the contribution of asymptomatic sars cov 2 infections to transmission a model based CORD-Papers-2021-10-25 (Version 1)

Title: The contribution of asymptomatic SARS-CoV-2 infections to transmission - a model-based analysis of the Diamond Princess outbreak
Abstract: Background: Some key gaps in the understanding of SARS-CoV-2 infection remain. One of them is the contribution to transmission from individuals experiencing asymptomatic infections. We aimed to characterise the proportion and infectiousness of asymptomatic infections using data from the outbreak on the Diamond Princess cruise ship. Methods: We used a transmission model of COVID-19 with asymptomatic and presymptomatic states calibrated to outbreak data from the Diamond Princess, to quantify the contribution of asymptomatic infections to transmission. Data available included the date of symptom onset for symptomatic disease for passengers and crew, the number of symptom agnostic tests done each day, and date of positive test for asymptomatic and presymptomatic individuals. Findings: On the Diamond Princess 74% (70-78%) of infections proceeded asymptomatically, i.e. a 1:3.8 case-to-infection ratio. Despite the intense testing 53%, (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. While the data did not allow identification of the infectiousness of asymptomatic infections, assuming no or low infectiousness resulted in posterior estimates for the net reproduction number of an individual progressing through presymptomatic and symptomatic stages in excess of 15. Interpretation: Asymptomatic SARS-CoV-2 infections may contribute substantially to transmission. This is essential to consider for countries when assessing the potential effectiveness of ongoing control measures to contain COVID-19.
Published: 5/11/2020
DOI: 10.1101/2020.05.07.20093849
DOI_URL: http://doi.org/10.1101/2020.05.07.20093849
Author Name: Emery, J C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/emery_j_c
Author Name: Russel, T W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/russel_t_w
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Hellewell, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hellewell_j
Author Name: Pearson, C A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pearson_c_a
Author Name: CMMID nCoV working group,
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cmmid_ncov_working_group
Author Name: Knight, G M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/knight_g_m
Author Name: Eggo, R M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/eggo_r_m
Author Name: Kucharski, A J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kucharski_a_j
Author Name: Funk, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/funk_s
Author Name: Flasche, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/flasche_s
Author Name: Houben, R M G J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/houben_r_m_g_j
Author Name: Atkins, Katherine E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/atkins_katherine_e
Author Name: Klepac, Petra
Author link: https://covid19-data.nist.gov/pid/rest/local/author/klepac_petra
Author Name: Jarvis, Christopher I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jarvis_christopher_i
Author Name: Davies, Nicholas G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/davies_nicholas_g
Author Name: Rees, Eleanor M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rees_eleanor_m
Author Name: Meakin, Sophie R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/meakin_sophie_r
Author Name: Rosello, Alicia
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rosello_alicia
Author Name: Van Zandvoort, Kevin
Author link: https://covid19-data.nist.gov/pid/rest/local/author/van_zandvoort_kevin
Author Name: Munday, James D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/munday_james_d
Author Name: Edmunds, John
Author link: https://covid19-data.nist.gov/pid/rest/local/author/edmunds_john
Author Name: Jombart, Thibaut
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jombart_thibaut
Author Name: Auzenbergs, Megan
Author link: https://covid19-data.nist.gov/pid/rest/local/author/auzenbergs_megan
Author Name: Bosse, Nikos I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bosse_nikos_i
Author Name: Leclerc, Quentin J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/leclerc_quentin_j
Author Name: Procter, Simon R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/procter_simon_r
Author Name: Deol, Arminder K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/deol_arminder_k
Author Name: Prem, Kiesha
Author link: https://covid19-data.nist.gov/pid/rest/local/author/prem_kiesha
Author Name: Medley, Graham
Author link: https://covid19-data.nist.gov/pid/rest/local/author/medley_graham
Author Name: Lowe, Rachel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lowe_rachel
Author Name: Clifford, Samuel
Author link: https://covid19-data.nist.gov/pid/rest/local/author/clifford_samuel
Author Name: Quaife, Matthew
Author link: https://covid19-data.nist.gov/pid/rest/local/author/quaife_matthew
Author Name: Diamond, Charlie
Author link: https://covid19-data.nist.gov/pid/rest/local/author/diamond_charlie
Author Name: Gibbs, Hamish P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gibbs_hamish_p
Author Name: Quilty, Billy J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/quilty_billy_j
sha: 9076c5c0ec9b55029f5ede3a3abe16d70fa63728
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: http://medrxiv.org/cgi/content/short/2020.05.07.20093849v1?rss=1 https://doi.org/10.1101/2020.05.07.20093849
