Cost effectiveness of preemptive school closures to mitigate pandemic influenza outbreaks of differing severity in the United States.

BACKGROUND: Nonpharmaceutical interventions (NPIs) may be considered as part of national pandemic preparedness as a first line defense against influenza pandemics. Preemptive school closures (PSCs) are an NPI reserved for severe pandemics and are highly effective in slowing influenza spread but have unintended consequences. METHODS: We used results of simulated PSC impacts for a 1957-like pandemic (i.e., an influenza pandemic with a high case fatality rate) to estimate population health impacts and quantify PSC costs at the national level using three geographical scales, four closure durations, and three dismissal decision criteria (i.e., the number of cases detected to trigger closures). At the Chicago regional level, we also used results from simulated 1957-like, 1968-like, and 2009-like pandemics. Our net estimated economic impacts resulted from educational productivity costs plus loss of income associated with providing childcare during closures after netting out productivity gains from averted influenza illness based on the number of cases and deaths for each mitigation strategy. RESULTS: For the 1957-like, national-level model, estimated net PSC costs and averted cases ranged from $7.5 billion (2016 USD) averting 14.5 million cases for two-week, community-level closures to $97 billion averting 47 million cases for 12-week, county-level closures. We found that 2-week school-by-school PSCs had the lowest cost per discounted life-year gained compared to county-wide or school district-wide closures for both the national and Chicago regional-level analyses of all pandemics. The feasibility of spatiotemporally precise triggering is questionable for most locales. Theoretically, this would be an attractive early option to allow more time to assess transmissibility and severity of a novel influenza virus. However, we also found that county-wide PSCs of longer durations (8 to 12 weeks) could avert the most cases (31-47 million) and deaths (105,000-156,000); however, the net cost would be considerably greater ($88-$103 billion net of averted illness costs) for the national-level, 1957-like analysis. CONCLUSIONS: We found that the net costs per death averted ($180,000-$4.2 million) for the national-level, 1957-like scenarios were generally less than the range of values recommended for regulatory impact analyses ($4.6 to 15.0 million). This suggests that the economic benefits of national-level PSC strategies could exceed the costs of these interventions during future pandemics with highly transmissible strains with high case fatality rates. In contrast, the PSC outcomes for regional models of the 1968-like and 2009-like pandemics were less likely to be cost effective; more targeted and shorter duration closures would be recommended for these pandemics.

Predictive scale-bridging simulations through active learning.

Throughout computational science, there is a growing need to utilize the continual improvements in raw computational horsepower to achieve greater physical fidelity through scale-bridging over brute-force increases in the number of mesh elements. For instance, quantitative predictions of transport in nanoporous media, critical to hydrocarbon extraction from tight shale formations, are impossible without accounting for molecular-level interactions. Similarly, inertial confinement fusion simulations rely on numerical diffusion to simulate molecular effects such as non-local transport and mixing without truly accounting for molecular interactions. With these two disparate applications in mind, we develop a novel capability which uses an active learning approach to optimize the use of local fine-scale simulations for informing coarse-scale hydrodynamics. Our approach addresses three challenges: forecasting continuum coarse-scale trajectory to speculatively execute new fine-scale molecular dynamics calculations, dynamically updating coarse-scale from fine-scale calculations, and quantifying uncertainty in neural network models.

Interplay of Mechanochemistry and Material Processes in the Graphite to Diamond Phase Transformation.

The manifestation of intramolecular strains in covalent systems is widely known to accelerate chemical reactions and open alternative reaction paths. This process is moderately well understood for isolated molecules and unimolecular processes. However, in condensed matter processes such as phase transformations, material properties and structure may influence typical mechanochemical effects. Therefore, we utilize steered molecular dynamics to induce out of plane strains in graphite and compress the system under a constant strain rate to induce phase transformation. We show that the out of plane strain allows phase transformations to initiate at small amounts of compressive strain. However, in contrast to typical mechanochemical results, the sum of compressive and out of plane work needed to form a diamond has a local minimum due to altered defect formation processes during phase transformation. Additionally, these altered processes slow the kinetics of the phase transformation, taking longer from initiation to total material transformation.

