15 million mobile phones used to track malaria

We all know that mosquitoes spread malaria. What we never quite realise is that humans also spread malaria — and quite significantly. In some places, the spread of malaria is directly linked to the mass movements of human populations. The movements of infected humans seem to increase the dispersal of parasites beyond what would be possible by mosquitoes alone.

Fifty years ago, when we first tried to eradicate malaria, it failed — and among the main culprits (along with drug resistance and unsustainable funding) was listed movements of human populations. Historically, movements of infected people from areas where malaria was still endemic to areas where the disease had been eradicated led to a resurgence of the disease. As people and populations move, they can increase their risk for acquiring the disease, or increase the risk of transmitting it.

As always wars and civil unrest tend to favour disease transmission, and malaria is no different. During the 1980s in Angola, 15 years of continuous war had displaced hundreds of thousands of people. As a direct result, malaria moved from sixth to first place as the leading cause of mortality. This, simply because the capital city Luanda underwent an unprecedented population increase — and the malaria endemicity rose along with it. In a population that wasn’t ready for it.

The relationship between malaria transmission and population movement is undoubtedly complex. Population movements that either place people at risk for malaria or cause them to pose a risk to others cannot be stopped. But it seems now they can be tracked and we can mitigate for it.

In June of 2008, the movements of approximately 15 million people in Kenya were tracked using their mobile phones. Tracked, not by governments or refugee aid organisations, but tracked by researchers from the Harvard School of Public Health. During a 12 month period, every call or text made by each individual to one of 11,920 cell towers located within the boundaries of 692 settlements was logged and recorded.

Surveillance is a term that loses more and more of its meaning with every single advance in technology. Usually we picture more nefarious intents and purposes for tracking citizens. Within that year in Kenya, starting points and destinations of all 15 million individuals were tracked — giving each person a primary settlement to call home and mapping their movements in relation to malaria prevalence. Researchers determined where each person spent most of their time based on the location of the majority of their call and text records — this was their home base.

Mapping that onto malaria prevalence for the entire country allowed researchers to estimate and infer an individual’s probability of being infected and the probability that visitors to the settlement will become infected. Researchers built up what was essentially a parasite movement network.

There was some directionality to the net movements of people and parasites between settlements. Settlements can either be characterised as “source” or a “sink” — net emitters of people and parasites are sources and net receivers are sinks. As the capital city, Nairobi and its immediate surroundings, become a major destination for both humans and parasites. And from there, two sources of importation of malaria parasites exist. First, individuals visiting endemic areas may become infected during their stay, depending on the malaria endemicity of the destination, and may carry parasites back to their primary settlement. And secondly, infected individuals can carry parasites with them towards other settlements.

In analysing the movements of people within Kenya, researchers discovered that returning residents contribute to some movements of parasites between regions within Lake Victoria and coastal areas. Nairobi imports the largest proportion of infections from returning residents — those infected coming back from journeys to the coast and central Kenya.

It turns out that the structures of the networks of parasite and people movements were remarkably stable over the course of the year. Meaning that elimination efforts can also be robust.

Spatial analysis using maps to associate geographic information with disease can be traced as far back as the 17th century. In this day and age we are able to gather vast amounts of data with relative ease. GPS systems mean it is possible to integrate highly accurate geographic location with virtually any measurable observation — in real time and across multiple measurable observations.

The ease of gathering this much human health-related data points to the question of ownership and confidentiality. Essentially, big data problems.

The idea is that with those vast amounts of data collected, mitigation will be easier — early warnings and detections, hazard preparedness and the like. It is a somewhat new concept in the way we approach global health. Taking into consideration the universal and trying to capture the entire picture. Of course, every intervention is local… but this times it starts out at the global.

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