Research page
International Economics

Schistosomiasis, Agriculture and Migration in Africa: a joint Economic and Ecological Analysis

Project Team:  Jean-Louis Arcand (Graduate Institute) ; Theophile Mande (Best – i3E, Ouagadougou) ; Javier Perez-Saez (Ecole Polytechnique Fédérale, Lausanne) ; Daniele Rinaldo (Graduate Institute) ; Penelope Vounatsou (Swiss Tropical and Public Health Institute, Basel)
Timeline: 2018-2022
Keywords: Migration ; Rural development ; Coffee ; Quality ; Smallholder Empowerment ; Sustainable Livelihoods ; Policy decision support
Funding organisation: SNIS


  • International Organisations: Departement of Control of Neglected Tropical Diseases at the WHO
  • NGOs, States or other institutions: BEST – i3E ; The Global Coffee Platform


Granted fund: 253,830 CHF


Project Summary: Schistosomiasis, bilharzia, or ‘snail fever’ is a chronic disease caused by flatworms native to sub-Saharan Africa. Transmitted through fresh water during daily activities, the disease has impairing impacts on people’s health, i.e. people do not necessarily die from it, but their quality of life is severely reduced. In 2016, 206 million people worldwide were suffering from Schistosomiasis.

As infected people are often too weak to work, the disease directly impacts the economic development of affected countries. Therefore the main objective of this project is to identify the impact of schistosomiasis on economic development in sub-Saharan Africa. In the process, the project will create economic indicators using agriculture and migration to explain the dynamics of the disease.

The main research questions to be addressed are:

  • What is the impact of schistosomiasis on agricultural production?
  • What is the place of schistosomiasis in the optimal resource allocation of farmers?
  • What is the impact that the development of water resources and human mobility have on spreading the disease?

The research methodology combines several axes: an empirical study on the impact of schistosomiasis on agricultural production; the development of a unitary dataset linking the characteristics of agricultural production to the variables characteristic to the evolution of schistosomiasis; the use of machine learning to select the model which best captures the link between the disease, agricultural development and migration.