impact of different cholera risk mitigation strategies to improve water and sanitation infrastructure, quality, and access, including their relative efficiency and potential to target the highest impact parts of the city. The fact that two Wards (Kanyama and Harry Nkumbula) were ranked as first and second highest impact for nearly all mitigation scenarios reflects the clear need to prioritize this region of the city. The potential for greatest impact here likely stems from the confluence of a number of factors: the very high concentration of cholera risk (meaning predicted incidence rates are high), the dense population (meaning larger numbers can potentially be averted), and the poor levels of access to water and sanitation at the time (meaning there was large scope to improve on current provision). All three factors, in turn, were driven by the nature of these neighborhoods which were characterized by high rates of poverty and informal settlements with inadequate infrastructure.

The approach developed here has a number of limitations. All analysis stemmed from a model that sought to explain the spatial variation in reported cases across the city. These case data were geolocated according to the home residence of the patient, but of more interest is the location at which the infection was acquired. In many cases, this may be in or close to the home, but in others the exposure may have occurred somewhere else entirely – for example at a workplace, when visiting another household in a different parts of the city, or in a public venue. In future analyses, it may be possible to obtain data representing patterns of human movement (for example as collected via mobile phone records) and include these in analysis of spatial risk patterns (Bengtsson et al. 2015). A second limitation concerns the counterfactual analysis. This relied on the necessary assumption that the empirical relationships captured in the modeling between cholera risk and each putative risk factor were causal rather than mere associative. For example, it was assumed that the observed relationship between poor sanitation and cholera meant that improving sanitation would reduce cholera risk. This seems largely defensible since there are established causal pathways linking the two, and since other factors were controlled for in the analysis, but results nevertheless remain contingent on the assumption of causation. For some risk factors, the causal pathway may be more indirect. The elimination of _E. coli_ contamination in piped water, for example, was estimated to yield reductions in cholera risk. While _E. coli_ is, of course, not the pathogen responsible for cholera, it is plausible that actions taken to eliminate it by improving water infrastructure and treatment would also be effective against _Vibrio cholerae_ itself. A third limitation pertains to the distinction between access to and use of water and sanitation infrastructure. This study deals solely with the question of access and does not claim to know the extent to which city residents are using the available WSS infrastructure. There is a lack of available data on the extent of household use of WSS infrastructure, but if it were later to become available, it could be incorporated in the model.

To compare the possible effort required to implement different scenarios, we used the beneficiary population in each geographic area that would have to receive the intervention. This is an inevitably