Search

BackgroundHuman African trypanosomiasis is a parasitic disease caused by trypanosomes among whichTrypanosoma brucei gambienseis responsible for a chronic form (gHAT) in West and Central Africa. Its elimination as a public health problem (EPHP) is being achieved. Côte d’Ivoire was one of the first countries to be validated by WHO in 2020 and this was particularly challenging as the country still reported around a hundred cases a year in the early 2000s. This article describes the strategies implemented including a mathematical model to evaluate the reporting results and infer progress towards sustainable elimination.MethodsThe control methods used combined both exhaustive and targeted medical surveillance strategies to diagnose and treat cases as well as vector control to reduce the risk of transmission in the most at risk areas. A mechanistic model was used to estimate the number of underlying infections and the probability of elimination of transmission (EoT) between 2000– 2021 in two endemic and two hypo-endemic health districts.ResultsBetween 2015 and 2019, nine gHAT cases were detected in two health districts in which the number of cases/10,000 inhabitants was far below 1, a necessary condition for validating the EPHP. Modelling estimated a slow but steady decline in transmission across the four health districts, bolstered in the two endemic health districts by the introduction of vector control. The decrease in underlying transmission in all health districts corresponds to a high probability that EoT has already occurred in Côte d’Ivoire.ConclusionThis success was achieved through a multi-stakeholder and multidisciplinary one health approach where research has played a major role in adapting tools and strategies to this large epidemiological transition to a very low prevalence. This integrated approach will need to continue to reach the verification of EoT in Côte d’Ivoire targeted by 2025.Author SummarySignificant efforts to controlTrypanosoma brucei gambiensehuman African trypanosomiasis (gHAT) have drastically reduced the prevalence of the disease and elimination of transmission (EoT) is targeted for 2030 by WHO. This reduction was particularly challenging in Côte d’Ivoire as it still faced epidemic episodes in the early 2000s. This large epidemiological transition to very low prevalence necessitated the adaptation and evolution of both medical and vector control strategies described in this article. A mathematical model was also used to retrospectively analyse case reporting results, indicating with high probability that local EoT has already been achieved in the four health districts analysed.With nine gHAT cases detected in two health districts between 2015 and 2019 and less than one case per 10,000 people per year in all health districts at national level over this five-year period, Côte d’Ivoire received validation by WHO of achievement of the elimination of the disease as a public health problem in 2020. These results combined with the modelling offer encouragement regarding reaching the verification of EoT targeted by 2025 in Côte d’Ivoire on condition of maintaining such multidisciplinary one health approach including research activities to continuously adapt it to the epidemiological transition to zero incidence.

Background The gambiense human African trypanosomiasis (gHAT) elimination programme in the Democratic Republic of Congo (DRC) routinely collects case data through passive surveillance and active screening, with several regions reporting no cases for several years, despite being endemic in the early 2000s. Methods We use mathematical models fitted to longitudinal data to estimate the probability that selected administrative regions have already achieved elimination of transmission (EOT) of gHAT. We examine the impact of active screening coverage on the certainty of model estimates for transmission and therefore the role of screening in the measurement of EOT. Results In 3 example health zones of Sud-Ubangi province, we find there is a moderate (>40%) probability that EOT has been achieved by 2018, based on 2000–2016 data. Budjala and Mbaya reported zero cases during 2017–18, and this further increases our respective estimates to 99.9% and 99.6% (model S) and to 87.3% and 92.1% (model W). Bominenge had recent case reporting, however, that if zero cases were found in 2021, it would substantially raise our certainty that EOT has been met there (99.0% for model S and 88.5% for model W); this could be higher with 50% coverage screening that year (99.1% for model S and 94.0% for model W). Conclusions We demonstrate how routine surveillance data coupled with mechanistic modeling can estimate the likelihood that EOT has already been achieved. Such quantitative assessment will become increasingly important for measuring local achievement of EOT as 2030 approaches.

Human African trypanosomiasis, caused by the gambiense subspecies of Trypanosoma brucei (gHAT), is a deadly parasitic disease transmitted by tsetse. Partners from around the world have stepped up efforts to eliminate the disease, and the Chadian government have had a particular focus on the previously high-prevalence setting of Mandoul. In this study, we evaluate the economic efficiency of the intensified strategies that were put in place from 2014 aimed at interrupting transmission of gHAT, and we make recommendations on the best way forward based on both epidemiological projections and cost-effectiveness. In our analysis we use a dynamic transmission model fit to epidemiological data from Mandoul to evaluate the cost-effectiveness of combinations of active screening, improved passive screening (defined as an expansion of the number of health posts capable of screening for gHAT), and vector control activities (the deployment of Tiny Targets). For cost-effectiveness analyses, our primary outcome is disease burden, denominated in disability-adjusted life-years (DALYs), and costs, denominated in 2020 US$. Although active and passive screening have enabled more rapid diagnosis and accessible treatment in Mandoul, the addition of vector control provided good value-for-money (at less than $750/DALY averted) and substantially increased the probability of reaching the 2030 elimination target for gHAT as set by the World Health Organization. Our transmission modelling and economic evaluations suggests that the gains have been made could be maintained by robust passive screening, and the inclusion of active screening and vector control strategies could be considered for other foci in the country with active transmission. Our analysis speaks to comparative efficiency, and it does not take into account all possible considerations; for instance, any cessation of on-going active screening should first consider that strong surveillance activities will be critical to verify elimination of transmission and to protect against the possible importation of infection from neighbouring endemic foci.Author summaryIn a drive to eliminate human African trypanosomiasis (gHAT or sleeping sickness) from Chad following a peak in 2002, the National Sleeping Sickness Control programme and their partners focused on making substantial changes to interventions within the high prevalence setting of Mandoul. These included the use of vector control starting in 2014 and improved screening in health facilities starting in 2015. To explore whether these interventions were an efficient use of resources we carried out a retrospective analysis using a dynamic transmission model fit to epidemiological data from Mandoul combined with a cost model. Our analysis indicated that active and passive screening have enabled more rapid diagnosis and accessible treatment in Mandoul compared to less ambitious interventions, and furthermore the addition of vector control provided good value-for-money and substantially increased the probability of reaching the 2030 elimination of transmission target for gHAT set by the World Health Organization. Looking forwards, our prospective analysis also considers the health economics of future strategies and, as part of this, concludes that the scaleback of vertical interventions appears cost-effective if passive screening remains robust in Mandoul. This could therefore enable shifting of resources to tackle other remaining foci in Chad.

