 Research article
 Open Access
On the transmission dynamics of Buruli ulcer in Ghana: Insights through a mathematical model
 Farai Nyabadza^{1} and
 Ebenezer Bonyah^{2}Email author
 Received: 20 May 2015
 Accepted: 26 October 2015
 Published: 6 November 2015
Abstract
Background
Mycobacterium ulcerans is know to cause the Buruli ulcer. The association between the ulcer and environmental exposure has been documented. However, the epidemiology of the ulcer is not well understood. A hypothesised transmission involves humans being bitten by the water bugs that prey on mollusks, snails and young fishes.
Methods
In this paper, a model for the transmission of Mycobacterium ulcerans to humans in the presence of a preventive strategy is proposed and analysed. The model equilibria are determined and conditions for the existence of the equilibria established. The model analysis is carried out in terms of the reproduction number \(\mathcal{R}_0\). The disease free equilibrium is found to be locally asymptotically stable for \(\mathcal{R}_0<1.\) The model is fitted to data from Ghana.
Results
The model is found to exhibit a backward bifurcation and the endemic equilibrium point is globally stable when \(\mathcal{R}_0>1.\) Sensitivity analysis showed that the Buruli ulcer epidemic is highly influenced by the shedding and clearance rates of Mycobacterium ulcerans in the environment. The model is found to fit reasonably well to data from Ghana and projections on the future of the Buruli ulcer epidemic are also made.
Conclusions
The model reasonably fitted data from Ghana. The fitting process showed data that appeared to have reached a steady state and projections showed that the epidemic levels will remain the same for the projected time. The implications of the results to policy and future management of the disease are discussed.
Keywords
 Buruli ulcer
 Transmission dynamics
 Basic reproduction number
 Sensitivity analysis
 Stability
Background
Buruli ulcer is caused by pathogenic bacterium where infection often leads to extensive destruction of skin and soft tissue through the formation of large ulcers usually on the legs or arms [28]. It is a devastating disease caused by Mycobacterium ulcerans. The ulcer is fast becoming a debilitating affliction in many countries [3]. It is named after a region called Buruli, near the Nile River in Uganda, where in 1961 the first large number of cases was reported. In Africa, close to 30,000 cases were reported between 2005 and 2010 [29]. Cote d’Ivoire, with the highest incidence, reported 2533 cases in 2010 [27]. This disease has dramatically emerged in several west African countries, such as Ghana, Cote d’Ivoire, Benin, and Togo in recent years [26].
The transmission mode of the ulcer is not well understood, however residence near an aquatic environment has been identified as a risk factor for the ulcer in Africa [6, 16, 25]. Transmission is thus likely to occur through contact with the environment [20]. Recent studies in West Africa have implicated aquatic bugs as transmission vectors for the ulcer [18, 24]. An attractive hypothesis for a possible mode of transmission to humans was proposed by Portaels et al. [22]: waterfiltering hosts (fish, mollusks) concentrate the Mycobacterium ulcerans bacteria present in water or mud and discharge them again to this environment, where they are then ingested by aquatic predators such as beetles and water bugs. These insects, in turn, may transmit the disease to humans by biting [18]. Person to person transmission is less likely. Aquatic bugs are insects found throughout temperate and tropical environments with abundant freshwater. They prey, according to their size, on mollusks, snails, young fishes, and the adults and larvae of other insects that they capture with their raptorial front legs and bite with their rostrum. These insects can inflict painful bites on humans as well. In Ghana, where Buruli ulcer is endemic, the water bugs are present in swamps and rivers, where human activities such as farming, fishing, and bathing take place [18].
Research on Buruli ulcer has focused mainly on the sociocultural aspects of the disease. The research recommends the need for Information, Education and Communication (IEC) intervention strategies, to encourage early case detection and treatment with the assumption that once people gain knowledge they will take the appropriate action to access treatment early [2]. IEC is defined as an approach which attempts to change or reinforce a set of behaviours to a targeted group regarding a problem. The IEC strategy is preventive in that it has a potential of enhancing control of the ulcer [5]. It is also important to note that Buruli ulcer is treatable with antibiotics. A combination of rifampin and streptomycin administered daily for 8 weeks has the potential to eliminate Mycobacterium ulcerans bacilli and promote healing without relapse.
Mathematical models have been used to model the transmission of many diseases globally. Many advances in the management of diseases have been born from mathematical modeling [11, 12, 14, 15]. Mathematical models can evaluate actual or potential control measures in the absence of experiments, see for instance [19]. To the best of our knowledge very few mathematical models have been formulated to analyse the transmission dynamics of Mycobacterium ulcerans. This could be largely due to the elusive epidemiology of the Buruli ulcer. Aidoo and Osei [3] proposed a mathematical model of the SIRtype in an endeavour to explain the transmission of Mycobacterium ulcerans and its dependence on arsenic. In this paper, we propose a model which takes into account the human population, water bugs as vectors and fish as potential reservoirs of Mycobacterium ulcerans following the transmission dynamics described in [8]. In addition we include the preventive control measures in a bid to capture the IEC strategy. Our main aim is to study the dynamics of the Buruli ulcer in the presence of a preventive control strategy, while emphasizing the role of the vector (water bugs) and fish and their interaction with the environment. The model is then validated using data from Ghana. This is crucial in informing policy and suggesting strategies for the control of the disease.
This paper is arranged as follows; in “Methods”, we formulate and establish the basic properties of the model. We also determine the steady states and analysed their stability. The results of this paper are given in “Results”. Parameter estimation, sensitivity analysis and the numerical results on the behavior of the model are also presented in this section. The paper is concluded in “Discussion”.
Methods
Model formulation

Mycobacterium ulcerans are transferred only from vector ( water bug) to the humans.

There is homogeneity of human, water bug and fish populations’ interactions.

Infected humans recover and are temporarily immune, but lose immunity.

Fish are preyed on by the water bugs.

Unlike some bacterial infections such as leprosy (caused by Mycobacterium leprae) and tuberculosis (caused by Mycobacterium tuberculosis), which are characterized by persontoperson contact transmission, it is hypothesized that Mycobacterium ulcerans is acquired through environmental contact and direct persontoperson transmission is rare [20].

Susceptible host (human population) can be infected through biting by an infectious vector (water bug). We represent the effective biting rate that an infectious vector has to susceptible host as \(\beta _H\) and the incidence of new infections transmitted by water bugs is expressed by standard incidence rate \( \displaystyle \beta _H \frac{S_H I_V}{N_H}.\) One can interpret \(\beta _H\) as a function of the biting frequency of the infected water bugs on humans, density of infectious water bugs per human, the probability that a bite will result in an infection and the efficacy of the IEC strategy. In particular we can set \(\beta _H=(1\epsilon )\tau \alpha \beta _H^*,\) where \(\epsilon \in (0,1)\) is the efficacy of the IEC strategy, \(\tau \) the number of water bugs per human host, \(\alpha \) the biting frequency (the biting rate of humans by a single water bug) and \(\beta _H^*\) the probability that a bite by an infected vector to a susceptible human will produce an infection.

Susceptible water bugs are infected at a rate \(\displaystyle \beta _V \frac{S_V I_F}{N_V}\) through predation of infected fish and \(\displaystyle \eta _v\beta _V \frac{S_V U}{K}\) representing other sources in the environment. Here \(\eta _V\) differentiates the infectivity potential of the fish from that of the environment.

Assuming fish prey on infected water bugs, susceptible fish are infected at a rate \(\displaystyle \beta _F\frac{S_F I_V}{N_F}\) through predation of infected fish and \(\displaystyle \eta _F\beta _F \frac{S_F U}{K}\) representing infection through the environment. Here \(\eta _F\) is a modification parameter that models the relative infectivity of fish from that of the environment.

The vector population and the fish populations are assumed to be constant. The growth functions are respectively given by \(g(N_V)\) and \(g(N_F),\) where$$\begin{aligned} g(N_V)=\mu _VN_V~~\mathrm{and}~~g(N_F)=\mu _FN_F. \end{aligned}$$
It is important to note that other types of functions can be chosen as growth functions. In this work we however assume that the growth functions are linear.

There is a proposed hypothesis that environmental mycobacteria in the bottoms of swamps may be mechanically concentrated by small waterfiltering organisms such as microphagous fish, snails, mosquito larvae, small crustaceans, and protozoa [8]. We assume that fish increase the environmental concentrations of Mycobacterium ulcerans at a rate \(\sigma _F.\) Humans are are assumed not to shed any bacteria into the environment.

Aquatic bugs release bacteria into the environment at a rate \(\sigma _V.\)

The model does not include a potential route of direct contact with the bacterium in water.

The birth rate of the human population is directly proportional to the size of the human population.

The recovery of infected individuals is assumed to occur both spontaneously and through treatment. Research has shown that localized lesions may spontaneously heal but, without treatment, most cases of Buruli ulcer result in physical deformities that often lead to physiological abnormalities and stigmas [4].
We now describe briefly, the transmission dynamics of Buruli ulcer:
Description of parameters used in the model
Symbol  Description 

\(\beta _H\)  The effective contact rate between the vector and susceptible humans 
\(\beta _V\)  The effective contact rate between fish and susceptible vectors 
\(\beta _F\)  The effective contact rate between the susceptible fish and Mycobacterium ulcerans 
\(\gamma \)  The recovery rate of infected humans 
\(\theta \)  The rate of loss of immunity of recovered humans 
\(\mu _H\)  Natural mortality rate/birth rate of the human population 
\(\mu _V\)  Natural mortality rate of the vector population 
\(\mu _F\)  Natural mortality rate of the fish population 
\(r_V\)  The growth rate of the vector population 
\(r_F\)  The growth rate of the fish population 
K  The environmental carrying capacity of the bacteria population 
\(\sigma _F\)  Rate of shedding of Mycobacterium ulcerans into the environment by fish 
\(\sigma _V\)  Rate of shedding of Mycobacterium ulcerans into the environment by the water bugs 
\(\mu _E\)  Rate at which Mycobacterium ulcerans are cleared from the environment 
Basic properties
Feasible region
Positivity of solutions
We desire to show that for any nonnegative initial conditions of system (2), say \(\displaystyle (S_{H0},I_{H0},I_{V0},I_{F0},U_0),\) the solutions remain nonnegative for all \(\displaystyle \tau \in [0,\infty ).\) We prove that all the state variables remain nonnegative and the solutions of the system (2) with positive initial conditions will remain positive for all \(\tau > 0\). We thus state the following lemma.
Lemma 1
Given that the initial conditions of system (2) are positive, the solutions \(S_H(\tau ),~I_H(\tau ),~I_V(\tau ),~I_F(\tau )\) and \(U(\tau )\) are nonnegative for all \(\tau >0\).
Proof
Steady states analysis
The disease free equilibrium
Theorem 1
The disease free equilibrium \(\mathbf{\mathcal {E}_0}\) whenever it exists, is locally asymptotically stable if \(\mathcal{R}_0 <1\) and unstable otherwise.
Proof
We note that \(\displaystyle \mathcal{R}_0\) is the model system (2)’s reproduction number and does not depend on the human population size. The model reproduction number is a sum of three terms. The terms \(R_0^1\) and \(R_0^2\) represent the contribution of fish and water bugs respectively to the infection dynamics. The term \(R_0^3,\) which is not very common in many epidemiological models, shows the combined contribution of the water bugs, fish and their shedding of Mycobacterium ulcerans into the environment. So, the infection is driven by the water bugs, fish and the density of the bacterium in the environment. The model reproduction number increases linearly with the shedding rates of the Mycobacterium ulcerans into the environment by fish and water bugs and the effective contact rates \(\beta _f\) and \(\beta _v\). It decreases with increasing removal rates of the fish and Mycobacterium ulcerans. So the control of the ulcer depends largely on environmental management.
The endemic equilibrium
If \(\triangle =0\), then \(f'(I_V^*)\) has only one real root with multiplicity two. This implies that \((I_V^*)^1=(I_V^*)^2 = \frac{a_2}{3a_3}\) and that \(f'(I_V^*)<0\). Thus the polynomial \(f(I_V^*)\) is a decreasing function. Given that \(f''(I_V^*)(\frac{a_2}{3a_3}) = 0,\) the turning point is a point of inflexion for \(f(I_V^*).\) The polynomial \(f(I_V^*)\) has only one endemic equilibrium.
For \(\triangle >0\), we consider two cases; \(a_1<0\) and \(a_1>0\). If \(a_1<0\), then \(a_1a_3>0\). This means that \(\sqrt{\triangle }<a_2\). Irrespective of the sign of \(a_2\), \(f'(I_V^*)\) has two real positive and distinct roots. This implies that (5) has two positive turning points. If \(f(0) = a_0>0\) i.e \(\mathcal {R}_0>1\) then, \(f(I_V^*)\) has at least one positive real root, and hence at least one endemic equilibrium. On the other hand, if \(f(0) = a_0<0\) then, \(f(I_V^*)\) has at most two positive real roots when \(\mathcal {R}_0<1\), and hence at most two endemic equilibria.
If \(a_1>0\), then \(a_1a_3<0\), which implies that \(\sqrt{\triangle }>a_2\). For \(a_2>0\), \(f'(I_V^*)\) has two real roots of opposite signs. Since \(f(0) = a_0>0\) for \(\mathcal{R}_0>1\), then, \(f(I_V^*)\) has one positive root. For \(a_2<0\), \(f'(I_V^*)\) has two negative real roots. Since \(f(0) = a_0<0\) for \(\mathcal{R}_0<1\), then, \(f(I_V^*)\) has no positive real roots, and consequently no endemic equilibria.
Furthermore, we can use the Descartes’ Rule of Signs [7] to explore the existence of endemic equilibrium (or equilibria) for \(\mathcal{R}_0<1\). We note the possible existence of backward bifurcation. The theorem below summarises the existence of endemic equilibria of the model system (2).
Theorem 2
 1
a unique endemic equilibrium point if \(\mathcal{R}_0>1\).
 2
has two endemic equilibria for \(\mathcal{R}_0^c<\mathcal{R}_0<1\) where \(\mathcal{R}_0^c\) is the critical threshold below which no endemic equilibrium exists.
Remark The evaluation of \(\mathcal{R}_0^c\) depends on the signs of \(a_2\) and \(a_1\) and the sign of the discriminant. The computations are algebraically involving and long and are not included here. Since the model system (2) possesses two endemic equilibria when \(\mathcal{R}_0^c<\mathcal{R}_0<1\), the model exhibits backward bifurcation for \(\mathcal{R}_0<1\).
The consequence of the above remark is that bringing \(\mathcal{R}_0\) below unity is not sufficient to eradicate the disease. For eradication, \(\mathcal{R}_0\) must be brought below the critical value \(\mathcal{R}_0^c\).
Global stability of the endemic equilibrium
Theorem 3
The endemic equilibrium point \(\mathbf{\mathcal {E}_1}\) of system (2), is globally asymptotically stable.
Proof
Therefore, \(\dot{\mathcal{V}} \le 0\) and by the LaSalle’s Extension [17], it implies that the omega limit set of each solution lies in an invariant set contained in \({\Omega }.\) The only invariant set contained in \(\Omega \) is the singleton \(\mathcal{E}_1\). This shows that each solution which intersects \(\mathbb {R}_+^5\) limits to the endemic equilibrium. This completes the proof. \(\square \)
Results
Parameter estimation
The biggest challenge in epidemic modeling is the estimation of parameters in the model validation process. In this section we endeavour to estimate some of the parameter values of system (2). The demographic parameters can be easily estimated from census population data. We begin by estimating the mortality rate \(\mu _h.\) We note that the average life expectancy of the human population in Ghana is 60 years [21]. This translates into \(\mu _h=0.017\) per year or equivalently \(4.6\times 10^{5}\) per day. Buruli ulcer is currently regarded as a vector borne disease. Recovery rates modelled by \(\gamma _h,\) of vector borne diseases range from \(1.6\times 10^{5}\) to 0.5 per day [23]. This translates to an average of between 0.00584 and 183 per year. The rate of loss of immunity \(\theta _h\) for vector borne diseases range between 0 and \(1.1\times 10^{2}\) per day[23]. The mortality rate of the water bugs is assumed to be 0.15 per day [3]. The rates per day can easily be transferred to yearly rates.
Parameter values used for the simulations and sensitivity analysis
Parameter  Value/range  Source 

\(\mu _h\)  \(4.5\times 10^{5}\)  [21] 
\(\gamma _h\)  \(1.6\times 10^{5}0.5\)  [23] 
\(\theta _h\)  \(01.1\times 10^{2}\)  [23] 
\(m_1,m_2\)  \(m_1<1,m_2>1\)  Estimated 
\(m_3,m_4,m_5\)  (0,1)  Estimated 
\(\beta _h,\beta _f,\beta _v \)  (0,1)  Estimated 
\({\eta _v}\)  (1,5)  Estimated 
\({\eta _f}\)  (0, 1)  Estimated 
\({\sigma _f,\sigma _v}\)  (0,1)  Estimated 
\({\mu _f}\)  \(3\times 10^{3}7\times 10^{3}\)  Estimated 
\(\mu _e\)  (0,1)  Estimated 
Sensitivity analysis
Data and the fitting process
Data on Buruli ulcer cases in Ghana
Year  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012 

Buruli ulcer cases  739  1159  1201  1096  1136  1300  1158  1428  1324  1292 
We fit the model system (2) to the data of Buruli ulcer cases expressed as fractions. We use the least squares curve fit routine (lsqcurvefit) in Matlab with optimisation to estimate the parameter values. Many parameters are known to lie within limits. A few parameters such as the demographic parameters are known [13] and it is thus important to estimate the others. The process of estimating the parameters aims at finding the best concordance between computed and observed data. One tedious way to do it is by trial and error or by the use of software programs designed to find parameters that give the best fit. Here, the fitting process involves the use of the least squarescurve fitting method. Matlab code is used where unknown parameter values are given a lower and upper bound from which the set of parameter values that produce the best fit are obtained.
Discussion
In this paper, a deterministic model on the dynamics of the Buruli ulcer in the presence of a preventive intervention strategy is presented. The model’s steady states are determined and their stabilities investigated in terms of the classic threshold \(\mathcal{R}_0.\) In disease transmission modelling, it is well known that a classical necessary condition for disease eradication is that the basic reproductive number \(\mathcal{R}_0,\) must be less than unity. The model has multiple endemic equilibria (in fact it exhibits a backward bifurcation). When a backward bifurcation occurs, endemic equilibria coexist with the disease free equilibrium for \(\mathcal{R}_0<1.\) This means that getting the classic threshold \(\mathcal{R}_0\) less than 1, might not be sufficient to eliminate the disease. Thus the existence of backward bifurcation has important public health implications. This might explain why the disease has persisted in the human population over time. The endemic equilibrium is found to be globally stable if \(\mathcal{R}_0>1.\)
The sensitivity analysis of model parameters showed some interesting results. These results suggest that efforts to remove Mycobacterium ulcerans and infected fish from the environment will greatly reduce the epidemic although the latter will be impracticable. This is because of the costs involved and the fact that many governments in affected areas operate on lean budgets.
The model is then fitted to data on the Buruli ulcer in Ghana. The model reasonably fits the data. The challenge in the fitting process was that the data appears to indicate that Buruli ulcer has reached a steady state. This then produced some parameter values that appeared unreasonable. Despite these challenges, the fit produced reasonable projections on the future of the ulcer. The model shows that in the near future, the number of cases will not change if everything remains the same. An important consideration that can be added to the model is the inclusion of probable policy shifts and the investigation of different scenarios on the progression of the epidemic as the policies change. Because not much of the disease is understood, parameter estimation was a daunting task. So we had to reasonably estimate some of the parameter using the hypothesis that Buruli ulcer is a vector borne disease. Due to the estimation of essential parameters sensitivity analysis was necessary and very important to determine how these parameters influence the model. The implications of varying some of the important epidemiological parameters such as the shedding rates were investigated. Important results were drawn through Figs. 6 and 7. The main result of this paper is that the management of Buruli ulcer depends mostly on the management of the environment.
Conclusions
This model can be improved by considering social interventions in the human population, modeled as functions and the inclusion of the different forms of treatment available as some individuals opt for traditional methods while others depend on the government health care system [1]. Social interventions include education, awareness, poverty reduction and provision of social services. While the mathematical representations of these interventions are insurmountable, they are vital to the dynamics of the disease and public health policy designs. Finally this model can be used to suggest the type of data that should be collected as research on the Buruli ulcer intensifies. The global burden of the disease and its epidemiology are not well understood, [28]. Clearly, gaps do exist in the nature and type of data available. Reports on the disease are often based on passive presentations of patients at health care facilities. As a result of the difficulties of accessing health care in affected areas, data on the disease is scanty.
Declarations
Authors’ contributions
FN designed the model and carried out the numerical simulations. EB did the mathematical analysis and writing of the manuscript. Both authors read and approved the final manuscript.
Acknowledgements
The first author acknowledges with gratitude the support from the Stellenbosch University, International office for the research visit that culminated into this manuscript. The second author acknowledge, with thanks, the support of the Department of Mathematics and Statistics, Kumasi Polytechnic.
Competing interests
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Authors’ Affiliations
References
 Agbenorku P, Donwi IK, Kuadzi P, Saunderson P. Buruli Ulcer: treatment challenges at three centres in Ghana. J Trop Med. 2012; doi:10.1155/2012/371915.PubMed CentralPubMedGoogle Scholar
 Ahorlu CK, Koka E, YeboahManu D, Lamptey I, Ampadu E. Enhancing Buruli ulcer control in Ghana through social interventions: a case study from the Obom subdistrict. BMC Public Health. 2013;13:59.PubMed CentralView ArticlePubMedGoogle Scholar
 Aidoo AY, OSei B. Prevalence of acquatic insects and arsenic concentration determine the geographical distribution of Mycobacterium ulcerans infection. Comput Math Method Med. 2007;8:235–44.View ArticleGoogle Scholar
 Boleira M, Lupi O, Lehman L, Asiedu KB, Kiszewski Ana Elisa. Buruli ulcer. Anais Brasileiros de Dermatologia. 2010;85(3):281–301.View ArticlePubMedGoogle Scholar
 Clift E. IEC interventions for health: a 20 year retrospective on dichotomies and directions. Int J Health Commun. 1998;3(4):367–75.View ArticleGoogle Scholar
 Debacker M, Portaels F, Aguiar J, Steunou C, Zinsou C, Meyers W, et al. Risk factors for Buruli ulcer, Benin. Emerg Infect Dis. 2006;12:1325–31.PubMed CentralView ArticlePubMedGoogle Scholar
 Descartes’ Rule of Signs, Available at: http://www.purplemath.com/modules/drofsign.htm, Acessed 2 Sept 2013.
 Eddyani M, OforiAdjei D, Teugels G, De Weirdt D, Boakye D, Meyers WM, Portaels F. Potential role for fish in transmission of Mycobacterium ulcerans disease (Buruli Ulcer): an environmental study. Appl Environ Microbiol. 2004;5679–81.Google Scholar
 Garba SM, Gumel AB, Abu Bakar MR. Backward bifurcaytion in dengue transmission dynamics. Math Biosci. 2008;215:11–25.View ArticlePubMedGoogle Scholar
 Gomero B. Latin Hypercube Sampling and Partial Rank Correlation Coefficient analysis applied to an optimal control problem, MSc Thesis, The University of Tennessee. 2012.Google Scholar
 Grassly NC, Fraser C. Mathematical models of infectious disease transmission. Nat Rev Microbiol. 2008;6:477–87.PubMedGoogle Scholar
 Grundmann H, Hellriegel B. Mathematical modelling: a tool for hospital infection control. Lancet Infect Dis. 2005;6(1):39–45.View ArticleGoogle Scholar
 Ghana Statistical Service, Available at: http://www.statsghana.gov.gh. Accessed Sept 2013.
 Houben RM, Dowdy DW, Vassall A, et al. How can mathematical models advance tuberculosis control in high HIV prevalence settings? Int J Tuber Lung Dis. 2014;18(5):509–14.View ArticleGoogle Scholar
 Huppert A, Katriel G. Mathematical modelling and prediction in infectious disease epidemiology. Clin Microbiol Infect. 2013;19:999–1005.View ArticlePubMedGoogle Scholar
 Jacobsen KH, Padgett JJ. Risk factors for Mycobacterium ulcerans infection. Int J Infect Dis. 2010;14(8):e677–81.View ArticlePubMedGoogle Scholar
 LaSalle JP. The stability of dynamical systems. In: CBMSNSF Regional Conference Series in Applied Mathematics 25, SIAM: Philadelphia. 1976.Google Scholar
 Marsollier L, Robert R, Aubry J, Andre JS, Kouakou H, Legras P, Manceau A, Mahaza C, Carbonnelle B. Aquatic insects as a vector for Mycobacterium ulcerans. Appl Environ Microbiol. 2002;68:4623–8.PubMed CentralView ArticlePubMedGoogle Scholar
 Marty R, Roze S, Bresse X, Largeron N, SmithPalmer J. Estimating the clinical benefits of vaccinating boys and girls against HPVrelated diseases in Europe. BMC Cancer. 2013;19:19. doi:10.1186/147124071310.Google Scholar
 Merritt RW, Walker ED, Small PLC, Wallace JR, Johnson PDR, et al. Ecology and transmission of buruli ulcer disease: a systematic review. PLoS Neglect Trop Dis. 2010;4(12):e911.View ArticleGoogle Scholar
 Population and Housing Census National Analytical Report, 2012. http://www.statsghana.gov.gh.
 Portaels F, Chemlal K, Elsen P, Johnson PD, Hayman JA, Hibble J, Kirkwood R, Meyers WM. Mycobacterium ulcerans in wild animals. Revue Scientifique et Technique. 2001;20:252–64.PubMedGoogle Scholar
 Rascalou G, Pontier D, Menu F, Gourbie‘re S. Emergence and prevalence of human vectorborne diseases in sink vector populations. PLoS One. 2012;7(5):e36858. doi:10.1371/journal.pone.0036858.PubMed CentralView ArticlePubMedGoogle Scholar
 Silva MT, Portaels F, Pedrosa J. Aquatic insects and Mycobacterium ulcerans: an association relevant to buruli ulcer control? PLoS Med. 2007;4(2):e63.PubMed CentralView ArticlePubMedGoogle Scholar
 Sopoh GE, Barogui YT, Johnson RC, Dossou AD, Makoutode M. Family relationship, water contact and occurrence of Buruli ulcer in Benin. PLoS Neglect Trop Dis. 2010;4(7):e746.View ArticleGoogle Scholar
 Stienstra Y, van der Graaf WTA, Asamoa K, van der Werf TS. Beliefs and attitudes toward buruli ulcer in Ghana. Am J Trop Med Hyg. 2002;67:207–13.PubMedGoogle Scholar
 Williamson HR, Benbow ME, Campbell LP, Johnson CR, Sopoh G, Barogui Y, Merritt RW, Small PLC. Detection of Mycobacterium ulcerans in the environment predicts prevalence of buruli ulcer in Benin, PLoS Neglect Trop Dis. 2012; e1506.Google Scholar
 World Health Organization. Buruli ulcer Mycobacterium ulcerans infection, http://www.who.int/buruli/en/.
 World Health Organization. Buruli ulcer: Number of new cases of Buruli ulcer reported (per year). http://apps.who.int/neglected_diseases/ntddata/buruli/buruli.html.