| Title:
|
Transmission dynamics of SARS-CoV-2 in a strictly-Orthodox Jewish community in the UK |
| Abstract:
|
Some social settings such as households and workplaces have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020 reaching 64.3% seroprevalence within 10 months and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data and a network model of people and places represented as a stochastic graph rewriting system we estimated the relative contribution of transmission in households schools and religious institutions to the epidemic and the relative risk of infection in each of these settings. All congregate settings were important for transmission with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher general-community transmission rate for women (3.3-fold) and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations. |
| Published:
|
2022-05-20 |
| Journal:
|
Sci Rep |
| DOI:
|
10.1038/s41598-022-12517-6 |
| DOI_URL:
|
http://doi.org/10.1038/s41598-022-12517-6 |
| Author Name:
|
Waites William |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/waites_william |
| Author Name:
|
Pearson Carl A B |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/pearson_carl_a_b |
| Author Name:
|
Gaskell Katherine M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/gaskell_katherine_m |
| Author Name:
|
House Thomas |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/house_thomas |
| Author Name:
|
Pellis Lorenzo |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/pellis_lorenzo |
| Author Name:
|
Johnson Marina |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/johnson_marina |
| Author Name:
|
Gould Victoria |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/gould_victoria |
| Author Name:
|
Hunt Adam |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/hunt_adam |
| Author Name:
|
Stone Neil R H |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/stone_neil_r_h |
| Author Name:
|
Kasstan Ben |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/kasstan_ben |
| Author Name:
|
Chantler Tracey |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/chantler_tracey |
| Author Name:
|
Lal Sham |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/lal_sham |
| Author Name:
|
Roberts Chrissy H |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/roberts_chrissy_h |
| Author Name:
|
Goldblatt David |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/goldblatt_david |
| Author Name:
|
Marks Michael |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/marks_michael |
| Author Name:
|
Eggo Rosalind M |
| Author link:
|
https://covid19-data.nist.gov/pid/rest/local/author/eggo_rosalind_m |
| sha:
|
252ce2b1d5374584c55148e89ba3456f3e01a95b |
| license:
|
cc-by |
| license_url:
|
https://creativecommons.org/licenses/by/4.0/ |
| source_x:
|
Medline; PMC |
| source_x_url:
|
https://www.medline.com/https://www.ncbi.nlm.nih.gov/pubmed/ |
| pubmed_id:
|
35595824 |
| pubmed_id_url:
|
https://www.ncbi.nlm.nih.gov/pubmed/35595824 |
| pmcid:
|
PMC9121858 |
| pmcid_url:
|
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9121858 |
| url:
|
https://www.ncbi.nlm.nih.gov/pubmed/35595824/
https://doi.org/10.1038/s41598-022-12517-6 |
| has_full_text:
|
TRUE |
| Keywords Extracted from Text Content:
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www.nature.com/reprints.Publisher
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Springer Nature
creat iveco mmons
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HDR UK |
| Extracted Text Content in Record:
|
First 5000 Characters:Some social settings such as households and workplaces, have been identified as high risk for SARS-CoV-2 transmission. Identifying and quantifying the importance of these settings is critical for designing interventions. A tightly-knit religious community in the UK experienced a very large COVID-19 epidemic in 2020, reaching 64.3% seroprevalence within 10 months, and we surveyed this community both for serological status and individual-level attendance at particular settings. Using these data, and a network model of people and places represented as a stochastic graph rewriting system, we estimated the relative contribution of transmission in households, schools and religious institutions to the epidemic, and the relative risk of infection in each of these settings. All congregate settings were important for transmission, with some such as primary schools and places of worship having a higher share of transmission than others. We found that the model needed a higher generalcommunity transmission rate for women (3.3-fold), and lower susceptibility to infection in children to recreate the observed serological data. The precise share of transmission in each place was related to assumptions about the internal structure of those places. Identification of key settings of transmission can allow public health interventions to be targeted at these locations.
The transmission dynamics of SARS-CoV-2 in settings such as households 1-5 , schools 6-8 and workplaces 9,10 has been the subject of considerable interest, since understanding the relative risk of transmission by setting 11-15 enables more effective targeting of public health interventions [16] [17] [18] [19] [20] [21] [22] to minimise the extent and impact of the epidemic. Characterisation of transmission dynamics and evaluation of interventions is often done with agent-based or network models where the network structure is either generated synthetically 23,24 or inferred from mobility data 25,26 . However, because dynamics are formulated in terms of interactions between individuals, the role of setting is implicit and can only be measured indirectly.
There is a need for further investigation into the importance of different transmission settings. Ideally information on attendance at those settings would be coupled with evidence of infection, to allow the relative importance of each setting to be disentangled.
We developed a transmission model where setting is explicit to examine the role of differing types of places and their relative contribution to SARS-CoV-2 transmission in a strictly-Orthodox Jewish community in the UK.
are represented in the data. The partition containing individuals, however, is incomplete; only about 10% of the population was sampled. Furthermore, the total sizes of most places is not known. This asymmetry means that special attention is required to the sensitivity of the results from a place-mediated transmission model on absolute population size. We address this by varying the size of the population. To decrease the population, we select households uniformly at random without replacement to remove, and remove those individuals who are members of the selected households. To increase the population, we select households uniformly at random with replacement and, for each, create a duplicate household whose members are connected to the same places as the role-model. This sensitivity analysis is then to simulate epidemics on these smaller or larger networks and check that the results hold.
