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

Abstract

COVID-19 created an unprecedented global public health crisis during 2020–2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted.

Maintaining face mask use before and after achieving different COVID-19 vaccination coverage levels: a modelling study

“…[43][44][45] Combining several layers of interventions can not only cover up these gaps but also further enhance each layer. [46][47][48] Our study shows that face mask use can be cost-effective and, in many cases, cost saving, meaning that face mask use would pay for itself. This finding provides strong support for governments, third-party payers, and other organisations to provide face masks to the general public.…”

Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches

“…In addition, the ML models “learned” a steeper increasing trend for % minority below 20%; this corresponds to a situation when the exponential assumption is violated. The ML models also “learned” that COVID-19 mortality rates increase with population density at the lower range but become “saturated” at higher densities [ 34 ], a fact that cannot be captured by geostatistical models with “naive” structures.

Fig.

…”

“…An association metric was also constructed to approximate effects captured by the ML models and showed that ML (even for small datasets) played a complementary role to advanced geostatistical models: those models can capture similar associations when an underlying exponential relation holds, but ML can further “learn” non-exponential patterns in the data, an attribute that is important for knowledge discovery. For instance, RF and XGBOOST detected nonlinear saturation effects of increasing population density that can be explained by the combined effects of “density-dependent” and “frequency-dependent” mechanisms [ 34 , 40 ].…”

“…New Jersey is one of the most ethnically, socioeconomically and environmentally diverse States; while it has on average the highest population density in the nation, its 565 municipalities include urban centers, suburban sprawl, shore towns, an aging industrial infrastructure, as well as agricultural land and forested areas. So, New Jersey constitutes a remarkably heterogeneous environmental landscape that has often been considered a “microcosm” representative of conditions occurring across the entire contiguous US [ 34 ].…”

Hybrid Prediction Model Based on Decomposed and Synthesized COVID-19 Cumulative Confirmed Data

“…It is challenging to accurately predict the drastic fluctuations in the actual numbers of infections and deaths due to changing external factors. In [6][7][8][9], the traditional Susceptible, Exposed, Infected, Recovered (SEIR) model was combined with some external factors such as quarantine, vaccination and other strategies for the prediction of the COVID-19 epidemic, and they achieved good results, but the parameters of the model, such as the mutation rate of the virus, the probability of recovery, etc., change dynamically during the evolution of the epidemic, and these models do not take this feature into account. So, there are still some problems.…”