Receiver operating characteristic (ROC) curves with area under the curve (AUC) values comparing model discrimination ability across three periods: pre-pandemic (2018–2019), pandemic (2020–2021), and post-pandemic (2022).

<p>The diagonal reference line represents random classification (AUC = 0.5), while perfect classification corresponds to AUC = 1.0. Shaded areas indicate 95% confidence intervals based on 1,000 bootstrap iterations. 95% CIs of AUC are given in parentheses.</p> <p>(TIF)</p>...

Повний опис

Збережено в:

Бібліографічні деталі
Автор:	Pikkanet Suttirat (13567965) (author)
Інші автори:	Sudarat Chadsuthi (3738406) (author), Charin Modchang (3738409) (author), Joacim Rocklöv (296647) (author)
Опубліковано:	2025
Предмети:	Ecology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified shapley additive explanation median incidence rate 878 &# 8211 861 &# 8211 2020 &# 8211 low risk based specific climatic variables model &# 8217 predicting leptospirosis risk model performance declined leptospirosis risk predictive performance integrating climatic testing periods targeted interventions study develops study demonstrates strongest predictors provincial level potential underreporting machine learning household size driving outbreaks complex mix complex interplay classify provinces agricultural features
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Receiver operating characteristic (ROC) curves with area under the curve (AUC) values comparing model discrimination ability across three periods: pre-pandemic (2018–2019), pandemic (2020–2021), and post-pandemic (2022).

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