The impact of long-term non-pharmaceutical interventions on COVID-19 epidemic dynamics and control: the value and limitations of early models

Abstract

Mathematical models of epidemics are important tools for predicting epidemic dynamics and evaluating interventions. Yet, because early models are built on limited information, it is unclear how long they will accurately capture epidemic dynamics. Using a stochastic SEIR model of COVID-19 fitted to reported deaths, we estimated transmission parameters at different time points during the first wave of the epidemic (March–June, 2020) in Santa Clara County, California. Although our estimated basic reproduction number ( R 0 ) remained stable from early April to late June (with an overall median of 3.76), our estimated effective reproduction number ( R E ) varied from 0.18 to 1.02 in April before stabilizing at 0.64 on 27 May. Between 22 April and 27 May, our model accurately predicted dynamics through June; however, the model did not predict rising summer cases after shelter-in-place orders were relaxed in June, which, in early July, was reflected in cases but not yet in deaths. While models are critical for informing intervention policy early in an epidemic, their performance will be limited as epidemic dynamics evolve. This paper is one of the first to evaluate the accuracy of an early epidemiological compartment model over time to understand the value and limitations of models during unfolding epidemics.

Computational models predicting the early development of the COVID-19 pandemic in Sweden: systematic review, data synthesis, and secondary validation of accuracy

“…the discovery that a large fraction of cases are asymptomatic and their contribution to disease transmission 3 , and a better understanding of transmission routes 4 , the early models developed during the beginning of the pandemic become obsolete. Despite their drawbacks compared to later models, it is of importance to study these early efforts in order to learn about their validity and prediction accuracy 5 , since this knowledge will improve our preparedness for coming pandemics.…”

Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey

“…Many of these methods have been applied to studies of COVID-19; CDC [43] has been reporting predictions of COVID-19 spread and outcomes from an ensemble of over 30 publicly available models, which can be broadly grouped into three main categories, i.e., compartmental models, individual and network-based models, and statistical and machine learning models. Classic compartmental models, such as the SEIR (Susceptible, Exposed, Infectious, Recovered Individuals) model, represent a standard and widely used method in infectious disease epidemiology [44,45]. A SEIR model employs systems of either deterministic or stochastic ODEs to describe the dynamics of an epidemic.…”

“…A stochastic compartmental SEIR (Susceptible, Exposed, Infectious, Recovered Individuals) modeling system with time-varying probabilistic transmission parameters [45] was implemented in a Sequential Quasi-Monte Carlo framework to simulate local COVID-19 spread dynamics at the county level across New Jersey. The present model divides the residents of each county into ten compartments (Figure 1).…”

“…In order to consider the uncertainties associated with different model parameters, we generated 2000 parameter sets (low discrepancy Sobol sequences [45]) by "randomly" drawing samples from the specified parameter ranges. We constructed the model based on each Sobol sequence and calibrated the corresponding 2000 SEIR sub-models for each of the 21 counties of the State of New Jersey.…”

Mathematical modeling of the impact of Omicron variant on the COVID-19 situation in South Korea

“…Among the researchers, statisticians, epidemiologists, and mathematicians contributed to formulating models to capture the transmission dynamics of COVID-19 and forecasting the evolution of the pandemic among different populations amidst government interventions. These mainly included statistical models [ 9 - 18 ], deep-learning models [ 19 - 24 ] and mathematical models [ 1 , 14 , 25 , 26 ].…”