Analysing the Combined Health, Social and Economic Impacts of the Corovanvirus Pandemic Using Agent-Based Social Simulation

Dignum, Frank; Dignum, Virginia; Davidsson, Paul; Ghorbani, Amineh; Hurk, Mijke van der; Jensen, Maarten; Kammler, Christian; Lorig, Fabian; Ludescher, Luis Gustavo; Melchior, Alexander; Mellema, René; Pastrav, Cezara; Vanhée, Loïs; Verhagen, Harko

doi:10.1007/s11023-020-09527-6

Cited by 39 publications

(37 citation statements)

References 14 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…strains, as well as various "change points" in the spreading rate 29,37,38 , studies of genomic surveillance data interpreted as complex networks [39][40][41] , dynamic models of social behaviour in times of health crises [42][43][44] and investigations of global socioeconomic effects of the COVID-19 pandemic 6,45,46 .…”

Section: Discussionmentioning

confidence: 99%

“…Thus, one future direction would be a comparison of the epidemic and intervention thresholds across the ABM and network-based models. Other avenues lead to analysis of precursors and critical thresholds for possible emergence of new strains, as well as various “change points” in the spreading rate 29 , 37 , 38 , studies of genomic surveillance data interpreted as complex networks 39 – 41 , dynamic models of social behaviour in times of health crises 42 – 44 and investigations of global socioeconomic effects of the COVID-19 pandemic 6 , 45 , 46 .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling transmission and control of the COVID-19 pandemic in Australia

et al. 2020

View full text Add to dashboard Cite

There is a continuing debate on relative benefits of various mitigation and suppression strategies aimed to control the spread of COVID-19. Here we report the results of agent-based modelling using a fine-grained computational simulation of the ongoing COVID-19 pandemic in Australia. This model is calibrated to match key characteristics of COVID-19 transmission. An important calibration outcome is the age-dependent fraction of symptomatic cases, with this fraction for children found to be one-fifth of such fraction for adults. We apply the model to compare several intervention strategies, including restrictions on international air travel, case isolation, home quarantine, social distancing with varying levels of compliance, and school closures. School closures are not found to bring decisive benefits unless coupled with high level of social distancing compliance. We report several trade-offs, and an important transition across the levels of social distancing compliance, in the range between 70% and 80% levels, with compliance at the 90% level found to control the disease within 13–14 weeks, when coupled with effective case isolation and international travel restrictions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Modelling transmission and control of the COVID-19 pandemic in Australia

et al. 2020

View full text Add to dashboard Cite

show abstract

“…All models are partial representations of reality and, given that the primary purpose of an epidemiological model is to address the efficacy of health care interventions, they isolate away certain factors and effects of interventions (economic and social) and are more accurate in predicting the spread of the disease. Other models 58,59 tradeoff epidemiological accuracy with accounting for social and economic effects, and may be more relevant for assessing the harms of mitigation measures.…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

Agent-based modeling for SARS-CoV-2 epidemic prediction and intervention assessment. A methodological appraisal

Maziarz

Zach

2020

Preprint

View full text Add to dashboard Cite

Our purpose is to assess epidemiological agent-based models-or ABMs-of the SARS-CoV-2 pandemic methodologically. The rapid spread of the outbreak requires fast-paced decision-making regarding mitigation measures. However, the evidence for the efficacy of non-pharmaceutical interventions such as imposed social distancing and school or workplace closures is scarce: few observational studies use quasi-experimental research designs, and conducting randomized controlled trials seems infeasible. Additionally, evidence from the previous coronavirus outbreaks of SARS and MERS lacks external validity, given the significant differences in contagiousness of those pathogens relative to SARS-CoV-2. To address the pressing policy questions that have emerged as a result of COVID-19, epidemiologists have produced numerous models that range from simple compartmental models to highly advanced agent-based models. These models have been criticized for involving simplifications and lacking empirical support for their assumptions. In order to address these voices and methodologically appraise epidemiological ABMs, we consider AceMod (the model of the COVID-19 epidemic in Australia) as an example of the modeling practice. Our case study shows that, although epidemiological ABMs involve simplifications of various sorts, the key characteristics of social interactions and the spread of SARS-CoV-2 are represented sufficiently accurately. This is the case because these modelers treat empirical results as inputs for constructing modeling assumptions and rules that the agents follow; and they use calibration to assert the adequacy to benchmark variables. Given this, we claim that the best epidemiological ABMs are models of actual mechanisms and deliver both mechanistic and difference-making evidence. Furthermore, the efficacy claims are not only internally valid but also adequately describe the effects of interventions in the targets of the models. We also discuss the limitations of ABMs and put forward policy recommendations.

show abstract

“…ABMs are touted in epidemiology for their ability to overcome limitations of traditional SIR models and their variations, which treat individuals as homogeneous, interactions as equal and global, and the spatial distribution of individuals as uniform [4,14]. As a result, ABMs have been developed to simulate seasonal influenza [26,27], pandemics including H1N1 [8,15,29], Ebola [30,36], and COVID-19 [6,12,16,37], and smaller outbreaks of small-pox [7], anthrax [9], the pneumonic plague [43], and dengue [19]. Agentbased simulations aim to forecast disease spread dynamics, estimate social and economic impacts, develop policy intervention strategies, and better understand the relationship between local processes and disease emergence.…”

Section: Agent-based Modeling For Disease Spreadmentioning

confidence: 99%

Data-driven mobility models for COVID-19 simulation

Pesavento

Chen

Yu³

et al. 2020

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities

View full text Add to dashboard Cite

Agent-based models (ABM) play a prominent role in guiding critical decision-making and supporting the development of effective policies for better urban resilience and response to the COVID-19 pandemic. However, many ABMs lack realistic representations of human mobility, a key process that leads to physical interaction and subsequent spread of disease. Therefore, we propose the application of Latent Dirichlet Allocation (LDA), a topic modeling technique, to foot-traffic data to develop a realistic model of human mobility in an ABM that simulates the spread of COVID-19. In our novel approach, LDA treats POIs as "words" and agent home census block groups (CBGs) as "documents" to extract "topics" of POIs that frequently appear together in CBG visits. These topics allow us to simulate agent mobility based on the LDA topic distribution of their home CBG. We compare the LDA based mobility model with competitor approaches including a naive mobility model that assumes visits to POIs are random. We find that the naive mobility model is unable to facilitate the spread of COVID-19 at all. Using the LDA informed mobility model, we simulate the spread of COVID-19 and test the effect of changes to the number of topics, various parameters, and public health interventions. By examining the simulated number of cases over time, we find that the number of topics does indeed impact disease spread dynamics, but only in terms of the outbreak's timing. Further analysis of simulation results is needed to better understand the impact of topics on simulated COVID-19 spread. This study contributes to strengthening human mobility representations in ABMs of disease spread.

show abstract

Analysing the Combined Health, Social and Economic Impacts of the Corovanvirus Pandemic Using Agent-Based Social Simulation

Cited by 39 publications

References 14 publications

Modelling transmission and control of the COVID-19 pandemic in Australia

Modelling transmission and control of the COVID-19 pandemic in Australia

Agent-based modeling for SARS-CoV-2 epidemic prediction and intervention assessment. A methodological appraisal

Data-driven mobility models for COVID-19 simulation

Contact Info

Product

Resources

About