From serological surveys to disease burden: a modelling pipeline for Chagas disease

Julia Ledien, Zulma M. Cucunubá, Gabriel Parra-Henao, Eliana Rodríguez-Monguí, Andrew P. Dobson, Susana B. Adamo, Luis Gerardo Castellanos, María Gloria Basáñez, Pierre Nouvellet

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In 2012, the World Health Organization (WHO) set the elimination of Chagas disease intradomiciliary vectorial transmission as a goal by 2020. After a decade, some progress has been made, but the new 2021-2030 WHO roadmap has set even more ambitious targets. Innovative and robust modelling methods are required to monitor progress towards these goals. We present a modelling pipeline using local seroprevalence data to obtain national disease burden estimates by disease stage. Firstly, local seroprevalence information is used to estimate spatio-temporal trends in the Force-of-Infection (FoI). FoI estimates are then used to predict such trends across larger and fine-scale geographical areas. Finally, predicted FoI values are used to estimate disease burden based on a disease progression model. Using Colombia as a case study, we estimated that the number of infected people would reach 506 000 (95% credible interval (CrI) = 395 000-648 000) in 2020 with a 1.0% (95%CrI = 0.8-1.3%) prevalence in the general population and 2400 (95%CrI = 1900-3400) deaths (approx. 0.5% of those infected). The interplay between a decrease in infection exposure (FoI and relative proportion of acute cases) was overcompensated by a large increase in population size and gradual population ageing, leading to an increase in the absolute number of Chagas disease cases over time. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.

Original languageEnglish (US)
Article number20220278
JournalPhilosophical Transactions of the Royal Society B: Biological Sciences
Volume378
Issue number1887
DOIs
StatePublished - Oct 9 2023

All Science Journal Classification (ASJC) codes

  • General Biochemistry, Genetics and Molecular Biology
  • General Agricultural and Biological Sciences

Keywords

  • Chagas disease
  • Force-of-Infection
  • Random Forest
  • burden of disease
  • infectious diseases
  • modelling

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