A Knowledge Driven Agent-Based Semantic Model for Epidemic Surveillance

2015 ◽  
Vol 09 (04) ◽  
pp. 433-457 ◽  
Author(s):  
Madiha Sahar ◽  
Nadra Guizani ◽  
Saleh M. Basalamah ◽  
Muhammad N. Ayyaz ◽  
Maaz Ahmad ◽  
...  
Keyword(s):  
Semantic Analysis ◽  
Temporal Dynamics ◽  
Probabilistic Approach ◽  
Disease Spread ◽  
Semantic Model ◽  
Population Demographics ◽  
Agent Based ◽  
Spread Model ◽  
Spatio Temporal ◽  
Population Interaction

In this paper we propose a probabilistic approach to synthesize an agent-based heterogeneous semantic model depicting population interaction and analyzing the spatio-temporal dynamics of an airborne epidemic, such as influenza, in a metropolitan area. The methodology is generic in nature and can generate a baseline population for cities for which detailed population summary tables are not available. The joint probabilities of population demographics are estimated using the International Public Use Microsimulation Data (IPUMS) sample set. Agents are assigned various activities based on several characteristics. The agent-based model for the city of Lahore, Pakistan is synthesized and a rule based disease spread model of influenza is simulated. The simulation results are visualized to produce semantic analysis for the spatio-temporal dynamics of the epidemic. The results show that the proposed model can be used by officials and medical experts to simulate an outbreak.

scholarly journals Epidemic Spreading in Urban Areas Using Agent-Based Transportation Models

2019 ◽  
Vol 11 (4) ◽  
pp. 92 ◽  
Author(s):  
Jürgen Hackl ◽  
Thibaut Dubernet
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Human Mobility ◽  
Urban Setting ◽  
Disease Spread ◽  
Model Parameters ◽  
Epidemic Spreading ◽  
Agent Based ◽  
Spread Model ◽  
Spread Dynamics

Human mobility is a key element in the understanding of epidemic spreading. Thus, correctly modeling and quantifying human mobility is critical for studying large-scale spatial transmission of infectious diseases and improving epidemic control. In this study, a large-scale agent-based transport simulation (MATSim) is linked with a generic epidemic spread model to simulate the spread of communicable diseases in an urban environment. The use of an agent-based model allows reproduction of the real-world behavior of individuals’ daily path in an urban setting and allows the capture of interactions among them, in the form of a spatial-temporal social network. This model is used to study seasonal influenza outbreaks in the metropolitan area of Zurich, Switzerland. The observations of the agent-based models are compared with results from classical SIR models. The model presented is a prototype that can be used to analyze multiple scenarios in the case of a disease spread at an urban scale, considering variations of different model parameters settings. The results of this simulation can help to improve comprehension of the disease spread dynamics and to take better steps towards the prevention and control of an epidemic.

scholarly journals Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues

2016 ◽  
Vol 13 (117) ◽  
pp. 20160112 ◽  
Author(s):  
Patrick Smadbeck ◽  
Michael P. H. Stumpf
Keyword(s):  
Developmental Biology ◽  
Large Scale ◽  
Cell Tracking ◽  
Temporal Dynamics ◽  
Growth Models ◽  
Lineage Tracing ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Spatio Temporal ◽  
Large Scale Simulations

Development is a process that needs to be tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny. A generic feature of developing and homeostatic tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end quickly. Computational and theoretical biologists/physicists have, in response, developed a range of modelling approaches, most notably agent-based modelling. These models seem to capture features observed in experiments, but can also become computationally expensive. Here, we develop complementary genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We show that with both bounded and unbounded growth simple, but universal scaling relationships allow us to connect coalescent theory with the fractal growth models extensively used in developmental biology. Using our genealogical perspective, it is possible to study bulk statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.

scholarly journals STOCHASTIC ANALYSIS OF A DETERMINISTIC AND SEASONALLY-FORCED SEI MODEL FOR IMPROVED DISEASE SPREAD SIMULATION

2017 ◽  
Vol 17 (04) ◽  
pp. 1750067 ◽  
Author(s):  
AYESHA SOHAIL ◽  
ZHI WU LI ◽  
MEHWISH IFTIKHAR ◽  
MABRUKA MOHAMED ◽  
O. ANWAR BÉG
Keyword(s):  
Rural Areas ◽  
Stochastic Analysis ◽  
Temporal Dynamics ◽  
Disease Spread ◽  
Stochastic Effects ◽  
Force Of Infection ◽  
The Past ◽  
Spatio Temporal ◽  
The Stability ◽  
Sei Model

The geographic distribution of different viruses has developed widely, giving rise to an escalating number of cases during the past two decades. The deterministic Susceptible, Exposed, Infectious (SEI) models can demonstrate the spatio-temporal dynamics of the diseases and have been used extensively in modern mathematical and mechano-biological simulations. This article presents a functional technique to model the stochastic effects and seasonal forcing in a reliable manner by satisfying the Lipschitz criteria. We have emphasized that the graphical portrayal can prove to be a powerful tool to demonstrate the stability analysis of the deterministic as well as the stochastic modeling. Emphasis is made on the dynamical effects of the force of infection. Such analysis based on the parametric sweep can prove to be helpful in predicting the disease spread in urban as well as rural areas and should be of interest to mathematical biosciences researchers.

