A structural model of corona virus behaviour for testing on data behaviour

We fit the logistic function, the reduced form of epidemic behaviour, to the data for deaths from Covid-19, for a wide variety of countries, with a view to estimating a causal model of the covid virus' progression. We then set out a structural model of the Covid virus behaviour based on evolutionary biology and social household behaviour; we estimated and tested this by indirect inference, matching its simulated logistic behaviour to that found in the data. In our model the virus' progression depends on the interaction of strategies by household agents, the government and the virus itself as programmed by evolution. Within these interactions, it turns out that there is substitution between government topdown direction (such as lockdown) and social reaction to available information on the virus behaviour. We also looked at experience of second waves, where we found that countries successfully limited second waves when they had had longer first waves and followed policies of localised reaction in the second.