Research Data Leeds Repository

LIS-MAL estimates of hydro-climatic suitability for malaria transmission in Africa (1971-2100)

Smith, Mark and Willis, Thomas and Alfieri, Lorenzo and James, William and Trigg, Mark and Yamazaki, Dai and Hardy, Andrew and Bisselink, Berny and De Roo, Ad and Macklin, Mark and Thomas, Chris (2020) LIS-MAL estimates of hydro-climatic suitability for malaria transmission in Africa (1971-2100). University of Leeds. [Dataset] https://doi.org/10.5518/786

Dataset description

Estimates of climatic suitability for malaria transmission in Africa over the periods 1971-2005, 2011-2040, 2041-2070 and 2071-2100. Seven climate projections using the high concentration scenario (RCP 8.5) were produced with EC-EARTH3-HR v3.1 by the Swedish Meteorological and Hydrological Institute (described in the Excel workbook). These were used to run the Lisflood hydrological model at 0.5 degree resolution and estimate hydro-climatic suitability for malaria transmission based on the Mordecai temperature ranges and Lisflood-predicted surface water availability. For each time period and each of the seven GCMs (i.e. 28 rasters) a 0.5 degree raster layer of the ‘number of suitable months for malaria transmission’ over Africa is presented in the form of a Geotiff. An Excel spreadsheet summarises these files in terms of total area in each 1 month category for the Lisflood estimates and the estimated changes in suitability between time periods. The population estimated to live within areas hydro-climatically suitable for malaria transmission is also presented, incorporating both present day estimates and future predictions. These data are broken down by country and summarised in terms of ‘population months’ for each time period.

Divisions:	Faculty of Environment > School of Geography
Related resources:	Location Type https://doi.org/10.1038/s41467-020-18239-5 Publication https://github.com/ec-jrc/lisflood-code Software
Date deposited:	30 Jul 2020 11:35
URI:	http://archive.researchdata.leeds.ac.uk/id/eprint/715
Additional details Additional details Creators: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Smith, Mark https://orcid.org/0000-0003-4361-9527 University Of Leeds https://doi.org/10.13039/501100000777 crossref Willis, Thomas https://orcid.org/0000-0003-0103-1805 University Of Leeds https://doi.org/10.13039/501100000777 crossref Alfieri, Lorenzo https://orcid.org/0000-0002-3616-386X European Commission, Joint Research Centre, Ispra, Italy James, William https://orcid.org/0000-0002-3273-4688 University Of Leeds https://doi.org/10.13039/501100000777 crossref Trigg, Mark https://orcid.org/0000-0002-8412-9332 University Of Leeds https://doi.org/10.13039/501100000777 crossref Yamazaki, Dai https://orcid.org/0000-0002-6478-1841 University of Tokyo, https://doi.org/10.13039/501100004721 crossref Hardy, Andrew https://orcid.org/0000-0002-7928-8873 Aberystwyth University https://doi.org/10.13039/501100000315 crossref Bisselink, Berny https://orcid.org/0000-0001-5203-0410 European Commission, Joint Research Centre, Ispra, Italy De Roo, Ad https://orcid.org/0000-0001-9859-9127 European Commission, Joint Research Centre, Ispra, Italy Macklin, Mark https://orcid.org/0000-0003-4167-2033 University of Lincoln Thomas, Chris https://orcid.org/0000-0003-4939-9959 University of Lincoln Type of data: Dataset Research funders: NERC, NERC Grant numbers: NE/H022740/1, NE/P013481/1 Publication date: 2020 Resource language: English Metadata language: English Research project title: HYDROMAL: Hydro-dynamic drivers of malaria transmission hazard in Africa., FLOODMAL. Publisher: University of Leeds Contact Email Address: M.W.Smith@leeds.ac.uk Last Modified: 08 Sep 2020 07:56

Files

Documentation

Dataset Summary [294kB] Dataset Summary [294kB] Dataset Summary [294kB] Visible to: Anyone File name: dataset_summary.pdf Content type: Documentation Description: Dataset description Mime-Type: application/pdf License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 294kB

Q_seasonlength description [713B] Q_seasonlength description [713B] Q_seasonlength description [713B] Visible to: Anyone File name: Readme.txt Content type: Documentation Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 713B

Data

Raw data [72kB] Raw data [72kB] Raw data [72kB] Visible to: Anyone File name: data.xlsx Content type: Data Description: Excel file with all quantitative data Mime-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 72kB

Q_seasonlength [273kB] Q_seasonlength [273kB] Q_seasonlength [273kB] Visible to: Anyone File name: LISMAL_Suitability.zip Content type: Data Description: TIF file for each forcing model and time period (in subfolders) Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 273kB

Program

Python code [11kB] Python code [11kB] Python code [11kB] Visible to: Anyone File name: Code.zip Content type: Program Mime-Type: application/zip License: MIT License File size: 11kB

mit_license.txt [1kB] mit_license.txt [1kB] mit_license.txt [1kB] Visible to: Anyone File name: mit_license.txt Content type: Program Mime-Type: text/plain File size: 1kB