Mapping urban physical distancing constraints, sub-Saharan Africa: a case study from Kenya.

With the onset of the coronavirus disease 2019 (COVID-19) pandemic, public health measures such as physical distancing were recommended to reduce transmission of the virus causing the disease. However, the same approach in all areas, regardless of context, may lead to measures being of limited effectiveness and having unforeseen negative consequences, such as loss of livelihoods and food insecurity. A prerequisite to planning and implementing effective, context-appropriate measures to slow community transmission is an understanding of any constraints, such as the locations where physical distancing would not be possible. Focusing on sub-Saharan Africa, we outline and discuss challenges that are faced by residents of urban informal settlements in the ongoing COVID-19 pandemic. We describe how new geospatial data sets can be integrated to provide more detailed information about local constraints on physical distancing and can inform planning of alternative ways to reduce transmission of COVID-19 between people. We include a case study for Nairobi County, Kenya, with mapped outputs which illustrate the intra-urban variation in the feasibility of physical distancing and the expected difficulty for residents of many informal settlement areas. Our examples demonstrate the potential of new geospatial data sets to provide insights and support to policy-making for public health measures, including COVID-19.

Avec l'apparition de la pandémie de maladie à coronavirus 2019 (COVID-19), des mesures de santé publique telles que la distanciation physique ont été mises en place afin de limiter la transmission du virus à l'origine de la maladie. Néanmoins, adopter la même approche dans toutes les régions sans tenir compte du contexte pourrait réduire l'efficacité de ces mesures et avoir des conséquences négatives imprévues, comme la perte des moyens de subsistance et l'insécurité alimentaire. Avant de planifier et de déployer des mesures utiles et adaptées à la situation en vue de ralentir la transmission au sein des communautés, il est impératif d'identifier les contraintes liées notamment aux lieux où la distanciation physique est impossible à respecter. Le présent document se concentre sur l'Afrique subsaharienne. Nous y avons présenté et évoqué les défis auxquels sont confrontés les habitants des implantations urbaines sauvages au cours de l'actuelle pandémie de COVID-19. Nous décrivons comment intégrer les nouveaux ensembles de données géospatiales pour obtenir des informations plus détaillées sur les contraintes locales liées à la distanciation physique et trouver des solutions alternatives permettant de limiter la transmission de la COVID-19 d'une personne à l'autre. Nous citons une étude de cas réalisée dans le comté de Nairobi, au Kenya, dont les résultats cartographiés illustrent les variations intra-urbaines qui déterminent la faisabilité de la distanciation physique et les difficultés que les habitants de nombreuses implantations sauvages sont susceptibles de rencontrer. Nos exemples révèlent le potentiel des nouveaux ensembles de données géospatiales dans l'analyse et l'élaboration des politiques et mesures de santé publique, y compris pour la COVID-19.

Con el inicio de la pandemia de la enfermedad por coronavirus de 2019 (COVID-19), se recomendaron medidas de salud pública como el distanciamiento físico para reducir la transmisión del virus causante de la enfermedad. Sin embargo, el mismo enfoque en todas las áreas, sin tener en cuenta el contexto, puede llevar a que las medidas sean de eficacia limitada y tengan consecuencias negativas imprevistas, como la pérdida de medios de vida y la inseguridad alimentaria. Un requisito previo para planificar y aplicar medidas eficaces y adecuadas al contexto para ralentizar la transmisión en la comunidad es conocer las limitaciones, como los lugares en los que no sería posible el distanciamiento físico. En este documento, centrado en el África subsahariana, se describen y discuten los desafíos a los que se enfrentan los residentes de los asentamientos urbanos informales en la actual pandemia de la COVID-19. Se describe cómo los nuevos conjuntos de datos geoespaciales pueden integrarse para proporcionar información más detallada sobre las limitaciones locales al distanciamiento físico y pueden informar la planificación de vías alternativas para reducir la transmisión de la COVID-19 entre las personas. Se incluye un estudio de caso del condado de Nairobi, Kenia, con resultados cartográficos que ilustran la variación intraurbana en la viabilidad del distanciamiento físico y la dificultad prevista para los residentes de muchas áreas de asentamientos informales. Los ejemplos que aquí se presentan demuestran el potencial de los nuevos conjuntos de datos geoespaciales para proporcionar información y apoyo a la elaboración de políticas sobre medidas de salud pública, entre ellas las relacionadas con la COVID-19.

