@article{ali:Science:2020,
  title = {Serial Interval of {{SARS}}-{{CoV}}-2 Was Shortened over Time by Nonpharmaceutical Interventions},
  author = {Ali, Sheikh Taslim and Wang, Lin and Lau, Eric H. Y. and Xu, Xiao-Ke and Du, Zhanwei and Wu, Ye and Leung, Gabriel M. and Cowling, Benjamin J.},
  year = {2020},
  month = jul,
  volume = {369},
  pages = {1106--1109},
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.abc9004},
  abstract = {Studies of novel coronavirus disease (COVID-19) have reported varying estimates of epidemiological parameters including serial interval distributions, i.e., the time between illness onset in successive cases in a transmission chain, and reproduction numbers. By compiling a line-list database of transmission pairs in mainland China, we show that mean serial intervals of COVID-19 have shortened substantially from 7.8 days to 2.6 days within a month (January 9 to February 13, 2020). This change is driven by enhanced non-pharmaceutical interventions, in particular case isolation. We also show that using real-time estimation of serial intervals allowing for variation over time, provides more accurate estimates of reproduction numbers than using conventionally fixed serial interval distributions. These findings could improve assessment of transmission dynamics, forecasting future incidence, and estimating the impact of control measures.},
  chapter = {Report},
  copyright = {Copyright \textcopyright{} 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/PBQXYYDX/Ali et al. - 2020 - Serial interval of SARS-CoV-2 was shortened over t.pdf},
  journal = {Science},
  language = {en},
  number = {6507},
  pmid = {32694200}
}

@article{ashcroft:SMW:2020,
  title = {{{COVID}}-19 Infectivity Profile Correction},
  author = {Ashcroft, Peter and Huisman, Jana S. and Lehtinen, Sonja and Bouman, Judith A. and Althaus, Christian L. and Regoes, Roland R. and Bonhoeffer, Sebastian},
  year = {2020},
  volume = {150},
  pages = {w20336},
  doi = {10.4414/smw.2020.20336},
  abstract = {Presymptomatic infections are spread over a longer time period before symptom onset than previously thought, which can have significant ramifications for contact tracing efforts.},
  copyright = {All rights reserved},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/AZGKQKEC/Ashcroft et al_2020_COVID-19 infectivity profile correction.pdf},
  journal = {Swiss Medical Weekly},
  language = {en}
}

@article{bi:TheLancetInfectiousDiseases:2020,
  title = {Epidemiology and Transmission of {{COVID}}-19 in 391 Cases and 1286 of Their Close Contacts in {{Shenzhen}}, {{China}}: A Retrospective Cohort Study},
  shorttitle = {Epidemiology and Transmission of {{COVID}}-19 in 391 Cases and 1286 of Their Close Contacts in {{Shenzhen}}, {{China}}},
  author = {Bi, Qifang and Wu, Yongsheng and Mei, Shujiang and Ye, Chenfei and Zou, Xuan and Zhang, Zhen and Liu, Xiaojian and Wei, Lan and Truelove, Shaun A. and Zhang, Tong and Gao, Wei and Cheng, Cong and Tang, Xiujuan and Wu, Xiaoliang and Wu, Yu and Sun, Binbin and Huang, Suli and Sun, Yu and Zhang, Juncen and Ma, Ting and Lessler, Justin and Feng, Tiejian},
  year = {2020},
  month = aug,
  volume = {20},
  pages = {911--919},
  publisher = {{Elsevier}},
  issn = {1473-3099, 1474-4457},
  doi = {10.1016/S1473-3099(20)30287-5},
  abstract = {{$<$}h2{$>$}Summary{$<$}/h2{$><$}h3{$>$}Background{$<$}/h3{$><$}p{$>$}Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures.{$<$}/p{$><$}h3{$>$}Methods{$<$}/h3{$><$}p{$>$}From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk.{$<$}/p{$><$}h3{$>$}Findings{$<$}/h3{$><$}p{$>$}Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91\%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95\% CI 20\textendash 22). Cases were isolated on average 4{$\cdot$}6 days (95\% CI 4{$\cdot$}1\textendash 5{$\cdot$}0) after developing symptoms; contact tracing reduced this by 1{$\cdot$}9 days (95\% CI 1{$\cdot$}1\textendash 2{$\cdot$}7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6{$\cdot$}27 [95\% CI 1{$\cdot$}49\textendash 26{$\cdot$}33] for household contacts and 7{$\cdot$}06 [1{$\cdot$}43\textendash 34{$\cdot$}91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11{$\cdot$}2\% (95\% CI 9{$\cdot$}1\textendash 13{$\cdot$}8), and children were as likely to be infected as adults (infection rate 7{$\cdot$}4\% in children {$<$}10 years \emph{vs} population average of 6{$\cdot$}6\%). The observed reproductive number (\emph{R}) was 0{$\cdot$}4 (95\% CI 0{$\cdot$}3\textendash 0{$\cdot$}5), with a mean serial interval of 6{$\cdot$}3 days (95\% CI 5{$\cdot$}2\textendash 7{$\cdot$}6).{$<$}/p{$><$}h3{$>$}Interpretation{$<$}/h3{$><$}p{$>$}Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the \emph{R}. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control.{$<$}/p{$><$}h3{$>$}Funding{$<$}/h3{$><$}p{$>$}Emergency Response Program of Harbin Institute of Technology, Emergency Response Program of Peng Cheng Laboratory, US Centers for Disease Control and Prevention.{$<$}/p{$>$}},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/KFS7RZV4/Bi et al_2020_Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close.pdf},
  journal = {The Lancet Infectious Diseases},
  language = {English},
  number = {8},
  pmid = {32353347}
}

@article{brooks:TheLancet:2020,
  title = {The Psychological Impact of Quarantine and How to Reduce It: Rapid Review of the Evidence},
  shorttitle = {The Psychological Impact of Quarantine and How to Reduce It},
  author = {Brooks, Samantha K. and Webster, Rebecca K. and Smith, Louise E. and Woodland, Lisa and Wessely, Simon and Greenberg, Neil and Rubin, Gideon James},
  year = {2020},
  volume = {395},
  pages = {912--920},
  publisher = {{Elsevier}},
  issn = {0140-6736, 1474-547X},
  doi = {10.1016/S0140-6736(20)30460-8},
  abstract = {{$<$}h2{$>$}Summary{$<$}/h2{$><$}p{$>$}The December, 2019 coronavirus disease outbreak has seen many countries ask people who have potentially come into contact with the infection to isolate themselves at home or in a dedicated quarantine facility. Decisions on how to apply quarantine should be based on the best available evidence. We did a Review of the psychological impact of quarantine using three electronic databases. Of 3166 papers found, 24 are included in this Review. Most reviewed studies reported negative psychological effects including post-traumatic stress symptoms, confusion, and anger. Stressors included longer quarantine duration, infection fears, frustration, boredom, inadequate supplies, inadequate information, financial loss, and stigma. Some researchers have suggested long-lasting effects. In situations where quarantine is deemed necessary, officials should quarantine individuals for no longer than required, provide clear rationale for quarantine and information about protocols, and ensure sufficient supplies are provided. Appeals to altruism by reminding the public about the benefits of quarantine to wider society can be favourable.{$<$}/p{$>$}},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/RBMVE6TH/Brooks et al_2020_The psychological impact of quarantine and how to reduce it.pdf},
  journal = {The Lancet},
  language = {English},
  number = {10227},
  pmid = {32112714}
}

