Population Age Group Sensitivity for COVID-19 Infections with Deep Learning

• URL: http://arxiv.org/abs/2307.00751v1
• Date: Mon, 3 Jul 2023 04:56:55 GMT
• Title: Population Age Group Sensitivity for COVID-19 Infections with Deep Learning
• Authors: Md Khairul Islam, Tyler Valentine, Royal Wang, Levi Davis, Matt Manner, Judy Fox
• Abstract summary: The COVID-19 pandemic has created unprecedented challenges for governments and healthcare systems worldwide. This study aimed to identify the most influential age groups in COVID-19 infection rates at the US county level.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The COVID-19 pandemic has created unprecedented challenges for governments and healthcare systems worldwide, highlighting the critical importance of understanding the factors that contribute to virus transmission. This study aimed to identify the most influential age groups in COVID-19 infection rates at the US county level using the Modified Morris Method and deep learning for time series. Our approach involved training the state-of-the-art time-series model Temporal Fusion Transformer on different age groups as a static feature and the population vaccination status as the dynamic feature. We analyzed the impact of those age groups on COVID-19 infection rates by perturbing individual input features and ranked them based on their Morris sensitivity scores, which quantify their contribution to COVID-19 transmission rates. The findings are verified using ground truth data from the CDC and US Census, which provide the true infection rates for each age group. The results suggest that young adults were the most influential age group in COVID-19 transmission at the county level between March 1, 2020, and November 27, 2021. Using these results can inform public health policies and interventions, such as targeted vaccination strategies, to better control the spread of the virus. Our approach demonstrates the utility of feature sensitivity analysis in identifying critical factors contributing to COVID-19 transmission and can be applied in other public health domains.

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