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Background: Universal access to safe drinking water and sanitation facilities is an essential human right, recognised in the Sustainable Development Goals as crucial for preventing disease and improving human wellbeing. Comprehensive, high-resolution estimates are important to inform progress towards achieving this goal. We aimed to produce highresolution geospatial estimates of access to drinking water and sanitation facilities. Methods: We used a Bayesian geostatistical model and data from 600 sources across more than 88 low-income and middle-income countries (LMICs) to estimate access to drinking water and sanitation facilities on continuous continent-wide surfaces from 2000 to 2017, and aggregated results to policy-relevant administrative units. We estimated mutually exclusive and collectively exhaustive subcategories of facilities for drinking water (piped water on or off premises, other improved facilities, unimproved, and surface water) and sanitation facilities (septic or sewer sanitation, other improved, unimproved, and open defecation) with use of ordinal regression. We also estimated the number of diarrhoeal deaths in children younger than 5 years attributed to unsafe facilities and estimated deaths that were averted by increased access to safe facilities in 2017, and analysed geographical inequality in access within LMICs. Findings: Across LMICs, access to both piped water and improved water overall increased between 2000 and 2017, with progress varying spatially. For piped water, the safest water facility type, access increased from 40·0% (95% uncertainty interval [UI] 39·4–40·7) to 50·3% (50·0–50·5), but was lowest in sub-Saharan Africa, where access to piped water was mostly concentrated in urban centres. Access to both sewer or septic sanitation and improved sanitation overall also increased across all LMICs during the study period. For sewer or septic sanitation, access was 46·3% (95% UI 46·1–46·5) in 2017, compared with 28·7% (28·5–29·0) in 2000. Although some units improved access to the safest drinking water or sanitation facilities since 2000, a large absolute number of people continued to not have access in several units with high access to such facilities (>80%) in 2017. More than 253 000 people did not have access to sewer or septic sanitation facilities in the city of Harare, Zimbabwe, despite 88·6% (95% UI 87·2–89·7) access overall. Many units were able to transition from the least safe facilities in 2000 to safe facilities by 2017; for units in which populations primarily practised open defecation in 2000, 686 (95% UI 664–711) of the 1830 (1797–1863) units transitioned to the use of improved sanitation. Geographical disparities in access to improved water across units decreased in 76·1% (95% UI 71·6–80·7) of countries from 2000 to 2017, and in 53·9% (50·6–59·6) of countries for access to improved sanitation, but remained evident subnationally in most countries in 2017. Interpretation: Our estimates, combined with geospatial trends in diarrhoeal burden, identify where efforts to increase access to safe drinking water and sanitation facilities are most needed. By highlighting areas with successful approaches or in need of targeted interventions, our estimates can enable precision public health to effectively progress towards universal access to safe water and sanitation. ; This work was primarily supported by a grant from the Gates Foundation (OPP1132415). LGA has received support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finance Code 001), Conselho Nacional de Desenvolvimento Científico e Tecnológico and Fundação de Amparo à Pesquisa do Estado de Minas Gerais. OOA acknowledges the Department of Science and Innovation, National Research Foundation, and DSI/NRF Centre of Excellence for Epidemiological Modelling and Analysis, Stellenbosch, South Africa. SMAl acknowledges the Department of Health Policy and Management, Faculty of Public Health, Kuwait University and International Centre for Casemix and Clinical Coding, Faculty of Medicine, National University of Malaysia for the approval and support to participate in this research project. HTA acknowledges Aksum University. MAu and CH are partly supported by a grant from the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. AAz acknowledges funding from the Gates Foundation (OPP1171700). ABad is supported by the Public Health Agency of Canada. TWB was supported by the Alexander von Humboldt Foundation through the Alexander von Humboldt Professor award, funded by the German Federal Ministry of Education and Research; the EU; the Wellcome Trust; and from National Institute of Child Health and Human Development of National Institutes of Health (NIH; R01-HD084233), National Institute on Aging of NIH (P01AG041710), National Institute of Allergy and Infectious Diseases of NIH (R01-AI124389 and R01-AI112339), as well as Fogarty International Center of NIH (D43-TW009775). DABen was supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the National Health Service, the NIHR, or the UK Department of Health and Social Care. GBB is supported by Sistema Nacional de Investigación (SNI) de la Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT) of Panamá. FCar acknowledges UID/ MULTI/04378/2019 and UID/QUI/50006/2019 support with funding from FCT/Ministério da Ciência, Tecnologia e Ensino Superior through national funds. VMC acknowledges her grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006. JDN acknowledges support from the Alexander von Humboldt Foundation. DBD acknowledges support from the Gates Foundation. KD is supported by a Wellcome Trust grant (number 201900/Z/16/Z) as part of his International Intermediate Fellowship. AGo acknowledges Sistema Nacional de Investigadores de Panamá (SNI), Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). CH is partly supported by a grant co-funded by European Fund for Regional Development through the Operational Program for Competitiveness (project ID P_40_382). SMSI is funded by a Fellowship from National Heart Foundation of Australia and Deakin University. MJ and the Serbian part of this GBD contribution was co-funded through grant OI175014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. JK is a recipient of the 2020 Benjamin V Cohen Peace Fellowship from Ball State University Center for Peace and Conflict Studies. YJK's work was supported by the Research Management Centre, Xiamen University Malaysia, grants number XMUMRF/2018-C2/ ITCM/0001. KKr is supported by a DST PURSE grant and UGC Center of Advanced Study awarded to the Department of Anthropology, Panjab University, Chandigarh, India. BL acknowledges support from the NIHR Oxford Biomedical Research Centre and the British Heart Foundation Centre of Research Excellence, Oxford. PTNM acknowledges the Council for the Development of Social Science Research in Africa. ANA acknowledges Debre Markos University for its support in-terms of office and internet access while reviewing this paper. AMSam received a fellowship from the Egyptian Fulbright Mission programme. MMS-M acknowledges the support of the Ministry of Education, Science and Technological Development of the Republic of Serbia (contract number 175087). AShi acknowledges the support of Health Data Research UK. MRS acknowledges the Clinical Research Development Center of Imam Reza Hospital, Kermanshah university of Medical sciences for their wise advice. JBS is part of Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III (ISCIII), Madrid, Spain. RT-S was supported in part by grant PI17/00719 from Instituto de Salud Carlos III–FEDER. BU acknowledges Manipal Academy of Higher Education, Manipal. TWij acknowledges the Migraine Foundation Australia and the Department of Medicine, Faculty of Medicine, University of Rajarata, Saliyapura, Anuradhapuraya, Sri Lanka. CSW was supported by the South African Medical Research Council. SBZ received a scholarship from the Australian Government research training program in support of his academic career. ; publishedVersion

