UTHealth SPH-COVID19仪表板——生物统计学和Data Science - Departments - The University of Texas Health Science Center at Houston (UTHealth) School of Public Health

About the data

实时数据是混乱的，我们在一些图表中看到了这一点。根据几个局限性，应考虑对该数据的解释。简要地，

数据源：德克萨斯州卫生服务部。
州级的日常数据（我们正在展示）将与县级仪表板不同。这是由于该县到州的报告滞后。
Testing and cases data are underestimated. This is because not all clinics are reporting data in a comprehensive, systematic manner. As time moves on, this data gets more and more accurate to reflect the "true" burden in Texas.
5月19日之后的Covid19阳性病例不包括抗体测试。
有针对较小县的数据转储。“数据转储”是一个日期，而不是随时间扩展的大量案例。例如，我们可能会在6月17日在安德森县看到这一点。这影响了投影，热点和R（t）分析。谨慎地解释。
在某些情况下，每日案件为阴性。这可能是由于回顾性县的校正。这些模型“平滑”数据以解释这些异常值，但是较大的离群值可能仍会对分析产生重大影响。
On March 24, 2020, all hospitals were mandated to report COVID19 hospitalizations to the状态。为了系统化报告，医院准备计划提供者（HPP）在德克萨斯州创建了8个亚组，负责收集医院数据并向州报告。为了保持整个州的报告一致，DSHSdefined key metrics。简要地，
- COVID19住院：确认的PCR或可疑COVID19。
- Suspected COVID19: 1) patient with respiratory illness AND no other obvious causes AND no travel within 14 days; 2) respiratory illness AND has been in contact with a confirmed COVID19 person; 3) severe respiratory illness AND requires hospitalization AND no other obvious explanation.
- 床的可用性：定义为包括医院向州报告的医院床，ICU床和涌现的床可用性。医院，ICU或涌现的床可用性只有在员工人数的情况下才能报告给该州。
- Ventilator availability: Total number of adult ventilators available, to include adult ventilators that are capable of ventilating a pediatric patient. Any device used to support, assist or control respiration through the application of positive pressure to the airway when delivered via an artificial airway.
  
  医院，城市和县可能会随着司法管辖区的增加或减少而调整医院病床的可用性。

统计建模的详细信息

R(t) was estimated using the R0 package in R. The generation time estimates were generated using a gamma distribution with mean 3.96 and standard deviation 4.75, based on estimates from Ganyani, et al. 2020. We investigated several options based on the literature and found this choice of distribution and parameters to not affect the results significantly. Some of the R(t) estimates may be unreliable for counties with “data dumps”. Interpret cautiously for these counties as we are not able to observe the trend over time. To smooth the data, we used 7-day moving averages in the R(t) estimation. Case reports are lagged from test administration (at the beginning the lag was closer to 2-3 weeks and by May it was closer to 2-5 days), thus what we currently are able to estimate is a reflection of tests performed several days prior. This lag may differ by county, hospital, and testing center. R(t) is only estimated for counties who have had at least 50 total cases.

Ganyani T，Kremer C，Chen D，Torneri A，Faes C，Wallinga J，Hens N.基于症状发作数据，估计冠状病毒病的发电间隔（Covid-19），2020年3月。2020年4月30日； 25（17）：2000257。

为了建立新案例的预测，建立了自动回归综合移动平均值（ARIMA）模型，建立在7天的新案例移动平均值上，以预测未来的10天。使用R中的Auto.arima函数选择了自动估计的顺序，差异程度和模型的移动平均值，根据Aikake信息标准选择最佳模型。如果每日最大病例的数量太低，则不会产生估计。这些预测的局限性包括：（1）这些预测纯粹是基于以前的数据趋势，目前尚未考虑任何协变量；（2）预测对于具有数据转储的县可能不可靠；（3）案例报告滞后于测试管理（一开始就接近2-3周，到5月，它已经接近2-5天），因此我们目前能够估计的是对执行测试的反映前几天。该滞后可能因县，医院和测试中心而有所不同。