Libotte Gustavo B, Dos Anjos Lucas, Almeida Regina C C, Malta Sandra M C, Silva Renato S
National Laboratory for Scientific Computing, Getúlio Vargas Av., 333, Quitandinha, Petrópolis, Rio de Janeiro, Brazil.
Nonlinear Dyn. 2022;107(3):1919-1936. doi: 10.1007/s11071-021-07069-9. Epub 2022 Jan 7.
Reliable data are essential to obtain adequate simulations for forecasting the dynamics of epidemics. In this context, several political, economic, and social factors may cause inconsistencies in the reported data, which reflect the capacity for realistic simulations and predictions. In the case of COVID-19, for example, such uncertainties are mainly motivated by large-scale underreporting of cases due to reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of models, we propose strategies capable of improving the ability to predict the spread of the diseases. Using a compartmental model in a COVID-19 study case, we show that the regularization of data by means of Gaussian process regression can reduce the variability of successive forecasts, improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.
可靠的数据对于获得充分的模拟以预测疫情动态至关重要。在这种情况下，一些政治、经济和社会因素可能导致报告数据出现不一致，而这些数据反映了进行现实模拟和预测的能力。例如，在新冠疫情中，此类不确定性主要是由于某些地区检测能力下降导致病例大规模漏报所致。为了减轻用于估计模型参数的数据中的噪声影响，我们提出了能够提高疾病传播预测能力的策略。在一个新冠疫情研究案例中使用 compartmental 模型，我们表明通过高斯过程回归对数据进行正则化可以减少连续预测的变异性，提高预测能力。我们还展示了采用随时间变化的 compartmental 模型参数的优势，这与通常采用恒定值的方法不同。
