World-Watching: German Industry: Structural Change Underway

[from Deutsche Bank Research]

Production in major industrial sectors in Germany has developed very differently in recent years under the impact of the coronavirus pandemic and energy price shock. For example, manufacturing in electrical engineering rose by 18% compared with the start of 2015. In the chemical industry, there has been a 20% decline over the same period. The differences are not only cyclical, but also structural. In the future, it will be more important to distinguish between Germany as an industrial location and the German industry.

Read the Germany blog [archived PDF].

Economics-Watching: Fourth-Quarter GDP Growth Estimate Inches Up

The growth rate of real gross domestic product (GDP) is a key indicator of economic activity, but the official estimate is released with a delay. Our GDPNow forecasting model provides a “nowcast” of the official estimate prior to its release by estimating GDP growth using a methodology similar to the one used by the U.S. Bureau of Economic Analysis.

GDPNow is not an official forecast of the Atlanta Fed. Rather, it is best viewed as a running estimate of real GDP growth based on available economic data for the current measured quarter. There are no subjective adjustments made to GDPNow—the estimate is based solely on the mathematical results of the model. In particular, it does not capture the impact of COVID-19 and social mobility beyond their impact on GDP source data and relevant economic reports that have already been released. It does not anticipate their impact on forthcoming economic reports beyond the standard internal dynamics of the model.

Recent forecasts for the GDPNow model are available here. More extensive numerical details—including underlying source data, forecasts, and model parameters—are available as a separate spreadsheet. You can also view an archive of recent commentaries from GDPNow estimates.

Please note that the Federal Reserve no longer supports the GDPNow app. Download the Federal Reserve’s EconomyNow app or go to the Atlanta Fed’s website to continue to get the latest GDP nowcast and more economic data.

Latest estimate: 3.9 percent — January 3, 2023

The GDPNow model estimate for real GDP growth (seasonally adjusted annual rate) in the fourth quarter of 2022 is 3.9 percent on January 3, up from 3.7 percent on December 23. After last week’s Advance Economic Indicators report from the U.S. Census Bureau and this morning’s construction spending release from the U.S. Census Bureau, the nowcasts of fourth-quarter real gross private domestic investment growth and fourth-quarter real government spending growth increased from 3.8 percent and 0.8 percent, respectively, to 6.1 percent and 1.0 percent, respectively, while the nowcast of the contribution of the change in real net exports to fourth-quarter real GDP growth decreased from 0.35 percentage points to 0.17 percentage points.

The Early Universe and the Future of Humanity/Xi Risks Losing the Middle Class

[from The Institute of Art and Ideas]

The Life and Philosophy of Martin Rees

An Interview with Martin Rees

Astronomer Royal and best-selling science author, Martin Rees pioneering early work led to evidence to contradict the Steady State theory of the universe and confirm the Big Bang. His influence then spread to the wider public—knighted in 1992, elevated to Baron in 2005, then giving the Reith Lectures in 2010. Most recently his attention has turned from the early universe to the future of humanity. In this interview, Lord Rees discusses the ideas and experiences which led to such an illustrious career.

Xi Risks Losing the Middle Class

The zero-COVID strategy has run its course

Kerry Brown | Professor of Chinese Studies and Director of Lau China Institute, King’s College London. He is the co-editor of the Journal of Current Chinese Affairs, and author of Xi: A Study in Power.

China is continuing with its tough zero-COVID policy. But the cracks in the economy and a discontent middle class mean that Xi’s Imperial-like governing style is under challenge, writes Kerry Brown.

China’s zero-COVID strategy operates in Chinese domestic politics a bit like Brexit does in the UK. Despite complaints from business networks and broader society about the negative impact on economic growth and citizens’ freedoms, it’s a policy commitment the government is sticking to no matter what.

Of course, no one voted for the draconian lockdowns implemented across China. And, unlike Brexit, the lockdowns are very much in line with expert advice in the country, rather than running against it. The Chinese Centre for Disease Control and Prevention (CCDC), the main governmental body advising the government over crisis response in this area, said in a weekly update last November that without comprehensive restraints on people’s movement and quarantines on anyone testing positive for the virus, the national health system would soon be overwhelmed with cases, and find itself in the same bind as those in the US or Europe did.

That the words of the experts have been taken so much in earnest is striking for a regime that previously hasn’t been shy to dismiss them. The Xi leadership may be confident in the way it speaks to the outside world, but it seems that it has the same profound wariness in the robustness of the country’s public health as everywhere else. Things have not been helped by clinical trials showing the Chinese vaccines – the only ones accepted in China – are not as effective as foreign ones where the length of protection is in question). On top of this, vaccine take-up by the elderly, the most vulnerable group, has been poor. It is easy to see therefore why the central government might be very cautious. What is harder to understand, however, is why the cautiousness has bordered on obsessiveness.

The Xi way of governing is increasingly almost imperial in style, with broad, high-level policy announcements made in Beijing, sometimes of almost Delphic succinctness.

