Human agency and infection rates: implications for social distancing during epidemics

Christopher Bronk Ramsey

Unless otherwise stated an R0 of 2.4 will be used, with a 95% range of interactivity of 100 and a maximum interaction rate set at 10 time the median. This gives at of 0.995 and a median value of t of 0.558. All outputs are expressed in cycles of infection, following the logic of this simple model; in the case of COVID-19 this is expected to correspond roughly to weeks. In practice with a distributed population, everything would be expected to lag more than this model would suggest. It is also important to note that the parameter modelled here is new infections, symptoms will lag this and detected cases (especially if they are on hospitalisation) will lag further, and deaths, further still. This model is best suited to looking at endemic infection within a single integrated population. The initial seed infection rate probability is chosen to be 0.0001 which is the level at which it is very obvious you have an issue; the seed infection is assumed to be distributed in proportion to individuals interactivity which fits with the overall logic of this model. For many of the scenarios it is useful to further split the population into groups with different interventions. For those cases, in this paper three groups have been used: 60% in group 1, 20% in group 2, which is assumed to be vulnerable, and 20% in group 3, which are assumed to be key workers and therefore not subject to some sanctions. Where mortality is discussed, this is assumed to be 0.5% for groups 1 and 3, and 5% for group 2; this gives an IFR of 1.4% overall which is the upper limit of what is expected [6] .