Human African Trypanosomiasis (sleeping sickness)

Human African trypanosomiasis, also known as sleeping sickness, is a vector-borne parasitic disease, transmitted by the tsetse fly.

HAT affects 24 countries in sub-Saharan Africa where tsetse flies are found, with disease typically fatal without treatment. Whilst over 37,000 cases were reported in 1998, recent case numbers have fallen to under 2200 in 2016 (Source: WHO, Over the past two decades, 97-98% of reported HAT cases have been caused by the parasite Trypanosoma brucei gambiense, while the remaining cases are caused by Trypanosoma brucei rhodesiense. Further information is available from the WHO ( The NTD Modelling Consortium has therefore focused on analysis and predictions for the gambiense form of the disease. Gambiene HAT is currently targeted for elimination as a public health problem by 2020 and zero transmission by 2030. This page gives an overview of the transmission dynamics for modellers who are new to modelling trypanosomiasis. At the bottom of the page are some questions about HAT which modelling can help address, as well as some relevant modelling papers.


Diagnostics and Surveillance: 

For HAT, diagnostics are an intrinsic part of medical intervention. Due to the challenges with administering current drugs, infection must be confirmed - at-risk populations are actively screened using a multi-tool algorithm including the card agglutination test for trypanosomes (CATT), microscopy to visualise parasites and a lumbar puncture to stage disease. Symptomatic individuals who self-present at fixed health facilities also undergo a similar diagnostic process. Recently a variety of rapid diagnostic tests (RDTs) have been used as part of both active and passive screening.

Strategies and uncertainty of disease burden may be improved by:

  • Carrying out active surveillance in areas with persistent low-level passive reporting
  • Identifying groups of people who are most exposed to tsetse bites and improve their recruitment to active screening

Future modelling and predictions could be made more robust by:

  • Estimating the proportion of HAT infections that produce long-term carriers and if these individuals all develop disease
  • Providing staged up-to-date case data to enhance modelling predictions to best reflect recent reporting and strategy


Infection Biology: 

Causal agent: Trypanosoma brucei gambiense or Trypanosoma brucei
Vector: tsetse flies

T.b. gambiense causes a chronic form of sleeping sickness, and it may be months or years before symptoms appear. During the primary stage of Trypanosoma infection, parasites can be found in the blood, lymph, and subcutaneous tissues. The symptoms of this stage include fever, headaches, joint pain, and itching.

During the secondary stage of infection, also called the neurological phase, parasites cross the blood-brain barrier to infect the brain and central nervous system. Symptoms of this stage include behaviour changes, sensory disturbances, lack of coordination, confusion, and disturbances of the sleep cycle. Without treatment, the majority of cases will die.


Disease Burden: 

The global burden of disease and risk have been mapped by the WHO ( and respectively).

Transmission Dynamics: 

The Trypanosoma parasites (T.b. gambiense and T.b. rhodesiense) are transmitted between tsetse (Glossina), humans, and animals.

Tsetse take blood meals from humans or animals every two to three days. Flies infected with Trypanosoma will inject the parasites into the human or animal that they are feeding on. The parasite undergoes several developmental stages within the mammalian host. Trypanosomes present in an infected human or animal can then be ingested by an uninfected tsetse fly taking its blood meal on the infected host. The trypanosomes will then undergo several more developmental stages within the fly, and eventually reach the salivary glands of the infected tsetse fly.  The newly infected tsetse fly will then transmit trypanosomes to its next host during the next blood meal, thus repeating the cycle of infection and transmission.

There are many different species and subspecies of tsetse that transmit trypanosomes, each with varying preferences for feeding on humans or animals. The Morsitans group tends to be found in savannah environments (e.g. G. morsitans sub-morsitans, G. pallidipes, G. synnertoni, and G. austeni). The Palpalis group is typically found in Riverine environments (e.g. G. palpalis palpalis, G. fuscipes fuscipes, G. tachinoides and G. palpalis gambiensis). The Fusca group is found in the forest (e.g. G. fusca fusca, G. brevipalpis, G. longipennis, and G. medicorum). (Source: Fevre et al. 2006

What modelling has told us about transmission dynamics:

  • Heterogeneous human populations likely drive disease dynamics. Modelling provides additional evidence of high-risk groups and non-participation of some groups in active screening.
  • Extremely low-level prevelences can persist in small populations (e.g. villages) for long durations due to the slow timescales associated with infection

Diagnosis and treatment of humans to reduce Trypanosoma reservoir:  serological tests can screen human blood for the presence of trypanosomes. If infection is confirmed, staging can be performed with a lumbar puncture to determine if trypanosomes are present in the CNS fluid. The drugs used for treatment depend on the stage of infection and must be administered intravenously.

Modelling in this project suggests that medical interventions in various foci have contributed to substantial reductions in transmission and reporting since the turn of the century. Current active screening has been moderately successful in reducing reported cases and most health zones in former Equateur province, DRC, are on track to locally achieve elimination as a public health problem by 2020, however the neighbouring former province of Bandundu has numerous health zones in which current active screening will be insufficient to achieve elimination goals.

Vector control to reduce tsetse population size: insecticide, traps, sterilized insect technique, paratransgenesis. In particular tiny targets have reduced tsetse population sizes by ~80% in Guinea and DRC, 90% in Uganda, and 99% in Chad.

Vector control has not been used at scale in many regions, however modelling analyses in regions where it has been utilised find that this foci are likely to succeed in eliminating HAT as a public health problem if this tsetse intervention continues (e.g. Boffa focus in Guinea and Mandoul focus in Chad). Predictions of vector control in DRC suggest that it could be helpful in the health zones which are predicted not to meet their target and to interrupt transmission to meet the 2030 goal of full elimination – even moderate vector control with 60% reduction could be sufficient.

Model comparison across for modelling groups has been used to assess possible benefits of improved strategies. In addition to standard active screening and passive surveillance, the complementary strategies of vector control, targeting high-risk people in active screening and improving access to passive detection and treatment were simulated. The models indicate that all of these improved interventions could greatly benefit HAT strategy and help to achieve elimination. In particular vector control has the potential to rapidly reduce transmission, and all models ranked this as the strategy which would most likely avert the most cases.


Health Economics: 

Dynamic modelling analyses of the costs and benefits of different approaches to controlling HAT are limited, however one study which examines these is found at Relevant health economic questions that can be answered through modelling are included below.

Modelling Questions: 

Can HAT be eliminated by 2020?

What is the optimal combination of interventions to eliminate HAT?

How could new developments in interventions facilitate or speed up the elimination of HAT?

Model code

We are committed to making our model code available for use by other modellers. Below are links to code used in a recent publication:

Rock et al. Data-driven models to predict the elimination of sleeping sickness in former Equateur province of DRC. Epidemics 2017. The code for the models can be found in Appendix A. Supplementary data.

Modelling publications