line 185.

At line 37, please begin the sentence "Our findings indicate that ..." or similar.

At line 183 and in other places, we are not sure that "Nowcasting" is a widely understood scientific term: please consider rephrasing.

Early in the methods section of your main text, please state whether the study had a protocol or prespecified analysis plan (and if so attach the document as an attachment, referred to in the text). Please highlight analyses that were not prespecified.

Please restructure the early part of the "Discussion" section of your main text: the first paragraph should summarize the study's main findings, with these being discussed in subsequent paragraphs.

Please remove the section on "Funding" from the end of the text (this information will appear in the metadata).

In your reference list, please ensure that all citations meet journal style (italics should be rendered in plain text). For reference 9, for example, formatting should be as follows: Fraser C, Riley S, Anderson RM, Ferguson NM. Factors that make an infectious disease outbreak controllable. Proc Natl Acad Sci U S A. 2004;101:6146-6151.

Please rename the STROBE attachment "S1_STROBE_Checklist". In the "Section" column, please add paragraph numbers to accompany the section where individual items appear (avoiding line or page numbers, which generally change in the event of publication).

Comments from Reviewers:

Any attachments provided with reviews can be seen via the following link:

[LINK]

16 Jun 2020

Dear Dr. Colizza,

On behalf of my colleagues and the academic editor, Dr. Richard Zachariah Aandahl, I am delighted to inform you that your manuscript entitled "Tracing and analysis of 288 early SARS-CoV-2 infections outside China: a modeling study" (PMEDICINE-D-20-00657R3) has been accepted for publication in PLOS Medicine.

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Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Adya Misra, PhD

Senior Editor

PLOS Medicine

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