A real-time big data & artificial intelligence platform to allow policy makers and citizens to understand the levels of physical distancing, movement and mobility at the village level
Muri is outside the Posyandu near her home in North Jakarta. It’s been closed for services since the start of the COVID-19 pandemic. In the seven months since the first detected case of COVID-19 in Indonesia, more than 222,000 Indonesians have contracted COVID-19, with 8841 deaths reported.
Carrying her 9-month-old daughter, Kinara, Muri tells us about the very real cost, emotional and financial, her family is paying to help slow the spread of this global pandemic.
“I’m scared, because we don’t know people’s condition, so I’m scared to go places,” she said
“My wish is for the COVID to end, my husband to be able to get work as usual again, his salary can get back to normal."
With no medicine or vaccine available, governments around the world are pursuing non-medical interventions. According to the best science available, handwashing, mask usage and maintaining physical distance are the most effective tools we have to combat the spread of COVID-19.
While the impact of the virus is devastating, the implementation of these containment policies carries their own cost. With more than 500,000 schools closed across Indonesia the peak of PSBB (Pembatasan Sosial Berskala Besar- Large-scale social restrictions), children are shouldering the cost of controlling this pandemic.
With lives and livelihoods at stake, it’s critical that policymakers have the best evidence available when making hard decisions, especially those related to PSBB.
Due to the rapidly changing nature of the pandemic, traditional data that we’d ordinarily rely on (from the Official Statistics from BPS (Badan Pusat Statistik – Indonesia’s National Statistics Office), face-to-face surveys are available either not at all or with too great a delay to assist policy makers.
Recognising this need for the best real-time evidence to inform policymaking, UNICEF Indonesia’s Data & Analytics team, working closely with our colleagues at MagicBox from UNICEF Headquarters, and in-country partners at the University of Indonesia created ‘UNICEF Mobility Insights’, a real-time big data & artificial intelligence platform to allow policy makers and citizens to understand the levels of physical distancing, movement and mobility at the village level, in real time.
How we do this
Using anonymised, aggregated mobile phone data from users of our partner Cuebiq’s services, we calculate user’s ordinary night-time location (Where they spend most of their time between the hours of 8pm-5am). Then, by observing their mobile locations during the day, we calculate how much time they spent at home each day. Using this, we’re able to provide granular, real time data about the level of PSBB compliance, at all administrative levels.
Working with our colleagues at the University of Indonesia, we’ve established a strong relationship between the levels of stay at home and a reduction in case rates. According to analysis by UNICEF & UI, in an area such as Jakarta a 1% increase in stay at home could save up to 500 lives.
There are 24.79 Million people living in poverty across Indonesia. By utilising data provided by TNP2K and BPS, we are able to answer the critical question: Is the ability to physical distance correlated with income or poverty? The answer is: Yes. Our analysis suggests that regions with the highest reported levels of poverty also have the lowest observed levels of physical distancing.
“The big-data stay-at-home measures are powerfully associated with transmission” says Paul Pronyk, the Chief of Child Survival and Development for UNICEF. “What is even more important is that these figures are both real-time and predictive. For example, if policy maker tightens restrictions today, we can observe changes in mobility instantly. If greater numbers of people staying at home, we can expect lower caseloads in 7-10 days. In the absence of a vaccine, this precision can make a huge difference.”
Observing in real time the reduction in ‘stay at home’ rates, on 14 September 2020 Governor Anies of DKI Jakarta re-implemented large scale social restrictions in DKI Jakarta. Because this data is timely, accurate and available immediately the office of the Governor is able to effectively target and monitor the implementation of these restrictions, ensuring that the most lives are saved at the lowest possible economic and social cost to Indonesia’s citizens.
Due to Indonesia’s high levels of digital opportunity and UNICEF Indonesia’s fortunate position as an organisation with advanced Machine Learning & Data Analytics capacity, we are uniquely placed to undertake this transformative big-data work, providing levels of insight that would have previously required Billions of Rupiah to gain. Thanks to the aggregated and anonymized mobility data provided by our partners at Cuebiq, we’re able to materially assist in the formation of evidence-based policy, ensuring a lower disease burden and a brighter future for Muri, Kinara, and every child.
UNICEF Indonesia wishes to express its sincere gratitude to the key donors that have contributed to this work, including Nokia through the Finnish Committee for UNICEF and the COVID-19 Solidarity Response Fund.