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Adaptive Epidemic Forecasting and Community Risk Evaluation of COVID-19
arXiv - CS - Computers and Society Pub Date : 2021-06-03 , DOI: arxiv-2106.02094 Vishrawas Gopalakrishnan, Sayali Navalekar, Pan Ding, Ryan Hooley, Jacob Miller, Raman Srinivasan, Ajay Deshpande, Xuan Liu, Simone Bianco, James H. Kaufman
arXiv - CS - Computers and Society Pub Date : 2021-06-03 , DOI: arxiv-2106.02094 Vishrawas Gopalakrishnan, Sayali Navalekar, Pan Ding, Ryan Hooley, Jacob Miller, Raman Srinivasan, Ajay Deshpande, Xuan Liu, Simone Bianco, James H. Kaufman
Pandemic control measures like lock-down, restrictions on restaurants and
gatherings, social-distancing have shown to be effective in curtailing the
spread of COVID-19. However, their sustained enforcement has negative economic
effects. To craft strategies and policies that reduce the hardship on the
people and the economy while being effective against the pandemic, authorities
need to understand the disease dynamics at the right geo-spatial granularity.
Considering factors like the hospitals' ability to handle the fluctuating
demands, evaluating various reopening scenarios, and accurate forecasting of
cases are vital to decision making. Towards this end, we present a flexible
end-to-end solution that seamlessly integrates public health data with tertiary
client data to accurately estimate the risk of reopening a community. At its
core lies a state-of-the-art prediction model that auto-captures changing
trends in transmission and mobility. Benchmarking against various published
baselines confirm the superiority of our forecasting algorithm. Combined with
the ability to extend to multiple client-specific requirements and perform
deductive reasoning through counter-factual analysis, this solution provides
actionable insights to multiple client domains ranging from government to
educational institutions, hospitals, and commercial establishments.
中文翻译:
COVID-19 的适应性流行病预测和社区风险评估
封锁、限制餐馆和聚会、保持社交距离等大流行控制措施已证明可有效遏制 COVID-19 的传播。然而,它们的持续执法会产生负面的经济影响。为了制定减少人民和经济困难同时有效对抗大流行的战略和政策,当局需要在正确的地理空间粒度上了解疾病动态。考虑医院处理波动需求的能力、评估各种重新开放方案以及准确预测病例等因素对决策至关重要。为此,我们提出了一种灵活的端到端解决方案,可将公共卫生数据与三级客户数据无缝集成,以准确估计重新开放社区的风险。其核心是最先进的预测模型,可自动捕捉传输和移动性的变化趋势。针对各种已发布基线的基准测试证实了我们预测算法的优越性。结合扩展到多个客户特定要求并通过反事实分析执行演绎推理的能力,该解决方案为从政府到教育机构、医院和商业机构的多个客户领域提供了可操作的见解。
更新日期:2021-06-07
中文翻译:
COVID-19 的适应性流行病预测和社区风险评估
封锁、限制餐馆和聚会、保持社交距离等大流行控制措施已证明可有效遏制 COVID-19 的传播。然而,它们的持续执法会产生负面的经济影响。为了制定减少人民和经济困难同时有效对抗大流行的战略和政策,当局需要在正确的地理空间粒度上了解疾病动态。考虑医院处理波动需求的能力、评估各种重新开放方案以及准确预测病例等因素对决策至关重要。为此,我们提出了一种灵活的端到端解决方案,可将公共卫生数据与三级客户数据无缝集成,以准确估计重新开放社区的风险。其核心是最先进的预测模型,可自动捕捉传输和移动性的变化趋势。针对各种已发布基线的基准测试证实了我们预测算法的优越性。结合扩展到多个客户特定要求并通过反事实分析执行演绎推理的能力,该解决方案为从政府到教育机构、医院和商业机构的多个客户领域提供了可操作的见解。