Modelling to infer the role of animals in gambiense human African trypanosomiasis transmission and elimination in the DRC

Abstract

Gambiense human African trypanosomiasis (gHAT) has been targeted for elimination of transmission (EoT) to humans by 2030. Whilst this ambitious goal is rapidly approaching, there remain fundamental questions about the presence of non-human animal transmission cycles and their potential role in slowing progress towards, or even preventing, EoT. In this study we focus on the country with the most gHAT disease burden, the Democratic Republic of Congo (DRC), and use mathematical modelling to assess whether animals may contribute to transmission in specific regions, and if so, how their presence could impact the likelihood and timing of EoT. By fitting two model variants—one with, and one without animal transmission—to the human case data from 2000–2016 we estimate model parameters for 158 endemic health zones of the DRC. We evaluate the statistical support for each model variant in each health zone and infer the contribution of animals to overall transmission and how this could impact predicted time to EoT. We conclude that there are 24/158 health zones where there is substantial to decisive statistical support for some animal transmission. However—even in these regions—we estimate that animals would be extremely unlikely to maintain transmission on their own. Animal transmission could hamper progress towards EoT in some settings, with projections under continuing interventions indicating that the number of health zones expected to achieve EoT by 2030 reduces from 68/158 to 61/158 if animal transmission is included in the model. With supplementary vector control (at a modest 60% tsetse reduction) added to medical screening and treatment interventions, the predicted number of health zones meeting the goal increases to 147/158 for the model including animal transmission. This is due to the impact of vector reduction on transmission to and from all hosts.

Towards the sustainable elimination of human African trypanosomiasis in Côte d’Ivoire using an integrated approach

“…Nevertheless, it is acknowledged that this model variant does not incorporate the possibility for non-human animal-tsetse transmission cycles, nor potentially self-curing asymptomatic human infections. Either of these two possibilities could lead to more transmission events per detected case, and therefore to more pessimistic model outcomes [55,56]. Despite this, the extremely low case reporting across several years in Côte d’Ivoire may indicate that these transmission routes (if they exist) are not sustaining transmission to humans; modelling analyses in the low-prevalence regions of the former Equateur province of the Democratic Republic of Congo [55] and the Mandoul focus of Chad [36] have found this kind of persistent low or zero reporting is suggestive of very limited or no infection contribution from non-human animals.…”

“…Either of these two possibilities could lead to more transmission events per detected case, and therefore to more pessimistic model outcomes [55,56]. Despite this, the extremely low case reporting across several years in Côte d'Ivoire may indicate that these transmission routes (if they exist) are not sustaining transmission to humans; modelling analyses in the low-prevalence regions of the former Equateur province of the Democratic Republic of Congo [55] and the Mandoul focus of Chad [36] have found this kind of persistent low or zero reporting is suggestive of very limited or no infection contribution from non-human animals. Furthermore, in the foci with vector control, the large reduction in tsetse population density will have reduced transmission between tsetse and any potential infection source (animal or human).…”

A modelling assessment of short- and medium-term risks of programme interruptions for gambiense human African trypanosomiasis in the DRC

“…This and other similar modelling analyses by the HAT MEPP project [ 17 , 29 , 33 , 42 ] have been used to support PNLTHA-DRC’s quantitative evaluation of their elimination strategy. Modelling cannot perfectly predict the future, and it is acknowledged that financial and operational challenges may sometimes prevent optimal strategies from being conducted.…”

Cost-effectiveness of end-game strategies against sleeping sickness across the Democratic Republic of Congo

“…In 69 health zones, this would mean more intense strategies than those that are CE at WTP=$500; in 31, 10, and 24 health zones there would be, a need for more AS, more VC, or both. Overall, the highest number of health zones where EoT-maximising strategies are not CE in Kasai Occidental (14), Kasai Oriental (15), and Maniema Katanga (10). There is one exception to the trends: in the health zone of Isangi, Mean AS will reach EoT with 0.91 probability and is the minimum-cost strategy, but the addition of targeted VC is cost-effective at WTP=$250 and WTP=$500.…”

“…Notably, in 48 health zones, the strategy that maximises EoT by 2030 still has a <90% probability of reaching EoT by 2030. Twenty-two of the 48 health zones are in Kasai Occidental (12) and Maniema-Katanga (10), while the rest are in Kasai Oriental (6), Bandundu Nord (5), Bandundu Sud (5), Bas-Uélé region (all 3 health zones), and Equateur Sud (2). All the health zones in Equateur Nord, the portion of Isangi coordination in Tschopo province, Kinshasa, and Kongo Centrale have a <90% probability of reaching EoT by 2030.…”