scholarly journals Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes

2021 ◽  
Vol 15 (1) ◽  
pp. e0009047
Author(s):  
Eyal Goldstein ◽  
Joseph J. Erinjery ◽  
Gerardo Martin ◽  
Anuradhani Kasturiratne ◽  
Dileepa Senajith Ediriweera ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Ecological Factors ◽  
Epidemiological Studies ◽  
Decision Makers ◽  
Agent Based ◽  
Snake Species ◽  
Relative Contribution ◽  
As Species ◽  
Spatio Temporal ◽  
The Tropics

Snakebite causes more than 1.8 million envenoming cases annually and is a major cause of death in the tropics especially for poor farmers. While both social and ecological factors influence the chance encounter between snakes and people, the spatio-temporal processes underlying snakebites remain poorly explored. Previous research has focused on statistical correlates between snakebites and ecological, sociological, or environmental factors, but the human and snake behavioral patterns that drive the spatio-temporal process have not yet been integrated into a single model. Here we use a bottom-up simulation approach using agent-based modelling (ABM) parameterized with datasets from Sri Lanka, a snakebite hotspot, to characterise the mechanisms of snakebite and identify risk factors. Spatio-temporal dynamics of snakebite risks are examined through the model incorporating six snake species and three farmer types (rice, tea, and rubber). We find that snakebites are mainly climatically driven, but the risks also depend on farmer types due to working schedules as well as species present in landscapes. Snake species are differentiated by both distribution and by habitat preference, and farmers are differentiated by working patterns that are climatically driven, and the combination of these factors leads to unique encounter rates for different landcover types as well as locations. Validation using epidemiological studies demonstrated that our model can explain observed patterns, including temporal patterns of snakebite incidence, and relative contribution of bites by each snake species. Our predictions can be used to generate hypotheses and inform future studies and decision makers. Additionally, our model is transferable to other locations with high snakebite burden as well.

scholarly journals An Agent-based Simulation of the SIRD model of COVID-19 Spread

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Infection Rate ◽  
Disease Spread ◽  
Agent Based Simulation ◽  
Effective Response ◽  
Agent Based ◽  
Human Interactions ◽  
The Past ◽  
Contact Rates ◽  
Spread Model ◽  
Development Phases

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.

scholarly journals Modelling epidermis homoeostasis and psoriasis pathogenesis

2015 ◽  
Vol 12 (103) ◽  
pp. 20141071 ◽  
Author(s):  
Hong Zhang ◽  
Wenhong Hou ◽  
Laurence Henrot ◽  
Sylvianne Schnebert ◽  
Marc Dumas ◽  
...  
Keyword(s):  
Immune System ◽  
Temporal Dynamics ◽  
Turnover Time ◽  
Quiescent State ◽  
Keratinocyte Proliferation ◽  
Agent Based ◽  
Central Transition ◽  
Pathological Conditions ◽  
Epidermal Turnover ◽  
Spatio Temporal

We present a computational model to study the spatio-temporal dynamics of epidermis homoeostasis under normal and pathological conditions. The model consists of a population kinetics model of the central transition pathway of keratinocyte proliferation, differentiation and loss and an agent-based model that propagates cell movements and generates the stratified epidermis. The model recapitulates observed homoeostatic cell density distribution, the epidermal turnover time and the multilayered tissue structure. We extend the model to study the onset, recurrence and phototherapy-induced remission of psoriasis. The model considers psoriasis as a parallel homoeostasis of normal and psoriatic keratinocytes originated from a shared stem cell (SC) niche environment and predicts two homoeostatic modes of psoriasis: a disease mode and a quiescent mode. Interconversion between the two modes can be controlled by interactions between psoriatic SCs and the immune system and by normal and psoriatic SCs competing for growth niches. The prediction of a quiescent state potentially explains the efficacy of multi-episode UVB irradiation therapy and recurrence of psoriasis plaques, which can further guide designs of therapeutics that specifically target the immune system and/or the keratinocytes.

scholarly journals A large majority of awake hippocampal sharp-wave ripples feature spatial trajectories with momentum

2021 ◽  
Author(s):  
Emma L Krause ◽  
Jan Drugowitsch
Keyword(s):  
Temporal Dynamics ◽  
Probabilistic Approach ◽  
Complete Characterization ◽  
Sharp Wave ◽  
Natural Movement ◽  
Spatio Temporal ◽  
Almost All ◽  
Spatial Trajectories

During periods of rest, hippocampal place cells feature bursts of activity called sharp-wave ripples (SWRs). Heuristic approaches to their analysis have revealed that a small fraction of SWRs appear to "simulate" trajectories through the environment - called awake hippocampal replay - while the functional role of a majority of these SWRs remains unclear. Applying a novel probabilistic approach to characterize the spatio-temporal dynamics embedded in SWRs, we instead show that almost all SWRs of foraging rodents simulate such trajectories through the environment. Furthermore, these trajectories feature momentum, that is, inertia in their velocities, that mirrors the animals' natural movement. This stands in contrast to replay events during sleep which seem to follow Brownian motion without such momentum. Lastly, interpreting the replay trajectories in the context of navigational planning revealed that similar past analyses were biased by the heuristic SWR sub-selection. Overall, our approach provides a more complete characterization of the spatio-temporal dynamics within SWRs, highlights qualitative differences between sleep and awake replay, and ought to support future, more detailed, and less biased analysis of the role of awake replay in navigational planning.

Sign in / Sign up

Export Citation Format

Share Document