@article{buitrago-garcia:PLOSMedicine:2020,
  title = {Occurrence and Transmission Potential of Asymptomatic and Presymptomatic {{SARS}}-{{CoV}}-2 Infections: {{A}} Living Systematic Review and Meta-Analysis},
  shorttitle = {Occurrence and Transmission Potential of Asymptomatic and Presymptomatic {{SARS}}-{{CoV}}-2 Infections},
  author = {{Buitrago-Garcia}, Diana and {Egli-Gany}, Dianne and Counotte, Michel J. and Hossmann, Stefanie and Imeri, Hira and Ipekci, Aziz Mert and Salanti, Georgia and Low, Nicola},
  year = {2020},
  volume = {17},
  pages = {e1003346},
  publisher = {{Public Library of Science}},
  issn = {1549-1676},
  doi = {10.1371/journal.pmed.1003346},
  abstract = {Background There is disagreement about the level of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We conducted a living systematic review and meta-analysis to address three questions: (1) Amongst people who become infected with SARS-CoV-2, what proportion does not experience symptoms at all during their infection? (2) Amongst people with SARS-CoV-2 infection who are asymptomatic when diagnosed, what proportion will develop symptoms later? (3) What proportion of SARS-CoV-2 transmission is accounted for by people who are either asymptomatic throughout infection or presymptomatic? Methods and findings We searched PubMed, Embase, bioRxiv, and medRxiv using a database of SARS-CoV-2 literature that is updated daily, on 25 March 2020, 20 April 2020, and 10 June 2020. Studies of people with SARS-CoV-2 diagnosed by reverse transcriptase PCR (RT-PCR) that documented follow-up and symptom status at the beginning and end of follow-up or modelling studies were included. One reviewer extracted data and a second verified the extraction, with disagreement resolved by discussion or a third reviewer. Risk of bias in empirical studies was assessed with an adapted checklist for case series, and the relevance and credibility of modelling studies were assessed using a published checklist. We included a total of 94 studies. The overall estimate of the proportion of people who become infected with SARS-CoV-2 and remain asymptomatic throughout infection was 20\% (95\% confidence interval [CI] 17\textendash 25) with a prediction interval of 3\%\textendash 67\% in 79 studies that addressed this review question. There was some evidence that biases in the selection of participants influence the estimate. In seven studies of defined populations screened for SARS-CoV-2 and then followed, 31\% (95\% CI 26\%\textendash 37\%, prediction interval 24\%\textendash 38\%) remained asymptomatic. The proportion of people that is presymptomatic could not be summarised, owing to heterogeneity. The secondary attack rate was lower in contacts of people with asymptomatic infection than those with symptomatic infection (relative risk 0.35, 95\% CI 0.10\textendash 1.27). Modelling studies fit to data found a higher proportion of all SARS-CoV-2 infections resulting from transmission from presymptomatic individuals than from asymptomatic individuals. Limitations of the review include that most included studies were not designed to estimate the proportion of asymptomatic SARS-CoV-2 infections and were at risk of selection biases; we did not consider the possible impact of false negative RT-PCR results, which would underestimate the proportion of asymptomatic infections; and the database does not include all sources. Conclusions The findings of this living systematic review suggest that most people who become infected with SARS-CoV-2 will not remain asymptomatic throughout the course of the infection. The contribution of presymptomatic and asymptomatic infections to overall SARS-CoV-2 transmission means that combination prevention measures, with enhanced hand hygiene, masks, testing tracing, and isolation strategies and social distancing, will continue to be needed.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/2KZ44IAG/Buitrago-Garcia et al_2020_Occurrence and transmission potential of asymptomatic and presymptomatic.pdf},
  journal = {PLOS Medicine},
  language = {en},
  number = {9}
}

@article{cheng:JAMAInternMed:2020,
  title = {Contact Tracing Assessment of {{COVID}}-19 Transmission Dynamics in {{Taiwan}} and Risk at Different Exposure Periods before and after Symptom Onset},
  author = {Cheng, Hao-Yuan and Jian, Shu-Wan and Liu, Ding-Ping and Ng, Ta-Chou and Huang, Wan-Ting and Lin, Hsien-Ho and {for the Taiwan COVID-19 Outbreak Investigation Team}},
  year = {2020},
  volume = {180},
  pages = {1156--1163},
  publisher = {{American Medical Association}},
  issn = {2168-6106},
  doi = {10.1001/jamainternmed.2020.2020},
  abstract = {{$<$}h3{$>$}Importance{$<$}/h3{$><$}p{$>$}The dynamics of coronavirus disease 2019 (COVID-19) transmissibility are yet to be fully understood. Better understanding of the transmission dynamics is important for the development and evaluation of effective control policies.{$<$}/p{$><$}h3{$>$}Objective{$<$}/h3{$><$}p{$>$}To delineate the transmission dynamics of COVID-19 and evaluate the transmission risk at different exposure window periods before and after symptom onset.{$<$}/p{$><$}h3{$>$}Design, Setting, and Participants{$<$}/h3{$><$}p{$>$}This prospective case-ascertained study in Taiwan included laboratory-confirmed cases of COVID-19 and their contacts. The study period was from January 15 to March 18, 2020. All close contacts were quarantined at home for 14 days after their last exposure to the index case. During the quarantine period, any relevant symptoms (fever, cough, or other respiratory symptoms) of contacts triggered a COVID-19 test. The final follow-up date was April 2, 2020.{$<$}/p{$><$}h3{$>$}Main Outcomes and Measures{$<$}/h3{$><$}p{$>$}Secondary clinical attack rate (considering symptomatic cases only) for different exposure time windows of the index cases and for different exposure settings (such as household, family, and health care).{$<$}/p{$><$}h3{$>$}Results{$<$}/h3{$><$}p{$>$}We enrolled 100 confirmed patients, with a median age of 44 years (range, 11-88 years), including 44 men and 56 women. Among their 2761 close contacts, there were 22 paired index-secondary cases. The overall secondary clinical attack rate was 0.7\% (95\% CI, 0.4\%-1.0\%). The attack rate was higher among the 1818 contacts whose exposure to index cases started within 5 days of symptom onset (1.0\% [95\% CI, 0.6\%-1.6\%]) compared with those who were exposed later (0 cases from 852 contacts; 95\% CI, 0\%-0.4\%). The 299 contacts with exclusive presymptomatic exposures were also at risk (attack rate, 0.7\% [95\% CI, 0.2\%-2.4\%]). The attack rate was higher among household (4.6\% [95\% CI, 2.3\%-9.3\%]) and nonhousehold (5.3\% [95\% CI, 2.1\%-12.8\%]) family contacts than that in health care or other settings. The attack rates were higher among those aged 40 to 59 years (1.1\% [95\% CI, 0.6\%-2.1\%]) and those aged 60 years and older (0.9\% [95\% CI, 0.3\%-2.6\%]).{$<$}/p{$><$}h3{$>$}Conclusions and Relevance{$<$}/h3{$><$}p{$>$}In this study, high transmissibility of COVID-19 before and immediately after symptom onset suggests that finding and isolating symptomatic patients alone may not suffice to contain the epidemic, and more generalized measures may be required, such as social distancing.{$<$}/p{$>$}},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/2M2C87N8/Cheng et al_2020_Contact Tracing Assessment of COVID-19 Transmission Dynamics in Taiwan and Risk.pdf},
  journal = {JAMA Internal Medicine},
  language = {en},
  number = {9}
}

