Impact of mobility restriction in COVID-19 superspreading events using agent-based model (original) (raw)
Related papers
Indonesian Journal of Electrical Engineering and Computer Science, 2022
Computer modelling and simulation methods are very important and play a critical role in the mitigation and response to the ongoing COVID-19 pandemic. In this study, we propose a computational modeling technique based on cellular automata (CA) with realistic proposed rules. The rules are designed to simulate the propagation of COVID-19 disease through a bounded area. Our proposed CA rules are novel in many respects. For example, we introduce rules regarding surface states not only the states of the person rules to the proposed model. In addition on, the classification of neighbors to nearest neighbors and range of neighbors based on cellular layers is explained. Moreover, the concepts of time generation and access time are deployed for the first time to model the propagation of the disease over time in this work. Further details of the proposed model including the topology of the defined area, the initial states of the cells and four-layer transfer mechanism are explained as well. This work may be considered a criterion of spreading for COVID-19 from point source in a defined population area. The results of the proposed algorithm represent the percentage of the population whose infectious status is described by different cellular state objects after a defined generation time. The results are compared under different circumstances and analyzed equanimity.
Epidemic spreading: Tailored models for COVID-19
Europhysics News
A very simple epidemic model proposed a century ago is the linchpin of the current mathematical models of the epidemic spreading of the COVID-19. Nowadays, the abstracted compartmentalisation of the population in susceptible, infected and recovered individuals, combined with precise information about the networks of mobility flows within geographical territories, is the best weapon of the physics community to forecast the possible evolution of contagions in the current pandemic scenario.
Modeling epidemics using cellular automata
Applied Mathematics and Computation, 2007
The main goal of this work is to introduce a theoretical model, based on cellular automata, to simulate epidemic spreading. Specifically, it divides the population into three classes: susceptible, infected and recovered, and the state of each cell stands for the portion of these classes of individuals in the cell at every step of time. The effect of population vaccination is also considered. The proposed model can serve as a basis for the development of other algorithms to simulate real epidemics based on real data.
An Agent-based Simulation of the SIRD model of COVID-19 Spread
INTERNATIONAL JOURNAL OF BIOLOGY AND BIOMEDICAL ENGINEERING, 2020
The COVID-19 pandemic has resulted in more than a million deaths worldwide and wreaked havoc on world economies. SARS-CoV-2, the virus that causes COVID-19, belongs to a family of coronaviruses that have appeared in the past; however, this virus has been proven to be more lethal and have a much higher infection rate than coronaviruses that have previously emerged. Vaccines for COVID-19 are still in development phases, with limited deployment, and the most effective response to the pandemic has been to adopt social distancing and, in extreme cases, complete lockdown. This paper adopts a modified SIRD (Susceptible, Infectious, Recovered, Deaths) disease spread model for COVID-19 and utilizes agent-based simulation to obtain the number of infections in four different scenarios. The simulated scenarios utilized different contact rates in order to identify their effects on disease spread. Our results confirmed that not taking strict precautionary procedures to prohibit human interactions will lead to increased infections and deaths, adversely affecting countries' healthcare infrastructure. The model is flexible, and other studies can use it to measure other parameters discovered in the future.
BMC Research Notes, 2014
Background The spread of an infectious disease is determined by biological and social factors. Models based on cellular automata are adequate to describe such natural systems consisting of a massive collection of simple interacting objects. They characterize the time evolution of the global system as the emergent behaviour resulting from the interaction of the objects, whose behaviour is defined through a set of simple rules that encode the individual behaviour and the transmission dynamic. Methods An epidemic is characterized trough an individual–based–model built upon cellular automata. In the proposed model, each individual of the population is represented by a cell of the automata. This way of modeling an epidemic situation allows to individually define the characteristic of each individual, establish different scenarios and implement control strategies. Results A cellular automata model to study the time evolution of a heterogeneous populations through the various stages of dis...
