DOI: 10.5061/dryad.s1rn8pkhw
title: Re-emergence of Aedes aegypti (Linnaeus) in Egypt under climate changes

description: Aedes aegypti, the primary vector of several medically significant
 arboviruses, including dengue fever, yellow fever, chikungunya, and Zika
 viruses, was successfully eradicated from Egypt in 1963. However, reports
 of its re-emergence and associated dengue outbreaks in southern Egyptian
 governorates since 2011 have raised concerns. This study aimed to model
 the current and future distribution of Ae. aegypti in Egypt. Locally
 collected occurrence data were combined with bioclimatic, anthropogenic,
 and biological environmental variables to identify key factors driving the
 distribution of Ae. aegypti. The modeling of maximum entropy (MaxEnt)
 showed good performance (AUC mean = 0.975; TSS mean = 0.789) and
 identified the density of the human population, the annual precipitation
 and the normalized difference vegetation index (NDVI) as key determinants
 of the habitat suitability of Ae. aegypti. The present-day predictions
 highlight the Nile Valley, Nile Delta, Fayoum Basin, Red Sea coast, and
 South Sinai as suitable habitats. The model projects a potential range
 expansion for Ae. aegypti under future climate change scenarios,
 particularly in the Nile Delta region. This expansion is expected to
 increase the suitable area for Ae. aegypti by 61-68% by 2050 and 64-69% by
 2070, depending on the climate scenarios. These findings can support
 decision-making regarding vector control and disease prevention strategies
 to protect both local populations and international travelers; in
 particular, Egypt is one of the world’s most important tourist
 destinations.
description: The study considers bioclimatic, anthropogenic, and biological
 environmental variables to understand <em>Aedes aegypti</em>
 habitat. <strong>Bioclimatic
 variables</strong>: Nineteen bioclimatic variables were sourced from
 the WorldClim database. To avoid the influence of highly correlated
 variables, the researchers conducted a principal component analysis and
 selected three key variables (annual precipitation, mean temperature of
 the warmest quarter, and temperature seasonality).
 <strong>Anthropogenic variable</strong>: Human
 population density data was obtained from the WorldPop dataset through the
 Google Earth Engine platform, using the median range from 2000 to
 2020. <strong>Biological environmental
 variable</strong>: Normalized difference vegetation index (NDVI)
 data, representing vegetation cover, was acquired from the Moderate
 Resolution Imaging Spectroradiometer (MODIS) MOD13A2 product on the Google
 Earth Engine platform, calculating the median range from 2000 to
 2015. <strong>Future Projections</strong>:
 To predict the impact of future changes, the researchers used five general
 circulation models (GCMs) from the WorldClim database and projected human
 population density data from Figshare for the years 2050 and 2070. They
 used two emission scenarios: a low emission scenario (ssp126; SSP1-RCP2.6)
 and a high emission scenario (ssp585; SSP5-RCP8.5). For future NDVI
 projections, the median NDVI range from 2000 to 2015 served as a proxy for
 the present, while the median NDVI for 2020 acted as a proxy for the
 future. <strong>Habitat suitability
 model</strong>: The researchers used MaxEnt software (version 3.4.4)
 to predict the current and future distribution of <em>Aedes
 aegypti</em>. MaxEnt is a modeling technique that uses presence-only
 data and is effective for projecting species distribution shifts under
 climate change. The model used 75% of the occurrence records as training
 data and 25% as test data. Tenfold bootstrapped replicates were used with
 a random seed to enhance model performance. The model's performance
 was evaluated using the area under the receiver operating characteristic
 curve (AUC) and the true skill statistic (TSS). To
 summarize the model results for current conditions, mean values across all
 runs were used. For future conditions, the mean across all means of all
 GCMs for each SSP scenario was calculated. The uncertainty index of the
 model predictions was estimated using the range (maximum-minimum) of
 predictions from ten replicate MaxEnt runs for current conditions and the
 range across all future model combinations within each SSP for future
 conditions.
description: # Re-emergence of Aedes aegypti (Linnaeus) in Egypt under climate changes
 ## General Information Created: December 2024 Authors: * Mustafa M.
 Soliman ([msoliman@cu.edu.eg](mailto:msoliman@cu.edu.eg)) | ORCID:
 0000-0002-0905-4473 * Abdallah M. Samy
 ([samy@sci.asu.edu.eg](mailto:samy@sci.asu.edu.eg)) | ORCID:
 0000-0003-3978-1134 * Magdi S. El-Hawagry
 ([elhawagry@cu.edu.eg](mailto:elhawagry@cu.edu.eg)) | ORCID:
 0000-0001-9162-5265 ## Dataset Description This dataset supports research
 modeling the current and future distribution of Aedes aegypti mosquitoes
 in Egypt using MaxEnt. The study combines locally collected data with
 bioclimatic, anthropogenic, and biological variables to identify key
 environmental factors influencing mosquito distribution. ### Data
 Collection * 48 Ae. aegypti occurrence records spanning 1925-2023 *
 Collected from nine Egyptian governorates: * Alexandria * Assiut * Aswan *
 Beni Suef * Cairo * Fayoum * Minya * Qena * Red Sea ### Data Sources 1.
 Bioclimatic Variables: * Nineteen variables from WorldClim database 2.
 Anthropogenic Data: * Human population density from WorldPop * Future
 population projections from Figshare (2050 and 2070) 3. Biological Data: *
 Normalized difference vegetation index (NDVI) from MODIS MOD13A2 product
 (Google Earth Engine platform) 4. Climate Projections: * Five general
 circulation models (GCMs) from WorldClim * Projection years: 2050 and 2070
 * Emission scenarios: * Low emission (ssp126; SSP1-RCP2.6) * High emission
 (ssp585; SSP5-RCP8.5) ## File Structure ├── Aedes_rarefied_points.csv #
 Occurrence records data ├── Layers_current/ # Current bioclimatic layers │
 └── *.asc # ASCII format climate data ├── Future_layers/ # Projected
 climate scenarios │ └── *.asc # ASCII format projected data └──
 Bias_file_rarefy_200/ # Bias files for MaxEnt modeling ## File Formats *
 Occurrence data: CSV format * Environmental layers: ASCII (.asc) format *
 All spatial data are clipped to the study area ## Usage Notes * The
 occurrence records were compiled from previous literature * All
 environmental layers are spatially aligned and processed for use in MaxEnt
 modeling * Bias files are included to account for sampling bias in the
 modeling process