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Estimating household contact matrices structure from easily collectable metadata.

Lorenzo Dall'AmicoJackie KleynhansLaetitia GauvinMichele TizzoniLaura OzellaMvuyo MakhasiNicole WolterBrigitte LanguageRyan G WagnerCheryl CohenStefano TempiaCiro Cattuto
Published in: PloS one (2024)
Contact matrices are a commonly adopted data representation, used to develop compartmental models for epidemic spreading, accounting for the contact heterogeneities across age groups. Their estimation, however, is generally time and effort consuming and model-driven strategies to quantify the contacts are often needed. In this article we focus on household contact matrices, describing the contacts among the members of a family and develop a parametric model to describe them. This model combines demographic and easily quantifiable survey-based data and is tested on high resolution proximity data collected in two sites in South Africa. Given its simplicity and interpretability, we expect our method to be easily applied to other contexts as well and we identify relevant questions that need to be addressed during the data collection procedure.
Keyphrases
  • electronic health record
  • high resolution
  • south africa
  • big data
  • minimally invasive
  • machine learning
  • human immunodeficiency virus
  • high speed