Anthem, Inc, Indianapolis, IN, United States.
J Med Internet Res. 2021 Apr 7;23(4):e24389. doi: 10.2196/24389.
The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number R, which is the expected number of secondary infections spread by a single infected individual.
We propose a simple method for estimating the time-varying infection rate and the R.
We used a sliding window approach with a Susceptible-Infectious-Removed (SIR) model. We estimated the infection rate from the reported cases over a 7-day window to obtain a continuous estimation of R. A proposed adaptive SIR (aSIR) model was applied to analyze the data at the state and county levels.
The aSIR model showed an excellent fit for the number of reported COVID-19 cases, and the 1-day forecast mean absolute prediction error was <2.6% across all states. However, the 7-day forecast mean absolute prediction error approached 16.2% and strongly overestimated the number of cases when the R was rapidly decreasing. The maximal R displayed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We found that the aSIR model can rapidly adapt to an increase in the number of tests and an associated increase in the reported cases of infection. Our results also suggest that intensive testing may be an effective method of reducing R.
The aSIR model provides a simple and accurate computational tool for continuous R estimation and evaluation of the efficacy of mitigation measures.
由于当地人口密度和政策措施的不同，COVID-19 大流行的动态变化也不同。在决策过程中，政策制定者会考虑有效繁殖数 R 的估计值，即单个感染者传播的二次感染数量的预期值。
我们提出了一种简单的方法来估计时变感染率和 R。
我们使用带有易感-感染-移除（SIR）模型的滑动窗口方法。我们从 7 天窗口内报告的病例中估计感染率，以获得 R 的连续估计。应用一种提出的自适应 SIR（aSIR）模型来分析州和县级别的数据。
aSIR 模型对报告的 COVID-19 病例数具有出色的拟合度，所有州的 1 天预测平均绝对预测误差均<2.6%。然而，当 R 迅速下降时，7 天预测平均绝对预测误差接近 16.2%，并严重高估了病例数。所有州的最大 R 值范围很广，从 2.0 到 4.5，纽约（4.4）和密歇根（4.5）的最高。我们发现，aSIR 模型可以快速适应检测数量的增加以及感染报告病例的相应增加。我们的结果还表明，密集检测可能是降低 R 的有效方法。
aSIR 模型为连续 R 估计和评估缓解措施的效果提供了一种简单而准确的计算工具。
