We study the effects of an intervention aimed at identifying and containing outbreaks in a network model of contagion where social distance is endogenous. The intervention induces a fall in the risk of contagion, to which agents respond by reducing social distance. If the intervention relies on infrequent or inaccurate testing, this crowding out effect may fully offset the intervention’s direct effect, so that the risk of contagion increases. In these circumstances, we show that “slow” interventions – which allow the outbreak to spread to immediate neighbors before being contained – may generate higher ex-ante welfare than “fast” ones and may even “crowd in” social distance. The theory thus identifies a trade off between (i) the swiftness of the intervention and (ii) the scope for crowding out. Simulations on a real world network confirm that the infection rate is not necessarily monotonically decreasing in the accuracy of the intervention and that slow interventions may outperform fast ones for intermediate levels of accuracy.

我们研究了一项干预措施的效果，该干预措施旨在识别和控制社交距离是内源性的网络传染模型中的爆发。干预导致传染风险下降，代理人通过减少社交距离来应对。如果干预依赖于不频繁或不准确的测试，这种挤出效应可能会完全抵消干预的直接效应，从而增加传染风险。在这些情况下，我们表明“缓慢”干预——允许疫情在被控制之前传播到近邻——可能比“快速”干预产生更高的事前福利，甚至可能“挤进”社会距离。因此，该理论确定了 (i) 干预的迅速性和 (ii) 挤出范围之间的权衡。