Among the agents involved in the disease transmission process, human hosts play a crucial role as their density, spatial location, demographic characteristics (e.g. age-risk profiles) and behaviour determine their exposure to infection. Any approach that requires the use of modelled disease rates or dynamics requires reasonable information on the resident human population for the time period one is intending to estimate risk. Where risks and spread of diseases are heterogeneous in space, population distributions and counts should ideally be resolved to higher levels of spatial detail than large regional estimates. Accurate and detailed information on population size and distribution are therefore of significant importance for deriving populations at risk and infection movement estimates in spatial epidemiological studies. For many low-income countries of the World, where disease burden is greatest, however, spatially detailed, contemporary census data do not exist. This is especially true for much of Africa, where currently available census data are often over a decade old, and at administrative boundary levels just below national-level.
We contributed to recent developments of the WorldPop database, more specifically on the AfriPop part of the project (see the map below from Linard et al. 2012). The WorlPop project aims to provide an open access archive of spatial demographic datasets for Africa, Asia and Central and South America to support development, disaster response and health applications. The methods used are designed with full open access and operational application in mind, using transparent, fully documented and shareable methods to produce easily updatable maps with accompanying metadata.
The MAUPP project is dedicated to the improvement of population distribution maps for urban areas in sub-Saharan Africa. Objectives are: * to improve intra-urban predictions of population densities * to develop continental-scale urban expansion models for Africa in order to produce predicted human population distribution datasets for the future
Our knowledge of human population distribution in space and time remains poor, especially in low income countries. At the moment, mapping of populations is constrained by the logistics of censuses and surveys, which just provide a single snapshot of population distributions every ten years and little information exists to inform on daily, monthly or seasonal changes in population distributions. Methods have been developed to produce accurate and cost-effective datasets depicting human population distribution for different time periods using mobile phone call activities. The number of communications and the number of phone users aggregated at the cell tower level are used to estimate the population density in the coverage area of the tower. Our findings show that maps made using mobile records are detailed, reliable and flexible enough to help inform infrastructure and emergency planners; particularly in low income countries, where recent population density information is often scarce. With similar data being collected every day by mobile phone network providers across the World, the prospect of being able to map contemporary and changing human population distributions over relatively short time intervals exists, paving the way to new applications and a near real-time understanding of patterns and processes in human geography.
We aim to work on further improvements of the population distribution models by (i) improving population disaggregation algorithms and predictor variables included in the modelling process, (ii) including urban expansion forecasts into predicted population distribution datasets and (iii) evaluate the potential of the wealth of spatio-temporal data provided by new technologies (mobile phones and social networks such as Twitter) to improve the spatial and temporal resolution of human population distribution datasets.
Dissemination is ensured throuhg the Worldpop database, where data can be visualised and downloaded.
Our main collaborators on this topic are Andy Tatem, Alessandro Sorichetta and Graeme Hornby (Univ. Southampton, UK), Andrea Gaughan and Forrest Stevens (Univ. Louisville, USA), Pierre Deville and Vincent Blondel (Université catholique de Louvain, Belgium), Eléonore Wolff, Taïs Grippa and Sabine Vanhuysse (ULB), Michal Shimoni and JuanFran Lopez (Royal Military Institute, Belgium)
Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009
C. Linard, C. W. Kabaria, M. Gilbert, A. J. Tatem, A. E. Gaughan, F. R. Stevens, A. Sorichetta, A. M. Noor, and R. W. Snow.
"International Journal of Digital Earth", Vol. 0, Issue 0, Pages 1-13, 2017.
Dynamic population mapping using mobile phone data
P. Deville, C. Linard, S. Martin, M. Gilbert, F. R. Stevens, A. E. Gaughan, V. D. Blondel, and A. J. Tatem.
"Proceedings of the National Academy of Sciences", Vol. 111, Issue 45, Pages 15888-15893, 2014.
Modelling spatial patterns of urban growth in Africa
C. Linard, A. J. Tatem, and M. Gilbert.
"Applied Geography", Vol. 44, Pages 23-32, 2013.
Population Distribution, Settlement Patterns and Accessibility across Africa in 2010
C. Linard, M. Gilbert, R. W. Snow, A. M. Noor, and A. J. Tatem.
"PLoS ONE", Vol. 7, Issue 2, Pages e31743, 2012.
Assessing the use of global land cover data for guiding large area population distribution modelling
C. Linard, M. Gilbert, and A. J. Tatem.
"GeoJournal", Vol. 76, Issue 5, Pages 525-538, 2011.