Disease Transmission Models for Public Health Decision‑Making: Designing Intervention Strategies for Schistosoma japonicum

Chapter Details

Pub Date: 8 Apr 2010
Pages: 12
Chapter Category: Parasitic Disease
Taken from the Book: Modelling Parasite Transmission and Control
Book Series: Special Books
Edited by: Edwin Michael and Robert C. Spear

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Disease Transmission Models for Public Health Decision‑Making: Designing Intervention Strategies for Schistosoma japonicum

Edmund Y.W. Seto and Elizabeth J. Carlton

About this Chapter

The purpose of infectious disease transmission modeling is often to understand the factors that are responsible for the persistence of transmission, the dynamics of the infection process and how best to control transmission. As such, there should be great potential to use mathematical models to routinely plan and evaluate disease control programs. In reality, there are many challenges that have precluded the practical use of disease models in this regard. One challenge relates to the mathematical complexity of the models, which has made it difficult for field workers and health officials to understand and use them. Another challenge is that, despite their mathematical complexity, models typically do not have sufficient structural complexity to consider many of the site‑specific epidemiologic and disease control details that the practicing health official routinely considers. Moreover, most modeling studies have not been sufficiently explicit or exemplary in explaining how field data may be incorporated into the models to impact public health decision‑making. In this chapter, we start with a classic model of schistosomiasis transmission and relate its key properties to the more detailed model of Schistosoma japonicum model presented in Chapter by Remais and Chapter by Spear. We then discuss how various controls (e.g., chemotherapy, snail control and sanitation) may be evaluated via the detailed model. We then demonstrate in a practical manner, using S. japonicum data from China, how field data may be incorporated to inform the practice of disease control. Finally, we present a new model structure that considers how heterogeneous populations are interconnected, which has particular relevance to understanding disease control and emergence in today’s highly mobile world.

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