https://ojs.wpro.who.int/ 1WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.4.001 Original Research C oronavirus disease 2019 (COVID-19) presents an enormous challenge to public health. By 18 April 2020, 140 million cases had been reported across 222 countries and areas, with an estimate of 3 million people having died.1 The overwhelming attention placed on COVID-19 and the volume of research published in the early months of this pandemic (over 4100 papers in PubMed to the end of April 2020) create challenges for public health responders attempting to understand the epidemiology of this disease. There is a need to distil and synthesize the findings that are most relevant to inform public health interventions. Estimates of the transmission parameters of a pathogen are required as soon as practicable, to inform the public health response. With known pathogens, public health responders can use data and estimates from previous outbreaks to make evidence-based deci- sions. However, with an emerging pathogen, such as severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2), past outbreaks may provide limited utility; hence, epidemic parameters must be estimated from early cases and detected transmission events. A successful outbreak response is informed by rapid data collection and analy- sis, to understand the dynamics of disease spread and identify appropriate, informed interventions. Understanding disease transmission of a new pathogen requires knowledge of the incubation period, serial interval and reproduction number. The basic re- production number is the expected or average number of secondary cases that result from one infected person if no individuals in the population are immune to the pathogen and no measures are in place to reduce spread. In practice, pathogens rarely propagate freely through a population because individuals change their behaviour or governments enact public health interventions. The effective reproduction number is the expected or average number of secondary cases in a population where some individuals are immune or interventions to limit spread are in place. a National Centre for Epidemiology and Population Health, Australian National University, Canberra, Australian Capital Territory, Australia. Published: 11 May 2021 doi: 10.5365/wpsar.2020.11.4.001 Early reports of epidemiological parameters of the COVID-19 pandemic Keeley Allen,a Amy Elizabeth Parrya and Kathryn Glassa Correspondence to Keeley Allen (email: Keeley.Allen@anu.edu.au) Background: The emergence of a new pathogen requires a rapid assessment of its transmissibility, to inform appropriate public health interventions. Methods: The peer-reviewed literature published between 1 January and 30 April 2020 on COVID-19 in PubMed was searched. Estimates of the incubation period, serial interval and reproduction number for COVID-19 were obtained and compared. Results: A total of 86 studies met the inclusion criteria. Of these, 33 estimated the mean incubation period (4–7 days) and 15 included estimates of the serial interval (mean 4–8 days; median length 4–5 days). Fifty-two studies estimated the reproduction number. Although reproduction number estimates ranged from 0.3 to 14.8, in 33 studies (63%), they fell between 2 and 3. Discussion: Studies calculating the incubation period and effective reproduction number were published from the beginning of the pandemic until the end of the study period (30 April 2020); however, most of the studies calculating the serial interval were published in April 2020. The calculated incubation period was similar over the study period and in different settings, whereas estimates of the serial interval and effective reproduction number were setting-specific. Estimates of the serial interval were shorter at the end of the study period as increasing evidence of pre-symptomatic transmission was documented and as jurisdictions enacted outbreak control measures. Estimates of the effective reproduction number varied with the setting and the underlying model assumptions. Early analysis of epidemic parameters provides vital information to inform the outbreak response. WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011 https://ojs.wpro.who.int/2 Allen et alEarly reports of epidemiological parameters of the COVID-19 pandemic and potentially not modifying their behaviour, this study refers to all estimates of the reproduction number as the effective reproduction number. We searched peer-reviewed published research articles from PubMed using the terms “coronavirus” AND “novel” OR “new” OR “covid” OR “Wuhan” OR “ncp” OR “ncov” for articles published online until 30 April 2020. The literature search ran from 24 February 2020 to 12 May 2020. All articles were imported to Zotero 5.0.87 for review. Eligible articles were reviewed for date of online publication, study period, sample size, setting, method of calculating epidemic parameters, assumptions used to inform these calculations and output measures (including the approach to estimating uncertainty). Studies were included in this review if they reported estimates of at least one of the relevant epidemic param- eters and were written in English. Any articles published before 1 November 2019, pre-prints, grey literature and case reports were excluded. Ethics and permissions Ethical approval was not sought for this review of exist- ing, publicly available peer-reviewed literature. RESULTS The PubMed search returned 4426 articles published online up to 30 April 2020. Of these articles, 3581 were excluded at the screening assessment and a further 759 at the eligibility assessment, giving a total