Evidence map›Paper›PMID 38774820›Full record

ArticleJournal of parallel and distributed computing2024

Novel multi-cluster workflow system to support real-time HPC-enabled epidemic science: Investigating the impact of vaccine acceptance on COVID-19 spread.

Parantapa Bhattacharya, Dustin Machi, Jiangzhuo Chen, Stefan Hoops, Bryan Lewis, Henning Mortveit, Srinivasan Venkatramanan, Mandy L Wilson, Achla Marathe, Przemyslaw Porebski and 8 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of parallel and distributed computing, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Parantapa BhattacharyaBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Dustin MachiBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Jiangzhuo ChenBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Stefan HoopsBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Bryan LewisBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Henning MortveitBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Srinivasan VenkatramananBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Mandy L WilsonBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Achla MaratheBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Przemyslaw PorebskiBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Brian KlahnBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Joseph OuttenBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Anil VullikantiBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Dawen XieBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Abhijin AdigaBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Shawn BrownHewlett Packard, San Francisco, USA.
Christopher BarrettBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.
Madhav MaratheBiocomplexity Institute, University of Virginia, Charlottesville, VA, USA.

Funding

Systems Analysis of Social Pathways of Epidemics to Reduce Health DisparitiesR01GM109718 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI MARATHE, ACHLA, VULLIKANTI, ANIL · 2014 to 2022
$3.6M
ACL HHS U01CK000589NCEZID CDC HHS U01 CK000589NIGMS NIH HHS R01 GM109718
6 · The paper itself

Abstract

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 10K 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 39.4K to 28.2K 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 28.2K to 29.9K (a 6% improvement) nationwide.

Identifiers

PMID38774820
PMCPMC11105799

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.