Multiple models for outbreak decision support in the face of uncertainty.

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Assessing K-12 school reopenings under different COVID-19 Spread scenarios - United States, school year 2020/21: A retrospective modeling study.

INTRODUCTION: School-age children play a key role in the spread of airborne viruses like influenza due to the prolonged and close contacts they have in school settings. As a result, school closures and other non-pharmaceutical interventions were recommended as the first line of defense in response to the novel coronavirus pandemic (COVID-19). METHODS: We used an agent-based model that simulates communities across the United States including daycares, primary, and secondary schools to quantify the relative health outcomes of reopening schools for the period of August 15, 2020 to April 11, 2021. Our simulation was carried out in early September 2020 and was based on the latest (at the time) Centers for Disease Control and Prevention (CDC)'s Pandemic Planning Scenarios released in May 2020. We explored different reopening scenarios including virtual learning, in-person school, and several hybrid options that stratify the student population into cohorts in order to reduce exposure and pathogen spread. RESULTS: Scenarios where cohorts of students return to school in non-overlapping formats, which we refer to as hybrid scenarios, resulted in significant decreases in the percentage of symptomatic individuals with COVID-19, by as much as 75%. These hybrid scenarios have only slightly more negative health impacts of COVID-19 compared to implementing a 100% virtual learning scenario. Hybrid scenarios can significantly avert the number of COVID-19 cases at the national scale-approximately between 28 M and 60 M depending on the scenario-over the simulated eight-month period. We found the results of our simulations to be highly dependent on the number of workplaces assumed to be open for in-person business, as well as the initial level of COVID-19 incidence within the simulated community. CONCLUSION: In an evolving pandemic, while a large proportion of people remain susceptible, reducing the number of students attending school leads to better health outcomes; part-time in-classroom education substantially reduces health risks.

Machine learning of consistent thermodynamic models using automatic differentiation.

We propose a data-driven method to describe consistent equations of state (EOS) for arbitrary systems. Complex EOS are traditionally obtained by fitting suitable analytical expressions to thermophysical data. A key aspect of EOS is that the relationships between state variables are given by derivatives of the system free energy. In this work, we model the free energy with an artificial neural network and utilize automatic differentiation to directly learn the derivatives of the free energy. We demonstrate this approach on two different systems, the analytic van der Waals EOS and published data for the Lennard-Jones fluid, and we show that it is advantageous over direct learning of thermodynamic properties (i.e., not as derivatives of the free energy but as independent properties), in terms of both accuracy and the exact preservation of the Maxwell relations. Furthermore, the method implicitly provides the free energy of a system without explicit integration.

Rational Design of Highly Stable and Active MXene-Based Bifunctional ORR/OER Double-Atom Catalysts.

Designing highly active and bifunctional oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) catalysts has attracted great interest toward metal-air batteries. Herein, an efficient solution to the search for MXene-based bifunctional catalysts is proposed by introducing non-noble metals such as Fe/Co/Ni at the surfaces. These results indicate that the ultrahigh activities in Ni1/Ni2- and Fe1/Ni2-modified MXene-based double-atom catalysts (DACs) for bifunctional ORR/OER are better than those of well-known unifunctional catalysts with low overpotentials, such as Pt(111) for the ORR and IrO2 (110) for the OER. Strain can profoundly regulate the catalytic activities of MXene-based DACs, providing a novel pathway for tunable catalytic behavior in flexible MXenes. An electrochemical model, based on density functional theory and theoretical polarization curves, is proposed to reveal the underlying mechanisms, in agreement with experimental results. Electronic structure analyses indicate that the excellent catalytic activities in the MXene-based DACs are attributed to the electron-capturing capability and synergistic interactions between Fe/Co/Ni adsorbents and MXene substrate. These findings not only reveal promising candidates for MXene-based bifunctional ORR/OER catalysts but also provide new theoretical insights into rationally designing noble-metal-free bifunctional DACs.