of 86 included studies. The results of the search and eligibility assessment are shown in Fig. 1. Of the 86 included studies, 15 calculated more than one epidemic parameter of interest. Sixty of the 86 studies used data from mainland China for part or all of their analysis, and 11 specifically analysed outbreak data from Hubei province or the city of Wuhan. Incubation period A total of 33 studies estimated the incubation period of COVID-19 (Table 1). Mean estimates were reported in 15 studies, ranging from 1.8 to 9.9 days; however, 44% of the mean estimates were 5–6 days. The shortest mean estimate (incubation period = 1.8 days) was calculated The distribution of the incubation period is crucial for determining the length of quarantine for potentially exposed individuals and travellers.2–4 Estimates of the serial interval provide public health responders with an idea of the time available to identify and isolate poten- tial cases before they can spread the disease to oth- ers.5,6 The reproduction number of a disease provides a population-wide estimate of the scale of a potential outbreak and a baseline to test the effectiveness of dif- ferent interventions in limiting disease transmission.7–9 Although highly influential, early estimates of the incu- bation period, serial interval and reproduction number are generally based on small sample sizes that may not be representative of the wider population at risk.7,9,10 Although some literature reviews have reviewed the epidemiology of COVID-19,11–14 they have not col- lated the estimates of epidemic parameters from the initial period of the COVID-19 pandemic. The aim of this study was to collate and compare the characteristics of the COVID-19 pandemic up to 30 April 2020. METHODS Studies that describe or estimate the epidemic char- acteristics of the COVID-19 pandemic until 30 April 2020 were collected. Epidemiological parameters were limited to the incubation period, the serial interval and the reproduction number. The incubation period is the length of time experienced by an individual case from the point of infection to the start of symptom onset. The serial interval refers to the mean length of time between successive cases in a chain of transmission, measured as the length of time from symptom onset in a primary case to symptom onset in a secondary case. Both the incubation period and serial interval in this analysis are measured in days. Over the course of the COVID-19 pandemic so far, governments have enacted public health interventions at different times and to different extents. Individual behaviours have changed at different rates as individuals have learned about COVID-19 and responded to media reports, government messaging and their understanding of risk. Several estimates of the reproduction number overlap periods when governments have enacted significant public health interventions. Although this study focuses on estimates from the early stages of the outbreak, when most of the population were susceptible WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011https://ojs.wpro.who.int/ 3 Early reports of epidemiological parameters of the COVID-19 pandemicAllen et al from returned travellers from Hubei province in China, using their last day of travel as their date of exposure.29 One study’s mean estimate of 9.9 days was calculated from a series of 14 cases in Viet Nam.33 A further 22 estimates of the incubation period were summarized by their median. These studies were generally reporting on a specific cluster or outbreak investigation, and median estimates largely ranged from 4 to 7 days. Estimates outside of this range were cal- culated from case series; for example, a median range of 1–4 days was found among eight participants39 and an estimated 8-day incubation period for a study involv- ing 19 participants.27 The distribution of the mean and median incubation estimates by sample size of the study is shown in Fig. 2. A further three studies only included a range of ob- served incubation periods. The longest incubation period from these studies was 16 days, recorded in an outbreak investigation in mainland China.36 Additional estimates of the 95th percentile of the incubation period ranged from 10.3 days (95% confidence interval [CI]: 8.6–14.1)17 to 14 days (95% CI: 12.2–15.9).47 Fig. 1. Preferred reporting items for systematic reviews and meta-analysis diagram of study selection Reasons for exclusion at screening assessment • Articles focused on diagnostics (n = 406) • Articles focused on therapeutics (n = 636) • Articles report laboratory studies (n = 330) • Articles are not peer-reviewed (n = 13) • Articles are not in English (n = 141) • Articles focused on emergency and health-care management (n = 686) • Articles are not related to COVID-19 (n = 228) • Case reports (n = 165) • Articles report guidelines or consensus statements (n = 177) • Editorials, commentaries or letters (n = 616) • Review articles (n = 158) • Articles focused on COVID-19 in animals (n = 25) Reasons for exclusion at eligibility assessment • Articles focused on diagnostics (n = 47) • Articles focused on therapeutics (n = 11) • Articles report laboratory studies (n = 35) • Articles are not peer-reviewed (n = 9) • Articles are not in English (n = 35) • Articles focused on emergency and health-care management (n = 67) • Articles are not related to COVID-19 (n = 11) • Case reports (n = 41) • Articles report guidelines or consensus statements (n = 1) • Editorials, commentaries or letters (n = 117) • Review articles (n = 109) • Articles focused on COVID-19 in animals (n = 1) • Epidemiological studies that do not contain