「Testing, Voluntary Social Distancing and the Spread of an Infection」というNBER論文をアセモグルらが上げている。著者はDaron Acemoglu(MIT)、Ali Makhdoumi(デューク大)、Azarakhsh Malekian(トロント大)、Asuman Ozdaglar(MIT)。

This paper studied the effects of testing on social activity and voluntary social distancing in the context of an epidemic. Social activity levels determine the (endogenous) social network over which contacts take place and an infection spreads. Testing enables authorities to identify and isolate infected individuals who spread the virus, and has been identified by the recent literature on COVID-19 and policymakers as a key tool for combating epidemics. Our analysis, however, shows that the impact of testing on the spread of an epidemic may be more complex because, knowing that tests will lead to the isolation of infected individuals, agents can increase their social activity levels and refrain from voluntary social distancing. As a result, our analysis established that the effects of testing on the spread of the infection can be non-monotonic—greater testing can lead to higher infection probabilities.
Our analysis also characterized the optimal testing policies. The same forces that lead to nonmonotonic comparative statics also imply that a benevolent social planner may prefer to leave her testing capacity partially or fully unused—because increasing testing can make the spread of the virus more likely. This implies that testing should often be combined with mandatory social distancing measures—which ensure that the adverse behavioral effects of testing can be countered by preventing excessively high social activity levels.
Our paper is part of a growing literature on the interaction between economic incentives and epidemiological dynamics. Two high-level contributions of our approach are to conceptualize the problem of endogenous behavior as one of social network formation and to use the percolation model rather than the SIR dynamic model. Both of these contributions can be useful beyond the confines of our specific question, but the robustness of our conclusions to relaxing both assumptions and adopting different modeling strategies need to be investigated. Other interesting areas for research include the analysis of optimal testing and tracing when tests lead to type I and type II errors and policy is constrained by privacy considerations and non-obedience (both in acquiescing to testing and following mandatory social distancing guidelines). Another interesting avenue is to enrich the setup to incorporate more heterogeneity and richer economic, social and epidemiological interactions so as to enable quantitative policy analysis.