COVID-19 reopening strategies at the county level in the face of uncertainty: Multiple Models for Outbreak Decision Support.

Policymakers make decisions about COVID-19 management in the face of considerable uncertainty. We convened multiple modeling teams to evaluate reopening strategies for a mid-sized county in the United States, in a novel process designed to fully express scientific uncertainty while reducing linguistic uncertainty and cognitive biases. For the scenarios considered, the consensus from 17 distinct models was that a second outbreak will occur within 6 months of reopening, unless schools and non-essential workplaces remain closed. Up to half the population could be infected with full workplace reopening; non-essential business closures reduced median cumulative infections by 82%. Intermediate reopening interventions identified no win-win situations; there was a trade-off between public health outcomes and duration of workplace closures. Aggregate results captured twice the uncertainty of individual models, providing a more complete expression of risk for decision-making purposes.

Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media.

Fine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids behave. At these scales, fluids are under confinement, affecting key properties such as density, viscosity, adsorption, etc. Pore-scale Lattice Boltzmann Methods (LBM) can simulate flow in complex pore structures relevant to predicting hydrocarbon production, but must be corrected to account for confinement effects. Molecular dynamics (MD) can model confinement effects but is computationally expensive in comparison. The hurdle to bridging MD with LBM is the computational expense of MD simulations needed to perform this correction. Here, we build a Machine Learning (ML) surrogate model that captures adsorption effects across a wide range of parameter space and bridges the MD and LBM scales using a relatively small number of MD calculations. The model computes upscaled adsorption parameters across varying density, temperature, and pore width. The ML model is 7 orders of magnitude faster than brute force MD. This workflow is agnostic to the physical system and could be generalized to further scale-bridging applications.

Ultrastrong π-Bonded Interface as Ductile Plastic Flow Channel in Nanostructured Diamond.

A combinational effect of nanostructured crystallites and π-bonded interfaces is much attractive in solving the conflict between strength/hardness and toughness to design extrinsically superhard materials with enhanced fracture toughness and/or other properties such as tunable electronic properties. In the present work, taking the experimentally observed π-bonded interfaces in nanostructured diamond as the prototype, we theoretically investigated their stabilities, electronic structures, and mechanical strengths with special consideration of the size effect of nanocrystallites or nanolayers. It is unprecedentedly found that the π-bonded interfaces exhibit tunable electronic semiconducting properties, superior fracture toughness, and anomalously large creep-like plasticity at the cost of minor losses in strength/hardness; such unique combination is uncovered to be attributed to the ductile bridging effect of the sp2 bonds across the π-bonded interface that dominates the localized plastic flow channel. As the length scale of nanocrystallites/nanolayers is lower than a critical value, however, the first failure occurring inside nanocrystallites/nanolayers features softening and embrittling. These findings not only provide a novel insight into the unique strengthening and toughening origin observed in ultrahard nanostructured diamonds consisting of nanotwins, nanocomposites, and nanocrystallites but also highlight a unique pathway by combining the nanostructured crystallites and the strongly bonded interface to design the novel superhard materials with superior toughness.

Rational Design of Flexible Two-Dimensional MXenes with Multiple Functionalities.

In the past decade, two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides (MXenes) have attracted attention and interest from the scientific community due to their superior mechanical strength and flexibility, physical/chemical properties, and multiple exciting functionalities. Among these materials, the ingenious and effective combination of the mechanical and functional properties of MXenes provides a promising opportunity for designing flexible and wearable devices. This review summarizes the recent research progress in the structural stabilities, mechanical strength and deformation mechanism, strain-tunable energy storages, and catalytic and thermoelectric properties along with certain strain modifications and strain-controllable electronic/topological properties of MXenes from a combined theoretical and experimental perspective and illustrates their electronic origins. Taking the design principles as a focus, the theoretical predictions provide guidance, while the experimental work gives a thorough validation, thus setting the foundation for the current scientific achievements, challenges, and prospects in the field of MXenes.

