PLoS Neglected Tropical Diseases (Mar 2022)

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

  • Kimberly M Fornace,
  • Laura Senyonjo,
  • Diana L Martin,
  • Sarah Gwyn,
  • Elena Schmidt,
  • David Agyemang,
  • Benjamin Marfo,
  • James Addy,
  • Ernest Mensah,
  • Anthony W Solomon,
  • Robin Bailey,
  • Chris J Drakeley,
  • Rachel L Pullan

DOI
https://doi.org/10.1371/journal.pntd.0010227
Journal volume & issue
Vol. 16, no. 3
p. e0010227

Abstract

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BackgroundAs 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 findingsHere, 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/significanceThis 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.