Data Journalism

Driven to the edge of chaos and distraction

How emerging technologies could help satisfy AI’s hunger for power and make algorithms more ethical

Photo by Tara Winstead from Pexels

Artificial intelligence has power and secrecy problems, encapsulated by an insatiable appetite for data, energy, and deep-learning models producing inscrutable, unaccountable outputs. A glimmer of hope that AI’s environmental and ethical impact could be counteracted comes from a confluence of emerging technologies.

AI research investment rose steadily since 2010, with a noticeable upward trend from India, China, and Korea before the pandemic struck.

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AI’s data diet

Artificial intelligence research is also driving its increasingly ravenous appetite for data and power.

According to the International Energy Agency, demand for data is “rising exponentially”, causing a rapid expansion of global data centres. Alongside power-hungry AI systems, estimated to have a substantial environmental impact, the combined effects are significant.

Two of the top three countries who have invested most heavily in AI research since 2010 also have the lowest proportion of power stations using non-renewable fuel.

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Over 8000 data centres crunch the world’s data, according to a briefing for the United States International Trade Commission using proprietary figures from Cloudscene, an Australian data intelligence firm.

A study of global data centre energy consumption shows how comprehensive, accessible energy consumption figures remain elusive. Due to a lack of location data, and “extrapolation” based models, current estimates lack certainty.

This data gap led me to create estimates based on my own analysis of publicly available data. Though this imperfect snapshot gave conspicuously low figures for some industry giants (e.g. Amazon, Google, et al.), it provided insight into some front-runners like Digital Realty and Equinix.

3 companies have over 100 data centres | Get the data here

Digital Realty leads the USA by virtue of building new data centres meeting the Environmental Protection Agency’s Energy Star rating.

Based on Cloudscene’s figures and official EPA data however, only around 7% of US data centres have this rating. 

Digital Realty also had the highest number of hyperscale data centres with at least 5000 servers spread over 10000 square feet.

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AI’s explosive growth means demand for servers with “high-end” Graphical Processing Units has “skyrocketed”, according to Telehouse.

Though not the dominant industry processor of choice, a significant uptick in GPUs dedicated to AI has led to uncertainty about their projected power footprint.

Emerging technology

One innovative AI technology which could mitigate environmental concerns is called reservoir computing at the “edge of chaos”.

Joel Hochstetter, a postgraduate student at the University of Sydney, Australia, introduced his team’s research into novel artificial neural networks, seeking to mimic the human brain, in a recent article commissioned by his university. I spoke with him to find out more about their work and its potential applications.

So essentially I study networks of tiny silver wires….when we stimulate these networks with electricity, we see…interesting electrical switching phenomena.

One of the aims of the work which we’re trying to harness are interesting dynamical behaviours…for…information processing tasks in a framework known as reservoir computing.”

Reservoir computing is startlingly diverse and due to its “low training cost”, and “fast learning” processes compared to conventional neural networks, could revolutionise artificial intelligence.

So if you think about a conventional, artificial neural network you have…layers and they’re fully connected. Then you have some inputs at the start and outputs at the end, and you have weights between the layers.

The way that you train these neural networks is that you go through and kind of fiddle with all the weights and go back and forth through the neural network, to try and optimise some kind of cost function, or basically get the highest accuracy you can.”

Alongside removing this intensive training process, a reservoir computer evolves autonomously “by its own intrinsic dynamics”, and is akin to the “last layer in a neural network.”

Experimental forms of reservoir computing show promise for substantial power efficiency savings with tasks like image classification. One study demonstrated a hundred-fold improvement over conventional neural networks with an equivalent level of accuracy.

I can’t give you any numbers…but if you think about how your regular kind of neural network has many dense layers, then it’s very computationally intensive.”

“If you do this on a regular computer, then you’re limited by…this…Von Neumann bottleneck, where your CPU is passing information between RAM and processing. Because you’re having to access the memory and then operate on [it], that slows you down.”

Transistors with memories

One promising technology being developed which might exceed contemporary computing limitations, whilst reducing AI’s carbon footprint, is called a “memristor”.

So essentially a memristor is…like an electrical device that has a memory of past stimuli. It’s different to a transistor because, for a transistor, if you turn all the voltages off everything is forgotten. 

In terms of energy efficiency these memristor devices overcome this Von Neumann bottleneck that I mentioned because processing and memory occurs at the same location. So you no longer have to pass information back and forth to do processing.”