A generalized solid strengthening rule for biocompatible Zn-based alloys, a comparison with Mg-based alloys.

Solid solution strengthening has been widely used in designing various high-performance biocompatible Mg-based alloys, but its transferability to other biocompatible metals such as Zn-based alloys is questionable or nearly absent. In the present study, an ab initio informed Peierls-Nabarro model and Leyson et al.'s strengthening model are used for a systematic investigation on solute strengthening in Zn-based alloys, which is compared with the widely studied Mg-based alloys. Although an inverse relationship was revealed between volume misfit εb and chemical misfit εSFE for both Zn-based and Mg-based alloys, most solutes would however result in positive εb and negative εSFE for Zn-based alloys, differing from Mg-based alloys. With εb and εSFE as two key descriptors, a generalized scaling diagram is finally drawn for a fast evaluation of solid solution strengthening in Zn-based alloys, indicating that the alkaline-earth and rare earth elements are better strengtheners for Zn-based alloys, which provides a general rule in designing novel biocompatible materials.

Designing ultrastrong 5d transition metal diborides with excellent stability for harsh service environments.

Much effort was devoted towards the rational design of ultrastrong transition metal borides (TMBs) with remarkable mechanical properties and excellent stabilities, owing to promising applications in machining, drilling tools and protective coatings for the aerospace industry. Although an enormous number of investigations have been performed on these TMBs under normal conditions, studies on the stability and mechanical strength in harsh high-pressure environments, which are critical for safe service behavior and a realistic understanding of stabilities and strengthening mechanisms, are yet nearly absent. In this work, taking 5d TMB2 (TM = Hf, Ta, W, Re, Os, Ir and Pt) as an illustration, we performed comprehensive high-throughput first-principles screening for thermodynamically stable and metastable structures under various pressures. Four experimentally observed structures are found to be thermodynamically feasible for most 5d TMB2 (TM = Hf, Ta, W, Re, Os and Ir) at 0 and 100 GPa. By exploiting orbital-decomposed electronic structures, we reveal that the pressure-induced stabilization and phase transitions of 5d TMB2 can be rationalized by the splitting of bonding and antibonding states around the Fermi level. Further investigations on the pressure-induced strengthening indicate that 5d TMB2 in the hP6[194] structure exhibit a profound strengthening effect under high pressure, which can be rationalized by the proposed strengthening factor η, but η fails in the oP6[59] structure due to the changed instability modes at different pressures. These findings suggest the necessity to explore the plasticity parameters for a realistic understanding of pressure-induced strengthening in TMBs, providing a strong argument for rules based on bond parameters at equilibrium in designing strong solids.

School dismissal as a pandemic influenza response: When, where and for how long?

We used individual-based computer simulation models at community, regional and national levels to evaluate the likely impact of coordinated pre-emptive school dismissal policies during an influenza pandemic. Such policies involve three key decisions: when, over what geographical scale, and how long to keep schools closed. Our evaluation includes uncertainty and sensitivity analyses, as well as model output uncertainties arising from variability in serial intervals and presumed modifications of social contacts during school dismissal periods. During the period before vaccines become widely available, school dismissals are particularly effective in delaying the epidemic peak, typically by 4-6 days for each additional week of dismissal. Assuming the surveillance is able to correctly and promptly diagnose at least 5-10% of symptomatic individuals within the jurisdiction, dismissals at the city or county level yield the greatest reduction in disease incidence for a given dismissal duration for all but the most severe pandemic scenarios considered here. Broader (multi-county) dismissals should be considered for the most severe and fast-spreading (1918-like) pandemics, in which multi-month closures may be necessary to delay the epidemic peak sufficiently to allow for vaccines to be implemented.

A synergetic stabilization and strengthening strategy for two-dimensional ordered hybrid transition metal carbides.