Background Gambiense human African trypanosomiasis (gHAT) is marked for elimination of transmission (EOT) by 2030, but the disease persists in several low-income countries. We examine the cost-effectiveness of four gHAT elimination strategies in Democratic Republic of Congo (DRC), which has the highest burden of gHAT. Methods We compared four strategies against gHAT by coupling a transmission model with a health outcomes model in five settings - spanning low- to high-risk. Alongside passive surveillance (PS) infixed health facilities, the strategies included active screening (AS) at average or high coverage levels, both alone or with vector control (VC). A scale-back algorithm was devised to simulate cessation of AS and VC when no cases were reported for three consecutive years. Outcomes were denominated in disability-adjusted life-years (DALYs) and costs until 2040 were denominated in 2018 US$. Results In high or moderate-risk settings, costs of gHAT strategies are primarily driven by AS and, if used, VC. Due to the cessation of AS and VC most investments (75-80%) will be made by 2030 and VC might be cost-saving while ensuring EOT. In low-risk settings, costs are driven by PS, and minimum-cost strategies consisting of AS and PS lead to EOT by 2030 with high probability. Conclusion In many settings, the case for EOT by 2030 is a sensible use of resources, and investments in gHAT will decelerate within this decade in moderate- and low-risk regions. Research in context Evidence before this study The databases PubMed and ScienceDirect were searched on 28 April 2020 to identify previous studies undertaken on cost-effectiveness of strategies to control gHAT. We used an unrestricted language search and dates from January 2000 to focus on contemporary interventions and prevalence. The search terms were “sleeping sickness” OR “trypanosomiasis” AND “cost effective”. We excluded studies on rhodesiense HAT, animal trypanosomiasis and Chagas disease. A few intervention-specific studies have examined the cost-effectiveness of different tools, such as treatment and diagnostic tools or algorithms, but only a cost-effective analysis by Sutherland and colleagues (2017) examines the potential of multi-faceted strategies to avert burden and achieve elimination. Sutherland and colleagues showed that the deployment of emerging technologies - new treatments, new diagnostics, and/or vector control - would make elimination of transmission (EOT) feasible and cost-effective at thresholds below $1500-$1800 per DALY averted in theoretical settings of low, medium, and high prevalence. Added value of this study Using a dynamic modelling framework, the present study is the first to perform a DRC-specific cost-effectiveness analysis for gHAT strategies that accounts for local epidemiological and operational characteristics. Using detailed cost-functions and updated information on the impact of interventions, we have projected the epidemiological and health economic implications by 2040 of gHAT control and EOT by 2030. We have simulated four alternative strategies combining passive surveillance with moderate and high coverage levels of active screening by mobile units. We have also simulated strategies with and without vector control. We found that in high-risk settings, strategies that lead to EOT are in line with strategies that aim to minimise costs, but in moderate-risk settings strategies with a high risk to EOT would require a relatively high willingness to pay to be considered cost-effective; this is similar to investments made for other infectious disease elimination programs. Implications of all the available evidence gHAT elimination strategies can represent a justifiable use of resources even in resource-constrained settings. Investments in EOT in the near-term will enable a good degree of reduced costs post-2030, and in high-incidence settings, these investments will pay back by 2040.

The World Health Organization recently launched its 2021-2030 roadmap, Ending the Neglect to Attain the Sustainable Development Goals, an updated call to arms to end the suffering caused by neglected tropical diseases. Modelling and quantitative analyses played a significant role in forming these latest goals. In this collection, we discuss the insights, the resulting recommendations and identified challenges of public health modelling for 13 of the target diseases: Chagas disease, dengue, gambiense human African trypanosomiasis (gHAT), lymphatic filariasis (LF), onchocerciasis, rabies, scabies, schistosomiasis, soil-transmitted helminthiases (STH), Taenia solium taeniasis/ cysticercosis, trachoma, visceral leishmaniasis (VL) and yaws. This piece reflects the three cross-cutting themes identified across the collection, regarding the contribution that modelling can make to timelines, programme design, drug development and clinical trials.

Background In recent years, a programme of vector control, screening and treatment of gambiense human African trypanosomiasis (gHAT) infections led to a rapid decline in cases in the Mandoul focus of Chad. To represent the biology of transmission between humans and tsetse, we previously developed a mechanistic transmission model, fitted to data between 2000 and 2013 which suggested that transmission was interrupted by 2015. The present study outlines refinements to the model to: (1) Assess whether elimination of transmission has already been achieved despite low-level case reporting; (2) quantify the role of intensified interventions in transmission reduction; and (3) predict the trajectory of gHAT in Mandoul for the next decade under different strategies. Method Our previous gHAT transmission model for Mandoul was updated using human case data (2000–2019) and a series of model refinements. These include how diagnostic specificity is incorporated into the model and improvements to the fitting method (increased variance in observed case reporting and how underreporting and improvements to passive screening are captured). A side-by-side comparison of fitting to case data was performed between the models. Results We estimated that passive detection rates have increased due to improvements in diagnostic availability in fixed health facilities since 2015, by 2.1-fold for stage 1 detection, and 1.5-fold for stage 2. We find that whilst the diagnostic algorithm for active screening is estimated to be highly specific (95% credible interval (CI) 99.9–100%, Specificity = 99.9%), the high screening and low infection levels mean that some recently reported cases with no parasitological confirmation might be false positives. We also find that the focus-wide tsetse reduction estimated through model fitting (95% CI 96.1–99.6%, Reduction = 99.1%) is comparable to the reduction previously measured by the decline in tsetse catches from monitoring traps. In line with previous results, the model suggests that transmission was interrupted in 2015 due to intensified interventions. Conclusions We recommend that additional confirmatory testing is performed in Mandoul to ensure the endgame can be carefully monitored. More specific measurement of cases, would better inform when it is safe to stop active screening and vector control, provided there is a strong passive surveillance system in place. Graphical Abstract

Background In line with movement restrictions and physical distancing essential for the control of the COVID-19 pandemic, WHO recommended postponement of all neglected tropical disease (NTD) control activities that involve community-based surveys, active case finding, and mass drug administration in April, 2020. Following revised guidance later in 2020, and after interruptions to NTD programmes of varying lengths, NTD programmes gradually restarted in the context of an ongoing pandemic. However, ongoing challenges and service gaps have been reported. This study aimed to evaluate the potential effect of the programmatic interruptions and strategies to mitigate this effect. Methods For seven NTDs, namely soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis, trachoma, visceral leishmaniasis, and human African trypanosomiasis, we used mathematical transmission models to simulate the effect of programme interruptions on the dynamics of each of these diseases in different endemic settings. We also explored the potential benefit of implementing mitigation strategies, primarily in terms of minimising the delays to control targets. Findings We show that the effect of the COVID-19-induced interruption in terms of delay to achieving elimination goals might in some cases be much longer than the duration of the interruption. For schistosomiasis, onchocerciasis, trachoma, and visceral leishmaniasis, a mean delay of 2–3 years for a 1-year interruption is predicted in areas of highest prevalence. We also show that these delays can largely be mitigated by measures such as additional mass drug administration or enhanced case-finding. Interpretation The COVID-19 pandemic has brought infectious disease control to the forefront of global consciousness. It is essential that the NTDs, so long neglected in terms of research and financial support, are not overlooked, and remain a priority in health service planning and funding. Funding Bill & Melinda Gates Foundation, Medical Research Council, and the UK Foreign, Commonwealth & Development Office.

