Effective Weapon

Effective weapon against the coronavirus 

Despite its proximity to China, Taiwan has only recorded 77 cases of coronavirus contamination. The country claims to have largely controlled the spread of the epidemic, which an international team of researchers mainly attributes to the emergency deployment of big data analytics and other technologies.

Stanford University's Chih-Hung Jason Wang said officials had mapped the spread of the pathogen from the start of the epidemic, integrating Taiwan's national health insurance database into its database on immigration and customs. Result: The country of 24 million people had detailed information on the travel history and symptoms of Taiwanese citizens for two weeks, which was shared with hospitals, clinics and pharmacies.

The authorities also followed foreign travelers with mobile phones and made them fill out health declaration forms; everyone in quarantine received a cell phone and was monitored by calls and visits. Taiwanese officials also used information technology to track the number of masks, isolation rooms and other sanitation available.

Having the right data to make the right decisions

Insight into the spread of the disease can help leaders respond more effectively to the epidemic. The data can help authorities identify the most vulnerable communities. The decisions that leaders will make in the coming weeks will shape the world for years to come. To make these decisions, you need the right data. In the fight against the coronavirus, an overview of preventive actions, population mobility, spread of the disease and the resilience of people and systems to cope with the virus can help public health and humanitarian officials to respond more effectively to the COVID-19 epidemic.

Yet today, public health leaders who make difficult choices lack high-resolution data on key questions such as: where is the disease likely to spread? Are there priorities areas that we need to contain to limit future spread? Where are the most vulnerable communities?

Aggregate mobility information from telecommunications data was used during the Ebola epidemic in West Africa and has been the subject of further research by the UNICEF Innovation Lab, Flowminder and others. Recently in Belgium, Dalberg Data Insights, one of the organizations mandated by the Belgian government to lead the data working group against COVID-19, analyzed aggregated and anonymized telecommunications data from the country's three telecommunications operators. The main objective is to understand trends in human mobility with regard to locking measures and to assess the risk of increased infection in a specific region. Overall, in Belgium, human mobility has decreased with an average of 54%, with some regions recording an even larger decrease. The crisis response team in Belgium can refer to this analysis regarding the impact of the measures imposed and indicate the risk of an outbreak of viruses and cases imported from other regions.

Living conditions in emerging countries may compromise ability to follow advice on how to behave

Another example of effective use of data comes from South Korea. The country is monitoring quarantined citizens with a mobile app, developed by the Ministry of the Interior and Security, as reported by the MIT Technology Review. The country's sense of urgency intensified after "patient 31" became a "super broadcaster" and is believed to be behind the rapid increase in cases. Quarantined people can use the app to communicate with local government case managers and report their symptoms. The person and the person responsible for the government file are informed if the person leaves the designated quarantine zone. The application is not mandatory and people can opt out. These measures, along with mass coronavirus tests in South Korea, have helped “flatten the curve” in the country. The number of daily confirmed cases peaked on February 29 and has declined since.

Identify communities at risk

Identifying the most vulnerable communities can be important for health officials to guide response efforts such as improving health infrastructure, allocating emergency funds and preventive measures. This is particularly relevant in emerging countries where living conditions can compromise the ability to follow advice on how to behave. It is difficult to wash your hands for 20 seconds or more with clean soap when your main source of water is a polluted river. Self-quarantine and self-isolation are unrealistic when you share a single room with other family members. And staying at home is impossible if you live day to day and have to go out twice a day to work, then refuel for the next meal.

Authorities can map areas where appropriate response capacity is compromised, at a high level of detail, using a combination of available primary data collection, data from national statistical offices and satellite images. The Location Analytics (LOCAN) team at Dalberg Research, based in Kenya, analyzes risk profiles in several African countries. The results are then fed back into epidemiological models as inputs for informed decision-making on the response to the crisis. A similar risk model, which relied on three key risk variables - people over the age of 60, regular smokers and those who use dirty cooking fuel in their homes - was developed and applied in Nigeria where health officials continue to report new cases despite a strong federal response.