relevant parameters (n = 275) Id en ti fi ed S cr ee n in g E lig ib ili ty In cl u d ed Records identified through PubMed search (n = 4426) Records screened (n = 4426) Records excluded (n = 3581) Full-text articles assessed for eligibility (n = 845) Full-text articles excluded, with reasons (n = 759) Studies included in the qualitative analysis (n = 86) WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011 https://ojs.wpro.who.int/4 Allen et alEarly reports of epidemiological parameters of the COVID-19 pandemic Study authors Online publication date Study period Sample size Setting Estimate (days)* Uncertainty estimate (days) Uncertainty measure Chan et al.15 24 January 2020 26 December 2019 –15 January 2020 5 Mainland China - 3–6 Range Li et al.16 29 January 2020 Up to 22 January 2020 10 Wuhan/Hubei 5.2 4.1–7.0 95% CI Backer, Klinkenberg and Wallinga17 6 February 2020 20 January 2020 –28 January 2020 88 International 6.4 5.6–7.7 95% CrI Ki and Task Force for 2019-nCoV18 9 February 2020 20 January 2020 –8 February 2020 28 Republic of Korea 3.9; [3.0] 0–15 Range Jiang, Rayner and Luo19 13 February 2020 Up to 8 February 2020 50 Mainland China 4.9 4.4–5.5 95% CI Linton et al.20 17 February 2020 17 December 2019 –31 January 2020 158 International 5.6; [4.6] 4.4–7.4; 3.7–5.7 95% CrI Xu et al.21 19 February 2020 10 January 2020 –26 January 2020 56 Mainland China [4] 3–5 IQR Tian et al.22 27 February 2020 20 January 2020 –10 February 2020 203 Mainland China [6.7] ± 5.2 SD Cai et al.23 28 February 2020 19 January 2020 –3 February 2020 10 Mainland China 6.5 2–10 Range Guan et al.24 28 February 2020 Up to 23 January 2020 291 Mainland China [4] 2–7 IQR Liu et al.25 3 March 2020 1 January 2020 –5 February 2020 58 Mainland China 6.0; [5.0] 3–8; 1–16 IQR; Range Lauer et al.26 10 March 2020 4 January 2020 –24 February 2020 181 International [5.1] 4.5–5.8 95% CI Zhao et al.27 12 March 2020 23 January 2020 –5 February 2020 19 Mainland China [8] 6–11 IQR Pung et al.28 16 March 2020 18 January 2020 –10 February 2020 17 Singapore [4] 3–6; 1–11 IQR; Range Leung29 18 March 2020 20 January 2020 –12 February 2020 105 Mainland China (trav- elled to Hubei) 1.8 1.0–2.7 95% CI 70 Mainland China (local transmission) 7.2 6.1–8.4 95% CI Chang et al.30 23 March 2020 28 January 2020 –9 February 2020 15 Mainland China [5] 1–6 Range Jin et al.31 24 March 2020 17 January 2020 –8 February 2020 21 Mainland China – GI symptoms [4] 3–7 IQR 195 Mainland China – No GI symptoms [5] 3–8 IQR Zhang et al.32 2 April 2020 19 January 2020 –17 February 2020 49 Mainland China 5.2 1.8–12.4 95% CI Le et al.33 2 April 2020 17 January 2020 –14 February 2020 12 Viet Nam 9.9 ± 5.2 SD Zhu and Chen34 2 April 2020 1 December 2019 –23 January 2020 Not specified Mainland China, Hong Kong (SAR) China, Macau (SAR) China, Taiwan (China) 5.67 1–14 Range Table 1. Estimated incubation period of COVID-19 from included epidemiological parameters studies published between 1 January and 30 April 2020 WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011https://ojs.wpro.who.int/ 5 Early reports of epidemiological parameters of the COVID-19 pandemicAllen et al *Mean estimates. Median estimates are shown in [square brackets]. Multiple estimates of incubation period for the same population within the same study are shown in the same row and separated by a semicolon. Estimates of the incubation period in the same study for different populations are shown in separate rows. CI: confidence interval; CrI: credible interval; GI: gastrointestinal; IQR: interquartile range; SD: standard deviation. Notes: Sample size reported in Table 1 is the sample size used to calculate the incubation period, not necessarily the whole study sample. All estimates are reported to one decimal place, except where stating findings from papers that did not provide that level of precision. Study authors Online publication date Study period Sample size Setting Estimate (days)* Uncertainty estimate (days) Uncertainty measure Han et al.35 6 April 2020 31 January 2020 –16 February 2020 25 Mainland China – adults [5] 3–12 Range 7 Mainland China – children [4] 2–12 Range Shen et al.36 7 April 2020 8 January 2020 –26 February 2020 6 Mainland China [7.5] 1–16 Range Sanche et al.37 7 April 2020 15 January 2020 –30 January 2020 24 Mainland China 4.2 3.5–5.1 95% CI Ghinai et al.38 8 April 2020 February–March 2020 15 United States of America 4.3; [4] 1–7 Range Huang et al.39 10 April 2020 23 January 2020 –20 February 2020 8 Mainland China [2] 1–4 Range Zheng et al.40 10 April 2020 17 January 2020 –7 February 2020 161 Mainland China [6] 3–8 Range Xia et al.41 12 April 2020 23 January 2020 –18 February 2020 10 China incl. Hong Kong (SAR) China, Macau (SAR) China, Taiwan (China) 7.0 ± 2.59; 2–14 SD; Range Chen et al.42 14 April 2020 28 January 2020 –11 February 2020 12 Mainland China 8.0 1–13 Range Song et al.43 23 April 2020 16 January 2020 –29 January 2020 22 Mainland China - 2–13 Range Jiang et al.44 23 April 2020 23 January 2020 –13 February 2020 4 Mainland China - 9–13 Range Nie et al.45 27 April 2020 19 January 2020 –8 February 2020 2907 Mainland China [5] 2–8 IQR Yu et al.46 29 April 2020 Up to 19 February 2020 132 Mainland China [7.2] 6.4–7.9 95% CI Bi et al.47 30 April 2020 14 January 2020 –12 February 2020 138 Mainland China [4.8] 4.2–5.4 95% CI Serial interval Of the 15 studies that included a serial interval, eight were published in April 2020. Mean serial interval es- timates were calculated in 14 studies and ranged from 3.1 to 7.5 days (Table 2). The estimated serial intervals were longer in stud- ies published at the start than at the end of the study