Two-dimensional (2D) transition metal carbides (MXenes) exhibit excellent thermodynamic stability and remarkable mechanical strength and flexibility, as well as rich functionality, which attract considerable interest due to their potential application for high-performance flexible and stretchable devices. However, premature phonon instability of some non-hybrid MXenes was recently found to intrinsically limit their strength and flexibility, evoking passionate curiosity in pursuing an effective solution for more impressive mechanical properties. In this work, on the basis of an alloying strengthening mechanism, a combinational strategy is proposed to build ordered hybrid M2''M'C2O2 (M'' = Mo, W; M' = Ti, Zr, Hf) with remarkable dynamic stability and superior mechanical properties by hindering the premature phonon instability originating from the outer transition metals. By means of comprehensive screening, symmetrical-Mo2TiC2O2 is interestingly found to possess excellent stability at equilibrium and outstanding tolerance to phonon instability during straining compared to its Ti counterpart, being attributed to the character of the robust Mo-dz2 and O-pz hybridization. Although similar optical phonon soft modes appear in Ti3C2O2 and Mo2TiC2O2 under multiple loadings, the latter is much stiffer during straining. An in-depth analysis of deformed electronic structures reveals that a strain-induced increasing density of states in the vicinity of the Fermi level mainly composed of Mo-dz2 states facilitates the fatal phonon softening in Mo2TiC2O2 under biaxial tension, while differing from the mechanical instability in Ti3C2O2 triggered by a Peierls transition. Our findings provide a novel stabilization and strengthening strategy for 2D materials, and pave a new way for searching for 2D material candidates in designing flexible devices.

Phonon-mediated stabilization and softening of 2D transition metal carbides: case studies of Ti2CO2 and Mo2CO2.

Two-dimensional transition metal carbides (MXenes) exhibit excellent thermodynamic stability, mechanical strength and flexibility, which make them promising candidates in flexible devices and reinforcements in nanocomposites. However, the dynamic stability may intrinsically determine the preferred adsorption sites of functional groups in MXenes and lead to premature failure under finite strain before approaching the elastic limits. It is found interestingly that different adsorption sites of the functional groups correspond to the different phonon stabilities and adsorption energies of MXenes, which can be attributed to different hybridization characteristics between the metal-d and O-pz states and delocalized electron behaviors around the metal atoms. Although both Ti2CO2 and Mo2CO2 possess high ideal strengths and superior flexibility, the premature phonon instabilities appear unexpectedly in distinct manners before approaching their elastic limits. An in-depth exploration of the soft modes and deformed electronic structures reveals that a continuously decreasing gap-opening at the Γ point in Ti2CO2 increases after in-plane phonon instability due to the pseudo Jahn-Teller effect, differing from the out-of-plane phonon instability and semiconductor-metal transition under biaxial tension observed in MoS2. Although Mo2CO2 shows similar failure modes to graphene under uniaxial/biaxial tensions, the band crossings around the Fermi level are found to be responsible for its metallic character and elastic/phonon instabilities by modifying the elastic energy or electronic band energy, different from the gap opening appearing in graphene. Our results shed light onto the profound effect of the phonon instability on the preferable structure and strengths of MXenes, providing theoretical guidance on designing flexible MXene devices, raising a great challenge to the conventional strengthening theory by simply counting bonds.

Designing flexible 2D transition metal carbides with strain-controllable lithium storage.