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 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 moderate or high 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 to 61 if animals are 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 animals. This is due to the impact of vector reduction on transmission to and from all hosts.Author summaryElimination of African sleeping sickness by 2030 is an ambitious goal, not least because of the unclear role that animals might play in transmission. We use mathematical models, fitted to case data from DRC to assess and quantify the contribution of animals to the human case burden.We found that 24/158 geographic regions included in this study had statistical evidence of animal transmission, although it appears extremely unlikely that animals could maintain transmission on their own. Animals could, however, delay elimination; using our model without animal transmission we predicted that 68 regions are expected to achieve elimination by 2030, whereas this reduces to 61 with animals. If vector control to reduce fly populations (which transmit the disease to and from hosts) are controlled in addition to medical interventions, then 147 regions are predicted to reach elimination by 2030 even with animal transmission.

Mathematical models of vector-borne infections, including malaria, often assume age-independent mortality rates of vectors, despite evidence that many insects senesce. In this study we present survival data on insecticide-resistant Anopheles gambiae s.l. from experiments in Côte d’Ivoire. We fit a constant mortality function and two age-dependent functions (logistic and Gompertz) to the data from mosquitoes exposed (treated) and not exposed (control) to insecticide-treated nets (ITNs), to establish biologically realistic survival functions. This enables us to explore the effects of insecticide exposure on mosquito mortality rates, and the extent to which insecticide resistance might impact the effectiveness of ITNs. We investigate this by calculating the expected number of infectious bites a mosquito will take in its lifetime, and by extension the vectorial capacity. Our results show that the predicted vectorial capacity is substantially lower in mosquitoes exposed to ITNs, despite the mosquitoes in the experiment being highly insecticide-resistant. The more realistic age-dependent functions provide a better fit to the experimental data compared to a constant mortality function and, hence, influence the predicted impact of ITNs on malaria transmission potential. In models with age-independent mortality, there is a great reduction for the vectorial capacity under exposure compared to no exposure. However, the two age-dependent functions predicted an even larger reduction due to exposure, highlighting the impact of incorporating age in the mortality rates. These results further show that multiple exposures to ITNs had a considerable effect on the vectorial capacity. Overall, the study highlights the importance of including age dependency in mathematical models of vector-borne disease transmission and in fully understanding the impact of interventions.Author summaryInterventions against malaria are most commonly targeted on the adult mosquitoes, which transmit the infection from person to person. One of the most important interventions are bed-nets, treated with insecticides. Unfortunately, extensive exposure of mosquitoes to insecticide has led to widespread evolution of insecticide resistance, which might threaten control strategies. Piecing together the overall impact of resistance on the efficacy of insecticide-treated nets is complex, but can be informed by the use of mathematical models. However, there are some assumptions that the models frequently use which are not realistic in terms of the mosquito biology. In this paper, we formulate a model that includes age-dependent mortality rates, an important parameter in vector control since control strategies most commonly aim to reduce the lifespan of the mosquitoes. By using novel data collected using field-derived insecticide-resistant mosquitoes, we explore the effects that the presence of insecticides on nets have on the mortality rates, as well as the difference incorporating age dependency in the model has on the results. We find that including age-dependent mortality greatly alters the anticipated effects of insecticide-treated nets on mosquito transmission potential, and that ignoring this realism potentially overestimates the negative impact of insecticide resistance.

Stochastic methods for modelling disease dynamics enable the direct computation of the probability of elimination of transmission. For the low-prevalence disease of human African trypanosomiasis (gHAT), we develop a new mechanistic model for gHAT infection that determines the full probability distribution of the gHAT infection using Kolmogorov forward equations. The methodology allows the analytical investigation of the probabilities of gHAT elimination in the spatially connected villages of different prevalence health zones of the Democratic Republic of Congo, and captures the uncertainty using exact methods. Our method provides a more realistic approach to scaling the probability of elimination of infection between single villages and much larger regions, and provides results comparable to established models without the requirement of detailed infection structure. The novel flexibility allows the interventions in the model to be implemented specific to each village, and this introduces the framework to consider the possible future strategies of test-and-treat or direct treatment of individuals living in villages where cases have been found, using a new drug.

Stochastic methods for modelling disease dynamics enable the direct computation of the probability of elimination of transmission. For the low-prevalence disease of human African trypanosomiasis (gHAT), we develop a new mechanistic model for gHAT infection that determines the full probability distribution of the gHAT infection using Kolmogorov forward equations. The methodology allows the analytical investigation of the probabilities of gHAT elimination in the spatially connected villages of different prevalence health zones of the Democratic Republic of Congo, and captures the uncertainty using exact methods. Our method provides a more realistic approach to scaling the probability of elimination of infection between single villages and much larger regions, and provides results comparable to established models without the requirement of detailed infection structure. The novel flexibility allows the interventions in the model to be implemented specific to each village, and this introduces the framework to consider the possible future strategies of test-and-treat or direct treatment of individuals living in villages where cases have been found, using a new drug.

BackgroundIn recent years, an integrated programme of vector control, screening and treatment of gambiense human African trypanosomiasis (gHAT) infections has led to a rapid decline in cases in the Mandoul disease focus of Chad. In this study, we assess whether elimination of transmission has already been achieved in the region despite low-level case reporting, quantify the role of intensified interventions in transmission reduction, and predict the trajectory of gHAT in Mandoul for the next decade under a range of control scenarios.MethodWe utilise human case data (2000–2019) to update a previous model of transmission of gHAT in Mandoul. We also test the updated model, which now has refined assumptions on diagnostic specificity of the current algorithm and an improved fitting method, via a data censoring approach.ResultsWe conclude that passive detection rates have increased due to improvements in diagnostic availability in fixed health facilities since 2015, by 2.1-fold for stage 1 detection, and by 1.5-fold for stage 2. We find that whilst the diagnostic algorithm for active screening is estimated to be highly specific (99.93%, 95% CI: 99.91–99.95%), the high screening level and limited remaining infection means that some recently reported cases might be false positives, especially the ones that were not parasitologically confirmed. We also find that the focus-wide tsetse vector reduction estimated through model fitting (99.1%, 95% CI: 96.1–99.6%) is comparable to the very high reduction previously measured by the decline in catches of tsetse from monitoring traps. In line with previous results, the model suggests that transmission was likely interrupted in 2015 as a result of intensified interventions.ConclusionsWe recommend that additional confirmatory testing is performed in Mandoul in order that the endgame can be carefully monitored now that infection levels are so low. More specific measurement of cases would better inform when it is safe to stop active screening and vector control.

