Influenza-Like Symptom Prediction by Analyzing Self-Reported Health Status and Human Mobility Behaviors

Ma, Fenglong; Zhong, Shiran; Gao, Jing; Bian, Ling

doi:10.1145/3307339.3342141

Cited by 7 publications

(1 citation statement)

References 19 publications

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“…Precision and recall are two separate perspectives of an accuracy measurement. Over‐ or under‐prediction of flows are inevitably reflected in either the precision or recall (Ali, Shamsuddin, & Ralescu, 2015; Japkowicz & Stephen, 2002; Ma, Zhong, Gao, & Bian, 2019). The F1 score is adopted to balance the two measurements in order to comprehensively reflect the prediction accuracy (Powers, 2011; Sasaki, 2007).…”

Section: Methodsmentioning

confidence: 99%

What drives disease flows between locations?

Zhong

Bian

2020

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Communicable diseases “flow” between locations. These flows dictate where and when certain communities will be affected. While the prediction of disease flows is essential for the timely intervention of epidemics, few studies have addressed this critical issue. This study predicts disease flows during an epidemic by considering the epidemiological, network, and temporal contextual factors using a deep learning approach. A series of scenario analyses helps identify the effects of these contextual factors on disease flows. Results show that the extended spatial–temporal effect of the epidemiological factors stimulates disease flows. The compound effects of the network factors enhance the transmission efficiency of these flows. Lastly, the temporal effect accelerates the combined effects of epidemiological and network factors on the flows. The findings of this study reveal the intricate nature of disease flows and lay a solid foundation for real‐time surveillance of epidemics and pandemics to inform timely interventions for a broad range of communicable diseases.

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Section: Methodsmentioning

confidence: 99%