period, with a mean interval of 7.5 days in late January 2020 and a mean of 4–5 days in early March 2020. Es- timates published from March 2020 onwards included transmission pairs with negative serial intervals, or intervals shorter than the incubation period, suggesting possible pre-symptomatic transmission. Mean estimates of the serial interval that included negative transmission pairs generally ranged from 3.9 to 5.8 days (Table 2). The four median serial interval estimates ranged from 1.0 to 5.4 days. Excluding the estimate of 2 days from a case series of eight cases,39 the median serial interval ranged from 4.0 to 5.4 days (Table 2). Reproduction number There were 90 estimates of the reproduction number from 52 studies across three World Health Organization (WHO) regions: Western Pacific Region, European Re- gion and Region of the Americas. Reproduction number estimates ranged from 0.3 to 14.8. Of the 90 reported WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011 https://ojs.wpro.who.int/6 Allen et alEarly reports of epidemiological parameters of the COVID-19 pandemic DISCUSSION This study provides a review of estimated epidemic parameters of the COVID-19 outbreak up to 30 April 2020. Estimates of the incubation period were similar across the study period, with a mean estimated value of 5–6 days and a range of 2–14 days. Estimates of the serial interval shortened over the study period, from 7.5 days in late January 2020 to a mean of 4–5 days in early March 2020. Estimates of the reproduction number varied in the studies collated up to 30 April 2020. Although some estimates of the reproduction number were as high as 14.8, over half were between 2 and 4. The higher esti- mates demonstrate the impact of the setting, individual behaviours and public health interventions – the highest estimates were associated with cruise ships,64,67,68 whereas the lowest estimates were generally calculated in areas with a rapid response to an outbreak.18,55,74,78 The incubation period reflects the growth of a virus in an individual, and thus is largely a biological function that would not be expected to vary with changes in hu- man behaviour and wider public health interventions. Variations in the incubation period reported in this study estimates, 33 estimates (37%) were between 2 and 3, and 20 estimates (22%) were between 3 and 4 (Table 3). The initial low estimate of 0.3 relied on the early assumption that the pathogen was primarily spread through zoonotic transmission.56 Other estimates of the reproduction number under 1 were reported in jurisdic- tions with rapid public health interventions during the study period, including the Republic of Korea and Singa- pore.18,55,74 The highest reproduction number estimate (14.8) was from analyses of transmission dynamics onboard the Diamond Princess cruise ship.67 The distribution of reproduction number estimates by the assumed serial interval is shown in Fig. 3. Just over half (n = 50) of the 90 reproduction number re- sults used an estimate of the serial interval to calculate the reproduction number. Serial interval estimates used to estimate the reproduction number ranged from 449 to 10 days, with the latter taken from the estimated serial interval for severe acute respiratory syndrome (SARS) in early outbreaks.100 Studies generally applied serial intervals from the earliest COVID-19 estimate of 7.5 days16 and the accepted serial interval of SARS of 8.4 days.100 Note: The confidence intervals (CIs) of estimates are not shown in the figure. CIs are reported in Table 1. The estimate from Nie et al.45 of a median of 5 days is not shown because the sample size (n = 2907) is significantly larger than other studies. Fig. 2. Incubation period estimates and sample size of study (n = 28 studies, 35 estimates) published between 1 January and 30 April 2020 0 2 4 6 8 10 12 0 50 100 150 200 250 300 350 In cu b at io n p er io d ( d ay s) Sample size Incubation period estimates and sample size of study (n = 28 studies, 35 estimates) In cu ba ti on p er io d (d ay s) Sample size WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011https://ojs.wpro.who.int/ 7 Early reports of epidemiological parameters of the COVID-19 pandemicAllen et al *Mean estimates. Median estimates are shown in [square brackets]. Multiple estimates of serial interval for the same population within the same study are shown in the same row and separated by a semicolon. Estimates of the serial interval in the same study for different populations are shown in separate rows. bCI: Bayesian confidence interval; CI: confidence interval; CrI: credible interval; SD: standard deviation. Notes: Sample size reported is the sample size used to calculate the serial interval, not necessarily the whole study sample. All estimates are reported to one decimal place, except where stating findings from papers that did not provide that level of precision. Table 2. Estimated serial interval from included COVID-19 epidemiological parameters studies published between 1 January and 30 April 2020 Study authors Online publication date Study period Sample size Transmission pairs Setting Estimate (days)* Uncertainty estimate (days) Uncertainty measure Li et al.16 29 January 2020 Up to 22 January 2020 10 6 Wuhan/Hubei 7.5 5.3–19.0 95% CI Ki and Task Force for 2019-nCoV18 9 February 2020 20 January 2020 –8 February 2020 28 12 Republic of Korea 6.6; [4.0] 3–15 Range Liu et al.25 3 March 2020 1 January 2020 –5 February 2020 15 single intracluster transmission cases 12 clusters Mainland China 5.5 - - 56 single co-exposure cases 56 clusters Mainland China 3.1 - - Nishiura et al.48 4 March 2020 