metadata: authors: 0: author: George Joseph
1: author: Sveta Milusheva
2: author: Sturrock,Hugh James William
3: author: Kashangura,Faith Maidei
4: author: Ayling,Sophie Charlotte Emi
5: author: Hoo,Yi Rong
count: Zimbabwe
docna: 0: docna: The Importance of Maintenance : Geospatial
            Analysis of Cholera Risk and Water and Sanitation
            Infrastructure in Harare, Zimbabwe
docty: Policy Research Working Paper
abstracts: cdata!: Understanding the specific factors
            associated with cholera outbreaks is an integral part of
            designing better approaches to mitigate their impact. This
            paper uses georeferenced case data from the cholera epidemic
            that occurred in Harare, Zimbabwe, from September 2018 to
            January 2019. The paper applies spatio-temporal modeling to
            understand how the outbreak unfolded and the factors
            associated with higher risk of being a reported case. The
            study highlighted a number of findings. First, using call
            detail records to estimate weekly population movement
            throughout the city, the results suggest that human movement
            helps to explain the spatiotemporal patterns of the cases
            observed. In addition, the results highlight a number of
            sociodemographic risk factors and suggest that there is a
            relationship between cholera risk and water infrastructure,
            with populations living in close proximity to the sewer
            network with high access to piped water being at higher
            risk. A possible explanation for this surprising observation
            is that sewer bursts led to the contamination of the piped
            water network, turning access to piped water, usually
            assumed to be protective, into a risk factor. Although
            further studies are required to test this hypothesis, if it
            is true, it highlights the importance of maintenance for
            achieving the Sustainable Development Goals of improved
            water and sanitation infrastructure.
display_title: The Importance of Maintenance :
            Geospatial Analysis of Cholera Risk and Water and Sanitation
            Infrastructure in Harare, Zimbabwe
listing_relative_url: /research/2023/02/34010566/importance-maintenance-geospatial-analysis-cholera-risk-water-sanitation-infrastructure-harare-zimbabwe
topicv3: Geographic Information Systems m1326400 663,Sanitation m1326453 2000,Water m1326522 137,Public Health m1327519 635
publishtoextweb_dt: 2023-02-28T00:00:00Z
dois: 10.1596/1813-9450-10327
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: NA
2: author_key: 1872564
3: author_key: NA
4: author_key: 1404724
5: author_key: 1598837
model_extractions: Policy Research Working Paper 10327 The Importance of Maintenance Geospatial Analysis of Cholera Risk and Water and Sanitation Infrastructure in Harare, Zimbabwe _George Joseph Sveta Milusheva Hugh Sturrock Faith Maidei Kashangura Sophie Charlotte Emi Ayling Yi Rong Hoo_ Water Global Practice February 2023
Policy Research Working Paper 10327

# **Abstract**

Understanding the specific factors associated with cholera outbreaks is an integral part of designing better approaches to mitigate their impact. This paper uses georeferenced case data from the cholera epidemic that occurred in Harare, Zimbabwe, from September 2018 to January 2019. The paper applies spatio-temporal modeling to understand how the outbreak unfolded and the factors associated with higher risk of being a reported case. The study highlighted a number of findings. First, using call detail records to estimate weekly population movement throughout the city, the results suggest that human movement helps to explain the spatiotemporal patterns of the cases observed. In addition, the results highlight a number of sociodemographic risk factors and suggest that there is a relationship between cholera risk and water infrastructure, with populations living in close proximity to the sewer network with high access to piped water being at higher risk. A possible explanation for this surprising observation is that sewer bursts led to the contamination of the piped water network, turning access to piped water, usually assumed to be protective, into a risk factor. Although further studies are required to test this hypothesis, if it is true, it highlights the importance of maintenance for achieving the Sustainable Development Goals of improved water and sanitation infrastructure.

This paper is a product of the Water Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at gjoseph@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The Importance of Maintenance: Geospatial Analysis of Cholera Risk and Water and Sanitation Infrastructure in Harare, Zimbabwe George Joseph<sup>1</sup> , Sveta Milusheva, Hugh Sturrock, Faith Maidei Kashangura, Sophie Charlotte Emi Ayling, Yi Rong Hoo Originally published in the <u>Policy Research Working Paper Series on</u> _February 2023._ This version is updated on _May 2023._ To obtain the originally published version, please email <u>prwp@worldbank.org.</u> JEL: I18; O55; Keywords: Water Supply, Sanitation, Targeting, Cholera, Public Health

