We present MacKenzie, a HPC-driven multi-cluster workflow system that was used repeatedly to configure and execute fine-grained US national-scale epidemic simulation models during the COVID-19 ...pandemic. Mackenzie supported federal and Virginia policymakers, in real-time, for a large number of “what-if” scenarios during the COVID-19 pandemic, and continues to be used to answer related questions as COVID-19 transitions to the endemic stage of the disease. MacKenzie is a novel HPC meta-scheduler that can execute US-scale simulation models and associated workflows that typically present significant big data challenges. The meta-scheduler optimizes the total execution time of simulations in the workflow, and helps improve overall human productivity.
As an exemplar of the kind of studies that can be conducted using Mackenzie, we present a modeling study to understand the impact of vaccine-acceptance in controlling the spread of COVID-19 in the US. We use a 288 million node synthetic social contact network (digital twin) spanning all 50 US states plus Washington DC, comprised of 3300 counties, with 12 billion daily interactions. The highly-resolved agent-based model used for the epidemic simulations uses realistic information about disease progression, vaccine uptake, production schedules, acceptance trends, prevalence, and social distancing guidelines. Computational experiments show that, for the simulation workload discussed above, MacKenzie is able to scale up well to 10 K CPU cores.
Our modeling results show that, when compared to faster and accelerating vaccinations, slower vaccination rates due to vaccine hesitancy cause averted infections to drop from 6.7M to 4.5M, and averted total deaths to drop from 39.4 K to 28.2 K across the US. This occurs despite the fact that the final vaccine coverage is the same in both scenarios. We also find that if vaccine acceptance could be increased by 10% in all states, averted infections could be increased from 4.5M to 4.7M (a 4.4% improvement) and total averted deaths could be increased from 28.2 K to 29.9 K (a 6% improvement) nationwide.
•Presents MacKenzie, a novel multi-cluster HPC job scheduler.•A novel workflow that reduces execution time and improves productivity.•Detailed, fine-grained, data driven epidemic models.•Detailed analysis of the COVID-19 vaccine allocation problem.•Real world workflow; has been used repeatedly brief VA and Federal policymakers.
Neighborhood effects have an important role in evacuation decision-making by a family. Owing to peer influence, neighbors evacuating can motivate a family to evacuate. Paradoxically, if a lot of ...neighbors evacuate, then the likelihood of an individual or family deciding to evacuate decreases, for fear of crime and looting. Such behavior cannot be captured using standard models of contagion spread on networks, e.g., threshold, independent cascade, and linear threshold models. Here, we propose a new threshold-based graph dynamical system model,
2mode-threshold
, which captures this dichotomy. We study theoretically the dynamical properties of
2mode-threshold
in different networks, and find significant differences from a standard threshold model. We build and characterize small world networks of Virginia Beach, VA, where nodes are geolocated families (households) in the city and edges are interactions between pairs of families. We demonstrate the utility of our behavioral model through agent-based simulations on these small world networks. We use it to understand evacuation rates in this region, and to evaluate the effects of modeling parameters on evacuation decision dynamics. Specifically, we quantify the effects of (1) network generation parameters, (2) stochasticity in the social network generation process, (3) model types (
2mode-threshold
vs. standard threshold models), (4)
2mode-threshold
model parameters, (5) and initial conditions, on computed evacuation rates and their variability. An illustrative example result shows that the absence of looting effect can overpredict evacuation rates by as much as 50%.
ObjectivesThis research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its ...residents.MethodsWe use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza.ResultsSimulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%–55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums.ConclusionsUnique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.
Residential energy demand dynamics at household level can be studied through demographic, behavioral and physical characteristics of the household. In this paper, we develop an agent-based model ...using a bottom-up approach to build disaggregated energy demand estimates at the household level at an hourly interval. A household level analysis is made possible via the use of synthetic populations for the urban and rural areas of Virginia, USA. The energy consumption estimate is based on householders' demographics, their behaviors and activities, ratings of appliances used in energy-related activities, space conditioning fuels, physical characteristics of the home, and weather conditions. Results from the simulation are then validated with actual demand curves from Rappahannock county in Virginia using dynamic time warping. The simulation results show that the model produces realistic energy demand profiles.
We aim to determine the economic and social impact of typical interventions proposed by the public health officials and preventive behavioral changes adopted by the private citizens in the event of a ..."flu-like" epidemic.
We apply an individual-based simulation model to the New River Valley area of Virginia for addressing this critical problem. The economic costs include not only the loss in productivity due to sickness but also the indirect cost incurred through disease avoidance and caring for dependents.
The results show that the most important factor responsible for preventing income loss is the modification of individual behavior; it drops the total income loss by 62% compared to the base case. The next most important factor is the closure of schools which reduces the total income loss by another 40%.
The preventive behavior of the private citizens is the most important factor in controlling the epidemic.
This research studies the impact of influenza epidemic in the slum and non-slum areas of Delhi, the National Capital Territory of India, by taking proper account of slum demographics and residents' ...activities, using a highly resolved social contact network of the 13.8 million residents of Delhi.
An SEIR model is used to simulate the spread of influenza on two different synthetic social contact networks of Delhi, one where slums and non-slums are treated the same in terms of their demographics and daily sets of activities and the other, where slum and non-slum regions have different attributes.
Differences between the epidemic outcomes on the two networks are large. Time-to-peak infection is overestimated by several weeks, and the cumulative infection rate and peak infection rate are underestimated by 10-50%, when slum attributes are ignored.
Slum populations have a significant effect on influenza transmission in urban areas. Improper specification of slums in large urban regions results in underestimation of infections in the entire population and hence will lead to misguided interventions by policy planners.
According to the U.S. Energy Information Administration (EIA), the residential sector accounts for one-third of the country’s energy consumption. This number is steadily increasing, posing a ...challenge to energy regulators as well as suppliers.Tomanage the growing demand for energy, there is a need for energy system optimization, especially on the demand side. This paper uses a first principles approach to build a high-resolution energy demand model, which can be used as a test bed by academicians as well as policy makers for performing such optimizations. This framework generates activity-based, building-level, time-dependent demand profiles. The model associates appliance usage with each household activity and calculates energy consumption based on the appliance energy rating, the duration of the energy consuming activity, and the type of activity performed by each household member. It also accounts for shared activities among household members to avoid double counting. Additionally, passive energy consumptions such as space heating/cooling, lighting, etc., are measured. Lastly, validation of the results obtained by this model against real-world data for Virginia is carried out. The results indicate that the modeling framework is robust and can be extended to other parts of the U.S. and beyond.
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