The integration of classification tree and Sequence Alignment Method for exploring groups of population based on daily time use data

Wongchavalidkul, Natachai; Piantanakulchai, Mongkut

doi:10.1016/j.asoc.2015.04.035

Cited by 3 publications

(3 citation statements)

References 41 publications

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“…The IPUMS is usually available for synthesizing the agent model but for only a small area of interest. The approach described in [32] is further extended to use this data for generating summary files for all agents.…”

Section: Preliminariesmentioning

confidence: 99%

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An Agent-based Rule Driven Knowledge Model for Epidemic Simulation: A Case Study of Lahore City.

Arslan,

Sahar,

Javaid

et al. 2022

VFAST trans. softw. eng.

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In order to mitigate the effects of disease spread, it is necessary to understand the Spatio-temporal dynamics of epidemic spread and response evaluation. For that purpose, an agent-based heterogeneous knowledge model is synthesized using a probabilistic approach that depicts population interactions. The approach can generate a baseline population synthesis for cities such as Lahore, Pakistan where the population is quite dense and agent-based modeling becomes quite complex. An agent-based Spatio-temporal model of airborne disease is analyzed (i.e., influenza) in a metropolitan area. Such diseases result in mass casualties’ events in the populous regions, every year. Using GIS shapefiles, prior knowledge of people activities and IPUMS data, the agent base model is synthesized and an epidemic spread scenario is simulated. The proposed model can be used by healthcare officials for disease spread simulation. The model is helpful when heterogeneity semantics in population bring complexity in computations. It brings different attributes like age, sex and social status under consideration while calculating the probability of being infected as the way of interaction between people is highly depend upon them. This proposed model takes spatial factor into account as the way an infection spreads depending upon spatial distribution of infected and susceptible ones.

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Section: Preliminariesmentioning

confidence: 99%

“…In this section, the population synthesis for an agent-based model for Lahore is presented. The computation approach is an extension of the methodology described in [32]. Four random variables are selected that are: education (Ed), gender (Ge), age (Ag), and family size (Fm).…”

Section: Baseline Population Synthesis Of An Agent-based Model For La...mentioning

confidence: 99%

An Agent-based Rule Driven Knowledge Model for Epidemic Simulation: A Case Study of Lahore City.

Arslan,

Sahar,

Javaid

et al. 2022

VFAST trans. softw. eng.

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“…It has predominantly been used in the biological sciences to measure the similarity between nucleic acid and amino acid sequences, identify functional conservation, and infer evolutionary relationships between species (Edgar, 2004;Felsenstein, 1988;Notredame et al, 2000;Sankoff and Kruskal, 1983;Thompson et al, 1994). Researchers in the social sciences have adapted this technique to describe similarity of activity performed by individuals, such as the sequence of chores performed in daily routine (Wilson et al, 1999;Wongchavalidkul and Piantanakulchai, 2015), order of locations visited by tourists (Shoval and Isaacson, 2007), and tasks in business processes (Bose and van der Aalst, 2010). SA could be applied in a similar manner to explore patterns of team activity that are associated with performance.…”

Section: Introductionmentioning

confidence: 99%

Modeling variation of clinical team processes with multiple sequence alignment

Bahr

Herzberg

Lambert

et al. 2019

Methodological Innovations

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Our objective was to model process variation of Emergency Medical Service teams responding to simulated pediatric emergencies and determine if sequence alignment distinguishes performance quality. We performed a retrospective process analysis by watching and coding activities in videos from standardized simulations of 42 Emergency Medical Service teams. Teams were classified into high-or low-performing groups based on the Clinical Teamwork Scale™. Activities were coded according to resuscitation tasks, performer, and times. We used ClustalG to align task sequences within and between groups, and measured similarity. Teams within and between performance levels had an average sequence similarity of 52 ± 7% and 50 ± 7%. Teams performed clinically appropriate tasks that varied in prioritization, for example, performing compressions or connecting the EKG monitor early. There was no statistical difference in gross similarity between groups but specific differences in prioritization may have had clinically meaningful implications. Alignment could improve by accounting for task duration and concurrency.

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