Individual-based distributed epidemic spread model

2010 ◽  
Author(s):  
Bisong Hu ◽  
Jianhua Gong
Keyword(s):  
Epidemic Spread ◽  
Spread Model

scholarly journals Modeling quarantine during epidemics and mass-testing using drones

2020 ◽  
Author(s):  
Leonid Sedov ◽  
Alexander Krasnochub ◽  
Valentin Polishchuk
Keyword(s):  
Differential Equations ◽  
Metropolitan Area ◽  
Extended Model ◽  
Epidemic Spread ◽  
Infected People ◽  
Peak Number ◽  
Infected Population ◽  
Sir Epidemic ◽  
Spread Model ◽  
Over Time

We extend the classical SIR epidemic spread model by introducing the “quarantined” compartment. We solve (numerically) the differential equations that govern the extended model and quantify how quarantining “flattens the curve” for the proportion of infected population over time. Furthermore, we explore the potential of using drones to deliver tests, enabling mass-testing for the infection; we give a method to estimate the drone fleet needed to deliver the tests in a metropolitan area. Application of our models to COVID-19 spread in Sweden shows how the proposed methods could substantially decrease the peak number of infected people, almost without increasing the duration of the epidemic.

scholarly journals COVID-19 Outbreak in Oman: Model-Driven Impact Analysis and Challenges

2020 ◽  
Author(s):  
Kashif Zia ◽  
Umar Farooq
Keyword(s):  
Impact Analysis ◽  
Epidemic Spread ◽  
Sultanate Of Oman ◽  
Real Numbers ◽  
Model Driven ◽  
Mobility Data ◽  
Testing Strategies ◽  
Spread Model ◽  
Rapid Spread ◽  
Simulation Results

AbstractMotivated by the rapid spread of COVID-19 all across the globe, we have performed simulations of a system dynamic epidemic spread model in different possible situations. The simulation, not only captures the model dynamic of the spread of the virus, but also, takes care of population and mobility data. The model is calibrated based on epidemic data and events specifically of Sultanate of Oman, which can easily be generalized. The simulation results are quite disturbing, indicating that, during a process of stringent social distancing and testing strategies, a small perturbation can lead to quite undesirable outcomes. The simulation results, although consistent in expected outcomes across changing parameters’ values, also indicate a substantial mismatch with real numbers. An analysis of what can be the reason of this mismatch is also performed. Within these contradictions, for Oman, regarding the eradication of epidemic, the future is not extremely alarming.

Epidemics

2017 ◽  
Author(s):  
Paul Charbonneau
Keyword(s):  
Scale Invariance ◽  
Small World ◽  
Complex Nature ◽  
Small World Network ◽  
Epidemic Spread ◽  
Fire Model ◽  
Random Walkers ◽  
Contagious Diseases ◽  
Spread Model ◽  
Forest Fire Model

This chapter examines the complex nature of the epidemic spread of contagious diseases. It first describes the model of epidemic spread constructed by adding random walks on a lattice to the forest-fire model before discussing the implementation of the epidemic “algorithm” using a minimal Python code. It then considers a representative simulation showing a time series of the number of infected and healthy random walkers, along with the behavior of the epidemic spread model and the dynamic self-organization of epidemic surges around a marginal infection rate of exactly unity. It also explores the scale invariance of a small-world network connecting twelve nodes. The chapter includes exercises and further computational explorations, along with a suggested list of materials for further reading.

scholarly journals Voting Rule Based Cellular Automata Epidemic Spread Model for Leptospirosis