One scenario is simply about the structures of decision-making in China. This was an issue right from the moment the variant started to appear in late 2019, and local officials in Wuhan stood accused of trying to hush the issue up, delaying reporting to the central authorities till things had already gone on too long. As a result of this, in February 2020 key officials in the city were sacked. But this is unlikely to change the fact that provincial officials are very risk averse under Xi, and that any central direction to manage the pandemic will be interpreted in the purest terms and executed to the letter.

This explains the completeness of the Xian government’s virtual incarceration of its 8 million population after just a few COVID cases at the end of 2021, the first of the more recent lockdowns. It also explains why the traditionally more free-thinking municipal authority of Shanghai and its similarly liberal approach was fiercely knocked back by Beijing last February, to make an example for any other provinces thinking of going their own way. The absolute prohibition on people moving from their homes there, in one of the most dynamic and lively cities of modern China, was perfect proof that if the government could bring about this situation there, it could do it anywhere.

This case study also reveals some important things about the Xi way of governing. It is increasingly almost imperial in style, with broad, high-level policy announcements made in Beijing, sometimes of almost Delphic succinctness, which are then handed down to various levels of government to do as they will. Exactly how and when the discussion amongst Xi and his Politburo colleagues on the best response to COVID happened is unclear. In a world where almost every political system seems to leak incessantly, the Chinese one is unique in maintaining its opacity and secretiveness – no mean achievement in the social media era.

The Communist Party is very aware of how relatively small incidents can mount up and then generate overwhelming force. It itself coined the Chinese phrase ‘a single spark can start a prairie fire.’

Rumors of clashes between Xi and his premier Li Keqiang on the effectiveness of the current response remain just that – rumors, with precious little hard evidence to back them up. Who in the current imperial system might dare to speak from the ranks and say that policy must change is unclear. Scientists should deal in hard facts – but we all know that science is susceptible to politicization. Experts in China have to offer their expertise in a highly political context. A declaration that the current approach is not fit for purpose can easily be reinterpreted as an attempt to launch an indirect attack on the core leader. With an important Congress coming up later this year, at which Xi is expected to be appointed for another five years in power, sensitivities are even more intense than normal. It is little wonder that the COVID strategy status quo settled on last year has not shifted.

Things, however, may well change, and change quickly. China is moving into tricky economic territory. The impact of the pandemic on global supply chains, along with the various stresses domestically on the housing market, and productivity, have shrunk expectations for growth. A predicted 6% in the earlier part of the year now looks overly ambitious. There is a real possibility China might experience a recession. At a moment like this, the government, which after all operates as a constant crisis and risk management entity, might do what it does best and prompt rapid, and dramatic, changes.

The handling of COVID-19 might look like further proof that Chinese politics under Xi is repressive and zero-sum. But even in an autocratic state like the current People’s Republic, the pandemic will not leave politics unchanged.

This doesn’t mean that China’s COVID-19 bind gets any easier. Like the country’s serious demographic challenges, with a rapidly aging population, the only thing the government will be picking an argument with is reality as it proceeds into the future. As with Europe and the US, being more liberal about facing COVID-19 will involve accepting some of the harsh consequences – rising fatalities, particularly for the elderly and vulnerable, and health systems put under enormous stress. In such a huge, complex country, and of enormous geopolitically importance, a misstep could easily lead to huge and unwanted consequences, generating discontent and triggering mass protests in a way reminiscent of 1989. The Communist Party is very aware of how relatively small incidents can mount up and then generate overwhelming force. It itself coined the Chinese phrase ‘a single spark can start a prairie fire.’ One such spark – the introduction of Marxism into China in the 1910s – led to its gaining of power three decades later.

The handling of COVID-19 might look like further proof that Chinese politics under Xi is repressive and zero-sum. But I suspect that even in an autocratic state like the current People’s Republic, the pandemic will not leave politics unchanged. In particular, the middle classes in cities like Shanghai have had their patience tested in recent months. This is the key group for Xi, the heart of his new innovative, more self-dependent, higher-quality service sector workers in an urbanized economy. Their support remains crucial if Xi is able to steer China towards the moment when it hopes it will become the world’s largest economy. Policies to try to placate them by addressing imbalances, critical environmental issues and improving public health are likely to only increase. Delivery however will be key.

Faced with a potentially life-threatening infectious disease, the Party can throw out injunctions and claim it has been the victim of bad luck. But an ailing economy and no clear signs of the government knowing how to manage this will prove a toxic mixture for it. Xi and his third term in office will be all about delivery. The question is whether, even with the formidable suite of powers he has, he can do this. Governing China has always been the ultimate political challenge. COVID-19 has made that even harder.

“2022 Monkeypox Outbreak: Situational Awareness” with Syra Madad [Zoom]

[from Harvard Kennedy School’s Belfer Center, part of Harvard University]

Thursday, July 21, 2:30-4:00 PM EDT

RSVP (Required)

The 2022 Monkeypox outbreak continues to expand with case counts mounting in many countries. This seminar will cover where we are in the global fight against monkeypox, where we may be headed as a nation, and what we need to do right now to mitigate the growing threat of monkeypox. Join Belfer Fellow Dr. Syra Madad in conversation with Kai Kupferschmidt, Dr. Krutika Kuppalli, Dr. Anne Rimoin, Dr. Boghuma Kabisen Titanji, and Dr. Jay K. Varma.

