Objectives The aim of this study was to conduct a rapid systematic review and meta-analysis of estimates of the incubation period of COVID-19. Design Rapid systematic review and meta-analysis of observational research. Setting International studies on incubation period of COVID-19. Participants Searches 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 measures Parameters of a lognormal distribution of incubation periods. Results The 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. Conclusions The 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.

Objectives Our 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. Design Rapid 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 sources Search 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. Results There 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. Conclusions There 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.

Successful eradication schemes for bovine tuberculosis (bTB) have been implemented in a number of European and other countries over the last 50 years. However, the islands of Britain and Ireland remain a significant aberration to this trend, with the recent exception of Scotland. Why have eradication schemes failed within these countries, while apparently similar programs have been successful elsewhere? While significant socio-economic and political factors have been discussed elsewhere as key determinants of disease eradication, here we review some of the potential ecological and epidemiological constraints that are present in these islands relative to other parts of Europe. We argue that the convergence of these potential factors may interact additively to diminish the potential of the present control programs to achieve eradication. Issues identified include heterogeneity of diagnostic testing approaches, the presence of an abundant wildlife reservoir of infection and the challenge of sustainably managing this risk effectively; the nature, size, density and network structure of cattle farming; potential effects of Mycobacterium bovis strain heterogeneity on disease transmission dynamics; possible impacts of concurrent endemic infections on the disclosure of truly infected animals; climatological differences and change coupled with environmental contamination. We further argue that control and eradication of this complex disease may benefit from an ecosystem level approach to management. We hope that this perspective can stimulate a new conversation about the many factors potentially impacting bTB eradication schemes in Britain and Ireland and possibly stimulate new research in the areas identified.

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.

In the European Union, the recommended ante-mortem diagnostic methods for bovine tuberculosis (bTB) include the single intradermal cervical comparative tuberculin (SICCT) test and the interferon-gamma (IFN-γ) test as an ancillary test. The SICCT test has a moderate sensitivity (Se) and high specificity (Sp), while the IFN-γ test has good Se, but a lower Sp than the SICCT test. A retrospective Bayesian latent class analysis was conducted on 71,185 cattle from 806 herds chronically infected with bTB distributed across Northern Ireland (NI) to estimate the Se and Sp of the common ante-mortem tests and meat inspection. Analyses were also performed on data stratified by farming type and herd location to explore possible differences in test performance given the heterogeneity in the population. The mean estimates in chronically infected herds were: (1) 'standard' SICCT: Se 40.5-57.7%, Sp 96.3-99.7%; (2) 'severe' SICCT: Se 49.0%-60.6%, Sp 94.4-99.4%; (3) IFN-γ(bovine-avian) using a NI optical density (OD) cut-off difference of 0.05: IFN-γ(B-A)NI: Se 85.8-93.0%, Sp 75.6-96.2%; (4) IFN-γ(bovine-avian) using a standard 'commercial' OD cut-off difference of 0.1: IFN-γ(B-A)0.1: Se 83.1-92.1%, Sp 83.1-97.3%; and (5) meat inspection: Se 49.0-57.1% Se, Sp 99.1-100%. Se estimates were lower in cattle from dairy farms than from beef farms. There were no notable differences in estimates by location of herds. Certain population characteristics, such as production type, might influence the ability of bTB tests to disclose truly infected cases.