> 1 George Joseph (corresponding author), Water Global Practice, World Bank, Washington DC 20433, email: <u>gjoseph@worldbank.org. Sveta Milusheva, Development Impact Evaluation, World Bank , Washington DC 20433, smilusheva@worldbank.org,  Hugh Sturrock, Co-Founder and Chief Science Officer at Locational.UK, hugh@locational.io,</u> Faith Maidei Kashangura, Land Management and Development Manager, City of Harare, fmashonga@gmail.com, Sophie Charlotte Emi Ayling,  Center for Advanced Spatial Analysis, University College, London UK, sayling@worldbank.org,and Yi Rong Hoo, World Bank, Washington DC 20433, <u>yhoo@worldbank.org.</u>

We would like to acknowledge the support of the Global Water Security and Sanitation Partnership (GWSP) of the World Bank and the ieConnect for Impact Program funded with UK aid from the UK government in completing this work. Also this analysis formed part of a broader collaboration of the World Bank with the Zimbabwe National Modeling Consortium. In particular the Biomedical Research and Training Institute - Institutional Review Board (BRTI-IRB) and the COVID-19 Modeling and Spatial analysis sub-group (modeling consortium); Mr. Tendayi Kureya, Executive Secretary of the Medical Research Council of Zimbabwe (MRC-Z); Dr Shungu Munyati, Director General of BRTI; Professor in Demography and Behavioral Science Simon Gregson and Dr. Mike Pickles, Research Fellow of Imperial College London (ICL). From the World Bank we would like to thank Chenjerai Sismayi from the Health, Nutrition and Population Global Practice of the World Bank as well as the Country Management Unit of the World Bank in Zimbabwe. The findings, interpretations and conclusions expressed in this paper do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank or the governments whom they represent. The World Bank does not guarantee the accuracy of the data included in this work.
The Importance of Maintenance: Geospatial Analysis of Cholera Risk and Water and Sanitation Infrastructure in Harare, Zimbabwe

# Introduction

The World Health Organization’s (WHO’s) Global Task Force on Cholera Control views cholera not just as a global threat to public health, but also an indicator of poverty and a mark of inequity[1]. Since 1970, cholera has become endemic in many countries in Sub-Saharan Africa [2] with annual case fatality ratios (CFRs) of 1.6% - the highest in the world [3]. While several oral cholera vaccines (OCVs) now exist, they only protect against cholera infection for up to two years, and they address the symptoms and not the cause. Large outbreaks continue to plague several countries, especially in Sub-Saharan Africa. Without addressing the pathways to contamination in these low-income settings with inadequate infrastructure, outbreaks will continue to be a risk.

Pathways for transmission can include contaminated water sources, both for drinking and domestic use [4][5], [6][7], poor sanitation [8][9][10]; contaminated food, poor personal hygiene [11] and poor drainage [11], [12]. These same pathways can also lead to other water-borne diseases such as typhoid, making it vital to understand which pathways are most important in a given setting in order to target resources effectively to reduce the spread of these diseases. Additionally, since cholera is a water-borne disease, the focus in terms of risk factors is usually on infrastructure and water sources, yet another important factor is mobility. If infected individuals travel from an area with a cholera outbreak to an area without any cases and the water/sewer networks are susceptible to contamination, the traveler could contaminate the water in this new location and lead to the spread of the disease [[13], [14]]. Studying the role of mobility could be important for helping to contain spread [15]–[17].

In particular, outbreaks in developing country contexts are usually attributed to where households have low access to WHO Joint Monitoring program classified ‘improved’ water and sanitation infrastructure such as networked water and sewer systems [2], [18 <mark>]. However, poorly maintained or operated network infrastructure can also fail to bring about the protection from cholera that it may seem to promise. There are examples from developed and middle-income countries where poor operation of sewerage infrastructure can lead to health and environmental hazards</mark> [19]–[21].

An understanding of the spatial distribution of cholera risk, and the factors driving risk, can support decision making around longer-term interventions to reduce the risk of outbreaks. Especially in cities with mixed service provision - where households may rely on piped water and point sources for drinking water, and on site sanitation or sewer networks for sanitation - there are a variety of potential improvements that could be made. These range from investing in repair or construction of network infrastructure (water or sewer pipes), to ensuring better access to protected point sources (boreholes, protected wells) or improving the quality of on-site sanitation (such as pit latrines or septic tanks). While in general, improving access to piped water is considered an improvement in infrastructure with public health benefits, piped water networks can become health 2
hazards if not properly maintained. For example in Zambia, Ashraf et al.(2017 <mark>) found that service interruptions were associated with an increased incidence of diarrheal disease, upper respiratory infections and typhoid fever</mark> [22 <mark>]. Equally, sewer networks are considered to be the top of the sanitation service ladder according to the Joint Monitoring Program of the WHO (JMP-WHO, 2020) with risks from onsite facilities more well documented in the literature (</mark> Bancalari & Martinez, 2018 **)** [23 <mark>]. Nevertheless, when bursts or leakages are not properly addressed, liquid sewage can potentially be even more hazardous than waste from onsite sanitation facilities [</mark> 24].

