Transmission models of Mycobacterium ulcerans: A systematic review

Rebecca Rasmussen; Katherine B. Gibney; Timothy P. Stinear; Jennifer A. Flegg; Patricia T. Campbell

doi:10.1371/journal.pntd.0013376

Abstract

Background: Buruli ulcer, caused by Mycobacterium ulcerans, is a neglected tropical disease affecting 33 countries worldwide. Elucidating the full transmission pathways of this infection remains an important active field of investigation, especially in Central and West Africa and Southeast Australia in the state of Victoria where disease burden is high. This systematic review (pre-registered on PROSPERO: CRD42023452944) provides an overview of mathematical transmission models of M. ulcerans and highlights future areas of investigation crucial to understanding transmission of Buruli ulcer and quantifying the impact of potential preventative interventions. Methodology/principal findings: We searched Scopus, PubMed, Embase and CAB abstracts on January 2025, and included studies which reported novel mechanistic models of M. ulcerans transmission. We qualitatively compared mathematical model structures, parameterisation methods, model analyses and author conclusions, and conducted a quality assessment of the studies using a modified Philips checklist. Twenty studies met the inclusion criteria; 18 performed theoretical analyses, while only five validated their model against empirical data, limiting conclusions and the implications for disease management. Seventeen studies focused on a water bug/fish/human system proposed for Central and West Africa with a diverse ranges of model structures. Three models described the mosquito/possum system of Southeast Australia using similar models, with none considering human populations. Conclusions/significance: This review highlights a large gap in modelling Buruli ulcer in Victoria: there are currently no models of this system that have been specifically formulated with data. Such models could be valuable for exploring and testing likely transmission scenarios. Future research should focus on developing models that incorporate local data and consider all potential transmission pathways to better understand the disease dynamics and evaluate potential interventions.

Author summary

Buruli ulcer is a neglected tropical disease caused by the bacterium Mycobacterium ulcerans. It poses a significant public health challenge in both Central and West Africa and Southeast Australia—Buruli ulcer causes stigmatising disfigurements and disabilities which often lead to personal economic instability. Despite this, the transmission mechanisms of M. ulcerans to humans remain poorly understood, impeding the development of effective control and prevention strategies. In Australia, researchers identified that mosquitoes and possums play a role in M. ulcerans transmission to humans, however, the complete transmission cycle is not understood. In Central/West Africa, the transmission vector remains unconfirmed. Given this knowledge gap, mathematical models have been developed with the aim of clarifying infection mechanisms. Our study presents the first systematic review of such models. We analysed various model structures and their relevance to observed transmission patterns and found crucial gaps—there were no mosquito models for Africa and no human models for Australia. Additionally, we found that most studies did not validate their model with real-world data. Our review of M. ulcerans transmission models highlights important avenues for future research, both in the context of Buruli ulcer and for other diseases with complex environmental interactions or multiple transmission routes.

Citation: Rasmussen R, Gibney KB, Stinear TP, Flegg JA, Campbell PT (2025) Transmission models of Mycobacterium ulcerans: A systematic review. PLoS Negl Trop Dis 19(8): e0013376. https://doi.org/10.1371/journal.pntd.0013376

