Covid-19 cases are dropping fast. Why?

‘One month ago, the CDC published the results of more than 20 pandemic forecasting models. Most projected that COVID-19 cases would continue to grow through February, or at least plateau. Instead, COVID-19 is in retreat in America. New daily cases have plunged, and hospitalizations are down almost 50 percent in the past month. This is not an artifact of infrequent testing, since the share of regional daily tests that are coming back positive has declined even more than the number of cases. Some pandemic statistics are foggy, but the current decline of COVID-19 is crystal clear.

**Four reasons: social distancing, seasonality, seroprevalence, and shots.’

Read here (The Atlantic, Feb 17, 2021) 

How influencers, celebrities, and FOMO [fear of missing out] can win over vaccine skeptics

‘Drawing from product innovation theory, Rohit Deshpandé and colleagues offer three recommendations to speed adoption of COVID-19 vaccines...

‘Governments are prioritizing certain groups to receive the vaccine, with medical professionals and certain government personnel at the top, followed by first responders and vulnerable populations, and then the general population. The diffusion of innovations model indicates that each of these groups will have five customer segments based on their willingness to get vaccinated earlier or later. For example, some medical professionals will be eager to get vaccinated early (the innovators, early adopters, and majority) while others will wait (the late majority and laggards).

‘So, how do we maximize the number of individuals in any prioritized group who are willing, if not eager, to get vaccinated as soon as possible?

‘The answer requires keen understanding of each segment, for example, of both the seniors in the early majority and the seniors that are laggards less keen on taking the vaccine. The diffusion of innovations research indicates that a combination of personal and societal factors influence the rate of adoption within and between segments factors, with the ultimate driver being word of mouth.

‘For the COVID-19 vaccine, the personal factors include people’s perceived efficacy and need for the vaccine, past immunization experiences, and opinions about vaccines more generally, along with those of their families.

‘Societal drivers include the advice of experts, media, and other influencers within their demographic, socioeconomic, and innovation adoption segment. Influencers will need to mitigate concerns about the “newness” of the vaccine, such as the probability of side effects and solutions when they occur. They will also need to reinforce the positive consequences of taking the vaccine, such as the ability to visit family, go to work, and have more entertainment options.’

Read here (Harvard Business School, Jan 29, 2021)

A new understanding of herd immunity

‘Gabriela Gomes studies chaos. Specifically, patterns in nonlinear dynamics... So with all its apparently chaotic eccentricities, the coronavirus was an ideal challenge for Gomes, a professor at the University of Strathclyde, in Glasgow, Scotland... Based on data from several countries in Europe, she said, her results show a herd-immunity threshold much lower than that of other models. “We just keep running the models, and it keeps coming back at less than 20 percent,” Gomes said. “It’s very striking.”

‘If that proves correct, it would be life-altering news. It wouldn’t mean that the virus is gone. But by Gomes’s estimates, if roughly one out of every five people in a given population is immune to the virus, that seems to be enough to slow its spread to a level where each infectious person is infecting an average of less than one other person. The number of infections would steadily decline...

There are two more arguments in the story, citing levels from 20 to 70 percent... The conclusion, as stated in the subhead: ‘The portion of the population that needs to get sick is not fixed. We can change it.’

Read here (The Atlantic, July 13, 2020)

How to make sense of all the Covid-19 projections? A new model combines them

‘The [University of Massachusetts Amherst] team unveiled the first version four weeks ago and ever since has been adding in more forecasts and updating the projections weekly. The latest update — released Tuesday — incorporates eight models, including some oft-cited ones, such as those built by the Imperial College London, the University of Washington Institute for Health Metrics and Evaluation, Columbia University and Northeastern University. (The team also sends each week's release to the CDC, which publishes a version with a slight time lag.)

‘The projections vary substantially — with the most pessimistic forecasting a total death toll of 120,000 by June 6 and the most optimistic forecasting 103,000 deaths by that date. But the models have been inching closer to each other. Over the past several weeks, the distance between the highest and lowest estimates has halved from a gap of 36,000 deaths two weeks ago to a gap of 17,000 deaths in the most recent update released Tuesday.’