About the Speakers

Dr. Anne Rimoin is a Professor of Epidemiology at the UCLA Fielding School of Public Health. She is the Gordon-Levin Endowed Chair in Infectious Diseases and Public Health. Dr. Rimoin is the director of the Center for Global and Immigrant Health and is an internationally recognized expert on emerging infections, global health, surveillance systems, and vaccination.

Rimoin has been working in the DRC since 2002, where she founded the UCLA-DRC Health Research and Training Program to train U.S. and Congolese epidemiologists to conduct high-impact infectious disease research in low-resource, logistically-complex settings.

Dr. Rimoin’s research focuses on emerging and vaccine-preventable diseases. It has led to fundamental understandings of the epidemiology of human monkeypox in post-eradication of smallpox, long-term immunity to Ebola virus in survivors and durability of immune response to Ebola virus vaccine in health workers in DRC. Her current research portfolio includes studies of COVID-19, Ebola, Marburg, Monkeypox and vaccine-preventable diseases of childhood.

Boghuma Kabisen Titanji (MD, MSc., DTM&H, PhD) is a Cameroonian born physician-scientist and Assistant Professor of Medicine at Emory University in Atlanta. She obtained her MD from the University of Yaoundé I in Cameroon and worked for two years after graduation as a medical officer, before pursuing post-graduate research training in London, United Kingdom. As an awardee of the prestigious Commonwealth Scholarship program, she obtained a Masters Degree in Tropical Medicine and International Health from the London School of Hygiene and Tropical Medicine, a diploma in Tropical Medicine and Hygiene from the Royal College of Physicians and a Ph.D. in Virology from University College London. Dr. Titanji joined Emory University School of Medicine in 2016 where she completed a residency in Internal Medicine, on the ABIM research pathway and a fellowship Infectious Diseases. She has three parallel career interests: translational and clinical research in HIV and emerging virus infections, science communication, and global health. Her clinical focus is general infectious diseases and people with HIV. Her current research focuses on chronic inflammation as a mediator of cardiovascular disease in people with HIV. She is passionate about leveraging translational research to improve the care of people with HIV, global health equity and using science to influence health policy through science communication and advocacy.

Jay K. Varma, MD is a Professor of Population Health Sciences and Director of the Cornell Center for Pandemic Prevention and Response at Weill Cornell Medicine. Dr. Varma is an expert on the prevention and control of diseases, having led epidemic responses, developed global and national policies, and led large-scale programs that have saved hundreds of thousands of lives in China, Southeast Asia, Africa, and the United States. After graduating magna cum laude with highest honors from Harvard, Dr. Varma completed medical school, internal medicine residency, and chief residency at the University of California, San Diego School of Medicine. From 2001-2021, he worked for the U.S. Centers for Disease Control and Prevention with postings in Atlanta, Thailand, China, Ethiopia, and New York City. From April 2020 – May 2021, he served as the principal scientific spokesperson and lead for New York City’s COVID-19 response. Dr. Varma has authored 143 scientific manuscripts, 13 essays, and one book.

Kai Kupferschmidt is a science journalist based in Berlin, Germany. He is a contributing correspondent for Science where he often covers infectious diseases. Kai received a diploma in molecular biomedicine from the University of Bonn, Germany and later visited the Berlin Journalism School. He has won several awards for his work, including the Journalism Prize of the German AIDS Foundation. Together with two colleagues he runs a podcast on global health called Pandemia [German]. He has also written two books, one about infectious diseases and one about the science of the color blue.

Krutika Kuppalli, MD, FIDSA is a Medical Officer for Emerging Zoonotic Diseases and Clinical Management in the Health Emergencies Program at the World Health Organization where she currently supports activities for the Monkeypox outbreak and COVID-19 pandemic. She completed her Internal Medicine residency and Infectious Diseases fellowship at Emory University, a Post-Doctoral Fellowship in Global Public Health at the University of California, San Diego and the Emerging Leader in Biosecurity Fellowship at the Johns Hopkins Center for Health Security. Dr. Kuppalli currently serves on the American Society of Tropical Medicine and Hygiene (ASTMH) Trainee Committee and is the Chair of the Infectious Diseases Society of America (IDSA) Global Health Committee.

Dr. Kuppalli was previously awarded the NIH Fogarty International Clinical Research Fellowship and conducted research in Southern India to understand barriers to care and how emerging infections impacted persons living with HIV/AIDS. She was the medical director of a large Ebola Treatment Unit in Sierra Leone during the 2014 West Africa Ebola outbreak, helped lead the development and implementation of pandemic response preparedness activities in resource limited settings, and has consulted on the development of therapeutics for emerging pathogens. Her clinical and research interests focus on health systems strengthening in resource limited settings, research and clinical care for emerging infections, outbreak preparedness and response, and policy. She has worked in numerous countries including Ethiopia, India, Sierra Leone, Uganda, and Haiti.

