The incubation period of COVID-19 – A rapid systematic review and meta-analysis of observational research

Abstract

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

Pre-symptomatic transmission of SARS-CoV-2 infection: a secondary analysis using published data

“…Tianjin. An alternative method, using maximum likelihood estimation, was used by He et al Case reports [11][12][13][14][15][16][17][18][19][20] and virological studies [21][22][23] . CC-BY-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

“…The pre-symptomatic transmission potential of COVID-19 has been highlighted by case reports [11][12][13][14][15][16][17][18][19][20]. The potential for pre-symptomatic transmission was also suggested by detection of viral genome in upper respiratory samples prior to symptoms [21][22][23].…”

Accounting for Global COVID-19 Diffusion Patterns, January–April 2020

“…Fixed effects are denoted as a i and δ t , representing the country and time fixed effects, respectively. We choose to examine the response of new mortality growth with respect to SI at horizons no shorter than two weeks, with reference to studies on the incubation and death periods: The incubation period is 6 days on average (McAloon et al 2020), and the death period (number of days from symptom onset to death) ranges from 12 to 15 days for different age groups on average according to CDC estimates. 12 The collection of estimates b γ h ð Þ for h = {14, 21, 28} trace out the dynamic impact of stringency policies on mortality growth at the weekly frequency.…”

Accounting for Global COVID-19 Diffusion Patterns, January-April 2020

“…Fixed effects are denoted as and , representing the country and time fixed effects, respectively. We choose to examine the response of new mortality growth with respect to SI at horizons no shorter than two weeks, with reference to studies on the incubation and death periods: The incubation period is 6 days on average (McAloon, et al, 2020), and the death period (number of days from symptom onset to death) ranges from 12 to 15 days for different age groups 9 Oxford's Government Response Tracker 10 Local projection methods have become widely popular for macroeconomic analysis (see Ramey and Zubairy (2018) for a recent application), specifically for estimating impulse response functions. They are shown to be robust to misspecification and very easily allow for non-linearities (which we introduce when studying heterogeneity).…”