In a recent Nature article, CEGA faculty co-director Josh Blumenstock discusses how machine learning can be applied to improve aid responses to COVID-19.
“Last week, I had an unexpected encounter on Zoom. The callers were senior government officials from the Togolese Republic, in West Africa.
Why? The president of Togo wants to send cash payments (equivalent to around US$20 per person) to around 525,000 vulnerable Togolese households this month. But, like most developing countries, Togo lacks good data on the economic situation of specific households, and certainly has no way of collecting this information in the middle of a pandemic. Cina Lawson, a cabinet minister in Togo, and Shegun Adjadi Bakari, one of the president’s senior advisers, called to find out how big data and machine learning might help them to find the people who need the payments most.
COVID-19’s spread and lockdowns in low-income countries are leaving hundreds of millions of poor and vulnerable people without work or income. The United Nations World Food Programme has warned of devastating famines — 265 million people in low- and middle-income countries are projected to suffer from acute hunger by the end of the year.
These are terrifying numbers. Policymakers such as Lawson and Bakari are scrambling to find ways to help their citizens survive the next few months. Big data and artificial intelligence can help.”
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