The novel coronavirus (SARS-CoV-2) emerged in late 2019 and spread globally in early 2020 Initial reports suggested the associated disease, COVID-19, produced rapid epidemic growth and caused high mortality As the virus sparked local epidemics in new communities, health systems and policy makers were forced to make decisions with limited information about the spread of the disease We developed a compartmental model to project COVID-19 healthcare demands that combined information regarding SARS-CoV-2 transmission dynamics from international reports with local COVID-19 hospital census data to support response efforts in three Metropolitan Statistical Areas (MSAs) in Texas, USA: Austin-Round Rock, Houston-The Woodlands-Sugar Land, and Beaumont-Port Arthur Our model projects that strict stay-home orders and other social distancing measures could suppress the spread of the pandemic Our capacity to provide rapid decision-support in response to emerging threats depends on access to data, validated modeling approaches, careful uncertainty quantification, and adequate computational resources IEEE

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早期 COVID-19 大流行建模：来自美国德克萨斯州的三个隔室模型案例研究

新型冠状病毒 (SARS-CoV-2) 于 2019 年底出现，并于 2020 年初在全球传播 初步报告表明，相关疾病 COVID-19 导致流行病快速增长并导致高死亡率随着该病毒在新社区引发局部流行病，卫生系统和政策制定者被迫在有关疾病传播的有限信息的情况下做出决定 我们开发了一个分区模型来预测 COVID-19 医疗保健需求，该模型将国际报告中有关 SARS-CoV-2 传播动态的信息与当地 COVID-19 医院相结合人口普查数据以支持美国德克萨斯州三个大都会统计区 (MSA) 的响应工作：奥斯汀-朗德罗克、休斯顿-林地-舒格兰、和博蒙特-亚瑟港 我们的模型表明，严格的居家令和其他社会疏离措施可以抑制大流行的传播 我们为应对新出现的威胁提供快速决策支持的能力取决于对数据的访问、经过验证的建模方法、谨慎不确定性量化和充足的计算资源 IEEE