Up to 12 February 2020 Not specified 28 – all pairs International [4.0] 3.1–4.9 95% CrI 18 – most certain pairs International [4.6] 3.5–5.9 95% CrI Pung et al.28 16 March 2020 Up to 15 February 2020 4 3 Singapore 3–8 Range Du et al.49 19 March 2020 21 January 2020 –8 February 2020 752 468 Mainland China 4.0 3.5–4.4 95% CI Wu et al.50 19 March 2020 1 December 2019 –28 February 2020 Not specified 43 International 7 5.8–8.1 95% CI Zhang et al.32 2 April 2020 19 January 2020 –17 February 2020 63 35 Mainland China 5.1 3.1–11.6 95% CI Ji et al.51 7 April 2020 23 January 2020 –27 March 2020 51 32 Wuhan/Hubei 6.5 6.3 SD Huang et al.39 10 April 2020 23 January 2020 –20 February 2020 9 8 Mainland China [1] 0–4 Range Wang et al.52 10 April 2020 11 January 2020 –16 February 2020 115 85 Wuhan/Hubei 5.5 ± 2.7 SD He et al.53 15 April 2020 7 January 2020 –4 March 2020 Not specified 77 International 5.8; [5.2] 4.8–6.8; 4.1–6.4 95% CI Kwok et al.54 23 April 2020 23 January 2020 –13 February 2020 38 26 Hong Kong (SAR) China 4.6 3.4–5.9 95% bCI 26 – adjusted for right truncation Hong Kong (SAR) China 4.8 3.5–6.9 95% CrI Bi et al.47 27 April 2020 14 January 2020 –12 February 2020 Not specified 48 Mainland China 6.3; [5.4] 5.2–7.6; 4.4–6.5 95% CI Ganyani et al.55 30 April 2020 14 January 2020 –27 February 2020 54 4 clusters Singapore 5.2 –3.4–13.9 95% CrI 114 16 clusters Mainland China 3.9 –4.5–12.5 95% CrI WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011 https://ojs.wpro.who.int/8 Allen et alEarly reports of epidemiological parameters of the COVID-19 pandemic Table 3. Estimated reproduction number from included COVID-19 epidemiological parameters studies published between 1 January and 30 April 2020 Study authors Online publication date Study period Sample size Method Setting Estimate Uncertainty interval Uncertainty measure Wu et al.56 23 January 2020 10 January 2020 –12 January 2020 41 Zoonotic transmission – Cauchemez et al. 2013111 Wuhan/ Hubei 0.3 0.17–0.44 95% CI Li et al.16 29 January 2020 Up to 22 January 2020 425 Transmission model with renewal equations Wuhan/ Hubei 2.2 1.4–3.9 95% CI Riou and Althaus57 30 January 2020 Up to 18 January 2020 50 Stochastic transmission model Wuhan/ Hubei 2.2 1.4–3.8 90% HDI Zhao et al.58 30 January 2020 10 January 2020 –24 January 2020 2033 Exponential growth model method Mainland China 2.24 –3.58 1.96–2.55 to 2.89– 4.39 95% CI Wu et al.59 31 January 2020 1 December 2019 –28 January 2020 55 Differential equation – SEIR compartment model International 2.68 2.47–2.86 95% CrI Zhao et al.60 1 February 2020 1 December 2019 –24 January 2020 41 Exponential growth model method Mainland China 2.56 2.49–2.63 95% CI Tang et al.61 7 February 2020 10 January 2020 –15 January 2020 41 Differential equation – SEIR compartment model Mainland China 6.47 5.71–7.23 95% CI Ki and Task Force for 2019- nCoV18 9 February 2020 20 January 2020 – 8 February 2020 26 Estimated from transmission chains Republic of Korea 0.48 0.25–0.84 95% CI Zhou et al.62 12 February 2020 Up to 25 January 2020 2820 Differential equation – SEIR compartment model Mainland China 2.83–3.28 - - Jung et al.63 14 February 2020 31 December 2019 –24 January 2020 92 Exponential growth model method Mainland China 2.1; 3.2 2.0–2.2; 2.7–3.7 95% CI Zhang et al.64 22 February 2020 Up to 16 February 2020 355 Cori et al. methodology112 Cruise ship 2.28 2.06–2.52 95% CI Lai et al.65 25 February 2020 Up to 4 February 2020 52 Coalescent-based exponential growth and a birth-death skyline method Mainland China 2.6 2.1–5.1 95% CI Chen et al.66 28 February 2020 7 December 2019 –1 January 2020 Not specified Bats-Hosts- Reservoir-People transmission network model Wuhan/ Hubei 3.58 - - Rocklov, Sjodin and Wilder- Smith67 28 February 2020 21 January 2020 –19 February 2020 3700 Differential equation – SEIR compartment model Cruise ship 14.8 - - Mizumoto and Chowell68 29 February 2020 20 January 2020 –17 February 2020 3711 Discrete time integral equation Cruise ship 5.8 0.6–11.0 95% CrI Fang, Nie and Penny69 6 March 2020 20 January 2020 –29 February 2020 35 329 Differential equation – SEIR compartment model Mainland China 2.35–3.21 - - WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011https://ojs.wpro.who.int/ 9 Early reports of epidemiological parameters of the COVID-19 pandemicAllen et al Study authors Online publication date Study period Sample size Method Setting Estimate Uncertainty interval Uncertainty measure Zhou et al.70 10 March 2020 10 January 2020 –31 January 2020 44 Differential equation – SEIR compartment model Mainland China 5.3167 - - Kucharski et al.71 11 March 2020 1 December 2019 –11 February 2020 Not specified Differential equation – SEIR compartment model Wuhan/ Hubei 2.35 1.15–4.77 95% CI Yang and Wang72 11 March 2020 23 January 2020 –10 February 2020 Not specified Differential equation – SEIR compartment model Wuhan/ Hubei 4.25 - - Zhao and Chen73 11 March 2020 20 January 2020 –30 January 2020 Not specified Differential equation – SEIR compartment model Mainland China 4.7092 - - Choi and Ki74 12 March 2020 29 December 2019 –3 January 2020 Not specified Differential equation – SEIR compartment model Wuhan/ Hubei 4.028 4.010– 4.046 95% CI 20 January 2020 –17 February 2020 30 Republic of Korea 0.555 0.509– 0.602 95% CI Kuniya75 13 March 2020 15 January 2020 –29 February 2020 239 Differential equation – SEIR compartment model Japan 2.6 2.4–2.8 95% CI Remuzzi and Remuzzi76 13 March 2020 19 February 2020 –8 March 2020 Unclear Exponential growth model method Italy 2.76–3.25 - - Li et al.77 16 March 2020 10 January 2020 –23 January 2020 801 Differential equation – SEIR compartment