High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa.

Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.

High-resolution population estimation using household survey data and building footprints.

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.

Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets.

Multi-temporal, globally consistent, high-resolution human population datasets provide consistent and comparable population distributions in support of mapping sub-national heterogeneities in health, wealth, and resource access, and monitoring change in these over time. The production of more reliable and spatially detailed population datasets is increasingly necessary due to the importance of improving metrics at sub-national and multi-temporal scales. This is in support of measurement and monitoring of UN Sustainable Development Goals and related agendas. In response to these agendas, a method has been developed to assemble and harmonise a unique, open access, archive of geospatial datasets. Datasets are provided as global, annual time series, where pertinent at the timescale of population analyses and where data is available, for use in the construction of population distribution layers. The archive includes sub-national census-based population estimates, matched to a geospatial layer denoting administrative unit boundaries, and a number of co-registered gridded geospatial factors that correlate strongly with population presence and density. Here, we describe these harmonised datasets and their limitations, along with the production workflow. Further, we demonstrate applications of the archive by producing multi-temporal gridded population outputs for Africa and using these to derive health and development metrics. The geospatial archive is available at https://doi.org/10.5258/SOTON/WP00650.

Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake.

INTRODUCTION: Sudden impact disasters often result in the displacement of large numbers of people. These movements can occur prior to events, due to early warning messages, or take place post-event due to damages to shelters and livelihoods as well as a result of long-term reconstruction efforts. Displaced populations are especially vulnerable and often in need of support. However, timely and accurate data on the numbers and destinations of displaced populations are extremely challenging to collect across temporal and spatial scales, especially in the aftermath of disasters. Mobile phone call detail records were shown to be a valid data source for estimates of population movements after the 2010 Haiti earthquake, but their potential to provide near real-time ongoing measurements of population displacements immediately after a natural disaster has not been demonstrated. METHODS: A computational architecture and analytical capacity were rapidly deployed within nine days of the Nepal earthquake of 25th April 2015, to provide spatiotemporally detailed estimates of population displacements from call detail records based on movements of 12 million de-identified mobile phones users. RESULTS: Analysis shows the evolution of population mobility patterns after the earthquake and the patterns of return to affected areas, at a high level of detail. Particularly notable is the movement of an estimated 390,000 people above normal from the Kathmandu valley after the earthquake, with most people moving to surrounding areas and the highly-populated areas in the central southern area of Nepal. DISCUSSION: This analysis provides an unprecedented level of information about human movement after a natural disaster, provided within a very short timeframe after the earthquake occurred. The patterns revealed using this method are almost impossible to find through other methods, and are of great interest to humanitarian agencies.

Research on child abuse in the U.S. armed forces.

Child maltreatment in the United States has provoked considerable interest in recent years. Child abuse and neglect are prevalent in all parts of American society. Although children of military personnel experience maltreatment, little research has been completed that compares child abuse rates in the military with those for civilian populations. Studies that have assessed child abuse in the armed forces have been based on official reports recorded in military Family Advocacy central registries. Because a standardized method for recording child abuse does not exist, conclusions regarding the prevalence of abuse are often inaccurate. We explore this and other methodological constraints that make estimating child abuse accurately difficult. A review of the literature also reveals that there are many correlates of child abuse unique to the military family. Finally, we discuss both the risk and protective factors within military life that may influence the occurrence of child maltreatment.