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Some mathematical and statistical issues in assessing the evidence for acquired immunity to schistosomiasis

Published online by Cambridge University Press:  04 August 2010

Valerie Isham
Affiliation:
University College London
Graham Medley
Affiliation:
University of Warwick
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Summary

Introduction

The problem of whether humans mount a protective immune response to schistosomiasis is both of basic, biological interest and important in the context of disease control. The immune response to this and other large parasites differs from that of microparasites, such as viruses and bacteria, and involves distinct branches of the immune system associated with IgE and eosinophils. The possibility of a protective immune response has consequences not just for vaccine development but also for the effectiveness of control programmes in general. Immune responses are altered by chemotherapy itself and may be involved in the schistosomicidal action of praziquantel, the main chemotherapeutic drug. The problem of demonstrating a protective response is closely related to that of quantifying the response, which would be essential for vaccine development.

In this paper we consider some mathematical, and especially statistical, problems that arise when assessing the evidence for immunity in man, illustrating these with data from our own studies in Kenya. We first briefly describe our studies in Kenya and outline the difficulty of interpreting simple age-intensity curves. The main part of the paper is divided into two sections, the first discussing two more sophisticated approaches to analysis of crosssectional data and the second reviewing the analysis of treatment-reinfection studies.

Schistosomiasis studies in Kenya

For more than a decade studies have been undertaken on Schistosoma mansoni infection in the Machakos District of Kenya, as a collaboration between the Kenyan Medical Research Institute (KEMRI), the Division of Vector Borne Diseases (DVBD) of the Kenyan Ministry of Health and the University of Cambridge.

Type
Chapter
Information
Models for Infectious Human Diseases
Their Structure and Relation to Data
, pp. 139 - 159
Publisher: Cambridge University Press
Print publication year: 1996

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