Editor: Eric H. Y. Lau, The University of Hong Kong, CHINA

Received: April 16, 2025; Accepted: July 21, 2025; Published: August 19, 2025

Copyright: © 2025 Rasmussen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: J.A.F.’s research is supported by the Australian Research Council (https://www.arc.gov.au/) (FT210100034) and the National Health and Medical Research Council (https://www.nhmrc.gov.au/) (APP2019093). K.B.G. is a recipient of an MRFF/NHMRC (https://www.nhmrc.gov.au/) Investigator Grant (MRF1193727). T.P.S.’s research is supported by the National Health and Medical Research Council of Australia (https://www.nhmrc.gov.au/) (GNT1196396, GNT1194325). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Buruli ulcer is a neglected tropical disease caused by the bacterium Mycobacterium ulcerans, involving the destruction of the skin and soft tissue. The disease has been documented in 33 countries worldwide [1], particularly in Central and West Africa [2, 3], and Southeast Australia [4]. Buruli ulcer presents a substantial public health burden: prevalence estimates in Central and West Africa range from 3.2 cases per 10,000 population in Côte d’Ivoire to 26.9 cases per 10,000 population in Benin [5]. Additionally, we are seeing a concerning geographic spread of Buruli ulcer in Southeast Australia [6]. Despite this, the mechanisms of transmission to humans have not been completely established [7], leading the WHO Buruli ulcer fact sheet to state “the mode of transmission is not known and there is no prevention for the disease” [1].

In Victoria, Australia, epidemiological investigations have identified the mosquito Aedes notoscriptus as a vector of M. ulcerans [8], and the common ringtail possum Pseudocheirus peregrinus as a mammal reservoir of the pathogen, shedding the bacteria into the environment through its faeces [8–10]. In contrast, the transmission dynamics in Central and West Africa remain less clear. While M. ulcerans has been detected in many aquatic taxa, including water bugs (Belostomatidae and Naucoridae) and mosquitoes [11–13], their role in the Central and West African transmission cycle is yet to be determined [7]. There are similarities in Buruli ulcer epidemiology across Africa and Australia: observed seasonal occurence of disease [14]; spatial overlap of Buruli ulcer cases in humans and environmental M. ulcerans [15]; and living near aquatic environments is associated with an increased risk of Buruli ulcer [16]. Importantly, experimental evidence has shown that subcutaneous inoculation with M. ulcerans is required for Buruli ulcer to develop in a mammalian host, not just contact with the skin (broken or unbroken) [17, 18]. This requirement, coupled with the close genetic links between the strains of M. ulcerans in Australia and Africa [19] and the fact that the bacterium is a specialist not a generalist [20], suggests that while the mammalian host may differ, the transmission mechanisms across both continents may be similar.

Despite these advances, important knowledge gaps remain in both geographical settings. In Southeast Australia, the complete transmission cycle, including the role of other potential vectors or reservoirs, is not fully understood. In African settings, the identity of the transmission vector(s) and potential animal reservoirs remain unconfirmed. Mathematical models are useful tools to explore these and other aspects of infection transmission. Such models are commonly used to clarify transmission dynamics, predict outbreaks, identify effective controls, and inform research priorities. For pathogens like M. ulcerans, mathematical models can help to disentangle the interactions between multiple biotic and abiotic factors that cannot be elucidated from observational data alone.

In this paper, we present the first systematic review of mathematical models of M. ulcerans transmission. The aims of this review are to qualitatively compare the mathematical models that have been used to describe the transmission dynamics of M. ulcerans, and to highlight any unanswered epidemiological questions that could be addressed by modelling. Principally, we aim to identify the relevance of existing M. ulcerans transmission model structures to observed transmission dynamics in Victoria, Australia.

Materials and methods

We report on a systematic review of transmission models for M. ulcerans according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement [21]. The review was pre-registered on PROSPERO (ID number CRD42023452944) and can be accessed at www.crd.york.ac.uk/PROSPERO/display_record.asp?ID=CRD42023452944. A protocol for this systematic review was not published.

Data sources and search strategy

We searched Scopus, PubMed, Embase and CAB Abstracts on August 21st 2023, with no language or publication date restrictions. The search was conducted again on January 31st 2025 and new studies were included before final analysis. In addition, we conducted forwards and backwards searches (on August 21st 2023 and again on January 31st 2025) using the identified articles and found papers outside of the chosen databases.

Our search consisted of terms to capture the pathogen, disease and model formulation. The same search strategy was used in each of the four databases (modified in structure to work with each database, see S1 Appendix). For example, in Scopus the following string was used:

TITLE-ABS-KEY (buruli OR mycobacterium OR bairnsdale OR mossman OR searls’) AND (ulcer*) AND (model* OR paramet* OR simulat* OR program* OR comput*).

