Modelling Excess Mortality in Covid-19-like Epidemics

Burda, Zdzislaw

We discuss a stochastic model to assess cumulative excess deaths during Covid-19-like epidemics for various non-pharmaceutic interventions. The model simulates three interrelated stochastic processes: epidemic spreading, availability of respiratory ventilators and changes in death statistics. Epidemic may spread either locally or globally. The local mode simulates virus transmission through contacts in the vicinity of the place of residence while the global mode simulates virus transmission through social mixing in public places, sport arenas, airports, etc, where many people meet, who live in remote geographic locations. Epidemic is modelled as a discrete time stochastic process on random geometric networks. In the simulations we assume that the basic reproduction number is $R_0=2.5$ and the infectious period lasts ca. ten days. We also assume that the virus leads to severe acute respiratory syndrome in about one percent of cases, which in turn almost surely lead to respiratory default and death, unless the patient receives an appropriate medical treatment supported by respiratory ventilation. For other parameters, like mortality rate or the number of respiratory ventilators per million of inhabitants, we take values typical for developed countries. We simulate populations of $10^5-10^6$ people. We compare different strategies: do-nothing, social distancing, reduction of social mixing and lockdown, assuming that there is no vaccine and no efficient medicine. The results of the simulations show that strategies that slow down the spread of epidemic too much are inefficient in reducing the cumulative excess of deaths. A hybrid strategy in which lockdown is in place for some time and is then completely released is inefficient as well.