Bangladeshi scientist Firdausi Qadri received the Ramon Magsaysay Award – the “Nobel Prize of Asia” – for her work in developing more affordable vaccines against cholera and typhoid – Copyright AFP/File MUNIR UZ ZAMAN
The seventh cholera pandemic, which began in 1961, continues to this day. The pandemic has affected millions of people each year and claimed tens of thousands of lives. The World Health Organization has recognized cholera as the longest-lasting pandemic in history. It spreads through the contamination of domestic water sources by the Vibrio cholerae Bacteria, often due to poor sanitation infrastructure.
In countries such as Bangladesh, where cholera is endemic due to high population density and inadequate access to clean water and sanitation, the disease poses a significant public health risk.
Some scientists believe that establishing an early warning system for local cholera risk in high-risk populations may be beneficial for the health of these areas. This includes measures such as encouraging households to adopt safer water, sanitation and hygiene practices, thereby reducing vulnerability to cholera infection.
Supported in part by NASA and managed by Resources for the Future, Kevin Boyle and colleagues at Moravian University, Penn State, and the University of Rhode Island investigated the feasibility of implementing a smartphone app to deliver cholera risk predictions to households as part of early warning efforts to mitigate the cholera threat in Bangladesh.
The study is entitled “Early warning systems, mobile technology and cholera defence: lessons from rural Bangladesh” and is published in The Journal of Environmental Economics and Management.
For the research, the team developed CholeraMap, an Android-based smartphone application that delivers cholera risk forecasts to households. The app allows users to access risk predictions – from low to medium to high – for both their community and their individual residences.
The app also provided guidance on understanding the risk of cholera in the area and provided important public health information to reduce the risk of cholera.
This type of reporting makes sense because cholera risk is seasonal and changing weather patterns make historical cholera risk predictions less reliable, indicating the need for predictive models. Environmental scientists worked on a predictive model for monthly cholera risk in Bangladesh. The researchers then examined how best to communicate these predictions directly to at-risk populations.
To develop the cholera threat model, scientists used remote sensing data such as rainfall observations, anomalies, forecasts, temperature and elevation, as well as data on population density and past cholera cases. This model was then calibrated for Matlab, a rural sub-district in Bangladesh.
To test the software, the CholeraMap application was installed on the smartphones of about 750 households via Matlab. Another 750 households received a companion app – CholeraApp – that provided publicly available information on cholera prevention. Finally, as a control, 500 households that did not have access to either app were tracked.
To assess the impact of CholeraMap on household behavior and health, researchers conducted surveys among participating households before and after installing the application. Respondents were asked about their household’s water use, sanitation and hygiene practices, their recent health experiences, water safety, and their experiences using CholeraMap.
The scientists discovered that households using smartphone apps were less reliant on surface water, a major source of cholera transmission. Surface water serves as an important source of water in Matlab, especially for activities such as washing clothes, washing dishes, and bathing. By minimizing their dependence on surface water, households using CholeraMap were taking proactive measures to reduce their risk of contracting the disease.
Overall, the analysis seems to show that people respond and act in their own interests when presented with better health-related information.
