Open Access Open Badges Research article

Estimates of the duration of the early and late stage of gambiense sleeping sickness

Francesco Checchi1*, João AN Filipe2, Daniel T Haydon3, Daniel Chandramohan1 and François Chappuis45

Author Affiliations

1 Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E7HT, UK

2 Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E7HT, UK

3 Division of Environmental and Evolutionary Biology, University of Glasgow, Glasgow G12 8QQ, UK

4 Médecins Sans Frontières, Swiss section, Rue de Lausanne 78, 1211 Geneva 21, Switzerland

5 Geneva University Hospitals, Travel and Migration Medicine Unit, Rue Micheli-du-Crest 24, 1211 Geneva 14, Switzerland

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BMC Infectious Diseases 2008, 8:16  doi:10.1186/1471-2334-8-16

Published: 8 February 2008



The durations of untreated stage 1 (early stage, haemo-lymphatic) and stage 2 (late stage, meningo-encephalitic) human African trypanosomiasis (sleeping sickness) due to Trypanosoma brucei gambiense are poorly quantified, but key to predicting the impact of screening on transmission. Here, we outline a method to estimate these parameters.


We first model the duration of stage 1 through survival analysis of untreated serological suspects detected during Médecins Sans Frontières interventions in Uganda and Sudan. We then deduce the duration of stage 2 based on the stage 1 to stage 2 ratio observed during active case detection in villages within the same sites.


Survival in stage 1 appears to decay exponentially (daily rate = 0.0019; mean stage 1 duration = 526 days [95%CI 357 to 833]), possibly explaining past reports of abnormally long duration. Assuming epidemiological equilibrium, we estimate a similar duration of stage 2 (500 days [95%CI 345 to 769]), for a total of nearly three years in the absence of treatment.


Robust estimates of these basic epidemiological parameters are essential to formulating a quantitative understanding of sleeping sickness dynamics, and will facilitate the evaluation of different possible control strategies.