During the COVID-19 pandemic Dr. Kuppalli served as a consultant for the San Francisco Department of Health and helped develop and operationalize a field hospital. She served as an expert witness to the U.S. Congress, Financial Services Committee Task Force on Artificial Intelligence (AI) about how digital technologies may be leveraged for exposure notification and contact tracing to improve the pandemic response. She also collaborated with the Brennan Center for Justice to develop guidelines to inform “Healthy in-person Voting” in advance of the 2020 U.S. election and testified before the U.S. House Select Subcommittee regarding these recommendations. Prior to her position at WHO, she was the medical lead for COVID-19 vaccine rollout at the Medical University of South Carolina (MUSC) and helped coordinate vaccine education events for the staff and community and oversaw the reporting of adverse vaccine events.

Since joining WHO in August 2021, Dr. Kuppalli has been part of the WHO headquarters incident management team (IMST) for COVID-19, the clinical characterization and management working group for COVID-19, the COVID-19 therapeutics steering committee, and is the technical focal point for the post COVID-19 condition (Long COVID) steering committee. She is a member of the secretariat on the scientific advisory group on the origins of emerging and re-emerging infectious diseases (SAGO) which was convened by the Director General to understand and investigate the origins of SARS-CoV-2 and other novel pathogens. More recently since the development of the multi-country monkeypox outbreak she has been part of the IMST at WHO as one of the clinical management focal points. In this capacity she was part of the WHO core group that helped write the recently published Clinical Management and Infection Prevention and Control guidelines for Monkeypox and advising on the clinical endpoints for the global CORE therapeutics protocol.

Dr. Kuppalli is recognized as a scientific expert in global health, biosecurity and outbreak response. She was recognized by NPR Source of The Week early in the pandemic as an expert to follow and named to Elemental’s 50 Experts to Trust in a Pandemic. She has been a frequent contributor to numerous domestic and international media outlets including The New York Times, NPR, Reuters, The Washington Post, Vox, Stat News, San Francisco Chronicle, Forbes, NBC Bay Area, BBC News.

Science-Watching: Forecasting New Diseases in Low-Data Settings Using Transfer Learning

[from London Mathematical Laboratory]

by Kirstin Roster, Colm Connaughton & Francisco A. Rodrigues

Abstract

Recent infectious disease outbreaks, such as the COVID-19 pandemic and the Zika epidemic in Brazil, have demonstrated both the importance and difficulty of accurately forecasting novel infectious diseases. When new diseases first emerge, we have little knowledge of the transmission process, the level and duration of immunity to reinfection, or other parameters required to build realistic epidemiological models. Time series forecasts and machine learning, while less reliant on assumptions about the disease, require large amounts of data that are also not available in early stages of an outbreak. In this study, we examine how knowledge of related diseases can help make predictions of new diseases in data-scarce environments using transfer learning. We implement both an empirical and a synthetic approach. Using data from Brazil, we compare how well different machine learning models transfer knowledge between two different dataset pairs: case counts of (i) dengue and Zika, and (ii) influenza and COVID-19. In the synthetic analysis, we generate data with an SIR model using different transmission and recovery rates, and then compare the effectiveness of different transfer learning methods. We find that transfer learning offers the potential to improve predictions, even beyond a model based on data from the target disease, though the appropriate source disease must be chosen carefully. While imperfect, these models offer an additional input for decision makers for pandemic response.

Introduction

Epidemic models can be divided into two broad categories: data-driven models aim to fit an epidemic curve to past data in order to make predictions about the future; mechanistic models simulate scenarios based on different underlying assumptions, such as varying contact rates or vaccine effectiveness. Both model types aid in the public health response: forecasts serve as an early warning system of an outbreak in the near future, while mechanistic models help us better understand the causes of spread and potential remedial interventions to prevent further infections. Many different data-driven and mechanistic models were proposed during the early stages of the COVID-19 pandemic and informed decision-making with varying levels of success. This range of predictive performance underscores both the difficulty and importance of epidemic forecasting, especially early in an outbreak. Yet the COVID-19 pandemic also led to unprecedented levels of data-sharing and collaboration across disciplines, so that several novel approaches to epidemic forecasting continue to be explored, including models that incorporate machine learning and real-time big data data streams. In addition to the COVID-19 pandemic, recent infectious disease outbreaks include Zika virus in Brazil in 2015, Ebola virus in West Africa in 2014–16, Middle East respiratory syndrome (MERS) in 2012, and coronavirus associated with severe acute respiratory syndrome (SARS-CoV) in 2003. This trajectory suggests that further improvements to epidemic forecasting will be important for global public health. Exploring the value of new methodologies can help broaden the modeler’s toolkit to prepare for the next outbreak. In this study, we consider the role of transfer learning for pandemic response.

