Impact of changes in protective behaviors and out-of-household activities by age on COVID-19 transmission and hospitalization in Chicago, Illinois.

PURPOSE: Even with an efficacious vaccine, protective behaviors (social distancing, masking) are essential for preventing COVID-19 transmission and could become even more important if current or future variants evade immunity from vaccines or prior infection. METHODS: We created an agent-based model representing the Chicago population and conducted experiments to determine the effects of varying adult out-of-household activities (OOHA), school reopening, and protective behaviors across age groups on COVID-19 transmission and hospitalizations. RESULTS: From September-November 2020, decreasing adult protective behaviors and increasing adult OOHA both substantially impacted COVID-19 outcomes; school reopening had relatively little impact when adult protective behaviors and OOHA were maintained. As of November 1, 2020, a 50% reduction in young adult (age 18-40) protective behaviors resulted in increased latent infection prevalence per 100,000 from 15.93 (IQR 6.18, 36.23) to 40.06 (IQR 14.65, 85.21) and 19.87 (IQR 6.83, 46.83) to 47.74 (IQR 18.89, 118.77) with 15% and 45% school reopening. Increasing adult (age ≥18) OOHA from 65% to 80% of prepandemic levels resulted in increased latent infection prevalence per 100,000 from 35.18 (IQR 13.59, 75.00) to 69.84 (IQR 33.27, 145.89) and 38.17 (IQR 15.84, 91.16) to 80.02 (IQR 30.91, 186.63) with 15% and 45% school reopening. Similar patterns were observed for hospitalizations. CONCLUSIONS: In areas without widespread vaccination coverage, interventions to maintain adherence to protective behaviors, particularly among younger adults and in out-of-household settings, remain a priority for preventing COVID-19 transmission.

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models.

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model's rule set. The model's internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

Structure and properties of binder gels formed in the system Mg(OH)2-SiO2-H2O for immobilisation of Magnox sludge.

A cementitious system for the immobilisation of magnesium rich Magnox sludge was produced by blending an Mg(OH)2 slurry with silica fume and an inorganic phosphate dispersant. The Mg(OH)2 was fully consumed after 28 days of curing, producing a disordered magnesium silicate hydrate (M-S-H) with cementitious properties. The structural characterisation of this M-S-H phase by (29)Si and (25)Mg MAS NMR showed clearly that it has strong nanostructural similarities to a disordered form of lizardite, and does not take on the talc-like structure as has been proposed in the past for M-S-H gels. The addition of sodium hexametaphosphate (NaPO3)6 as a dispersant enabled the material to be produced at a much lower water/solids ratio, while still maintaining the fluidity which is essential in practical applications, and producing a solid monolith. Significant retardation of M-S-H formation was observed with larger additions of phosphate, however the use of 1 wt% (NaPO3)6 was beneficial in increasing fluidity without a deleterious effect on M-S-H formation. This work has demonstrated the feasibility of using M-S-H as binder to structurally immobilise Magnox sludge, enabling the conversion of a waste into a cementitious binder with potentially very high waste loadings, and providing the first detailed nanostructural description of the material thus formed.