model Mainland China 2.38 2.03–2.77 95% CrI Shim et al.78 17 March 2020 20 January 2020 –26 February 2020 6284 Generalized growth model Republic of Korea 1.5 1.4–1.6 95% CI Du et al.49 19 March 2020 21 January 2020 –8 February 2020 752 Not stated Mainland China 1.32 1.16–1.48 95% CI Wu et al.50 19 March 2020 1 December 2019 –28 February 2020 45 771 Differential equation – SEIR compartment model Wuhan/ Hubei 1.94 1.83–2.06 95% CrI Yuan et al.79 28 March 2020 23 February 2020 –9 March 2020 Not specified Exponential growth model method; Wallinga time dependent method Italy 3.27; 3.10 3.17–3.38; 2.21–4.11 95% CI France 6.32; 6.56 5.72–6.99; 2.04–12.26 95% CI Spain 5.08; 3.95 4.51–5.74; 0–10.19 95% CI Germany 6.07; 4.43 5.51–6.69; 1.83–7.92 95% CI Anastassopoulou et al.80 31 March 2020 11 January 2020 –10 February 2020 Not specified Differential equation – SEIR compartment model Wuhan/ Hubei 4.6 3.56–5.65 90% CI Ferretti et al.81 31 March 2020 Up to end March 2020 40 transmission pairs Exponential growth model method Mainland China 2 1.7–2.5 90% CI Huang et al.82 31 March 2020 13 January 2020 –9 March 2020 80 754 Differential equation – SEIR compartment model Mainland China 2.23–2.51 - - WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011 https://ojs.wpro.who.int/10 Allen et alEarly reports of epidemiological parameters of the COVID-19 pandemic Study authors Online publication date Study period Sample size Method Setting Estimate Uncertainty interval Uncertainty measure Tian et al.83 31 March 2020 31 December 2019 –23 January 2020 Not specified Differential equation – SEIR compartment model Mainland China 3.15 3.04–3.26 95% BCI Zhu and Chen34 2 April 2020 1 December 2019 –23 January 2020 Not specified Poisson Transmission Model Mainland China 2.47 2.39–2.55 95% CI Sanche et al.37 7 April 2020 15 January 2020 –30 January 2020 140 Differential equation – SEIR compartment model Mainland China 5.7 3.8–8.9 95% CI Zhao et al.84 8 April 2020 1 December 2019 –8 January 2020 Not specified Differential equation – SEIR compartment model Wuhan/ Hubei 2.5 2.4–2.7 95% CI Pan, Liu and Wang85 10 April 2020 5 December 2019 –8 March 2020 32 583 Cori et al. methodology112 Wuhan/ Hubei 3.82 3.72–3.93 95% CrI Abbott et al.86 14 April 2020 Up to 25 January 2020 1975 Stochastic branching process model Mainland China 2.8–3.8 - - Puci et al. 14 April 2020 22 March 2020 –29 March 2020 975 Differential equation – SEIR compartment model Italy 1.82 1.51–2.01 95% CI Du et al.87 16 April 2020 1 December 2019 –22 January 2020 19 Exponential growth method Mainland China 1.9 1.47–2.59 95% CrI Torres-Roman et al.88 17 April 2020 6 March 2020 –15 March 2020 Not specified Cori et al. methodology112 Peru 2.97 - - Tsang et al.89 20 April 2020 15 January 2020 –3 March 2020 Not specified Exponential growth model Mainland China 2.8–3.5 - - Muniz- Rodriguez et al.90 22 April 2020 19 February 2020 –19 March 2020 978 Exponential growth model; renewal equations method Islamic Republic of Iran 4.4; 3.5 3.9–4.9; 1.3–8.1 95% CI Zhuang et al.91 22 April 2020 Up to 5 March 2020 Not specified Stochastic model, maximum likelihood estimation approach Italy 2.6; 3.3 2.3–2.9; 3.0–3.6 95% CI Republic of Korea 2.6; 3.2 2.3–2.9; 2.9–3.5 95% CI Gatto et al.92 23 April 2020 24 February 2020 –23 March 2020 107 Differential equation – SEIR compartment model Italy 3.6 3.49–3.84 95% CI Han et al.93 23 April 2020 21 January 2020 –15 February 2020 482 Exponential growth model method Mainland China 2.9 1.8–4.5 95% CI Caicedo-Ochoa et al.94 25 April 2020 Up to 23 March 2020 (first 10 days after reaching 25 cases in each location) Not specified Cori et al. methodology112 Two serial intervals used: 7.5 days; 4.7 days Spain 6.48; 2.9 5.97–7.02; 2.67–3.14 95% CrI Italy 6.41; 2.83 6.11–6.71; 2.70–2.96 95% CrI Ecuador 12.86; 3.95 12.05–13.68; 3.70–4.21 95% CrI Panama 7.19; 3.67 6.37–8.08; 3.25–4.13 95% CrI Brazil 6.53; 2.91 5.85–7.25; 2.60–3.23 95% CrI Chile 5.79; 2.67 5.32–6.28; 2.45–2.89 95% CrI Colombia 5.65; 2.67 5.04–6.29; 2.38–2.98 95% CrI Peru 5.24; 2.36 4.68–5.83; 2.11–2.63 95% CrI Mexico 4.94; 2.42 4.37–5.56; 2.14–2.72 95% CrI WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011https://ojs.wpro.who.int/ 11 Early reports of epidemiological parameters of the COVID-19 pandemicAllen et al Study authors Online publication date Study period Sample size Method Setting Estimate Uncertainty interval Uncertainty measure Bi et al.47 27 April 2020 14 January 2020 –12 February 2020 48 Estimated from transmission chains Mainland China 0.4 0.3–0.5 95% CI Distante et al.95 27 April 2020 Up to 29 March 2020 Not specified Exponential growth method Italy 3.6 - - Ndairou et al.96 27 April 2020 4 January 2020 –9 March 2020 Not specified Differential equation – SEIR compartment model Wuhan/ Hubei 0.945 - - Peirlinck et al.97 27 April 2020 21 January 2020 –4 April 2020 311 357 Differential equation – SEIR compartment model United States of America 5.3 ± 0.95 SD Adegboye et al.98 28 April 2020 27 February 2020 –11 April 2020 318 Cori et al. methodology112 Nigeria 2.71 - - Ganyani et al.55 30 April 2020 14 January 2020 –27 February 2020 91 Exponential growth model method Singapore 1.25 1.17–1.34 95% CrI 135 Exponential growth model method Mainland China 1.41 1.26–1.58 95% CrI Ivorra et al.99 30 April 2020 1 December 2019 –29 March 2020 Not specified Differential equation – SEIR compartment model Mainland China 4.2732 - - Multiple estimates of the reproduction number for the same population within the same study are shown in the same row and separated by a semicolon. Estimates of the incubation period in the same study for different