Forecasting Cholera Using Remote Sensing

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Cholera is a major disease threatening millions each year, where its symptoms are caused by a bacteria called Vibrio cholerae that contaminates food and water. It is usually caused by contaminated water used for agriculture or drinking. Recently, NASA has developed techniques so that it can forecast the potential for cholera outbreaks to occur, helping scientists and healthcare workers to act before the outbreak occurs. This project has included collaboration with  the Department for International Development (DFID) in the UK, the Met Office, which provides public weather data in the UK, NASA, and US-based scientists.

The NASA forecasting tool effectively takes satellite observations on environmental conditions and combines the data with information on infrastructure and conditions in a country. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua satellites measures air and water temperatures, while precipitation data from the Global Precipitation Measurement, a satellite-based precipitation measuring instrument, are used together. The phytoplankton in surroundings waters are also measured through MODIS. Data collected on sanitation and clean water infrastructure are added to this where a model is then run to forecast areas cholera is likely to be evident. Effectively the presence of these factors to a high degree mean that the probability of cholera occurrence is more likely. Smaller areas are broken down in the region of analysis to the size of counties in the United States. The model has demonstrated 92% accuracy in its first test case in Yemen, showing its utility particularly where on the ground access makes it difficult to measure cholera.[1]

Map of predicted cholera risk based on analysis and satellite data in Yemen, June 2017. Blue color indicates low risk of cholera while red color indicates high risk of cholera. The map on the right shows the actual number of cholera cases in June 2017. The red area represent reported cholera cases. Credits: West Virginia University/Antar Jutla

Map of predicted cholera risk based on analysis and satellite data in Yemen, June 2017. Blue color indicates low risk of cholera while red color indicates high risk of cholera. The map on the right shows the actual number of cholera cases in June 2017. The red area represent reported cholera cases.
Credits: West Virginia University/Antar Jutla

Back in 2000, it was already demonstrated conclusively that cholera had certain environmental conditions that allow it to flourish. It was also demonstrated in that study that satellite data can be used to forecast the outbreak of cholera by taking climate data from 1992–1995 in Bangladesh and combining it with available satellite data that included Advanced Very High Resolution Radiometer (AVHRR) data. In effect, this demonstrated that real-time estimates could be made provided data can be rapidly sent to scientists working to forecast where cholera outbreaks could be found. It was after this period more studies began to develop and demonstrate links between environmental conditions observed through satellite data and cholera outbreaks.[2]

The potential of forecasting cholera also allows it to combine with other efforts that are similarly trying to combine environmental data and various infrastructure factors to forecast cholera spread. One effort has used environmental data from Hurricane Matthew in 2016 that affected Haiti. The model in this case can estimate near real-time spread and effects of a cholera outbreak based on ongoing vaccination efforts and not only environmental factors. The potential is satellite data and data and modelling combined using NASA’s tool could allow not only forecasts of potential cholera to be made but also allow humanitarian efforts to respond to ongoing cholera cases through targeted and optimized healthcare efforts. This makes it possible to also minimize cholera spread and not just forecast its occurance.[3]

Maps of the reported cholera cases and the associated incidence during October 2016 at the communal level with the detailed weekly dynamics for the departments most affected after Matthew (Grande Anse (a), Sud (b) and Ouest (c)), and for the whole country (panel d). Figure: Pasetto et al., 2018

Maps of the reported cholera cases and the associated incidence during October 2016 at the communal level with the detailed weekly dynamics for the departments most affected after Matthew (Grande Anse (a), Sud (b) and Ouest (c)), and for the whole country (panel d). Figure: Pasetto et al., 2018

As these new studies and efforts are beginning to demonstrate the potential to understand cholera outbreaks, other outbreaks, such as those caused by rotavirus, could now also be determined by forecasting using satellite data capturing temperature and rainfall conditions. In Bangladesh, similar to Yemen, 1-2 months prior to an outbreak of a rotavirus, when conditions were 16°C to 21°C at night and there were a high number of rainfall days within a month, appeared to be critical in determining the timing of an outbreak. Similarly, MODIS data can aid in this by showing if those environmentally-driven events are evident and that information could provide a forecast potential which could be acted upon prior to an outbreak.[4]

What these studies demonstrate is that we are now at the point where major health outbreaks driven by environmental and other factors, including poor infrastructure, could be determined well in advance prior to their occurrence. Other models could even be combined that allow healthcare workers to then optimize treatment once outbreaks occur. Satellite data now permits such forecasting to be done in remote region and areas that might have relatively poor on the ground data gathering potential and healthcare facilities.

References

[1]    For more on NASA’s new cholera forecasting tool and satellite systems used for this, see: https://www.nasa.gov/press-release/nasa-investment-in-cholera-forecasts-helps-save-lives-in-yemen.

[2]    For an early study on the relationship between cholera, climate data, and using satellite data, see:  Lobitz, B., Beck, L., Huq, A., Wood, B., Fuchs, G., Faruque, A. S. G., & Colwell, R. (2000). Climate and infectious disease: Use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proceedings of the National Academy of Sciences, 97(4), 1438–1443. https://doi.org/10.1073/pnas.97.4.1438.

[3]    For more on the effort to model cholera using ongoing vaccination efforts and environmental data, see:  Pasetto, D., Finger, F., Camacho, A., Grandesso, F., Cohuet, S., Lemaitre, J. C., … Rinaldo, A. (2018). Near real-time forecasting for cholera decision making in Haiti after Hurricane Matthew. PLOS Computational Biology, 14(5), e1006127. https://doi.org/10.1371/journal.pcbi.1006127.

[4]    For forecasting rota virus outbreaks using satellite data, see: Hasan, M. A., Mouw, C., Jutla, A., & Akanda, A. S. (2018). Quantification of Rotavirus Diarrheal Risk Due to Hydroclimatic Extremes Over South Asia: Prospects of Satellite-Based Observations in Detecting Outbreaks. GeoHealth, 2(2), 70–86. https://doi.org/10.1002/2017GH000101

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