Read here (NPR or National Public Radio, US, May 13, 2020)

Sympathy for the epidemiologists: Paul Krugman

‘...the White House probably likes IHME [University of Washington’s Institute for Health Metrics and Evaluation] less today than it did yesterday: the institute just drastically revised its projected death total upward, from 72,000 to 134,000. Documents obtained by The New York Times suggest that modelers within the U.S. government have also revised death projections sharply upward...

‘So let me give a shout-out to the hard-working, much-criticized epidemiologists trying to get this pandemic right. You may take a lot of abuse when you get it wrong, which you unavoidably will on occasion. But you’re doing what must be done. Also, welcome to my world.’

Read here (New York Times, May 5, 2020)

French doctors say they found a Covid-19 patient from December

‘There's new evidence that the coronavirus may have been in France weeks earlier than was previously thought. Doctors at a Paris hospital say they've found evidence that one patient admitted in December was infected with Covid-19. If verified, this finding would show that the virus was already circulating in Europe at that time -- well before the first known cases were diagnosed in France or hotspot Italy.’

Read here (CNN, May 4, 2020)

SARS-COV-2 was already spreading in France in late December 2019

Summary of the report:

• Covid-19 was already spreading in France in late December 2019, a month before the official first cases in the country.
• Early community spreading changes our knowledge of covid-19 epidemic.
• This new case changes our understanding of the epidemic and modeling studies should adjust to this new data.

Read here (Science Direct, May 3, 2020)

For the record: Two game-changing studies from Imperial College that affected Europe-wide policies

The Imperial College COVID-19 Response Team in London produced two studies that influenced policy in Europe in a big way. In particular, it helped push Britain to switch its strategy from one based on ‘herd immunity’ to that of "suppression".

(1) March 16: ‘Suppression the only viable strategy’

The first study, published March 16, 2020, concluded that ‘epidemic suppression is the only viable strategy at the current time. The social and economic effects of the measures which are needed to achieve this policy goal will be profound. Many countries have adopted such measures already, but even those countries at an earlier stage of their epidemic (such as the UK) will need to do so imminently.’

Read here (Imperial College, March 16, 2020)

(2) March 30: ‘59,000 lives saved in 11 European countries via non-pharmacologial interventions, between 7 to 43 million individuals infected -- as of March 31’

The second study, published March 30, 2020, said that ‘with current interventions remaining in place to at least the end of March, we estimate that interventions across all 11 countries will have averted 59,000 deaths up to 31 March [95% credible interval 21,000-120,000]. Many more deaths will be averted through ensuring that interventions remain in place until transmission drops to low levels.

‘We estimate that, across all 11 countries between 7 and 43 million individuals have been infected with SARS-CoV-2 up to 28th March, representing between 1.88% and 11.43% of the population. The proportion of the population infected to date – the attack rate - is estimated to be highest in Spain followed by Italy and lowest in Germany and Norway, reflecting the relative stages of the epidemics.’

Read here (Imperial College, March 30, 2020)

Coronavirus lockdown measures may have saved 59,000 lives in Europe already, says new study by Imperial College

 ‘At least 59,000 lives have already been saved in 11 European countries due to the social distancing measures introduced to stem the spread of Covid-19, new modelling suggests.

‘According to the analysis, 370 deaths have already been averted in the UK - where a nationwide lockdown came into effect just one week ago - while Italian interventions have saved 38,000 lives to date.

‘But the study also shows that the continent remains a long way from developing “herd immunity”, whereby the vast majority of people have caught, recovered and become immune to the coronavirus.

‘The modelling, published yesterday by Imperial College, London, analyses the impact of lockdown in 11 European countries, including the UK.’

Read here (Telegraph, March 30, 2020)

The new astrology: By fetishising mathematical models, economists turned economics into a highly paid pseudoscience

‘Nonetheless, surveys indicate that economists see their discipline as ‘the most scientific of the social sciences’. What is the basis of this collective faith, shared by universities, presidents and billionaires? Shouldn’t successful and powerful people be the first to spot the exaggerated worth of a discipline, and the least likely to pay for it?

‘In the hypothetical worlds of rational markets, where much of economic theory is set, perhaps. But real-world history tells a different story, of mathematical models masquerading as science and a public eager to buy them, mistaking elegant equations for empirical accuracy.’

[Note: This is an old article published April 4, 2016. We should read this in the context of the epidemiological modelling being used now to forecast Covid-19.]

Read here (Aeon, April 4, 2016)