Insights from quantitative and mathematical modelling on the proposed 2030 goal for gambiense human African trypanosomiasis (gHAT)

Measuring the target

In the long term, reaching EOT will lead to zero detected cases; however, the two objectives are not equivalent - it is possible either that zero case detections could occur without EOT or, conversely, that gHAT detections could be observed even after EOT.

EOT before zero reporting. Achieving EOT may not immediately lead to zero detected cases as there is often a long period between infection and detection (several years is typical¹⁶, although in extreme cases this could be decades¹⁷). As EOT is approached, the choice of confirmatory diagnostics becomes increasingly important as imperfect test specificity, even current algorithms with ~99.9% specificity, can lead to false positive cases. As we approach the 2030 goal, more rigorous methodologies (e.g. the laboratory-based trypanolysis test with 100% specificity¹⁸) should help to circumnavigate this problem.

Zero reporting but not EOT. Achieving zero detected cases does not mean that there is EOT for numerous reasons. The first possibility is that screening does not identify all remaining infections at peri-elimination. Only some of the population at risk is regularly screened; modelling^5,6,9 suggests that some high-risk individuals (~20% of the population) may not attend active screenings and data show that not all settlements in high-risk areas are screened annually (around 50% of villages in a high-endemicity region of DRC were screened in any given year^4,19). Coverage may improve with mini mobile teams (screening otherwise inaccessible villages) or door-to-door screening (likely increasing the number of high-risk people participating), but pockets of infection could still be missed. Furthermore, large areas of DRC (Figure 1), South Sudan and Central African Republic with potential transmission are not regularly screened due to regional conflicts. Secondly, even where there is a functional health system, there is a high probability of underreporting; models for Bandundu, DRC, suggest that only around 20% of gHAT cases that escape active detection are identified by passive detection (Model W in Rock et al.⁹), corresponding to ~63% of all infections being unreported.

Figure 1. Geographic availability of gambiense human African trypanosomiases (gHAT) data across the Democratic Republic of the Congo.

Colours represent numbers of reported cases in health zones from the last five years of data set. Health zones that never (2000–2016) report cases or active screening are coloured grey, whilst zones with <5% mean active screening coverage (during 2012–2016) are shown with black dots. This figure has been adapted from data presented in Franco et al.¹ under a CC-BY 4.0 license and with permission from Dr Erick Mwamba Miaka, director of the National HAT Control Programme (PNLTHA) of the Democratic Republic of the Congo.

Models can be used in order to explore the predictive power of one or more years of zero detected cases in estimating the likelihood of EOT. A stochastic village-level model (replaying active screening from 2000-16 for 559 settlements in Yasa Bonga & Mosango, Bandundu, DRC) focussed on detecting zero cases under active screening. This model strongly indicates that three or more consecutive rounds of finding zero cases is sufficient to reach >90% positive predictive value (PPV) of local EOT across typical village sizes and where screenings that achieve <10% coverage were ignored (Figure 2)¹⁹. There is higher certainty of EOT in smaller settlements and only using active screenings with >50% coverage as a measure could reduce the number of screening rounds needed to have high confidence. Current WHO guidelines recommend conducting three consecutive years of active screening with zero detections in a village before stopping⁷, therefore providing high confidence of local EOT prior to cessation. Modelling suggests that factoring screening coverage and population size into future guidelines could further improve certainty that EOT is met before stopping and could reduce the number of zero detections required for smaller settlements if coverage is sufficient. Scaling these insights from the village to larger spatial scales is confounded by spatial correlations and reinvasion, suggesting an intelligent and reactive surveillance methodology is required.

Figure 2. Probability of elimination of transmission (EOT) at the village-level based on case reporting.

The positive predictive value (PPV) of zero case detections to assess whether local EOT has occurred is the probability of zero human infection given consecutive active screenings with no cases found and no passive reporting in between (for >2 screenings). Model parameterization is for Yasa Bonga and Mosango health zones in the Democratic Republic of the Congo. The left figure uses all active screenings with >10% coverage, while the right figure is restricted to screenings of >50% coverage. The yellow region indicates >90% confidence that EOT has been met locally. This figure has been reproduced with permission from Davis et al.¹⁹.

Finally, there is potential for circulation of infection in animal reservoirs or persistence in asymptomatic individuals, which could lead to resurgence even after zero human reporting²⁰.

Technical feasibility

Models predict that for some regions (e.g. Equateur, DRC) continuation of the current medical-only strategy could achieve local EOT by 2030¹¹; however, in other regions (particularly some of Bandundu, DRC) this strategy may need to be supplemented with additional or improved interventions, even in areas likely to meet EPHP by 2020¹². Local EOT may be unsustainable without continued control due to the risk of reinvasion from other infected areas.

