A Discrete-Event, Simulated Social Agent-Based Network Transmission (DESSABNeT) model for communicable diseases: Method and validation using SARS-CoV-2 data in three large Australian cities Article Swipe
Nicolas J. C. Stapelberg
,
Nicolas R. Smoll
,
Marcus Randall
,
Dinesh Palipana
,
Bryan Bui
,
Kristine Macartney
,
Gulam Khandaker
,
André Wattiaux
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0251737
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0251737
Importance During pandemics Agent Based Models (ABMs) can model complex, fine-grained behavioural interactions occurring in social networks, that contribute to disease transmission by novel viruses such as SARS-CoV-2. Objective We present a new agent-based model (ABM) called the Discrete-Event, Simulated Social Agent based Network Transmission model (DESSABNeT) and demonstrate its ability to model the spread of COVID-19 in large cities like Sydney, Melbourne and Gold Coast. Our aim was to validate the model with its disease dynamics and underlying social network. Design DESSABNeT relies on disease transmission within simulated social networks. It employs an epidemiological SEIRD+M (Susceptible, exposed, infected, recovered, died and managed) structure. One hundred simulations were run for each city, with simulated social restrictions closely modelling real restrictions imposed in each location. Main outcome(s) and measure(s) The mean predicted daily incidence of COVID-19 cases were compared to real case incidence data for each city. R eff and health service utilisation outputs were compared to the literature, or for the Gold Coast with daily incidence of hospitalisation. Results DESSABNeT modelled multiple physical distancing restrictions and predicted epidemiological outcomes of Sydney, Melbourne and the Gold Coast, validating this model for future simulation work. Conclusions and relevance DESSABNeT is a valid platform to model the spread of COVID-19 in large cities in Australia and potentially internationally. The platform is suitable to model different combinations of social restrictions, or to model contact tracing, predict, and plan for, the impact on hospital and ICU admissions, and deaths; and also the rollout of COVID-19 vaccines and optimal social restrictions during vaccination.
Related Topics
Concepts
Communicable disease
Contact tracing
Social distance
Transmission (telecommunications)
Epidemiology
Incidence (geometry)
Agent-based model
Computer science
Pandemic
Social network (sociolinguistics)
Geography
Disease transmission
Disease
Operations research
Medicine
Simulation
Environmental health
Public health
Coronavirus disease 2019 (COVID-19)
Engineering
Telecommunications
Artificial intelligence
Social media
Virology
Infectious disease (medical specialty)
Mathematics
Pathology
World Wide Web
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0251737
- OA Status
- gold
- Cited By
- 12
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3163739396
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3163739396Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0251737Digital Object Identifier
- Title
-
A Discrete-Event, Simulated Social Agent-Based Network Transmission (DESSABNeT) model for communicable diseases: Method and validation using SARS-CoV-2 data in three large Australian citiesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-05-21Full publication date if available
- Authors
-
Nicolas J. C. Stapelberg, Nicolas R. Smoll, Marcus Randall, Dinesh Palipana, Bryan Bui, Kristine Macartney, Gulam Khandaker, André WattiauxList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0251737Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0251737Direct OA link when available
- Concepts
-
Communicable disease, Contact tracing, Social distance, Transmission (telecommunications), Epidemiology, Incidence (geometry), Agent-based model, Computer science, Pandemic, Social network (sociolinguistics), Geography, Disease transmission, Disease, Operations research, Medicine, Simulation, Environmental health, Public health, Coronavirus disease 2019 (COVID-19), Engineering, Telecommunications, Artificial intelligence, Social media, Virology, Infectious disease (medical specialty), Mathematics, Pathology, World Wide Web, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 5, 2023: 2, 2022: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
25Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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