Department of Graphics and Digital Technology, School of Urban Design, Wuhan University, Wuhan 430072, China.
Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China.
Int J Environ Res Public Health. 2021 Jan 26;18(3):1090. doi: 10.3390/ijerph18031090.
Potential unreported infection might impair and mislead policymaking for COVID-19, and the contemporary spread of COVID-19 varies in different counties of the United States. It is necessary to estimate the cases that might be underestimated based on county-level data, to take better countermeasures against COVID-19. We suggested taking time-varying Susceptible-Infected-Recovered (SIR) models with unreported infection rates (UIR) to estimate factual COVID-19 cases in the United States.
Both the SIR model integrated with unreported infection rates (SIRu) of fixed-time effect and SIRu with time-varying parameters (tvSIRu) were applied to estimate and compare the values of transmission rate (TR), UIR, and infection fatality rate (IFR) based on US county-level COVID-19 data.
Based on the US county-level COVID-19 data from 22 January (T) to 20 August (T) in 2020, SIRu was first tested and verified by Ordinary Least Squares (OLS) regression. Further regression of SIRu at the county-level showed that the average values of TR, UIR, and IFR were 0.034%, 19.5%, and 0.51% respectively. The ranges of TR, UIR, and IFR for all states ranged from 0.007-0.157 (mean = 0.048), 7.31-185.6 (mean = 38.89), and 0.04-2.22% (mean = 0.22%). Among the time-varying TR equations, the power function showed better fitness, which indicated a decline in TR decreasing from 227.58 (T) to 0.022 (T). The general equation of tvSIRu showed that both the UIR and IFR were gradually increasing, wherein, the estimated value of UIR was 9.1 (95%CI 5.7-14.0) and IFR was 0.70% (95%CI 0.52-0.95%) at T.
Despite the declining trend in TR and IFR, the UIR of COVID-19 in the United States is still on the rise, which, it was assumed would decrease with sufficient tests or improved countersues. The US medical system might be largely affected by severe cases amidst a rapid spread of COVID-19.
潜在的未报告感染可能会影响和误导 COVID-19 的决策制定，而 COVID-19 在不同的美国县的传播情况也不同。有必要根据县级数据来估计可能被低估的病例，以便更好地应对 COVID-19。我们建议采用带有未报告感染率（UIR）的时变易感-感染-恢复（SIR）模型来估计美国的实际 COVID-19 病例。
我们应用固定时间效应的 SIR 模型与具有时变参数的 SIRu（tvSIRu）来估计和比较基于美国县级 COVID-19 数据的传播率（TR）、UIR 和感染致死率（IFR）的值。
根据美国 2020 年 1 月 22 日（T）至 8 月 20 日（T）期间的县级 COVID-19 数据，我们首先通过最小二乘法（OLS）回归测试和验证了 SIRu。进一步对县级 SIRu 的回归显示，TR、UIR 和 IFR 的平均值分别为 0.034%、19.5%和 0.51%。所有州的 TR、UIR 和 IFR 范围从 0.007-0.157（均值=0.048）、7.31-185.6（均值=38.89）和 0.04-2.22%（均值=0.22%）。在时变 TR 方程中，幂函数显示出更好的拟合度，表明 TR 从 227.58（T）下降到 0.022（T）。tvSIRu 的通用方程表明，UIR 和 IFR 都在逐渐增加，其中 UIR 的估计值为 9.1（95%CI 5.7-14.0），IFR 为 0.70%（95%CI 0.52-0.95%）在 T 时。
尽管 TR 和 IFR 呈下降趋势，但美国 COVID-19 的 UIR 仍在上升，据假设，随着检测的增加或应对措施的改善，UIR 会下降。美国医疗系统可能会受到 COVID-19 快速传播带来的严重病例的严重影响。