The World Health Organization recently launched its 2021-2030 roadmap, Ending the Neglect to Attain the Sustainable Development Goals, an updated call to arms to end the suffering caused by neglected tropical diseases. Modelling and quantitative analyses played a significant role in forming these latest goals. In this collection, we discuss the insights, the resulting recommendations and identified challenges of public health modelling for 13 of the target diseases: Chagas disease, dengue, gambiense human African trypanosomiasis (gHAT), lymphatic filariasis (LF), onchocerciasis, rabies, scabies, schistosomiasis, soil-transmitted helminthiases (STH), Taenia solium taeniasis/ cysticercosis, trachoma, visceral leishmaniasis (VL) and yaws. This piece reflects the three cross-cutting themes identified across the collection, regarding the contribution that modelling can make to timelines, programme design, drug development and clinical trials.

Stochastic methods for modelling disease dynamics enables the direct computation of the probability of elimination of transmission (EOT). For the low-prevalence disease of human African trypanosomiasis (gHAT), we develop a new mechanistic model for gHAT infection that determines the full probability distribution of the gHAT infection using Kolmogorov forward equations. The methodology allows the analytical investigation of the probabilities of gHAT elimination in the spatially-connected villages of the Kwamouth and Mosango health zones of the Democratic Republic of Congo, and captures the uncertainty using exact methods. We predict that, if current active and passive screening continue at current levels, local elimination of infection will occur in 2029 for Mosango and after 2040 in Kwamouth, respectively. Our method provides a more realistic approach to scaling the probability of elimination of infection between single villages and much larger regions, and provides results comparable to established models without the requirement of detailed infection structure. The novel flexibility allows the interventions in the model to be implemented specific to each village, and this introduces the framework to consider the possible future strategies of test-and-treat or direct treatment of individuals living in villages where cases have been found, using a new drug.

Background Gambiense human African trypanosomiasis (gHAT) is a disease targeted for elimination of transmission (EOT) by 2030, however the likelihood of achieving it is unknown. We utilised modelling to study the impact of currently-available intervention methods on transmission across the Democratic Republic of Congo (DRC) – which accounts for ∼ 70% of global burden – and highlight regions requiring intensified interventions. Methods A model previously fitted to case data in DRC was used to predict cases and new infections under four future strategies in 168 health zones. The strategies comprise of medical interventions – active and passive screening (AS and PS) and some include large-scale vector control (VC). In each health zone, we estimate the median year of EOT and the probability of EOT by 2030 under each and compute the least ambitious strategy predicted to achieve EOT by 2030. Findings The model predicts 42 health zones are very likely to achieve EOT (> 90% probability) using medical-only strategies continued at mean coverage levels; this increases to 52 when AS coverage is increased to maximum previous coverage. In all VC strategies, health zones are predicted to meet EOT by 2030, although there are several where increasing low AS coverage could achieve this. Interpretation This analysis provides a priority list for consideration for supplementary VC implementation (Bagata, Bandundu, Bolobo, Kikongo, Kwamouth and Masi Manimba in former Bandundu province) in conjunction with the recent AS coverage. Funding Bill Melinda Gates Foundation [OPP1177824, OPP1184344, OPP1156227, OPP1186851, and OPP1155293] and Belgian Development Agency (ENABEL). Research in context Evidence before this study On 30th April 2020 we searched PubMed and ScienceDirect to identify previous predictive modelling studies of gHAT in DRC using the search terms “model” AND “Democratic Republic of Congo” OR “DRC” AND “try-panosomiasis” OR “sleeping sickness”. There are numerous modelling studies which have looked at estimating the impact of a variety of strategies on transmission and elimination, however many utilise infection prevalence categories for performing simulations rather than location-specific data. For DRC, modelling studies have made projections at a province-level (i.e. Bandundu), and for health zones (i.e all of Equateur and some in Bandundu), concluding that there is high heterogeneity in underlying transmission, consequently whether medical-only strategies will suffice to meet elimination of transmission (EOT) by 2030. They find that supplemental, large-scale vector control would be expected to result in rapid EOT across settings. Two high-endemicity, village-level studies suggest that regular, high-coverage screening is needed to achieve EOT within 15 years without additional interventions. Added value of this study This study presents predictions for EOT across the whole DRC for the first time. Since DRC has the highest disease burden it is critical to understand how far current tools might go towards achieving this 2030 target across the country, and how strategies may need to be adapted for specific locations in the endgame. It also provides a priority list for regions requiring intensified interventions. Implications of all the available evidence Our analysis suggests that, whilst many regions of DRC are expected to meet the EOT goal by 2030 with medical-only strategies, for some regions current strategies may need to be bolstered to achieve EOT within the next decade. Although some regions could consider increasing coverage of active screening, vector control appears a desirable supplemental intervention in several specific high-prevalence locations.

Due to the COVID-19 pandemic, many key neglected tropical disease (NTD) activities have been postponed. This hindrance comes at a time when the NTDs are progressing towards their ambitious goals for 2030. Mathematical modelling on several NTDs, namely gambiense sleeping sickness, lymphatic filariasis, onchocerciasis, schistosomiasis, soil-transmitted helminthiases (STH), trachoma, and visceral leishmaniasis, shows that the impact of this disruption will vary across the diseases. Programs face a risk of resurgence, which will be fastest in high-transmission areas. Furthermore, of the mass drug administration diseases, schistosomiasis, STH, and trachoma are likely to encounter faster resurgence. The case-finding diseases (gambiense sleeping sickness and visceral leishmaniasis) are likely to have fewer cases being detected but may face an increasing underlying rate of new infections. However, once programs are able to resume, there are ways to mitigate the impact and accelerate progress towards the 2030 goals., The COVID-19 pandemic has disrupted neglected tropical disease programs. Modelling shows the impact of this will vary across diseases. Once interventions are reintroduced, there are opportunities for mitigating the delay and accelerating progress towards the 2030 goals.

