Top 10 Most Affected Areas
Context
Dengue fever, a viral illness transmitted by Aedes (Aedes aegypti) mosquitoes, is a growing public health concern in tropical regions worldwide. In Colombia, the disease has seen significant variations in incidence from 2007 to 2018, with the eastern regions of the country consistently reporting the highest rates. These areas, characterized by warm temperatures and tropical landscapes, provide ideal conditions for the Aedes mosquitoes, which thrive in humid environments with stagnant water.
This project explores the geographical factors that influence the spread of dengue in Colombia. By mapping dengue trends over time, we can better understand how regional variations and climate conditions contribute to the disease's distribution. The heatmap visual presents a clear picture of dengue incidence across the country, while the top 10 municipal bar graph highlights the areas most affected by the virus. Through this analysis, we aim to find patterns and conclusions on the complex relationship between environmental and infrastructural factors that shape the spread of this mosquito-borne disease in Colombia.
Top 10 Most Affected Areas
Context
Stars represent Colombia's most populous cities. Click on a star or on a bar in the graph to zoom.
In addition to time series values, our dataset contains general statistics of each muicipality not directly tied to a certain year. These include literacy rates, secondary education rates, unemployment rates, percentage of households without water, and percentage of households without internet access. The goal of mapping these statistics is to determine if there are any geographical trends that correlate with these factors, and to analyze if these factors have an influence on the incidence rates of dengue fever. Specifically, we want to determine if there exists a correlation between the municipalities with high incidence rates and high disparities.
Incidence, Temperature, and Precipitation data are dependent on the slider in the first map. Update the slider to see more yearly data.
You might have noticed a few interesting occurences in the Incidence map. First, there are a few locations where the incidence rate remains relatively low thoughout the years, despite having neighboring municipalities with drastically changing indicence rates. Specifically, there seems to be a cluster of municipalities in central and southwestern Colombia that share this trait. Additionally, these regions have historically low temperature and rainfall recordings. Is there an outside variable that can explain these coincidences? The map below showcases the geographical landscape of Colombia, as well as topographic markers describing the elevation in those regions. By mapping these additional features, we aim to capture more information about the relationship between incidence rates and the potential factors that correspond to them.
Topography lines describe the elevation above sea level (in meters).
Through the analysis of dengue fever cases in Colombia from 2007 to 2018, several important trends emerge. First, municipalities like Florencia, Fortul, and Nilo consistently appear as hotspots in different years, highlighting that certain areas are more vulnerable due to localized environmental or social factors. Epidemic years, such as 2010, show dramatic spikes in incidence rates, suggesting that dengue outbreaks can escalate rapidly under specific conditions. Conversely, endemic years like 2011, 2017, and 2018 show much lower case numbers, indicating periods of relative control. The shifting locations of outbreaks suggest that while some areas improve (e.g., Florencia after 2011), others can become new centers for the virus (e.g., Nilo in later years). Importantly, western Pacific coastal municipalities remain consistently low in incidence, likely due to their colder and wetter climates, which inhibit mosquito breeding. Overall, this analysis emphasizes the dynamic nature of dengue outbreaks, the need for targeted public health interventions in high-risk municipalities, and the importance of understanding local environmental and socio-economic factors that drive these patterns.