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 Spatio-temporal control of mutualism in legumes helps spread symbiotic nitrogen fixation

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Benoit Daubech ◽  
Philippe Remigi ◽  
Ginaini Doin de Moura ◽  
Marta Marchetti ◽  
Cécile Pouzet ◽  
...  
Keyword(s):  
N2 Fixation ◽  
Temporal Dynamics ◽  
Root Nodules ◽  
Bacterial Species ◽  
Ecological Factors ◽  
Initial Inoculum ◽  
Selective Forces ◽  
Spatio Temporal ◽  
Plant Population Size ◽  
The Relationship

Mutualism is of fundamental importance in ecosystems. Which factors help to keep the relationship mutually beneficial and evolutionarily successful is a central question. We addressed this issue for one of the most significant mutualistic interactions on Earth, which associates plants of the leguminosae family and hundreds of nitrogen (N2)-fixing bacterial species. Here we analyze the spatio-temporal dynamics of fixers and non-fixers along the symbiotic process in the Cupriavidus taiwanensis–Mimosa pudica system. N2-fixing symbionts progressively outcompete isogenic non-fixers within root nodules, where N2-fixation occurs, even when they share the same nodule. Numerical simulations, supported by experimental validation, predict that rare fixers will invade a population dominated by non-fixing bacteria during serial nodulation cycles with a probability that is function of initial inoculum, plant population size and nodulation cycle length. Our findings provide insights into the selective forces and ecological factors that may have driven the spread of the N2-fixation mutualistic trait.

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 The spatio-temporal distribution of COVID-19 infection in England between January and June 2020

2021 ◽  
Vol 149 ◽  
Author(s):  
Richard Elson ◽  
Tilman M. Davies ◽  
Iain R. Lake ◽  
Roberto Vivancos ◽  
Paula B. Blomquist ◽  
...  
Keyword(s):  
Urban Areas ◽  
Temporal Dynamics ◽  
Disease Risk ◽  
Spatial Scales ◽  
Temporal Distribution ◽  
Health Resources ◽  
Epidemiological Studies ◽  
Accurate Identification ◽  
Multiple Introductions ◽  
Spatio Temporal

Abstract The spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed. A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk. Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first 6 months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on 23 March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout. In terms of controlling transmission, the most important practical application of our results is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

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 The spatio-temporal distribution of COVID-19 infection in England between January and June 2020

2021 ◽  
Author(s):  
Richard Elson ◽  
Tilman M. Davies ◽  
Iain R. Lake ◽  
Roberto Vivancos ◽  
Paula B. Blomquist ◽  
...  
Keyword(s):  
Urban Areas ◽  
Temporal Dynamics ◽  
Disease Risk ◽  
Spatial Scales ◽  
Temporal Distribution ◽  
Health Resources ◽  
Epidemiological Studies ◽  
Accurate Identification ◽  
Multiple Introductions ◽  
Spatio Temporal

AbstractThe spatio-temporal dynamics of an outbreak provide important insights to help direct public health resources intended to control transmission. They also provide a focus for detailed epidemiological studies and allow the timing and impact of interventions to be assessed.A common approach is to aggregate case data to administrative regions. Whilst providing a good visual impression of change over space, this method masks spatial variation and assumes that disease risk is constant across space. Risk factors for COVID-19 (e.g. population density, deprivation and ethnicity) vary from place to place across England so it follows that risk will also vary spatially. Kernel density estimation compares the spatial distribution of cases relative to the underlying population, unfettered by arbitrary geographical boundaries, to produce a continuous estimate of spatially varying risk.Using test results from healthcare settings in England (Pillar 1 of the UK Government testing strategy) and freely available methods and software, we estimated the spatial and spatio-temporal risk of COVID-19 infection across England for the first six months of 2020. Widespread transmission was underway when partial lockdown measures were introduced on the 23rd March 2020 and the greatest risk erred towards large urban areas. The rapid growth phase of the outbreak coincided with multiple introductions to England from the European mainland. The spatio-temporal risk was highly labile throughout.In terms of controlling transmission, the most important practical application is the accurate identification of areas within regions that may require tailored intervention strategies. We recommend that this approach is absorbed into routine surveillance outputs in England. Further risk characterisation using widespread community testing (Pillar 2) data is needed as is the increased use of predictive spatial models at fine spatial scales.

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.

Sign in / Sign up

Export Citation Format

Share Document