a modelling study for designing a multi layered surveillance approach to detect the CORD-Papers-2021-10-25 (Version 1)

Title: A Modelling Study for Designing a Multi-layered Surveillance Approach to Detect the Potential Resurgence of SARS-CoV-2
Abstract: Background: Countries achieving control of COVID-19 after an initial outbreak will continue to face the risk of SARS-CoV-2 resurgence. This study explores surveillance strategies for COVID-19 containment based on polymerase chain reaction tests. Methods: Using a dynamic SEIR-type model to simulate the initial dynamics of a COVID-19 introduction, we investigate COVID-19 surveillance strategies among healthcare workers, hospital patients, and community members. We estimate surveillance sensitivity as the probability of COVID-19 detection using a hypergeometric sampling process. We identify test allocation strategies that maximise the probability of COVID-19 detection across different testing capacities. We use Beijing, China as a case study. Findings: Surveillance subgroups are more sensitive in detecting COVID-19 transmission when they are defined by more COVID-19 specific symptoms. In this study, fever clinics have the highest surveillance sensitivity, followed by respiratory departments. With a daily testing rate of 0.07/1000 residents, via exclusively testing at fever clinic and respiratory departments, there would have been 598 [95% eCI: 35, 2154] and 1373 [95% eCI: 47, 5230] cases in the population by the time of first case detection, respectively. Outbreak detection can occur earlier by including non-syndromic subgroups, such as younger adults in the community, as more testing capacity becomes available. Interpretation: A multi-layer approach that considers both the surveillance sensitivity and administrative constraints can help identify the optimal allocation of testing resources and thus inform COVID-19 surveillance strategies. Funding: Bill & Melinda Gates Foundation, National Institute of Health Research (UK), National Institute of Health (US), the Royal Society, and Wellcome Trust.
Published: 6/29/2020
DOI: 10.1101/2020.06.27.20141440
DOI_URL: http://doi.org/10.1101/2020.06.27.20141440
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Gong, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/gong_w
Author Name: Clifford, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/clifford_s
Author Name: Sundaram, M E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sundaram_m_e
Author Name: CMMID COVID Working Group,
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cmmid_covid_working_group
Author Name: Jit, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jit_m
Author Name: Flasche, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/flasche_s
Author Name: Klepac, P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/klepac_p
sha: bc591943d56741a446b97c15b76d64c9683f8bd0
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: http://medrxiv.org/cgi/content/short/2020.06.27.20141440v1?rss=1 https://doi.org/10.1101/2020.06.27.20141440
has_full_text: TRUE
Keywords Extracted from Text Content: COVID-19 patients SEIR-type SARS-CoV-2 eCI bronchus COVID-19 patients t. UK Anal non-SARS-CoV-2 respiratory pathogens medRxiv non-HCW patient Fred Lunnon W. coronavirus 2 medRxiv preprint Figure S2 SARS-CoV-2 Tracheal HCWs medRxiv preprint Figure 2 medRxiv preprint Figure S3 SF medRxiv preprint Marginal Wuhan Eggo Prem K Figure 1 Wuhan, Figure 2 medRxiv preprint Figure S2 outpatients ∈ {1 medRxiv preprint Figure S6 patients people SEIR-type https://github.com/yangclaraliu/covid_surveillance_strategy WFG Kucharski AJ compartment I c medRxiv preprint Davies NG Shanghai IDC-19 SE3I3R COVID-19 layer medRxiv preprint Figure S5 medRxiv preprint Figure 4 SC Jit M. persons I c Harbin, e1005697 medRxiv preprint Supplemental Material human PK medRxiv preprint Figure S4 PCRnegative UR individuals − HCW cerebral on-ward lung cancer medRxiv preprint Figure S1 SARS-CoV hemisphere MES medRxiv preprint S eCI Coronavirus disease 2019 Cook AR
