The ability to collect and learn from large amounts of data has been a major driver of innovation over recent decades. Everything from healthcare – think patient analytics, wearables and the COVID-19 response – to transportation – Uber and Lyft – to entertainment – Netflix – is now driven by data and stats.
However, the ability to collect good data, the ability to extract insights from it, and the skills to transform those insights into change are not spread evenly across the world.
Taking a page from the way MSF sends medical staff and expertise to developing countries, some organizations are beginning to do the same with statistics. But in general, the need to improve domestic statistical capacity in developing countries remains largely unmet.
We are two mathematicians at the University of Colorado Boulder and part of a project called the Interdisciplinary Statistical Analysis Laboratory that works to develop statistical infrastructure around the world. The aim of the program is to help build data science infrastructure in developing countries. In 10 countries and still going, we have started “Statistics Laboratories” – academic centers that train young statisticians to collaborate on important local statistical projects.
Where stats matter
The benefit of a program like Doctors Without Borders is clear – the group provides medical care. It’s hard to see the benefit of improved statistical power but it could be just as important.
For example, during the cholera outbreak in London in 1854, John Snow used statistical data collection and analysis to identify and shut down a contaminated water pump. Later that year, Florence Nightingale, founder of Modern Nursing, used statistics to show that simple hygiene measures can dramatically reduce infection and death in hospitals.
Each year, the World Bank ranks countries on a scale of 1-100. One represents a complete lack of basic statistical data and the ability to analyze, 100 represents the statistical power of a developed country like the United States According to the 2020 report, the average statistical power of countries in Sub-Saharan Africa, South Asia and Latin America is 57.1, 69.8 and 70.1 respectively .
This disparate statistical ability has played an important role in the spread of the epidemic. Strong data collection and analysis of COVID-19 cases has allowed some countries – such as Nigeria and the US – to better respond to initial outbreaks and take an informed approach when reopening sections of the economy.
The LISA 2020 network has grown to include more than 30 statistical laboratories. Credit: Eric Vance/Lisa, CC BY-ND
Unfortunately, during the pandemic, 80% of national statistical offices in low-to-middle-income countries have indicated that they need additional support to carry out important data collection and analysis.
Just as good data can lead to good decisions, a lack of data can often lead to less effective decisions. For example, during the 2014 to 2016 Ebola epidemic in Liberia, the government initially did not have access to accurate, real-time mortality data or effective analysis tools. This shortage has prevented public health authorities from responding quickly and effectively to the outbreak. Once the government introduced a system for collecting data over the phone, officials were better able to assign doctors and nurses to where they needed it.
Statistics in ecology, health and politics
The idea for a multidisciplinary statistical analysis laboratory started in Northwest Africa, on the borders of Western Sahara and Mauritania. One of us, Eric Vance, was in the middle of a five-year assignment that traveled the world before earning his Ph.D. At a border checkpoint in the middle of an ancient minefield, he accidentally met a biologist who was studying a desert fox in the desert.
When the biologist found out that Vance was studying statistics, his eyes lit up, and he said, “Oh, statistician! I have questions for you.” But before Vance could offer any help, he had to board a bus and cross the mine-strewn border when Vance returned to the United States, realizing the vast need for statistical capacity and education in developing countries. To address this gap, he launched the global LISA 2020 network in 2012.
The goal of the program is to provide local university students with the skills and tools to do the statistics they need to drive development. We help local professors set up a statistical lab at the universities where they work. These statistics laboratories are collaborative centers where local professors teach students to provide statistical advice to academics, businesses, and other policy makers. While students learn statistics, they are also using their technical skills to make real local change.
One of our partner laboratories works with the Independent National Electoral Commission of Nigeria. Together, they assess the accuracy, completeness, consistency and reliability of data within Nigeria’s Continuing Voter Registration Policy to explore ways to improve the electoral process for voters.
In Ethiopia, another local laboratory is helping the Ethiopian government improve birth and death registration. The goal of using surveys, effective database management, and statistical training programs is to improve health outcomes.
Since its launch in 2012, our network of statistical labs has grown exponentially, with particularly strong roots in Africa, South Asia and Brazil. As of July 2021, it consists of 31 statistical laboratories located in 10 low- and middle-income countries.
As statistics continue to play a more important role in society, equal access to data resources in developing countries is becoming more and more important.
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Introduction of the conversation
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