children may not be as effective. In general, the WHO review concludes that North American and Australian data from the 1918 pandemic indicate that community level nonpharmaceutical measures were not ‘demonstrably’ useful historically.

Recent studies of NPIs during the 1918 pandemic have examined the evidence. Hatchett _et al._ (2007)<sup>56</sup> analyze data from 17 cities that implemented some form of NPIs and varied in their peak or overall mortality rates during the fall 1918 pandemic wave. The most common NPI configurations included social distancing and case isolation. The authors found that school, theatre, and church closures were most effective. By statistically analyzing the difference in pandemic outcomes given the policies employed, the authors conclude that timing of the interventions was a key explanatory factor for peak mortality, but not overall mortality. In other words, city governments who initiated interventions sooner after an outbreak began in their jurisdiction were able to ‘flatten’ the epidemic curve, and also limit the total deaths<sup>57</sup> (a proxy for the infection attack rate). Again, this points to how the demonstration effect of prior outbreaks allows cities with later outbreaks to time their interventions better and achiev overall rede uctions in _R_ .

The reduction in overall mortality could have been much larger if the effective interventions had continued for a longer period. As it happened, cities with effective community interventions protected their citizens from the first wave, but this kept a larger number susceptible. When the interventions were lifted prematurely (usually after mortality rates began to decline), the cities experienced a second wave as infectious individuals restarted an epidemic. In comparison, second waves were relatively smaller in cities with poor policy implementation in the first wave, even if such cities may have had larger overall mortality. Epidemiologically, the cities with effective but prematurely lifted interventions did not reduce _R_ to a low enough level to achieve substantial herd immunity. These insights are corroborated by Bootsma _et al._ (2007)<sup>34</sup> , who fit the 1918 data to an SEIR model and test similar hypotheses. Bootsma _et al._ conclude that transitory community‐level policies have the potential to reduce attack rates (and hence mortality) by 30‐40%, but higher reductions require sustaining the social distancing and movement restriction policies for longer durations, imposing greater social and economic costs.

Besides the insights on timing and length of policies, both studies make a point not considered before about individual reactions. The 1918 data indicates that rising mortality rates caused people to react by reducing risky behavior. This manifested most visibly as lower rates of social contact, and possibly as greater personal hygiene or mask‐wearing. Such ‘prevalence‐elastic’ behavior has been theorized before for other diseases. In statistical analysis, such reactive behavior plus indirect effects from family members reducing contacts to stay at home to care for the sick boosts the effectiveness of social distancing policies. But, in realistic epidemic settings, the results of over‐dependence on reactive behavior may be difficult to predict. Poor information availability – especially on mortality which is the most observable indicator but occurs with a lag to transmission – may mean that private reactive behavior is too little and too late. Prompt and trustworthy public health communication is required to reinforce the positive factors, as seen during SARS.

_Prospective modeling_ : Carrat _et al._ (2006)<sup>58</sup> using French data on effective contact rates in an influenza epidemic, simulate community transmission of pandemic influenza and find local nonpharmaceutical policies more effective compared to antiviral prophylaxis. A 28