Eligibility criteria

Peer-reviewed studies were eligible for inclusion if they described a mathematical model of the M. ulcerans transmission mechanism—including mechanistic, compartmental, agent-based, spatial, or numerical models, both deterministic and stochastic. We did not include laboratory models, species distribution models or statistical models purely based on data. Unpublished manuscripts and conference abstracts were not eligible for inclusion.

Screening and data collection

Screening and selection were undertaken in Covidence [22]. Two reviewers (RR and PC) screened the references independently, and resolved conflicts through discussion with JF. The two reviewers were blinded to each other’s decisions.

One reviewer (RR) extracted data from each included study, specifically: primary author, year of publication and the modelled location as well as model purpose, structure and parameterisation, and the results and conclusions of each study. These results included model outputs related to the basic reproduction number (R₀), key parameters in sensitivity analyses and outcomes of any analyses. We included results of numerical simulations where they promoted conclusions unrelated to the information already extracted. For example, we excluded discussion of any numerical simulations related to R₀, where R₀ was an analytical result elsewhere in the paper.

Data synthesis

In this review, we qualitatively compared mathematical model structures, parameterisation methods, model analyses and conclusions drawn by authors. We also assessed the applicability of the model choice in the context of present-day knowledge and the relevance of the model choice to observed transmission dynamics in Victoria, Australia. We grouped studies first by type of analysis, either being purely theoretical or containing some application to real-world data to compare high level model structures and analyses. Then we grouped the studies by the modelled location, either Central and West Africa or Southeast Australia for comparison of location-specific model features.

One reviewer (RR) used a modified Philips checklist [23] (excluding unrelated questions) for assessing the risk of bias in the included studies as well as the extent to which they answered their research question (see S2 Appendix), as recommended by the Cochrane Handbook [24].

Infectious disease model terminology

For readers less familiar with mathematical models, we provide the following brief explanation of compartmental mechanistic models of infectious diseases to guide interpretation of the results of our review. These kinds of models make up the majority of the studies included in this review, and those that employ different model structures use similar concepts. For more in depth reading, see [25, 26].

Compartments: Compartmental models consist of populations broken down into mutually exclusive epidemiological stages (“compartments”). Relevant compartments may include:

susceptible compartments (S), containing individuals of a species that have not yet been exposed to infection but are susceptible to infection
exposed compartments (E) containing individuals who have been infected with a pathogen but are not yet capable of transmitting the infection
infectious compartments (I) containing those capable of transmitting the infection
recovered/removed compartments (R) containing those that are no longer capable of transmitting infection

Models may include other mutually exclusive compartments, for example a treatment compartment (T) containing individuals who are receiving treatment for the disease. Individuals from the populations in each compartment move through these stages at rates governed by a set of parameters.

For diseases that are spread via vectors such as mosquitoes, similar compartments to those above may be included for the vector population. Environmental sources of infection can also be modelled, typically as a separate compartment with which the host and vector populations come into contact. Susceptible individuals can become infected through contact with an infected organism or contaminated environment at rates that are either dependent on the density of the infected organism (density-dependent transmission) or independent of the organism density (frequency-dependent transmission).

Reproduction number: The basic reproduction number R₀ is defined as the average number of new infections caused by a single infectious individual in a totally susceptible population and is specific to the pathogen and population [25]. If R₀>1 then an outbreak of the infection will occur (on average) and if R₀<1 then, on average, the infection will die out without an outbreak. Since R₀ is typically straight-forward to calculate given a transmission model (at least numerically), it is often used as a proxy for the potential impact of the disease.

