either all ones or all zeros to observations where the ratio is undefined. Imputing all ones assumes that, if cases had reported, they would have all been confirmed; imputing all zeros assumes that, if cases had reported, none would have tested positive. Figure E.9(a) plots the average ratio of confirmed to total cases across control and treated sections. Looking at either bound, there is no meaningful difference in these ratios, and the confidence intervals overlap throughout the study period.<sup>46</sup> In Section E.20, we write down a model to clarify what must be assumed for our results to reflect a change in exposure (as opposed to reporting). For confirmed cases to increase while the share of confirmed to total to remains unchanged, one must conjecture that the treatments dramatically increased reporting by asymptomatic individuals while having negligible effects among those showing possible signs of the virus. This strains credulity: one cannot preemptively test for Ebola, so individuals without symptoms have no reason to report; moreover, qualitative accounts suggest the crisis deterred unexposed individuals from visiting clinics, even when they had other healthcare needs (Elston et al. 2016).

# **4.3.2 Surveillance**

Surveillance is challenging in a setting like rural Sierra Leone: if individuals or families want to avoid health workers, they are unlikely to be detected given the difficulties of canvassing sparsely populated regions with limited road networks (Richards et al. 2015; Olu et al. 2016; McNamara et al. 2016). Nonetheless, we look for indications of intensified top-down efforts in treated sections as a possible alternative explanation for the increase in reported cases.

First, contact tracing is central to disease surveillance efforts. In our control sections 59 percent of confirmed cases were subject to contact tracing, compared to just 22 percent in CM and 28 percent in NFA (Table E.16). Second, we use three measures derived from the VHF data (all measured at the section-level): (1) the probability that a case received laboratory testing to confirm or rule out an infection; (2) the average number of days that passed between a case being reported and lab testing; and (3) the number of unique case workers (logged) that entered information into the VHF. We expect these variables to proxy for top-down surveillance efforts during the crisis. In Table E.17, we find no significant differences for these variables across treatment and control.

Finally, using data from Sierra Leone’s National Ebola Response Center (NERC) and the UN Mission for Ebola Emergency Response (UNMEER), we count the number of Ebola-specific treatment facilities in each section (see Section E.1). There were three types of specialized facilities: Ebola Treatment Units

> 46 It is possible that the ratio of confirmed to total cases could stay constant if there was an increase in the number of probable and suspected cases. Figure E.9(b) repeats the bounding exercise but uses the ratio of confirmed to confirmed plus negative cases. This exercise delivers the same conclusion, as the number of probable and suspected cases are small and unaffected by treatment (see Table E.6.

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