Transfer learning refers to a collection of techniques that apply knowledge from one prediction problem to solve another, often using machine learning and with many recent applications in domains such as computer vision and natural language processing. Transfer learning leverages a model trained to execute a particular task in a particular domain, in order to perform a different task or extrapolate to a different domain. This allows the model to learn the new task with less data than would normally be required, and is therefore well-suited to data-scarce prediction problems. The underlying idea is that skills developed in one task, for example the features that are relevant to recognize human faces in images, may be useful in other situations, such as classification of emotions from facial expressions. Similarly, there may be shared features in the patterns of observed cases among similar diseases.

The value of transfer learning for the study of infectious diseases is relatively under-explored. The majority of existing studies on diseases remain in the domain of computer vision and leverage pre-trained neural networks to make diagnoses from medical images, such as retinal diseases, dental diseases, or COVID-19. Coelho and colleagues (2020) explore the potential of transfer learning for disease forecasts. They train a Long Short-Term Memory (LSTM) neural network on dengue fever time series and make forecasts directly for two other mosquito-borne diseases, Zika and Chikungunya, in two Brazilian cities. Even without any data on the two target diseases, their model achieves high prediction accuracy four weeks ahead. Gautam (2021) uses COVID-19 data from Italy and the USA to build an LSTM transfer model that predicts COVID-19 cases in countries that experienced a later pandemic onset.

These studies provide empirical evidence that transfer learning may be a valuable tool for epidemic forecasting in low-data situations, though research is still limited. In this study, we aim to contribute to this empirical literature not only by comparing different types of knowledge transfer and forecasting algorithms, but also by considering two different pairs of endemic and novel diseases observed in Brazilian cities, specifically (i) dengue and Zika, and (ii) influenza and COVID-19. With an additional analysis on simulated time series, we hope to provide theoretical guidance on the selection of appropriate disease pairs, by better understanding how different characteristics of the source and target diseases affect the viability of transfer learning.

Zika and COVID-19 are two recent examples of novel emerging diseases. Brazil experienced a Zika epidemic in 2015–16 and the WHO declared a public health emergency of global concern in February 2016. Zika is caused by an arbovirus spread primarily by mosquitoes, though other transmission methods, including congenital and sexual have also been observed. Zika belongs to the family of viral hemorrhagic fevers and symptoms of infection share some commonalities with other mosquito-borne arboviruses, such as yellow fever, dengue fever, or chikungunya. Illness tends to be asymptomatic or mild but can lead to complications, including microcephaly and other brain defects in the case of congenital transmission.

Given the similarity of the pathogen and primary transmission route, dengue fever is an appropriate choice of source disease for Zika forecasting. Not only does the shared mosquito vector result in similar seasonal patterns of annual outbreaks, but consistent, geographically and temporally granular data on dengue cases is available publicly via the open data initiative of the Brazilian government.

COVID-19 is an acute respiratory infection caused by the novel coronavirus SARS-CoV-2, which was first detected in Wuhan, China, in 2019. It is transmitted directly between humans via airborne respiratory droplets and particles. Symptoms range from mild to severe and may affect the respiratory tract and central nervous system. Several variants of the virus have emerged, which differ in their severity, transmissibility, and level of immune evasion.

Influenza is also a contagious respiratory disease that is spread primarily via respiratory droplets. Infection with the influenza virus also follows patterns of human contact and seasonality. There are two types of influenza (A and B) and new strains of each type emerge regularly. Given the similarity in transmission routes and to a lesser extent in clinical manifestations, influenza is chosen as the source disease for knowledge transfer to model COVID-19.

For each of these disease pairs, we collect time series data from Brazilian cities. Data on the target disease from half the cities is retained for testing. To ensure comparability, the test set is the same for all models. Using this empirical data, as well as the simulated time series, we implement the following transfer models to make predictions.

  • Random forest: First, we implement a random forest model which was recently found to capture well the time series characteristics of dengue in Brazil. We use this model to make predictions for Zika without re-training. We also train a random forest model on influenza data to make predictions for COVID-19. This is a direct transfer method, where models are trained only on data from the source disease.
  • Random forest with TrAdaBoost: We then incorporate data from the target disease (i.e., Zika and COVID-19) using the TrAdaBoost algorithm together with the random forest model. This is an instance-based transfer learning method, which selects relevant examples from the source disease to improve predictions on the target disease.
  • Neural network: The second machine learning algorithm we deploy is a feed-forward neural network, which is first trained on data of the endemic disease (dengue/influenza) and applied directly to forecast the new disease.
  • Neural network with re-training and fine-tuning: We then retrain only the last layer of the neural network using data from the new disease and make predictions on the test set. Finally, we fine-tune all the layers’ parameters using a small learning rate and low number of epochs. These models are examples of parameter-based transfer methods, since they leverage the weights generated by the source disease model to accelerate and improve learning in the target disease model.
  • Aspirational baseline: We compare these transfer methods to a model trained only on the target disease (Zika/COVID-19) without any data on the source disease. Specifically, we use half the cities in the target dataset for training and the other half for testing. This gives a benchmark of the performance in a large-data scenario, which would occur after a longer period of disease surveillance.