@article{clifford:medRxiv:2020,
  title = {Strategies to Reduce the Risk of {{SARS}}-{{CoV}}-2 Re-Introduction from International Travellers},
  author = {Clifford, Samuel and Quilty, Billy J. and Russell, Timothy W. and Liu, Yang and Chan, Yung-Wai Desmond and Pearson, Carl A. B. and Eggo, Rosalind M. and Endo, Akira and Group, CMMID COVID-19 Working and Flasche, Stefan and Edmunds, W. John},
  year = {2020},
  pages = {2020.07.24.20161281},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.07.24.20161281},
  abstract = {{$<$}p{$>$}To mitigate SARS-CoV-2 transmission risks from international travellers, many countries currently use a combination of up to 14 days of self-quarantine on arrival and testing for active infection. We used a simulation model of air travellers arriving to the UK from the EU or the USA and the timing of their stages of infection to evaluate the ability of these strategies to reduce the risk of seeding community transmission. We find that a quarantine period of 8 days on arrival with a PCR test on day 7 (with a 1-day delay for test results) can reduce the number of infectious arrivals released into the community by a median 94\% compared to a no quarantine, no test scenario. This reduction is similar to that achieved by a 14-day quarantine period (median 99\% reduction). Shorter quarantine periods still can prevent a substantial amount of transmission; all strategies in which travellers spend at least 5 days (the mean incubation period) in quarantine and have at least one negative test before release are highly effective (e.g. a test on day 5 with release on day 6 results in a median 88\% reduction in transmission potential). Without intervention, the current high prevalence in the US (40 per 10,000) results in a higher expected number of infectious arrivals per week (up to 23) compared to the EU (up to 12), despite an estimated 8 times lower volume of travel in July 2020. Requiring a 14-day quarantine period likely results in less than 1 infectious traveller each entering the UK per week from the EU and the USA (97.5th percentile). We also find that on arrival the transmission risk is highest from pre-symptomatic travellers; quarantine policies will shift this risk increasingly towards asymptomatic infections if eventually-symptomatic individuals self-isolate after the onset of symptoms. As passenger numbers recover, strategies to reduce the risk of re-introduction should be evaluated in the context of domestic SARS-CoV-2 incidence, preparedness to manage new outbreaks, and the economic and psychological impacts of quarantine.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/LEJ59YDT/Clifford et al_2020_Strategies to reduce the risk of SARS-CoV-2 re-introduction from international.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{ferretti:medRxiv:2020,
  title = {The Timing of {{COVID}}-19 Transmission},
  author = {Ferretti, Luca and Ledda, Alice and Wymant, Chris and Zhao, Lele and Ledda, Virginia and Dorner, Lucie Abeler- and Kendall, Michelle and Nurtay, Anel and Cheng, Hao-Yuan and Ng, Ta-Chou and Lin, Hsien-Ho and Hinch, Rob and Masel, Joanna and Kilpatrick, A. Marm and Fraser, Christophe},
  year = {2020},
  pages = {2020.09.04.20188516},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.09.04.20188516},
  abstract = {{$<$}p{$>$}The timing of SARS-CoV-2 transmission is a critical factor to understand the epidemic trajectory and the impact of isolation, contact tracing and other non- pharmaceutical interventions on the spread of COVID-19 epidemics. We examined the distribution of transmission events with respect to exposure and onset of symptoms. We show that for symptomatic individuals, the timing of transmission of SARS-CoV-2 is more strongly linked to the onset of clinical symptoms of COVID-19 than to the time since infection. We found that it was approximately centered and symmetric around the onset of symptoms, with three quarters of events occurring in the window from 2-3 days before to 2-3 days after. However, we caution against overinterpretation of the right tail of the distribution, due to its dependence on behavioural factors and interventions. We also found that the pre-symptomatic infectious period extended further back in time for individuals with longer incubation periods. This strongly suggests that information about when a case was infected should be collected where possible, in order to assess how far into the past their contacts should be traced. Overall, the fraction of transmission from strictly pre-symptomatic infections was high (41\%; 95\%CI 31-50\%), which limits the efficacy of symptom-based interventions, and the large fraction of transmissions (35\%; 95\%CI 26-45\%) that occur on the same day or the day after onset of symptoms underlines the critical importance of individuals distancing themselves from others as soon as they notice any symptoms, even if they are mild. Rapid or at-home testing and contextual risk information would greatly facilitate efficient early isolation.{$<$}/p{$>$}},
  annotation = {Code and data @ http://doi.org/10.5281/zenodo.4033022},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/E3ETMFZ8/Ferretti et al_2020_The timing of COVID-19 transmission.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{ferretti:Science:2020,
  title = {Quantifying {{SARS}}-{{CoV}}-2 Transmission Suggests Epidemic Control with Digital Contact Tracing},
  author = {Ferretti, Luca and Wymant, Chris and Kendall, Michelle and Zhao, Lele and Nurtay, Anel and {Abeler-D{\"o}rner}, Lucie and Parker, Michael and Bonsall, David and Fraser, Christophe},
  year = {2020},
  volume = {368},
  publisher = {{American Association for the Advancement of Science}},
  issn = {0036-8075, 1095-9203},
  doi = {10.1126/science.abb6936},
  abstract = {Instantaneous contact tracing New analyses indicate that severe acute respiratory syndrome\textendash coronavirus 2 (SARS-CoV-2) is more infectious and less virulent than the earlier SARS-CoV-1, which emerged in China in 2002. Unfortunately, the current virus has greater epidemic potential because it is difficult to trace mild or presymptomatic infections. As no treatment is currently available, the only tools that we can currently deploy to stop the epidemic are contact tracing, social distancing, and quarantine, all of which are slow to implement. However imperfect the data, the current global emergency requires more timely interventions. Ferretti et al. explored the feasibility of protecting the population (that is, achieving transmission below the basic reproduction number) using isolation coupled with classical contact tracing by questionnaires versus algorithmic instantaneous contact tracing assisted by a mobile phone application. For prevention, the crucial information is understanding the relative contributions of different routes of transmission. A phone app could show how finite resources must be divided between different intervention strategies for the most effective control. Science, this issue p. eabb6936 Structured Abstract INTRODUCTIONCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome\textendash coronavirus 2 (SARS-CoV-2), has clear potential for a long-lasting global pandemic, high fatality rates, and incapacitated health systems. Until vaccines are widely available, the only available infection prevention approaches are case isolation, contact tracing and quarantine, physical distancing, decontamination, and hygiene measures. To implement the right measures at the right time, it is of crucial importance to understand the routes and timings of transmission. RATIONALEWe used key parameters of epidemic spread to estimate the contribution of different transmission routes with a renewal equation formulation, and analytically determined the speed and scale for effective identification and contact tracing required to stop the epidemic. RESULTSWe developed a mathematical model for infectiousness to estimate the basic reproductive number R0 and to quantify the contribution of different transmission routes. To parameterize the model, we analyzed 40 well-characterized source-recipient pairs and estimated the distribution of generation times (time from infection to onward transmission). The distribution had a median of 5.0 days and standard deviation of 1.9 days. We used published parameters for the incubation time distribution (median 5.2 days) and the epidemic doubling time (5.0 days) from the early epidemic data in China.The model estimated R0 = 2.0 in the early stages of the epidemic in China. The contributions to R0 included 46\% from presymptomatic individuals (before showing symptoms), 38\% from symptomatic individuals, 10\% from asymptomatic individuals (who never show symptoms), and 6\% from environmentally mediated transmission via contamination. Results on the last two routes are speculative. According to these estimates, presymptomatic transmissions alone are almost sufficient to sustain epidemic growth.To estimate the requirements for successful contact tracing, we determined the combination of two key parameters needed to reduce R0 to less than 1: the proportion of cases who need to be isolated, and the proportion of their contacts who need to be quarantined. For a 3-day delay in notification assumed for manual contact tracing, no parameter combination leads to epidemic control. Immediate notification through a contact-tracing mobile phone app could, however, be sufficient to stop the epidemic if used by a sufficiently high proportion of the population.We propose an app, based on existing technology, that allows instant contact tracing. Proximity events between two phones running the app are recorded. Upon an individual's COVID-19 diagnosis, contacts are instantly, automatically, and anonymously notified of their risk and asked to self-isolate. Practical and logistical factors (e.g., uptake, coverage, R0 in a given population) will determine whether an app is sufficient to control viral spread on its own, or whether additional measures to reduce R0 (e.g., physical distancing) are required. The performance of the app in scenarios with higher values of R0 can be explored at https://bdi-pathogens.shinyapps.io/covid-19-transmission-routes/. CONCLUSIONGiven the infectiousness of SARS-CoV-2 and the high proportion of transmissions from presymptomatic individuals, controlling the epidemic by manual contact tracing is infeasible. The use of a contact-tracing app that builds a memory of proximity contacts and immediately notifies contacts of positive cases would be sufficient to stop the epidemic if used by enough people, in particular when combined with other measures such as physical distancing. An intervention of this kind raises ethical questions regarding access, transparency, the protection and use of personal data, and the sharing of knowledge with other countries. Careful oversight by an inclusive advisory body is required. {$<$}img class="fragment-image" aria-describedby="F1-caption" src="https://science.sciencemag.org/content/sci/368/6491/eabb6936/F1.medium.gif"/{$>$} Download high-res image Open in new tab Download Powerpoint Instant contact tracing can reduce the proportion of cases that need to be isolated and contacts who need to be quarantined to achieve control of an epidemic.Subject A becomes symptomatic after having had contact with other people in different settings the day before. Contacts are notified and quarantined where needed. In the inset, the green area indicates the success rates needed to control an epidemic with R0 = 2 (i.e., negative growth rates after isolating cases and quarantining their contacts). The newly emergent human virus SARS-CoV-2 (severe acute respiratory syndrome\textendash coronavirus 2) is resulting in high fatality rates and incapacitated health systems. Preventing further transmission is a priority. We analyzed key parameters of epidemic spread to estimate the contribution of different transmission routes and determine requirements for case isolation and contact tracing needed to stop the epidemic. Although SARS-CoV-2 is spreading too fast to be contained by manual contact tracing, it could be controlled if this process were faster, more efficient, and happened at scale. A contact-tracing app that builds a memory of proximity contacts and immediately notifies contacts of positive cases can achieve epidemic control if used by enough people. By targeting recommendations to only those at risk, epidemics could be contained without resorting to mass quarantines (``lockdowns'') that are harmful to society. We discuss the ethical requirements for an intervention of this kind. Instantaneous contact tracing and notifications by mobile phone app could potentially stop the COVID-19 epidemic. Instantaneous contact tracing and notifications by mobile phone app could potentially stop the COVID-19 epidemic.},
  chapter = {Research Article},
  copyright = {Copyright \textcopyright{} 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/67CQBFSA/Ferretti et al. - 2020 - Quantifying SARS-CoV-2 transmission suggests epide.pdf;/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/M5HQFZRM/abb6936-Data-S1.csv},
  journal = {Science},
  language = {en},
  number = {6491},
  pmid = {32234805}
}

