The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison

Journal of Biomedical Informatics
Yirong ChenAlex R Cook

Abstract

Accurate and timely prediction for endemic infectious diseases is vital for public health agencies to plan and carry out any control methods at an early stage of disease outbreaks. Climatic variables has been identified as important predictors in models for infectious disease forecasts. Various approaches have been proposed in the literature to produce accurate and timely predictions and potentially improve public health response. We assessed how the machine learning LASSO method may be useful in providing useful forecasts for different pathogens in countries with different climates. Separate LASSO models were constructed for different disease/country/forecast window with different model complexity by including different sets of predictors to assess the importance of different predictors under various conditions. There was a more apparent cyclicity for both climatic variables and incidence in regions further away from the equator. For most diseases, predictions made beyond 4 weeks ahead were increasingly discrepant from the actual scenario. Prediction models were more accurate in capturing the outbreak but less sensitive to predict the outbreak size. In different situations, climatic variables have different levels of importanc...Continue Reading

Citations

Oct 6, 2020·PloS One·Yan HaoYanping Bai
Sep 17, 2021·PLoS Neglected Tropical Diseases·Laith Hussain-AlkhateebSilvia Runge-Ranzinger
Sep 26, 2020·Journal of Biomedical Informatics·Matheus Henrique Dal Molin RibeiroLeandro Dos Santos Coelho
Jan 8, 2022·PLoS Neglected Tropical Diseases·Emmanuelle SylvestreAndré Cabié

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Software Mentioned

SARIMA
LASSO
R
EpiWeek

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