Multiple modelling approaches have shown that improvements to passive surveillance, targeting active screening to include high-risk groups, and VC could all result in reduced transmission and lead to EOT by 2030 with higher probability than the current medical-only strategy^9,21. Whilst it may not be necessary to implement VC across all settings, modelling consistently finds that VC averts infections fastest amongst the considered strategies. Modelling also suggests that the use of other new technologies (i.e. new oral drugs and RDTs) could lead to EOT but with lower probability and slower timelines than VC¹³.

Operational feasibility

Modelling results are generally based on assumptions that the health system retains similar or better functionality over the next 10 years. Political instability, conflict, or a reduced priority for tackling the reduced number of future cases could all lead to less control being applied in the future. Models are therefore making assumptions that screening and other controls follow recent trends.

Ability to sustain achievement of the goal

Stopping large-scale control activities against gHAT too soon could be problematic for EOT. Modelling was used to explore potential resurgence following attainment of EPHP in Guinea²², concluding that the presence of animal reservoirs would likely lead to resurgence following cessation of screening and vector control, but resurgence was unlikely if transmission was anthroponotic. Indeed, interruption of medical interventions in Guinea during the Ebola outbreak has shown that early cessation of activities in low prevalence settings can still lead to resurgence over three years¹⁰. In contrast, regions that maintained VC but stopped medical intervention observed a decrease in prevalence during the same time period²³.

Even if an area has reached local EOT, there is a concern that cessation of activities could be risky if nearby places have on-going transmission. Modelling reinvasion of HAT in villages in DRC that have achieved local EOT suggests short-term reinvasion is likely (>70%) from a single infected person, but less likely to cause persistent infection for ~15 years (<20%)¹⁹.

Considerations of costs and allocative efficiency

The burden of NTDs falls in resource-poor settings, and it is of utmost importance to efficiently use the resources available. A cost-effectiveness analysis for gHAT across settings of different transmission intensities has found that VC combined with other new technologies (diagnostics and drugs) is likely to be highly cost-effective in high-transmission settings (i.e. cost-effective for WTP thresholds >$386/disability-adjusted life year [DALY] averted). In moderate-transmission settings this strategy is only likely to be cost-effective for high WTP thresholds (>$1509/DALY averted), with medical-only strategies using new technologies likely to be preferable for lower WTP thresholds¹³. Unfortunately, cost-effectiveness in this traditional net-benefits framework does not always align with the goal of EOT by 2030, as strategies that are cost-effective (in terms of DALYs versus costs) may not be sufficient to meet the EOT goal. For example, in Sutherland et al.¹³, VC strategies were generally required to have high predicted probability of EOT by 2030, despite having low probabilities of being cost-effective in moderate- or low-transmission settings.

An analysis on the affordability of gHAT intervention and patient financial impact estimated that the total costs of a global control or elimination programme would be substantial (depending on the programme, between US$410.9 million and US$1.2 billion, compared to US$630.6 million for control activities in 2013-2020)²⁴. Alleviation of impoverishment and catastrophic health expenditures for households due to gHAT infection can only be achieved through elimination, rather than control, programmes.

Systematic non-participation in screening

Several modelling studies suggest that there is systematic non-participation in active screening, with high-risk individuals less likely to participate; the models without this heterogeneity were unable to match the observed longitudinal patterns of cases across different regions^5,6. More detailed data on age and gender of screening participants and gHAT cases could help better elucidate key groups in the population most responsible for transmission.

Animal reservoirs

Although prevalence of gHAT infection in animals is nonzero, estimates are uncertain and the role of infected animals in onward transmission is unclear²⁰. One modelling study using point prevalence data from animals in Cameroon suggested that animals constitute a possible transmission reservoir, implying that control targeting only human cases would be unable to eliminate gHAT due to persistence in animals²⁵, whereas modelling using longitudinal human data (Guinea, DRC, Chad) suggest that there is comparable statistical support for models with and without an animal reservoir^5,6,22. However, animals are unlikely to be able to sustain transmission on their own in Chad⁵.

Modelling suggests that VC, including spraying livestock, would reduce any possible transmission from animals, although pockets of sustained transmission could occur away from human activities²⁶.

Asymptomatic reservoirs

Asymptomatic infections in humans have been considered in a few transmission models. In some^9,21, the role of these infections in maintaining transmission or causing resurgence was unclear, although one modelling study using data from Guinea found both asymptomatic and clinical human infections were necessary for gHAT to persist (assuming no animal reservoir) and concluded that passive surveillance alone was not sufficient for gHAT monitoring in the approach to elimination²⁷.

Movement

So far, little attention has been paid to the movement of people in the modelling literature on gHAT; however, this may be important in areas that recently achieved disease-free status. Particular regions of concern would include formally endemic areas with both high influxes of refugees/internally displaced people and limited surveillance.