Data analysis and forecasting at McMaster University
Our research group is led by three faculty who have been engaged in epidemiological modelling research for more then 20 years:
- 17 Oct 2021 forecast
- 20 Sep 2021 forecast
- 5 Jun 2021 forecast
- 20 May 2021 forecast
- 9 May 2021 forecast
- 19 Apr 2021. Early prediction of Ontario’s third COVID-19 wave
Canadian public data repository
Advice to governments
We are providing comments and modelling results to several government organizations that have sought our help.
McMasterPandemic R package
McMasterPandemic is an R package that provides tools for parameter estimation, simulation, and forecasting infectious disease outbreaks (using compartmental epidemic models). Follow the link to learn more about the package, including how to install and use it. The functionality of this package is evolving rapidly. We will improve the documentation as time permits.
epigrowthfit R package
epigrowthfit is an R package for estimating epidemic growth rates. It was developed for the purpose of studying growth rates of historical epidemics, but it can be used to study real-time outbreaks. The methodology and philosophy are based on:
Ma J, Dushoff J, Bolker BM, Earn DJD (2014). Bulletin of Mathematical Biology, (1), 245–260, Estimating initial epidemic growth rates.
The package is available freely on github. We will improve the documentation as time permits.
COVID-19 publications involving our group
Papst I, Li M, Champredon D, Bolker BM, Dushoff J, Earn DJD (12 Apr 2021). BMC Public Health , 706, Age-dependence of healthcare interventions for COVID-19 in Ontario, Canada.
Park SW, Cornforth MC, Dushoff J, Weitz JS (June 2020). Epidemics The time scale of asymptomatic transmission affects estimates of epidemic potential in the COVID-19 outbreak..
Weitz JS, Beckett SJ, Coenen AR, Demory D, Dominguez-Mirazo M, Dushoff J, Leung C-Y, Li G, Măgălie A, Park SW, Rodriguez-Gonzalez R, Shivam S, Zhao CY (7 May 2020). Nature Medicine, Modeling shield immunity to reduce COVID-19 epidemic spread.
Barton CM, Alberti M, Ames D, Atkinson, J-A, Bales J, Burke E, Chen M, Diallo SY, Earn DJD, Fath B, Feng Z, Gibbons C, Hammond R, Heffernan J, Houser H, Hovmand PS, Kopainsky B, Mabry P L, Mair C, Meier P, Niles R, Nosek B, Osgood N, Pierce S, Polhill JG, Prosser L, Robinson E, Rosenzweig C, Sankaran S, Stange K, and Tucker G (2020). Science (6490), 482–483, Transparency of COVID-19 models.
Park SW, Bolker BM, Champredon D, Earn DJD, Li M, Weitz JS, Grenfell BT, Dushoff J (2020). J. R. Soc. Interface , 20200144, Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak.
Park SW, Sun K, Champredon D, Li M, Bolker BM, Earn DJD, Weitz JS, Grenfell BT, Dushoff J (Posted to github 04 Jun 2020). Cohort-based approach to understanding the roles of generation and serial intervals in shaping epidemiological dynamics
Weitz JS, Park SW, Eksin C, Dushoff J (Posted to medRxiv 19 May 2020). Moving Beyond a Peak Mentality: Plateaus, Shoulders, Oscillations and Other ‘Anomalous’ Behavior-Driven Shapes in COVID-19 Outbreaks.
Park SW, Sun K, Viboud C, Grenfell BT, Dushoff J (Posted to medRxiv 30 Mar 2020). Potential roles of social distancing in mitigating the spread of coronavirus disease 2019 (COVID-19) in South Korea.