A revolutionary computer-based prediction system has helped drastically reduce the number of cholera cases and deaths in war-torn Yemen.
According to the BBC, Yemen was home to 50,000 cases of waterborne diseases a month in 2017, with 2,000 sufferers - mostly young children - eventually dying from their illnesses. In 2018, that number has plummeted to around 2,500 a month and it’s possibly all thanks to a new computer prediction system that can spot outbreaks before they even happen.
The ongoing civil war in Yemen, which erupted in 2015, was greatly contributed to one of the largest cholera epidemics in recent years, with fighting destroying sanitation, sewage and water supplies across the nation. All told, this has lead to over one million cases of waterborne infection in 2017 - a number which massively strained the limited resources of aid workers on the ground.
Faced with this, the UK's Department for International Development worked closely with the Met Office weather service to develop a system which can predict likely outbreak locations, allowing charities and agencies to better concentrate their efforts and resources.
How To Predict a Cholera Outbreak
Firstly, the Met Office uses its weather satellites to monitor and track weather patterns over Yemen in order to locate future areas of particularly heavy rainfall. Such downpours can often overwhelm sewage and sanitation systems resulting in the contamination of drinking water and aiding the spread of disease.
This information is then fed through a computer model which can further highlight potential issue areas. The algorithm, developed by the University of Maryland’s Professor Rita Colwell and West Virginia University’s Dr Antar Jutla, takes into account additional information such as population density, seasonal temperature and local infrastructure and provides workers on the ground with a list of high risk locations.
Charities on the ground, such as Unicef, then use this data to better direct their operations, which frequently consist of distributing hygiene kits, chlorine tablets and jerry cans for water storage. Perhaps more importantly, workers also conduct local educational initiatives, teaching Yeminis basic sanitation as well as advice for drawing clean water. Although much of the aid provided on the ground is of a tried and trusted method, the computer model has allowed them to distribute it much more effectively - sometimes up to four weeks before heavy rainfall arrives.
Professor Charlotte Watts, chief scientific adviser for the Department of International Development accepts that other factors, such as local structures and geography, may have also had an effect on the reduction of cholera cases in Yemen. However, the reduction following the introduction of the prediction system is so marked, she believes it has also had a significant role in reducing the number of deaths and infections resulting from cholera.
Now, she hopes to develop the system further and possibly extend the warning period from four to eight weeks, allowing workers on the ground time to establish more robust solutions - such as vaccination programmes. Ultimately, she also hopes the system can be used to impact upon other illnesses also tied to weather patterns, such as malaria and dengue fever. Indeed, an American medical startup, AIME, is already working on such a system.