Understanding Dynamics of Pandemic Models to Support Predictions of COVID-19 Transmission: Parameter Sensitivity Analysis of SIR-Type Models

Abstract

Despite efforts made to model and predict COVID-19 transmission, large predictive uncertainty remains. Failure to understand the dynamics of the nonlinear pandemic prediction model is an important reason. To this end, local and multiple global sensitivity analysis approaches are synthetically applied to analyze the sensitivities of parameters and initial state variables and community size (N) in susceptible-infected-recovered (SIR) and its variant susceptible-exposed-infected-recovered (SEIR) models and basic reproduction number ( R0 ), aiming to provide prior information for parameter estimation and suggestions for COVID-19 prevention and control measures. We found that N influences both the maximum number of actively infected cases and the date on which the maximum number of actively infected cases is reached. The high effect of N on maximum actively infected cases and peak date suggests the necessity of isolating the infected cases in a small community. The protection rate and average quarantined time are most sensitive to the infected populations, with a summation of their first-order sensitivity indices greater than 0.585, and their interactions are also substantial, being 0.389 and 0.334, respectively. The high sensitivities and interaction between the protection rate and average quarantined time suggest that protection and isolation measures should always be implemented in conjunction and started as early as possible. These findings provide insights into the predictability of the pandemic models by estimating influential parameters and suggest how to effectively prevent and control epidemic transmission.

Evaluation of the EsteR Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study

“…Sensitivity analysis is a widely used tool, and in the literature, it has been used for various COVID-19–related research questions (eg, it has been used to analyze epidemiological models [ 5 , 6 , 8 ] or understand the transmission of COVID-19 [ 7 ]). Weng and Yi [ 9 ] conducted a sensitivity analysis to estimate the COVID-19 incubation time based on a systematic literature review.…”

“…First, a sensitivity analysis of the model parameters was carried out. In research, sensitivity analyses have been recently used to analyze the COVID-19 transmission dynamics and which regulations allow for control of the pandemic [ 5 - 9 ]. For our statistical models, the aim of the sensitivity analysis was to determine how a variation in the model parameters influences the answers provided in the tools.…”

Quantifying the Risk of General Health and Early COVID-19 Spread in Residential Buildings with Deep Learning and Expert-augmented Machine Learning

“…In the research stream of COVID-19 studies, the proposed method helps to capture the uncertainty of COVID-19 transmission in the total built environment. In fact, owing to the dynamics of the nonlinear pandemic prediction model [54] and the small case counts, modeling and tracing COVID-19 transmission at the early stage of spread remains challenging. We not only established an approach from the data selection, the socio-ecological system as the theoretical framework, and an effective learning approach of both deep neural networks and expert-augmented learning, to quantify the relationship between internal built environment features' importance to the general health of residents of the studied buildings in District A and these same buildings' cumulative COVID-19 case counts but also generalized and validated the relationship to another sample of buildings in District B to predict the COVID-19 case counts these District B buildings had accumulated.…”

Approaches to the Simulation of Personal Income Variations During Epidemics

“…Как известно [20], для моделей типа SIR параметры β, γ наиболее чувствительные и определяют характер поведения кривых, являющихся решением модели (1). Критика таких моделей, помимо плохого прогноза на долгосрочный временной период, основывается на предположении однородности населения без учета дифференциации населения по социальным признакам: например, возраст или соблюдение/несоблюдение противоковидных ограничений.…”