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Table 2 Summary of the different distributed lag non-linear models (DLNMs) characterizing the relationship between clinical incidence and ante natal clinic (ANC) parasite prevalence

From: Using ante-natal clinic prevalence data to monitor temporal changes in malaria incidence in a humanitarian setting in the Democratic Republic of Congo

Acronym Endemicity effect Lagged effect Number of parameters AIC RMSE (rolling cross-validation)
LE Linear No lagged effects 6 3859.2 0.0667
LELL Linear Linear 7 3116.6 0.0563
LENL Linear Non-linear 13 3116.0 0.0564
NE Hill function No lagged effects 8 3499.8 0.1126
NELL Hill function Linear 9 2982.0 0.05434
NENL Hill function Non-linear 15 2978.9 0.05431
  1. The second and third columns indicate the shape of the basis function used to characterize how the relationship is influenced by endemicity and the lagged effect. Models are compared using Akaike information criterion (AIC, lowest value in italic indicating most parsimonious model) and root mean squared error (RMSE, lowest value in italic indicating most predictive model)