The remainder of this paper is organized as follows. The models are described in more technical detail in Section 2. Section 3 shows the results of the synthetic and empirical predictions. Finally, Section 4 discusses practical implications of the analyses.

Access the full paper [via institutional access or paid download].

Coronavirus Update: Fall Boosters Could Have Bits of Omicron

[from ScienceNews Coronavirus Update, by Erin Garcia de Jesús]

For all the coronavirus variants that have thrown pandemic curve balls—including alpha, beta, gamma, deltaCOVID-19 vaccines have stayed the same. That could change this fall.

Yesterday, an advisory committee to the U.S. Food and Drug Administration met to discuss whether vaccine developers should update their jabs to include a portion of the omicron variant—the version of the coronavirus that currently dominates the globe. The verdict: The omicron variant is different enough that it’s time to change the vaccines. Exactly how is up in the air; the FDA still has to weigh in and decide what versions of the coronavirus will be in the shot.

“This doesn’t mean that we are saying that there will be boosters recommended for everyone in the fall,” Amanda Cohn, chief medical officer for vaccine policy at the U.S. Centers for Disease Control and Prevention said at the June 28 advisory meeting. “But my belief is that this gives us the right vaccine for preparation for boosters in the fall.”

The decision to update COVID-19 vaccines didn’t come out of nowhere. In the two-plus years that the coronavirus has been spreading around the world, it has had a few “updates” of its own—mutating some of its proteins that allow the virus to more effectively infect our cells or hide from our immune systems.

Vaccine developers had previously crafted vaccines to tackle the beta variant that was first identified in South Africa in late 2020. Those were scrapped after studies showed that current vaccines remained effective.

The current vaccines gave our immune systems the tools to recognize variants such as beta and alpha, which each had a handful of changes from the original SARS-CoV-2 virus that sparked the pandemic. But the omicron variant is a slipperier foe. Lots more viral mutations combined with our own waning immunity mean that omicron can gain a foothold in the body. And vaccine protection isn’t as good as it once was at fending off COVID-19 symptoms.

The shots still largely protect people from developing severe symptoms, but there has been an uptick in hospitalizations and deaths among older age groups, Heather Scobie, deputy team lead of the CDC’s Surveillance and Analytics Epidemiology Task Force said at the meeting. And while it’s impossible to predict the future, we could be in for a tough fall and winter, epidemiologist Justin Lessler of the University of North Carolina at Chapel Hill said at the meeting. From March 2022 to March 2023, simulations project that deaths from COVID-19 in the United States might number in the tens to hundreds of thousands.

A switch to omicron-containing jabs may give people an extra layer of protection for the upcoming winter. PfizerBioNTech presented data at the meeting showing that updated versions of its mRNA shot gave clinical trial participants a boost of antibodies that recognize omicron. One version included omicron alone, while the other is a twofer, or bivalent, jab that mixes the original formulation with omicron. Moderna’s bivalent shot boosted antibodies too. Novavax, which developed a protein-based vaccine that the FDA is still mulling whether to authorize for emergency use, doesn’t have an omicron-based vaccine yet, though the company said its original shot gives people broad protection, generating antibodies that probably will recognize omicron.

Pfizer and Moderna both updated their vaccines using a version of omicron called BA.1, which was the dominant variant in the United States in December and January. But BA.1 has siblings and has already been outcompeted by some of them.

Since omicron first appeared late last year, “we’ve seen a relatively troubling, rapid evolution of SARS-CoV-2,” Peter Marks, director of the FDA’s Center for Biologics Evaluation and Research in Silver Spring, Maryland, said at the advisory meeting.

Now, omicron subvariants BA.2, BA.2.12.1, BA.4 and BA.5 are the dominant versions in the United States and other countries. The CDC estimates that roughly half of new U.S. infections the week ending June 25 were caused by either BA.4 or BA.5. By the time the fall rolls around, yet another new version of omicron—or a different variant entirely—may join their ranks. The big question is which of these subvariants to include in the vaccines to give people the best protection possible.

BA.1, the version already in the updated vaccines, may be the right choice, virologist Kanta Subbarao said at the FDA meeting. An advisory committee to the World Health Organization, which Subbarao chairs, recommended on June 17 that vaccines may need to be tweaked to include omicron, likely BA.1. “We’re not trying to match [what variants] may circulate,” Subbarao said. Instead, the goal is to make sure that the immune system is as prepared as possible to recognize a wide variety of variants, not just specific ones. The hope is that the broader the immune response, the better our bodies will be at fighting the virus off even as it evolves.

The variant that is farthest removed from the original virus is probably the best candidate to accomplish that goal, said Subbarao, who is director of the WHO’s Collaborating Center for Reference and Research on Influenza at the Doherty Institute in Melbourne, Australia. Computational analyses of how antibodies recognize different versions of the coronavirus suggest that BA.1 is probably the original coronavirus variant’s most distant sibling, she said.

