Evidence map›Paper›PMID 38603243›Full record

ArticleThe international journal of high performance computing applications2022

Digital transformation of droplet/aerosol infection risk assessment realized on "Fugaku" for the fight against COVID-19.

Kazuto Ando, Rahul Bale, ChungGang Li, Satoshi Matsuoka, Keiji Onishi, Makoto Tsubokura

Abstract read
In one paragraph

Article in The international journal of high performance computing applications, 2022. 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
  2. Article
  3. Article
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

6 authors.

Kazuto AndoRIKEN Center for Computational Science, Kobe, Japan.ORCID https://orcid.org/0000-0003-2123-1177
Rahul BaleRIKEN Center for Computational Science, Kobe, Japan.ORCID https://orcid.org/0000-0003-4586-2782
ChungGang LiRIKEN Center for Computational Science, Kobe, Japan.
Satoshi MatsuokaRIKEN Center for Computational Science, Kobe, Japan.
Keiji OnishiRIKEN Center for Computational Science, Kobe, Japan.
Makoto TsubokuraRIKEN Center for Computational Science, Kobe, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The fastest supercomputer in 2020, Fugaku, has not only achieved digital transformation of epidemiology in allowing end-to-end, detailed quantitative modeling of COVID-19 transmissions for the first time but also transformed the behavior of the entire Japanese public through its detailed analysis of transmission risks in multitudes of societal situations entailing heavy risks. A novel aerosol simulation methodology was synthesized out of a combination of a new CFD methods meeting industrial demands in the solver, CUBE (Jansson et al., 2019), which not only allowed the simulations to scale massively with high resolution required for micrometer virus-containing aerosol particles but also enabled extremely rapid time-to-solution due to its ability to generate the digital twins representing multitudes of societal situations in a matter of minutes, attaining true overall application high performance; such simulations have been running for the past 1.5°years on Fugaku, cumulatively consuming top supercomputer-class resources and the communicated by the media as well as becoming the basis for official public policies.

Indexed as

building cube methodcomputational fluid dynamicsCOVID-19dirty CADdroplet/aerosol transmissionimmersed boundary methodsocietal behavioral change

Identifiers

PMID38603243
PMCPMC9548498

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.