Intervention analyses: Interventions aimed at controlling either the spread of the pathogen or mitigating the severity of disease, given infection, are often incorporated in compartmental models. This may be achieved by changing parameter values governing flows between model compartments. For example “optimal control analyses”, where control variables are introduced into the model to represent means of mitigating the spread of the pathogen, can be used to optimise the effect of interventions. Alternatively, modellers may include additional compartments to allow different outcomes of infection. For example, inclusion of a treatment compartment as above may be used to allow for more rapid recovery of treated individuals. These techniques can inform potential avenues for real-world disease control.

The role of vectors in the transmission of an infectious disease may be either mechanical (carried between hosts on the body of the vector e.g. Yersinia pestis and fleas) or biological (vector becomes infected and must become infectious before transmission to a new host can occur e.g. Plasmodium spp. and mosquitoes). In the case of biological transmission, the life cycle of the vector and the natural history of the pathogen are modelled to capture the impact that their interaction has on transmission.

Results

Summary of studies included in the review

We performed a full text screening of 26 articles for eligibility for inclusion. Six of these were excluded because they were reviews [27], were abstract only [28, 29], detailed a generic transmission model instead of one specific to M. ulcerans [30, 31] or contained only a statistical model [32]. Of the 20 studies included in the systematic review, 14 were found in the database search, and a forwards and backwards search of these yielded a further six eligible studies (Fig 1). The following sections detail characteristics of the included models and results of the studies, for more summary tables and a quality assessment, see S2 Appendix.

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Fig 1. PRISMA flow chart.

PRISMA flow chart depicting article selection process. Constructed with Covidence [22].

https://doi.org/10.1371/journal.pntd.0013376.g001

Characteristics of included models

The following description refers to model identification numbers (ID) assigned in Table 1, which provides an overview of the included M. ulcerans transmission models. Further, a “phylogenetic tree” demonstrating the commonalities between these models is provided in Fig 2 and a network of compartments used in the transmission models is provided in Fig 3.

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Fig 2. “Phylogenetic tree” of models.

A “phylogenetic tree” representing the commonalities between models from the selected articles. Each “tree node” represents a differentiation in model structure. Model attributes, corresponding with the column headings, are found over each “branch”. The IDs for the models (listed in Table 1) described by each set of attributes are at the terminal end of each “branch”. Here we denote M. ulcerans as “MU” and describe model compartments with letter signifiers (for example, “SIR Humans” for a susceptible-infectious-recovered compartmental model for humans).

https://doi.org/10.1371/journal.pntd.0013376.g002

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Fig 3. Compilation of model compartments.

Compartment model diagram depicting the frequency of compartments in reviewed models and transition/transmission pathways. Compartments are susceptible (S), exposed (E), infectious (I), treated (T) and removed (R) with subscripts H for humans, R for reservoirs, V for vectors and compartment U for environmental M. ulcerans. S_Low and S_High represent susceptible individuals at low and high risk, respectively. I_S and I_NS represent infectious individuals seeking treatment or not seeking treatment. R_Dis represents individuals who acquire a disability as a result of having Buruli ulcer. U_R and represent reservoir and vector associated bacteria. Compartment box sizes represent the relative frequency of these compartments in the reviewed models. Black arrows indicate transitions of individuals between compartments, red dashed arrows represent M. ulcerans transmission routes and green arrows represent shedding of M. ulcerans from infectious compartments into the environment. Thickness of the arrows represent the relative frequency of pathways in the reviewed models. To see the IDs for the models (listed in Table 1) containing each pathway and compartment, see S3 Appendix.

https://doi.org/10.1371/journal.pntd.0013376.g003

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Table 1. Overview of Buruli ulcer transmission models.

