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COVID-19 Research
Our work in modelling the COVID-19 pandemic.
An interdisciplinary and cross-institutional group was convened by Caroline Colijn and Dan Coombs (UBC), with support from PIMS, to produce real-time models of the pandemic, focusing on British Columbia and Canada. Information about the group can be found .
Pre-symptomatic transmission
On February 18 and 19 MAGPIE research group hosted a hackathon, using data from the coronavirus outbreak to explore epidemic modelling. As a result of some of the work done by hackathon participants, a paper has now been published in eLife on the incubation period and serial interval distribution using data from clusters in Singapore and Tianjin. The paper concludes that pre-symptomatic transmission is occuring. Read the paper and a news article on the findings .
Citation: . Lauren C. Tindale, Michelle Coombe, Jessica E. Stockdale, Emma S. Garlock, Wing Yin Venus Lau, Manu Saraswat, Yen-Hsiang Brian Lee, Louxin Zhang, Dongxuan Chen, Jacco Wallinga, Caroline Colijn. eLife 2020;9:e57149
Quantifying the impact of COVID-19 control measures using a Bayesian model of physical distancing
Wondering how effective social distancing practices will be on "flattening the curve"? Several members of the MAGPIE research group are co-authors on a preprint describing a Bayesian model estimating the impact of physical distancing measures using local data from BC. Read the paper .
You can see the effect of different social distancing scenarios, as well as create your own scenarios and see how the pandemic changes on this shiny app (). An article was published in the Globe and Mail containing graphs based on these models. You can read the article .
Citation: . Sean C. Anderson, Andrew M. Edwards, Madi Yerlanov, Nicola Mulberry, Jessica E. Stockdale, Sarafa A. Iyaniwura, Rebeca C. Falcao, Michael C. Otterstatter, Michael A. Irvine, Naveed Z. Janjua, Daniel Coombs, Caroline Colijn. PLoS Computational Biology. doi:https://doi.org/10.1371/journal.pcbi.1008274
How much leeway is there to relax Covid-19 control measures?
We estimate how much leeway there is to reduce control measures in a range of national and regional jurisdictions that have experienced different COVID-19 epidemics. The article examines the risks associated with different levels of reopening and finds a high risk of exceeding past peek sizes. The authors recommend a cautious approach to reopening that incorporates strong monitoring for changes in transmission.
Citation: Sean C. Anderson, Nicola Mulberry, Andrew M. Edwards, Jessica E. Stockdale, Sarafa A. Iyaniwura, Rebeca C. Falcao, Michael C. Otterstatter, Naveed Z. Janjua, Daniel Coombs, Caroline Colijn. Epidemics Vol 35, June 2021. doi: https://doi.org/10.1016/j.epidem.2021.100453
Long time frames to detect the impact of changing Covid-19 control measures
We explored the time frame between a change in COVID-19 measures at the population level and the observable impact of such a change on cases. This study used data from British Columbia, Canada and found that it takes three weeks or more to detect a substantial difference in case counts after a change in population level control measures.
Citation: . Jessica E Stockdale, Renny Doig, Joosung Min, Nicola Mulberry, Liangliang Wang, Lloyd T Elliott, Caroline Colijn. medRxiv 2020.06.14.20131177; doi: https://doi.org/10.1101/2020.06.14.20131177
Event-specific interventions to minimize COVID-19 transmission
With the lack of a Covid-19 treatment or vaccine, interventions such as hand washing, masks, social distancing, and "social bubbles" are used to limit community transmission. In a now published paper, we provide a quantitative framework to determine which interventions are likely to have the most impact in which settings and introduce the concept of "event R", the expected number of new infections due to the presence of a single infected individual at an event. We obtain a fundamental relationship between event R and four parameters: transmission intensity, duration of exposure, the proximity of individuals, and the degree of mixing. We use reports of small outbreaks to establish event R and transmission intensity in a range of settings. This information can be used to re-open economies with principled measures to reduce COVID-19 transmission.