@article{gostic:eLife:2020,
  title = {Estimated Effectiveness of Symptom and Risk Screening to Prevent the Spread of {{COVID}}-19},
  author = {Gostic, Katelyn and Gomez, Ana CR and Mummah, Riley O and Kucharski, Adam J and {Lloyd-Smith}, James O},
  editor = {Franco, Eduardo and Ferguson, Neil M and McCaw, James M},
  year = {2020},
  volume = {9},
  pages = {e55570},
  publisher = {{eLife Sciences Publications, Ltd}},
  issn = {2050-084X},
  doi = {10.7554/eLife.55570},
  abstract = {Traveller screening is being used to limit further spread of COVID-19 following its recent emergence, and symptom screening has become a ubiquitous tool in the global response. Previously, we developed a mathematical model to understand factors governing the effectiveness of traveller screening to prevent spread of emerging pathogens (Gostic et al., 2015). Here, we estimate the impact of different screening programs given current knowledge of key COVID-19 life history and epidemiological parameters. Even under best-case assumptions, we estimate that screening will miss more than half of infected people. Breaking down the factors leading to screening successes and failures, we find that most cases missed by screening are fundamentally undetectable, because they have not yet developed symptoms and are unaware they were exposed. Our work underscores the need for measures to limit transmission by individuals who become ill after being missed by a screening program. These findings can support evidence-based policy to combat the spread of COVID-19, and prospective planning to mitigate future emerging pathogens.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/567Q9T9I/Gostic et al_2020_Estimated effectiveness of symptom and risk screening to prevent the spread of.pdf},
  journal = {eLife}
}

@article{grantz:medRxiv:2020,
  title = {Maximizing and Evaluating the Impact of Test-Trace-Isolate Programs},
  author = {Grantz, Kyra H. and Lee, Elizabeth C. and McGowan, Lucy D'Agostino and Lee, Kyu Han and Metcalf, C. Jessica E. and Gurley, Emily S. and Lessler, Justin},
  year = {2020},
  pages = {2020.09.02.20186916},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  issn = {2018-6916},
  doi = {10.1101/2020.09.02.20186916},
  abstract = {{$<$}p{$>$}\textbf{Background:} Test-trace-isolate programs are an essential part of COVID-19 control that offer a more targeted approach than many other non-pharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. \textbf{Methods and Findings:} We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, \emph{R}, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of case detection, speed of isolation, contact tracing completeness and speed of quarantine using parameters consistent with COVID-19 transmission (\emph{R}\textsubscript{0}=2.5, generation time 6.5 days). We show that \emph{R} is most sensitive to changes to the proportion of infections detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (\&lt;30\%). Although test-trace-isolate programs can contribute substantially to reducing \emph{R}, exceptional performance across all metrics is needed to bring \emph{R} below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Formally framing the dynamical process also indicates that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of program performance are sensitive to assumptions about COVID-19 natural history, our qualitative findings are robust across numerous sensitivity analyses. \textbf{Conclusions:} Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic, and can alleviate the need for more restrictive social distancing measures.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/AIHH84H8/Grantz et al_2020_Maximizing and evaluating the impact of test-trace-isolate programs.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{guglielmi:Nature:2020,
  title = {Fast Coronavirus Tests: What They Can and Can't Do},
  shorttitle = {Fast Coronavirus Tests},
  author = {Guglielmi, Giorgia},
  year = {2020},
  month = sep,
  volume = {585},
  pages = {496--498},
  publisher = {{Nature Publishing Group}},
  doi = {10.1038/d41586-020-02661-2},
  abstract = {Rapid antigen tests are designed to tell in a few minutes whether someone is infectious. Will they be game changers?},
  copyright = {2020 Nature},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/ULU2WNBC/Guglielmi_2020_Fast coronavirus tests.pdf},
  journal = {Nature},
  language = {en},
  number = {7826}
}

@article{he:NatMed:2020,
  title = {Temporal Dynamics in Viral Shedding and Transmissibility of {{COVID}}-19},
  author = {He, Xi and Lau, Eric H. Y. and Wu, Peng and Deng, Xilong and Wang, Jian and Hao, Xinxin and Lau, Yiu Chung and Wong, Jessica Y. and Guan, Yujuan and Tan, Xinghua and Mo, Xiaoneng and Chen, Yanqing and Liao, Baolin and Chen, Weilie and Hu, Fengyu and Zhang, Qing and Zhong, Mingqiu and Wu, Yanrong and Zhao, Lingzhai and Zhang, Fuchun and Cowling, Benjamin J. and Li, Fang and Leung, Gabriel M.},
  year = {2020},
  volume = {26},
  pages = {672--675},
  publisher = {{Nature Publishing Group}},
  issn = {1546-170X},
  doi = {10.1038/s41591-020-0869-5},
  abstract = {Presymptomatic transmission of SARS-CoV-2 is estimated to account for a substantial proportion of COVID-19 cases.},
  copyright = {2020 The Author(s), under exclusive licence to Springer Nature America, Inc.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/PEZ9Z49D/41591_2020_869_MOESM4_ESM.r;/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/Q832NZJK/He-NatMed-2020.pdf},
  journal = {Nature Medicine},
  language = {en},
  number = {5}
}

@misc{IATA:2020,
  title = {Traveler {{Survey Reveals COVID}}-19 {{Concerns}}},
  author = {IATA},
  year = {2020},
  abstract = {IATA released public opinion research showing the willingness to travel being tempered by concerns over the risks of catching COVID-19 during air travel. The industry's re-start plans address passenger's main concerns.},
  howpublished = {https://www.iata.org/en/pressroom/pr/2020-07-07-01/},
  language = {en}
}
% == BibTeX quality report for IATA:2020:
% ? Title looks like it was stored in title-case in Zotero

