ObjectivesThe aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19.DesignRapid systematic review and meta-analysis of observational research.SettingInternational studies on incubation period of COVID-19.ParticipantsSearches were carried out in PubMed, Google Scholar, Embase, Cochrane Library as well as the preprint servers MedRxiv and BioRxiv. Studies were selected for meta-analysis if they reported either the parameters and CIs of the distributions fit to the data, or sufficient information to facilitate calculation of those values. After initial eligibility screening, 24 studies were selected for initial review, nine of these were shortlisted for meta-analysis. Final estimates are from meta-analysis of eight studies.Primary outcome measuresParameters of a lognormal distribution of incubation periods.ResultsThe incubation period distribution may be modelled with a lognormal distribution with pooled mu and sigma parameters (95% CIs) of 1.63 (95% CI 1.51 to 1.75) and 0.50 (95% CI 0.46 to 0.55), respectively. The corresponding mean (95% CIs) was 5.8 (95% CI 5.0 to 6.7) days. It should be noted that uncertainty increases towards the tail of the distribution: the pooled parameter estimates (95% CIs) resulted in a median incubation period of 5.1 (95% CI 4.5 to 5.8) days, whereas the 95th percentile was 11.7 (95% CI 9.7 to 14.2) days.ConclusionsThe choice of which parameter values are adopted will depend on how the information is used, the associated risks and the perceived consequences of decisions to be taken. These recommendations will need to be revisited once further relevant information becomes available. Accordingly, we present an R Shiny app that facilitates updating these estimates as new data become available.

ObjectivesOur objective was to review the literature on the inferred duration of the infectious period of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, and provide an overview of the variation depending on the methodological approach.DesignRapid scoping review. Literature review with fixed search terms, up to 1 April 2020. Central tendency and variation of the parameter estimates for infectious period in (A) asymptomatic and (B) symptomatic cases from (1) virological studies (repeated testing), (2) tracing studies and (3) modelling studies were gathered. Narrative review of viral dynamics.Information sourcesSearch strategies developed and the following searched: PubMed, Google Scholar, MedRxiv and BioRxiv. Additionally, the Health Information Quality Authority (Ireland) viral load synthesis was used, which screened literature from PubMed, Embase, ScienceDirect, NHS evidence, Cochrane, medRxiv and bioRxiv, and HRB open databases.ResultsThere was substantial variation in the estimates, and how infectious period was inferred. One study provided approximate median infectious period for asymptomatic cases of 6.5–9.5 days. Median presymptomatic infectious period across studies varied over <1–4 days. Estimated mean time from symptom onset to two negative RT-PCR tests was 13.4 days (95% CI 10.9 to 15.8) but was shorter when studies included children or less severe cases. Estimated mean duration from symptom onset to hospital discharge or death (potential maximal infectious period) was 18.1 days (95% CI 15.1 to 21.0); time to discharge was on average 4 days shorter than time to death. Viral dynamic data and model infectious parameters were often shorter than repeated diagnostic data.ConclusionsThere are limitations of inferring infectiousness from repeated diagnosis, viral loads and viral replication data alone and also potential patient recall bias relevant to estimating exposure and symptom onset times. Despite this, available data provide a preliminary evidence base to inform models of central tendency for key parameters and variation for exploring parameter space and sensitivity analysis.

The serial interval is the time between symptom onsets in an infector–infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector–infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19. Objective Conduct a review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters. Methods We conducted a rapid review of the literature covering the period 1 January 2020 and 21 August 2020, following predefined eligibility criteria. Forty scientific papers met our inclusion criteria and were included in the review. Results The mean of the serial interval ranged from 3.03 to 7.6 days, based on 38 estimates, and the median from 1.0 to 6.0 days (based on 15 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.95 to 5.20 days. One estimate of 5.0 days was provided for the median of the generation time. Discussion Estimates of the serial interval and the generation time are very dependent on the specific factors that apply at the time that the data are collected, including the level of social contact. Consequently, the estimates may not be entirely relevant to other environments. Therefore, local estimates should be obtained as soon as possible. Careful consideration should be given to the methodology that is used. Real-time estimations of the serial interval/generation time, allowing for variations over time, may provide more accurate estimates of reproduction numbers than using conventionally fixed serial interval/generation time distributions.

