Simulating the Evolution of Infectious Agents Through Human Interaction (original) (raw)
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The spread of infectious diseases such as COVID-19, flu influenza, malaria, dengue, mumps, and rubella in a population is a big threat to public health. The infectious diseases spread from one person to another person through close contact. Without proper planning, an infectious disease can become an epidemic and can result in large human and financial losses. To better respond to the spread of infectious disease and take measures for its control, the public health authorities need models and simulations to study the spread of such diseases. In this paper, an agent-based simulation engine is presented that models the spread of infectious diseases in the population. The simulation takes as an input the human-to-human interactions, population dynamics, disease transmissibility and disease states and shows the spread of disease over time. The simulation engine supports non-pharmaceutical interventions and shows its impact on the disease spread across locations. A unique feature of this...
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Current statistical models to simulate pandemics miss the most relevant information about the close atomic interactions between individuals which is the key aspect of virus spread. Thus, they lack a proper visualization of such interactions and their impact on virus spread. In the field of computer graphics, and more specifically in computer animation, there have been many crowd simulation models to populate virtual environments. However, the focus has typically been to simulate reasonable paths between random or semi-random locations in a map, without any possibility of analyzing specific individual behavior. We propose a crowd simulation framework to accurately simulate the interactions in a city environment at the individual level, with the purpose of recording and analyzing the spread of human diseases. By simulating the whereabouts of agents throughout the day by mimicking the actual activities of a population in their daily routines, we can accurately predict the location and duration of interactions between individuals, thus having a model that can reproduce the spread of the virus due to human-to-human contact. Our results show the potential of our framework to closely simulate the virus spread based on real agent-to-agent contacts. We believe that this could become a powerful tool for policymakers to make informed decisions in future pandemics and to better communicate the impact of such decisions to the general public.
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Proceedings of the 7th ACM SIGSPATIAL International Workshop on Geospatial Simulation, 2024
The COVID-19 pandemic has reshaped societies and brought to the forefront simulation as a tool to explore the spread of the diseases including that of agent-based modeling. Efforts have been made to ground these models on the world around us using synthetic populations that attempt to mimic the population at large. However, we would argue that many of these synthetic populations and therefore the models using them, miss the social connections which were paramount to the spread of the pandemic. Our argument being is that contagious diseases mainly spread through people interacting with each other and therefore the social connections need to be captured. To address this, we create a geographically-explicit synthetic population along with its social network for the Western New York (WNY) Area. This synthetic population is then used to build a framework to explore a hypothetical contagious disease inspired by various of COVID-19. We show simulation results from two scenarios utilizing this framework, which demonstrates the utility of our approach capturing the disease dynamics. As such we show how basic patterns of life along with interactions driven by social networks can lead to the emergence of disease outbreaks and pave the way for researchers to explore the next pandemic utilizing agent-based modeling with geographically explicit social networks.
2020
: The spreading of viruses in a population depends not only on biological factors and the availability of effective pharmaceutical products, but also on human behaviour. Since in the case of CoViD-19, there are currently no pharmaceutical measures available, the discussion focuses on non-pharmaceutical measures such as social distancing and quarantines. However, whether and to what extent these measures achieve the desired effects, does not only depend on medical parameters but also on human behaviour: A sufficient number of individuals need to change their social behaviour to successfully contain the spreading of CoViD-19. We offer an interactive computer simulation that allows us to compare different policy measures and social rules such as social distancing and quarantine in terms of their potential for virus containment. In the following text, we describe the basic structure as well as some implications of a simulation designed to model the effectiveness of different measures ...
We have created a model based on artificial agents that describes some aspects of the diffusion dynamics of COVID-19, especially aspects related to the link between social behaviour and contagion dynamics. Starting from the first part of this article [1], published on April 2, we investigated how we can link a particular socio-cultural attitude to some effects on the lethality of the epidemic. As a starting data, we show the effect that the epidemic had in terms of difference between the number of deaths in the first quarter of 2019 and the first quarter of 2020, in 1,698 Italian municipalities [2]. Once we have established how the incidence of the virus was very irregular, we assume that one of the causes may be some social behaviour, through the simulation model described in the previous publication [1], which has been appropriately revised.
2020
The spreading of viruses in a population depends not only on biological factors and the availability of effective pharmaceutical products, but also on human behaviour. Since in the case of Covid-19, there are currently no pharmaceutical measures available, the discussion focuses on non-pharmaceutical measures such as social distancing and quarantines. However, whether and to what extent these measures achieve the desired effects, does not only depend on medical parameters but also on human behaviour: A sufficient number of individuals need to change their social behaviour to successfully contain the spreading of Covid-19. We offer an interactive computer simulation that allows us to compare different policy measures and social rules such as social distancing and quarantine in terms of their potential for virus containment. In the following text, we describe the basic structure as well as some implications of a simulation designed to model the effectiveness of different measures to c...
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.
City-Scale Simulation of Covid-19 Pandemic & Intervention Policies Using Agent-Based Modelling
2021 Winter Simulation Conference (WSC), 2021
During the Covid-19 pandemic, most governments across the world imposed policies like lock-down of public spaces and restrictions on people's movements to minimize the spread of the virus through physical contact. However, such policies have grave social and economic costs, and so it is important to pre-assess their impacts. In this work we aim to visualize the dynamics of the pandemic in a city under different intervention policies, by simulating the behavior of the residents. We develop a very detailed agent-based model for a city, including its residents, physical and social spaces like homes, marketplaces, workplaces, schools/colleges etc. We parameterize our model for Kolkata city in India using ward-level demographic and civic data. We demonstrate that under appropriate choice of parameters, our model is able to reproduce the observed dynamics of the Covid-19 pandemic in Kolkata, and also indicate the counter-factual outcomes of alternative intervention policies.