A tale of two parasites:statistical modelling to support disease control programmes in Africa

Diggle, Peter and Giorgi, Emanuele and Atsame, Julienne and Ella, Sylvie Ntsame and Ogoussan, Kisito and Gass, Katherine (2019) A tale of two parasites:statistical modelling to support disease control programmes in Africa. Statistical Science. ISSN 0883-4237 (In Press)

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Abstract

Vector-borne diseases have long presented major challenges to the health of rural communities in the wet tropical regions of the world, but especially in sub-Saharan Africa. In this paper we describe the contribution that statistical modelling has made to the global elimination programme for one vector-borne disease, onchocerciasis. We explain why information on the spatial distribution of a second vector-borne disease, Loa loa, is needed before communities at high risk of onchocerciasis can be treated safely with mass distribution of ivermectin, an antifiarial medication. We show how a model-based geostatistical analysis of Loa loa prevalence survey data can be used to map the predictive probability that each location in the region of interest meets a WHO policy guideline for safe mass distribution of ivermectin and describe two applications: one is to data from Cameroon that assesses prevalence using traditional blood-smear microscopy; the other is to Africa-wide data that uses a low-cost questionnaire-based method. We describe how a recent technological development in image-based microscopy has resulted in a change of emphasis from prevalence alone to the bivariate spatial distribution of prevalence and the intensity of infection amongst infected individuals. We discuss how statistical modelling of the kind described here can contribute to health policy guidelines and decisionmaking in two ways. One is to ensure that, in a resource-limited setting, prevalence surveys are designed, and the resulting data analysed, as efficiently as possible. The other is to provide an honest quantification of the uncertainty attached to any binary decision by reporting predictive probabilities that a policy-defined condition for action is or is not met. Vector-borne diseases have long presented major challenges to the health of rural communities in the wet tropical regions of the world, but especially in sub-Saharan Africa. In this paper we describe the contribution that statistical modelling has made to the global elimination programme for one vector-borne disease, onchocerciasis. We explain why information on the spatial distribution of a second vector-borne disease, Loa loa, is needed before communities at high risk of onchocerciasis can be treated safely with mass distribuiton of ivermectin, an antiflarial medication. We show how a model-based geostatistical analysis of Loa loa prevalence survey data can be used to map the predictive probability that each location in the region of interest meets a WHO policy guideline for safe mass distribution of ivermectin and describe two applications: one to data from Cameroon that assesses prevalence using traditional blood-smear microscopy; one to Africa-wide data that uses a low-cost questionnaire-based method. We describe how a recent technological development in image-based microscopy has resulted in a change of emphasis from prevalence alone to the bivariate spatial distribution of prevalence and the intensity of infection amongst infected individuals. We discuss how statistical modelling of the kind described here can contribute to health policy guidelines and decision-making in two ways. One is to ensure that, in a resourcelimited setting, prevalece surveys are designed, and the resulting data analysed, as efficiently as possible. The other is to provide an honest quantification of the uncertainy attached to any binary decision by reporting predictive probabilities that a policy-defined condition for action is or is not met.

Item Type:
Journal Article
Journal or Publication Title:
Statistical Science
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600
Subjects:
ID Code:
137729
Deposited By:
Deposited On:
10 Oct 2019 13:35
Refereed?:
Yes
Published?:
In Press
Last Modified:
03 Apr 2020 05:21