The first column contains the article ID assigned for ease of reference and is used throughout this review—this is different from the article reference number shown in the second column. ID 2a refers to the base model in article 2, and 2b refers to the model presented in the supplimentary material of paper ID 2. Model characteristics present in model 2b and not in 2a are shown in brackets.

https://doi.org/10.1371/journal.pntd.0013376.t001

Of the 20 included studies, 15 papers modelled the water bug/human/fish dynamic in Central and West Africa (IDs 1,3– 13,16,17,19), three modelled the possum/mosquito dynamic in Australia (IDs 14,15,18), one (ID 2) modelled an arbitrary number of unspecified populations in Central and West Africa and one (ID 20) modelled a general system of M. ulcerans, macroinvertebrates and fish (Fig 2 and Table 1).

Of the 16 models based in Central and West Africa most (n = 12) focused on water bugs as vectors (predominantly Belostomatidae and Naucoridae) (IDs 1,3,5,6,8,10,12,13,16,17,19), while four did not consider a population of vectors (IDs 4,7,9,11). None of the Central and West African models explored the potential of Buruli ulcer being a mosquito-borne disease, although paper ID 2 did sample mosquitoes, as part of the data for their network model, and found both mosquitoes and water bugs PCR positive for M. ulcerans (Fig 2 and Table 1). All three Australian models considered populations of possums, mosquitoes and environmental M. ulcerans (Fig 2 and Table 1). Only two models that included vectors modelled an exposed period for them (only IDs 14,18), so most models have assumed that M. ulcerans is transmitted mechanically rather than biologically (which would require an extrinsic latency period, represented in models as a vector exposed period) (Fig 3).

Only four of the 15 models that included humans (IDs 1,3– 13,16,17,19) modelled an exposed period for them (IDs 7,9,13,19; Fig 3). In models where humans did not contribute to transmission, the absence of an exposed compartment meant the modelled human dynamics occurred 4–5 months earlier than they would have with an exposed compartment (representing an intrinsic latency period). In models where humans did contribute to transmission, either directly or by shedding, the absence of an exposed compartment may have inhibited the ability to realistically describe dynamics. Human shedding was only present in the models where there were no other compartments able to shed (no vectors or reservoirs, IDs 4 and 11; Fig 2)—so while human shedding may not be biologically relevant it may have been used to create non-trivial model dynamics when there were no other infectious compartments. The majority of models (n = 19) were deterministic, while one model (ID 7) used stochastic delays for compartmental transitions (Table 1).

Theoretical analysis

Force of infection (FOI).

Of the models reviewed, eight imposed a frequency dependent FOI from infectious to susceptible individuals for all transmission routes (IDs 1,3,5,6,10,12,13,17). Of these eight models, seven modelled environmental M. ulcerans. All but one of these used a linear force of infection from environmental M. ulcerans to vectors and reservoir hosts dependent on the carrying capacity of the environmenteak (IDs 3,5,6,10,12,17), while ID 13 used a frequency dependent FOI for environmental M. ulcerans to water bugs. Seven models used density dependent FOI for all populations (IDs 2a,8,9,14,15,16,18), including all the possum/mosquito models. ID 19 used a density dependent FOI for transmission from humans to vectors and a frequency dependent FOI for transmission from vectors to humans (as per the Ross-Macdonald model of malaria transmission [52, 53]). Models that included environmental M. ulcerans transmission to humans all used a Michaelis-Menten function for the FOI, where the transmission rate plateaus as the level of environmental M. ulcerans increases (IDs 2b,4,5,10,11). Paper ID 20 assumed M. ulcerans in the environment attaches to fish and aquatic macro-invertebrates (assumed mechanical transmission) at a rate described by a Michaelis-Menten function.

Sensitivity analyses and reproduction numbers.

