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.

Livestock production in Africa is key to national economies, food security and rural livelihoods, and > 85% of livestock keepers live in extreme poverty. With poverty elimination central to the Sustainable Development Goals, livestock keepers are therefore critically important. Foot-and-mouth disease is a highly contagious livestock disease widespread in Africa that contributes to this poverty. Despite its US$2.3 billion impact, control of the disease is not prioritized: standard vaccination regimens are too costly, its impact on the poorest is underestimated, and its epidemiology is too weakly understood. Our integrated analysis in Tanzania shows that the disease is of high concern, reduces household budgets for human health, and has major impacts on milk production and draft power for crop production. Critically, foot-and-mouth disease outbreaks in cattle are driven by livestock-related factors with a pattern of changing serotype dominance over time. Contrary to findings in southern Africa, we find no evidence of frequent infection from wildlife, with outbreaks in cattle sweeping slowly across the region through a sequence of dominant serotypes. This regularity suggests that timely identification of the epidemic serotype could allow proactive vaccination ahead of the wave of infection, mitigating impacts, and our preliminary matching work has identified potential vaccine candidates. This strategy is more realistic than wildlife-livestock separation or conventional foot-and-mouth disease vaccination approaches. Overall, we provide strong evidence for the feasibility of coordinated foot-and-mouth disease control as part of livestock development policies in eastern Africa, and our integrated socioeconomic, epidemiological, laboratory and modelling approach provides a framework for the study of other disease systems.