An Agent-Based Simulation of the Spread of Dengue Fever

Mahmood, Imran; Jahan, M. Sarwar; Groen, Derek; Javed, Aneela

doi:10.1007/978-3-030-50420-5_8

Cited by 4 publications

(16 citation statements)

References 17 publications

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“…Triangular (9,14) days [27] The probability of recovery for symptomatic individuals relying on their own immune system for recovery. 0.962 [47], [48] ′…”

Section: (37)mentioning

confidence: 99%

“…The ABM using the discrete event simulation method is proposed due to its industry recognition [13][14][15][16][17][18][19][20], [38][39] .…”

Section: Model Assumption and Explanationmentioning

confidence: 99%

“…Merler [13] modeled the movements of individuals using the ABM, i.e., seeking assistance in health care facilities, the movements of individuals taking care of patients infected with Ebola virus who were not admitted to hospital, and the attendance of funerals. Mahmood [14] extended the traditional SEIR model and proposed an ABM to analyze interactions between a host and a vector such that the spread of a disease in a given area over time can be forecasted. Hunter [15] used open data and created a data-driven ABM to simulate the spread of an airborne infectious disease in 33 different Irish towns to study the correlations between the simulation results and the town characteristics (i.e., the population, area and age structures).…”

Section: Introductionmentioning

confidence: 99%

“…Notably, however, their models all run in supercomputing centers. Actually, "agent-based models are computationally intensive" is industry-recognized [13][14][15][16][17][18][19][20], [38][39] , which is the primary reason for the work presented in this paper. In terms of the nature of the model, an ABM is a pseudodistributed system [27] , i.e., it is a collection of objects that are concurrently active and communicate with each other (despite the concurrency being simulated by the engine).…”

Section: Introductionmentioning

confidence: 99%

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An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions

et al. 2021

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The traditional agent-based model requires high computing power of the central processing unit. Thus, an improved agent-based model combined with the discrete event simulation method is proposed. The result of the equation-based Susceptible-Exposed-Infective-Asymptomatic-Recovered (SEIAR) model with the same parameter combination, which has been demonstrated to be effective, is used to verify the validity of this improved agent-based model. Additionally, an analysis based on simulation results of the Contact Tracing Measure (CTM), Location-Based Checking-Testing Measure (LCTM), Lockdown Measure (LM), Mobile Cabin Isolation and Hospital Measure (MCHM) is presented. The simulation results show that implementing long-term lockdown measures has the best effect on epidemic control. Moreover, according to the simulation results, we inferred that using only nonpharmaceutical epidemic prevention measures may result in a second outbreak of COVID-19 owing to the risk of asymptomatic transmission.

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“…Triangular (9,14) days [27] The probability of recovery for symptomatic individuals relying on their own immune system for recovery. 0.962 [47], [48] ′…”

Section: (37)mentioning

confidence: 99%

“…The ABM using the discrete event simulation method is proposed due to its industry recognition [13][14][15][16][17][18][19][20], [38][39] .…”

Section: Model Assumption and Explanationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions

et al. 2021

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“…ABS is increasingly used to model emerging diseases, such as COVID-19, where the key disease parameters and assumptions are provisional or unknown (Mahmood, Jahan et al, 2020;Miksch et al, 2014;Moghadas et al, 2017;Tuomisto et al, 2020;Venkatramanan et al, 2018). ABS supports the simulation many agents and their interactions, allowing researchers to recreate and predict the emergence of complex phenomena from individual behaviours (Railsback & Grimm, 2019).…”

Section: Agent-based Disease Modelling Approachesmentioning

confidence: 99%

FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions

Mahmood

Suleimenova

Sassoon

et al. 2020

Journal of Simulation

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The recent Covid-19 outbreak has had a tremendous impact on the world, and many countries are struggling to help incoming patients and at the same time, rapidly enact new public health measures such as lock downs. Many of these decisions are guided by the outcomes of so-called Susceptible-Exposed-Infectious-Recovered (SEIR) models that operate on a national level. Here we introduce the Flu And Coronavirus Simulator (FACS), a simulation tool that models the viral spread at the sub-national level, incorporating geospatial data sources to extract buildings and residential areas in a region. Using FACS, we can model Covid-19 spread at the local level, and provide estimates of the spread of infections and hospital arrivals for different scenarios. We validate the simulation results with the ICU admissions obtained from the local hospitals in the UK. Such validated models can be used to support local decision-making for an effective health care capability response to the epidemic.

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Effects of rodents' behaviours on leptospirosis spread: an individual-based modeling approach

Chong¹,

Tiong²,

Labadin³

et al. 2022

Math. Model. Comput.

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Leptospirosis is a zoonotic disease common in tropical and subtropical regions. This infectious disease is endemic in Sarawak, Malaysia. In recent years, the threat of leptospirosis is on an increasing trend in Sarawak since 2010. The traditional compartmental models assume that the population is homogeneous and shares the same characteristics and behaviours. However, each individual in the population has different and unique behaviour in the real world. Thus, this paper aims to model the leptospirosis spread by adopting the individual-based modeling approach to address the heterogeneity that affects the transmission of the disease. Rodents' behaviours such as active period duration and movement range are incorporated into the model. From the sensitivity analyses, the results show that the movement range of the rodents has a significant impact on the spread of the disease compared to the active period duration. The comparison between simulation results and the actual prevalence data in Sarawak is performed to validate the model. Through regression analysis, the correlations of determination for three outbreaks in Sarawak for the year 2017 are more than 90%. In addition, the normal probability plots for three outbreaks indicate the points follow the line well and are normally distributed. This shows that the proposed individual-based model can predict leptospirosis transmission.

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An Agent-Based Simulation of the Spread of Dengue Fever

Cited by 4 publications

References 17 publications

An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions

An Improved Agent-Based Model Using Discrete Event Simulation for Nonpharmaceutical Interventions

FACS: A geospatial agent-based simulator for analysing COVID-19 spread and public health measures on local regions

Effects of rodents' behaviours on leptospirosis spread: an individual-based modeling approach

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