An agent-based transmission model of COVID-19 for re-opening policy design

Rodríguez, Alma; Cuevas, Erik; Zaldívar, Daniel; Morales-Castañeda, Bernardo; Sarkar, Ram; Houssein, Essam H.

doi:10.1016/j.compbiomed.2022.105847

Cited by 14 publications

(18 citation statements)

References 65 publications

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“…Specifically, compared with previous works, infection on a contact network estimated from human GPS data mainly captures the transmission between cities and nations [27][28][29]. Agentbased models using random walks to describe human movements can only study the microscopic actions within a building or a district [18,19,21,22]. The multi-level hierarchical container structures in the Mob-Cov provides a more realistic and accurate description of individuals moving cross multiple length scales while maintaining a reasonable computational cost.…”

Section: Discussionmentioning

confidence: 99%

“…[14]. In addition, some agent-based models consider individual movements in a continuous physical space [18,19,21,22,24]. For example, Cuevas [18] studied how the combination of local random walks and long directed movements of individuals influences COVID-19 transmission.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Dehning et al [6] combined the susceptible-infectedrecovered (SIR) model with Bayesian parameter inference to detect change points of disease spreading rate using COVID-19 case numbers measured in Germany. On the other hand, agentbased models consider the individual differences, microscale policies and detailed actions, which can provide a comprehensive understanding of the transmission dynamics as well as a flexible framework for implementing various intervention rules [12][13][14][15][16][17][18][19][21][22][23][24][25]. The agent-based models have been used to study the spread of COVID-19 in Australia [12], Singapore [13] and France [14] under various interventions [25].…”

Section: Introductionmentioning

confidence: 99%

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A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

Chen

Jiang

et al. 2022

Preprint

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The COVID-19 pandemic has created an urgent need for mathematical models that can project epidemic trends and evaluate the effectiveness of mitigation strategies. To forecast the transmission of COVID-19, a major challenge is the accurate assessment of the multi-scale human mobility and how they impact the infection through close contacts. By combining the stochastic agent-based modeling strategy and hierarchical structures of spatial containers corresponding to the notion of places in geography, this study proposes a novel model, Mob-Cov, to study the impact of human traveling behaviour and individual health conditions on the disease outbreak and the probability of zero COVID in the population. Specifically, individuals perform power-law type of local movements within a container and global transport between different-level containers. Frequent long movements inside a small-level container (e.g. a road or a county) and a small population size reduce the local crowdedness of people and the disease infection and transmission. In contrast, travels between large-level containers (e.g. cities and nations) facilitate global disease spread and outbreak. Moreover, dynamic infection and recovery in the population are able to drive the bifurcation of the system to a "zero-COVID" state or a "live with COVID" state, depending on the mobility patterns, population number and health conditions. Reducing total population and local people accumulation as well as restricting global travels help achieve zero-COVID. In summary, the Mob-Cov model considers more realistic human mobility in a wide range of spatial scales, and has been designed with equal emphasis on performance, low simulation cost, accuracy, ease of use and flexibility. It is a useful tool for researchers and politicians to investigate the pandemic dynamics and plan actions against the disease.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

Chen

Jiang

et al. 2022

Preprint

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“…Lastly, in AbCSim, the transmission probability from interpersonal contact is a parameter linked not only to strictly epidemiological aspects, because

is also multiplied or modulated by the variable v , which evolves throughout the year and is inversely proportional to the daily mean temperature. According to studies that define COVID-19 as a seasonal disease [ [49] , [50] , [51] , [52] ], the

value is affected by the variable v ( Temp ), which reaches its maximum value (the unit) on July 15 and its lowest value (0.5) on January 15, when the daily mean temperature is higher in the southern hemisphere.…”

Section: Methodsmentioning

confidence: 99%

“…One of the simplest but least realistic is the random walk model. There are also models that take advantage of a given regularity in human activities to propose a daily schedule or agenda for each individual [ 10 , 27 , 52 , 59 ]. In this paper, we propose the use of Markov models for the modeling of agent mobility since they have demonstrated a good relationship between complexity and realism, with good results in other applications.…”

Section: Methodsmentioning

confidence: 99%

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models

Pais

Godano

Juarez

et al. 2023

Computers in Biology and Medicine

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Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

Ajagbe

Adigun

2023

Multimed Tools Appl

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Deep learning (DL) is becoming a fast-growing field in the medical domain and it helps in the timely detection of any infectious disease (IDs) and is essential to the management of diseases and the prediction of future occurrences. Many scientists and scholars have implemented DL techniques for the detection and prediction of pandemics, IDs and other healthcare-related purposes, these outcomes are with various limitations and research gaps. For the purpose of achieving an accurate, efficient and less complicated DL-based system for the detection and prediction of pandemics, therefore, this study carried out a systematic literature review (SLR) on the detection and prediction of pandemics using DL techniques. The survey is anchored by four objectives and a state-of-the-art review of forty-five papers out of seven hundred and ninety papers retrieved from different scholarly databases was carried out in this study to analyze and evaluate the trend of DL techniques application areas in the detection and prediction of pandemics. This study used various tables and graphs to analyze the extracted related articles from various online scholarly repositories and the analysis showed that DL techniques have a good tool in pandemic detection and prediction. Scopus and Web of Science repositories are given attention in this current because they contain suitable scientific findings in the subject area. Finally, the state-of-the-art review presents forty-four (44) studies of various DL technique performances. The challenges identified from the literature include the low performance of the model due to computational complexities, improper labeling and the absence of a high-quality dataset among others. This survey suggests possible solutions such as the development of improved DL-based techniques or the reduction of the output layer of DL-based architecture for the detection and prediction of pandemic-prone diseases as future considerations.

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An agent-based transmission model of COVID-19 for re-opening policy design

Cited by 14 publications

References 65 publications

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

A stochastic agent-based model to evaluate COVID-19 transmission influenced by human mobility

City-scale model for COVID-19 epidemiology with mobility and social activities represented by a set of hidden Markov models

Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review

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