Our paper in Journal of Simulation ‘How simulation modelling can help reduce the impact of COVID-19‘ setting out how simulation modelling can help in the fight against COVID-19 and subsequent epidemics and pandemics. Click here to access the paper.
Modelling has been used extensively by all national governments and the World Health Organisation in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community.
In 1959, Charles Lindblom wrote The Science of “Muddling Through”, advocating an incremental approach to public policy and management. “Muddling through” does not work for pandemics. The science of pandemics is dominated by epidemiology, not behavioral science. The delay between policy decisions (or indecisions) and the resultant high UK death rate needs tracing back from the prime minister and his advisers to Cobra, the chief scientific adviser, and the Scientific Advisory Group for Emergencies. However, this is one form of contact tracing that can be delayed until the inevitable public inquiry.
Dr Duncan Robertson Fellow in Management Studies, St Catherine’s College, University of Oxford, UK
The real problem with Coronavirus Covid-19 is that when the health service becomes overloaded, the death rate goes up significantly. So, it is imperative that we keep the number of cases at any one time below or as near as possible to NHS capacity.
The Government’s model relies on shifting the peak. I am not clear that we are doing this fast enough. We are an outlier compared to other countries in our social distancing policy response.
There is a tension between epidemiologists – those who study how diseases spread (a very established science started around the time of the Spanish Flu 100 years ago), and behavioural scientists who study how people behave and react.
Epidemiologists have models validated against past pandemics. Behavioral scientists do not.
There are economic and social costs of social distancing. But if you delay social distancing too much, there are potentially very real human costs in increased mortality. Doctors will need to make very difficult moral decisions on who to treat and who not to treat.
I do not understand why, if the intention is to create herd immunity, why we are not isolating our vulnerable population, especially those in care homes.
Behavioral insights are great when you are trying to get people to pay their tax bills on time. And if people don’t, it doesn’t really matter. With a pandemic, if you get the timing wrong, more people die unnecessarily. Then you look back from your computer and say, yes, we got that behavioral model wrong while doctors and nurses on the front line are exposed to extra cases that could put their own lives at risk.
Here’s a video to show what we should be trying to do:
The United Kingdom is at a crossroads, an ideological battle between natural science and behavioral science. Let’s hope for all our sakes we get this one right.
Boris Johnson, the UK Prime Minister, is facing a dilemma. When do we go from the so-called containment phase for controlling Covid-19 Coronavirus, to the delay phase.
In the medical / natural science corner, is the Chief Medical Officer, Professor Chris Whitty, who has presented himself calmly, reassuringly, as completely on top of his brief. He is a physician and an epidemiologist (as well as a lawyer, and an MBA). His evidence at the newly formed House of Commons Coronavirus Committee was calm, frank, precise. He is exactly the sort of advisor that any government would be proud to have. Flatten the peak. Delay the virus spread. Keep the height of the peak low. Save lives.
In the behavioral science corner is, well, I am not sure who. Maybe it’s the Chief Scientific Advisor, who highlighted the need to take account of behavioral science. Yes, please do. It’s a wicked problem, and please include more complex social modelling.
But what we are now seeing is what the Director General of the World Health Organization (up until now also criticised for its seemingly political response to the issue) could be referring to as ‘alarming levels of inaction’.
I do hope, however, that Boris Johnson is being guided by the science, both behavioral and epidemiological, and not by advisors who profess to be superforecasters. You don’t have to be a superforecaster to forecast that if we get this wrong, many will die unnecessarily.
The 2011 film Contagion, starring the spectacularly ill-fated Gwyneth Paltrow, is a dramatization of a viral pandemic starting in pretty analagous circumstances to the current Wuhan Coronavirus (2019-nCoV) outbreak. It’s a good film, and is a great introduction to the work of Centers for Disease Control (CDCs) that monitor the spread – the epidemiology – of the disease. There are two scenes where R-nought, or R0, are described:
Despite the blogger character in the clip describing the spread, using a R0 of 2, as being a problem you can do on a napkin, it takes a little more thinking about. He also seems a bit confused about R0, talking about growth from 2 to 4 to 16, to 256, to 65,536 each day. That’s not what R0 is – it is not a rate, and actually if the rate was 2, this would mean 2 to 4 to 8 to 16 to 32 etc., each time doubling the number. It is possible that he is thinking that there are two generations each per day, but that’s not whatR0 is.
So, on to the professionals:
The CDC epidemiologist in the clip is more on point (despite having sloppy notation with no subscripts). This is better – it shows the reproduction number for the infection – note again, this is not a rate – no time dimension is involved – it basically shows the number of cases on average each case generates.
This population modelling – so called SIR (Susceptible, Infected, Recovered) system dynamics modelling – is just one of several approaches that can be used to model contagion across a population. My recent paper ‘Spatial Transmission Models: A Taxonomy and Framework’ sets out a review of what they are and the advantages and disadvantages of each. In brief, we can model the population numbers, the individual agents that carry the virus, the network of contacts between infected individuals, or the regions or cells in which individuals are located (city districts, for example). The paper is available to read by clicking on the link here.