Industry has yet to exploit the nascent technology beyond “speeding up existing algorithms”.

Commercially, I think companies like IBM would be doing research into this but no one’s using this technology yet, because it’s limited by the material side in terms of – it’s not quite reliable enough yet – but it’s promising.”

An almost chaotic solution

Joel’s team discovered that their memristive system performs more effectively when pushed to the brink of disorder, a delicate equilibrium between stability and instability known in scientific literature as the “edge of chaos“.

Essentially it’s been hypothesised that, for a wide range of dynamical systems… – like brains or gene regulatory networks for example – being close to what’s called the edge of chaos or criticality, where you’re near some kind of phase transition between two different, completely distinct regimes, might give you optimal performance in different ways.

If you think about a regime where the system is very ordered, [it is]…predictable and many parts of the system are either doing the same thing, or they’re not doing anything at all. By contrast, in a regime where a dynamical system might be chaotic, different parts of the system are uncorrelated and…all over the place in how they’re working. Somewhere in the middle between this kind of unpredictable chaotic regime, and this kind of ordered, slowly changing system, you might be able to have the greatest…coherence…complexity and randomness.”

Memristive technologies driven to the edge of chaos could be applied to myriad complex computational tasks, providing solutions to managing the relentless rise of big data and increasingly sophisticated, power-hungry AI.

Joel shared what he hoped the technology might be used for in the future:

Looking forward to the next 15-20 years…predicting chaotic or unpredictable time series. For example, if you were trying to predict the weather or the stock market. Another thing that we might be able to do with these networks is to handle other kinds of streaming data, like video data.”

I asked Joel if he thought it could dramatically improve the way data centres currently function.

Definitely. I think so. Once we accomplish the initial challenges then I think looking long-term this would be the hope: That we could process large data sets, like you described.”

Unlocking AI’s black box

As AI becomes ubiquitous, experts have questioned the trustworthiness of automated systems underpinned by opaque algorithms, which are steeped in hidden biases but so complex that they are inscrutable to the researchers deploying them.

This has sparked controversy, like Google’s recent decision to censor critical research by Dr Timnit Gebru, former co-lead for ethical AI research, who co-authored a paper looking at the “environmental and ethical implications of large-scale AI language models”. The situation raised several eyebrows.

I spoke to David Morales, a Phd student at the university of Granada working on “AI explainability for machine learning algorithms”, whose team discovered a new method of distracting image classification algorithms to make them more transparent using “visual explanation techniques”.

The problem with machine learning algorithms is that they are known as black boxes because the algorithm gets an input, and you get an output, but you…don’t know why the algorithm made that decision.”

“Visual explanation techniques try to explain machine learning algorithms.”

“So…usually the algorithm learns by itself how to classify an image. We modified this training process in order to force the algorithm to discover new features and…regions of interest.”

Though requiring human intervention whilst running a “classical” algorithm, before using the new technique to query the results, this could be simplified to improve efficiency.

“Data scientists have to learn to improve visual explanation techniques…to develop new deep learning…and…machine learning models to make them more trustworthy and interpretable.”

New algorithms could be more accountable and energy efficient, whilst enabling humanity to learn from AI.

“At the end it’s just one algorithm…not two, so I think the energy [footprint] will be much lower.”

“We can learn to perform many tasks in a better way if we can see how artificial intelligence resolved these problems.”

Get involved

The “pernicious effect” of AI bias is being tackled by organisations like the USA’s National Institute of Standards and Technology, who are about to publish a special paper on the topic, aiming to identify and manage AI biases whilst improving trust in algorithms. NIST wants public input on their proposals, which can be submitted by “completing the template form (in Excel format) and sending it to“[𝟏]


[𝟏] Link will open on the NIST website. Author has no affiliation to NIST.

Data Journalism, Journalism

NHS Staff at breaking point, beds running out, backlogs, delays, and Brexit.

What does the future hold for the NHS?

Urgent and emergency care situation reports show how the NHS almost ran out of critical care beds during the second wave of the pandemic. Hospitals in London reached 95% of their capacity by January 2021, closely followed by the South East and the East of England.

The situation was perilously close to being an unmitigated disaster as the second national lockdown began across the UK. Yet many of the pressures faced by the NHS were predicted in advance.

Five Coronavirus spikes piercing the NHS

In early 2020, a study by the Health Foundation predicted five distinct areas of impact linked to the pandemic.

