Modeling the epidemic dynamics of COVID-19: Agent-based approach including molecular dynamics simulation and SEIR type methods (original) (raw)

Modelling the epidemic dynamics of COVID-19 with consideration of human mobility

International Journal of Data Science and Analytics

So far COVID-19 has resulted in mass deaths and huge economic losses across the world. Various measures such as quarantine and social distancing have been taken to prevent the spread of this disease. These prevention measures have changed the transmission dynamics of COVID-19 and introduced new challenges for epidemic modelling and prediction. In this paper, we study a novel disease spreading model with two important aspects. First, the proposed model takes the quarantine effect of confirmed cases on transmission dynamics into account, which can better resemble the real-world scenario. Second, our model incorporates two types of human mobility, where the intra-region human mobility is related to the internal transmission speed of the disease in the focal area and the inter-region human mobility reflects the scale of external infectious sources to a focal area. With the proposed model, we use the human mobility data from 24 cities in China and 8 states in the USA to analyse the disease spreading patterns. The results show that our model could well fit/predict the reported cases in both countries. The predictions and findings shed light on how to effectively control COVID-19 by managing human mobility behaviours.

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.

An agent-based approach for modeling dynamics of contagious disease spread

International Journal of Health Geographics, 2009

The propagation of communicable diseases through a population is an inherent spatial and temporal process of great importance for modern society. For this reason a spatially explicit epidemiologic model of infectious disease is proposed for a greater understanding of the disease's spatial diffusion through a network of human contacts.

An agent based model for assessing transmission dynamics and health systems burden for COVID-19

Indonesian Journal of Electrical Engineering and Computer Science

Coronavirus disease of 2019 (COVID-19) pandemic has caused over 230 million infections with more than 4 million deaths worldwide. Researches have been using various mathematical and simulation techniques to estimate the future trends of the pandemic to help the policymakers and healthcare fraternity. Agent-based models (ABM) could provide accurate projections than the compartmental models that have been largely used. The present study involves a simulation of ABM using a synthetic population from India to analyze the effects of interventions on the spread of the disease. A disease model with various states representing the possible progression of the disease was developed and simulated using AnyLogic. The results indicated that imposing stricter non-pharmaceutical interventions (NPI) lowered the peak values of infections, the proportion of critical patients, and the deceased. Stricter interventions offer a larger time window for the healthcare fraternity to enhance preparedness. The...

High‐Resolution Agent‐Based Modeling of COVID‐19 Spreading in a Small Town

Advanced Theory and Simulations

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An Agent-Based Model of Epidemic Spread Using Human Mobility and Social Network Information

2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, 2011

The recent adoption of ubiquitous computing technologies has enabled capturing large amounts of human behavioral data. The digital footprints computed from these datasets provide information for the study of social and human dynamics, including social networks and mobility patterns, key elements for the effective modeling of virus spreading. Traditional epidemiologic models do not consider individual information and hence have limited ability to capture the inherent complexity of the disease spreading process. To overcome this limitation, agent-based models have recently been proposed as an effective approach to model virus spreading. However, most agent-based approaches to date have not included real-life data to characterize the agents' behavior. In this paper we propose an agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading. The proposed approach is applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus. Our simulations indicate that the restricted mobility due the government mandates reduced by 10% the peak number of individuals infected by the virus and postponed the peak of the pandemic by two days.

Impact of mobility restriction in COVID-19 superspreading events using agent-based model

PLOS ONE, 2021

COVID-19 pandemic is an immediate major public health concern. The search for the understanding of the disease spreading made scientists around the world turn their attention to epidemiological studies. An interesting approach in epidemiological modeling nowadays is to use agent-based models, which allow to consider a heterogeneous population and to evaluate the role of superspreaders in this population. In this work, we implemented an agent-based model using probabilistic cellular automata to simulate SIR (Susceptible-Infected-Recovered) dynamics using COVID-19 infection parameters. Differently to the usual studies, we did not define the superspreaders individuals a priori, we only left the agents to execute a random walk along the sites. When two or more agents share the same site, there is a probability to spread the infection if one of them is infected. To evaluate the spreading, we built the transmission network and measured the degree distribution, betweenness, and closeness c...

A local Agent-Based Model of COVID-19 spreading and interventions

HAL (Le Centre pour la Communication Scientifique Directe), 2023

This research presents a simulation model that combines metapopulation geospatial data with the SEIR epidemiological model to simulate a city of up to 250,000 residents while considering various factors such as virus transmission rate, disease severity, and prevention and control measures. This model can assist decision-makers in exploring different pandemic response strategies, including lockdowns, social distancing, mass testing, contact tracing, and vaccination. This simulation aims to provide decision-makers with a better understanding of the implications of their choices and enable them to make informed real-time decisions to manage a health crisis.

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.

DIRECT SIMULATION OF THE COVID-19 EPIDEMIC

Scientifica, 2019

We introduce an agent-based model to simulate the epidemiological dynamics of COVID-19. Most computational models proposed to study this epidemic do no take into account human mobility. We present a direct simulation model where mobility plays a key role and propose as well four quarantine strategies. The results show that the no-quarantine strategy does lead to a high peak of contagions with no rebound. Quarantined strategies, for their part, show a re-emergence of the epidemic with smaller and softer peaks.