@article{jiang:medRxiv:2020,
  title = {Is a 14-Day Quarantine Period Optimal for Effectively Controlling Coronavirus Disease 2019 ({{COVID}}-19)?},
  author = {Jiang, Xue and Niu, Yawei and Li, Xiong and Li, Lin and Cai, Wenxiang and Chen, Yucan and Liao, Bo and Wang, Edwin},
  year = {2020},
  month = mar,
  pages = {2020.03.15.20036533},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.15.20036533},
  abstract = {{$<$}p{$>$}Background The outbreak of a new coronavirus (SARS-CoV-2) disease (Covid-19) has become pandemic. To be more effectively controlling the disease, it is critical to set up an optimal quarantine period so that about 95\% of the cases developing symptoms will be retained for isolation. At the moment, the WHO-established quarantine period is 14 days based on previous reports which had studied small sizes of hospitalized cases (10 and \textasciitilde 100, respectively), however, over 80\% of adult- and 95\% of child-cases were not necessary to stay in hospitals, and therefore, had not been hospitalized. Therefore, we are questioning if the current-inferred median incubation time is representative for the whole Covid-19 population, and if the current quarantine period is optimal. Methods We compiled and analyzed the patient-level information of 2015 laboratory-confirmed Covid-19 cases including 99 children in 28 Chinese provinces. This cohort represents a wide-range spectrum of Covid-19 disease with both hospitalized and non-hospitalized cases. Results The full range of incubation periods of the Covid-19 cases ranged from 0 to 33 days among 2015 cases. There were 6 (0.13\%) symptom-free cases including 4 females with a median age of 25.5 years and 2 males with a median age of 36 years. The median incubation period of both male and female adults was similar (7-day) but significantly shorter than that (9-day) of child cases (P=0.02). This cohort contained 4 transmission generations, and incubation periods of the cases between generations were not significantly different, suggesting that the virus has not been rapidly adapted to human beings. Interestingly, incubation periods of 233 cases (11.6\%) were longer than the WHO-established quarantine period (14 days). Data modeling suggested that if adults take an extra 4-day or 7-day of isolation (i.e., a quarantine period of 18 or 21 days), 96.2\% or 98.3\%, respectively, of the people who are developing symptoms will be more effectively quarantined. Patients transmitted via lunch/dinner parties (i.e., gastrointestinal tract infection through oral transmission) had a significantly longer incubation period (9-day) than other adults transmitted via respiratory droplets or contaminated surfaces and objects (P\&lt;0.004). Conclusions The whole Covid-19 population including both hospitalized and non-hospitalized cases had a median incubation period of 7-day for adults, which is 1.8-day longer than the hospitalized cases reported previously. An extension of the adult quarantine period to 18 days or 21 days could be more effective in preventing virus-spreading and controlling the disease. The cases transmitted by lunch/dinner parties could be infected first in the gastrointestinal tract through oral transmission and then infected in the respiratory system so that they had a longer incubation period.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/4I896VCI/Jiang et al_2020_Is a 14-day quarantine period optimal for effectively controlling coronavirus.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{kucharski:TheLancetInfectiousDiseases:2020,
  title = {Effectiveness of Isolation, Testing, Contact Tracing, and Physical Distancing on Reducing Transmission of {{SARS}}-{{CoV}}-2 in Different Settings: A Mathematical Modelling Study},
  shorttitle = {Effectiveness of Isolation, Testing, Contact Tracing, and Physical Distancing on Reducing Transmission of {{SARS}}-{{CoV}}-2 in Different Settings},
  author = {Kucharski, Adam J. and Klepac, Petra and Conlan, Andrew J. K. and Kissler, Stephen M. and Tang, Maria L. and Fry, Hannah and Gog, Julia R. and Edmunds, W. John and Emery, Jon C. and Medley, Graham and Munday, James D. and Russell, Timothy W. and Leclerc, Quentin J. and Diamond, Charlie and Procter, Simon R. and Gimma, Amy and Sun, Fiona Yueqian and Gibbs, Hamish P. and Rosello, Alicia and van Zandvoort, Kevin and Hu{\'e}, St{\'e}phane and Meakin, Sophie R. and Deol, Arminder K. and Knight, Gwen and Jombart, Thibaut and Foss, Anna M. and Bosse, Nikos I. and Atkins, Katherine E. and Quilty, Billy J. and Lowe, Rachel and Prem, Kiesha and Flasche, Stefan and Pearson, Carl A. B. and Houben, Rein M. G. J. and Nightingale, Emily S. and Endo, Akira and Tully, Damien C. and Liu, Yang and {Villabona-Arenas}, Julian and O'Reilly, Kathleen and Funk, Sebastian and Eggo, Rosalind M. and Jit, Mark and Rees, Eleanor M. and Hellewell, Joel and Clifford, Samuel and Jarvis, Christopher I. and Abbott, Sam and Auzenbergs, Megan and Davies, Nicholas G. and Simons, David},
  year = {2020},
  month = oct,
  volume = {20},
  pages = {1151--1160},
  publisher = {{Elsevier}},
  issn = {1473-3099, 1474-4457},
  doi = {10.1016/S1473-3099(20)30457-6},
  abstract = {{$<$}h2{$>$}Summary{$<$}/h2{$><$}h3{$>$}Background{$<$}/h3{$><$}p{$>$}The isolation of symptomatic cases and tracing of contacts has been used as an early COVID-19 containment measure in many countries, with additional physical distancing measures also introduced as outbreaks have grown. To maintain control of infection while also reducing disruption to populations, there is a need to understand what combination of measures\textemdash including novel digital tracing approaches and less intensive physical distancing\textemdash might be required to reduce transmission. We aimed to estimate the reduction in transmission under different control measures across settings and how many contacts would be quarantined per day in different strategies for a given level of symptomatic case incidence.{$<$}/p{$><$}h3{$>$}Methods{$<$}/h3{$><$}p{$>$}For this mathematical modelling study, we used a model of individual-level transmission stratified by setting (household, work, school, or other) based on BBC Pandemic data from 40 162 UK participants. We simulated the effect of a range of different testing, isolation, tracing, and physical distancing scenarios. Under optimistic but plausible assumptions, we estimated reduction in the effective reproduction number and the number of contacts that would be newly quarantined each day under different strategies.{$<$}/p{$><$}h3{$>$}Results{$<$}/h3{$><$}p{$>$}We estimated that combined isolation and tracing strategies would reduce transmission more than mass testing or self-isolation alone: mean transmission reduction of 2\% for mass random testing of 5\% of the population each week, 29\% for self-isolation alone of symptomatic cases within the household, 35\% for self-isolation alone outside the household, 37\% for self-isolation plus household quarantine, 64\% for self-isolation and household quarantine with the addition of manual contact tracing of all contacts, 57\% with the addition of manual tracing of acquaintances only, and 47\% with the addition of app-based tracing only. If limits were placed on gatherings outside of home, school, or work, then manual contact tracing of acquaintances alone could have an effect on transmission reduction similar to that of detailed contact tracing. In a scenario where 1000 new symptomatic cases that met the definition to trigger contact tracing occurred per day, we estimated that, in most contact tracing strategies, 15 000\textendash 41 000 contacts would be newly quarantined each day.{$<$}/p{$><$}h3{$>$}Interpretation{$<$}/h3{$><$}p{$>$}Consistent with previous modelling studies and country-specific COVID-19 responses to date, our analysis estimated that a high proportion of cases would need to self-isolate and a high proportion of their contacts to be successfully traced to ensure an effective reproduction number lower than 1 in the absence of other measures. If combined with moderate physical distancing measures, self-isolation and contact tracing would be more likely to achieve control of severe acute respiratory syndrome coronavirus 2 transmission.{$<$}/p{$><$}h3{$>$}Funding{$<$}/h3{$><$}p{$>$}Wellcome Trust, UK Engineering and Physical Sciences Research Council, European Commission, Royal Society, Medical Research Council.{$<$}/p{$>$}},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/GFWL5F55/Kucharski et al_2020_Effectiveness of isolation, testing, contact tracing, and physical distancing.pdf},
  journal = {The Lancet Infectious Diseases},
  language = {English},
  number = {10},
  pmid = {32559451}
}

@article{kucirka:AnnalsofInternalMedicine:2020,
  title = {Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction\textendash Based {{SARS}}-{{CoV}}-2 Tests by Time since Exposure},
  author = {Kucirka, Lauren M. and Lauer, Stephen A. and Laeyendecker, Oliver and Boon, Denali and Lessler, Justin},
  year = {2020},
  volume = {173},
  pages = {262--267},
  issn = {0003-4819},
  doi = {10.7326/M20-1495},
  annotation = {https://github.com/HopkinsIDD/covidRTPCR},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/FGBZAAC3/Kucirka et al. - 2020 - Variation in False-Negative Rate of Reverse Transc.pdf},
  journal = {Annals of Internal Medicine},
  number = {4}
}

@article{lauer:AnnInternMed:2020,
  title = {The {{Incubation Period}} of {{Coronavirus Disease}} 2019 ({{COVID}}-19) {{From Publicly Reported Confirmed Cases}}: {{Estimation}} and {{Application}}},
  shorttitle = {The {{Incubation Period}} of {{Coronavirus Disease}} 2019 ({{COVID}}-19) {{From Publicly Reported Confirmed Cases}}},
  author = {Lauer, Stephen A. and Grantz, Kyra H. and Bi, Qifang and Jones, Forrest K. and Zheng, Qulu and Meredith, Hannah R. and Azman, Andrew S. and Reich, Nicholas G. and Lessler, Justin},
  year = {2020},
  month = mar,
  volume = {172},
  pages = {577--582},
  publisher = {{American College of Physicians}},
  issn = {0003-4819},
  doi = {10.7326/M20-0504},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/SCPPSI8A/Lauer et al_2020_The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly.pdf},
  journal = {Annals of Internal Medicine},
  number = {9}
}
% == BibTeX quality report for lauer:AnnInternMed:2020:
% ? Title looks like it was stored in title-case in Zotero

@article{Lehtinen:Interface:2021,
  title = {On the Relationship between Serial Interval, Infectiousness Profile and Generation Time},
  author = {Lehtinen, Sonja and Ashcroft, Peter and Bonhoeffer, Sebastian},
  year = {2021},
  month = jan,
  volume = {18},
  pages = {20200756},
  doi = {10.1098/rsif.2020.0756},
  abstract = {The timing of transmission plays a key role in the dynamics and controllability of an epidemic. However, observing generation times\textemdash the time interval between the infection of an infector and an infectee in a transmission pair\textemdash requires data on infection times, which are generally unknown. The timing of symptom onset is more easily observed; generation times are therefore often estimated based on serial intervals\textemdash the time interval between symptom onset of an infector and an infectee. This estimation follows one of two approaches: (i) approximating the generation time distribution by the serial interval distribution or (ii) deriving the generation time distribution from the serial interval and incubation period\textemdash the time interval between infection and symptom onset in a single individual\textemdash distributions. These two approaches make different\textemdash and not always explicitly stated\textemdash assumptions about the relationship between infectiousness and symptoms, resulting in different generation time distributions with the same mean but unequal variances. Here, we clarify the assumptions that each approach makes and show that neither set of assumptions is plausible for most pathogens. However, the variances of the generation time distribution derived under each assumption can reasonably be considered as upper (approximation with serial interval) and lower (derivation from serial interval) bounds. Thus, we suggest a pragmatic solution is to use both approaches and treat these as edge cases in downstream analysis. We discuss the impact of the variance of the generation time distribution on the controllability of an epidemic through strategies based on contact tracing, and we show that underestimating this variance is likely to overestimate controllability.},
  copyright = {All rights reserved},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/KE66CH6D/Lehtinen et al_2021_On the relationship between serial interval, infectiousness profile and.pdf},
  journal = {J. R. Soc. Interface},
  number = {174}
}
% == BibTeX quality report for Lehtinen:Interface:2021:
% ? Possibly abbreviated journal title J. R. Soc. Interface

