In the last months, the world has been confronted with the outbreak of COVID-19, an infectious disease caused by a novel corona virus closely related to the virus that caused SARS. Information (and unfortunately disinformation) on the disease spreads even faster than the disease itself. Many institutions concerned with public health, like the World Health Organisation, HealthMap, and Johns Hopkins University, offer web-based interactive maps and other visuals, to track the number and location of disease cases in real time.
Nowadays, we take this kind of presentation of information almost for granted. However, several major steps had to be taken before all this was possible. First of all, the idea of using a map to depict and analyze its clustering in relation to geography had to emerge. Additionally, adding exchangeable layers with additional information to existing maps required sophisticated printing techniques. But layering images over maps is still completely different from adding layers with adaptable data over maps: computer technology made this possible. The next requirement is the transition from static to dynamic maps: advances in high resolution aerial photography, satellites, and data technology led to map services like those offered by among others ESRI, Google Maps and OpenStreetMaps. And to get all this information to you in real time, of course, we needed the World Wide Web.
John Snow's Cholera Map
The famous map taken from John Snow's report included in the 'Report on the cholera outbreak in the Parish of St. James, Westminster, during the autumn of 1854' is the best known example of a disease map. Although not the first map of a disease outbreak, it is a very fine example of a link between a disease and topographic information.
The map was used in a report presented to the Cholera Inquiry Committee a year after the outbreak occurred. The report, and especially the part written by Snow, reads like a detective story, and can be found online at the Welcome Trust. Snow explains how he made inquiries in the neighborhood on were water was obtained. He found that the water from the pump at Marlborough Street contained inpurities visible to the naked eye, so people avoided that pump, and went to the one in Broad Street. One source told him on the bad smell of water from the pump on Broad Street. Now, Snow asked permission to make a list of cholera deaths in the subdistricts of Soho, as registered at the General Register Office. He then did field research: "On proceeding to the spot, I found that nearly al the deaths had taken place within a short distance of the pump in Broad Street." So, he recognized the topographical pattern on the site. He did not need to make a map to notice the pattern. He then had a talk with the Board of Guardians of the parish: "In consequence of what I said, the handle of the pump was removed on the following day."
So, in contrary to common believe, it was not the map that solved the problem. The map was made to give a committee a visual summary of the brilliant thinking of a brilliant mind.
The map (Snow himself named it a diagram of the topography of the outbreak) has no legend, but Snow explains the map in the report. "All the deaths from Cholera [...] are shewn by black lines in the situation of the houses in which they occurred, or in which the fatal attacks were contracted." He continues: "The inner dotted line on the map shews the various points which have been found by careful measurement to be at an equal distance by the nearest road from the pump on Broad Street and the surrounding pumps." Such a line is nowadays called an isodistance line. As can be seen, the map is helpful not only to identify the pattern, but also the outliers and exceptions. which in some cases were people who sent their servants to the Broad Street pump because they liked the water more than that from other pumps.
Representation of data
Snow used black bars to indicate individual deaths. In modern disease maps, as can be seen in the three COVID-19 maps above, dots are used. But, there is a more important difference: Snow uses one symbol per event. This requires knowledge on the exact location of every event. The COVID-19 maps have less detail: the WHO and Johns Hopkins map register cases by country (or province in case of China). The map from HealthMap uses registration by region or city. Therefore, individual cases are clustered, and the size of the cluster is represented by the size (and / or color) of the dot. This is of course more practical in case of a global outbreak, since otherwise international databases with address details of every case would be needed. Additionally, we all would feel pretty uncomfortable if you could zoom into the map to such a detail that everybody would be able to link cases to identifiable addresses and people.
However, too much clustering results in less informative maps. For instance, the clustering by country in the WHO map obscures the fact that most of the case in the United States (at least up to march 4th 2020), occurred at the West-Coast. Another example is the clustering of cases at the German part of the border between Germany and the Netherlands, in a part of Europe where borders only exist on maps.
A problem with maps like Snow's and the COVID-19 map, is that they only show absolute numbers of cases. If you want to make assessments of risk, it is essential to know the number of the complete population. For instance, on march 4th 2020, there were 26 cases of COVID-19 in Iceland and 30 in Canada. The size of the dot on the WHO map is the same.
However, Iceland has around 364.000 inhabitants, whereas Canada has 37.5 million. So, the disease is much more prevalent per 100.000 inhabitants in Iceland as it is in Canada, but the map does not inform about that.
An interesting difference between the COVID-19 maps shown, is the use of color. The WHO map is quite neutral. The other to look more dramatic and alarming: a black background, cases in red and yellow, colors usually associated with danger.
So, how do these maps help in combating a disease? Well, firstly, they inform the public. They increase awareness for preventive measures. But there is much more data available to health organizations and governments, that can be combined with the information on cases. Risk factors can be added, like population density, poverty, poor access to hygiene measures, information on the age of inhabitants, etcetera, etcetera. Combining all these data can be helpful in making predictive models, with can guide and optimize the way money and resources are deployed to combat the outbreak.
So a lot has changed since 1855. Back then, Snow used a map to explain what had happened a year before. Today, a map will predict what will be happening tomorrow.
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