doi: medRxiv preprint Word Count: 3156 2 2 ABSTRACT 2 7Background: Reliable estimates of the incubation period are important for decision making around the 2 8 control of infectious diseases. Knowledge of the incubation period distribution can be used directly to 2 9 inform decision-making or as inputs into mathematical models. 3 0Objectives: The aim of this study was to conduct a rapid systematic review and meta-analysis of 3 1 estimates of the incubation periods of COVID-19. 3 2 Design: Rapid systematic review and meta-analysis of observational research 3 3 Data sources: Publications on the electronic databases PubMed, Google Scholar, MedRxiv and BioRxiv 3 4were searched. The search was not limited to peer-reviewed published data, but also included pre-print 3 5 articles. 6Study appraisal and synthesis methods: Studies were selected for meta-analysis if they reported either 3 7 the parameters and confidence intervals of the distributions fit to the data, or sufficient information to 3 8 facilitate calculation of those values. The majority of studies suitable for inclusion in the final analysis 3 9 modelled incubation period as a lognormal distribution. We conducted a random effects meta-analysis of 4 0 the parameters of this distribution. 4 1 Results: The incubation period distribution may be modelled with a lognormal distribution with pooled 4 2 mu and sigma parameters of 1.63 (1.51, 1.75) and 0.50 (0.45, 0.55) respectively. The corresponding mean 4 3

Background: Understanding the extent of virus transmission that can occur before symptom onset is vital for targeting control measures against the global pandemic of COVID-19.Objective: Estimation of (1) the proportion of pre-symptomatic transmission of COVID-19 that can occur and (2) timing of transmission relative to symptom onset. Design: Secondary analysis of published dataData sources: Meta-analysis of COVID-19 incubation period and a rapid systematic review of serial interval and generation time, which are published separately.Methods: Simulations were generated of incubation period and of serial interval or generation time. From these, transmission times relative to symptom onset were calculated and the proportion of pre-symptomatic transmission was estimated. Results:A total of 23 estimates of serial interval and five estimates of generation time from 17 publications were included. These came from nine different data source categories (presented here in descending order of the proportion of pre-symptomatic transmission):Hong Kong, Tianjin, pooled data from Hong Kong and Shenzhen, Singapore, Mainland China excluding Hubei, mixed sources, Shenzhen, northern Italy and Wuhan. Transmission time relative to symptom onset ranged from a mean of 2.05 days before symptom onset for Hong Kong to 1.72 days after symptom onset for Wuhan. Proportion of pre-symptomatic transmission ranged from 33.7% in Wuhan to 72.7% in Hong Kong. Based on individual estimates, transmission time relative to symptom onset ranged from mean of 2.95 days before symptom onset to 1.72 days after symptom onset and proportion of pre-symptomatic transmission ranged from 33.7% to 79.9%. Simple unweighted pooling of estimates based on serial intervals resulted in a mean time of transmission of 0.67 days before symptoms, and an estimated 56.1% of transmission occurring in the pre-symptomatic period. Conclusions:Contact rates between symptomatic infectious and susceptible people are likely to influence the proportion of pre-symptomatic transmission. There is substantial potential for pre-symptomatic transmission of COVID-19 in a range of different contexts. Our work suggests that transmission of SARS-CoV-2 is most likely in the day before symptom onset whereas estimates suggesting most pre-symptomatic transmission highlighted a mean transmission times almost 3 days before symptom onset. These findings highlight the urgent need for extremely rapid and effective case detection, contact tracing and quarantine measures if strict social distancing measures are to be eased.