Tag: Wuhan

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.

Science-Watching: Proton-Coupled Energy Transfer/Omicron Spike Function

[from Science, First Release Notification for July 21]

Proton-Coupled Energy Transfer in Molecular Triads

Abstract

A photochemical mechanism was experimentally discovered and denoted proton-coupled energy transfer (PCEnT). A series of anthracene–phenol–pyridine triads formed the local excited anthracene state after light excitation at ca. 400 nm, which led to fluorescence around 550 nm from the phenol–pyridine unit. Direct excitation of phenol–pyridine would have required light around 330 nm, but the coupled proton transfer within the phenol–pyridine unit lowered its excited state energy so that it could accept excitation energy from anthracene. Singlet-singlet energy transfer thus occurred despite the lack of spectral overlap between the anthracene fluorescence and the phenol–pyridine absorption. Moreover, theoretical calculations indicated negligible charge transfer between the anthracene and phenol–pyridine units. PCEnT was suggested as an elementary reaction of possible relevance to biological systems and future photonic devices.

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

Omicron Spike Function and Neutralizing Activity Elicited by a Comprehensive Panel of Vaccines

Abstract

The SARS-CoV-2 Omicron variant of concern comprises several sublineages with BA.2 and BA.2.12.1 having replaced the previously dominant BA.1, and BA.4 and BA.5 increasing in prevalence worldwide. We show that the large number of Omicron sublineage spike mutations lead to enhanced ACE2 binding, reduced fusogenicity, and severe dampening of plasma neutralizing activity elicited by infection or seven clinical vaccines relative to the ancestral virus. Administration of a homologous or heterologous booster based on the Wuhan-Hu-1 spike sequence markedly increased neutralizing antibody titers and breadth against BA.1, BA.2, BA.2.12.1, and BA.4/5 across all vaccines evaluated. Our data suggest that although Omicron sublineages evade polyclonal neutralizing antibody responses elicited by primary vaccine series, vaccine boosters may provide sufficient protection against Omicron-induced severe disease.

Read the full paper. [Archived PDF]

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].