The purple and green lines represent the prior 2-week average of daily rain and temperature averages.
The purple and green lines represent the prior 2-week average of daily rain and temperature averages.
The left graph represent other known etiologies and the right graph represent viral etiologies. The dashed lines do not represent standardized density heights so the heights for V = 0 and V = 1 …
The right graph represents viral etiologies and the left graph represents other known etiologies.
'PresPtnt' refers to the predictive model using the presenting patient’s information. 'Pre-test' refers tot he use of pre-test odds based on prior patients’ predictive models. 'Climate' refers to …
AUC’s and confidence intervals for post-test odds used in the 80%training and 20%testing iteration.
Individual plot titles show the proportion of data used in training.
Pre-test refers to the use of prior patient predictions. Individual plot titles show the site left out of training.
Greyed rows are variables that would be accessible for providers in LMICs at the time of presentation. Table 1 is reproduced from Brintz et al., 2020, PLoS Negl Trop Dis., published under the …
Viral etiology | |
---|---|
Variable name | Variance reduction |
Age | 51.6 |
Season | 29.0 |
Blood in stool | 26.1 |
Height-for-age Z-score | 24.7 |
Vomiting | 23.0 |
Breastfeeding | 22.0 |
Mid-upper arm circumference | 20.9 |
Respiratory rate | 18.5 |
Wealth index | 18.3 |
Body Temperature | 16.7 |
PresPtnt refers to the model fit using presenting patient information.
Country | Test set size | Formula | AUC (95% CI) |
---|---|---|---|
Kenya | 79 | Pre-test * PresPtnt | 0.65 (0.53–0.77) |
PresPtnt * Seasonal | 0.66 (0.54–0.78) | ||
PresPtnt | 0.63 (0.51–0.75) | ||
Mali | 88 | Pre-test * PresPtnt | 0.74 (0.61–0.86) |
PresPtnt * Seasonal | 0.78 (0.66–0.89) | ||
PresPtnt | 0.75 (0.62–0.87) | ||
Pakistan | 108 | Pre-test * PresPtnt | 0.81 (0.72–0.89) |
PresPtnt * Seasonal | 0.8 (0.72–0.88) | ||
PresPtnt | 0.81 (0.73–0.89) | ||
India | 119 | Pre-test * PresPtnt | 0.84 (0.76–0.91) |
PresPtnt * Seasonal | 0.85 (0.78–0.92) | ||
PresPtnt | 0.81 (0.74–0.89) | ||
The Gambia | 80 | Pre-test * PresPtnt | 0.89 (0.82–0.96) |
PresPtnt * Seasonal | 0.87 (0.79–0.94) | ||
PresPtnt | 0.78 (0.67–0.88) | ||
Mozambique | 66 | Pre-test * PresPtnt | 0.88 (0.79–0.97) |
PresPtnt * Seasonal | 0.9 (0.82–0.98) | ||
PresPtnt | 0.77 (0.66–0.89) | ||
Bangladesh | 141 | Pre-test * PresPtnt | 0.91 (0.82–1) |
PresPtnt * Seasonal | 0.93 (0.88–0.99) | ||
PresPtnt | 0.95 (0.92–0.99) |
Additionally, + and - refer to our model indicating a true positive or false positive, respectively, based on the threshold for each model which achieves a 0.90 or 0.95 specificity. Only patients …
Specificity=0.90 | Specificity=0.95 | |||||
---|---|---|---|---|---|---|
Model | Addl. diag. (Se.,Sp.) | Auc (95% CI) | True + | False + | True + | False + |
Pre-test * PresPtnt | None | 0.839 (0.809–0.869) | 88 | 29 | 60 | 16 |
(0.7, 0.7) | 0.876 (0.849–0.902) | 102 | 31 | 78 | 16 | |
(0.7, 0.95) | 0.933 (0.914–0.952) | 132 | 31 | 123 | 16 | |
(0.9, 0.95) | 0.972 (0.960–0.984) | 154 | 34 | 147 | 18 | |
PresPtnt * Seasonal | None | 0.830 (0.798–0.861) | 70 | 25 | 54 | 11 |
(0.7, 0.7) | 0.870 (0.842–0.897) | 101 | 27 | 68 | 14 | |
(0.7, 0.95) | 0.931 (0.912–0.951) | 130 | 27 | 121 | 16 | |
(0.9, 0.95) | 0.971 (0.959–0.984) | 154 | 30 | 149 | 18 | |
PresPtnt | None | 0.809 (0.776–0.842) | 66 | 31 | 41 | 15 |
(0.7, 0.7) | 0.857 (0.827–0.886) | 98 | 33 | 68 | 16 | |
(0.7, 0.95) | 0.925 (0.904–0.946) | 129 | 33 | 117 | 18 | |
(0.9, 0.95) | 0.968 (0.955–0.981) | 153 | 34 | 149 | 18 |
Frequency table of pathogens in which the post-test odds formulation with varying specifity (Sp.) chosen have false positives.
The table shows the factor () used to simulate induced conditional dependence between two covariates and their average conditional correlation. Additionally, it shows the average AUC resulting …
AUC | |||
---|---|---|---|
1D-KDE | 2D-KDE | ||
-2.000 | −0.894 | 0.725 | 0.830 |
-1.000 | −0.709 | 0.758 | 0.828 |
-0.500 | −0.446 | 0.824 | 0.838 |
0.000 | 0.002 | 0.838 | 0.836 |
0.500 | 0.448 | 0.836 | 0.836 |
1.000 | 0.708 | 0.831 | 0.840 |
2.000 | 0.894 | 0.810 | 0.836 |