International audience ; Background : The 2018–2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces in the Democratic Republic of the Congo (DRC) is the largest ever recorded in the DRC. It has been declared a Public Health Emergency of International Concern. The outbreak emerged in a region of chronic conflict and insecurity, and directed attacks against health care workers may have interfered with disease response activities. Our study characterizes and quantifies the broader conflict dynamics over the course of the outbreak by pairing epidemiological and all available spatial conflict data.Methods : We build a set of conflict variables by mapping the spatial locations of all conflict events and their associated deaths in each of the affected health zones in North Kivu and Ituri, eastern DRC, before and during the outbreak. Using these data, we compare patterns of conflict before and during the outbreak in affected health zones and those not affected. We then test whether conflict is correlated with increased EVD transmission at the health zone level.Findings : The incidence of conflict events per capita is ~ 600 times more likely in Ituri and North Kivu than for the rest of the DRC. We identified 15 time periods of substantial uninterrupted transmission across 11 health zones and a total of 120 bi-weeks. We do not find significant short-term associations between the bi-week reproduction numbers and the number of conflicts. However, we do find that the incidence of conflict per capita was correlated with the incidence of EVD per capita at the health zone level for the entire outbreak (Pearson's r = 0.33, 95% CI 0.05–0.57). In the two provinces, the monthly number of conflict events also increased by a factor of 2.7 in Ebola-affected health zones ( p value < 0.05) compared to 2.0 where no transmission was reported and 1.3 in the rest of the DRC, in the period between February 2019 and July 2019.Conclusion : We characterized the association between variables documenting broad conflict ...

International audience ; Background : The 2018–2019 Ebola virus disease (EVD) outbreak in North Kivu and Ituri provinces in the Democratic Republic of the Congo (DRC) is the largest ever recorded in the DRC. It has been declared a Public Health Emergency of International Concern. The outbreak emerged in a region of chronic conflict and insecurity, and directed attacks against health care workers may have interfered with disease response activities. Our study characterizes and quantifies the broader conflict dynamics over the course of the outbreak by pairing epidemiological and all available spatial conflict data.Methods : We build a set of conflict variables by mapping the spatial locations of all conflict events and their associated deaths in each of the affected health zones in North Kivu and Ituri, eastern DRC, before and during the outbreak. Using these data, we compare patterns of conflict before and during the outbreak in affected health zones and those not affected. We then test whether conflict is correlated with increased EVD transmission at the health zone level.Findings : The incidence of conflict events per capita is ~ 600 times more likely in Ituri and North Kivu than for the rest of the DRC. We identified 15 time periods of substantial uninterrupted transmission across 11 health zones and a total of 120 bi-weeks. We do not find significant short-term associations between the bi-week reproduction numbers and the number of conflicts. However, we do find that the incidence of conflict per capita was correlated with the incidence of EVD per capita at the health zone level for the entire outbreak (Pearson's r = 0.33, 95% CI 0.05–0.57). In the two provinces, the monthly number of conflict events also increased by a factor of 2.7 in Ebola-affected health zones ( p value < 0.05) compared to 2.0 where no transmission was reported and 1.3 in the rest of the DRC, in the period between February 2019 and July 2019.Conclusion : We characterized the association between variables documenting broad conflict levels and EVD transmission. Such assessment is important to understand if and how such conflict variables could be used to inform the outbreak response. We found that while these variables can help characterize long-term challenges and susceptibilities of the different regions they provide little insight on the short-term dynamics of EVD transmission.

This is an Open Access article under the CC BY license. ; BACKGROUND: Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. METHODS: We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. FINDINGS: The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5-7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34-0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52-0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13-0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92-0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. INTERPRETATION: Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. ; info:eu-repo/semantics/publishedVersion