Ethics. The study was approved by the London School of Hygiene and Tropical Medicine Ethics Committee (Ref 22532). The study was performed in accordance with all relevant guidelines and regulations. Verbal informed consent was given during the telephone survey and written consent provided prior to phlebotomy. Parents provided written consent for children.
The model and supporting functions for postprocessing the simulation data is available at https:// git. sr. ht/ ~wwait es/ ortho dox-rewri ting.
The simulator for the extended version of the κ-calculus that we use here is implemented as part of the NetABC package (https:// git. sr. ht/ ~wwait es/ netabc).
www.nature.com/scientificreports/ We previously documented 27 64.3% (95% CI 61.6-67.0%) SARS-CoV-2 seroprevalence in November 2020 in this community which was more than five times the estimated seroprevalence of the wider metropolitan area 28 . We collected data on attendance at community institutions from all members of 394 households, approximately 10% of the total community's population. These data afford a unique opportunity to estimate the relative contributions of different settings to SARS-CoV-2 transmission and to understand what dynamics could have given rise to the particular pattern of seroprevalence observed.
To analyse transmission, we represented the community as a bipartite network of people and places. We constructed a transmission model using an extended version of the κ-calculus 29 to |
| Keywords Extracted from PMC Text:
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× |
| Extracted PMC Text Content in Record:
|
First 5000 Characters:The transmission dynamics of SARS-CoV-2 in settings such as households1–5, schools6–8 and workplaces9,10 has been the subject of considerable interest, since understanding the relative risk of transmission by setting11–15 enables more effective targeting of public health interventions16–22 to minimise the extent and impact of the epidemic. Characterisation of transmission dynamics and evaluation of interventions is often done with agent-based or network models where the network structure is either generated synthetically23,24 or inferred from mobility data25,26. However, because dynamics are formulated in terms of interactions between individuals, the role of setting is implicit and can only be measured indirectly.
There is a need for further investigation into the importance of different transmission settings. Ideally information on attendance at those settings would be coupled with evidence of infection, to allow the relative importance of each setting to be disentangled.
We developed a transmission model where setting is explicit to examine the role of differing types of places and their relative contribution to SARS-CoV-2 transmission in a strictly-Orthodox Jewish community in the UK.
We previously documented27 64.3% (95% CI 61.6–67.0%) SARS-CoV-2 seroprevalence in November 2020 in this community which was more than five times the estimated seroprevalence of the wider metropolitan area28. We collected data on attendance at community institutions from all members of 394 households, approximately 10% of the total community's population. These data afford a unique opportunity to estimate the relative contributions of different settings to SARS-CoV-2 transmission and to understand what dynamics could have given rise to the particular pattern of seroprevalence observed.
To analyse transmission, we represented the community as a bipartite network of people and places. We constructed a transmission model using an extended version of the \documentclass[12pt]{minimal}
\usepackage{amsmath}
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\usepackage{amsfonts}
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\usepackage{amsbsy}
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\usepackage{upgreek}
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\begin{document}$$\kappa$$\end{document}κ-calculus29 to implement a transmission model as a stochastic graph rewriting system30,31, a generalisation of how explicit epidemic dynamics are usually formulated on networks32. In this model, individuals have disease progression states and transmission is mediated by place, with a separate transmission process for each setting or kind of place. These place-mediated transmission processes are augmented with population-wide well-mixed transmission processes akin to general community transmission outside of the defined set of places. We fit the transmission rate parameters of this model to the measured distributions of positive test results from the seroprevalence survey. We used the fitted model to analyse the contribution of different places to transmission within the community. This process is shown schematically in Fig. S1.
We surveyed 1942 people in 374 households from a community of approximately 20,000 people in November and early December 2020. 33% of the population were under 10 years of age and 60% under 20, which is a higher percentage than the surrounding metropolitan area. Survey data included household membership and composition, which school children attended, which place of worship individuals attended, and which ritual bath adult men attended (adult men attend ritual baths collectively, women attend individually but no specific data were available about the latter). 1377 people from 309 households also provided a blood sample from which we found 64.3% had IgG antibodies to the SARS-CoV-2 spike protein, ranging from 50% in under 10 year olds to 75% in over 10 year olds27.
We observed a strong partitioning of attendance at places by both age and sex (Fig. 1B,C). Schools, both primary (under 13) and secondary (13 and over), were predominantly segregated by sex. The vast majority of individuals reporting a connection with a place of worship and all of those reporting attendance at a ritual bath were adult (18+) men.
We used the information reported in the household survey to generate a bipartite network of people and places (illustrative example in Fig. 1A. In this network there is an edge between every individual and each place with which they reported an association. We found that the greatest mean degree was in primary schools, and the least within households (Table 1).
Using a transmission model with varying susceptibility to infection by age, we fitted the rate parameters for each setting to the empirical distributions of household infection from the serosurvey using the sequential Monte-Carlo method for approximate Bayesian computation33 (Fig. 2C, Table 1). As well as place-mediated transmission processes, we included several transmission processes directly between individ |
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