Village-scale persistence and elimination of gambiense human African trypanosomiasis

• Published in: PLoS neglected tropical diseases Vol. 13; no. 10; p. e0007838
• Main Authors: Davis, Christopher N., Rock, Kat S., Mwamba Miaka, Erick, Keeling, Matt J.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.10.2019; Public Library of Science (PLoS)
• Subjects: African trypanosomiasis; Analysis; Biology; Biology and Life Sciences; Congo - epidemiology; Dynamics; Epidemics; Epidemiology; Forecasting; Geographic Mapping; Health; Health Policy; Humans; Incidence; Infections; Infectious diseases; Local population; Mass Screening - methods; Mathematics; Measles; Medicine and Health Sciences; Organizations; Parasites; Persistence; Population; Population Density; Populations; Prevalence; Probability theory; Public Health; Public health movements; Risk assessment; Screening; Stochastic models; Stochasticity; Towns; Trends; Tropical climate; Tropical diseases; Trypanosoma brucei gambiense; Trypanosomiasis; Trypanosomiasis, African - epidemiology; Vector-borne diseases; World Health Organization; Congo; United Kingdom > UK
• ISSN: 1935-2735; 1935-2727; 1935-2735
• DOI: 10.1371/journal.pntd.0007838

Gambiense human African trypanosomiasis (gHAT) is one of several neglected tropical diseases that is targeted for elimination by the World Health Organization. Recent years have seen a substantial decline in the number of globally reported cases, largely driven by an intensive process of screening and treatment. However, this infection is highly focal, continuing to persist at low prevalence even in small populations. Regional elimination, and ultimately global eradication, rests on understanding the dynamics and persistence of this infection at the local population scale. Here we develop a stochastic model of gHAT dynamics, which is underpinned by screening and reporting data from one of the highest gHAT incidence regions, Kwilu Province, in the Democratic Republic of Congo. We use this model to explore the persistence of gHAT in villages of different population sizes and subject to different patterns of screening. Our models demonstrate that infection is expected to persist for long periods even in relatively small isolated populations. We further use the model to assess the risk of recrudescence following local elimination and consider how failing to detect cases during active screening events informs the probability of elimination. These quantitative results provide insights for public health policy in the region, particularly highlighting the difficulties in achieving and measuring the 2030 elimination goal.