This paper uses geo-spatial analysis to explore the role of water and sanitation infrastructure in cholera risk. The study focuses on Harare, Zimbabwe, using the cholera outbreak of September - December 2018 as a case study. We use spatial modeling, detailed data on infrastructure and mobility, and georeferenced case data to identify factors associated with cholera risk. Second, we model different scenarios of infrastructural improvements and compare which of them contributes the most to reducing such risk. Third, we look at how the implementation of infrastructural improvements can be selectively targeted by ward throughout the city in order to potentially reduce cost and maximize health impacts.

# Methods

## Ethics Statement

<mark>This study was approved by the Medical Research Council of Zimbabwe. The approval number is MRCZ/E/302 and it was assessed as exempt. All data obtained and herein reported were anonymized prior to analysis.</mark>

## The Study Area

Zimbabwe is a landlocked Sub-Saharan African country with a population of just under 15 million people (World Bank, 2021). It is divided into 60 districts and bordered by Zambia, Mozambique, Botswana and South Africa. Its capital, on which this analysis is focused, is Harare, a city with a population of 1.5 million inhabitants (UN, 2012) and 46 wards. Zimbabwe has experienced cholera outbreaks dating back to 1971 with the worst occurring in 2008/9 which resulted in 4,288 reported deaths over 60 of its 62 districts [25]. The focus of this paper is a more localized but severe outbreak which took place between September and December 2018 and was concentrated in the capital, Harare. Of almost 10,000 cases, 93% (9,755) were reported in Harare, resulting in 46 deaths. According to the latest DHS data, 30% of Harare’s population is connected to an existing sewer network, compared to just 12% in neighboring country capital Lusaka, Zambia (DHS, 2015 & 2018).

All the data detailed in the paragraphs below was cleared for use by the Medical Research Council of Zimbabwe (MRC-Z). The approval letter can be found in the supporting information.

3
# Case data

Georeferenced cholera case data were available for the Harare area collected by the Ministry of Health and Child Care (MoHCC) following the World Health Organization (WHO) guidelines. The first case was recorded on September 4, 2018, and the last on January 9, 2019. <mark>WHO criteria for case diagnosis state that any patient aged 5 years or more presenting with acute watery diarrhea and severe dehydration where cholera is not known to be occurring, or any patient 2 years or older presenting with acute watery diarrhea where cholera is known to be occurring can be suspected as a cholera case. Confirmation is achieved by sampling a subset of patients during the course of the epidemic.</mark><sup>2</sup> <mark>Date of diagnosis, gender, age and geo-location of the case’s dwelling are also collected.</mark> For the purpose of spatio-temporal analyses, cases were aggregated across 7-day bins from September 1, 2018. Age and gender of the patients is not taken into account in this analysis. A total of 9,890 cases were included in the analysis after cleaning.

# Population Characteristics Data

Population density data were available from Facebook HDX[27]. Small Area Estimates (SAEs) for poverty produced by the World Bank were used for ward area estimates of poverty. Household access to water, sanitation and hand washing infrastructure was taken from the DHS 2015. This was available at the cluster level for 60 clusters for Harare and the surrounding area, and 44 inside the city.

# Water and Sanitation  Infrastructure and Environment Data

Data on water and sanitation infrastructure in the city was collected from a variety of sources. The water and sanitation network shape files for the city were obtained with the support of the City of Harare’s (CoH) Water and GIS departments. Complaints data were digitized for 7,179 water network breakage reports and 24,713 sewer burst location reports from around the city with the support of Harare’s GIS department. Water quality samples for 23 sampling points, administrative boundaries for water supply (DMAs) (165 suburbs), and the location of 362 markets and public facilities were also collected and digitized by City of Harare’s GIS department. Data on 1,848 private borehole locations was provided by Manyame, Upper Mazowe and Nyagui Sub-catchment area operators, for which 1,721 were inside Harare. Finally, a groundwater vulnerability index was developed by Ziva, based on a combination of geological factors (fault index, soil permeability, depth to groundwater, topographic wetness index and fissure flow).

