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.