Frédéric Pivetta at Real Impact Analytics: “Big data is a hot business topic. It turns out to be an equally hot topic for the non profit sector now that we know the vital role analytics can play in addressing public health issues and reaching sustainable development goals.
Big players like IBM just announced they will help fight Zika by analyzing social media, transportation and weather data, among other indicators. Telecom data takes it further by helping to predict the spread of disease, identifying isolated and fragile communities and prioritizing the actions of aid workers.
The power of telecom data
Human mobility contributes significantly to epidemic transmission into new regions. However, there are gaps in understanding human mobility due to the limited and often outdated data available from travel records. In some countries, these are collected by health officials in the hospitals or in occasional surveys.
Telecom data, constantly updated and covering a large portion of the population, is rich in terms of mobility insights. But there are other benefits:
- it’s recorded automatically (in the Call Detail Records, or CDRs), so that we avoid data collection and response bias.
- it contains localization and time information, which is great for understanding human mobility.
- it contains info on connectivity between people, which helps understanding social networks.
- it contains info on phone spending, which allows tracking of socio-economic indicators.
Aggregated and anonymized, mobile telecom data fills the public data gap without questioning privacy issues. Mixing it with other public data sources results in a very precise and reliable view on human mobility patterns, which is key for preventing epidemic spreads.
Using telecom data to map epidemic risk flows
So how does it work? As in any other big data application, the challenge is to build the right predictive model, allowing decision-makers to take the most appropriate actions. In the case of epidemic transmission, the methodology typically includes five steps :
- Identify mobility patterns relevant for each particular disease. For example, short-term trips for fast-spreading diseases like Ebola. Or overnight trips for diseases like Malaria, as it spreads by mosquitoes that are active only at night. Such patterns can be deduced from the CDRs: we can actually find the home location of each user by looking at the most active night tower, and then tracking calls to identify short or long-term trips. Aggregating data per origin-destination pairs is useful as we look at intercity or interregional transmission flows. And it protects the privacy of individuals, as no one can be singled out from the aggregated data.
- Get data on epidemic incidence, typically from local organisations like national healthcare systems or, in case of emergency, from NGOs or dedicated emergency teams. This data should be aggregated on the same level of granularity than CDRs.
- Knowing how many travelers go from one place to another, for how long, and the disease incidence at origin and destination, build an epidemiological model that can account for the way and speed of transmission of the particular disease.
- With an import/export scoring model, map epidemic risk flows and flag areas that are at risk of becoming the new hotspots because of human travel.
- On that base, prioritize and monitor public health measures, focusing on restraining mobility to and from hotspots. Mapping risk also allows launching prevention campaigns at the right places and setting up the necessary infrastructure on time. Eventually, the tool reduces public health risks and helps stem the epidemic.
That kind of application works in a variety of epidemiological contexts, including Zika, Ebola, Malaria, Influenza or Tuberculosis. No doubt the global boom of mobile data will proof extraordinarily helpful in fighting these fierce enemies….(More)”