You’re reading Reaction. Subscribe here to receive editor Iain Martin’s weekly newsletter, daily columnists including Tim Marshall, unlimited access to all the stories by our brilliant team of journalists, and our daily email with analysis every evening.
The transmission of respiratory viruses is poorly understood. However, the models used by SAGE to justify draconian restrictions are far too simplistic – they are based on a handful of assumptions that have not been adjusted in the light of real world evidence, despite numerous forecasting failures. First, they assume that every individual is equally susceptible to every variant. SAGE therefore assumes that each outbreak will lead to uncontrolled, exponential viral spread unless there is a material reduction in human interactions.
Why haven’t lockdowns worked? There are broadly two types of respiratory virus. There are those that spread person to person – like measles – in a continuous chain of transmission, uninterrupted by season and with every susceptible contact falling ill. Then there are those we do not understand so well, like influenza, which are much more complex. Instead of the simplistic close contact model, which assumes Covid spreads like measles, we should perhaps consider an alternative more sophisticated model based on influenza. The influenza virus model is unusual – it is predicated on the majority being exposed to a particular airborne virus but, oddly, only a minority appear to be susceptible to each year’s variant. To complicate matters further, influenza can also spread person to person.
The spread of influenza is difficult to model and the cause of the surges in transmission seen each winter is not fully understood. However, influenza has been measured for centuries, enabling interesting patterns to be discerned. Spread does appear to occur person-to-person but only a trickle of cases occur in the summer months before there is sudden exponential growth leading to a winter surge. This annual surge also happens in autumn in milder climates like Australia and California.
Each winter between 5 per cent and 15 per cent of the population somehow become susceptible to the new circulating influenza ‘variant’ (aka strain) – and to date no one can explain why the percentage is so small. Spending an hour in indoor environments in winter is sufficient to expose everyone inside to an infectious dose of influenza, but the majority remain uninfected – perhaps because they are not susceptible. After the 5-15 per cent cohort of susceptible individuals in a particular year are infected, a temporary quasi-herd immunity is reached. Cases therefore fall, reaching negligible levels until the next winter. Clear Gompertz curves are seen, although only affecting part of the population.
The following winter, those who were previously infected remain immune but a further 5-15 per cent become susceptible, somehow. No-one understands what exactly causes these people to become susceptible in winter when they were not the previous winter nor in the summer. A novel influenza virus can take up to eleven winters before full herd immunity is reached for that particular type of influenza virus.
Sign up for our FREE Reaction Weekend Email
Read the week's best-read articles on politics, business and geopolitics
Receive offers and exclusive invites
Plus uplifting cultural commentary
The poorly understood winter trigger that precipitates an influenza surge actually occurs twice each winter and usually the second half sees a different ‘variant’ surge and predominate. Influenza was present for the first half of winter 2019/20 but disappeared globally for the second half at the exact time that SARS-CoV-2 surged, 3 weeks earlier in Italy than in Sweden and the UK. Although these are quite different viruses, the fact that SARS-CoV-2 surged at the exact time that we would have expected a new influenza variant to rise suggests that the influenza transmission model is a viable candidate to examine further for COVID.
The critical point is that many more people are exposed to influenza every year than are infected, because it is airborne and infuses throughout indoor enclosed spaces. The majority are protected by their immune system and the remainder succumb. Vaccination is generally thought to have had an impact on influenza associated hospitalisations and mortality but the evidence it has significantly reduced transmission and infection is weak.
Comparing the transmission of SARS-CoV-2 to influenza is not the equivalent of dismissing COVID as being like ‘flu. In a certain subset, COVID causes more hospitalisations than influenza and results in greater demand for intensive care. However, how we respond to it is predicated on understanding how it transmits, so considering the influenza model is important.
Although we do have evidence of significant person-to-person close contact transmission of SARS-CoV-2, there are many areas of ambiguity such that this cannot be the only route of transmission, once again supporting the ‘influenza spread’ hypothesis to explain the spread of COVID.
The person-to-person close contact model cannot explain certain oddities of influenza transmission. For hundreds of years there have been reports of outbreaks of influenza in boats that have been at sea for weeks with no human contact. It is now clear that SARS-CoV-2 can be transmitted as aerosols through the air, like influenza, and it has been isolated from hospital ventilation systems. In addition, there is a growing body of evidence of numerous viruses present in the troposphere (four to 12 miles above us) which fall to ground level under the right environmental conditions. For decades the simultaneous appearance of genetically identical influenza virus around the world could not be explained, but tropospheric spread may explain this phenomenon.
The simplistic person-to-person close contact model cannot explain certain oddities of Covid either. There was an outbreak of a thousand cases diagnosed within two days of each other in a garment factory in Sri Lanka, without a super-spreader, at a time when there was minimal community Covid. An Argentinian fishing vessel had an outbreak after five weeks at sea, despite everyone testing negative before setting sail. There have been several occasions when Australian authorities have struggled to understand the source of Delta variant infections in the community at times of very low prevalence. Canada publish their test and trace data and 40 per cent of COVID cases in Canada, even at low prevalence, never have an identified source of transmission.
SAGE has never explained how key workers, including hospital staff, who have been continually exposed, could remain unaffected by the original and Alpha variants only to succumb to the Delta variant months later. The household transmission rate for SARS-CoV-2 is around one in 10 – is this because of good luck, or because the other nine in 10 people sharing living quarters with an infected person are not susceptible to that particular variant?
The influenza model of transmission is a hypothesis that requires testing, which could start by interviewing those on the Diamond Princessto see how many have been infected with subsequent variants.
Real world evidence has repeatedly shown that the simplistic approach adopted by SAGE – and others – has failed. No explanations have been offered for the lack of correlation between changes in human behaviour and viral prevalence. Early models were always more likely to be inaccurate but as more data has appeared the refusal to adjust the models becomes less forgivable. Numerous scientists have been pointing out the faults in the SAGE models for well over a year. Rather than SAGE listening, debating and adjusting their hypothesis, in a scientific way, dissenting voices have been quashed. The latest failures of the SAGE models must be a reality check. Other hypotheses, including the influenza model, need to be given due consideration and overly simplistic models, which fail to explain the patterns in real world data, must be discarded for good.
Dr Craig (@ClareCraigPath) has been a pathologist since 2001 – she worked in the NHS and reached consultant level in 2009. She specialised in cancer diagnostics including diagnostic testing for cancer within mass screening programmes, and was the day-to-day pathology lead for the cancer arm of the 100,000 Genomes Project. Subsequently she has worked on artificial intelligence for cancer diagnostics.