The global health community has earmarked a number of diseases for elimination or eradication, and these goals have often been praised on the premise of long-run cost-savings. However, decision-makers must contend with a multitude of demands on health budgets in the short- or medium-term, and costs-per-case often rise as the burden of a disease falls, rendering such efforts beyond the cost-effective use of scarce resources. In addition, these decisions must be made in the presence of substantial uncertainty regarding the feasibility and costs of elimination or eradication efforts. Therefore, analytical frameworks are necessary to consider the additional effort for reaching global goals, like elimination or eradication, that are beyond the cost-effective use of country resources. We propose a modification to the net-benefit framework to consider the implications of switching from an optimal strategy, in terms of cost-per-burden-averted, to a strategy with a higher likelihood of meeting the global target of elimination of transmission by a specified date. We illustrate the properties of our framework by considering the economic case of efforts to eliminate transmission of gambiense human African trypanosomiasis (gHAT), a vector-borne parasitic disease in West and Central Africa, by 2030.Significance StatementVarious diseases are earmarked for elimination by the global health community. While the health economic implications of elimination have been discussed before, the combination of uncertainty, cost-effectiveness in terms of cases averted, and elimination in the face of rising per-case costs has not been tackled before. We propose an approach that considers the tension between the dual objectives of cost-effectiveness and elimination while incorporating uncertainty in these objectives. We apply our method to strategies against human African trypanosomiasis in three settings, but this method could be directly applied to simulation-based studies of the cost-effectiveness of other disease elimination efforts. The method yields common metrics of efficiency when stakeholders have different objectives.

Many control programmes against neglected tropical diseases have been interrupted due to the coronavirus disease 2019 (COVID-19) pandemic, including those that rely on active case finding. In this study we focus on gambiense human African trypanosomiasis (gHAT), where active screening was suspended in the Democratic Republic of Congo (DRC) due to the pandemic. We use two independent mathematical models to predict the impact of COVID-19 interruptions on transmission and reporting and achievement of the 2030 elimination of transmission (EOT) goal for gHAT in two moderate-risk regions of the DRC. We consider different interruption scenarios, including reduced passive surveillance in fixed health facilities, and whether this suspension lasts until the end of 2020 or 2021. Our models predict an increase in the number of new infections in the interruption period only if both active screening and passive surveillance were suspended, and with a slowed reduction—but no increase—if passive surveillance remains fully functional. In all scenarios, the EOT may be slightly pushed back if no mitigation, such as increased screening coverage, is put in place. However, we emphasise that the biggest challenge will remain in the higher-prevalence regions where EOT is already predicted to be behind schedule without interruptions unless interventions are bolstered.

Background Gambiense human African trypanosomiasis (gHAT) has been brought under control recently with village-based active screening playing a major role in case reduction. In the approach to elimination, we investigate how to optimise active screening in villages in the Democratic Republic of Congo, such that the expenses of screening programmes can be efficiently allocated whilst continuing to avert morbidity and mortality. Methods We implement a cost-effectiveness analysis using a stochastic gHAT infection model for a range of active screening strategies and, in conjunction with a cost model, we calculate the net monetary benefit (NMB) of each strategy. We focus on the high-endemicity health zone of Kwamouth in the Democratic Republic of Congo. Results High-coverage active screening strategies, occurring approximately annually, attain the highest NMB. For realistic screening at 55% coverage, annual screening is cost-effective at very low willingness-to-pay thresholds (20.4 per disability adjusted life year (DALY) averted), only marginally higher than biennial screening (14.6 per DALY averted). We find that, for strategies stopping after 1, 2 or 3 years of zero case reporting, the expected cost-benefits are very similar. Conclusions We highlight the current recommended strategy—annual screening with three years of zero case reporting before stopping active screening—is likely cost-effective, in addition to providing valuable information on whether transmission has been interrupted.

Accurate models are crucial for predicting the spread of vector-borne diseases, and for developing appropriate control policies. Simple models often ignore finer details of vector biology, commonly due to lack of pertinent field data. However, for tsetse (Glossina spp), vectors of the parasites causing debilitating human and livestock trypanosomiasis in Africa, extensive field and laboratory data facilitate improved models and predictions of vector control outcomes. We review studies on the effects of environmental temperature, and fly age and sex, on survival and reproduction in tsetse, savannah species particularly–emphasizing the extreme maternal investment and sensitivity of early life stages to high temperatures. We consider implications of these results for predictive models of tsetse populations, and of the transmission and control of African trypanosomiasis. We discuss how further research on vectors, and improved models of vector populations and disease dynamics, can lead to improved predictions of vector abundance and disease spread.

A key challenge for many infectious diseases is to predict the time to extinction under specific interventions. In general, this question requires the use of stochastic models which recognize the inherent individual-based, chance-driven nature of the dynamics; yet stochastic models are inherently computationally expensive, especially when parameter uncertainty also needs to be incorporated. Deterministic models are often used for prediction as they are more tractable; however, their inability to precisely reach zero infections makes forecasting extinction times problematic. Here, we study the extinction problem in deterministic models with the help of an effective ‘birth–death’ description of infection and recovery processes. We present a practical method to estimate the distribution, and therefore robust means and prediction intervals, of extinction times by calculating their different moments within the birth–death framework. We show that these predictions agree very well with the results of stochastic models by analysing the simplified susceptible–infected–susceptible (SIS) dynamics as well as studying an example of more complex and realistic dynamics accounting for the infection and control of African sleeping sickness ( Trypanosoma brucei gambiense ).

Many control programmes against neglected tropical diseases have been interrupted due to COVID-19 pandemic, including those that rely on active case finding. In this study we focus on gambiense human African trypanosomiasis (gHAT), where active screening was suspended in the Democratic Republic of Congo (DRC) due to the pandemic. We use two independent mathematical models to predict the impact of COVID-19 interruptions on transmission and reporting, and the achievement of 2030 elimination of transmission (EOT) goal for gHAT in two moderate-risk regions of DRC. We consider different interruption scenarios, including reduced passive surveillance in fixed health facilities, and whether this suspension lasts until the end of 2020 or 2021. Our models predict an increase in the number of new infections in the interruption period only if both active screening and passive surveillance were suspended, and with slowed reduction - but no increase - if passive surveillance remains fully functional. In all scenarios, the EOT may be slightly pushed back if no mitigation such as increased screening coverage is put in place. However, we emphasise that the biggest challenge will remain in the higher prevalence regions where EOT is already predicted to be behind schedule without interruptions unless interventions are bolstered.

