Executive Summary
This report analyzes a nationwide georeferenced dataset compiled by the National Malaria Eradication Service (SENEPA). It contains 20,619 records of disease vectors (Phlebotominae, Triatominae, and Culicidae) collected between 1998 and 2023.
The objective is to provide a reproducible and interactive tool for public health decision-making, allowing the exploration of spatial and temporal records of vectors associated with Dengue, Chagas, Malaria, Leishmaniasis, and Yellow Fever in Paraguay.
1. Taxonomic Composition
The following chart displays the diversity of species documented for each disease. It highlights the dominant species and the relative surveillance records per vector group.
How to explore:
- Select a disease from the dropdown menu to filter species.
- Hover over points for exact frequency counts and family classification.
2. Temporal Trends (1998-2023)
This section shows the accumulation of records over 25 years. Note that sampling effort is heterogeneous, often increasing during outbreak years or specific campaigns.
How to explore:
Use the Range Slider at the bottom to focus on specific years.
The solid line shows annual records; the dashed line shows cumulative growth.
3. Geographic Distribution
This interactive spatial map shows where vectors have been detected across Paraguay’s 17 departments. Each occurrence is color-coded by its associated disease to facilitate regional comparisons.
How to explore:
- Toggle Layers: Use the control panel (top right corner) to check or uncheck specific diseases, allowing you to isolate or overlay different vector groups.
- Inspect Records: Click on any colored point to open a pop-up displaying the specific disease, scientific species name, and collection year.
- Zoom & Pan: Use the zoom tools (+ / -) or scroll with your mouse to inspect specific localities or urban centers in greater detail.
- Color Guide: Reference the legend (bottom right) for a quick overview of the disease categories.
3.1. General Distribution
3.2. Geographic Hotspots and Density
This interactive spatial map highlights the areas with the highest concentration of vector records across Paraguay’s 17 departments. The density map utilizes a heatmap (Viridis scale) to pinpoint historical surveillance hotspots and potential ecological niches for each specific vector group.
To enhance spatial context and readability, the visualization features customizable base map layers, allowing for better visual contrast depending on the distribution density of the selected disease.
Vector records hotspots
How to explore:
- Select Disease (Left Dropdown): Use the menu in the top-left corner of the map to toggle between different diseases (e.g., Dengue, Chagas, Malaria). The heatmap will automatically update to reflect the spatial footprint of the selected vectors.
- Change Base Map (Right Dropdown): Use the menu in the top-right corner to switch the background map style. You can choose between a standard detailed view (Open Street Map), a light minimalist canvas (Carto Positron), or a dark mode map (Carto Dark) which provides excellent contrast for the bright heatmap colors.
- Zoom & Pan: Scroll with your mouse or use the + / - buttons on the map to zoom into specific localities, urban centers, or rural districts. Click and drag to navigate across different regions of the country.
- Hover for Details: Move your cursor over the colored areas to see the precise latitude and longitude coordinates.
Vector records hotspots by desease
Dengue Vector records hotspots in two neighborhoods of Asunción
4. Spatio-Temporal Evolution of Disease Vectors
This dynamic animation synthesizes the geographic and temporal dimensions of the dataset, illustrating the historical progression of vector detections across Paraguay from 1998 to 2023. By animating occurrence density over time, this visualization enables the identification the observation of geographic shifts during specific epidemiological events, and the evaluation of long-term surveillance footprints.
How to explore this visualization:
- Automate the Timeline: Click the Play (▶) button located beneath the map to initiate the chronological sequence. Observe how vector presence expands or contracts year by year.
- Manual Timeline Scrubbing: Drag the slider at the bottom of the map to navigate freely across the 25-year period. This is particularly useful for performing direct comparisons between specific epidemiological periods (e.g., comparing pre- and post-intervention years, or evaluating specific outbreak decades like pre/post-2010).
- Inspect Localized Hotspots: While the animation is running or paused, hover your cursor over the high-density areas (colored hotspots). A tooltip will display the specific vector species detected and the corresponding year of the record.
- Adjust Geographic Context: Use the zoom controls (+/-) and drag the map to focus on specific departments or districts without interrupting the temporal animation.
4.1. Spatiotemporal visualization of the complete dataset
4.2. Spatiotemporal visualization of urban yellow fever vector records
4.3. Spatiotemporal visualization of yellow fever vector records
4.4. Spatiotemporal visualization of malaria vector records
4.5. Spatiotemporal visualization of Leishmaniosis vector records
4.6. Spatiotemporal visualization of Chagas vector records
4.7. Spatiotemporal visualization of Dengue vector records
Note on Dengue data: Due to the inability to access national surveillance records for dengue vectors, the data presented here correspond exclusively to the “TOPA DENGUE” project, which is restricted to two neighborhoods in the city of Asunción.
5. Searchable Data Repository
The table below provides access to the 20,619 standardized records. Each entry includes taxonomic, administrative, and spatial metadata
Observation on Date Imputation: The
date_auxfield was generated to handle missing date values. When an exact date was unavailable, it was imputed as January 1st of the corresponding reporting year.
How to use:
Use the Search bar for specific species or localities.
Use Column Filters to isolate specific years or departments.