Where sensitivity analyses were conducted for models focusing on human/water bug/fish populations, a high level of infection was most positively correlated with: rate of water bug transmission to humans and human transmission to water bugs (IDs 8,13); water bug population size (ID 5); probability of seeking treatment (ID 7); and rate of water bug and environmental M. ulcerans transmission to fish (ID 6). The same sensitivity analyses showed a high level of infection was most negatively correlated with environmental M. ulcerans decay rate (IDs 5,6,11); water bug death rate (ID 5,8,13) and rate of human uptake into treatment (IDs 11,13). These dependencies were reflected in the reproduction numbers of human/water bug/fish models (IDs 1,3-13,16,17,19) (Table 2, Fig 3). Transmission from the environment to humans was only present in the reproduction number for two of the models (IDs 4,11) despite featuring in five separate models (IDs 4,5,10,11,16). The duration of the human exposed period was a factor in only one reproduction number (ID 19) despite human exposed compartments being included in three other models (IDs 7,9,13,19) (Table 2 and Fig 3). Of the 15 human/water bug/fish papers (IDs 1,3– 13,16,17,19), we were able to obtain R₀ values for 7 (IDs 3, 5, 10 – 13, 19). Four of these papers predicted a Buruli ulcer outbreak in their studied system (i.e. R₀>1; ID 5, 11, 12, 19; Table 2).

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Table 2. Basic reproduction numbers, theoretical analysis techniques and results (excluding paper IDs 2 and 9, which did not produce theoretical results).

Parameter and reproduction number values are reported where available. Note parameter units have not been standardised as information was often limited. Reproduction number dependence (positive (+) or negative (-)) and sensitivity analysis correlations are shown in brackets. Here we denote M. ulcerans as “MU”.

https://doi.org/10.1371/journal.pntd.0013376.t002

Among the possum/mosquito models, there were no sensitivity analyses conducted (IDs 14,15,18). Reproduction numbers most frequently positively depended on: the rate of possum shedding; possum and mosquito birth; and transmission from environment to possum mosquito to possum and possum to mosquito. These reproduction numbers most frequently negatively depended on the rate of possum and mosquito death (Table 2). It is important to note that there was limited variation within these possum/mosquito models—paper IDs 14 and 18 share the same reproduction number (same model construction) and ID 15 only differs in having no exposed period (Fig 3). The parameter values considered in paper IDs 14 and 18 resulted in biologically implausible values of R₀ (, whereas the R₀ value calculated for paper ID 15 was biologically relevant, and predicted an outbreak (R₀ = 2.16, Table 2).

Optimal control analysis.

Across the board, when authors investigated optimal control variables in their system (IDs 3,4,8,13,14,15,16), where more controls were applied simultaneously, there was less disease. These analyses were not used in conjunction with data and so, despite the conclusion that we should implement as many controls as possible, this lacks support from real-world data or experiments.

Applied models (IDs 2,6,9,11,19)

Here we report results and conclusions from studies where they have applied their models to a data set (Table 3). For further analysis of model applications by authors see S2 Appendix.

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Table 3. Applied analysis techniques and results.

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Paper IDs 6, 11 and 19 all fit their models to human incidence data in order to estimate several unknown model parameters. These models fit the data reasonably well, however all three model fits resulted in parameters seemingly outside the bounds of biological plausibility. For instance, paper ID 6 predicted that: the population of water bugs is a fifth as big as the human population; it takes an average of 17 days for humans to recover from Buruli ulcer (as opposed to months [1]); there are 10 times as many fish as water bugs; water bugs only live for seven days on average (six months to a year [56]); and fish only live for 20 days (at least eight weeks [57]). Paper ID 11 predicted that humans take an average of six days to recover from Buruli ulcer with treatment (several months [3]) and that the half-saturation constant is negligible compared to the initial environmental M. ulcerans concentration and so the transmission rate to humans is not modified by this concentration. Paper ID 19 predicted that the average life expectancy of someone living with Buruli ulcer is five years from infection (the disease is rarely fatal [1]), and the predicted latency period for M. ulcerans in humans is two years (closer to four months [58, 59]).

