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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. Global climate change and rapid urbanization in sub-Saharan Africa (SSA) are likely to affect the epidemiology of vector-borne diseases such as malaria in urban and peri-urban areas. In this context, a better understanding of intra-urban malaria risk and its determinants has become even more urgent. Malaria risk has often been modeled at the national scale from Demographic and Health Surveys (DHS), which are periodically conducted in more than 90 developing countries. However

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements., QC 20221206

We applied Weather Research and Forecasting (WRF) model's Multilayer Urban Canopy Model (MLUCM) to simulate the urban climate of Singapore during a hot period in April 2016. The high-resolution local climate zone (LCZ) map was used as urban land use/land cover data in order to study the intraurban variability in different LCZ classes. The LCZ map for Singapore was developed by adopting the World Urban Database and Access Portal Tools (WUDAPT) methodology based on satellite remote sensing imageries and building height data. The coupled WRF/MLUCM model was validated using meteorological data from stations across Singapore. Higher index of agreement compared to observations and lower root-mean-squared error of 2-m temperature, relative humidity, and global horizontal irradiance showed satisfactory model performance. A sensitivity analysis of initial and boundary conditions helped in determining the model configuration with the least error for quantifying the urban heat island (UHI) effect. The diurnal cycle and the spatial pattern of UHI were investigated, and it was found that the mean UHI intensity peaked in the early morning at 2.2 °C, reaching 3.6 °C in the Compact High Rise (LCZ1) areas. The anthropogenic heat due to indoor air conditioning was found to play a major role in all the processes studied, while the effect of the different land use types was most pronounced during nighttime and least visible near noon. The UHI circulation developed near the central catchment is found to prevent sea breezes from further propagating inland.

Malaria burden is increasing in sub-Saharan cities because of rapid and uncontrolled urbanization. Yet very few studies have studied the interactions between urban environments and malaria. Additionally, no standardized urban land-use/land-cover has been defined for urban malaria studies. Here, we demonstrate the potential of local climate zones (LCZs) for modeling malaria prevalence rate (PfPR2-10) and studying malaria prevalence in urban settings across nine sub-Saharan African cities. Using a random forest classification algorithm over a set of 365 malaria surveys we: (i) identify a suitable set of covariates derived from open-source earth observations; and (ii) depict the best buffer size at which to aggregate them for modeling PfPR2-10. Our results demonstrate that geographical models can learn from LCZ over a set of cities and be transferred over a city of choice that has few or no malaria surveys. In particular, we find that urban areas systematically have lower PfPR2-10 (5%-30%) than rural areas (15%-40%). The PfPR2-10 urban-to-rural gradient is dependent on the climatic environment in which the city is located. Further, LCZs show that more open urban environments located close to wetlands have higher PfPR2-10. Informal settlements - represented by the LCZ 7 (lightweight lowrise) - have higher malaria prevalence than other densely built-up residential areas with a mean prevalence of 11.11%. Overall, we suggest the applicability of LCZs for more exploratory modeling in urban malaria studies. © 2020 The Author(s). Published by IOP Publishing Ltd., SCOPUS: ar.j, info:eu-repo/semantics/published