interventions targeting nonsymptomatic cases can be important to prevent local outbreaks CORD-Papers-2022-06-02 (Version 1)

Title: Interventions targeting nonsymptomatic cases can be important to prevent local outbreaks: COVID-19 as a case-study
Abstract: Background During infectious disease epidemics a key question is whether cases travelling to new locations will trigger local outbreaks. The risk of this occurring depends on a range of factors such as the transmissibility of the pathogen the susceptibility of the host population and crucially the effectiveness of local surveillance in detecting cases and preventing onward spread. For many pathogens presymptomatic and/or asymptomatic (together referred to here as nonsymptomatic) transmission can occur making effective surveillance challenging. In this study using COVID-19 as a case-study we show how the risk of local outbreaks can be assessed when nonsymptomatic transmission can occur. Methods We construct a branching process model that includes nonsymptomatic transmission and explore the effects of interventions targeting nonsymptomatic or symptomatic hosts when surveillance resources are limited. Specifically we consider whether the greatest reductions in local outbreak risks are achieved by increasing surveillance and control targeting nonsymptomatic or symptomatic cases or a combination of both. Findings Seeking to increase surveillance of symptomatic hosts alone is typically not the optimal strategy for reducing outbreak risks. Adopting a strategy that combines an enhancement of surveillance of symptomatic cases with efforts to find and isolate nonsymptomatic hosts leads to the largest reduction in the probability that imported cases will initiate a local outbreak. Interpretation During epidemics of COVID-19 and other infectious diseases effective surveillance for nonsymptomatic hosts can be crucial to prevent local outbreaks. Funding UKRI-BBSRC Wellcome Trust UKRI-MRC FCDO EDCTP2 Christ Church (Oxford).
Published: 2020-11-07
DOI: 10.1101/2020.11.06.20226969
DOI_URL: http://doi.org/10.1101/2020.11.06.20226969
Author Name: Lovell Read F A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lovell_read_f_a
Author Name: Funk S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/funk_s
Author Name: Obolski U
Author link: https://covid19-data.nist.gov/pid/rest/local/author/obolski_u
Author Name: Donnelly C A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/donnelly_c_a
Author Name: Thompson R N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/thompson_r_n
sha: 6e19f98e73d255a15976a7166891dea6ec8acc78
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: http://medrxiv.org/cgi/content/short/2020.11.06.20226969v1?rss=1 https://doi.org/10.1101/2020.11.06.20226969
has_full_text: TRUE
Keywords Extracted from Text Content: hosts COVID-31 19 − https://doi.org/10.1101 /2020 doi: hosts Red dotted lines 651 medRxiv preprint red 321 circle https://doi.org/10.1101 https://doi.org/10 7 + 566 . medRxiv preprint 620 Figure S4 · 5 D C. 9 B medRxiv ∈ F. individuals · 3 https://doi A. blue region 302 red dotted lines contours · 2 · Red dotted lines Figure 3C https://doi.org/10.1101/2020.11.06.20226969 doi Figure 1B green × contacts https://doi.org/10 lines red dashed line · 8 Figure 2B Figure 3A C. · 6 red dots infectors COVID-19 pre-378 Figure 3B Figure 3D org/10 medRxiv preprint Figure S1 Red dotted 375 lines medRxiv preprint 9 · 1 10·6 0·630 line Δ 7 red circle · 4 · 7. 631 632 633 Figure S5 blue line line red circles Figure 2B -D
Extracted Text Content in Record: First 5000 Characters:Background 25 During infectious disease epidemics, a key question is whether cases travelling to new locations 26 will trigger local outbreaks. The risk of this occurring depends on a range of factors, such as the 27 transmissibility of the pathogen, the susceptibility of the host population and, crucially, the 28 effectiveness of local surveillance in detecting cases and preventing onward spread. For many 29 pathogens, presymptomatic and/or asymptomatic (together referred to here as nonsymptomatic) 30 transmission can occur, making effective surveillance challenging. In this study, using COVID-31 19 as a case-study, we show how the risk of local outbreaks can be assessed when 32 nonsymptomatic transmission can occur. 33 34 Methods 35 We construct a branching process model that includes nonsymptomatic transmission, and explore 36 the effects of interventions targeting nonsymptomatic or symptomatic hosts when surveillance 37 resources are limited. Specifically, we consider whether the greatest reductions in local outbreak 38 risks are achieved by increasing surveillance and control targeting nonsymptomatic or 39 symptomatic cases, or a combination of both. 40 41 Findings 42 Seeking to increase surveillance of symptomatic hosts alone is typically not the optimal strategy 43 for reducing outbreak risks. Adopting a strategy that combines an enhancement of surveillance of 44 symptomatic cases with efforts to find and isolate nonsymptomatic hosts leads to the largest 45 reduction in the probability that imported cases will initiate a local outbreak. 46 During epidemics of COVID-19 and other infectious diseases, effective surveillance for 49 nonsymptomatic hosts can be crucial to prevent local outbreaks. analytically using a branching process model and found that effective isolation of infectious 67 hosts leads to a substantial reduction in the outbreak risk. 68 69 . CC-BY-NC-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 copyright holder for this preprint this version posted November 7, 2020. ; https://doi. org/10.1101 org/10. /2020 to transmit a pathogen while not showing symptoms. For COVID-19, the incubation period has 71 been estimated to last approximately five or six days on average, 9,10 and presymptomatic 72 transmission can occur during that period. [11] [12] [13] [14] Additionally, asymptomatic infected individuals 73 (those who never develop symptoms) are also thought to contribute to transmission. 11,15,16 74 75 Motivated by the need to assess the risk of outbreaks outside China early in the COVID-19 76 pandemic, we show how the risk that imported cases will lead to local outbreaks can be 77 estimated using a branching process model, including nonsymptomatic individuals in the model 78 explicitly. We explore the effects of interventions that aim to reduce this risk. Under the 79 assumption that detected infected hosts are isolated effectively, we consider whether it is most 80 effective to dedicate resources to enhancing surveillance targeting symptomatic individuals, to 81 instead focus on increasing surveillance for nonsymptomatic individuals, or to use a combination 82 of these approaches. 83 84 We show that the maximum reduction in outbreak risk almost always corresponds to a mixed 85 7 138 CC-BY-NC-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 copyright holder for this preprint this version posted November 7, 2020. ; https://doi. org/10.1101 org/10. /2020 Since this research was motivated by the need to estimate outbreak risks outside China in the 167 initial stages of the COVID-19 pandemic, we used a baseline set of parameter values in our 168 analyses that was informed by studies conducted during this pandemic (Table 1) . Where 169 possible, these parameter values were obtained from existing literature. However, we also 170 . CC-BY-NC-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. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2020. Relative isolation rate of nonsymptomatic individuals without intensified surveillance (compared to symptomatic individuals) = 0 · 1 Assumed (for different values, see Figure S7 ) Rate at which presymptomatic individuals develop symptoms = 0 · 5 days $" Recovery rate of symptomatic individuals = 1/8 days $" . CC-BY-NC-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. (which was not certified by peer review) The copyright holder for this preprint this version posted November 7, 2020. ; https://doi.org/10.1101 https://doi.org/10. /2020 Recovery rate of asymptomatic individuals = 0 ·
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