Results in physics, 2021
Currently, there is a global pandemic of COVID-19. To assess its prevalence, it is necessary to have adequate models that allow real-time modeling of the impact of various quarantine measures by the state. The SIR model, which is implemented using a multi-agent system based on mobile cellular automata, was improved. The paper suggests ways to improve the rules of the interaction and behavior of agents. Methods of comparing the parameters of the SIR model with real geographical, social and medical indicators have been developed. That allows the modeling of the spatial distribution of COVID-19 as a single location and as the whole country consisting of individual regions that interact with each other by transport, taking into account factors such as public transport, supermarkets, schools, universities, gyms, churches, parks. The developed model also allows us to assess the impact of quarantine, restrictions on transport connections between regions, to take into account such factors as the incubation period, the mask regime, maintaining a safe distance between people, and so on. A number of experiments were conducted in the work, which made it possible to assess both the impact of individual measures to stop the pandemic and their comprehensive application. A method of comparing computertime and dynamic parameters of the model with real data is proposed, which allowed assessing the effectiveness of the government in stopping the pandemic in the Chernivtsi region, Ukraine. A simulation of the pandemic spread in countries such as Slovakia, Turkey and Serbia was also conducted. The calculations showed the highaccuracy matching of the forecast model with real data.
Investigating dynamics of COVID-19 spread and containment with agent-based modeling
Governments, policy makers and officials around the globe are trying to mitigate the effects and progress of the COVID-19 pandemic by making decisions which will save the most lives and impose the least costs. Making these decisions needs a comprehensive understanding about the dynamics by which the disease spreads. In this work, we propose an epidemic agent-based model that simulates the spread of the disease. We show that the model is able to generate an important aspect of the pandemic: multiple waves of infection. A key point in the model description is the aspect of ‘fear’ which can govern how agents behave under different conditions. We also show that the model provides an appropriate test-bed to apply different containment strategies and this work presents the results of applying two such strategies: testing, contact tracing, and travel restriction. The results show that while both strategies could result in flattening the epidemic curve and significantly reduce the maximum n...
Ecological Modelling, 2000
A cellular automaton model for the effects of population movement and vaccination on epidemic propagation is presented. Each cellular automaton cell represents a part of the total population that may be found in one of three states: infected, immunized and susceptible. As parts of the population move randomly in the cellular automaton lattice, the disease spreads. We study the effect of two population movement parameters on the epidemic propagation: the distance of movement and the percentage of the population that moves. Furthermore, the model is extended to include the effect of the vaccination of some parts of the population on epidemic propagation. The model establishes the acceleration of the epidemic propagation because of the increment, of the percentage of the moving population, or of the maximum distance of population movement. On the contrary, the effect of population vaccination reduces the epidemic propagation. The proposed model can serve as a basis for the development of algorithms to simulate real epidemics based on real data.
A cellular automata to model epidemics
2013
Compartmental models are very popular in epidemiology (8), they provide excellent results when the populations satisfy certain hypotheses as large population size or population homogeneity (11), the complexity of this models is low making their analysis intuitive. In the other hand, they ignore important factors inherent to the problem, such as the nature of contacts between individuals and population heterogeneity (4 5 18). Cellular automata models are adequate to describe natural systems consisting of a massive collection of simple objects. They represent the global system behavior as a colection of simpler objects or cells. In this paper we propouse a cellular automata model to study the time evolution of a heterogeneous population through the various stages of disease resulting from the individuals interactions (epidemic). We validate the model with real data of flu that hit Geneva (Switzerland) in 1918 and then we will test the model under different assumptions discussing the r...
A Spatial Markov Chain Cellular Automata Model for the Spread of Viruses
2020
We consider a Spatial Markov Chain model for the spread of viruses. The model is based on the principle to represent a graph connecting nodes, which represent humans. The vertices between the nodes represent relations between humans. In this way, a graph is connected in which the likelihood of infectious spread from person to person is determined by the intensity of interpersonal contact. Infectious transfer is determined by chance. The model is extended to incorporate various lockdown scenarios.