A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (for example, warmer environments¹, younger populations^2,3,4) have yet to be framed within a comprehensive analysis. We synthesized factors hypothesized to drive the pace and burden of this pandemic in SSA during the period from 25 February to 20 December 2020, encompassing demographic, comorbidity, climatic, healthcare capacity, intervention efforts and human mobility dimensions. Large diversity in the probable drivers indicates a need for caution in interpreting analyses that aggregate data across low- and middle-income settings. Our simulation shows that climatic variation between SSA population centers has little effect on early outbreak trajectories; however, heterogeneity in connectivity, although rarely considered, is likely an important contributor to variance in the pace of viral spread across SSA. Our synthesis points to the potential benefits of context-specific adaptation of surveillance systems during the ongoing pandemic. In particular, characterizing patterns of severity over age will be a priority in settings with high comorbidity burdens and poor access to care. Understanding the spatial extent of outbreaks warrants emphasis in settings where low connectivity could drive prolonged, asynchronous outbreaks resulting in extended stress to health systems.

Measuring inequality in access to safe drinking-water and sanitation is proposed as a component of international monitoring following the expiry of the Millennium Development Goals. This study aims to evaluate the utility of census data in measuring geographic inequality in access to drinking-water and sanitation. Spatially referenced census data were acquired for Colombia, South Africa, Egypt, and Uganda, whilst non-spatially referenced census data were acquired for Kenya. Four variants of the dissimilarity index were used to estimate geographic inequality in access to both services using large and small area units in each country through a cross-sectional, ecological study. Inequality was greatest for piped water in South Africa in 2001 (based on 53 areas (N) with a median population (MP) of 657,015; D = 0.5599) and lowest for access to an improved water source in Uganda in2008 (N = 56; MP = 419,399; D = 0.2801). For sanitation, inequality was greatest for those lacking any facility in Kenya in 2009 (N = 158; MP = 216,992; D = 0.6981), and lowest for access to an improved facility in Uganda in 2002 (N = 56; MP = 341,954; D = 0.3403). Although dissimilarity index values were greater for smaller areal units, when study countries were ranked in terms of inequality, these ranks remained unaffected by the choice of large or small areal units. International comparability was limited due to definitional and temporal differences between censuses. This five-country study suggests that patterns of inequality for broad regional units do often reflect inequality in service access at a more local scale. This implies household surveys designed to estimate province-level service coverage can provide valuable insights into geographic inequality at lower levels. In comparison with household surveys, censuses facilitate inequality assessment at different spatial scales, but pose challenges in harmonising water and sanitation typologies across countries.

Water point mapping databases, generated through surveys of water sources such as wells and boreholes, are now available in many low and middle income countries, but often suffer from incomplete coverage. To address the partial coverage in such databases and gain insights into spatial patterns of water resource use, this study investigated the use of a maximum entropy (MaxEnt) approach to predict the geospatial distribution of drinking-water sources, using two types of unimproved sources in Kenya as illustration. Geographic locations of unprotected dug wells and surface water sources derived from the Water Point Data Exchange (WPDx) database were used as inputs to the MaxEnt model alongside geological/hydrogeological and socio-economic covariates. Predictive performance of the MaxEnt models was high (all > 0.9) based on Area Under the Receiver Operator Curve (AUC), and the predicted spatial distribution of water point was broadly consistent with household use of these unimproved drinking-water sources reported in household survey and census data. In developing countries where geospatial datasets concerning drinking-water sources often have necessarily limited resolution or incomplete spatial coverage, the modelled surface can provide an initial indication of the geography of unimproved drinking-water sources to target unserved populations and assess water source vulnerability to contamination and hazards.

Following the recent expiry of the United Nations’ 2015 Millennium Development Goals (MDGs), new international development agenda covering 2030 water, sanitation and hygiene (WASH) targets have been proposed, which imply new demands on data sources for monitoring relevant progress. This study evaluates drinking-water and sanitation classification systems from national census questionnaire content, based upon the most recent international policy changes, to examine national population census’s ability to capture drinking-water and sanitation availability, safety, accessibility, and sustainability. In total, 247 censuses from 83 low income and lower-middle income countries were assessed using a scoring system, intended to assess harmonised water supply and sanitation classification systems for each census relative to the typology needed to monitor the proposed post-2015 indicators of WASH targets. The results signal a lack of international harmonisation and standardisation in census categorisation systems, especially concerning safety, accessibility, and sustainability of services in current census content. This suggests further refinements and harmonisation of future census content may be necessary to reflect ambitions for post-2015 monitoring.

Roads have major impacts on wildlife, and the most direct negative effect is through deadly collisions with vehicles, i.e., roadkill. Amphibians are the most frequently road-killed animal group. Due to the significant differences between urban and rural environments, the potential urban-rural differences in factors driving amphibian roadkill risks should be incorporated into the planning of mitigation measures. Drawing on a citizen-collected roadkill dataset from Taiwan island, we present a MaxEnt based modelling analysis to examine potential urban-rural differences in landscape features and environmental factors associated with amphibian road mortality. By incorporating with the Global Human Settlement Layer Settlement Model—an ancillary human settlement dataset divided by built-up area and population density—amphibian roadkill data were divided into urban and rural data sets, and then used to create separate models for urban and rural areas. Model diagnostics suggested good performance (all AUCs > 0.8) of both urban and rural models. Multiple variable importance evaluations revealed significant differences between urban and rural areas. The importance of environmental variables was evaluated based on percent contribution, permutation importance and the Jackknife test. According to the overall results, road density was found to be important in explaining the amphibian roadkill in rural areas, whilst precipitation of warmest quarter was found to best explain the amphibian roadkill in the urban context. The method and outputs illustrated in this study can be useful tools to better understand amphibian road mortality in urban and rural environments and to inform mitigation assessment and conservation planning.

Water safety planning is an approach to ensure safe drinking-water access through comprehensive risk assessment and water supply management from catchment to consumer. However, its uptake remains low in rural areas. Participatory mapping, the process of map creation for resource management by local communities, has yet to be used for rural water safety planning. In this mixed methods study, to evaluate the validity of participatory mapping outputs for rural water safety planning and assess community understanding of water safety, 140 community members in Siaya County, Kenya, attended ten village-level participatory mapping sessions. They mapped drinking-water sources, ranked their safety and mapped potential contamination hazards. Findings were triangulated against a questionnaire survey of 234 households, conducted in parallel. In contrast to source type ranking for international monitoring, workshop participants ranked rainwater’s safety above piped water and identified source types such as broken pipes not explicitly recorded in water source typologies often used for formal monitoring. Participatory mapping also highlighted the overlap between livestock grazing areas and household water sources. These findings were corroborated by the household survey and subsequent participatory meetings. However, comparison with household survey data suggested participatory mapping outputs omitted some water sources and landscape-scale contamination hazards, such as open defecation areas or flood-prone areas. In follow-up visits, participant groups ranked remediation of rainwater harvesting systems as the most acceptable intervention to address hazards. We conclude that participatory mapping can complement other established approaches to rural water safety planning by capturing informally managed source use and facilitating community engagement.