Efficient flexible energy storage systems have received tremendous attention due to their enormous potential applications in self-powering portable electronic devices, including roll-up displays, electronic paper, and "smart" garments outfitted with piezoelectric patches to harvest energy from body movement. Unfortunately, the further development of these technologies faces great challenges due to a lack of ideal electrode materials with the right electrochemical behavior and mechanical properties. MXenes, which exhibit outstanding mechanical properties, hydrophilic surfaces, and high conductivities, have been identified as promising electrode material candidates. In this work, taking 2D transition metal carbides (TMCs) as representatives, we systematically explored several influencing factors, including transition metal species, layer thickness, functional group, and strain on their mechanical properties (e.g., stiffness, flexibility, and strength) and their electrochemical properties (e.g., ionic mobility, equilibrium voltage, and theoretical capacity). Considering potential charge-transfer polarization, we employed a charged electrode model to simulate ionic mobility and found that ionic mobility has a unique dependence on the surface atomic configuration influenced by bond length, valence electron number, functional groups, and strain. Under multiaxial loadings, electrical conductivity, high ionic mobility, low equilibrium voltage with good stability, excellent flexibility, and high theoretical capacity indicate that the bare 2D TMCs have potential to be ideal flexible anode materials, whereas the surface functionalization degrades the transport mobility and increases the equilibrium voltage due to bonding between the nonmetals and Li. These results provide valuable insights for experimental explorations of flexible anode candidates based on 2D TMCs.

Graph-based linear scaling electronic structure theory.

We show how graph theory can be combined with quantum theory to calculate the electronic structure of large complex systems. The graph formalism is general and applicable to a broad range of electronic structure methods and materials, including challenging systems such as biomolecules. The methodology combines well-controlled accuracy, low computational cost, and natural low-communication parallelism. This combination addresses substantial shortcomings of linear scaling electronic structure theory, in particular with respect to quantum-based molecular dynamics simulations.

New insight into the helium-induced damage in MAX phase Ti3AlC2 by first-principles studies.

In the present work, the behavior of He in the MAX phase Ti3AlC2 material is investigated using first-principle methods. It is found that, according to the predicted formation energies, a single He atom favors residing near the Al plane in Ti3AlC2. The results also show that Al vacancies are better able to trap He atoms than either Ti or C vacancies. The formation energies for the secondary vacancy defects near an Al vacancy or a C vacancy are strongly influenced by He impurity content. According to the present results, the existence of trapped He atoms in primary Al vacancy can promote secondary vacancy formation and the He bubble trapped by Al vacancies has a higher tendency to grow in the Al plane of Ti3AlC2. The diffusion of He in Ti3AlC2 is also investigated. The energy barriers are approximately 2.980 eV and 0.294 eV along the c-axis and in the ab plane, respectively, which means that He atoms exhibit faster migration parallel to the Al plane. Hence, the formation of platelet-like bubbles nucleated from the Al vacancies is favored both energetically and kinetically. Our calculations also show that the conventional spherical bubbles may be originated from He atoms trapped by C vacancies. Taken together, these results are able to explain the observed formation of bubbles in various shapes in recent experiments. This study is expected to provide new insight into the behaviors of MAX phases under irradiation from electronic structure level in order to improve the design of MAX phase based materials.

Mesodynamics with implicit degrees of freedom.

Mesoscale phenomena--involving a level of description between the finest atomistic scale and the macroscopic continuum--can be studied by a variation on the usual atomistic-level molecular dynamics (MD) simulation technique. In mesodynamics, the mass points, rather than being atoms, are mesoscopic in size, for instance, representing the centers of mass of polycrystalline grains or molecules. In order to reproduce many of the overall features of fully atomistic MD, which is inherently more expensive, the equations of motion in mesodynamics must be derivable from an interaction potential that is faithful to the compressive equation of state, as well as to tensile de-cohesion that occurs along the boundaries of the mesoscale units. Moreover, mesodynamics differs from Newton's equations of motion in that dissipation--the exchange of energy between mesoparticles and their internal degrees of freedom (DoFs)--must be described, and so should the transfer of energy between the internal modes of neighboring mesoparticles. We present a formulation where energy transfer between the internal modes of a mesoparticle and its external center-of-mass DoFs occurs in the phase space of mesoparticle coordinates, rather than momenta, resulting in a Galilean invariant formulation that conserves total linear momentum and energy (including the energy internal to the mesoparticles). We show that this approach can be used to describe, in addition to mesoscale problems, conduction electrons in atomic-level simulations of metals, and we demonstrate applications of mesodynamics to shockwave propagation and thermal transport.