@article{li:NEJM:2020,
  title = {Early Transmission Dynamics in {{Wuhan}}, {{China}}, of Novel Coronavirus\textendash Infected Pneumonia},
  author = {Li, Qun and Guan, Xuhua and Wu, Peng and Wang, Xiaoye and Zhou, Lei and Tong, Yeqing and Ren, Ruiqi and Leung, Kathy S. M. and Lau, Eric H. Y. and Wong, Jessica Y. and Xing, Xuesen and Xiang, Nijuan and Wu, Yang and Li, Chao and Chen, Qi and Li, Dan and Liu, Tian and Zhao, Jing and Liu, Man and Tu, Wenxiao and Chen, Chuding and Jin, Lianmei and Yang, Rui and Wang, Qi and Zhou, Suhua and Wang, Rui and Liu, Hui and Luo, Yinbo and Liu, Yuan and Shao, Ge and Li, Huan and Tao, Zhongfa and Yang, Yang and Deng, Zhiqiang and Liu, Boxi and Ma, Zhitao and Zhang, Yanping and Shi, Guoqing and Lam, Tommy T. Y. and Wu, Joseph T. and Gao, George F. and Cowling, Benjamin J. and Yang, Bo and Leung, Gabriel M. and Feng, Zijian},
  year = {2020},
  volume = {382},
  pages = {1199--1207},
  doi = {10.1056/NEJMoa2001316},
  abstract = {Original Article from The New England Journal of Medicine \textemdash{} Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus\textendash Infected Pneumonia},
  copyright = {Copyright \textcopyright{} 2020 Massachusetts Medical Society. All rights reserved.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/765KRZV9/Li et al. - 2020 - Early Transmission Dynamics in Wuhan, China, of No.pdf},
  journal = {New England Journal of Medicine},
  language = {en}
}

@article{linton:J.Clin.Med.:2020,
  title = {Incubation {{Period}} and {{Other Epidemiological Characteristics}} of 2019 {{Novel Coronavirus Infections}} with {{Right Truncation}}: {{A Statistical Analysis}} of {{Publicly Available Case Data}}},
  shorttitle = {Incubation {{Period}} and {{Other Epidemiological Characteristics}} of 2019 {{Novel Coronavirus Infections}} with {{Right Truncation}}},
  author = {Linton, Natalie M. and Kobayashi, Tetsuro and Yang, Yichi and Hayashi, Katsuma and Akhmetzhanov, Andrei R. and Jung, Sung-mok and Yuan, Baoyin and Kinoshita, Ryo and Nishiura, Hiroshi},
  year = {2020},
  month = feb,
  volume = {9},
  pages = {538},
  publisher = {{Multidisciplinary Digital Publishing Institute}},
  doi = {10.3390/jcm9020538},
  abstract = {The geographic spread of 2019 novel coronavirus (COVID-19) infections from the epicenter of Wuhan, China, has provided an opportunity to study the natural history of the recently emerged virus. Using publicly available event-date data from the ongoing epidemic, the present study investigated the incubation period and other time intervals that govern the epidemiological dynamics of COVID-19 infections. Our results show that the incubation period falls within the range of 2\textendash 14 days with 95\% confidence and has a mean of around 5 days when approximated using the best-fit lognormal distribution. The mean time from illness onset to hospital admission (for treatment and/or isolation) was estimated at 3\textendash 4 days without truncation and at 5\textendash 9 days when right truncated. Based on the 95th percentile estimate of the incubation period, we recommend that the length of quarantine should be at least 14 days. The median time delay of 13 days from illness onset to death (17 days with right truncation) should be considered when estimating the COVID-19 case fatality risk.},
  copyright = {http://creativecommons.org/licenses/by/3.0/},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/3SE8NDLX/Linton et al_2020_Incubation Period and Other Epidemiological Characteristics of 2019 Novel.pdf;/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/GB9AXSQV/linton_supp_tableS1_S2_8Feb2020.xlsx},
  journal = {Journal of Clinical Medicine},
  language = {en},
  number = {2}
}
% == BibTeX quality report for linton:J.Clin.Med.:2020:
% ? Title looks like it was stored in title-case in Zotero

@article{ma:medRxiv:2020,
  title = {Epidemiological Parameters of Coronavirus Disease 2019: A Pooled Analysis of Publicly Reported Individual Data of 1155 Cases from Seven Countries},
  shorttitle = {Epidemiological Parameters of Coronavirus Disease 2019},
  author = {Ma, Shujuan and Zhang, Jiayue and Zeng, Minyan and Yun, Qingping and Guo, Wei and Zheng, Yixiang and Zhao, Shi and Wang, Maggie H. and Yang, Zuyao},
  year = {2020},
  month = mar,
  pages = {2020.03.21.20040329},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.03.21.20040329},
  abstract = {{$<$}p{$>$}Background The outbreak of coronavirus disease 2019 (COVID-19) has been declared a pandemic by the World Health Organization, while several key epidemiological parameters of the disease remain to be clarified. This study aimed to obtain robust estimates of the incubation period, upper limit of latent period (interval between exposure of infector and infectee), serial interval, time point of exposure and basic reproduction number (R0) of COVID-19. Methods Between late February and early March of 2020, the individual data of laboratory confirmed cases of COVID-19 were retrieved from 10728 publicly available reports released by the health authorities of and outside China and from 1790 publications identified in PubMed and CNKI. To be eligible, a report had to contain the data that allowed for estimation of at least one parameter. As relevant data mainly came from clustering cases, the clusters for which no evidence was available to establish transmission order were all excluded to ensure accuracy of estimates. Additionally, only the cases with an exposure period spanning 3 days or less were included in the estimation of parameters involving exposure date, and a simple method for determining exposure date was adopted to ensure the error of estimates be small (\&lt; 0.3 day). Depending on specific parameters, three or four of normal, lognormal, Weibull, and gamma distributions were fitted to the datasets and the results from appropriate models were presented. Findings In total, 1155 cases from China, Japan, Singapore, South Korea, Vietnam, Germany and Malaysia were included for the final analysis. The mean and standard deviation were 7.44 days and 4.39 days for incubation period, 2.52 days and 3.95 days for the upper limit of latent period, 6.70 days and 5.20 days for serial interval, and -0.19 day (i.e., 0.19 day before symptom onset of infector) and 3.32 days for time point of exposure. R0 was estimated to be 1.70 and 1.78 based on two different formulas. For 39 (6.64\%) cases, the incubation periods were longer than 14 days. In 102 (43.78\%) infector-infectee pairs, transmission occurred before the symptom onsets of infectors. In 27 (3.92\%) infector-infectee pairs, symptom onsets of infectees occurred before those of infectors. Stratified analysis showed that incubation period and serial interval were consistently longer for those with less severe disease and for those whose primary cases had less severe disease. Asymptomatic transmission was also observed. Interpretation This study obtained robust estimates of several key epidemiological parameters of COVID-19. The findings support current practice of 14-day quarantine of persons with potential exposure, but also suggest that longer monitoring periods might be needed for selected groups. The estimates of serial interval, time point of exposure and latent period provide consistent evidence on pre-symptomatic transmission. This together with asymptomatic transmission and the generally longer incubation and serial interval of less severe cases suggests a high risk of long-term epidemic in the absence of appropriate control measures.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. The copyright holder for this pre-print is the author. All rights reserved. The material may not be redistributed, re-used or adapted without the author's permission.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/GID6334R/Ma et al_2020_Epidemiological parameters of coronavirus disease 2019.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{nicola:InternationalJournalofSurgery:2020,
  title = {The Socio-Economic Implications of the Coronavirus Pandemic ({{COVID}}-19): {{A}} Review},
  shorttitle = {The Socio-Economic Implications of the Coronavirus Pandemic ({{COVID}}-19)},
  author = {Nicola, Maria and Alsafi, Zaid and Sohrabi, Catrin and Kerwan, Ahmed and {Al-Jabir}, Ahmed and Iosifidis, Christos and Agha, Maliha and Agha, Riaz},
  year = {2020},
  volume = {78},
  pages = {185--193},
  issn = {1743-9191},
  doi = {10.1016/j.ijsu.2020.04.018},
  abstract = {The COVID-19 pandemic has resulted in over 4.3 million confirmed cases and over 290,000 deaths globally. It has also sparked fears of an impending economic crisis and recession. Social distancing, self-isolation and travel restrictions have lead to a reduced workforce across all economic sectors and caused many jobs to be lost. Schools have closed down, and the need for commodities and manufactured products has decreased. In contrast, the need for medical supplies has significantly increased. The food sector is also facing increased demand due to panic-buying and stockpiling of food products. In response to this global outbreak, we summarise the socio-economic effects of COVID-19 on individual aspects of the world economy.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/KETBV8NE/Nicola et al_2020_The socio-economic implications of the coronavirus pandemic (COVID-19).pdf},
  journal = {International Journal of Surgery},
  language = {en}
}

