Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology.

BACKGROUND: Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts. METHODS: A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m. RESULTS: The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation. CONCLUSIONS: This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.

Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators.

BACKGROUND: The rapid and often uncontrolled rural-urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa's population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents. As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. METHODS: Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2-10 years (PfPR2-10) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR2-10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population. RESULTS: The results suggest that the spatial distribution of PfPR2-10 in both cities is diverse and highly variable across the urban fabric. Dense informal settlements exhibit a positive relationship with PfPR2-10 and hotspots of malaria prevalence were found near suitable vector breeding sites such as wetlands, marshes and riparian vegetation. In both cities, there is a clear separation of higher risk in informal settlements and lower risk in the more affluent neighborhoods. Additionally, areas associated with urban agriculture exhibit higher malaria prevalence values. CONCLUSIONS: The outcome of this research highlights that populations living in informal settlements show higher malaria prevalence compared to those in planned residential neighborhoods. This is due to (i) increased human exposure to vectors, (ii) increased vector density and (iii) a reduced capacity to cope with malaria burden. Since informal settlements are rapidly expanding every year and often house large parts of the urban population, this emphasizes the need for systematic and consistent malaria surveys in such areas. Finally, this study demonstrates the importance of remote sensing as an epidemiological tool for mapping urban malaria variations at large spatial extents, and for promoting evidence-based policy making and control efforts.

Correction to: Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs.

Readers should note an additional Acknowledgment for this article: Dana Thomson is funded by the Economic and Social Research Council grant number ES/5500161/1.

Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs.

Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1 × 1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data-ideally to be made free and publicly available-and offer lay descriptions of some of the difficulties in generating such data products.

Spatial machine learning for predicting physical inactivity prevalence from socioecological determinants in Chicago, Illinois, USA.

The increase in physical inactivity prevalence in the USA has been associated with neighborhood characteristics. While several studies have found an association between neighborhood and health, the relative importance of each component related to physical inactivity or how this value varies geographically (i.e., across different neighborhoods) remains unexplored. This study ranks the contribution of seven socioecological neighborhood factors to physical inactivity prevalence in Chicago, Illinois, using machine learning models at the census tract level, and evaluates their predictive capabilities. First, we use geographical random forest (GRF), a recently proposed nonlinear machine learning regression method that assesses each predictive factor's spatial variation and contribution to physical inactivity prevalence. Then, we compare the predictive performance of GRF to geographically weighted artificial neural networks, another recently proposed spatial machine learning algorithm. Our results suggest that poverty is the most important determinant in the Chicago tracts, while on the other hand, green space is the least important determinant in the rise of physical inactivity prevalence. As a result, interventions can be designed and implemented based on specific local circumstances rather than broad concepts that apply to Chicago and other large cities. Supplementary Information: The online version contains supplementary material available at 10.1007/s10109-023-00415-y.

Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys.

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale.