Abstract
Mathematical modelling of helminth infections has the potential to inform policy and guide research for the control and elimination of human helminthiases. However, this potential, unlike in other parasitic and infectious diseases, has yet to be realised. To place contemporary efforts in a historical context, a summary of the development of mathematical models for helminthiases is presented. These efforts are discussed according to the role that models can play in furthering our understanding of parasite population biology and transmission dynamics, and the effect on such dynamics of control interventions, as well as in enabling estimation of directly unobservable parameters, exploration of transmission breakpoints, and investigation of evolutionary outcomes of control. The Disease Reference Group on Helminth Infections (DRG4), established in 2009 by the Special Programme for Research and Training in Tropical Diseases (TDR), was given the mandate to review helminthiases research and identify research priorities and gaps. A research and development agenda for helminthiasis modelling is proposed based on identified gaps that need to be addressed for models to become useful decision tools that can support research and control operations effectively. This agenda includes the use of models to estimate the impact of large-scale interventions on infection incidence; the design of sampling protocols for the monitoring and evaluation of integrated control programmes; the modelling of co-infections; the investigation of the dynamical relationship between infection and morbidity indicators; the improvement of analytical methods for the quantification of anthelmintic efficacy and resistance; the determination of programme endpoints; the linking of dynamical helminth models with helminth geostatistical mapping; and the investigation of the impact of climate change on human helminthiases. It is concluded that modelling should be embedded in helminth research, and in the planning, evaluation, and surveillance of interventions from the outset. Modellers should be essential members of interdisciplinary teams, propitiating a continuous dialogue with end users and stakeholders to reflect public health needs in the terrain, discuss the scope and limitations of models, and update biological assumptions and model outputs regularly. It is highlighted that to reach these goals, a collaborative framework must be developed for the collation, annotation, and sharing of databases from large-scale anthelmintic control programmes, and that helminth modellers should join efforts to tackle key questions in helminth epidemiology and control through the sharing of such databases, and by using diverse, yet complementary, modelling approaches.
Highlights
It is generally accepted that mathematical models have an important role to play in our understanding of the processes underlying observed epidemiological patterns of the helminthic diseases that afflict humankind
An exception to this is the use of the microsimulation model ONCHOSIM by the Onchocerciasis Control Programme in West Africa (OCP) [2]
In 1975, the commencement of the Onchocerciasis Control Programme in West Africa (OCP) would act as a catalyst for the use of epidemiological models in large-scale interventions [2], and in 1982 Dietz [20] presented deterministic and stochastic onchocerciasis models. From this time onwards there has been a great increase in the development of mathematical models for human helminthiases; we focus here on some salient contributions, highlighting the models of Anderson and co-workers [21,22,23,24,25] in the 1980s, and the stochastic microsimulation approaches [1,26,27,28] and their deterministic counterparts [29,30,31] of the 1990s
Summary
It is generally accepted that mathematical models have an important role to play in our understanding of the processes underlying observed epidemiological patterns of the helminthic diseases that afflict humankind. These include: i) encapsulating current understanding of the population biology of the parasite, enabling study and description of the determinants of endemic (pre-control) equilibrium; ii) facilitating exploration of the impact on infection and morbidity of different control scenarios (e.g., single intervention strategies, modes of delivery, combinations of interventions); iii) estimating unknown or unobservable parameters by fitting models to data; iv) investigating the conditions for parasite elimination and the behaviour of the host–parasite system in the vicinity of transmission breakpoints; and v) exploring the evolutionary outcomes of control (e.g., spread of anthelmintic resistance).
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