Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates.
Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates.
复制标题
人口分层可实现重新开放政策对死亡率和住院率的建模影响。
DOI：
10.1016/j.jbi.2021.103818
复制
发表时间：
2021-07
影响因子：
4.5
通讯作者：
Jiang X
中科院分区：
文献类型：
--
作者：
Huang T;Chu Y;Shams S;Kim Y;Annapragada AV;Subramanian D;Kakadiaris I;Gottlieb A;Jiang X
研究地方政策对近期住院率和死亡率的影响。我们引入了一种新的风险分层SIR-HCD模型，该模型引入了新的变量来模拟低接触（例如，在家工作）和高接触（例如，现场工作）亚群的动态，同时共享参数来控制它们各自的R0(t)随时间的变化。我们对2020年5月1日至2020年10月4日期间德克萨斯州哈里斯县每日报告的COVID-19住院率和累积死亡率数据进行了模型测试，这些数据来自多个来源（USA FACTS、美国劳工统计局、东南德克萨斯州区域咨询委员会COVID-19报告、TMC每日新闻和约翰霍普金斯大学县级死亡率报告）。在第一阶段和第二阶段重新开放期间，我们评估了我们的模型在德克萨斯州哈里斯县（大休斯顿地区人口最多的县）的预测准确性。我们的模型不仅优于其他竞争模型，而且还支持反事实分析，以模拟当地环境中未来政策的影响，这在现有方法中是独一无二的。死亡率和住院率受到当地隔离和重新开放政策的显著影响。现有模型不能以明确和可解释的方式直接说明这些政策对感染、住院和死亡率的影响。我们的工作是试图通过将这些信息纳入模型来改进对这些趋势的预测，从而支持决策。我们的工作是一次及时的努力，试图模拟在地方政策影响下流行病的动态。
Study the impact of local policies on near-future hospitalization and mortality rates. We introduce a novel risk-stratified SIR-HCD model that introduces new variables to model the dynamics of low-contact (e.g., work from home) and high-contact (e.g., work on-site) subpopulations while sharing parameters to control their respective R0(t) over time. We test our model on data of daily reported hospitalizations and cumulative mortality of COVID-19 in Harris County, Texas, from May 1, 2020, until October 4, 2020, collected from multiple sources (USA FACTS, U.S. Bureau of Labor Statistics, Southeast Texas Regional Advisory Council COVID-19 report, TMC daily news, and Johns Hopkins University county-level mortality reporting). We evaluated our model’s forecasting accuracy in Harris County, TX (the most populated county in the Greater Houston area) during Phase-I and Phase-II reopening. Not only does our model outperform other competing models, but it also supports counterfactual analysis to simulate the impact of future policies in a local setting, which is unique among existing approaches. Mortality and hospitalization rates are significantly impacted by local quarantine and reopening policies. Existing models do not directly account for the effect of these policies on infection, hospitalization, and death rates in an explicit and explainable manner. Our work is an attempt to improve prediction of these trends by incorporating this information into the model, thus supporting decision-making. Our work is a timely effort to attempt to model the dynamics of pandemics under the influence of local policies.
影响因子：
4.5
作者：
Kwon J;Grady C;Feliciano JT;Fodeh SJ
通讯作者：
Fodeh SJ
DOI：
10.1098/rspa.1927.0118
发表时间：
1927-08-01
期刊：
PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES A-CONTAINING PAPERS OF A MATHEMATICAL AND PHYSICAL CHARACTER
影响因子：
--
作者：
Kermack, WO;McKendrick, AG
通讯作者：
McKendrick, AG
影响因子：
7.8
作者：
da Silva, Ramon Gomes;Dal Molin Ribeiro, Matheus Henrique;Coelho, Leandro dos Santos
通讯作者：
Coelho, Leandro dos Santos