The World Health Organization 2030 goals for Taenia solium: insights and perspectives from transmission dynamics modelling

Taenia solium (TS), responsible for porcine cysticercosis, human taeniasis and (neuro)cysticercosis, was included in the World Health Organization neglected tropical disease (NTD) roadmap published in 2012. Targets set in this roadmap have not been met, but T. solium has been included in the consultation process for the new 2030 goals proposed for priority NTDs. Taenia solium transmission dynamics models can contribute to this process. A recent review has compared existing T. solium transmission models, identifying their similarities and differences in structure, parameterization and modelled intervention approaches. While a formal model comparison to investigate the impact of interventions is yet to be conducted, the models agree on the importance of coverage for intervention effectiveness and on the fact that human- and pig-focused interventions can be optimally combined. One of these models, cystiSim, an individual-based, stochastic model has been used to assess field-applicable interventions, some currently under evaluation in on-going trials in Zambia. The EPICYST, population-based, deterministic model has highlighted, based on simulating a generic sub-Saharan Africa setting, the higher efficacy (measured as the percentage of human cysticercosis cases prevented) of biomedical interventions (human and pig treatment and pig vaccination) compared to improved husbandry, sanitation, and meat inspection. Important questions remain regarding which strategies and combinations thereof provide sustainable solutions for severely resource-constrained endemic settings. Defining realistic timeframes to achieve feasible targets, and establishing suitable measures of effectiveness for these targets that can be quantified with current monitoring and evaluation tools, are current major barriers to identifying validated strategies. Taenia solium transmission models can support setting achievable 2030 goals; however, the refinement of these models is first required. Incorporating socio-economic elements, improved understanding of underlying biological processes, and consideration of spatial dynamics are key knowledge gaps that need addressing to support model development.