2015 ◽  
Vol 8 (4) ◽  
pp. 337
Author(s):  
Senthil Athithan ◽  
Vidya Prasad Shukla ◽  
Sangappa Ramachandra Biradar
Keyword(s):  
Cellular Automata ◽  
Epidemic Spread ◽  
Rule Based ◽  
Voting Rule ◽  
Spread Model

scholarly journals Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

2021 ◽  
Vol 260 ◽  
pp. 112420
Author(s):  
C. Camino ◽  
R. Calderón ◽  
S. Parnell ◽  
H. Dierkes ◽  
Y. Chemin ◽  
...  
Keyword(s):  
Plant Traits ◽  
Xylella Fastidiosa ◽  
Remotely Sensed ◽  
Epidemic Spread ◽  
Spread Model ◽  
Almond Orchards

scholarly journals COVID-19 Outbreak: Model-Driven Impact Analysis Comparing Oman and Pakistan

2021 ◽  
pp. 38-54
Author(s):  
Kashif Zia ◽  
Umar Farooq ◽  
Muhammad Shafi
Keyword(s):  
Impact Analysis ◽  
Epidemic Spread ◽  
Real Numbers ◽  
Model Driven ◽  
Mobility Data ◽  
Testing Strategies ◽  
Spread Model ◽  
Rapid Spread ◽  
Simulation Results ◽  
Expected Outcomes

Motivated by the rapid spread of COVID-19 all across the globe, we haveperformed simulations of a system dynamic epidemic spread model in differentpossible situations. The simulation, not only captures the model dynamic of thespread of the virus, but also, takes care of population and mobility data. Themodel is calibrated based on epidemic data and events as they happened. Thesimulation results are quite disturbing, indicating that, during a process of stringent social distancing and testing strategies, a small perturbation can lead toquite undesirable outcomes. The simulation results, although consistent in expected outcomes across changing parameters’ values, also indicate a substantialmismatch with real numbers. An analysis of what can be the reason of this mismatch is also performed. Within these contradictions, a comparative analysisof COVID-19 outbreak between two geographically close but demographicallyvery different countries – that is Oman and Pakistan – is performed.

scholarly journals Dynamic Cellular Automata Based Epidemic Spread Model for Population in Patches with Movement

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Senthil Athithan ◽  
Vidya Prasad Shukla ◽  
Sangappa Ramachandra Biradar
Keyword(s):  
Cellular Automata ◽  
Computing Time ◽  
Evacuation Planning ◽  
Epidemic Spread ◽  
Homogeneous Population ◽  
Cellular Automata Model ◽  
Computing Power ◽  
Spread Model ◽  
Enormous Number ◽  
Patchy Population

Epidemiology is the study of spread of diseases among the group of population. If not controlled properly, the epidemic would cause an enormous number of problems and lead to pandemic situation. Here in this paper we consider the situation of populated areas where people live in patches. A dynamic cellular automata model for population in patches is being proposed in this paper. This work not only explores the computing power of cellular automata in modeling the epidemic spread but also provides the pathway in reduction of computing time when using the dynamic cellular automata model for the patchy population when compared to the static cellular automata which is used for a nonpatchy homogeneous population. The variation of the model with movement of population among the patches is also explored which provides an efficient way for evacuation planning and vaccination of infected areas.

scholarly journals Epidemic Spread Modeling with Time Variant Infective Population Using Pushdown Cellular Automata

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Senthil Athithan ◽  
Vidya Prasad Shukla ◽  
Sangappa Ramachandra Biradar
Keyword(s):  
Cellular Automata ◽  
Real Time ◽  
Disease Spread ◽  
Abu Dhabi ◽  
Epidemic Spread ◽  
Time Data ◽  
Cellular Automata Model ◽  
Spread Model ◽  
The World ◽  
Real Time Data

The world without a disease is a dream of any human being. The disease spread if not controlled could cause an epidemic situation to spread and lead to pandemic. To control an epidemic we need to understand the nature of its spread and the epidemic spread model helps us in achieving this. Here we propose an epidemic spread model which considers not only the current infective population around the population but also the infective population which remain from the previous generations for computing the next generation infected individuals. A pushdown cellular automata model which is an enhanced version of cellular automata by adding a stack component is being used to model the epidemic spread and the model is validated by the real time data of H1N1 epidemic in Abu Dhabi.

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