Severe illness & Death

The authors considered how the direct impact of Covid-19, measured in terms of severe illness, hospitalisations, and deaths, would almost break the NHS. The latest data back up their grim forecast.

Dr Alex Stockdale, NIHR Academic Clinical Lecturer in Clinical Infection, Microbiology & Immunology at the University of Liverpool, who worked on Covid wards during the first wave, said:

“Everybody was really afraid. Non-infection specialists particularly, because they hadn’t really dealt with infectious diseases. All of us had quite profound insomnia, lying awake at night worrying ‘is it going to be me next’?”

Existing socioeconomic inequalities were also exacerbated by the pandemic, with black and minority ethnic communities disproportionately affected

Acute system shake up

Acute care was also expected to suffer from decreased capacity, caused by the massive surge in demand of Covid-19. This transpired as thousands of people were discharged to free up beds for Covid patients, staff were redeployed en masse, enormous numbers of operations were cancelled, and GP appointments increasingly took place remotely.

Non-covid related hospitalisations fell dramatically during the first lockdown, with accident and emergency admissions dropping by 31.4% overall compared to 2019, according to a recent data release by NHS Digital. Around 45% fewer Children aged 0-4, and 21-27% fewer young adults aged 18-25 were admitted to hospitals than the previous year.

Non-acute care and GP clinics

Non-urgent, routine healthcare normally dealt with by GPs was also dramatically impacted by the pandemic. Cancer Research repeatedly raised concerns about the relative lack of early cancer diagnoses, which is likely to lead to adverse outcomes.

Dr Stockdale echoed their concerns: 

“What we’re seeing now is lots of late diagnoses of cancer. That’s going to be the biggest shadow: Cancer related death over the next 24 months.”  

Cancer surgery has also dropped since the first lockdown, meaning that many urgent referrals received alternative treatments like radiotherapy.

Remote GP appointments increased significantly during the pandemic. According to NHS data, between May and July 2020 almost 50% of all appointments were conducted by telephone, exceeding the number of face to face appointments. Previously, from October 2019 until March 2020, in-person appointments accounted for 80% of the total.

A GP from Kent, who spoke on condition of anonymity, said: 

Remote appointments can actually take longer than seeing someone face to face, because…you can’t pick up…non-verbal cues and use your intuition.”

“There is…technology to ask patients to send images…for example of a rash. Sometimes this can be a real time saver, but other times…you have to [see them] face to face…to get a clear view.”

They also worried about elderly, deprived, and vulnerable patients “missing out on access because of digital exclusion.”

A report by Health Watch concurred that “many people [were] struggling to access care”, and found the lack of clear communication about changes to the appointments system “frustrating” and “confusing”.

Delays and failures

Research by the British Medical Journal showed a “record high of 4.46 million” patients were awaiting planned treatment in November 2020, and “2.3 million people” were still “waiting for surgical care” earlier this year.

Repurposed wards, operating theatres and outpatient clinics, staff redeployment, absence due to sickness or self-isolation, protective protocols slowing down hospital treatment, and “delays or failures in patient testing”, were all cited as possible contributing factors to increased waiting times.

A decimated & demoralised workforce 

A report by the Health Foundation examined how perennial staff shortages hampered the NHS’ pandemic response. Despite a recruitment drive there were over 80000 staff vacancies in June 2020. Almost 50% of these were nursing jobs, keeping the UK below the OECD average for nurses per capita.

Evidence also suggests that NHS staff have increased levels of anxiety, depression, stress, and PTSD. Female nurses and ethnic minority workers were particularly affected, with the latter having “50% greater risk” of “high PTSD symptoms”, and being significantly more worried about getting Covid-19 than their white counterparts.

The NHS Staff survey 2020 provided a parallel perspective. Overall, 44% of staff reported “feeling unwell” due to “work related stress” during the pandemic, an increase of 10% from 2019.

Post-pandemic recovery in the shadow of Brexit

A report by the Nuffield Trust outlined the complexity of Brexit for the NHS. From the prospect of investor-state dispute clauses in the controversial Trans-Pacific trade deal allowing private companies to challenge public health regulation, to rising medicine prices, changes in the mutual recognition of qualifications affecting NHS recruitment, UK immigration rules ensuring that social care applicants cannot pass minimum “salary or skills thresholds” for visas, foreign nurses finding it harder to accept jobs, data sharing disruption, and the burgeoning crisis in Northern Ireland, the future looks fraught with problems.

