How to choose the best control strategy? Mathematical models as a tool for pre-intervention evaluation on a macroparasitic disease

During the last century, emerging diseases have increased in number, posing a severe threat for human health. Zoonoses, in particular, represent the 60% of emerging diseases, and are a big challenge for public health due to the complexity of their dynamics. Mathematical models, by allowing an a priori analysis of dynamic systems and the simulation of different scenarios at once, may represent an efficient tool for the determination of factors and phenomena involved in zoonotic infection cycles, but are often underexploited in public health. In this context, we developed a deterministic mathematical model to compare the efficacy of different intervention strategies aimed at reducing environmental contamination by macroparasites, using raccoons (Procyon lotor) and their zoonotic parasite Bayilsascaris procyonis as a model system. The three intervention strategies simulated are raccoon depopulation, anthelmintic treatment of raccoons and faeces removal. Our results show that all these strategies are able to eliminate the parasite egg population from the environment, but they are effective only above specific threshold coverages. Host removal and anthelmintic treatment showed the fastest results in eliminating the egg population, but anthelmintic treatment requires a higher effort to reach an effective result compared to host removal. Our simulations show that mathematical models can help to shed light on the dynamics of communicable infectious diseases, and give specific guidelines to contain B. procyonis environmental contamination in native, as well as in new, areas of parasite emergence. In particular, the present study highlights that identifying in advance the appropriate treatment coverage is fundamental to achieve the desired results, allowing for the implementation of cost- and time-effective intervention strategies.Author summary: Emerging infectious diseases involve complex systems on which background information is often lacking. Mathematical modelling, by providing a dynamic analysis of interactions between populations, can help our understanding of a disease transmission and maintenance, allowing for the simulation and comparison of intervention strategies aimed at controlling disease spread. We applied mathematical modelling to baylisascariasis, a disease caused by accidental ingestion of Baylisascaris procyonis eggs by humans, with consequences including fatal or severe neurologic disease. B. procyonis is an intestinal roundworm of raccoons, which shed eggs in the environment with their faeces. We modelled this system to simulate three different ways to reduce egg contamination and exposure risk for humans: raccoon depopulation, anthelmintic treatment of raccoons and faeces removal. Our models revealed that raccoon depopulation is the most efficient strategy to eliminate B. procyonis eggs, providing specific information on the minimum effort required to achieve their elimination, and showing that mathematical modelling is an efficient tool to identify the best strategy to control a disease beforehand, without wasting time and resources.