Evidence map›Paper›PMID 33786413›Full record

ArticleAdvanced theory and simulations2021

High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town.

Agnieszka Truszkowska, Brandon Behring, Jalil Hasanyan, Lorenzo Zino, Sachit Butail, Emanuele Caroppo, Zhong-Ping Jiang, Alessandro Rizzo, Maurizio Porfiri

Abstract read
In one paragraph

Article in Advanced theory and simulations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
  2. How Will AI Shape the Future of Pandemic Response? Early Clues From Data Analytics.Risk analysis : an official publication of the Society for Risk Analysis · 2025
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Modeling COVID-19 Transmission Dynamics: A Bibliometric Review.International journal of environmental research and public health · 2022
    Review
  15. Article
  16. Article
  17. Agent Simulation Model of COVID-19 Epidemic Agent-Based on GIS: A Case Study of Huangpu District, Shanghai.International journal of environmental research and public health · 2022
    Article
  18. Article
  19. Article
  20. 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

9 authors.

Agnieszka TruszkowskaDepartment of Mechanical and Aerospace Engineering Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA.ORCID https://orcid.org/0000-0002-2516-2298
Brandon BehringDepartment of Mechanical and Aerospace Engineering Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA.ORCID https://orcid.org/0000-0002-0200-0704
Jalil HasanyanDepartment of Mechanical and Aerospace Engineering Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA.ORCID https://orcid.org/0000-0003-3142-2620
Lorenzo ZinoFaculty of Science and Engineering University of Groningen Nijenborgh 4 9747 AG Groningen The Netherlands.ORCID https://orcid.org/0000-0002-0946-6523
Sachit ButailDepartment of Mechanical Engineering Northern Illinois University DeKalb IL 60115 USA.ORCID https://orcid.org/0000-0001-9785-7374
Emanuele CaroppoMental Health Department Local Health Unit ROMA 2 00174 Rome Italy.
Zhong-Ping JiangDepartment of Electrical and Computer Engineering Tandon School of Engineering New York University 370 Jay Street Brooklyn NY 11201 USA.
Alessandro RizzoDepartment of Electronics and Telecommunications Politecnico di Torino 10129 Torino Italy.ORCID https://orcid.org/0000-0002-2386-3146
Maurizio PorfiriDepartment of Mechanical and Aerospace Engineering Tandon School of Engineering New York University Six MetroTech Center Brooklyn NY 11201 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY-one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches-in hospitals or drive-through facilities-and vaccination strategies that could prioritize vulnerable groups. Decision-making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.

Indexed as

agent‐based modelsCOVID‐19epidemiologyvaccination

Identifiers

PMID33786413
PMCPMC7995144

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.