New-generation individual based models for infectious diseases transmission (original) (raw)

A Probabilistic Individual-based Model for Infectious Diseases Outbreaks

Jurnal Teknologi, 2013

The mathematical modelling of infectious diseases is a large research area with a wide literature. In the recent past, most of the scientific contributions focused on compartmental models. However, the increasing computing power is pushing towards the development of individual models that consider the disease transmission and evolution at a very fine-grained level. In the paper, the authors give a short state of the art of compartmental models, summarise one of the most know individual models, and describe both a generalization and a simulation algorithm.

Modeling the Spread of Infectious Diseases: A Review

Wiley Series in Probability and Statistics, 2014

Monitoring, analyzing, and predicting the impact of infectious diseases on the wellbeing of a society is the cornerstone of identifying effective ways to prevent, control, and manage disease spreads. It is a common perception that every infectious disease is transmitted through space and time from one individual to another in its own special spreading network in the environment. The use of models in public health decisionmaking has become increasingly important in the study of the spread of disease, designing interventions to control and prevent further outbreaks, and limiting their devastating effects on a population (

Modified Individual-Level Models of Infectious Disease

2011

Infectious disease models can be used to understand mechanisms of the spread of diseases and thus, may effectively guide control policies for potential outbreaks. Deardon et al. (2010) introduced a class of individual-level models (ILMs) which are highly flexible. Parameter estimates for ILMs can be achieved by means of Markov chain Monte Carlo (MCMC) methods within a Bayesian framework. Here, we introduce an extended form of ILM, described by Deardon et al. (2010), and compare this model with the original ILM in the context of a simple spatial system. The two spatial ILMs are fitted to 70 simulated data sets and a real data set on tomato spotted wilt virus (TSWV) in pepper plants (Hughes et al., 1997). We find that the modified ILM is more flexible than the original ILM and may fit some data sets better.

A comparison of simulation models applied to epidemics

Journal of Artificial Societies and Social Simulation the, 2002

This paper presents a new approach to infectious disease analysis through computer simulation. The case study concerns the spread of Bovine Leukemia, a viral pathology sustained by a retrovirus from the same family as HIV that exclusively strikes cattle within dairy farms. Although analytical models of epidemic spread have been implemented, their practical use is often difficult, above all for predictive and quantitative analysis. Computer simulation provides a new possible approach, and here we apply two methodologies: "System Dynamics" and "Agent Based". Furthermore the case study is used like a workbench to illustrate the differences between the two approaches and to explain how these techniques can help with the understanding of the problem. At the same time epidemiological researchers are able to do a preliminary "what-if" analysis with the purpose of assessing the system's behaviour under various conditions and evaluating which alternative sanitary policies to adopt. Thanks to model results, experts have reached their first suppositions in order to fight the endemic behaviour of Bovine Leukemia. The models implemented can easily be extended to collect the details of the system to be investigated more efficiently and to allow more refined analyses to be made.

Modeling the spread of infectious disease in human populations

American Journal of Physical Anthropology, 1990

For the past 20 years, there has been an epidemic associated with the development of mathematical models to describe the spread of disease. This epidemic shows no signs yet of dying out. Four major topics related to this discipline are discussed here, including the following: 1) a n introduction to the basic assumptions and general framework common to most epidemic models; 2 ) a discussion of the major questions addressed by epidemic modelers; 3) a brief outline of several of the approaches used in the development of disease models; and 4) reviews of models that have been developed for influenza, malaria, and AIDS. The utility of these models and suggestions for contributions that anthropologists can make to this field are also discussed.

Simulating the Evolution of Infectious Agents Through Human Interaction

2020 IEEE 26th International Symposium for Design and Technology in Electronic Packaging (SIITME)

Human interaction and behavior were proven to be decisive factors for disease spread among the members of a community. Recent events, determined by the COVID-19 outbreak, forced us to develop methods of control, in order to lower the reproductive number and overall, reduce the number of cases. We propose a software simulation method centered on in what manner a virus spreads inside a closed space. This approach required the implementation of a simplified model of human behavior, while considering the factors that influence the rate of transmission between individuals. Agent-based simulation allowed us to identify different control and prevention means, which are efficient in reducing the natural evolution of a pandemic. The results of the analysis offered insight into the rate of spread and human behavior, or habits that led to a significant increase in the total number of infected agents. Recent literature discussed the simulation of disease spread among the inhabitants of larger areas or studied the way that the pathogen can be spread from person to person through droplets containing the virus. Though we strongly consider that previous studies offer important insight, preventing the spread among smaller groups can let us maintain our activity in a safe and supervised manner. The model has been validated by a series of simulations with respect to COVID-19 real data.

Use of Mathematical Models in Epidemiology to Predict Infectious

Partners Universal Multidisciplinary Research Journal (PUMRJ), 2024

Mathematical models play a key role in epidemiology, providing a powerful tool for predicting and controlling the spread of infectious diseases. This paper examines the use of mathematical models to analyze the dynamics of infectious diseases, assess the impact of health interventions, and predict future outbreaks. Initially, the structure of basic models such as SIR (Susceptible, Infected, Recovered) and their modifications to take into account factors such as population heterogeneity, social networks, and seasonal changes will be discussed. Next, model parameterization and calibration techniques will be explored to ensure accurate predictions in the context of data collected in real-time. The results show that mathematical models can be a valuable tool for public health policies, helping to identify optimal strategies for the prevention and control of infectious diseases. In conclusion, this analysis highlights the importance of the continued development of epidemiological models for improving the response to future epidemics and pandemics.

SIM-D: An Agent-Based Simulator for Modeling Contagion in Population

Applied Sciences, 2020

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...