Citizen Brief

The Models for the COVID-19 Pandemic 

Background 

How epidemiological models of COVID-19 help us estimate the true number of infections:  Ourworldindata

"There is something common to modern "liberal" and Sunni-Salafi education:   They teach students answers rather than how to ask questions." - Nassim Taleb

Wrong but Useful — What Covid-19 Epidemiologic Models Can and Cannot Tell Us I. Holmdahl and C. Buckee N Engl J Med 2020;383:303-305 | Published Online May 15, 2020

Experts and Trump’s advisers doubt White House’s 240,000 coronavirus deaths estimate

Inside the White House’s effort to create a projected death toll

 

Don’t Believe the COVID-19 Models  That’s not what they’re for. Zeynez Tufeck

 

Special report: The simulations driving the world’s response to COVID-19 (Nature) When Neil Ferguson visited the heart of British government in London’s Downing Street, he was much closer to the COVID-19 pandemic than he realized. Ferguson, a mathematical epidemiologist at Imperial College London, briefed officials in mid-March on the latest results of his team’s computer models, which simulated the rapid spread of the coronavirus SARS-CoV-2 through the UK population. 

 

Christopher J.L. Murray Professor, IHME Director, Chair, Department of Health Metrics Sciences

 March 26, 2020 Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator days and deaths by US state in the next 4 months

Nonetheless, a new model released Thursday by the University of Washington’s School of Medicine is one of the first to forecast a national peak. It projects that the peak in daily U.S. deaths will arrive in mid-April, and the tail end of that curve, subsiding below 10 daily deaths, will arrive by the first week of June.

https://covid19.healthdata.org/projections 

COVID-19 Aerosol Transmission Estimator

Modeling the propagation of COVID-19 by aerosol transmission ONLY (Scientific document Excel) 

Modeling the Pandemic  

Vision:

By November 15th, 2020 the Coronavirus that causes COVID 19 will be contained and managed with public health strategies.

 

Goal:

To utilize best evidence and data to  contain the spread of COVID 19 virus and limit the impact on individuals, communities, healthcare system and the economy.

 

The Challenge:

Provide an evidence-based strategy for minimizing the spread of the COVID 19 virus and the damage it causes . 

 

Background:

The COVID 19 Pandemic  has been spreading across the globe since November 2019.  The challenges associated with containing the Pandemic are complex but not overwhelming. A scientifically informed, coordinated effort involving individuals, communities and requires a multi-prong approach. 

Five Questions to Ask About Model Results  

  • What is the purpose and time frame of this model? For example, is it a purely statistical model intended to provide short-term forecasts or a mechanistic model investigating future scenarios? These two types of models have different limitations.

  • What are the basic model assumptions? What is being assumed about immunity and asymptomatic transmission, for example? How are contact parameters included?

  • How is uncertainty being displayed? For statistical models, how are confidence intervals calculated and displayed? Uncertainty should increase as we move into the future. For mechanistic models, what parameters are being varied? Reliable modeling descriptions will usually include a table of parameter ranges — check to see whether those ranges make sense.

  • If the model is fitted to data, which data are used? Models fitted to confirmed COVID-19 cases are unlikely to be reliable. Models fitted to hospitalization or death data may be more reliable, but their reliability will depend on the setting.

  • Is the model general, or does it reflect a particular context? If the latter, is the spatial scale — national, regional, or local — appropriate for the modeling questions being asked and are the assumptions relevant for the setting? Population density will play an important role in determining model appropriateness, for example, and contact-rate parameters are likely to be context-specific.

Recorded on March 27, 2020 Dr. Jay Bhattacharya is a professor of medicine at Stanford University. He is a research associate at the National Bureau of Economic Research and a senior fellow at both the Stanford Institute for Economic Policy Research and the Stanford Freeman Spogli Institute. His March 24, 2020, article in the Wall Street Journal questions the premise that “coronavirus would kill millions without shelter-in-place orders and quarantines.”

Niall Ferguson: COVID-19 in the Light of History and Network Science

Estimating actual COVID 19 cases (novel corona virus infections) in an area based on deaths  Mar 14, 2020

“All models are wrong, some are useful,” said a great scientist once. In this video, I walk you through an analysis that I hope sharpens your understanding of this pandemic and focuses you on what really matters.

What is a confidence interval?

Modeling the Public Health and Economic Response 

The Penn Wharton Budget Model's projections for U.S. states reopening.

Coronavirus Policy Response Simulator: Health and Economic Effects of State Reopenings

The Nation 

The CDC and its forecast

Institute for Health Metrics and Evaluation (IHME) projected hospital resource use based on COVID-19 deaths.

Coronavirus experts presents sobering (Modeling) data about projected US deaths 1,070,679 views Mar 31, 2020

Github Projections 

The State 
Flattening-the-curve-2.png

The States and their forecasts

County and cities 
Flattening-the-curve-2.png

The Counties and their forecasts