Analysis of COVID-19 Spread in Tokyo through an Agent-Based Model with Data Assimilation

Sun, Chang; Richard, Serge; Miyoshi, Takemasa; Tsuzu, Naohiro

doi:10.3390/jcm11092401

Cited by 8 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatial and temporal scales of these models varied considerably. These models studied the infection spread over a variety of geographical locations ranging from a large building (school or hospital) to a country and the temporal scales of the modelling outcomes range from a couple of weeks to several months [34][35][36][37][38][39][40][41][42][43][44][45][46]. Agent-based modelling tools such as Covasim [29] and PanSim [33] have been used to model infection spread in cities and countries over a period of a year.…”

Section: Discussionmentioning

confidence: 99%

“…A number of epidemiological models of COVID-19 infection have been developed using agent-based modelling approach to investigate various non-pharmaceutical intervention strategies such as social distancing, usage of masks, contact tracing and subsequently these models also explored the spatial aspects of infection spread [26][27][28][29][30][31][32][33]. Particularly, it is used to investigate various levels of interaction details of the infection propagation in healthcare facilities [34,35], educational institutions [36][37][38][39], cities [40][41][42], states and countries [43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK

Rahaman

Barik

2023

R. Soc. open sci.

View full text Add to dashboard Cite

Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK

Rahaman

Barik

2023

R. Soc. open sci.

View full text Add to dashboard Cite

show abstract

“…They found that to minimize COVID-19-related hospitalizations and deaths, elderly and vulnerable persons should be prioritized for vaccination until further vaccines are available. Sun et al [49] introduced an agent-based model together with a particle filter approach as a method for studying the evolution of COVID-19. With this model, they introduced a novel method for evaluating the effective reproduction number.…”

Section: Abms In Epidemic Modelingmentioning

confidence: 99%

Adaptive Learning in Agent-Based Models: An Approach for Analyzing Human Behavior in Pandemic Crowding

Romero,

Escudero

2023

ASI

View full text Add to dashboard Cite

This study assesses the impact of incorporating an adaptive learning mechanism into an agent-based model simulating behavior on a university campus during a pandemic outbreak, with the particular case of the COVID-19 pandemic. Our model not only captures individual behavior, but also serves as a powerful tool for assessing the efficacy of geolocalized policies in addressing campus overcrowding and infections. The main objective is to demonstrate RL’s effectiveness in representing agent behavior and optimizing control policies through adaptive decision-making in response to evolving pandemic dynamics. By implementing RL, we identify different temporal patterns of overcrowding violations, shedding light on the complexity of human behavior within semi-enclosed environments. While we successfully reduce campus overcrowding, the study recognizes its limited impact on altering the pandemic’s course, underlining the importance of comprehensive epidemic control strategies. Our research contributes to the understanding of adaptive learning in complex systems and provides insights for shaping future public health policies in similar community settings. It emphasizes the significance of considering individual decision-making influenced by adaptive learning, implementing targeted interventions, and the role of geospatial elements in pandemic control. Future research directions include exploring various parameter settings and updating representations of the disease’s natural history to enhance the applicability of these findings. This study offers valuable insights into managing pandemics in community settings and highlights the need for multifaceted control strategies.

show abstract

“…This formula indicates that as state dimensionality grows, particle requirements increase exponentially. Moreover, particle filters have seen applications in traffic estimation [66] and epidemiology [67,68], among others. In epidemiology, [69] initiated the application of particle filters, while [70] introduced the Smart Beam Particle Filter (SBPF) for epidemic forecasting.…”

Section: Particle Filtermentioning

confidence: 99%

Data Assimilation for Agent-Based Models

Ghorbani,

Nazari-Heris

et al. 2023

Mathematics

View full text Add to dashboard Cite

This article presents a comprehensive review of the existing literature on the topic of data assimilation for agent-based models, with a specific emphasis on pedestrians and passengers within the context of transportation systems. This work highlights a plethora of advanced techniques that may have not been previously employed for online pedestrian simulation, and may therefore offer significant value to readers in this domain. Notably, these methods often necessitate a sophisticated understanding of mathematical principles such as linear algebra, probability theory, singular value decomposition, optimization, machine learning, and compressed sensing. Despite this complexity, this article strives to provide a nuanced explanation of these mathematical underpinnings. It is important to acknowledge that the subject matter under study is still in its nascent stages, and as such, it is highly probable that new techniques will emerge in the coming years. One potential avenue for future exploration involves the integration of machine learning with Agent-based Data Assimilation (ABDA, i.e., data assimilation methods used for agent-based models) methods.

show abstract

Analysis of COVID-19 Spread in Tokyo through an Agent-Based Model with Data Assimilation

Cited by 8 publications

References 44 publications

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK

Adaptive Learning in Agent-Based Models: An Approach for Analyzing Human Behavior in Pandemic Crowding

Data Assimilation for Agent-Based Models

Contact Info

Product

Resources

About