Some members of the FDA advisory committee disagreed with choosing BA.1, instead saying that they’d prefer vaccines that include a portion of BA.4 or BA.5. With BA.1 largely gone, it may be better to follow the proverbial hockey puck where it’s going rather than where it’s been, said Bruce Gellin, chief of Global Public Health Strategy with the Rockefeller Foundation in Washington, D.C. Plus, BA.4 and BA.5 are also vastly different from the original variant. Both BA.4 and BA.5 have identical spike proteins, which the virus uses to break into cells and the vaccines use to teach our bodies to recognize an infection. So when it comes to making vaccines, the two are somewhat interchangeable.

There are some real-world data suggesting that current vaccines offer the least amount of protection from BA.4 and BA.5 compared with other omicron subvariants, Marks said. Pfizer also presented data showing results from a test in mice of a bivalent jab with the original coronavirus strain plus BA.4/BA.5. The shot sparked a broad immune response that boosted antibodies against four omicron subvariants. It’s unclear what that means for people.

Not everyone on the FDA advisory committee agreed that an update now is necessary—two members voted against it. Pediatrician Henry Bernstein of Zucker School of Medicine at Hofstra/Northwell in Uniondale, N.Y., noted that the current vaccines are still effective against severe disease and that there aren’t enough data to show that any changes would boost vaccine effectiveness. Pediatric infectious disease specialist Paul Offit of Children’s Hospital of Philadelphia said that he agrees that vaccines should help people broaden their immune responses, but he’s not yet convinced omicron is the right variant for it.

Plenty of other open questions remain too. The FDA could authorize either a vaccine that contains omicron alone or a bivalent shot, although some data hinted that a bivalent dose might spark immunity that could be more durable. Pfizer and Moderna tested their updated shots in adults. It’s unclear what the results mean for kids. Also unknown is whether people who have never been vaccinated against COVID-19 could eventually start with such an omicron-based vaccine instead of the original two doses.

Maybe researchers will get some answers before boosters start in the fall. But health agencies need to make decisions now so vaccine developers have a chance to make the shots in the first place. Unfortunately, we’re always lagging behind the virus, said pediatrician Hayley Gans of Stanford University. “We can’t always wait for the data to catch up.”

COVID-19 and “Naïve Probabilism”

[from the London Mathematical Laboratory]

In the early weeks of the 2020 U.S. COVID-19 outbreak, guidance from the scientific establishment and government agencies included a number of dubious claims—masks don’t work, there’s no evidence of human-to-human transmission, and the risk to the public is low. These statements were backed by health authorities, as well as public intellectuals, but were later disavowed or disproven, and the initial under-reaction was followed by an equal overreaction and imposition of draconian restrictions on human social activities.

In a recent paper, LML Fellow Harry Crane examines how these early mis-steps ultimately contributed to higher death tolls, prolonged lockdowns, and diminished trust in science and government leadership. Even so, the organizations and individuals most responsible for misleading the public suffered little or no consequences, or even benefited from their mistakes. As he discusses, this perverse outcome can be seen as the result of authorities applying a formulaic procedure of “naïve probabilism” in facing highly uncertain and complex problems, and largely assuming that decision-making under uncertainty boils down to probability calculations and statistical analysis.

This attitude, he suggests, might be captured in a few simple “axioms of naïve probabilism”:

Axiom 1: more complex the problem, the more complicated the solution.

This idea is a hallmark of naïve decision making. The COVID-19 outbreak was highly complex, being a novel virus of uncertain origins, and spreading through the interconnected global society. But the potential usefulness of masks was not one of these complexities. The mask mistake was consequential not because masks were the antidote to COVID-19, but because they were a low cost measure the effect of which would be neutral at worst; wearing a mask can’t hurt in reducing the spread of a virus.

Yet the experts neglected common sense in favor of a more “scientific response” based on rigorous peer review and sufficient data. Two months after the initial U.S. outbreak, a study confirmed the obvious, and masks went from being strongly discouraged to being mandated by law. Precious time had been wasted, many lives lost, and the economy stalled.

Crane also considers another rule of naïve probabilism:

Axiom 2: Until proven otherwise, assume that the future will resemble the past.

In the COVID-19 pandemic, of course, there was at first no data that masks work, no data that travel restrictions work, no data of human-to-human transmission. How could there be? Yet some naïve experts took this as a reason to maintain the status quo. Indeed, many universities refused to do anything in preparation until a few cases had been detected on campus—at which point they had some data, as well as hundreds or thousands of other as yet undetected infections.

Crane touches on some of the more extreme examples of his kind of thinking, which assumes that whatever can’t be explained in terms of something that happened in the past is speculative, non-scientific and unjustifiable:

“This argument was put forward by John Ioannidis in mid-March 2020, as the pandemic outbreak was already spiralling out of control. Ioannidis wrote that COVID-19 wasn’t a ‘once-in-a-century pandemic,’ as many were saying, but rather a ‘once-in-a-century data-fiasco’. Ioannidis’s main argument was that we knew very little about the disease, its fatality rate, and the overall risks it poses to public health; and that in face of this uncertainty, we should seek data-driven policy decisions. Until the data was available, we should assume COVID-19 acts as a typical strain of the flu (a different disease entirely).”