Alongside issues related to Brexit, the NHS is on the verge of integrated care systems reshaping the current model of healthcare. New partnerships between NHS providers and local government are set to be fully operational by April 2022. The proposed changes have been met with scepticism.

What next for the NHS? 

Coronavirus shows no sign of disappearing in the near future. A “chronically underfunded” and understaffed NHS and social care system looks likely, at the very least, to have to maintain a delicate balance between:  

And much more besides. 

As the pandemic ebbs and flows these challenges represent the tip of an increasingly unfathomable iceberg. The question is whether the British government, healthcare providers, and key stakeholders can work together to steer the NHS Titanic through the tumultuous, storm-tossed waves threatening to sink the ship before any lifeboats can be released. 

Photo by Markus Spiske from Pexels

Data Journalism

Unsolved and unsafe: Police forces across England and Wales struggling to tackle 8 out of 10 crimes as Greater Manchester Police found to be failing victims

The vast majority of all recorded crimes have not resulted in any suspects being charged since November 2017, according to an exclusive new analysis of police data.

Police in England and Wales have consistently failed to bring criminals to justice over the past three years, with many forces closing over 80% of investigations without any suspects being identified or prosecuted.

A significant number have charged suspects in fewer than 10% of cases.

The new analysis also shows little to no improvement has occurred during the pandemic, prior to which a YouGov survey revealed that Britons already expressed staggeringly low levels of confidence in the police’s ability to solve crime.

A recent resident of Chorlton-cum-Hardy in Manchester shared his experience of becoming a victim of crime, and his lack of confidence in the police’s ability to help, after moving there from a “quiet, peaceful village” in Cambridgeshire.

Manchester cityscape during a storm \ Image by Paul Rhoades from Pixabay

Ryan Buxton, a musician and guitar tutor, moved to Manchester in the spring of 2018 with a sense of excitement and expectation of encountering a vibrant city with a “rip-roaring” music scene reminiscent of “New York in the 1980s”, which he imagined would have been “terrible and terrifying in some respects”, but also “very exciting, very interesting in others.”

“In my head I’d painted this romantic and slightly intimidating picture of Manchester being this kind of big industrial place…you know, like you have to really be on your toes, being mugged all the time, or worse, I suppose.”

Shortly after arriving in the area, Ryan quickly found himself subjected to bicycle theft, criminal damage, and attempted burglary.

We did have a bike stolen from our lock-up and the new neighbours below us had both of their bikes stolen when they moved in.

I had my car keyed in town and there was no obvious reason why.”

He decided not to report these crimes to the police, as he felt that they would have “much bigger things on their mind.”

I can’t imagine a huge metropolitan police force having time for bike theft.

Just seemed like a process that was probably pointless I guess.”

He also recalled the experience of a friend who was out busking in the city centre.

“My friend Olly had his headphones stolen. He was walking down the street towards Piccadilly. I think it was during the day, to be honest, and somebody on their bike had cycled past and just taken them off his head.[They were] five miles away before he’d had a chance to do anything about it.

You don’t make your intention to be [near Piccadilly] after a certain time in my opinion. You don’t want to invite anything and if you were to be there at that time of night, maybe you’d be increasing your chances of something happening like that.”

Despite being affected by the sobering realities of inner city crime, Ryan still found it difficult to reconcile his daily experience of Manchester with the more disturbing headlines he often read in the local newspaper.

“Something I would say I’ve noticed is the stories that you hear tend to be quite shocking, I suppose. I find it hard to square what I see on a day to day basis with what I hear about happening in Rochdale or Oldham, other places like that…”

Greater Manchester Police were recently placed into special measures due to their failure to record thousands of reported crimes.

One person castigated the force on social media, claiming that they had ignored his pleas for help following a robbery in December 2020:

Yet long before they were placed into special measures, Greater Manchester Police had significant problems finding suspects to charge.

Of the top five police forces with the highest unsolved crime rate, Greater Manchester Police recorded the biggest rise from November 2017 until the middle of 2019. Beyond this point they failed to accurately record crime and outcomes data, leading to a rapid decline in numbers.

The dramatic increase in investigations being closed without suspects raises serious questions about the force’s real success rate.

Try selecting 1-2 forces / Scores vs Ranks

According to the ONS, their inability to record crimes was initially due to “the implementation of a new IT system”, which meant that the force were “unable to supply data for the period July 2019 to March 2020.” 

