COVID-19 has presented itself with an extreme impact on the resources of its epi-centres. In Uganda, there is uncertainty about what will happen especially in the main urban hub, the Greater Kampala Metropolitan Area (GKMA). Consequently, public health professionals have scrambled into resource-driven strategies and planning to tame the spread. This paper, therefore, deploys spatial modelling to contribute to an understanding of the spatial variation of COVID-19 vulnerability in the GKMA using the socioeconomic characteristics of the region. Based on expert opinion on the prevailing novel Coronavirus, spatially driven indicators were generated to assess vulnerability. Through an online survey and auxiliary datasets, these indicators were transformed, classified, and weighted based on the BBC vulnerability framework. These were spatially modelled to assess the vulnerability indices. The resultant continuous indices were aggregated, explicitly zoned, classified, and ranked based on parishes. The resultant spatial nature of vulnerability to COVID-19 in the GKMA sprawls out of major urban areas, diffuses into the peri-urban, and thins into the sparsely populated areas. The high levels of vulnerability (24.5% parishes) are concentrated in the major towns where there are many shopping malls, transactional offices, and transport hubs. Nearly half the total parishes in the GKMA (47.3%) were moderately vulnerable, these constituted mainly the parishes on the outskirts of the major towns while 28.2% had a low vulnerability.

The success of disaster management efforts demands meaningful integration of data that is geographically dispersed and owned by stakeholders in various sectors. However, the difficulty in finding, accessing and reusing interoperable vocabularies to organise disaster management data creates a challenge for collaboration among stakeholders in the disaster management cycle on data integration tasks. Thus the need to implement FAIR principles that describe the desired features ontologies should possess to maximize sharing and reuse by humans and machines. In this review, we explore the extent to which sharing and reuse of disaster management knowledge in the domain is inline with FAIR recommendations. We achieve this through a systematic search and review of publications in the disaster management domain based on a predefined inclusion and exclusion criteria. We then extract social-technical features in selected studies and evaluate retrieved ontologies against the FAIR maturity model for semantic artefacts. Results reveal that low numbers of ontologies representing disaster management knowledge are resolvable via URIs. Moreover, 90.9% of URIs to the downloadable disaster management ontology artefacts do not conform to the principle of uniqueness and persistence. Also, only 1.4% of all retrieved ontologies are published in semantic repositories and 84.1% are not published at all because there are no repositories dedicated to archiving disaster domain knowledge. Therefore, there exists a very low level of Findability (1.8%) or Accessibility (5.8%), while Interoperability and Reusability are moderate (49.1% and 30.2 % respectively). The low adherence of disaster vocabularies to FAIR Principles poses a challenge to disaster data integration tasks because of the limited ability to reuse previous knowledge during disaster management phases. By using FAIR indicators to evaluate the maturity in sharing, discovery and integration of disaster management ontologies, we reveal potential research opportunities for managing reusable and evolving knowledge in the disaster community.

One of the grand challenges of disaster management is for stakeholders to be able to discover, access, integrate and analyze task-appropriate disaster data together with their associated algorithms and work-flows. Even with a growing number of initiatives to publish disaster data using open principles, integration and reuse are still difficult due to existing interoperability barriers within datasets. Several frameworks for assessing data interoperability exist but do not generate best practice solutions to existing barriers based on the assessment they use. In this study, we assess disaster data interoperability in Uganda and identify generic solutions to interoperability challenges in the context of disaster data. Semi-structured interviews and focus group discussions were used to collect qualitative data from stakeholders in the disaster sector in Uganda. Data interoperability was measured to provide an understanding of interoperability in the disaster sector. Interoperability maturity is measured using qualitative methods, while data compatibility metrics are computed from identifiers in the RDF-triple model. Results indicate high syntactic and technical interoperability maturity for disaster data. On the contrary, there exists considerable semantic and legal interoperability barriers that hinder disaster data integration and reuse. A mapping of the interoperability challenges in the disaster management sector to solutions reveals a potential to reuse established patterns for managing interoperability. These include; the federated pattern, linked data patterns, broadcast pattern, rights and policy harmonization patterns, dissemination and awareness pattern, ontology design patterns among others. Thus a systematic approach to combining patterns is critical to managing data interoperability barriers among actors in the disaster management ecosystem.

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Listeria monocytogenes is a bacterium that is responsible for causing Listeria, a disease that has a wide range of adverse effects such as meningitis, bacteremia, complications during pregnancy, and other fatal illnesses especially among those whose immune systems are compromised. The purpose of this study was to establish hotspot candidate sites in New York State where the L. monocytogenes pathogen could be found. Several suitability criteria which include proximity to water, pasture, forests, and urban development and slope among others in New York State were considered in this analysis. This study assessed which spatial habitat factors influence habitat suitability of the L. monocytogenes pathogen in the forested areas of New York State. Multicriteria evaluation was used to integrate the different habitat factors using their different weights expressed using probability distributions. Spatially explicit uncertainty and sensitivity analysis (UA and SA) was carried out to examine the robustness of habitat suitability analysis. Suitability maps were generated and summarized using an average suitability map, a standard deviation uncertainty map, and sensitivity maps. Results showed that the shallowest depth to a wet soil layer (measured annually) and proximity to water are the habitat factors which contribute the most and individually to the distribution and survival of this pathogen. The slope gradient is singly insignificant but influential when associated with other factors like temperature, soil organic matter content, volume of water soil can store, proximity to forest, urban development, and pasture among others. It was established that water is the key habitat factor that favors the survival of this pathogen. Also, the ability to spatially model zoonotic pathogen hotspots is important in zoonoses control, informing and influencing policy. From these results, it is important to maintain the water quality of the water sources (lakes, rivers, ponds) and ensure that there is limited runoff from slopes.