The main aims of REACT were to:


  1.       Improve our understanding of intra-urban malaria risk
  2.       Improve our understanding of inter-urban malaria risk


These aims were supported by a set of intermediate work packages that acted as data providers to feed the malaria models. To achieve our objectives, we used multi-source RS information, ranging from low-resolution (LR), high resolution (HR) and very-high resolution (VHR) satellite derived data as well as openly available ancillary datasets such as OpenStreetMaps. The first objective (intra-urban malaria) was largely captured by combining VHR and HR land use/land cover (LULC) products. Our highlights include:


·         To our knowledge, we developed the first high resolution (100 meters) intra-urban malaria models, relying on EO-data for two sub-Saharan African (SSA) cities. 


·         Due to the high thematic level of our input LULC maps, we could make strong associations of malaria prevalence with types of urban slums and biophysical characteristics, expanding our current knowledge of urban malaria.


·         A publication in the leading geo-health journal:


o   Georganos, Stefanos, et al. "Modelling and mapping the intra-urban spatial distribution of Plasmodium falciparum parasite rate using very-high-resolution satellite derived indicators." International journal of health geographics 19.1 (2020): 1-18.




The second objective (inter-urban malaria) was largely captured through a restructuring of the initially foreseen plan. This was due to the poor availability and scarcity of enough malaria data points (both temporally and spatially). Consequently, and using the SC recommendations as an anchor, instead of producing a typological clustering of the 24 cities, we successfully tested the transferability potential of high-moderate resolution malaria models through a set of 9 cities harnessing the strength of Local Climate Zones (LCZ) and climatic information. Our highlights include:


·         To our knowledge, the first attempt to create transferable malaria models using EO-data in SSA.


·         LCZ were demonstrated to be potent predictions of malaria prevalence in several cities, while models trained in other areas could provide a better insight on malaria risk than single city models.


·         A peer-reviewed article in one of the leading environmental journals in the field:


o   Brousse, Oscar, et al. "Can we use local climate zones for predicting malaria prevalence across sub-Saharan African cities?." Environmental Research Letters 15.12 (2020): 124051.