One connector, four scholarly indexes, real DOIs and legal free PDFs. Works in Claude and ChatGPT.
Paste it into Claude or ChatGPT as a custom connector. 3-day free trial.
Kiesha Prem, Kevin van Zandvoort, Petra Klepac, Rosalind M. Eggo, Nicholas G. M. Davies, Alex Richard Cook, Mark Jit
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. Author summary The risk of contracting a directly transmitted infectious disease such as the Coronavirus Disease 2019 (COVID-19) depends on who interacts with whom. Such person-to-person interactions vary by age and locations—e.g., at home, at work, at school, or in the community—due to the different social structures. These social structures, and thus contact patterns, vary across and within countries. Although social contact patterns can be measured using contact surveys, the majority of countries around the world, particularly low- and middle-income countries, lack nationally representative contact surveys. A simple way to present contact data is to use matrices where the elements represent the rate of contact between subgroups such as age groups represented by the columns and rows. In 2017, we generated age- and location-specific synthetic contact matrices for 152 geographical regions by adapting contact pattern data from eight European countries using country-specific data on household size, school and workplace composition. We have now updated these matrices with the most recent data (Demographic Household Surveys, World Bank, UN Population Division) extending the coverage to 177 geographical locations, covering 97.2% of the world’s population. We also quantified contact patterns in rural and urban settings. When compared to out-of-sample empirically-measured contact patterns, we found that the synthetic matrices reproduce the main features of these contact patterns.
Formatted from the source metadata, not typed by hand. For a whole reference list, use the citation generator or ask your assistant through Find Research.
Prem, K., Zandvoort, K. V., Klepac, P., Eggo, R. M., Davies, N. G. M., Cook, A. R., & Jit, M. (2021). Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era. PLoS Computational Biology, 17(7), e1009098. https://doi.org/10.1371/journal.pcbi.1009098
Prem, Kiesha, et al. "Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era." PLoS Computational Biology, vol. 17, no. 7, 2021, pp. e1009098, https://doi.org/10.1371/journal.pcbi.1009098.
Prem, Kiesha, Kevin van Zandvoort, Petra Klepac, Rosalind M. Eggo, Nicholas G. M. Davies, Alex Richard Cook, and Mark Jit. "Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era." PLoS Computational Biology 17, no. 7 (2021): e1009098. https://doi.org/10.1371/journal.pcbi.1009098.
Prem K., Zandvoort K. V., Klepac P. [и др.] Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era // PLoS Computational Biology. — 2021. — Vol. 17, № 7. — P. e1009098. — URL: https://doi.org/10.1371/journal.pcbi.1009098
@article{prem2021projecting,
title = {{Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era}},
author = {Kiesha Prem and Kevin van Zandvoort and Petra Klepac and Rosalind M. Eggo and Nicholas G. M. Davies and Alex Richard Cook and Mark Jit},
journal = {PLoS Computational Biology},
year = {2021},
volume = {17},
number = {7},
pages = {e1009098},
doi = {10.1371/journal.pcbi.1009098},
url = {https://doi.org/10.1371/journal.pcbi.1009098}
}TY - JOUR AU - Prem, Kiesha AU - Zandvoort, Kevin van AU - Klepac, Petra AU - Eggo, Rosalind M. AU - Davies, Nicholas G. M. AU - Cook, Alex Richard AU - Jit, Mark TI - Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era JO - PLoS Computational Biology VL - 17 IS - 7 SP - e1009098 PY - 2021 DA - 2021/07/26 DO - 10.1371/journal.pcbi.1009098 UR - https://doi.org/10.1371/journal.pcbi.1009098 ER -
Add Find Research to Claude or ChatGPT and ask in plain language. The assistant reads this record, finds the papers around it and cites only what it actually found:
Setup takes about a minute: add the connector, then start the free trial.
Record from OpenAlex; open-access location from Unpaywall and the indexes. Find Research links only to legal copies.