I approached this assignment with a data-first, narrative second perspective. I work on developing strategies for combating vector-borne diseases so the allied fields of epidemiology and geography seemed like a promising place to look for a story. One of the major vectors for diseases that affect humans (i.e. Zika, dengue fever, malaria, chikungunya) is the mosquito. There are several different kinds, but for the most part, they share a common taste in habitat. Places that are close to water, particularly stagnant freshwater, and get warm (as low as 60F, but ideally 70-80+ F) As I poked around the web trying to find workable datasets with this in mind, I came to find well-indexed mosquito and vector-borne disease maps from Singapore, provided by the government. I used Google MyMaps to create layers on this map that you can use to toggle between the overlays and I’ll walk you through some of the highlights.
One of the first things to know about Singapore, from an epidemiological perspective, is that it’s dense. There are over 5 million people living in an area under 300 square miles. Its the third most densely populated country in the world. While its very prosperous, it also faces a high level of income inequality — more unequal than the US and on par with Equador and Saudi Arabia (according to its Gini coefficient).
There were over 3000 cases of dengue reported in Singapore in 2018 an while the government maintains a high level of engagement in managing mosquito populations, the problem remains endemic. The National Environment Agency provides records of the location and number of reported dengue cases over a 14 day cycle, the last of which ended on November 7th, 2018.
This is a heat-map of the clusters of dengue fever in Singapore. The areas that are blue-green are clusters in the single digits and the double digits are in orange and yellow. We can see that cases not evenly distriubted throughout the country and most clusters are within a mile of another cluster.
Zooming in on the largest cluster, with 51 cases (just under half of the total of all sites), we can see that the areas of stagnant water (where mosquitos breed) were found exclusively in homes. Water collects in every-day, household-objects, as is does in public places and construction sites. It seems personal homes, perhaps due to lack of knowledge, are the entry point for the spread of the disease. This is a question we can explore further by looking at where the breeding sites for aedes aegypti were found in the graph
Overlaying the aedes aegypti breeding sites with the dengue clusters, something interesting pops up — the graphs don’t match. While there are breeding sites in areas that there are clusters, there are not clusters everywhere there are breeding sites. This is interesting because it indicates that there is something going on, education, norms, or otherwise, that is breaking the line of transmission in these areas. But, you could say, maybe what we see in the breeding overlay is actually a representation of all the areas where aedes aegypti mosquito breeds and not all mosquitos have the virus responsible. In which case, the next map to look at would be the overlay of the areas receptive to malaria
In the above map, the “natural” habitat suitable for the malaria vector (anopheles gambiae) serves as a proxy for “natural” habitat suitable for aedes egypti. This overlay shows that a large portion of the mosquito breeding sites are, in fact, outside of the malaria receptive areas. While this map can party be explained by a lack of sampling depth for breeding grounds in the receptive areas, the fact remains that areas that were not expected to be breeding grounds simply are. Life in the Anthropocene for the mosquito is booming.
Taking a step back and looking at the dengue clusters in relationship to the hotels, we can get a sense of the places that tourists are likely to be. This can serve as a proxy for areas that might be thought of as desirable. It surprised me to see that while there are some clusters close to hotel locations, the hotels seem to carefully avoid them. I was then interested to delve into looking at how sharp the economic divide might be at those borders. While I wasn’t able to find data that was easily transferable into the format of this map, below are two maps that can give some insight. The first is of the train-system throughout Singapore and the second is a property heat-map. I chose to look into the train system to see if there was a relationship between transport hubs and dengue clusters in terms of 1) disease mobility and 2) if economic centres (well-serviced by transit as a proxy) faced the same intensity of disease compared to less economically active areas. While perhaps useful, these might be questions that are beyond the scope of this assignment. The property heat-map can give some more understanding of where the affluent and economically disadvantaged live.