Yet the new data analysis shows that from multiple perspectives, Greater Manchester Police were already struggling to keep a lid on crimes prior to this point.

Other people who claimed to have been victims of criminal activity in Greater Manchester shared their perspectives with harrowing stories of being deserted by the police when they tried to report crimes, and potentially life-threatening situations, shortly after the Manchester Evening News first broke the story of the force being placed in special measures.

The Mayor of Greater Manchester, Andy Burnham, and Bev Hughes, Greater Manchester’s Deputy Mayor for Policing, Crime and Criminal Justice, suggested that the pressures of the pandemic played a significant role in the force’s subsequent issues with data collection.

A recent report by Her Majesty’s Inspectorate of Constabulary and Fire and Rescue Services said that the force’s litany of problems included:

“Failing to identify, record and investigate around one in four reports of violent crime and to safeguard victims of many of these crimes. This includes behavioural crimes, such as harassment, stalking and coercive controlling behaviour, crimes amounting to domestic abuse and those reported by other agencies involving vulnerable adults and children…

Wrongly and prematurely closing substantial numbers of recorded crime investigations, including a high proportion of crimes involving vulnerable victims, as not supported by the victim, but without the evidence to show this to be the case.”

Greater Manchester Police, the Police Federation, and Greater Manchester Combined Authority were all approached for an interview but declined to comment. 

These withering assessments of police forces in England and Wales have coincided with a report in the Guardian which claims that police forces in England and Wales may face new crime reduction targets “in return for government providing the money for 20,000 new officers”, a flagship Conservative manifesto promise dubbed “Safer Streets” by Boris Johnson.

Yet the push to incentivise police forces to reduce the number of recorded crimes, especially “Homicide and serious violence”, in return for funding which has already been pledged, makes no mention of how unsuccessful they have been at dealing with current levels of crime.

Violence and sexual offences were the most frequently recorded crimes in England, Wales, and Northern Ireland during the past three years, followed by criminal damage and arson.

Cambridgeshire Constabulary, Greater Manchester, West Midlands, Bedfordshire, and West Yorkshire Police, had the highest number of unsolved crimes across all categories in England and Wales, with each force closing over 84% of cases without managing to identify or prosecute any suspects.

The Metropolitan Police also showed a remarkable rise in the total number of unsolved crimes in 2020, bucking the trend set by other police forces, who saw crime levelling off or dipping slightly as the pandemic took hold.

*Crime type data is taken from police “street crime” datasets, which are separate from their “outcomes” datasets, as the latter lack any data with respect to crime types. This means the final figures for “unsolved” crimes will be different (usually higher) than the “street crime” values above.

By contrast, Greater Manchester Police appeared to go in the opposite direction due to the significant issues they have had with data collection.

With the true picture of criminal activity in Greater Manchester over the past two years all but erased by the force’s negligence, the impact of crime upon tens of thousands of victims is impossible to accurately measure.

Featured photo by Anete Lusina from Pexels

Full methodology for the data analysis conducted using RStudio is available on Github. For outcomes data analysis, click here. For street crime data analysis, click here.

Data Journalism

Revealed: 80% of UK adults ready to trust a coronavirus vaccine

Why herd immunity could be achievable despite anti-vax attitudes


Source: Alexander Koch on

Three recent studies suggest a substantial majority of UK adults are ready to be vaccinated against Covid-19, making herd immunity possible, and potentially banishing the coronavirus pandemic to the realm of bad memories and fever-dreams. If 79% of UK adults receive an effective vaccine, this would match or surpass the hypothetical threshold scientists expect a population typically needs in order to defeat a disease like Coronavirus.

Aneesh Thakur, assistant professor of vaccine design and delivery at the university of Copenhagen, though quick to caution against making generalisations, explained that the ‘R’ number has a crucial role to play in how much of the population needs to be vaccinated to ensure success:

Assuming that on average [the ‘R’ number] is 2.5-3, then around 70% of the population should be vaccinated to get herd immunity in order to prevent further spread within the population. We cannot generalise, but that is a theoretical estimation.” 

– Professor Aneesh Thakur, university of Copenhagen, September 2020.