Extracted Text Content in Record: First 5000 Characters:Background: Countries achieving control of COVID-19 after an initial outbreak will continue to face the risk of SARS-CoV-2 resurgence. This study explores surveillance strategies for COVID-19 containment based on polymerase chain reaction tests. Methods: Using a dynamic SEIR-type model to simulate the initial dynamics of a COVID-19 introduction, we investigate COVID-19 surveillance strategies among healthcare workers, hospital patients, and community members. We estimate surveillance sensitivity as the probability of COVID-19 detection using a hypergeometric sampling process. We identify test allocation strategies that maximise the probability of COVID-19 detection across different testing capacities. We use Beijing, China as a case study. Findings: Surveillance subgroups are more sensitive in detecting COVID-19 transmission when they are defined by more COVID-19 specific symptoms. In this study, fever clinics have the highest surveillance sensitivity, followed by respiratory departments. With a daily testing rate of 0.07/1000 residents, via exclusively testing at fever clinic and respiratory departments, there would have been 598 [95% eCI: 35, 2154] and 1373 [95% eCI: 47, 5230] cases in the population by the time of first case detection, respectively. Outbreak detection can occur earlier by including non-syndromic subgroups, such as younger adults in the community, as more testing capacity becomes available. Interpretation: A multi-layer approach that considers both the surveillance sensitivity and administrative constraints can help identify the optimal allocation of testing resources and thus inform COVID-19 surveillance strategies. Coronavirus disease 2019 (COVID- 19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and was first detected in Wuhan, China towards the end of 2019. 1 On 11 Mar 2020, the World Health Organisation declared COVID-19 a global pandemic. 2 Within six months of its emergence, COVID-19 has led to over eight million reported cases and over 450,000 reported deaths globally. 3 Many countries and regions have succeeded in reducing COVID-19 incidence after the initial epidemics. Nevertheless, it is unlikely that SARS-CoV-2 will be eradicated in the near future. Given the high transmissibility of the pathogen, 4,5 the non-trivial proportion of infectious individuals showing mild to no symptoms, 6 the non-specific nature of symptoms, 7 the highly intertwined global travel network, and a lack of effective pharmaceutical measures for prevention or therapy, 8 countries that successfully contain the initial spread of COVID-19 will likely continue to face risks introduced by international travellers and unidentified local cases. With physical distancing measures gradually easing, sporadic infection clusters have already been observed. 9 To prevent these sporadic infection clusters from seeding new epidemics, rapid infection detection is vital. Containment strategies, such as case isolation and contact tracing, can quickly be overwhelmed if transmission remains undetected for too long. Thus, sustainable, cost-effective, and highly sensitive surveillance systems for SARS-CoV-2 are essential to the success of COVID-19 containment. This study explores different surveillance systems that maximise the probability of COVID-19 detection using polymerase chain reaction (PCR) while minimising the material and human resources required. We consider a hypothetical city with the population size, age structure, and healthcare infrastructure similar to that of Beijing, China. Fever clinics, a triage system that emerged during the 2003 outbreak of SARS-CoV, play a crucial role in COVID-19 response in China 10 and could be considered a potentially effective surveillance option elsewhere. The framework introduced is relevant to containment strategies in countries exiting the initial phases of COVID-19 epidemics, where only a small number of cases are observed sporadically. The Epidemic Process is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 29, 2020. . https://doi.org/10.1101/2020.06.27.20141440 doi: medRxiv preprint S: Susceptible; E: Exposed; I pc : Pre-clinical Infectious; I c : Clinical Infectious; I sc : Sub-clinical Infectious; + : PCR-positive Removed following I c ; and + : PCR-positive Removed following I sc ; − : PCRnegative Removed. Solid box indicates compartments that affect the force of infection; dashed box indicates compartments detectable by PCR-based surveillance. We simulate the spread of SARS-CoV-2 using a deterministic age-stratified compartmental SEIR-type model ( Figure 1 ). 11 Additionally, the infectious compartment is split into I pc , I c and I sc to account for differences in disease progression. The compartment I c represents infectious individuals whose symptoms are sufficiently severe ("clinical" illnesses) for them to se
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