Gambiense human African trypanosomiasis (gHAT) has been brought under control recently with village-based active screening playing a major role in case reduction. In the eve of elimination, we investigate how to optimise active screening in villages in the Democratic Republic of Congo, such that the expenses of screening programmes can be efficiently allocated while continuing to avert morbidity and mortality. We implement a cost-effectiveness analysis using a stochastic gHAT infection model for a range of active screening strategies and we calculate the net monetary benefit (NMB) of each strategy. High-coverage active screening strategies, occurring approximately annually, attain the highest NMB. We find that, for strategies stopping after one to three years of zero case reporting, the expected cost-benefits are very similar and we highlight the current recommended strategy (three years before stopping) is likely cost-effective, in addition to providing valuable information on whether transmission has been interrupted.

Gambiense human African trypanosomiasis (gHAT) is a virulent disease declining in burden but still endemic in West and Central Africa. Although it is targeted for elimination of transmission by 2030, there remain numerous questions about the drivers of infection and how these vary geographically. In this study we focus on the Democratic Republic of Congo (DRC), which accounted for 84% of the global case burden in 2016, to explore changes in transmission across the country and elucidate factors which may have contributed to the persistence of disease or success of interventions in different regions. We present a Bayesian fitting methodology, applied to 168 endemic health zones (∼100,000 population size), which allows for calibration of a mechanistic gHAT model to case data (from the World Health Organization HAT Atlas) in an adaptive and automated framework. It was found that the model needed to capture improvements in passive detection to match observed trends in the data within former Bandundu and Bas Congo provinces indicating these regions have substantially reduced time to detection. Health zones in these provinces generally had longer burn-in periods during fitting due to additional model parameters. Posterior probability distributions were found for a range of fitted parameters in each health zone; these included the basic reproduction number estimates for pre-1998 (R0) which was inferred to be between 1 and 1.14, in line with previous gHAT estimates, with higher median values typically in health zones with more case reporting in the 2000s. Previously, it was not clear whether a fall in active case finding in the period contributed to the declining case numbers. The modelling here accounts for variable screening and suggests that underlying transmission has also reduced greatly—on average 96% in former Equateur, 93% in former Bas Congo and 89% in former Bandundu—Equateur and Bandundu having had the highest case burdens in 2000. This analysis also sets out a framework to enable future predictions for the country., Author summary Gambiense human African trypanosomiasis (gHAT; sleeping sickness) is a deadly disease targeted for elimination of transmission by 2030, however there are still several unknowns about what factors influence continued transmission and how this changes with geographic location. In this study we focus on the Democratic Republic of Congo (DRC), which reported 84% of the global cases in 2016 to try and explain why some regions of the country have had more success than others in bringing down case burden. To achieve this we used a state-of-the-art statistical framework to match a mathematical gHAT model to reported case data for 168 regions with some case reporting during 2000–2016. The analysis indicates that two former provinces, Bandundu and Bas Congo had substantial improvements to case detection in fixed health facilities in the time period. Overall, all provinces were estimated to have reductions in (unobservable) transmission including ∼96% in former Equateur. This is reassuring as case finding effort has decreased in that region. The model fitting presented here will allow predictions of gHAT under alternative intervention strategies to be performed in future studies.

Epidemics of human African trypanosomiasis (HAT) in the twentieth century led to millions of deaths. However, since the start of the twenty-first century, there has been a continued decline in the number of reported cases, due to increased investment and prioritisation of control efforts. Systematic screening of at-risk areas and widespread access to increasingly advanced diagnostics and treatments, along with much improved vector control, has all helped to make disease elimination achievable in the near future. Despite the progress, the danger of disease resurgence is well known for HAT, and continued surveillance and treatment availability are essential. Additionally, many uncertainties regarding HAT transmission remain and combine to make potential disease eradication a complete unknown.

Background The World Health Organization targeted Trypanosoma brucei gambiense human African trypanosomiasis (gHAT) for elimination as a public health problem and for elimination of transmission. To measure gHAT elimination success with prevalences close to zero, highly specific diagnostics are necessary. Such a test exists in the form of an antibody-mediated complement lysis test, the trypanolysis test, but biosafety issues and technological requirements prevent its large-scale use. We developed an inhibition ELISA with high specificity and sensitivity that is applicable in regional laboratories in gHAT endemic countries. Methods The T. b. gambiense inhibition ELISA (g-iELISA) is based on the principle that binding of monoclonal antibodies to specific epitopes of T. b. gambiense surface glycoproteins can be inhibited by circulating antibodies of gHAT patients directed against the same epitopes. Using trypanolysis as reference test, the diagnostic accuracy of the g-iELISA was evaluated on plasma samples from 739 gHAT patients and 619 endemic controls and on dried blood spots prepared with plasma of 95 gHAT and 37 endemic controls. Results Overall sensitivity and specificity on plasma were, respectively, 98.0% (95% CI 96.7–98.9) and 99.5% (95% CI 98.6–99.9). With dried blood spots, sensitivity was 92.6% (95% CI 85.4–97.0), and specificity was 100% (95% CI 90.5–100.0). The g-iELISA is stable for at least 8 months when stored at 2–8°C. Conclusion The g-iELISA might largely replace trypanolysis for monitoring gHAT elimination and for postelimination surveillance. The g-iELISA kit is available for evaluation in reference laboratories in endemic countries., The novel Trypanosoma brucei gambiense inhibition ELISA (g-iELISA) is a high-throughput diagnostic applicable in regional laboratories for monitoring gambiense-HAT elimination. On plasma samples, sensitivity was 98.0% and specificity 99.5%, while on dried blood spots sensitivity was 92.6% and specificity 100%.

BackgroundThe first COVID-19 case in Kenya was confirmed on March 13th, 2020. Here, we provide forecasts for the potential incidence rate, and magnitude, of a COVID-19 epidemic in Kenya based on the observed growth rate and age distribution of confirmed COVID-19 cases observed in China, whilst accounting for the demographic and geographic dissimilarities between China and Kenya.MethodsWe developed a modelling framework to simulate SARS-CoV-2 transmission in Kenya, KenyaCoV. KenyaCoV was used to simulate SARS-CoV-2 transmission both within, and between, different Kenyan regions and age groups. KenyaCoV was parameterized using a combination of human mobility data between the defined regions, the recent 2019 Kenyan census, and estimates of age group social interaction rates specific to Kenya. Key epidemiological characteristics such as the basic reproductive number and the age-specific rate of developing COVID-19 symptoms after infection with SARS-CoV-2, were adapted for the Kenyan setting from a combination of published estimates and analysis of the age distribution of cases observed in the Chinese outbreak.ResultsWe find that if person-to-person transmission becomes established within Kenya, identifying the role of subclinical, and therefore largely undetected, infected individuals is critical to predicting and containing a very significant epidemic. Depending on the transmission scenario our reproductive number estimates for Kenya range from 1.78 (95% CI 1.44 −2.14) to 3.46 (95% CI 2.81-4.17). In scenarios where asymptomatic infected individuals are transmitting significantly, we expect a rapidly growing epidemic which cannot be contained only by case isolation. In these scenarios, there is potential for a very high percentage of the population becoming infected (median estimates: >80% over six months), and a significant epidemic of symptomatic COVID-19 cases. Exceptional social distancing measures can slow transmission, flattening the epidemic curve, but the risk of epidemic rebound after lifting restrictions is predicted to be high.

