Novel mathematical models estimate the screening efforts required to eliminate and eradicate sleeping sickness.
Researchers have created and tested five models for predicting sleeping sickness prevalence in a given village and further analysis of one reveals estimates of the screening frequency required to eliminate (less than 1 case per 10,000) and eradicate (zero cases) the disease. The paper published in PLOS Computational Biology will serve as a basis for evidence-based disease control policies to improve the frequency of screening towards eradication.
Human African Trypanosomiasis (HAT), also known as sleeping sickness, is a serious parasitic disease that is still prevalent in the Democratic Republic of the Congo and surrounding countries, despite decades of sustained screening programs. It is estimated that there were 20,000 cases in the year 2012 and that 70 million people from 36 Sub-Saharan countries are at risk of sleeping sickness infection.
Planning decisions, which determine which villages to screen and at what time interval to screen them, have a direct impact on the risk and the magnitude of a sleeping sickness outbreak. The WHO highlights that existing literature does not provide evidence-based guidelines on planning decisions and a wide variety of screening intervals have been applied in different control programs. Up until now there was little known about what screening interval is required to eliminate (less than 1 case per 10,000) and ultimately eradicate (zero cases) the disease.
In this paper, researchers from the Erasmus School of Economics, the Erasmus Medical Center, the Institute of Tropical Medicine, the University of Kinshasa, and the national sleeping sickness control program of the DRC present mathematical analyses to estimate the screening frequency required to eliminate (less than 1 case per 10,000) and eradicate (zero cases) the disease. These estimates are village specific and take screening participation levels and the accuracy of the diagnostic test into account.
The estimates calculated from their models can serve as a basis for evidence-based disease control policies to improve the frequency of screening towards eradication. They can guide decision making on village specific frequencies, on diagnostic tests, and on participation level targets which will contribute to working towards the targets set by the WHO. One such target is to eliminate sleeping sickness as a public health problem by 2020, which is defined as having less than one new case per 10,000 inhabitants in at least 90% of the disease foci.
The model estimates that the latter is only realistic for foci currently having less than 10 cases per 10,000 and for which the fraction of cases detected in a screening round is roughly above 75%. Moreover, under current under conditions, the model estimates that a screening interval of less than 15 months should be maintained to ultimately eradicate the disease.
This research serves as a basis for further modeling and optimization studies on sleeping sickness control. Whilst this paper focusses on strategic implications, future research will consider operational decisions. One particular challenge is to determine the actual schedules and routes for the mobile teams, so as to maximize their expected impact. Techniques and algorithms from the fields of operations research and management science will be employed to develop tools that support evidence-based scheduling and routing.
Part of the research was supported by a grant from the Bill & Melinda Gates Foundation: OPP1084252. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Article: Forecasting Human African Trypanosomiasis Prevalences from Population Screening Data Using Continuous Time Models, de Vries H, Wagelmans APM, Hasker E, Lumbala C, Lutumba P, Vlas SJd, et al., PLOS Computational Biology, doi:10.1371/journal.pcbi.1005103, published 22 September 2016.