populations are shown in separate rows. bCI: Bayesian confidence interval; CI: confidence interval; CrI: credible interval; HDI: high density interval; SD: standard deviation; SEIR: susceptible-exposed-infect- ed-recovered. Notes: Sample size reported is the sample size used to calculate the serial interval, not necessarily the whole study sample. All estimates are reported to the number of decimal places provided in each study. Fig. 3. Estimated reproduction number and serial interval of the model (n = 23 studies, 50 estimates) published between 1 January and 30 April 2020 0 2 4 6 8 10 12 14 16 0 2 4 6 8 10 12 Es ti m at ed r ep ro d u ct io n n u m b er Serial interval used in model (days) Estimated reproduction number and serial interval of the model (n = 23 studies, 50 estimates) Note: The confidence intervals (CIs) of estimates are not shown in the figure. CIs are reported in Table 3. E st im at ed r ep ro du ct io n n um b er Serial interval used in model (days) WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011 https://ojs.wpro.who.int/12 Allen et alEarly reports of epidemiological parameters of the COVID-19 pandemic and time periods. The reproduction number was estimated to be 2.2 in studies published in January and February 2020,16,57 but increased to 4 in articles published in March and April 2020.72,74,80 The epidemiological parameters reviewed share some similarities to that of SARS and Middle East respiratory syndrome (MERS), two diseases caused by coronaviruses that have caused significant outbreaks in the early 21st century. The estimates of the range and mean of the incubation period of COVID-19 are similar to that of SARS (2–10 days, mean of 5–6 days)2,100,107 and MERS (2–14 days, median of 5–6 days).107,108 However, the estimated serial interval for COVID-19 is shorter than the observed intervals for SARS (8.4 days)100 and MERS (7.6–12.6 days).108,109 The later estimates of the COVID-19 serial interval published in April 2020 are shorter than the estimates for the incubation period, suggesting the potential for pre-symptomatic transmission, which has not been observed for SARS or MERS.100,108,110 The estimated reproduction number of COVID-19 is similar to the estimates for the 2002–2003 SARS outbreak.100 This study has some important limitations. It provides a descriptive assessment and does not in- clude meta-analysis or recalculations of results. The use of different methods and different outputs from each study limits the capacity for meta-analysis. This review may also be impaired by publication bias. Several included studies were based on small sample sizes, which led to imprecise results. The ongoing pandemic requires the active involvement of public health researchers to assess unfolding situations and advise on local responses. Fulfilling crucial roles as the pandemic unfolded may have limited the potential to publish findings, restricting our understanding of epi- demic parameters in real time and reducing the repre- sentativeness of the results. This potential publication bias may also explain in part the overrepresentation of data from mainland China although COVID-19 has led to outbreaks worldwide. Nevertheless, the early pub- lished estimates included in this study have been used worldwide to inform public health responses, and they provide the best available evidence in the timeframe of this study. Only studies written in English were included in this review. This excludes many early estimates writ- may, in part, result from the study designs adopted. Sev- eral estimates of the incubation period were reported di- rectly from cluster investigations, often with low sample sizes. Studies with more than 20 participants had less variation between estimates than studies with smaller sample sizes. The definition of exposure, including the potential for continuous exposure in a household, may also have influenced results by artificially lengthening or shortening the incubation period, depending on study design and differences in local epidemiological reporting protocols. The serial interval and reproduction number are likely to be influenced by public health interventions, social behaviours and political decisions. Estimates of these two epidemic characteristics are therefore set- ting-specific, which may explain the variance across the results in this study. The serial interval estimates also changed as new information about the pathogen came to light, primarily the potential for pre-symptomatic and pauci-symptomatic transmission.101–106 However, these revised estimates of the serial interval were rarely used to revise reproduction number estimates. A longer serial interval results in a higher estimate of the reproduction number. The earliest published estimate by Li et al.’s study (first published online on 29 January 2020)16 of six transmission pairs in Wuhan was higher than most of the later estimates. That estimate was applied as an assumed serial interval in 10 studies published in March and April 2020,37,63–65,68,85,89,93–95 despite not being used in Li et al.’s own calculation of the reproduction number.16 These early studies have been used to inform national and regional responses to the COVID-19 pandemic, and they demonstrate the importance of and reliance on early estimates to inform future research and public health decision-making. Variations in the estimated reproduction number may also occur due to other assumptions applied in calculations. The initial estimate of the reproduction number of 0.3 assumed zoonotic transmission as the primary mode of transmission, based on the informa- tion available at the time.56 