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. 2022 Mar 8;16(3):e0010227.
doi: 10.1371/journal.pntd.0010227. eCollection 2022 Mar.

Characterising spatial patterns of neglected tropical disease transmission using integrated sero-surveillance in Northern Ghana

Kimberly M Fornace  1   2 , Laura Senyonjo  3 , Diana L Martin  4 , Sarah Gwyn  4 , Elena Schmidt  3 , David Agyemang  5 , Benjamin Marfo  6 , James Addy  6 , Ernest Mensah  7 , Anthony W Solomon  1   8 , Robin Bailey  1 , Chris J Drakeley  1 , Rachel L Pullan  1
Affiliations

Characterising spatial patterns of neglected tropical disease transmission using integrated sero-surveillance in Northern Ghana

Kimberly M Fornace et al. PLoS Negl Trop Dis. .

Abstract

Background: As prevalence decreases in pre-elimination settings, identifying the spatial distribution of remaining infections to target control measures becomes increasingly challenging. By measuring multiple antibody responses indicative of past exposure to different pathogens, integrated serological surveys enable simultaneous characterisation of residual transmission of multiple pathogens.

Methodology/principal findings: Here, we combine integrated serological surveys with geostatistical modelling and remote sensing-derived environmental data to estimate the spatial distribution of exposure to multiple diseases in children in Northern Ghana. The study utilised the trachoma surveillance survey platform (cross-sectional two-stage cluster-sampled surveys) to collect information on additional identified diseases at different stages of elimination with minimal additional cost. Geostatistical modelling of serological data allowed identification of areas with high probabilities of recent exposure to diseases of interest, including areas previously unknown to control programmes. We additionally demonstrate how serological surveys can be used to identify areas with exposure to multiple diseases and to prioritise areas with high uncertainty for future surveys. Modelled estimates of cluster-level prevalence were strongly correlated with more operationally feasible metrics of antibody responses.

Conclusions/significance: This study demonstrates the potential of integrated serological surveillance to characterise spatial distributions of exposure to multiple pathogens in low transmission and elimination settings when the probability of detecting infections is low.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Study site in Northern Ghana; administrative shapefiles obtained from National Information Technology Agency (NITA), Government of Ghana (https://data.gov.gh/dataset/shapefiles-all-districts-ghana-170-districts).
The boundaries and names shown and the designations used on this map do not imply the expression of any opinion whatsoever on the part of the authors, or the institutions with which they are affiliated, concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
Fig 2
Fig 2
Posterior estimates of the probability of exceeding 10% seroprevalence of high responders to A) Trachoma Pgp3; B) Lymphatic filariasis Wb123; C) Onchoceriasis Ov16; D) Strongyloidiasis NIE; E) Schistosomiasis SEA; F) Giardiasis VSP3; administrative shapefiles obtained from National Information Technology Agency (NITA), Government of Ghana (https://data.gov.gh/dataset/shapefiles-all-districts-ghana-170-districts).
Fig 3
Fig 3
Combining disease measures to A) identify regions with high probabilities of exceeding 10% seroprevalence (exceedance probabilities > 70%); and B) identify regions with high uncertainty (exceedance probabilities 40–60%); administrative shapefiles obtained from National Information Technology Agency (NITA), Government of Ghana (https://data.gov.gh/dataset/shapefiles-all-districts-ghana-170-districts).
Fig 4
Fig 4. Arithmetic mean MFI values per cluster by region.
See this image and copyright information in PMC

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