@article{nussbaumer-streit:Cochrane:2020,
  title = {Quarantine Alone or in Combination with Other Public Health Measures to Control {{COVID}}-19: A Rapid Review},
  shorttitle = {Quarantine Alone or in Combination with Other Public Health Measures to Control {{COVID}}-19},
  author = {{Nussbaumer-Streit}, Barbara and Mayr, Verena and Dobrescu, Andreea Iulia and Chapman, Andrea and Persad, Emma and Klerings, Irma and Wagner, Gernot and Siebert, Uwe and Ledinger, Dominic and Zachariah, Casey and Gartlehner, Gerald},
  year = {2020},
  issn = {1465-1858},
  doi = {10.1002/14651858.CD013574.pub2},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/R72N6G54/Nussbaumer-Streit et al_2020_Quarantine alone or in combination with other public health measures to control.pdf},
  journal = {Cochrane Database of Systematic Reviews},
  language = {en},
  number = {9}
}

@article{parmet:NEJM:2020,
  title = {Covid-19 \textemdash{} {{The Law}} and {{Limits}} of {{Quarantine}}},
  author = {Parmet, Wendy E. and Sinha, Michael S.},
  year = {2020},
  volume = {382},
  pages = {e28},
  doi = {10.1056/NEJMp2004211},
  abstract = {Perspective from The New England Journal of Medicine \textemdash{} Covid-19 \textemdash{} The Law and Limits of Quarantine},
  copyright = {Copyright \textcopyright{} 2020 Massachusetts Medical Society. All rights reserved.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/8CUWKWZ8/Parmet_Sinha_2020_Covid-19 — The Law and Limits of Quarantine.pdf},
  journal = {New England Journal of Medicine},
  language = {en},
  number = {15}
}
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@article{quilty:Eurosurveillance:2020,
  title = {Effectiveness of Airport Screening at Detecting Travellers Infected with Novel Coronavirus (2019-{{nCoV}})},
  author = {Quilty, Billy J. and Clifford, Sam and {CMMID nCoV working group} and Flasche, Stefan and Eggo, Rosalind M.},
  year = {2020},
  volume = {25},
  pages = {2000080},
  publisher = {{European Centre for Disease Prevention and Control}},
  issn = {1560-7917},
  doi = {10.2807/1560-7917.ES.2020.25.5.2000080},
  abstract = {We evaluated effectiveness of thermal passenger screening for 2019-nCoV infection at airport exit and entry to inform public health decision-making. In our baseline scenario, we estimated that 46\% (95\% confidence interval: 36 to 58) of infected travellers would not be detected, depending on incubation period, sensitivity of exit and entry screening, and proportion of asymptomatic cases. Airport screening is unlikely to detect a sufficient proportion of 2019-nCoV infected travellers to avoid entry of infected travellers.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/UM92L483/Quilty et al_2020_Effectiveness of airport screening at detecting travellers infected with novel.pdf},
  journal = {Eurosurveillance},
  language = {en},
  number = {5}
}

@article{quilty:medRxiv:2020,
  title = {Quarantine and Testing Strategies in Contact Tracing for {{SARS}}-{{CoV}}-2},
  author = {Quilty, Billy J. and Clifford, Samuel and Flasche, Stefan and Kucharski, Adam J. and {CMMID COVID-19 Working Group} and Edmunds, W. John},
  year = {2020},
  month = aug,
  pages = {2020.08.21.20177808},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  doi = {10.1101/2020.08.21.20177808},
  abstract = {{$<$}p{$>$}Previous work has indicated that contact tracing and isolation of index case and quarantine of potential secondary cases can, in concert with physical distancing measures, be an effective strategy for reducing transmission of SARS-CoV-2. Currently, contacts traced manually through the NHS Test and Trace scheme in the UK are asked to self-isolate for 14 days from the day they were exposed to the index case, which represents the upper bound for the incubation period. However, following previous work on screening strategies for air travellers it may be possible that this quarantine period could be reduced if combined with PCR testing. Adapting the simulation model for contact tracing, we find that quarantine periods of at least 10 days combined with a PCR test on day 9 may largely emulate the results from a 14-day quarantine period in terms of the averted transmission potential from secondary cases (72\% (95\%UI: 3\%, 100\%) vs 75\% (4\%, 100\%), respectively). These results assume the delays from testing index cases9 and tracing their contacts are minimised (no longer than 4.5 days on average). If secondary cases are traced and quarantined 1 day earlier on average, shorter quarantine periods of 8 days with a test on day 7 (76\% (7\%, 100\%)) approach parity with the 14 day quarantine period with a 1 day longer delay to the index cases9 test. However, the risk of false-negative PCR tests early in a traced case9s infectious period likely prevents the use of testing to reduce quarantine periods further than this, and testing immediately upon tracing, with release if negative, will avert just 17\% of transmission potential on average. In conclusion, the use of PCR testing is an effective strategy for reducing quarantine periods for secondary cases, while still reducing transmission of SARS-CoV-2, especially if delays in the test and trace system can be reduced, and may improve quarantine compliance rates.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/5J4Y8BFI/Quilty et al_2020_Quarantine and testing strategies in contact tracing for SARS-CoV-2.pdf;/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/N77GRUMU/2020.08.21.20177808v3.full.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{russell:TheLancetPublicHealth:2020,
  title = {Effect of Internationally Imported Cases on Internal Spread of {{COVID}}-19: A Mathematical Modelling Study},
  shorttitle = {Effect of Internationally Imported Cases on Internal Spread of {{COVID}}-19},
  author = {Russell, Timothy W. and Wu, Joseph T. and Clifford, Sam and Edmunds, W. John and Kucharski, Adam J. and Jit, Mark},
  year = {2020},
  month = dec,
  volume = {0},
  publisher = {{Elsevier}},
  issn = {2468-2667},
  doi = {10.1016/S2468-2667(20)30263-2},
  abstract = {{$<$}h2{$>$}Summary{$<$}/h2{$><$}h3{$>$}Background{$<$}/h3{$><$}p{$>$}Countries have restricted international arrivals to delay the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These measures carry a high economic and social cost, and might have little effect on COVID-19 epidemics if there are many more cases resulting from local transmission compared with imported cases. Our study aims to investigate the extent to which imported cases contribute to local transmission under different epidemic conditions.{$<$}/p{$><$}h3{$>$}Methods{$<$}/h3{$><$}p{$>$}To inform decisions about international travel restrictions, we calculated the ratio of expected COVID-19 cases from international travel (assuming no travel restrictions) to expected cases arising from internal spread, expressed as a proportion, on an average day in May and September, 2020, in each country. COVID-19 prevalence and incidence were estimated using a modelling framework that adjusts reported cases for under-ascertainment and asymptomatic infections. We considered different travel scenarios for May and September, 2020: an upper bound with estimated travel volumes at the same levels as May and September, 2019, and a lower bound with estimated travel volumes adjusted downwards according to expected reductions in May and September, 2020. Results were interpreted in the context of local epidemic growth rates.{$<$}/p{$><$}h3{$>$}Findings{$<$}/h3{$><$}p{$>$}In May, 2020, imported cases are likely to have accounted for a high proportion of total incidence in many countries, contributing more than 10\% of total incidence in 102 (95\% credible interval 63\textendash 129) of 136 countries when assuming no reduction in travel volumes (ie, with 2019 travel volumes) and in 74 countries (33\textendash 114) when assuming estimated 2020 travel volumes. Imported cases in September, 2020, would have accounted for no more than 10\% of total incidence in 106 (50\textendash 140) of 162 countries and less than 1\% in 21 countries (4\textendash 71) when assuming no reductions in travel volumes. With estimated 2020 travel volumes, imported cases in September, 2020, accounted for no more than 10\% of total incidence in 125 countries (65\textendash 162) and less than 1\% in 44 countries (8\textendash 97). Of these 44 countries, 22 (2\textendash 61) had epidemic growth rates far from the tipping point of exponential growth, making them the least likely to benefit from travel restrictions.{$<$}/p{$><$}h3{$>$}Interpretation{$<$}/h3{$><$}p{$>$}Countries can expect travellers infected with SARS-CoV-2 to arrive in the absence of travel restrictions. Although such restrictions probably contribute to epidemic control in many countries, in others, imported cases are likely to contribute little to local COVID-19 epidemics. Stringent travel restrictions might have little impact on epidemic dynamics except in countries with low COVID-19 incidence and large numbers of arrivals from other countries, or where epidemics are close to tipping points for exponential growth. Countries should consider local COVID-19 incidence, local epidemic growth, and travel volumes before implementing such restrictions.{$<$}/p{$><$}h3{$>$}Funding{$<$}/h3{$><$}p{$>$}Wellcome Trust, UK Foreign, Commonwealth \& Development Office, European Commission, National Institute for Health Research, Medical Research Council, and Bill \& Melinda Gates Foundation.{$<$}/p{$>$}},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/KWLGEC85/Russell et al_2020_Effect of internationally imported cases on internal spread of COVID-19.pdf},
  journal = {The Lancet Public Health},
  language = {English},
  number = {0}
}

