reconstructing the global dynamics of under ascertained covid 19 cases and infections CORD-Papers-2021-10-25 (Version 1)

Title: Reconstructing the global dynamics of under-ascertained COVID-19 cases and infections
Abstract: Background: Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. Methods: Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever >= to 37.5C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the case fatality ratio (CFR) as an assumed baseline. We then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. Results: We estimate that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.38% (Bangladesh) to 99.6% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6th July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 17.8 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally, we estimated seroprevalence for each country. Despite low case detection in some countries, our results that adjust for this still suggest that all countries have had only a small fraction of their populations infected as of July 2020. Conclusions: We found substantial under-ascertainment of symptomatic cases, particularly at the peak of the first wave of the SARS-CoV-2 pandemic, in many countries. Reported case counts will therefore likely underestimate the rate of outbreak growth initially and underestimate the decline in the later stages of an epidemic. Although there was considerable under-reporting in many locations, our estimates were consistent with emerging serological data, suggesting that the proportion of each country's population infected with SARS-CoV-2 worldwide is generally low.
Published: 7/8/2020
DOI: 10.1101/2020.07.07.20148460
DOI_URL: http://doi.org/10.1101/2020.07.07.20148460
Author Name: Golding, N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/golding_n
Author Name: Russell, T W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/russell_t_w
Author Name: Abbott, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/abbott_s
Author Name: Hellewell, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hellewell_j
Author Name: Pearson, C A B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pearson_c_a_b
Author Name: van Zandvoort, K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/van_zandvoort_k
Author Name: Jarvis, C I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jarvis_c_i
Author Name: Gibbs, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gibbs_h
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Eggo, R M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/eggo_r_m
Author Name: Edmunds, J W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/edmunds_j_w
Author Name: Kucharski, A J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/kucharski_a_j
sha: 0d469c3aaf7e04dc781bee698e6b4e44146776d7
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: http://medrxiv.org/cgi/content/short/2020.07.07.20148460v1?rss=1 https://doi.org/10.1101/2020.07.07.20148460
has_full_text: TRUE
Keywords Extracted from Text Content: SARS-CoV-2 COVID-19 France Figure S2 France -Belarus 5,25,26 medRxiv HPC https://doi.org/10.1101/2020.07.07.20148460 doi S7 COVID-19 OurWorldInData dCFR wpp2019 R Kendall's correlation coefficient CrI US coronavirus SARS-CoV-2 Figures 1 and 2) AJK medRxiv preprint Figures S5 TWR confirmation-to-outcome NG SARS-CoV-2 antibodies SARS-CoV-2 UK containable
Extracted Text Content in Record: First 5000 Characters:Background: Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. Methods: Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever >= 37.5°C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the case fatality ratio (CFR) as an assumed baseline. We then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. We estimate that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.38% (Bangladesh) to 99.6% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6th July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 17.8 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally, we estimated seroprevalence for each country. Despite low case detection in some countries, our results that adjust for this still suggest that all countries have had only a small fraction of their populations infected as of July 2020. We found substantial under-ascertainment of symptomatic cases, particularly at the peak of the first wave of the SARS-CoV-2 pandemic, in many countries. Reported case counts will therefore likely underestimate the rate of outbreak growth initially and underestimate the decline in the later stages of an epidemic. Although there was considerable under-reporting in many locations, our estimates were consistent with emerging serological data, suggesting that the proportion of each country's population infected with SARS-CoV-2 worldwide is generally low. The pandemic of the novel coronavirus SARS-CoV-2 has caused 11.7 million confirmed cases and 538,818 deaths as of 6 h July 2020 (1) . As a precautionary measure, or in response to locally detected outbreaks, countries have introduced control measures with varying degrees of stringency (1), including isolation and quarantine; school and workplace closures; bans on social gatherings; physical distancing and face coverings; and stay-at-home orders (2, 3) . Several features of SARS-CoV-2 make accurate detection during an ongoing epidemic challenging (4) (5) (6) , including high transmissibility (3, (7) (8) (9) ; an incubation period with a long-tailed distribution (10) ; pre-symptomatic transmission (11) ; and the existence of asymptomatic infections, which may also contribute to transmission (12). These attributes mean that infections can go undetected (13) and that countries may only detect and report a fraction of their infections (3, 14) . Understanding the extent of unreported infections in a given country is crucial for situational awareness. If the true size of the epidemic can be estimated, this enables a more reliable assessment of how and when non-pharmaceutical interventions (NPIs) should be both introduced, as infections rise, or relaxed as infections fall (3) . Estimates of infection prevalence are also important for obtaining accurate measures of transmission: if the proportion of infections reported declines as the epidemic rises, the number of confirmed cases will grow slower than the actual underlying epidemic. Likewise, if detection rises as the epidemic declines, it may appear that transmission is not declining as fast as it is in reality. Underdetection of cases also makes it challenging to estimate at what stage of the epidemic a particular country is (15) : viewed in isolation, case incidence data could reflect a very large undetected epidemic, or a smaller, better reported epidemic. To estimate how the levels of under-ascertainment vary over time, we present a modelling framework that combines data on reported cases and deaths, and published severity estimates. We apply our methods to countries that have reported more than ten deaths to date, then use these underascertainment estimates to reconstruct global epidemics in all cou
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