projecting contact matrices in 177 geographical regions an update and comparison CORD-Papers-2022-06-02 (Version 1)
| Title: | Projecting contact matrices in 177 geographical regions: an update and comparison with empirical data for the COVID-19 era |
|---|---|
| Abstract: | Mathematical models have played a key role in understanding the spread of directly-transmissible infectious diseases such as Coronavirus Disease 2019 (COVID-19) as well as the effectiveness of public health responses. As the risk of contracting directly-transmitted infections depends on who interacts with whom mathematical models often use contact matrices to characterise the spread of infectious pathogens. These contact matrices are usually generated from diary-based contact surveys. However the majority of places in the world do not have representative empirical contact studies so synthetic contact matrices have been constructed using more widely available setting-specific survey data on household school classroom and workplace composition combined with empirical data on contact patterns in Europe. In 2017 the largest set of synthetic contact matrices to date were published for 152 geographical locations. In this study we update these matrices with the most recent data and extend our analysis to 177 geographical locations. Due to the observed geographic differences within countries we also quantify contact patterns in rural and urban settings where data is available. Further we compare both the 2017 and 2020 synthetic matrices to out-of-sample empirically-constructed contact matrices and explore the effects of using both the empirical and synthetic contact matrices when modelling physical distancing interventions for the COVID-19 pandemic. We found that the synthetic contact matrices reproduce the main traits of the contact patterns in the empirically-constructed contact matrices. Models parameterised with the empirical and synthetic matrices generated similar findings with few differences observed in age groups where the empirical matrices have missing or aggregated age groups. This finding means that synthetic contact matrices may be used in modelling outbreaks in settings for which empirical studies have yet to be conducted. |
| Published: | 2020-07-25 |
| DOI: | 10.1101/2020.07.22.20159772 |
| DOI_URL: | http://doi.org/10.1101/2020.07.22.20159772 |
| Author Name: | Prem Kiesha |
| Author link: | https://covid19-data.nist.gov/pid/rest/local/author/prem_kiesha |
| Author Name: | Zandvoort Kevin van |
| Author link: | https://covid19-data.nist.gov/pid/rest/local/author/zandvoort_kevin_van |
| Author Name: | Klepac Petra |
| Author link: | https://covid19-data.nist.gov/pid/rest/local/author/klepac_petra |
| Author Name: | Eggo Rosalind M |
| Author link: | https://covid19-data.nist.gov/pid/rest/local/author/eggo_rosalind_m |
| Author Name: | Davies Nicholas G |
| Author link: | https://covid19-data.nist.gov/pid/rest/local/author/davies_nicholas_g |
| Author Name: | Group Centre for the Mathematical Modelling of Infectious Diseases COVID Working |
| Author link: | https://covid19-data.nist.gov/pid/rest/local/author/group_centre_for_the_mathematical_modelling_of_infectious_diseases_covid_working |
| Author Name: | Cook Alex R |
| Author link: | https://covid19-data.nist.gov/pid/rest/local/author/cook_alex_r |
| Author Name: | Jit Mark |
| Author link: | https://covid19-data.nist.gov/pid/rest/local/author/jit_mark |
| license: | cc-by |
| license_url: | https://creativecommons.org/licenses/by/4.0/ |
| source_x: | MedRxiv; WHO |
| source_x_url: | https://www.who.int/ |
| who_covidence_id: | #20159772 |
| url: | http://medrxiv.org/cgi/content/short/2020.07.22.20159772v1?rss=1 https://doi.org/10.1101/2020.07.22.20159772 |
| has_full_text: | FALSE |
| G_ID: | projecting_contact_matrices_in_177_geographical_regions_an_update_and_comparison |
