In a first of its kind, a group of scientists predicted a cholera outbreak in Yemen by using satellites to gather information regarding precipitation, water storage, temperatures, and land around the country.

Cholera is a water-borne disease caused by the bacterium called Vibrio cholarae. Its symptoms include diarrhoea, vomiting, and muscle cramps, and in severe cases, may even lead to the death of the patient by acute dehydration. The disease may be endemic, occurring at regular intervals, especially around coastal areas. In such a case, the local people are often equipped to deal with the disease effectively. It may also take the form of an epidemic, causing deaths by the thousands due to a lack of proper treatment.

In such a case, a prior warning is often effective in reducing the number of deaths caused by the disease. While such warnings provide the authorities with enough time to equip themselves with adequate vaccines and medicines, an inaccurate prediction may also lead to panicking. Hence, accuracy in such cases is imperative, an objective that was successfully reached by the group of scientists in May 2017, to predict the Cholera epidemic that occurred in the month of June of the same year in Yemen.

A developing country like Yemen often faces problems of poor sanitation and lack of clean drinking water, which makes it prone to diseases like Cholera. Furthermore, Yemen’s political instability and civil unrest do not make it easy for scientists to collect data by perusing the locations and settling on data points. Satellites, on the other hand, provide such data from the sky, without necessitating the scientists’ physical presence in the country.

Jutla, a member of the team that predicted the epidemic said that due to their inability to go to war-torn Yemen, they had validated their algorithms based on data from the Bengal Delta and parts of Africa. This was the reason why they had not issued any prior warning since an inaccurate prediction would have caused unnecessary panic. But the known to be successful prediction has provided the group with a renewed confidence in their model. It provides them with the hope that further development of this innovation may prove to be helpful in evading and eradicating such epidemics in times to come.