  According to UNICEF, pneumonia is the leading cause of death in children
under 5. 70% of worldwide pneumonia deaths occur in only 15 countries,
including Bangladesh. Previous research has indicated a potential association
between the incidence of pneumonia and the presence of cross-ventilation in
slum housing in Dhaka, Bangladesh. The objective of this research is to
establish a validated computational framework that can predict ventilation
rates in slum homes to support further studies investigating this correlation.
To achieve this objective we employ a building thermal model (BTM) in
combination with uncertainty quantification (UQ). The BTM solves for the
time-evolution of volume-averaged temperatures in a typical home, considering
different ventilation configurations. The UQ method propagates uncertainty in
model parameters, weather inputs, and physics models to predict mean values and
95% confidence intervals for the quantities of interest, namely temperatures
and ventilation rates in terms of air changes per hour (ACH). The model
predictions are compared to on-site field measurements of air and thermal mass
temperatures, and of ACH. The results indicate that the use of standard cross-
or single-sided ventilation models limits the accuracy of the ACH predictions;
in contrast, a model based on a similarity relationship informed by the
available ACH measurements can produce more accurate predictions with
confidence intervals that encompass the measurements for 12 of the 17 available
data points.
