Pandemic modeling, good and bad

What kind of epidemiological modeling works well, and what kind doesn’t? This is determined by the nature of the target: the relevant causal relations are unstable across contexts, which tells against any modeling that assumes otherwise. I look at two influential examples from the Covid pandemic. The first example is the paper from Imperial College, London, that projected future infection rates under various policy scenarios, and that in March 2020 was influential in persuading the UK government to impose a lockdown (Ferguson et al 2020). Because it assumes stability, this first example of modeling fails: it carries no epistemic force at all. A different modeling strategy is required, one that is less ambitious but more effective. This is illustrated by the second example: the paper, also from Imperial College, London, that in December 2020 first estimated the transmissibility of the Alpha variant (Volz et al 2020). This second, contextual example of modeling works well