Background: Gambiense human African trypanosomiasis (gHAT) is marked for elimination of transmission (EOT) by 2030, but the disease persists in several low-income countries. We examine the cost-effectiveness of four gHAT elimination strategies in Democratic Republic of Congo (DRC), which has the highest burden of gHAT. Methods: We compared four strategies against gHAT by coupling a transmission model with a health outcomes model in five settings – spanning low- to high-risk. Alongside passive surveillance (PS) in fixed health facilities, the strategies included active screening (AS) at average or high coverage levels, both alone or with vector control (VC). A scale-back algorithm was devised to simulate cessation of AS and VC when no cases were reported for three consecutive years. Outcomes were denominated in disability-adjusted life-years (DALYs) and costs until 2040 were denominated in 2018 US$. Results: In high or moderate-risk settings, costs of gHAT strategies are primarily driven by AS and, if used, VC. Due to the cessation of AS and VC most investments (75-80%) will be made by 2030 and VC might be cost-saving while ensuring EOT. In low-risk settings, costs are driven by PS, and minimum-cost strategies consisting of AS and PS lead to EOT by 2030 with high probability. Conclusion: In many settings, the case for EOT by 2030 is a sensible use of resources, and investments in gHAT will decelerate within this decade in moderate- and low-risk regions. Funding Statement: This work was supported by the Bill and Melinda Gates Foundation (www.gatesfoundation.org) through the Human African Trypanosomiasis Modelling and Economic Predictions for Policy (HAT MEPP) project [OPP1177824] (MA, CH, REC, PB, MJK, KSR, and FT), through the NTD Modelling Consortium [OPP1184344, OPP1156227, and OPP1186851] (KSR and MJK), and through the TRYP-ELIM-BANDUNDU project [OPP1155293] (EMM and RS) and the Directorate-general Development Cooperation and Humanitarian Aid (EMM). Declaration of Interests: The authors have no conflicts of interest to disclose. Ethics Approval Statement: Due to the anonymised and aggregated nature of the gHAT case data, no ethical approval was required for secondary use of routinely collected national programme data. Costs were modeled based on freely available data in the literature, and therefore, this study is exempt from ethics board approval.

Background Gambiense human African trypanosomiasis ([gHAT] sleeping sickness) is a vector-borne disease that is typically fatal without treatment. Intensified, mainly medical-based, interventions in endemic areas have reduced the occurrence of gHAT to historically low levels. However, persistent regions, primarily in the Democratic Republic of Congo (DRC), remain a challenge to achieving the World Health Organization’s goal of global elimination of transmission (EOT). Methods We used stochastic models of gHAT transmission fitted to DRC case data and explored patterns of regional reporting and extinction. The time to EOT at a health zone scale (~100 000 people) and how an absence of reported cases informs about EOT was quantified. Results Regional epidemiology and level of active screening (AS) both influenced the predicted time to EOT. Different AS cessation criteria had similar expected infection dynamics, and recrudescence of infection was unlikely. However, whether EOT has been achieved when AS ends is critically dependent on the stopping criteria. Two or three consecutive years of no detected cases provided greater confidence of EOT compared with a single year (~66%–75% and ~82%–84% probability of EOT, respectively, compared with 31%–51%). Conclusions Multiple years of AS without case detections is a valuable measure to assess the likelihood that the EOT target has been met locally.

Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, which aspects of the available data and level of data aggregation, such as separation by disease stage, would be most useful for better understanding transmission dynamics and improving model reliability in making future predictions of control and elimination strategies.Author summaryHuman African tryposonomiasis (HAT), also known as sleeping sickness, is a parasitic disease with over 65 million people estimated to be living at risk of infection. Sleeping sickness consists of two stages: the first one is relatively mild but the second stage is usually fatal if untreated. The World Health Organization has targeted HAT for elimination as a public health problem by 2020 and for elimination of transmission by 2030. Regular monitoring updates indicate that 2020 elimination goals are likely to be achieved. This monitoring relies mainly on case report data that is collected through medical-based control activities — the main strategy employed so far in HAT control. This epidemiological data are also used to calibrate mathematical models that can be used to analyse current interventions and provide projections of potential intensified strategies.We investigated the role of the type and level of aggregation of this HAT case data on model calibrations and projections. We highlight that the lack of detailed epidemiological information, such as missing stage of disease or truncated time series data, impacts model recommendations for strategy choice: it can misrepresent the underlying HAT epidemiology (for example, the ratio of stage 1 to stage 2 cases) and increase uncertainty in predictions. Consistently including new data from control activities as well as enriching it through cross-sectional (e.g. demographic or behavioural data) and geo-located data is likely to improve modelling accuracy to support planning, monitoring and adapting HAT interventions.