Citation: Paul Tupper, Madi Yerlanov, Himani Boury, Caroline Colijn. Proceedings of the National Academy of Sciences of the United States of America Dec 2020, 117 (50) 32038-32045; DOI: 10.1073/pnas.2019324117
More COVID-19 research
L. Wang, J. Min, R. Doig, L.T. Elliott, C. Colijn. (August 4, 2022). Canadian J. Statistics; doi: https://doi.org/10.1002/cjs.11722
M.R.P. Parker, J. Cao, L.L.E. Cowen, L.T. Elliott, J. Ma. (July 12, 2022). medRxiv; doi: https://doi.org/10.1101/2022.07.11.22277508
C.D. Fibke, Y. Joffres, J.R. Tyson, C. Colijn, N.Z. Janjua, C. Fjell, N. Prystajecky, A. Jassem, H. Sbihi. (July 4, 2022). Frontiers in Public Health, 10:915363; doi: https://doi.org/10.3389/fpubh.2022.915363
P. Tupper, S. Pai, COVID Schools Canada, C. Colijn. (June 13, 2022). medRxiv; doi: https://doi.org/10.1101/2021.12.07.21267381
C.A. Smith, C.A. Yates, B. Ashby. (April 26, 2022). PLoS Global Public Health, 2(4):e0000298; doi: https://doi.org/10.1371/journal.pgph.0000298
S.A. Iyaniwura, R.C. Falcão, N. Ringa, P.A. Adu, M. Spencer, M. Taylor, C. Colijn, D. Coombs, N.Z. Janjua, M.A. Irvine, M. Otterstatter. (9 April 2022). Epidemics, 39, 100559.
C. Colijn, D.J.D. Earn, J. Dushoff, N.H. Ogden, M. Li, N. knox, G. Van Domselaar, K. Franklin, G. Jolly, S.P. Otto. (April 6, 2022). Canadian Communicable Disease Report, 48(4):131-139; doi: https://doi.org/10.14745%2Fccdr.v48i04a03
B. Sobkowiak, K. Kamelian, J.E.A. Zlosnik, J. Tyson, A. Gonçalves da Silva, L.M.N. Hoang, N. Prystajecky, C. Colijn. (March 15, 2022). medRxiv; doi: https://doi.org/10.1101/2022.03.10.22272213
J.E. Stockdale, K. Susvitasari, P. Tupper, B. Sobkowiak, N. Mulberry, A. Gonçalves da Silva, A.E. Watt, N. Sherry, C. Minko, B.P. Howden, C.R. Lane, C. Colijn. (March 3, 2022). medRxiv; doi: https://doi.org/10.1101/2022.02.23.22271355
J.E. Stockdale, S. Anderson, A. Edwards, S. Iyaniwura, N. Mulberry, M. Otterstatter, N.Z. Janjua, D. Coombs, C. Colijn, M. Irvine. (January 12, 2022). Royal Society Open Science, 9(1), 211710.
C.A. Smith, B. Ashby. (January 1, 2022). medRxiv; doi: https://doi.org/10.1101/2022.01.13.22269154
E.B. Are, Y. Song, J.E. Stockdale, P. Tupper, C. Colijn. (December 19, 2021). medRxiv; doi: https://doi.org/10.1101/2021.12.18.21268002
P. Tupper, S.P. Otto, C. Colijn. (December 2, 2021). FACETS, 6(): 1993-2001. https://doi.org/10.1139/facets-2021-0016
M.R.P. Parker, Y. Li, L.T. Elliott, J. Ma, L.L.E. Cowen. (November 1, 2021). The Canadian Journal of Statistics, 49(4):1018-1038; doi: https://doi.org/10.1002/cjs.11664
N. Mulberry, P. Tupper, E. Kirwin, C. McCabe, C. Colijn. (October 13, 2021). PLOS Global Public Health 1(10): e0000020
J.E. Stockdale, R. Doig, J. Min, N. Mulberry, L. Wang, L.T. Elliot, C. Colijn. (October 7, 2021). Eurosurveillance, 26(40):2001204; doi: https://doi.org/10.2807/1560-7917.ES.2021.26.40.2001204
P. Tupper, C. Colijn. (July 8, 2021). PLOS Computational Biology 17(7): e1009120.
P. Liu, L. McQuarrie, Y. Song, C. Colijn. (April 28, 2021). J. R. Soc. Interface.182021003620210036
E.B. Are, C. Colijn. (January 26, 2021). medRxiv; doi: https://doi.org/10.1101/2021.01.22.21250308
L. Grandjean, A. Saso, A.T. Ortiz, T. Lam, J. Hatcher, R. Thistlethwayte, M. Harris, T. Best, M. Johnson, H. Wagstaffe, E. Ralph, A. Mai, C. Colijn, J. Breuer, M. Buckland, K. Gilmour, D. Goldblatt, the Co-Stars Study Team. (November 23, 2020). medRxiv; doi: https://doi.org/10.1101/2020.11.20.20235697
L. Grandjean, A. Saso, A.T. Ortiz, T. Lam, J. Hatcher, R. Thistlethwaite, M. Harris, T. Best, M. Johnson, H. Wagstaffe, E. Ralph, A. Mai, C. Colijn, J. Breuer, M. Buckland, K. Gilmour, D. Goldblatt, The Co-Stars Study Team. (July 22, 2020). medRxiv 2020.07.16.20155663; doi: https://doi.org/10.1101/2020.07.16.20155663