A Cellular Automaton Framework for Infectious Disease Spread Simulation

In this paper, a cellular automaton framework for processing the spatiotemporal spread of infectious diseases is presented. The developed environment simulates and visualizes how infectious diseases might spread, and hence provides a powerful instrument for health care organizations to generate disease prevention and contingency plans. In this study, the outbreak of an avian flu like virus was modeled in the state of Tyrol, and various scenarios such as quarantine, effect of different medications on viral spread and changes of social behavior were simulated

The influence of risk perception in epidemics: a cellular agent model

Our work stems from the consideration that the spreading of a disease is modulated by the individual's perception of the infected neighborhood and his/her strategy to avoid being infected as well. We introduced a general ``cellular agent'' model that accounts for a hetereogeneous and variable network of connections. The probability of infection is assumed to depend on the perception that an individual has about the spreading of the disease in her local neighborhood and on broadcasting media. In the one-dimensional homogeneous case the model reduces to the DK one, while for long-range coupling the dynamics exhibits large fluctuations that may lead to the complete extinction of the disease

Understanding The Influence Of Participants\u27 Preferences On The Affiliation Network Of Churches Using Agent-based Modeling

As the affiliation network of churches may potentially benefit public health, the impact of participants’ preferences on the affiliation network bears further study. This paper attempts to use agent-based modeling techniques associated with geographic information to study the affiliation network between churches and participants. Using churches in ZIP Code 30318 in Atlanta in Georgia, this study specifies the preferences of participants as personal radii and choice patterns. Results suggest (1) personal radii of participants are positively related to the size of affiliation network and the centralities of churches; (2) the change of choice pattern of participants can lead to a sharp reduction in size of the affiliation network of churches; (3) The centralities of churches among the affiliation network are corresponding to population density of census tracts. Findings can be used to understand the formulation of affiliation network of churches and their relationship with participants’ preferences

Epidemiological cellular automata: a case study involving AIDS

The spread of disease is a major health concern in many parts of the world. In the absence of vaccines and treatments, the only method to stop the spread of disease is to control population movements. Human mobility is one of the causes of the geographical spread of emergent human infectious diseases and plays a key role in human-mediated bio-invasion, the dominant factor in the global biodiversity crisis. One of the most serious emergent infectious diseases in the last 30 years or so is AIDS (acquired immunodeficiency syndrome), where multiple pathogen species infect a human body. HIV/AIDS is now considered much more commonplace than previously thought. AIDS leads to interaction effects between the pathogens that may alter previously understood patterns of disease spread. There has been longstanding interest in how to model population movements in order to find optimal control strategies for a particular disease. The simulation models proposed here use cellular automata based on sound mathematical principles and epidemiological theory to model HIV/AIDS to provide a suitable framework to study the spatial spread of disease in different scenarios. This work investigates how probabilistic parameters affect the model in terms of time, location, gender, age and subgroups of the population. The cellular automaton modelling approach is used to forecast numbers of cases in different subgroups. An approach using wavelet transforms analysis is illustrated to understand the impact of delay on the spread of infectious disease. The results confirm that the higher the frequency, then the slower the spread of disease and vice versa. The thesis concludes with showing how co-infection can be modelled in future work on a theoretical base

Virus Replication Strategies and the Critical CTL Numbers Required for the Control of Infection

Vaccines that elicit protective cytotoxic T lymphocytes (CTL) may improve on or augment those designed primarily to elicit antibody responses. However, we have little basis for estimating the numbers of CTL required for sterilising immunity at an infection site. To address this we begin with a theoretical estimate obtained from measurements of CTL surveillance rates and the growth rate of a virus. We show how this estimate needs to be modified to account for (i) the dynamics of CTL-infected cell conjugates, and (ii) features of the virus lifecycle in infected cells. We show that provided the inoculum size of the virus is low, the dynamics of CTL-infected cell conjugates can be ignored, but knowledge of virus life-histories is required for estimating critical thresholds of CTL densities. We show that accounting for virus replication strategies increases estimates of the minimum density of CTL required for immunity over those obtained with the canonical model of virus dynamics, and demonstrate that this modeling framework allows us to predict and compare the ability of CTL to control viruses with different life history strategies. As an example we predict that lytic viruses are more difficult to control than budding viruses when net reproduction rates and infected cell lifetimes are controlled for. Further, we use data from acute SIV infection in rhesus macaques to calculate a lower bound on the density of CTL that a vaccine must generate to control infection at the entry site. We propose that critical CTL densities can be better estimated either using quantitative models incorporating virus life histories or with in vivo assays using virus-infected cells rather than peptide-pulsed targets

Modeling The Spatiotemporal Dynamics Of Cells In The Lung

Multiple research problems related to the lung involve a need to take into account the spatiotemporal dynamics of the underlying component cells. Two such problems involve better understanding the nature of the allergic inflammatory response to explore what might cause chronic inflammatory diseases such as asthma, and determining the rules underlying stem cells used to engraft decellularized lung scaffolds in the hopes of growing new lungs for transplantation. For both problems, we model the systems computationally using agent-based modeling, a tool that enables us to capture these spatiotemporal dynamics by modeling any biological system as a collection of agents (cells) interacting with each other and within their environment. This allows to test the most important pieces of biological systems together rather than in isolation, and thus rapidly derive biological insights from resulting complex behavior that could not have been predicted beforehand, which we can then use to guide wet lab experimentation. For the allergic response, we hypothesized that stimulation of the allergic response with antigen results in a response with formal similarity to a muscle twitch or an action potential, with an inflammatory phase followed by a resolution phase that returns the system to baseline. We prepared an agent-based model (ABM) of the allergic inflammatory response and determined that antigen stimulation indeed results in a twitch-like response. To determine what might cause chronic inflammatory diseases where the twitch presumably cannot resolve back to baseline, we then tested multiple potential defects to the model. We observed that while most of these potential changes lessen the magnitude of the response but do not affect its overall behavior, extending the lifespan of activated pro-inflammatory cells such as neutrophils and eosinophil results in a prolonged inflammatory response that does not resolve to baseline. Finally, we performed a series of experiments involving continual antigen stimulation in mice, determining that there is evidence in the cytokine, cellular and physiologic (mechanical) response consistent with our hypothesis of a finite twitch and an associated refractory period. For stem cells, we made a 3-D ABM of a decellularized scaffold section seeded with a generic stem cell type. We then programmed in different sets of rules that could conceivably underlie the cell\u27s behavior, and observed the change in engraftment patterns in the scaffold over selected timepoints. We compared the change in those patterns against the change in experimental scaffold images seeded with C10 epithelial cells and mesenchymal stem cells, two cell types whose behaviors are not well understood, in order to determine which rulesets more closely match each cell type. Our model indicates that C10s are more likely to survive on regions of higher substrate while MSCs are more likely to proliferate on regions of higher substrate