Background Gambian sleeping sickness or HAT (human African trypanosomiasis) is a neglected tropical disease caused by Trypanosoma brucei gambiense transmitted by riverine species of tsetse. A global programme aims to eliminate the disease as a public health problem by 2020 and stop transmission by 2030. In the South of Chad, the Mandoul area is a persistent focus of Gambian sleeping sickness where around 100 HAT cases were still diagnosed and treated annually until 2013. Pre-2014, control of HAT relied solely on case detection and treatment, which lead to a gradual decrease in the number of cases of HAT due to annual screening of the population. Methods Because of the persistence of transmission and detection of new cases, we assessed whether the addition of vector control to case detection and treatment could further reduce transmission and consequently, reduce annual incidence of HAT in Mandoul. In particular, we investigated the impact of deploying ‘tiny targets’ which attract and kill tsetse. Before tsetse control commenced, a census of the human population was conducted and their settlements mapped. A pre-intervention survey of tsetse distribution and abundance was implemented in November 2013 and 2600 targets were deployed in the riverine habitats of tsetse in early 2014, 2015 and 2016. Impact on tsetse and on the incidence of sleeping sickness was assessed through nine tsetse monitoring surveys and four medical surveys of the human population in 2014 and 2015. Mathematical modelling was used to assess the relative impact of tsetse control on incidence compared to active and passive screening. Findings The census indicated that a population of 38674 inhabitants lived in the vicinity of the Mandoul focus. Within this focus in November 2013, the vector is Glossina fuscipes fuscipes and the mean catch of tsetse from traps was 0.7 flies/trap/day (range, 0–26). The catch of tsetse from 44 sentinel biconical traps declined after target deployment with only five tsetse being caught in nine surveys giving a mean catch of 0.005 tsetse/trap/day. Modelling indicates that 70.4% (95% CI: 51–95%) of the reduction in reported cases between 2013 and 2015 can be attributed to vector control with the rest due to medical intervention. Similarly tiny targets are estimated to have reduced new infections dramatically with 62.8% (95% CI: 59–66%) of the reduction due to tsetse control, and 8.5% (95% 8–9%) to enhanced passive detection. Model predictions anticipate that elimination as a public health problem could be achieved by 2018 in this focus if vector control and screening continue at the present level and, furthermore, there may have been virtually no transmission since 2015. Conclusion This work shows that tiny targets reduced the numbers of tsetse in this focus in Chad, which may have interrupted transmission and the combination of tsetse control to medical detection and treatment has played a major role in reducing in HAT incidence in 2014 and 2015., Author summary A global programme aims to eliminate Gambian sleeping sickness (Human African Trypanosomiasis, HAT) as a public health problem by 2020. Gambian HAT is a neglected tropical disease caused by trypanosomes spread by tsetse flies and its control has relied largely on detection and treatment of human cases. In the Mandoul focus of southern Chad, regular screening of the human population (~39000 people) between 2002 and 2013 resulted in the detection and treatment of ~100 cases/year. We examined whether even better control might be achieved through the addition of vector control to medical screening. In February 2014, 2600 insecticide-treated targets (‘Tiny Targets’) were deployed in areas where tsetse were present; tsetse are attracted to the targets and on contacting it they pick up a lethal dose of insecticide. Monitoring of the tsetse population, using a network of 44 traps operated regularly between November 2013 and October 2016, showed that the mean daily catch of tsetse declined by 99.99%, from 0.7 tsetse/trap before targets were deployed to 0.005 tsetse/trap. The number of HAT cases detected by a programme of active screening also declined during this period. Mathematical modelling of the number of HAT cases reported during the period 2000–2015 suggests that 70% of the decline in cases during 2014–2015 was due to vector control. The model also suggests that the combination of these interventions may have interrupted transmission and may to lead to the elimination of sleeping sickness in the Mandoul focus by 2020. The results from Mandoul provide further empirical and theoretical evidence that the global elimination of Gambian HAT can be achieved through the integrated use of (i) case detection and treatment and (ii) vector control.

The leishmaniases comprise a complex of diseases characterized by clinical outcomes that range from self-limiting to chronic, and disfiguring and stigmatizing to life threatening. Diagnostic methods, treatments, and vector and reservoir control options exist, but deciding the most effective interventions requires a quantitative understanding of the population level infection and disease dynamics. The effectiveness of any set of interventions has to be determined within the context of operational conditions, including economic and political commitment. Mathematical models are the best available tools for studying quantitative systems crossing disciplinary spheres (biology, medicine, economics) within environmental and societal constraints. In 2005, the World Health Assembly and government health ministers of India, Nepal, and Bangladesh signed a Memorandum of Understanding to eliminate the life threatening form of leishmaniasis, visceral leishmaniasis (VL), on the Indian subcontinent by 2015 through a combination of early case detection, improved treatments, and vector control. The elimination target is

Epidemiological modelling has a vital role to play in policy planning and prediction for the control of vectors, and hence the subsequent control of vector-borne diseases. To decide between competing policies requires models that can generate accurate predictions, which in turn requires accurate knowledge of vector natural histories. Here we highlight the importance of the distribution of times between life-history events, using short-lived midge species as an example. In particular we focus on the distribution of the extrinsic incubation period (EIP) which determines the time between infection and becoming infectious, and the distribution of the length of the gonotrophic cycle which determines the time between successful bites. We show how different assumptions for these periods can radically change the basic reproductive ratio (R0) of an infection and additionally the impact of vector control on the infection. These findings highlight the need for detailed entomological data, based on laboratory experiments and field data, to correctly construct the next-generation of policy-informing models., Author Summary The basic reproductive ratio (R0) is a crucial measure of transmission intensity, lying at the interface between mathematical modelling and policy decision making. If control measures can induce a situation where R0 ≤ 1 for a sustained period of time then the pathogen must be eradicated. For diseases spread by short-lived insect vectors a modeller can not calculate R0 without addressing questions of chance such as, “What percentage of vectors will survive their extrinsic incubation period (EIP) to become infectious?”. Classical Ross-Macdonald theory provides answers for the modeller by making certain concrete assumptions, such as a fixed length EIP and exponentially distributed times between vector blood-meals. Using bluetongue virus spread by biting midges as an exemplar we demonstrate that biologically plausible alterations to the classical assumptions can significantly change the modeller’s prediction of R0 with both serious over-estimation and under-estimation being possible. The important modelling/control question, “How does R0 respond to increased vector per-capita mortality?”, is also found to depend strongly on details of the vector life-cycle expressed in the language of probability distributions.

Background The virulent vector-borne disease, Gambian human African trypanosomiasis (HAT), is one of several diseases targeted for elimination by the World Health Organization. This article utilises human case data from a high-endemicity region of the Democratic Republic of Congo in conjunction with a suite of novel mechanistic mathematical models to address the effectiveness of on-going active screening and treatment programmes and compute the likely time to elimination as a public health problem (i.e.

The neglected tropical disease (NTD) visceral leishmaniasis (VL) has been targeted by the WHO for elimination as a public health problem on the Indian subcontinent by 2017 or earlier. To date there is a surprising scarcity of mathematical models capable of capturing VL disease dynamics, which are widely considered central to planning and assessing the efficacy of interventions. The few models that have been developed are examined, highlighting the necessity for better data to parameterise and fit these and future models. In particular, the characterisation and infectiousness of the different disease stages will be crucial to elimination. Modelling can then assist in establishing whether, when, and how the WHO VL elimination targets can be met.

Human African trypanosomiasis (HAT), commonly called sleeping sickness, is caused by Trypanosoma spp. and transmitted by tsetse flies (Glossina spp.). HAT is usually fatal if untreated and transmission occurs in foci across sub-Saharan Africa. Mathematical modelling of HAT began in the 1980s with extensions of the Ross-Macdonald malaria model and has since consisted, with a few exceptions, of similar deterministic compartmental models. These models have captured the main features of HAT epidemiology and provided insight on the effectiveness of the two main control interventions (treatment of humans and tsetse fly control) in eliminating transmission. However, most existing models have overestimated prevalence of infection and ignored transient dynamics. There is a need for properly validated models, evolving with improved data collection, that can provide quantitative predictions to help guide control and elimination strategies for HAT.