Conclusion to Week 1

During this week you have considered how a pandemic can modelled to help inform those making policy.

We began with the kind of questions policy makers and citizens have about epidemics such as COVID-19:

• How many people will be infected and how many will recover?
• How many people will die?
• How likely is it that I or a loved one will die?
• How different is this from the usual seasonal illnesses and morbidity rates?
• Why don’t we just let the virus take it’s natural course?
• Why did we need the lock down? When can it be ended?
• When can I see my friends, children and grandchildren again?
• When is the best time for me to stop isolating
• Does my government have the right policies?

To begin to answer these questions we looked at some of the data available on websites such as

https://www.worldometers.info/coronavirus.

Step 1.3 collected some of the vital statistics of COVID-19 while Step 1.4 discussed transmission. Step 1.5 discussed crises in healthcare systems.

The Kermac-McKendrick SIR model was presented in Steps 1.6 and 1.7 as the basis for discussing herd immunity and flattening the curve in Step 1.8. Herd immunity is usually most relevant to policy in the context of vaccination.

Steps 1.9 and 1.10 summarised the Imperial College report and its impact. Step 1.11 considered policy makers’ common claim that they are driven by science.

Steps 1.12, 1.13, 1.4 and 1.15 considered prediction and forecasting in policy making, noting that some systems are inherently unpredictable but useful things can be known about their future behaviour. Complex Systems Science was presented as a way ahead to support policymakers.

In Week 2 you will see how computer modelling can be used to investigate policy questions.

What do you think

Has course so far given you a better understanding of the epidemic process? Has the SIR model helped you to understand the way epidemics cascade and die away. Do you now understand what is meant by ‘herd immunity’ and ‘flattening the curve’. Do you think modelling is useful for policy? Perhaps you have reservation about modelling in a policy context? Whatever your views please share and discuss them.

The following short quiz is to enable you to check that you have covered the main points of the course so far.