A Tale of Two Parasites: Statistical Modelling to Support Disease Control Programmes in Africa

• Diggle, Peter J. ^[1] ; Emanuele Giorgi ^[1] ; Julienne Atsame ^[2] ; Ella, Sylvie Ntsame ^[2] ; Kisito Ogoussan ^[3] ; Katherine Gass ^[4]
  1. [1] Lancaster University

     Lancaster University

     Lancaster, Reino Unido

     
  2. [2] Control Program of Parasitic Diseases, Ministry of Health, Libreville, Gabon
  3. [3] USAID’s Act to End Neglected Tropical Diseases West, FHI360, Washington, DC, USA
  4. [4] Task Force for Global Health, Decatur, Georgia, USA

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• Localización: Statistical science, ISSN 0883-4237, Vol. 35, Nº. 1, 2020 (Ejemplar dedicado a: Statistics and Science), págs. 42-50
• Idioma: inglés
• DOI: 10.1214/19-sts738
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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 vectorborne 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 questionnairebased 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 among 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.