Prevention And Mitigation Of Epidemics: Biodiversity Conservation And Confinement Policies

The relation between biodiversity loss and frequency/probability of zoonose pandemic risk is now well documented in the literature. In this article we present a first model to integrate this phenomenon in the context of a general equilibrium dynamic economic set-up. The occurrence of pandemic episodes is modeled as Poissonian leaps in stochastic economic variables. The planner can intervene in the economic and epidemiological dynamics in two ways: first (prevention), by deciding to preserve a greater quantity of biodiversity, thus decreasing the probability of a pandemic occurring, and second (mitigation), by reducing the death toll through a partial blockage of economic activity. The class of social welfare functional considered has, as polar cases, a total utilitarian and an average utilitarian specifications. It implicitly considers, at the same time, the effects of policies on mortality and on the productive capacity of the country. Thanks to the Epstein-Zin specification of preferences, we can distinguish between risk aversion and fluctuation aversion. The model is explicitly solved and the optimal policy completely described. The qualitative dependence of the optimal intervention as a function of natural, productivity and preference parameters is discussed. In particular the optimal lockdown is shown to be more severe in societies valuing more human life, and the optimal biodiversity conservation is shown to be more relevant for more “forward looking” societies, with a small discount rate and a high degree of altruism towards individuals of future generations. We also show that societies accepting a large welfare loss to mitigate the pandemics are also societies doing a lot of prevention, not to have to incur the loss too often. After calibrating the model with COVID-19 pandemic data we compare the mitigation efforts predicted by the model with those of the recent literature and we study the optimal prevention-mitigation policy mix.