Investigation of mixture modelling algorithms as a tool for determining the statistical likelihood of serological exposure to Filariasis utilizing historical data from the Lymphatic Filariasis Surveillance Program in Vanuatu

Joseph, Hayley, Sullivan, Sarah, Wood, Peter, Melrose, Wayne, Taleo, Fasihah, and Graves, Patricia (2019) Investigation of mixture modelling algorithms as a tool for determining the statistical likelihood of serological exposure to Filariasis utilizing historical data from the Lymphatic Filariasis Surveillance Program in Vanuatu. Tropical Medicine and Infectious Disease, 4 (1). 45.

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Abstract

As the prevalence of lymphatic filariasis declines, it becomes crucial to adequately eliminate residual areas of endemicity and implement surveillance. To this end, serological assays have been developed, including the Bm14 Filariasis CELISA which recommends a specific optical density cut-off level. We used mixture modelling to assess positive cut-offs of Bm14 serology in children in Vanuatu using historical OD (Optical Density) ELISA values collected from a transmission assessment survey (2005) and a targeted child survey (2008). Mixture modelling is a statistical technique using probability distributions to identify subpopulations of positive and negative results (absolute cut-off value) and an 80% indeterminate range around the absolute cut-off (80% cut-off). Depending on programmatic choices, utilizing the lower 80% cut-off ensures the inclusion of all likely positives, however with the trade-off of lower specificity. For 2005, country-wide antibody prevalence estimates varied from 6.4% (previous cut-off) through 9.0% (absolute cut-off) to 17.3% (lower 80% cut-off). This corroborated historical evidence of hotspots in Pentecost Island in Penama province. For 2008, there were no differences in the prevalence rates using any of the thresholds. In conclusion, mixture modelling is a powerful tool that allows closer monitoring of residual transmission spots and these findings supported additional monitoring which was conducted in Penama in later years. Utilizing a statistical data-based cut-off, as opposed to a universal cut-off, may help guide program decisions that are better suited to the national program.

Item ID: 57860
Item Type: Article (Research - C1)
ISSN: 2414-6366
Keywords: mixture modelling; filariasis; CELISA; R statistics; elimination; surveillance; serology; Bm14
Additional Information:

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licensesby/4.0/)

Funders: National Health and Medical Research Council (NHMRC)
Projects and Grants: NHMRC Peter Doherty Fellowship GNT1052580
Date Deposited: 17 Oct 2019 01:24
FoR Codes: 11 MEDICAL AND HEALTH SCIENCES > 1107 Immunology > 110705 Humoural Immunology and Immunochemistry @ 50%
11 MEDICAL AND HEALTH SCIENCES > 1108 Medical Microbiology > 110803 Medical Parasitology @ 25%
11 MEDICAL AND HEALTH SCIENCES > 1117 Public Health and Health Services > 111706 Epidemiology @ 25%
SEO Codes: 92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920404 Disease Distribution and Transmission (incl. Surveillance and Response) @ 50%
92 HEALTH > 9204 Public Health (excl. Specific Population Health) > 920499 Public Health (excl. Specific Population Health) not elsewhere classified @ 50%
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