Safe Water Optimization Tool

Research & Development

The SWOT is built on years of water safety research conducted in refugee camps around the world. Here are the key papers that underlie the SWOT.

Unpaired Data Modelling with the SWOT

De Santi, M., Khan, U.T., & Ali, S. I. (2023)

White Paper. Dahdaleh Institute for Global Health Research: Toronto, Canada.

SWOT EO Version 1.5 Updates

De Santi, M., Khan, U.T., & Ali, S. I. (2023)

White Paper. Dahdaleh Institute for Global Health Research: Toronto, Canada.

Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?

De Santi, M., Ali, S. I., Arnold, M., Fesselet, J., Hyvarinen, A., & Khan, U.T. (2022)

PLOS Water, 1(9): e0000040

Evidence-based chlorination targets for household water safety in humanitarian settings: Recommendations from a multi-site study in refugee camps in South Sudan, Jordan, and Rwanda

Ali, S. I., Ali, S. S., & Fesselet, J. (2021)

Water Research, 189(116642).

Forecasting point-of-consumption chlorine residual in refugee settlements using ensembles of artificial neural networks

De Santi, M., Khan, U.T., Arnold, M., Fesselet, J., & Ali, S.I. (2021)

npj Clean Water 4, 35.

The Safe Water Optimization Tool Artificial Neural Network Analytics for the SWOT v2.0

De Santi, M., & Ali, S. I. (2021)

White Paper. Dahdaleh Institute for Global Health Research: Toronto, Canada.

Effectiveness of emergency water treatment practices in refugee camps in South Sudan

Ali, S. I., Ali, S. S., & Fesselet, J. (2015)

Bulletin of the World Health Organization, 93(8), 550–558.

Case Studies

The SWOT has been implemented in emergency settings around the world. These case studies illustrate how the SWOT was used to improve water safety in a range of humanitarian water supply use cases.

Research & Development

Unpaired Data Modelling with the SWOT

SWOT EO Version 1.5 Updates

Modelling point-of-consumption residual chlorine in humanitarian response: Can cost-sensitive learning improve probabilistic forecasts?

Evidence-based chlorination targets for household water safety in humanitarian settings: Recommendations from a multi-site study in refugee camps in South Sudan, Jordan, and Rwanda

Forecasting point-of-consumption chlorine residual in refugee settlements using ensembles of artificial neural networks

The Safe Water Optimization Tool Artificial Neural Network Analytics for the SWOT v2.0

Effectiveness of emergency water treatment practices in refugee camps in South Sudan

Case Studies

⬢ Nigeria | Anka

⬢ Nigeria | Mbawa

⬢ Tanzania | Nyarugusu

Implementation Locations