# Population Mobility Data

We use anonymous and aggregated call detail record (CDR) data provided by two mobile network operators in Zimbabwe covering the period of the outbreak (September 2018-February 2019). Anonymous CDR data contain information on the location of the nearest cellphone tower through which any calls or texts made or received are routed. By comparing the locations of where these

> 2 It is standard practice in epidemic response that ‘Once an outbreak is declared, there is no need to confirm all suspected cases. The clinical case definition is sufficient to monitor epidemiological trends’. [26]

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<!-- Start of picture text --> Geological vulnerability Distance to medium sized market (m) Sanitation risk Proportion with piped water<br>ny52z' Pa , ER FicSeeoy263as 0.300.25. 15000 ‘ 0.605 0806<br>o& C3 if Dag?oe a 0. 0.101520 500010000 gf _ o30201 0402<br>0<br>2<br>2<br>Proporton with unimproved water Poverty Private borehole density Water burst density<br>5<br>x‘ 0.20 0.15 0.6<br>2 5 0.15 0.08 0.10 Oa<br>° 0.10 0.06 0s 0.3<br>gz 0.05 0.04 . 54<br>' 0.02 0.00 0.0<br>2<br>2<br>Sewer burst density Distance to water network (decimal degrees) Distance to sewer network (decimal degrees)<br>xr’‘r rde 0.07 om 4 0.10<br>52 18 wet 2 0.050.06 y 0.08<br>o7 1.0 - - Ph 0.040.03 >. 0.060.04<br>2 0.5 0.02 7 0.02<br>*°0.0 >0:00 ~~ # 0.00<br>2<br>30.8 30.9 31.0 31.1 31.2 31.3 314 30.8 30.9 31.0 31.1 31.2 31.3 314 30.8 30.9 31.0 31.1 31.2 31.3 314<br><!-- End of picture text -->
<!-- Start of picture text --> Week starting 2018-09-01 Week starting 2018-09-08 Week starting 2018-09-15<br>~ ~ ~<br>S<br>Lad<br>2<br>Lad<br>z Mean imported<br>cases/person/day<br>o<br>2 il, ,. i, 0.0025<br>0.0020<br>Week starting 2018-09-22— Week starting 2018-09-29~— Week starting 2018-10-067 0.0015:<br>n8‘ 0.00100.0005<br>e 0.0000<br>&<br>a<br>eS<br>2‘_N y ,<br>30.9 31.0 34 31.2 31.3 30.9 31.0 344 31.2 31.3 0.9 31.0 314 31.2 31.3<br><!-- End of picture text -->
## Scenario Modeling

To explore the potential impact of different public health intervention scenario <mark>s (Table 1), w</mark> e adjusted the relevant covariate and used the final model to predict a counterfactual. For this exercise we refit the final model without the imported incidence variable as this would change under the different scenarios – i.e. if there were no sewer bursts, transmission might be predicted to be lower which in turn would reduce the incidence of imported infections to other wards. For each scenario, we also ranked the wards (3<sup>rd</sup> administrative level) by the expected change in number of cases that would result from that scenario. This allowed us to identify where interventions could have been targeted and the expected impact of such targeting. We also calculated the population of each ward to allow us to create an efficiency index, estimated as: f f f % ffhaaffaaff f ffaaccffcc EEf ff= % ooff HHaaHHaaHHff ppoop ppaappffooff ppaaHHaaffppfftt

|**Scenario**|**Change to covariate layers**|
|---|---|
|No sewer bursts throughout city|Sewerburst density set to 0 throughout<br>Harare|
|No sewer bursts within 200m of the water line|Sewerburst density set to 0 within 200m of<br>waterline|
|No sewer bursts in high (>median) density burst<br>areas|Sewerburst<br>density<br>set<br>to<br>0<br>where<br>sewerburst density>median|
|No sewer bursts in highest (75th percentile)<br>density burst areas|Sewerburst<br>density<br>set<br>to<br>0<br>where<br>sewerburst density>75<sup>th</sup>percentile|
|Piped water provided to entire city|Proportion with piped water set to 1|
|Piped water provided to areas within 500m of the<br>existing piped water network|Proportion with piped water set to 1 within<br>500m of the water network|
|Sewer access provided to entire city|Distance to sewer and sanitation risk both<br>set to 0|
|Sewer access provided to areas within 500m of<br>the existing sewer network|Distance to sewer and sanitation risk both<br>set to 0 within 500m of sewer network|
|Piped water and sewer access provided to entire<br>city|Proportion with piped water set to 1,<br>distance to sewer and sanitation risk both set<br>to 0|
|Piped water provided within 500m of existing<br>piped water network and sewer access provided<br>within 500m of existing sewer network|Proportion with piped water set to 1 within<br>500m of the water network, Distance to<br>sewer and sanitation risk both set to 0 within<br>500m of sewer network|