The method applied may also influence the final estimate of the reproduction number. This is evident in the studies estimating the reproduction number of the Wuhan outbreak from De- cember 2019 to mid-February 2020, which increased in later publications that used the same data sources WPSAR Vol 12, No 2, 2021 | doi: 10.5365/wpsar.2020.11.3.011https://ojs.wpro.who.int/ 13 Early reports of epidemiological parameters of the COVID-19 pandemicAllen et al reference values to enable a timely response to potential future outbreaks of COVID-19 and any future emerging coronaviruses and other potential pandemic diseases. Conflicts of interest None declared. Funding None. References 1. Weekly epidemiological update on COVID-19 - 20 April 2021. Ge- neva: World Health Organization; 2021. Available from: https:// www.who.int/publications/m/item/weekly-epidemiological-update- on-covid-19---20-april-2021, accessed 23 April 2021. 2. Farewell VT, Herzberg AM, James KW, Ho LM, Leung GM. SARS incubation and quarantine times: when is an exposed individual known to be disease free? Stat Med. 2005 Nov 30;24(22):3431– 45. doi:10.1002/sim.2206 pmid:16237660 3. Nishiura H. Early efforts in modeling the incubation period of in- fectious diseases with an acute course of illness. Emerg Themes Epidemiol. 2007 May 11;4(1):2. doi:10.1186/1742-7622-4-2 pmid:17466070 4. Nishiura H. Determination of the appropriate quarantine period fol- lowing smallpox exposure: an objective approach using the incubation period distribution. Int J Hyg Environ Health. 2009 Jan;212(1):97– 104. doi:10.1016/j.ijheh.2007.10.003 pmid:18178524 5. Fine PEM. The interval between successive cases of an infectious disease. Am J Epidemiol. 2003 Dec 1;158(11):1039–47. https:// doi.org/10.1093/aje/kwg251 PMID:14630599 6. Ma Y, Horsburgh CR, White LF, Jenkins HE. Quantifying TB trans- mission: a systematic review of reproduction number and se- rial interval estimates for tuberculosis. Epidemiol Infect. 2018 Sep;146(12):1478–94. doi:10.1017/S0950268818001760 7. Becker NG, Wang D, Clements M. Type and quantity of data needed for an early estimate of transmissibility when an infec- tious disease emerges. Euro Surveill. 2010 Jul 1;15(26):19603. pmid:20619130 8. Caley P, Philp DJ, McCracken K. Quantifying social distancing arising from pandemic influenza. J R Soc Interface. 2008 Jun 6;5(23):631– 9. doi:10.1098/rsif.2007.1197 pmid:17916550 9. Nishiura H, Chowell G, Safan M, Castillo-Chavez C. Pros and cons of estimating the reproduction number from early epidemic growth rate of influenza A (H1N1) 2009. Theor Biol Med Model. 2010 Jan 7;7(1):1. doi:10.1186/1742-4682-7-1 pmid:20056004 10. Mercer GN, Glass K, Becker NG. Effective reproduction num- bers are commonly overestimated early in a disease outbreak. Stat Med. 2011 Apr 30;30(9):984–94. doi:10.1002/sim.4174 pmid:21284013 11. Chang T-H, Wu J-L, Chang L-Y. Clinical characteristics and diag- nostic challenges of pediatric COVID-19: A systematic review and meta-analysis. J Formos Med Assoc. 2020 May;119(5):982–9. doi:10.1016/j.jfma.2020.04.007 pmid:32307322 12. Park M, Cook AR, Lim JT, Sun Y, Dickens BL. A systematic re- view of COVID-19 epidemiology based on current evidence. J Clin Med. 2020 Mar 31;9(4):967. doi:10.3390/jcm9040967 pmid:32244365 ten in Mandarin and Korean, which also limits the representativeness of this analysis. Furthermore, this analysis was limited to peer-reviewed published journal articles indexed in PubMed, which represents only a fraction of the literature published on the COVID-19 pandemic. The current pandemic has seen the pro- liferation of pre-print articles and increased attention on their results. Grey literature published by WHO, national governments and other organizations were also omitted. In times of emergency, pre-prints and grey literature may provide new information in a timely manner; however, this review focused only on estima- tions of epidemic parameters that have been subject to external peer review. Pandemics are inherently uncertain times. The challenges of the ongoing COVID-19 pandemic are com- pounded by SARS-CoV-2 being a new pathogen, which public health and clinical professionals have had to rap- idly assess, understand and respond to. Early estimates can provide useful interim guidance for public health decision-making. This is particularly true for transmis- sion that is driven by biological characteristics, such as the incubation period. Epidemic characteristics that are influenced by human behaviours and public health interventions are less certain and require interpretation within the context of data collection and analysis of the study. Reliance on data from small sample sizes and specific settings is necessary in the context of an out- break, but it also limits the generalizability of findings to other contexts. Uncertainty in epidemic characteristics should not mean that we do not act. Although earlier estimates may rely on less-than-ideal sample sizes and sample struc- tures, they are necessary to facilitate decision-making in a timely manner. However, reliance on the first estimates published may limit or bias our understanding of new data. The increasing availability of pre-print articles pro- vides an outlet for urgent distribution of findings during an outbreak of a novel pathogen, provided preliminary findings are interpreted with caution before peer review. This study underscores the ongoing challenge and ever- present need for outbreak investigations and research to be both timely and frequently updated, to provide the best evidence to guide interventions. 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