Inside the Greenhouse

Resources

 

Models and Forecasts

MIDAS – Online Portal for COVID-19 Modeling Research (link)

U.K.  Scientific Pandemic Influenza Group on Modelling (SPI-M) Modelling
Summary (2018 report, link) (advisory subcommittee)

Public Health Agency of Canada, 2020. COVID-19 in Canada: Using data and modelling to inform public health action: Technical Briefing for Canadians, 9 April (PDF).

Begley, S. 2020. Influential Covid-19 model uses flawed methods and shouldn’t guide U.S. policies, critics sayStat, 17 April.

Bender, M. and R. Ballhaus, 2020. Trump’s Coronavirus Focus Shifts to Reopening Economy, Defending His ResponseThe Washington Post, 17 April.

Wan, W. and C. Johnson, 2020. America’s most influential coronavirus model just revised its estimates downward. But not every model agrees. The Washington Post, 8 April.

Wan, W. 2020. Experts and Trump’s advisers doubt White House’s 240,000 coronavirus deaths estimateThe Washington Post, 2 April.

Koerth et al. 2020. Why It’s So Freaking Hard To Make A Good COVID-19 Model, FiveThirtyEight, 31 March.

Wan, W. and A. Blake, 2020. Coronavirus modelers factor in new public health risk: Accusations their work is a hoaxThe Washington Post, 27 March.

IHME Covid-19 Projections (link) based on: IHME COVID-19 health service utilization forecasting team. Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator days and deaths by US state in the next 4 months. MedRxiv. 26 March 2020.

Enserink, M. and K. Kupferschmidt, 2020. Mathematics of life and death: How disease models shape national shutdowns and other pandemic policies, Science, 25 March.

Rivers, C. et al. 2020. Modernizing and Expanding Outbreak Science to Support Better Decision Making During Public Health Crises: Lessons for COVID-19 and Beyond, Johns Hopkins Center for Health Security, 24 March. (PDF).

Rivers, C., Chretien, J.P., Riley, S., Pavlin, J.A., Woodward, A., Brett-Major, D., Berry, I.M., Morton, L., Jarman, R.G., Biggerstaff, M. and Johansson, M.A., 2019. Using “outbreak science” to strengthen the use of models during epidemicsNature communications10(1), pp.1-3.

Chowell, G., Sattenspiel, L., Bansal, S., & Viboud, C. (2016). Mathematical models to characterize early epidemic growth: A reviewPhysics of life reviews18, 66-97.

Glasser, J. W., Hupert, N., McCauley, M. M., & Hatchett, R. (2011). Modeling and public health emergency responses: Lessons from SARSEpidemics3(1), 32-37.