Paper IDs 2 and 9 both made biological conclusions based on their model outputs. Paper ID 9 used their model to predict that environmental transmission is responsible for more human Buruli ulcer cases than water bug transmission. Paper ID 2 used their model to investigate which invertebrate taxa are more crucial to the transmission cycle and found that Oligochaeta—a taxon of worms—could play an integral part in the transmission network. They predicted that the removal of Oligochaeta from the system would significantly decrease the M. ulcerans prevalence in the system. Both results contradict the widely assumed role of water bugs in transmission of M. ulcerans in Africa by other included studies.

Grouping the applied papers by the location modelled (Ghana: IDs 6 and 11, Cameroon: IDs 9 and 19) and comparing common parameters we see little similarity in values (see S4 Appendix). IDs 6 and 11 only share similar human death rates and these are taken from demographic data and not estimated through model fitting. IDs 9 and 19 do not have any similar parameter values. The lack of similarity in fit parameters indicates that model outputs are quite sensitive to the model structure.

Discussion

This systematic review is the first to examine transmission model structures and assumptions of the dynamics of Buruli ulcer, a neglected tropical disease. We identified 20 studies that developed mechanistic M. ulcerans transmission models, focusing primarily on the dynamics in Central and West Africa (mostly Ghana and Cameroon) or Southeast Australia, despite reports of the disease in 33 countries [1]. Models of Central and West Africa predominantly explored aquatic transmission cycles, whereas Australian models focused on mosquito vectors and possum hosts. Most models were theoretical with few attempts to validate against empirical data, and those that did may not have fully captured the primary mechanisms of transmission (see S2 Appendix for quality assessment).

The reviewed studies included many variations on the multi-population compartment model and in general, these adhere to the known epidemiology of Buruli ulcer. The majority of models included a vector compartment, reflecting the evidence for transmission of M. ulcerans via biting vectors [17, 18] and most models treated humans as dead-end hosts. However, there were deviations from these established mechanisms where models prioritized non-trivial dynamics in simpler frameworks over real-world applicability. The majority of studies formulated a reproduction number for their model but only half of the reviewed studies provided enough information to calculate an R₀ value. Considering that all papers were focused on systems with Buruli ulcer outbreaks, only four studies resulted in biologically plausible reproduction numbers; others either predicted no outbreak at all or were much larger than is biologically relevant. This may further indicate a limitation in the applicability of some of the reviewed frameworks.

Models for Central and West Africa focused on the interactions between water bugs, fish, and humans. These models reflected key aspects of African Buruli ulcer epidemiology, such as age-related differences in disease (ID 4) [62, 63] and seasonal peaks in incidence [13, 14] modeled through dynamic transmission and shedding rates (ID 10). Although these structural frameworks could be combined with data to test existing transmission hypotheses, relatively few authors utilized data for model validation. Given the largely unknown transmission mechanisms, these authors’ hypotheses remain untested. Furthermore, no explicit mosquito models were developed for the African context, despite ongoing debates about the routes of transmission, particularly regarding water bugs [2, 7]. The conclusions of article IDs 2 and 9 add to the body of evidence that vectors other than water bugs may warrant more consideration in Africa. With sufficient data, a mathematical model of appropriate structure could be used to further test these potential transmission routes.

In contrast, the Victorian context in Southeast Australia offers a more established understanding of local M. ulcerans transmission, which was reflected in the similarities between model structures. Existing models involve interactions among mosquitoes, possums, and the environment. Notably, IDs 14 and 18 incorporated an exposed compartment for possums, acknowledging the long latency period associated with the infection. While these models provide a foundation for exploring environmental dynamics, they do not explore transmission to humans and the resulting dynamics, and none were validated against empirical data. This highlights a significant gap in modelling M. ulcerans transmission in Victoria; specifically formulated models in combination with data are essential to address key transmission hypotheses. Current models for Africa and Australia involve different transmission cycles, but the strength of evidence supporting the involvement of hosts and vectors differs between the settings. The literature suggests an Australian model should capture a mosquito, vertebrate host and human cycle. Whether a similar transmission cycle exists in Africa could be explored using a modelling framework.