Table 1: Scenarios modeled and corresponding changes to covariate layers

# Results

Figure 3 shows the distribution of cases temporally and spatially. Cases were concentrated towards the first few weeks in the south-west area of the city in the Glen View and Budiriro neighborhoods.

7
<!-- Start of picture text --> P Number of cases<br>re [sto<br>|<br>4% % 8 aes<br>*g oD<br>sae:<br>8<br>4<br>p<br>fP<br>° €&<br><!-- End of picture text -->
<!-- Start of picture text --> 3000-<br>c<br>= . %E 750- |<br>=<br>_<br>Qo, a ir<br>. 2000 =<br>g<br>g - 7 .<br>o wo<br>2o%4500- **<br>o<br>.<br>= * ao °<br>Ss a Py<br>© 1000- = :<br>3 * ra<br>= ~ 250-<br>3gj<br>2<br>(e) ai . *<br>. Oo2 e.<br>*<br>O- O-<br>fh) 1000 2000 3000 fn) 250 500 750<br>Predicted number of cases per week Predicted number of cases per hexgrid cell<br><!-- End of picture text -->
<!-- Start of picture text --> =<br>Z Distance- to sewer (km)<br>EyE3 =o—i<br>3“15 = — i<br>5 — 10<br>3 — 7»<br>5<br>~125-<br>pd 02 aa o6 OB<br>Proportion with piped water<br><!-- End of picture text -->
<!-- Start of picture text --> Week 1 Week 2 Week 3<br>0-0.001<br>Z : i 5 ; Cases0.001 /1000/7- 0.01 day period<br>0.01-0.1<br>01-1<br>Week 4 : Week 5 Week 6 Rend<br>? i i 2 — 5 100- 1000<br>~~ | | 4 : | 2 4 < ; A é 10-100<br><!-- End of picture text -->
|Piped water provided to areas<br>within 500m of the existing piped<br>water network|7.55 (755%)|1726325|0.66|-11.44|
|---|---|---|---|---|
|Sewer access provided to entire<br>city|1.71E+16|2599530|1|-1.71E+16|
|Sewer access provided to areas<br>within 500m of the existing sewer<br>network|-0.54|1459135|0.56|0.96|
|Piped water and sewer access<br>provided to entire city|5.03E+16|2599530|1|-5.03E+16|
|Piped water provided within|||||
|500m of existing piped water<br>network<br>and<br>sewer<br>access|||||
|provided within 500m of existing<br>sewer network|2.91 (291%)|1830207|0.7|-4.15|

**Table 3: Scenarios and results of respective changes in water and sanitation coverage across the city in terms of reducing cholera risk.**

In terms of opportunities to spatially target intervention options, results show that for all four scenarios involving preventing sewer bursts, the same five wards were ranked 1<sup>st</sup> to 5<sup>th</sup> (Figure 7).<sup>3</sup> Targeting these five wards could have prevented nearly 70% of cases.

> 3 Ranking was conducted by calculating the difference between the total cases in the ward predicted under a given scenario and the observed cases in the ward, aggregating the cases from all the grid cells in a ward.

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_D -- no sewer bursts in highest (75th percentile) density burst areas. Right – the cumulative reduction (%) in cases achieved, and population targeted, from targeting wards in order of priority._

# Discussion

There are a few key findings that come out of this research and have direct policy implications. The modeling suggested that sanitation risk, poverty, sewer burst density and incidence of imported cases were all risk factors for cholera. Meanwhile, increasing geological vulnerability, distance to medium sized market, proportion with unimproved or other water source, private borehole density and distance to water network were also correlated, but negatively, meaning they acted as protective factors. The correlation of poor sanitation more generally with cholera risk [12] as well as sewer bursts are well known in the literature [20] as well as specifically being known to be an issue in the context of a previous cholera outbreak in Zimbabwe [35]. The association with poverty is also unsurprising [36]. Associated factors with poverty and market size are often identified in the literature such as population density, overcrowding and low educational status [37], [38]. The protective character of proximity to the water network is also supported by a similar study in Lusaka, Zambia [12] **.** Surprising findings from this work include the fact that proportion of those with unimproved water source is a protective factor and that increases in geological vulnerability is also a protective factor. The former can be explained by the proximity of piped water sources to sewers with a high number and density of bursts. However, the association of increased geological vulnerability with reduced cholera risk is unexpected, not substantiated in the literature and requires further exploration.