It appears that a substantial proportion of the UK public is ready to put their trust in a vaccine, meaning that it would be possible to meet this theoretical threshold. Comparing data from surveys conducted by King’s College London, YouGov, and University College London, a clear pattern of positive attitudes to taking a coronavirus vaccine emerges. In contrast to widespread media coverage of anti-vax attitudes in the national press, most recently in response to the figures released by UCL, a significant majority of respondents signalled that they were ready to get vaccinated:

Survey results per study – KCL / YouGov / UCL:

UCL survey did not offer respondents a “don’t know” option

Chart: Miguel Roca | Sources (click to Getthedata): KCL / YouGov / UCL | Fri Sep 25 2020

UCL Covid-19 Social Attitudes Survey:

The largest and most recent dataset shown above is the landmark study conducted by University College London, sponsored by the Nuffield foundation, which has been tracking the psychological and social impact of the pandemic on a weekly basis since the original Coronavirus lockdown began. Their evidence overwhelmingly suggests that “on balance”, a significant majority of UK adults have a positive attitude to taking a Coronavirus vaccine. Nearly 80% of UCL survey respondents, taken from a sample of over 70000 people, said that they were very likely, moderately likely, or more likely than not to take a safe, effective vaccine against Covid-19:

“Positive/negative” = varying degrees of how likely/unlikely people thought they would be to take a vaccine.

Chart: Miguel Roca |Fri 25 Sep 2020 | Getthedata

Herd Immunity:

As confirmed cases of Coronavirus rise exponentially across the UK and parts of Europe, society must pin its hopes of stopping the pandemic on an effective vaccine. Estimates for a successful vaccination strategy which could lead to safe and effective herd immunity range from between 43% and 67% of the global population, meaning that an 80% vaccination rate should comfortably meet the required target to put the brakes on the pandemic.

Whilst the estimated “threshold” for herd immunity differs considerably between different diseases, and exists within a hypothetical range, if a sufficiently high proportion of the UK adult population were immunised against Covid-19, it should comfortably match the threshold for related diseases such as SARS1 and influenza:

Chart: Miguel Roca | Sources (click to Getthedata): Statista / IJRR / Harvard / Our World in Data | Wed Sep 23 2020

Although some scientists are cautiously optimistic about the possibility of a mass immunisation program, provided that the vaccines are highly effective against the virus, others remain sceptical and caution against making unsubstantiated predictions about vaccine-induced herd immunity to Covid-19. Dr Alexander Stockdale, NIHR Academic clinical lecturer in clinical infection, microbiology and immunology at the university of Liverpool, stressed the need to resist jumping to conclusions in the absence of real world data:

The level of herd immunity necessary for COVID-19 control is unknown given that we haven’t got a vaccine with evaluable data and these estimates rest on a number of assumptions yet to be validated. I don’t think we could say as such there is scientific consensus at all as these are predictive models not actual data. 

For example, the WHO has suggested a threshold of 50% disease risk reduction for approval of a candidate vaccine. There is a debate about whether disease reduction would translate to a reduction in transmission given that the type of immunity induced by vaccination may not be sterilising, i.e. it might reduce severe disease but not necessarily reduce transmission to the same degree. 

Answering a related question on how effective a vaccine would need to be in order to halt the pandemic if it were administered to 70-80% of the population, Dr Stockdale said:

In general terms vaccine coverage must be higher if efficacy is lower. I cannot provide an estimate as there are still many unknowns here – the proof is in the pudding and evaluation of this must wait for the approval of a vaccine and publication of the phase 3 trial data! There may be surprises along the way and we may be in for a bumpy ride.

For example, issues of fair vaccine allocation, differential efficacy in different populations, the potential effect of rare but serious side effects on population uptake, the role of anti-science and anti-vaccination influence over time. 

Dr Alexander Stockdale, university of Liverpool, September 2020.

As reported in the Financial Times, with 300 potential vaccine candidates in the pipeline – 9 of which have already proceeded to phase 3 clinical trials – the flood of data helping humanity make sense of its latest invisible pathological enemy continues rushing down our digital waterways at breakneck pace.

According to Devi Sridhar, professor and chair of global public health at Edinburgh university medical school, based on other diseases which have plagued humanity throughout history, vaccine-induced herd immunity combined with other measures presents our best realistic hope of controlling and/or eradicating Covid-19. By contrast, since so-called natural herd immunity has never been achieved for many of these deadly pathogens, pursuing this latter, highly controversial approach looks likely to be a dangerously ineffective strategy against the novel Coronavirus:

End Notes

1. This is assuming that the median herd immunity threshold for SARS, which based on these datasets is 65% (between 50-80%), is similar to SARS-Cov2.