Benefits Illustration 13: Healthcare

General points

Healthcare applications combine policy and operational use of location data, to assess particular healthcare problems and plan and manage actions to address them. Population growth and population aging put increasing pressure on healthcare services, which collectively form a significant proportion of GDP. As we have seen from the COVID-19 pandemic, some of the largest scale and most impactful analysis using location data comes in this sector, together with some of the most innovative and costly solutions (e.g. mobile tracking and contact tracing applications), involving logistical challenges and high risk / impact scenarios. Location intelligence has never meant so much globally as it has during the COVID-19 outbreak.

As with environment problems, diseases do not respect national boundaries and multinational intelligence and solutions are needed. Environment issues are also in various respects related to health issues. This is also the case for social and economic issues. With so much interconnection in different sectors and geographies, the importance of interoperability in data, systems and organisational response (in terms of capability, capacity and logistics) is vital. COVID-19 has also highlighted the importance of data in driving solutions, from quickly understanding the nature of the problem through genome sequencing through, developing vaccines, carrying out clinical trials, and giving regulatory clearance in different locations, developing vaccine supply capacity and rolling out vaccines globally, modelling scenarios to decide on recovery and residence measures, providing healthcare where needed to deal with cases, and taking action to address the economic, social and long term health impacts related to the pandemic.

Citizens have also come to rely on location-based information to understand the rationale for lockdowns and manage their activities in response to the challenges. Availability of reliable information from government and policies and actions taken based on the data has been an important factor for governments in maintaining the trust of their citizens.

Relevant location data involved in dealing with COVID-19, as well as other disease and disaster scenarios, includes: COVID-19 infections data, map reference information including administrative areas, places frequented by many people, transport networks, mobility information, address data, demographic data, land cover, air pollution, water sources, and health facilities, and health and other emergency services data. Such information needs to be available and shared amongst the many different parties involved in deciding on policy and managing the outbreak. Interoperability of data is key as well as the ability to handle a high frequency of update to enable accurate modelling and decisions to be taken with as much certainty as possible.

In April 2020, the European Commission published “Commission Recommendation (EU) 2020/518 on a common Union toolbox for the use of technology and data to combat and exit from the COVID-19 crisis”, in particular concerning mobile applications and the use of anonymised mobility data. Also published were “Guidelines 04/2020 on the use of location data and contact tracing tools in the context of the COVID-19 outbreak”.

Case studies

The John Hopkins COVID-19 Dashboard, based on ESRI software, become an important global tool to track and monitor the virus and the UN Statistics Division has made the data available in various formats and provides access through an API and geo-services.

At a national level, countries have implemented data solutions in rapid timeframes. Some countries with experience of SARS, such as South Korea, had approaches and solutions they could build upon. South Korea also enhanced their applications vary rapidly, for example the contact tracing system used to locate people attending a religious event in Daegu which resulted in mass infections. The system analysed data provided by credit card, transport and mobile companies. Many developed countries, however, lacked the necessary preparations and hurriedly built solutions.

The Czech Republic developed a ‘smart quarantine’ system which involved creating memory maps by processing individuals movements obtained from mobile phone data. Individuals have to give their consent. Thanks to location sharing, the probability of infection can be calculated and informed decisions taken. Take-up reached around 1.5m people by mid-2020 (out of a population of 10.5m). While this is good, there are limitations in the Czech Republic, and other countries with similar applications, where critical mass is needed.

In Germany, policy makers introduced a system to deal with local infection hotspots using NUTS 3 level administrative units. Restrictions were adapted according to the percentage of cases for the relevant populations. Interventions were varied over time as the progress of the outbreak evolved. The benefits of this localised approach were seen in dealing with an outbreak at a meat processing plant in June 2020, where 400 people were infected and appropriate quarantine measures were quickly put in place.

France introduced measures to move out of the containment phase based on department level red, amber and green classifications, taking into account the incidence of new cases, the virus reproduction factor, hospital bed occupancy rates, number of tests performed and rate of positive tests.

To support EU member states, Eurostat has performed spatial analysis to detail risk areas, using population data, combined with a European healthcare services dataset, transport networks and address data.

Version: EULF Blueprint v5 (draft)