has_full_text: TRUE
Keywords Extracted from Text Content: COVID-19 ERC SARS-CoV-2 1:3.8 COVID-19 coronaviruses medRxiv preprint Deviance Information Criterion 1:3.3-1:4.4 PI 1-3 1:3.8 SARS-CoV LibBi (28) Figure 1E RMGJH 1,304 (1, 416) individuals individuals Yokohama RBi (29) line https://github.com/thimotei/covid19_asymptomatic_trans within-passenger globe medRxiv preprint ; D 1-44 medRxiv preprint A passengers SARS-CoV-2 medRxiv GitHub Southeast Asia 5th sigmoid Figure 1A
Extracted Text Content in Record: First 5000 Characters:Some key gaps in the understanding of SARS-CoV-2 infection remain. One of them is the contribution to transmission from individuals experiencing asymptomatic infections. We aimed to characterise the proportion and infectiousness of asymptomatic infections using data from the outbreak on the Diamond Princess cruise ship. We used a transmission model of COVID-19 with asymptomatic and presymptomatic states calibrated to outbreak data from the Diamond Princess, to quantify the contribution of asymptomatic infections to transmission. Data available included the date of symptom onset for symptomatic disease for passengers and crew, the number of symptom agnostic tests done each day, and date of positive test for asymptomatic and presymptomatic individuals. On the Diamond Princess 74% (70-78%) of infections proceeded asymptomatically, i.e. a 1:3.8 case-toinfection ratio. Despite the intense testing 53%, (51-56%) of infections remained undetected, most of them asymptomatic. Asymptomatic individuals were the source for 69% (20-85%) of all infections. While the data did not allow identification of the infectiousness of asymptomatic infections, assuming no or low infectiousness resulted in posterior estimates for the net reproduction number of an individual progressing through presymptomatic and symptomatic stages in excess of 15. Asymptomatic SARS-CoV-2 infections may contribute substantially to transmission. This is essential to consider for countries when assessing the potential effectiveness of ongoing control measures to contain COVID-19. ERC Starting Grant (#757699), Wellcome trust (208812/Z/17/Z), HDR UK (MR/S003975/1) Evidence before this study It is known that a non-trivial proportion of infections with SARS-CoV-2 remain asymptomatic, and there is evidence that asymptomatic individuals contribute to transmission. However, empirical estimates for the proportion of infections that remain asymptomatic are often difficult to interpret due to opportunistic sampling frames combined with low and imbalanced participation from individuals with and without symptoms, which have resulted in a wide range of values (between 6-96%), with a suggestion of variation across age-groups. Quantitative estimates for the contribution of asymptomatic SARS-CoV-2 infections to ongoing transmission are absent. In this study we calibrated a mechanistic transmission model to data from the Diamond Princess cruise ship outbreak, which is unique in that it occurred in a closed population, nearly all individuals were tested regardless of symptoms at least once and detailed open access data are available. Our datadriven model found that 74% (95% posterior interval (PI) = 70-78%) of SARS-CoV-2 infections proceeded asymptomatically, a case-infection ratio of 1:3.8 (1:3.3-1:4.4). We found that because systematic testing irrespective of symptoms was only implemented in the last days before disembarkation, over half (53%, (51-56%) of infected individuals were not detected during this outbreak. Our model provides the first quantitative estimates of the proportion of all transmission driven by asymptomatic individuals. In a context of rapid and near complete case-isolation as well as quarantine, asymptomatic infections cases were responsible for 69% (20-85%) of all new infections. Remaining transmission was equally distributed between the presymptomatic and symptomatic phases of COVID-19 which is in line with previous findings. Part of the remaining uncertainty is due to the relative infectiousness of asymptomatic individuals, which we were unable to estimate. However, an exploration of the scenarios with a low relative infectiousness (e.g. 0-25% compared to symptomatic individuals) showed that to replicate the data a very high net reproduction number was required for individuals progressing through presymptomatic and symptomatic stages (15.5-29.1) . The ongoing COVID-19 pandemic has spread rapidly across the globe, and the number of individuals infected with SARS-CoV-2, outstrips the number of reported cases (1, 2) . One key reason for this may be that a substantial proportion of cases proceed asymptomatically, i.e. they either do not experience, or are not aware of symptoms throughout their infection but despite that can transmit to others. In this sense, asymptomatic infections differ from presymptomatic ones, which describes the part of the incubation period before symptoms develop during which onward transmission is possible. While pre-and asymptomatic individuals do not directly contribute to morbidity or mortality in an outbreak, they can contribute to ongoing transmission, as has been shown for COVID-19, (3) (4) (5) and other diseases (6) (7) (8) . Particularly, purely symptom-based interventions (e.g., self-isolation upon onset of disease) will not interrupt transmission from asymptomatic individuals and hence may be insufficient for outbreak control if a substantial proportion of transmission originates from pre-and asymptomatic infections.
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