Suggestions to include spontaneous behavioural responses to a growing epidemic as default in scenario models might sound simple and obvious, but it's worth considering the assumptions involved... 1/
What magnitude of behaviour change in response to epidemic would you assume as a baseline scenario? And how would this relate to transmission reduction? 2/
Would you assume disruptive-but-ineffective change (e.g. avoiding low risk interactions and continuing to have high risk ones) or well-targeted change (e.g. using more rapid tests effectively)? 3/
And what would you assume are the trigger points for this change? Rising cases/hospitalisations/deaths nationally? Media speculation about policy changes? Infections in people's first or second degree local social network? Spread of attitudes in social groups? All of these? 4/
If you're extrapolating patterns from other settings (e.g. earlier waves in places without stringent policies) how would you adjust for confounders like sick leave policies, test availability, increased vaccine coverage etc? 5/
Would you assume people will be less spontaneously cautious because they've been vaccinated, or more cautious because of variant uncertainty and knowledge of potential for waning responses? 6/
Or, for simplicity, would you assume behaviour just changes at specific points in time to go back to earlier stages of the epidemic, and show the resulting dynamics (which is what some models have previously done, e.g. below) 7/
Scenario that assume 'no change' in behaviour are clearly making a strong assumption. But as above illustrates, deviations from this scenario also require strong assumptions, often with multiple decision points... 8/
This shows the value of thinking through steps involved and assumptions that would be required, rather than just saying 'oh well, obviously X would happen...' 9/
It will gradually become clear how behaviour changed in different locations independent of measures during Omicron waves, just as we can study earlier waves. But how confident would you be that we'll see the same again for a future variant? 9/9
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Below analysis was two years ago (bbc.co.uk/news/health-51…). As well as providing an early warning about the COVID threat, it’s a good illustration of what is often an under-appreciated point: if we want to make sense of epidemic data and dynamics in real-time, we need models… 1/
At the time, only 41 cases of 2019-nCoV (aka COVID-19) had been reported in Wuhan. But two exported cases had just been detected in Thailand and one in Japan. How plausible was it that there were really just 41 cases in Wuhan? 2/
To answer this, we need to outline a model: if there are X cases in Wuhan, and travellers leave to different destinations at given rates, how likely is it we'd observe those three exported cases? With this model outlined, we can then use it to infer X given the observed data. 3/
Given below observation, an obvious question to ask is 'did any models consider a scenario that could give insight into what a Tier 4-like change in behaviour could look like?' 1/
If we look at scenarios that assume a prolonged return to behaviour levels seen in earlier steps of the reopening in England (e.g. Step 0, S1, S2 etc. below from 11 Dec: cmmid.github.io/topics/covid19…), models predict far lower levels of hospitalisations and deaths. 2/
Could summarise above scenarios as: 'If you want epidemic curve to look like that, this is the sort of virus characteristics and behaviour change that would be required - whether behaviour change comes from a policy, or pin hopes on spontaneous individual-level change' 3/
Some thoughts on the evolutionary trajectory of SARS-CoV-2 so far, how it compares to other viruses, and what might happen next... 1/
For seasonal coronaviruses & flu, we see a pattern of 'antigenic turnover' over time - circulating viruses give rise to new variants that escape prior immunity against infection, immunity builds against these new variants, then these in turn spawn new variants... 2/
When infections evolve to escape immunity like this, we typically end up with an evolutionary tree that looks like a lopsided ladder as new variants sequentially replace their 'parent' variant lineages (below from: journals.plos.org/ploscompbiol/a…) 3/
Flu is often quoted as an example of 'living alongside' an infection (even though countries work hard to reduce disease burden via vaccination). But I think sexually transmitted infections are also an important example to consider... 1/
For STIs, preaching abstinence to prevent infection has gradually fallen out of favour (e.g. thelancet.com/journals/langl…). Instead, societies deal with ongoing circulation of these infections using other tools... 2/
Testing & informing partners gives people information on their infection status (and treatment can reduce duration of infectiousness). Contacts can be made less risky with protection (e.g. condoms). And susceptibility can be reduced through vaccination (or, for HIV, PrEP)... 3/
Unfortunately, this oft-quoted Spectator tracker of COVID 'scenarios vs outcomes' seems at best muddled and at worst actively misleading. A thread on some weird comparisons - and how to do better critiques... data.spectator.co.uk/category/sage-… 1/
First plot is comparison of scenarios for increased R values with later data. Crucially, these weren't predictions about what R would be (R estimate that week was 0.9-1.1, so pretty flat epidemic). Rather, report showed what could happen if R increased beyond current range... 2/
Not sure why they cut out the R=2 scenario from original plot (which would've made it obvious these were illustrative - assets.publishing.service.gov.uk/government/upl…). TBF SPI-M plot could have included horizontal line to illustrate what R=1 looks like, but don't need a model to draw a flat line... 3/
Any writer who claims 'UK models always overestimate COVID numbers' is either lazy or trying to mislead their readers. A few illustrations... 1/
A surprisingly under-reported example is early UK scenario models (although @anthonybmasters & @d_spiegel correctly flag it in 'Covid by Numbers'). Some point estimates from initial 'recurrent lockdown' scenarios were lower than what actually occurred:
Then there’s the July 2020 report from @acmedsci, which used Imperial modelling. It was designed to be a plausible worst case scenario, not a forecast, but ended up strikingly close to reality (with Alpha increasing risk & vaccines decreasing it): 3/