Misses the time dimension: Expiring Eviction Moratoriums and COVID-19 Incidence

Methods: The study included 44 U.S. states that instituted eviction moratoriums., followed from March 13th to September 3rd, 2020….

Findings: Twenty-seven states lifted eviction moratoriums during the study period. COVID-19 incidence in states that lifted their moratoriums was 1.6 (95% CI 1.0,2.3) times the incidence of states that maintained their moratoriums at 10 weeks post-lifting and grew to a ratio of 2.1 (CI 1.1,3.9) at ≥16 weeks. Mortality in states that lifted their moratoriums was 1.6 (CI 1.2,2.3) times the mortality of states that maintained their moratoriums at 7 weeks post-lifting and grew to a ratio of 5.4 (CI 3.1,9.3) at ≥16 weeks. These results translate to an estimated 433,700 excess cases (CI 365200,502200) and 10,700 excess deaths (CI 8900,12500) nationally.


Lifting eviction moratoriums was associated with increased COVID-19 incidence and mortality, supporting the public health rationale for use of eviction moratoriums to prevent the spread of COVID-19.

Source: Expiring Eviction Moratoriums and COVID-19 Incidence and Mortality by Kathryn M. Leifheit, Sabriya L. Linton, Julia Raifman, Gabriel Schwartz, Emily A. Benfer, Frederick J Zimmerman, Craig Pollack :: SSRN

Cases exploded in all states from roughly Sep-Oct onward until collapsing in January. This study fails to take in to account the time dimension – which is that nearly everywhere, given time, has the same outcomes. Consequently, what they think they have discerned is likely a factor of the time dimension and nothing to do with eviction moratoriums.

Irrational Covid Fears – The New York Times

It’s a classic example of human irrationality about risk. We often underestimate large, chronic dangers, like car crashes or chemical pollution, and fixate on tiny but salient risks, like plane crashes or shark attacks.

One way for a risk to become salient is for it to be new. That’s a core idea behind Calabresi’s fable. He asks students to consider whether they would accept the cost of vehicle travel if it did not already exist. That they say no underscores the very different ways we treat new risks and enduring ones.

Source: Irrational Covid Fears – The New York Times

Years ago, John Stossel proposed a similar scenario to an audience. He was aware of a new energy system that could heat homes at cheaper cost and reduce green house gases too. But it came with a risk: they estimated about 450 people would die per year due to issues with the technology in the home setting.

Would you approve use of this system?

Almost all of the audience said no – but then when audience member asked, “Is this by chance natural gas?” Which it was. It illustrates how we take some risks for granted – but new risks not so much.

Why practicality is essential to public health

Public mitigations require voluntary compliance; heavy handed police enforcement will never work. This means “sell, don’t tell” strategies.

Many mitigations are ineffective because they are not practical. Sure, rich white-collar workers can work from home and order delivery of their needs – but that is outsourcing their risk to a generally lower paid, less privileged individual. When 71% of the workforce is declared essential, we can’t really lock down anyway.

This example illustrates what happens when we lost practicality:

The thread explains this with more examples – and notes that public health has been disconnected from a real world understanding.

Public health also needs to address their steadfast refusal to acknowledge significant inconsistencies in their messaging. That they have no explanation for this ruins their message. TX and MS ended mask mandates and were predicted to be disaster zones – instead, they’ve continued downwards (even in spite of the Texas Rangers game and filled stadium two weeks ago) while MI, NJ and NY maintain tight restrictions – and got worse.

Meanwhile, the Twitterati remain stuck in nonsensical hysteria. Such a dignified professional response, eh? His response cannot be justified based on any data or historical understanding of how vaccines work, but what ever, he’s a highly paid opinionator.

Disease Models

Saw these shared on social media today.

This is the model from last December projecting daily new cases under various scenarios. The column chart at bottom are the actual real world numbers that occurred. Since they didn’t know which scenario would happen, their range went from nothing to infinity.

The next chart is projected deaths due to Covid-19, from mid-January (solid red line). The actual real world deaths are plotted in the individual red points, below, and even well below the 95% confidence band.

This table compares the March 2020 Imperial College London (ICL, Neil Ferguson) projected deaths with actual deaths. Note – the actual deaths in Japan were over 9,000 at end of March – the value of 10 is an error in this table.

Disease models appear to be unfit for any purpose. We have seen this before.

Oregon’s disease model – projecting only 3 weeks into the future, was completely wrong 11 out of 13 times.

Do lock downs work?

In a peer-reviewed paper now published in Biometrics, I find that, in all three cases, Covid-19 levels were probably falling before lockdown. A separate paper, with colleague Ernst Wit, comes to the same conclusion for the first two lockdowns, by the alternative approach of re-doing Imperial College’s major modelling study of the epidemic in 2020. In light of this, the Imperial College claim that new infections were surging right up until lockdown one — causing about 20,000 avoidable deaths — seems rather questionable.

Source: Covid and the lockdown effect: a look at the evidence | The Spectator

Elsewhere, last November, a Harvard epidemiologist said that anyone questioning lock down policies should be held “for some form of accountability” and said those questioning lock downs are connected to right wing interests.

It can be difficult for a society to make forward progress in finding the truth when questions are prohibited and censorship is common.

Covid-19 sniffing dogs coming to airports and public spaces next?

While the world awaits a widely available COVID-19 vaccine, availability of testing is limited in many regions and can be further compounded by shortages of reagents, prolonged processing time and delayed results. One approach to rapid testing is to leverage the volatile organic compound (VOC) signature of SARS-CoV-2 infection. Detection dogs, a biological sensor of VOCs, were utilized to investigate whether SARS-CoV-2 positive urine and saliva patient samples had a unique odor signature. The virus was inactivated in all training samples with either detergent or heat treatment. Using detergent-inactivated urine samples, dogs were initially trained to find samples collected from hospitalized patients confirmed with SARS-CoV-2 infection, while ignoring samples collected from controls. Dogs were then tested on their ability to spontaneously recognize heat-treated urine samples as well as heat-treated saliva from hospitalized SARS-CoV-2 positive patients. Dogs successfully discriminated between infected and uninfected urine samples, regardless of the inactivation protocol, as well as heat-treated saliva samples. Generalization to novel samples was limited, particularly after intensive training with a restricted sample set. A unique odor associated with SARS-CoV-2 infection present in human urine as well as saliva, provides impetus for the development of odor-based screening, either by electronic, chemical, or biological sensing methods. The use of dogs for screening in an operational setting will require training with a large number of novel SARS-CoV-2 positive and confirmed negative samples.

Source: Discrimination of SARS-CoV-2 infected patient samples by detection dogs: A proof of concept study