The modeling suggests that there was an interaction between the proportion of households with piped water and distance to the sewer network. In the case of Harare, it suggests that closer to the sewer network (at 0km), the risk of cholera is slightly higher, the higher the proportion of households who have piped water. However, this relationship switches direction in areas >2km from the sewer network, where the higher the proportion of households with piped water, the lower the risk of cholera. This implies that there is a higher risk of having a piped water connection for households which are proximate to the sewer network in Harare, which we believe to be due to the high rate of sewer bursts in the city at the time of the outbreak. The most accurate way of testing whether this is the case would be through water quality testing data at different points along the network. Although water quality testing data was provided for this analysis, there were insufficient data points to be able to make such a comparison. In the absence of such data, this analysis can still provide some useful insights into investments that can reduce sewer bursts particularly in areas which are closer to the piped water network, and thereby offer the greatest health benefits in terms of reducing cholera risk.

We identify that spatially, the risk of sewer bursts on this occasion was concentrated in five wards in the south west of the city. On the assumption that sewer bursts do not occur at random, but are spatially correlated due to underlying weaknesses in the network in those areas, targeting these five wards promises to reduce cases across the city by up to 70%. Using the data from the bursts reported and analyzed, there appeared to be limited benefits (in terms of reducing cholera risk) in investing in repairing sewer bursts throughout the city as it would have led to a .87 reduction in 14
risk, versus a 0.86 reduction in risk when concentrating on the areas where the top 25% of sewer bursts are concentrated. The efficiency and cost effectiveness of reducing cholera risk is therefore much greater when concentrating areas that fall within the top 25th percentile of sewer bursts. It is also more efficient to provide piped water to within 500 meters of the existing network (7.55), rather than to extend access to the whole city (8.2) when considering their relative impact on reducing the number of cholera cases.

Finally, we also see how extending access to the sewer network in its state at the time of the data collection in fact seems to increase cholera risk. This finding may seem perverse given that according to the Joint Monitoring Program of the WHO, which set the standards for sanitation globally, sewered sanitation is at the top of the improved sanitation ladder. However, it serves to highlight the importance of proper maintenance of existing sanitation networks rather than necessarily emphasizing new construction. This is particularly important in a context where the institutional capacity and financial resources to maintain large-scale sanitation systems may be lacking.

An important limitation of the study is that the analysis presented relies on observational data and thus we draw correlations and associations, rather than drawing causal inferences. This clearly has important implications when interpreting the scenario analysis. Additionally, there are likely to be some errors in case data such as the locations and times of case identification. Similarly, a number of layers used in the analysis, such as proportion of households with access to piped water, were generated using DHS data, which have been spatially obfuscated. As a result, this may have introduced error into the layer, which could obscure, or falsely lead to, associations with cases. In addition, sewer and water burst density was based on spatiotemporally referenced complaints data, which is obviously a proxy measure with a number of reported and recorded issues. Data from direct inspections of sewer and water networks would have been a more reliable measure but such data was not available to the researchers.

The focus of this analysis has been to study what factors might be associated with a cholera outbreak and could explain the spatial heterogeneity in the outbreak. An area of future research would be to explore what may lead to some of these factors, especially sewer bursts, in order to be able to predict areas at risk for future outbreaks. Above all, this analysis shows the importance of emphasizing maintenance above and beyond new construction, especially when thinking of extending an already poorly maintained system.

# Acknowledgments

The authors acknowledge with thanks the financial support received from the Global Water Security and Sanitation Partnership (GWSP). The authors also acknowledge the cooperation of officials from City of Harare (CoH) Water and GIS Departments, in particular Toine Ramaker from VEI, Dr. Manangazira from the Ministry of Health, Zimbabwe; in country counterparts at the World Health Organization (WHO); and the Country Management Unit (CMU) of the World Bank in Zimbabwe, in particular then Country Manager Mukami Kariuki, Senior Operations Officer Tonderai Fadzai Naome Mukonoweshuro and Health Specialist Consultant Chenjerai Sisimayi for facilitating contact with counterparts and local dissemination of findings.

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