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Gambiense human African trypanosomiasis (gHAT) is a disease in West and Central Africa spread by tsetse. A key method to combat the spread of the disease is the deployment of tiny targets which kill tsetse. Monitoring of the tsetse population can allow us to ascertain the effectiveness of tiny targets in a given area and use this information to inform whether deployments should continue. However, the usefulness of this monitoring has been called into question. Here, we determine the monetary benefit of this monitoring in reducing the burden of gHAT and lowering costs.

We calculated the benefit of conducting twice-yearly monitoring over a two-year period of vector control, and how this changed for different locations and different monitoring strategies. We found these efforts were cost-effective at a willingness to pay threshold of $153 for the health zone of Mulumba, where we were able to determine the optimal monitoring strategy, but it was not cost-effective in the health zone of Vanga.

This work will provide a framework to ascertain the benefit of further monitoring efforts in more complex decision-making concerning gHAT, and could also be applied to other infectious diseases.

Stochastic simulations are often used to model real-world phenomena such as infectious disease dynamics. In this modelling, differing strategies are often compared to one another by comparing the model outputs each strategy results in. Hash-based matching, pseudo-random number generation is an approach for stochastic simulations that was originally developed by Pearson and Abbott in the hashprng package to overcome challenges with comparing model simulations in a way that considers the dependency between model outputs. We demonstrate how methods based on this approach grant considerable benefit when comparing different strategies, and show when each of our three proposed methods ought to be used. We illustrate our methods with two epidemiological models: one simple model of a vaccine-preventable infection and one complex model of African sleeping sickness, which can be controlled through multiple interventions. We show how our Bernoulli hashing method works very well for simple models, and a variation of it can be used for more complex models in certain cases. Additionally, we discuss the properties of our methods for considering counterfactual scenarios and note that, compared to other attempts to obtain perfect counterfactuals, they demonstrate advantages in computational complexity and their application to a wider variety of models.

Background

Gambiense human African trypanosomiasis (gHAT) is a vector-borne disease with hundreds of thousands of people living at risk across Sub-Saharan Africa. Uganda, having reduced gHAT cases reported annually from 948 in 2000 to zero local cases since 2020, is a frontrunner to be verified by the World Health Organization (WHO) as having achieved elimination of transmission (EoT) of gHAT. It is now crucial to quantify the impact of the interventions deployed, quantify whether the last transmission event (LTE) and last case have already occurred, and determine the resources required to sustain the gains.

Methods

We employed a suite of mechanistic compartmental gHAT models, fitted to data from seven districts (Adjumani, Amuru, Arua, Koboko, Maracha, Moyo, and Yumbe) in Uganda that reported gHAT cases during 2000–2022, to address these questions. By combining and weighting the evidence from each model variant, we captured the uncertainty of achieving different elimination metrics across Uganda. Additionally, we utilised the dynamic transmission model outputs to perform a health economic analysis, identifying the most cost-effective strategies to avert disease burden.

Results

Predictions estimate that Uganda had the LTE in or before 2021 with high certainty ( 95%). The model suggests that the local Ugandan case reported in 2019 may have been the last, but that there remains a 1.13% chance that cases could be reported after 2025. The cost-effectiveness analysis recommends that as transmission dynamics remain similarly low across all strategies, the Stop 2026 strategy, in which there is only passive screening from 2026 and subsequent reactive screening and vector control are only implemented if cases are detected, has the highest probability of being cost-effective and is predicted to have an economic cost of $193K ($161K–$220K) between 2026 and 2040.

Conclusions

To sustain Uganda’s success whilst ensuring efficient resource use, this modelling analysis recommends implementing the Stop 2026 strategy in all seven analysed districts, which focuses on maintaining strong passive screening post-2026. This recommendation aligns with the current gHAT strategy in Uganda.

In 2025 Guinea was validated as achieving elimination as a public health problem for the highly pathogenic, vector-borne infection, gambiense human African trypanosomiasis (gHAT) after reaching several years of low-level case reporting. gHAT cases in Guinea have overall seen a large decrease between 2000 and 2024, however there have been notable fluctuations. Transmission modelling was used to assess these trends in observed cases for the foci Boffa East, Boffa West, Dubréka, and Forécariah and evaluate transmission changes. This study quantifies the impact of interruptions due to Ebola and the introduction of new interventions (particularly the rapid diagnostic tests in the passive health system and vector control) in each focus.

The model suggests that transmission of gHAT has fallen 97% (83–100%) between 2000 and 2024, with disease burden measured in disability-adjusted life years (DALYs) reduced by 94% (63–100%). We estimated that Ebola interruptions caused an additional 1,147 gHAT DALYs due to the suspension of gHAT activities, however passive screening improvements and the introduction of vector control likely averted 1,719 and 9,038 DALYs respectively. This study quantifies the impact of Ebola related interruptions on gHAT transmission and disease burden and highlights the success of medical and vector control interventions in Guinea.

Avian influenza A(H5N1) poses a public health risk due to its pandemic potential should the virus mutate to become human-to-human transmissible. To date, reported influenza A(H5N1) human cases have typically occurred in the lower respiratory tract with a high case fatality rate. There is prior evidence of some influenza A(H5N1) strains being a small number of amino acid mutations away from achieving droplet transmissibility, possibly allowing them to be spread between humans. We present a mechanistic within-host influenza A(H5N1) infection model, novel for its explicit consideration of the biological differences between the upper and lower respiratory tracts. We then estimate a distribution of viral lifespans and effective replication rates in human H5N1 influenza cases. By combining our within-host model with a viral mutation model, we determine the probability of an infected individual generating a droplet transmissible strain of influenza A(H5N1) through mutation. For three mutations, we found a peak probability of approximately 10^-3 that a human case of H5N1 influenza produces at least one virion during the infectious period. Our findings provide insights into the risk of differing infectious pathways of influenza A(H5N1) (namely avian–human versus avian–mammal–human routes), demonstrating the three-mutation pathway being a cause of concern in human cases.