Unfortunately, waiting for the data also means waiting too long, if it turns out that the virus turns out to be more serious. This is like waiting to hit the tree before accepting that the available data indeed supports wearing a seatbelt. Moreover, in the pandemic example, this “lack of evidence” argument ignores other evidence from before the virus entered the United States. China had locked down a city of 10 million; Italy had locked down its entire northern region, with the entire country soon to follow. There was worldwide consensus that the virus was novel, the virus was spreading fast and medical communities had no idea how to treat it. That’s data, and plenty of information to act on.

Crane goes on to consider a 3rd axiom of naïve probabilism, which aims to turn ignorance into a strength. Overall, he argues, these axioms, despite being widely used by many prominent authorities and academic experts, actually capture a set of dangerous fallacies for action in the real world.

In reality, complex problems call for simple, actionable solutions; the past doesn’t repeat indefinitely (i.e., COVID-19 was never the flu); and ignorance is not a form of wisdom. The Naïve Probabilist’s primary objective is to be accurate with high probability rather than to protect against high-consequence, low-probability outcomes. This goes against common sense principles of decision making in uncertain environments with potentially very severe consequences.

Importantly, Crane emphasizes, the hallmark of Naïve Probabilism is naïveté, not ignorance, stupidity, crudeness or other such base qualities. The typical Naïve Probabilist lacks not knowledge or refinement, but the experience and good judgment that comes from making real decisions with real consequences in the real world. The most prominent naïve probabilists are recognized (academic) experts in mathematical probability, or relatedly statistics, physics, psychology, economics, epistemology, medicine or so-called decision sciences. Moreover, and worryingly, the best known naïve probabilists are quite sophisticated, skilled in the art of influencing public policy decisions without suffering from the risks those policies impose on the rest of society.

Read the paper. [Archived PDF]

BEA News: Gross Domestic Product (Third Estimate), Corporate Profits, and GDP by Industry, Fourth Quarter and Year 2021

(from the U.S. Bureau of Economic Analysis)

The U.S. Bureau of Economic Analysis (BEA) has issued the following news release today:

Real gross domestic product (GDP) increased at an annual rate of 6.9 percent in the fourth quarter of 2021, following an increase of 2.3 percent in the third quarter. The increase was revised down 0.1 percentage point from the “second” estimate released in February. The acceleration in the fourth quarter was led by an acceleration in inventory investment, upturns in exports and residential fixed investment and an acceleration in consumer spending. In the fourth quarter, COVID-19 cases resulted in continued restrictions and disruptions in the operations of establishments in some parts of the country. Government assistance payments in the form of forgivable loans to businesses, grants to state and local governments, and social benefits to households all decreased as provisions of several federal programs expired or tapered off.

Profits from current production (corporate profits with inventory valuation and capital consumption adjustments) increased $20.4 billion in the fourth quarter, compared with an increase of $96.9 billion in the third quarter.

Private goods-producing industries increased 5.4 percent, private services-producing industries increased 8.5 percent, and government increased 0.1 percent. Overall, 19 of 22 industry groups contributed to the fourth-quarter increase in real GDP.

Read the full report [Archived PDF].

Germany-Watching: Economics (U.S. Money Market Fund Reform)

from Deutsche Bundesbank Eurosystem’s Bundesbank Research Centre:

You Can’t Always Get What You Want (Where You Want It): Cross-Border Effects of the U.S. Money Market Fund Reform [PDF]

Authors: Daniel Fricke, Stefan Greppmair, Karol Paludkiewicz

Non-technical summary
Research Question

Money market funds (MMFs) are an important part of the growing segment of non-bank financial intermediaries. This paper contributes to this literature by analyzing the cross-border effects of the 2014 U.S. MMF reform, which was implemented several years prior to the EU Regulation. We study whether euro area MMFs received inflows as a consequence of the reform and investigate the (unintended) economic effects on the basis of the non-synchronized implementation dates of the regulatory changes in the U.S. and the EU.

Contribution

To the best of our knowledge, we are the first to examine the cross-border effects of the 2014 U.S. MMF reform. Prior work has shown that the reform led to a substantial decline of the institutional prime segment in the U.S. (MMFs that invest primarily in non-sovereign debt instruments). Moreover, these funds increased their risk-taking due to the increased competition and newly imposed liquidity restrictions left these funds more prone to large outflows (run risks).

Results

We document both positive and negative effects of the U.S. reform on institutional MMFs in the euro area. These funds, particularly those from the prime segment, experienced substantial inflows from foreign investors around the implementation of the U.S. reform and we show that these cross-border flows were largely motivated by the search for money-like instruments. While euro area MMFs reduced their risk-taking, the industry as a whole has become more concentrated and possibly more exposed to run risks. This risk materialized in the COVID-19 induced stress period during which these funds faced large outflows by foreign investors.

Read the full discussion paper [archived PDF].