Additionally, no models have been created for M. ulcerans transmission in settings other than Victoria and Central and West Africa despite Buruli ulcer being documented in 33 countries, including Japan, China, Papua New Guinea and the Americas [5]. While prevalence is lower in these areas, as we have seen in Victoria over the last decade, case numbers can quickly increase, and the disease can spread to new areas, due to currently unknown mechanisms [4]. Developing a greater range of region-specific models could facilitate timely responses to future outbreaks.

Due to the similarity of the model structures, we were able to efficiently explore model differences, but the limitations of the models—particularly their infrequent use of data—restricted our ability to make comparisons regarding M. ulcerans dynamics. Nonetheless, our systematic review has identified gaps and we are able to make suggestions for future modelling work.

This review did not include systematic searches in languages other than English, which may have led to the omission of relevant studies, potentially from other contexts. Additionally, our focus on mechanistic transmission models may have constrained our findings; exploring other disease models, such as spatial or statistical frameworks, could yield further insights. It was also clear that our initial database choices did not encompass all the existing M. ulcerans modelling literature, although we attempted to overcome this with forwards and backwards searches.

This is the first systematic review of mechanistic transmission models of M. ulcerans. We aimed to investigate the transmission model structures and parametrisation methods that have been used to describe M. ulcerans transmission dynamics. We collated models, compared structures and analysis techniques, and concluded that while existing models provide a foundation, large gaps still exist in the field. We also sought to determine the relevance of existing model structures to observed transmission dynamics in Victoria, Australia. Few models have been developed specifically for the Victorian setting, and none of these model structures were validated with empirical data. While uncertainties in epidemiology in both the Central and West African and Victorian contexts complicate comparisons, differences in proposed dynamics between settings [7] suggest that models cannot be directly applied to different regions. Therefore, a large gap remains in mechanistic modelling of Buruli ulcer dynamics in Victoria, particularly with regard to human disease burden. Such models could be key in uncovering characteristics of the disease and identifying approaches to Buruli ulcer control.

Supporting information

S1 Appendix. Full search strategy.

Full search strings for each of the four databases searched (PubMed, Scopus, Embase classic + Embase, CAB abstracts (CAB direct)).

https://doi.org/10.1371/journal.pntd.0013376.s001

(PDF)

S2 Appendix. Quality review.

Contains a table and discussion of the quality review for each included study, following a modified Philips Checklist.

https://doi.org/10.1371/journal.pntd.0013376.s002

(PDF)

S3 Appendix. Full compartment model diagram.

Contains Fig 3 from the main text with the addition of model ID labels showing which models used each compartment and transition/transmission routes.

https://doi.org/10.1371/journal.pntd.0013376.s003

(PDF)

S4 Appendix. Applied analyses: fitted parameter values.

Contains a table comparing the fitted values of like parameters from included studies that applied their models to epidemiological data.

https://doi.org/10.1371/journal.pntd.0013376.s004

(PDF)

S1 PRISMA Checklist. PRISMA 2020 expanded checklist.

Contains a table of recommended systematic review elements and text excerpts from this review where we have met those recommendations.

https://doi.org/10.1371/journal.pntd.0013376.s005

(PDF)

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Figures

Abstract

Author summary

Introduction

Materials and methods

Data sources and search strategy

Eligibility criteria

Screening and data collection

Data synthesis

Infectious disease model terminology

Results

Summary of studies included in the review

Characteristics of included models

Theoretical analysis

Force of infection (FOI).

Sensitivity analyses and reproduction numbers.

Optimal control analysis.

Applied models (IDs 2,6,9,11,19)

Discussion

Supporting information

S1 Appendix. Full search strategy.

S2 Appendix. Quality review.

S3 Appendix. Full compartment model diagram.

S4 Appendix. Applied analyses: fitted parameter values.

S1 PRISMA Checklist. PRISMA 2020 expanded checklist.

References