@article{salathe:SwissMed.Wkly.:2020,
  title = {{{COVID}}-19 Epidemic in {{Switzerland}}: On the Importance of Testing, Contact Tracing and Isolation},
  shorttitle = {{{COVID}}-19 Epidemic in {{Switzerland}}},
  author = {Salath{\'e}, Marcel and Althaus, Christian L. and Neher, Richard and Stringhini, Silvia and Hodcroft, Emma and Fellay, Jacques and Zwahlen, Marcel and Senti, Gabriela and Battegay, Manuel and {Wilder-Smith}, Annelies and Eckerle, Isabella and Egger, Matthias and Low, Nicola},
  year = {2020},
  month = mar,
  volume = {150},
  publisher = {{EMH Media}},
  doi = {10.4414/smw.2020.20225},
  abstract = {A liberal approach to testing for SARS-CoV-2 in Switzerland is needed as part of the package of control measures.},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/LETC66EP/Salathé et al_2020_COVID-19 epidemic in Switzerland.pdf},
  journal = {Swiss Medical Weekly},
  language = {en},
  number = {1112}
}

@article{WHO:quarantine,
  title = {Considerations for Quarantine of Contacts of {{COVID}}-19 Cases},
  author = {WHO},
  year = {2020},
  volume = {WHO/2019-nCoV/IHR\_Quarantine/2020.3},
  abstract = {Interim guidance},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/5IWS56K6/WHO-2019-nCoV-IHR_Quarantine-2020.3-eng.pdf},
  language = {en}
}
% == BibTeX quality report for WHO:quarantine:
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@article{xia:medRxiv:2020,
  title = {Transmission of Corona Virus Disease 2019 during the Incubation Period May Lead to a Quarantine Loophole},
  author = {Xia, Wei and Liao, Jiaqiang and Li, Chunhui and Li, Yuanyuan and Qian, Xi and Sun, Xiaojie and Xu, Hongbo and Mahai, Gaga and Zhao, Xin and Shi, Lisha and Liu, Juan and Yu, Ling and Wang, Meng and Wang, Qianqian and Namat, Asmagvl and Li, Ying and Qu, Jingyu and Liu, Qi and Lin, Xiaofang and Cao, Shuting and Huan, Shu and Xiao, Jiying and Ruan, Fengyu and Wang, Hanjin and Xu, Qing and Ding, Xingjuan and Fang, Xingjie and Qiu, Feng and Ma, Jiaolong and Zhang, Yu and Wang, Aizhen and Xing, Yuling and Xu, Shunqing},
  year = {2020},
  pages = {2020.03.06.20031955},
  publisher = {{Cold Spring Harbor Laboratory Press}},
  issn = {2003-1955},
  doi = {10.1101/2020.03.06.20031955},
  abstract = {{$<$}p{$>$}Background: The ongoing outbreak of novel corona virus disease 2019 (COVID-19) in Wuhan, China, is arousing international concern. This study evaluated whether and when the infected but asymptomatic cases during the incubation period could infect others. Methods: We collected data on demographic characteristics, exposure history, and symptom onset day of the confirmed cases, which had been announced by the Chinese local authorities. We evaluated the potential of transmission during the incubation period in 50 infection clusters, including 124 cases. All the secondary cases had a history of contact with their first-generation cases prior to symptom onset. Results: The estimated mean incubation period for COVID-19 was 4.9 days (95\% confidence interval [CI], 4.4 to 5.4) days, ranging from 0.8 to 11.1 days (2.5th to 97.5th percentile). The observed mean and standard deviation (SD) of serial interval was 4.1{$\pm$}3.3 days, with the 2.5th and 97.5th percentiles at -1 and 13 days. The infectious curve showed that in 73.0\% of the secondary cases, their date of getting infected was before symptom onset of the first-generation cases, particularly in the last three days of the incubation period. Conclusions: The results indicated the transmission of COVID-9 occurs among close contacts during the incubation period, which may lead to a quarantine loophole. Strong and effective countermeasures should be implemented to prevent or mitigate asymptomatic transmission during the incubation period in populations at high risk.{$<$}/p{$>$}},
  copyright = {\textcopyright{} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NoDerivs 4.0 International), CC BY-ND 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/77YPJM22/Xia et al_2020_Transmission of corona virus disease 2019 during the incubation period may lead.pdf},
  journal = {medRxiv},
  language = {en}
}

@article{zhang:TheLancetInfectiousDiseases:2020,
  title = {Evolving Epidemiology and Transmission Dynamics of Coronavirus Disease 2019 Outside {{Hubei}} Province, {{China}}: A Descriptive and Modelling Study},
  shorttitle = {Evolving Epidemiology and Transmission Dynamics of Coronavirus Disease 2019 Outside {{Hubei}} Province, {{China}}},
  author = {Zhang, Juanjuan and Litvinova, Maria and Wang, Wei and Wang, Yan and Deng, Xiaowei and Chen, Xinghui and Li, Mei and Zheng, Wen and Yi, Lan and Chen, Xinhua and Wu, Qianhui and Liang, Yuxia and Wang, Xiling and Yang, Juan and Sun, Kaiyuan and Longini, Ira M. and Halloran, M. Elizabeth and Wu, Peng and Cowling, Benjamin J. and Merler, Stefano and Viboud, Cecile and Vespignani, Alessandro and Ajelli, Marco and Yu, Hongjie},
  year = {2020},
  month = jul,
  volume = {20},
  pages = {793--802},
  publisher = {{Elsevier}},
  issn = {1473-3099, 1474-4457},
  doi = {10.1016/S1473-3099(20)30230-9},
  abstract = {{$<$}h2{$>$}Summary{$<$}/h2{$><$}h3{$>$}Background{$<$}/h3{$><$}p{$>$}The coronavirus disease 2019 (COVID-19) epidemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), began in Wuhan city, Hubei province, in December, 2019, and has spread throughout China. Understanding the evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy.{$<$}/p{$><$}h3{$>$}Methods{$<$}/h3{$><$}p{$>$}We collected individual information from official public sources on laboratory-confirmed cases reported outside Hubei in mainland China for the period of Jan 19 to Feb 17, 2020. We used the date of the fourth revision of the case definition (Jan 27) to divide the epidemic into two time periods (Dec 24 to Jan 27, and Jan 28 to Feb 17) as the date of symptom onset. We estimated trends in the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (\emph{R}\textsubscript{t}) at the provincial level.{$<$}/p{$><$}h3{$>$}Findings{$<$}/h3{$><$}p{$>$}We collected data on 8579 cases from 30 provinces. The median age of cases was 44 years (33\textendash 56), with an increasing proportion of cases in younger age groups and in elderly people (ie, aged {$>$}64 years) as the epidemic progressed. The mean time from symptom onset to hospital admission decreased from 4{$\cdot$}4 days (95\% CI 0{$\cdot$}0\textendash 14{$\cdot$}0) for the period of Dec 24 to Jan 27, to 2{$\cdot$}6 days (0{$\cdot$}0\textendash 9{$\cdot$}0) for the period of Jan 28 to Feb 17. The mean incubation period for the entire period was estimated at 5{$\cdot$}2 days (1{$\cdot$}8\textendash 12{$\cdot$}4) and the mean serial interval at 5{$\cdot$}1 days (1{$\cdot$}3\textendash 11{$\cdot$}6). The epidemic dynamics in provinces outside Hubei were highly variable but consistently included a mixture of case importations and local transmission. We estimated that the epidemic was self-sustained for less than 3 weeks, with mean \emph{Rt} reaching peaks between 1{$\cdot$}08 (95\% CI 0{$\cdot$}74\textendash 1{$\cdot$}54) in Shenzhen city of Guangdong province and 1{$\cdot$}71 (1{$\cdot$}32\textendash 2{$\cdot$}17) in Shandong province. In all the locations for which we had sufficient data coverage of \emph{Rt, Rt} was estimated to be below the epidemic threshold (ie, {$<$}1) after Jan 30.{$<$}/p{$><$}h3{$>$}Interpretation{$<$}/h3{$><$}p{$>$}Our estimates of the incubation period and serial interval were similar, suggesting an early peak of infectiousness, with possible transmission before the onset of symptoms. Our results also indicate that, as the epidemic progressed, infectious individuals were isolated more quickly, thus shortening the window of transmission in the community. Overall, our findings indicate that strict containment measures, movement restrictions, and increased awareness of the population might have contributed to interrupt local transmission of SARS-CoV-2 outside Hubei province.{$<$}/p{$><$}h3{$>$}Funding{$<$}/h3{$><$}p{$>$}National Science Fund for Distinguished Young Scholars, National Institute of General Medical Sciences, and European Commission Horizon 2020.{$<$}/p{$>$}},
  file = {/Users/ashcroft/polybox/aa-myFiles/aa-Bibliography/storage/LUU24RGU/Zhang et al_2020_Evolving epidemiology and transmission dynamics of coronavirus disease 2019.pdf},
  journal = {The Lancet Infectious Diseases},
  language = {English},
  number = {7},
  pmid = {32247326}
}