Jessica Hamzelou at New Scientist notes the systematic errors researchers and scientists have found with the modeling COVID Act Now relies on:

Chen Shen at the New England Complex Systems Institute, a research group in Cambridge, Massachusetts, and his colleagues argue that the Imperial team’s model is flawed, and contains ‘incorrect assumptions’. They point out that the Imperial team’s model doesn’t account for the availability of tests, or the possibility of ‘super-spreader events’ at gatherings, and has other issues.

Among other issues, COVID Act Now lists the “Known Limitations” of their model. Here are a few that seem especially alarming, considering they generate a model for each individual state:

• Many of the inputs into this model (hospitalization rate, hospitalization rate) are based on early estimates that are likely to be wrong.

• Demographics, populations, and hospital bed counts are outdated. Demographics for the USA as a whole are used, rather than specific to each state.

• The model does not adjust for the population density, culturally-determined interaction frequency and closeness, humidity, temperature, etc in calculating R0.

• This is not a node-based analysis, and thus assumes everyone spreads the disease at the same rate. In practice, there are some folks who are ‘super-spreaders,’ and others who are almost isolated.

So why is the organization or seemingly innocent online mapping tool using inaccurate algorithms to scaremonger leaders into tanking the economy? Politics, of course.

Founders of the site include Democratic Rep. Jonathan Kreiss-Tomkins and three Silicon Valley tech workers and Democratic activists — Zachary Rosen, Max Henderson, and Igor Kofman — who are all also donors to various Democratic campaigns and political organizations since 2016. Henderson and Kofman donated to the Hillary Clinton campaign in 2016, while Rosen donated to the Democratic National Committee, recently resigned Democratic Rep. Katie Hill, and other Democratic candidates. Prior to building the COVID Act Now website, Kofman created an online game designed to raise $1 million for the eventual 2020 Democratic candidate and defeat President Trump. The game’s website is now defunct.

Perhaps the goal of COVID Act Now was never to provide accurate information, but to scare citizens and government officials into to implementing rash and draconian measures. The creators even admit as much with the caveat that “this model is designed to drive fast action, not predict the future.”

They generated this model under the guise of protecting communities from overrun hospitals, a trend that is not on track to happen as they predicted. Not only is the data false, and looking more incorrect with each passing day, but the website is optimized for a disinformation campaign.

A social media share button prompts users to share their models and alarming graphs on Facebook and Twitter with the auto-fill text, “This is the point of no return for intervention to prevent X’s hospital system from being overloaded by Coronavirus.”

The daunting phrase, the “point of no return,” is the same talking point being repeated by government officials justifying their shelter-in-place orders and filling local news headlines.

Democrats are not going to waste such a rich political opportunity as a global pandemic. Americans already witnessed Speaker of the House Nancy Pelosi and House Democrats attempt to take advantage of an economic recession with a pipe-dream relief bill this week. Projects like COVID Act Now are another attempt to play the same political games, but with help from unknown, behind-the-scenes Democratic activists instead.

Our community leaders, the mayors and the city councils, deserve better than to be swindled by a handful Silicon Valley tech bros. Our governors and state officials deserve better data and analysis than a Democratic activists’ model that doesn’t adjust for important geographical factors like population density or temperature. Americans and their families deserve better than to be jobless, hopeless, and quarantined because of a single website’s inaccurate and hyperbolic hospitalization models.