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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)