de Andres P L, de Andres-Bragado L, Hoessly L
ICMM, Consejo Superior de Investigaciones Cientificas, Madrid, Spain.
Department of Biology, University of Fribourg, Fribourg, Switzerland.
Front Appl Math Stat. 2021 May 21;7:650716. doi: 10.3389/fams.2021.650716. eCollection 2021.
The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. The dynamics of such public-health threats can often be efficiently analyzed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derive the three free parameters for both models in several cases and test them against the amount of data needed to bring accuracy in predictions. The SHR model is ≈ ±2% accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar accuracy a fortnight before. All the analyzed cases assert the utility of SHR and SIR approximants as a valuable tool to forecast the disease's evolution. Finally, we have studied simulated stochastic individual-based SIR dynamics, which yields a detailed spatial and temporal view of the disease that cannot be given by SIR or SHR methods.
新冠疫情对全球人类生命造成了毁灭性影响，凸显了预测其发展态势的工具的必要性。此类公共卫生威胁的动态变化通常可以通过简单模型进行有效分析，这些模型有助于及时做出定量的政策决策。我们对一个用于传染病的易感-感染-康复模型（SIR）的最简版本与基于对数正态分布的简单最小二乘统计启发式回归（SHR）进行了基准测试。我们在几种情况下推导出了这两个模型的三个自由参数，并根据预测准确性所需的数据量对其进行了测试。SHR模型在病例每日曲线的第二个拐点过后约20天的准确率约为±2%，而SIR模型在此前两周就达到了类似的准确率。所有分析案例均表明，SHR和SIR近似法作为预测疾病演变的有价值工具具有实用性。最后，我们研究了基于个体的模拟随机SIR动态变化，它能给出疾病详细的时空视图，这是SIR或SHR方法无法做到的。
