Validation of COVID-19 spread model by early cases from Spain

The present-day distribution of Bantu languages is commonly thought to reflect the early stages of the Bantu Expansion, the greatest migration event in African prehistory. Using 1149 radiocarbon dates linked to 115 pottery styles recovered from 726 sites throughout the Congo rainforest and adjacent areas, we show that this is not the case. Two periods of more intense human activity, each consisting of an expansion phase with widespread pottery styles and a regionalization phase with many more local pottery styles, are separated by a widespread population collapse between 400 and 600 CE followed by major resettlement centuries later. Coinciding with wetter climatic conditions, the collapse was possibly promoted by a prolonged epidemic. Comparison of our data with genetic and linguistic evidence further supports a spread-over-spread model for the dispersal of Bantu speakers and their languages.

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Quantifying parametric uncertainty in the Rothermel model

International Journal of Wildland Fire ◽

10.1071/wf07070 ◽

2008 ◽

Vol 17 (5) ◽

pp. 638 ◽

Cited By ~ 15

Author(s):

Edwin Jimenez ◽

M. Yousuff Hussaini ◽

Scott Goodrick

Keyword(s):

Variance Reduction ◽

Input Parameter ◽

Parametric Uncertainty ◽

Fire Spread ◽

The United States ◽

Linear System Of Equations ◽

Spread Model ◽

Non Linear System ◽

The Impact ◽

Sensitivity Derivative

The purpose of the present work is to quantify parametric uncertainty in the Rothermel wildland fire spread model (implemented in software such as BehavePlus3 and FARSITE), which is undoubtedly among the most widely used fire spread models in the United States. This model consists of a non-linear system of equations that relates environmental variables (input parameter groups) such as fuel type, fuel moisture, terrain, and wind to describe the fire environment. This model predicts important fire quantities (output parameters) such as the head rate of spread, spread direction, effective wind speed, and fireline intensity. The proposed method, which we call sensitivity derivative enhanced sampling, exploits sensitivity derivative information to accelerate the convergence of the classical Monte Carlo method. Coupled with traditional variance reduction procedures, it offers up to two orders of magnitude acceleration in convergence, which implies that two orders of magnitude fewer samples are required for a given level of accuracy. Thus, it provides an efficient method to quantify the impact of input uncertainties on the output parameters.

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Evaluating a model for predicting active crown fire rate of spread using wildfire observations

Canadian Journal of Forest Research ◽

10.1139/x06-174 ◽

2006 ◽

Vol 36 (11) ◽

pp. 3015-3028 ◽

Cited By ~ 61

Author(s):

Martin E Alexander ◽

Miguel G Cruz

Keyword(s):

Fire Management ◽

Model Performance ◽

The United States ◽

Fire Research ◽

Management Decision ◽

Crown Fire ◽

Predictive Capacity ◽

Data Set ◽

Rate Of Spread ◽

Spread Model

We evaluated the predictive capacity of a rate of spread model for active crown fires (M.G. Cruz, M.E. Alexander, and R.H. Wakimoto. 2005. Can. J. For. Res. 35: 1626–1639) using a relatively large (n = 57) independent data set originating from wildfire observations undertaken in Canada and the United States. The assembled wildfire data were characterized by more severe burning conditions and fire behavior in terms of rate of spread and the degree of crowning activity than the data set used to parameterize the crown fire rate of spread model. The statistics used to evaluate model adequacy showed good fit and a level of uncertainty considered acceptable for a wide variety of fire management and fire research applications. The crown fire rate of spread model predicted 42% of the data with an error lower then ±25%. Mean absolute percent errors of 51% and 60% were obtained for Canadian and American wildfires, respectively. The characteristics of the data set did not allow us to determine where model performance was weaker and consequently identify its shortcomings and areas of future improvement. The level of uncertainty observed suggests that the model can be readily utilized in support of operational fire management decision making and for simulations in fire research studies.

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A Stochastic SIR Epidemic System with a Nonlinear Relapse

Discrete Dynamics in Nature and Society ◽

10.1155/2018/5493270 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Ali El Myr ◽

Abdelaziz Assadouq ◽

Lahcen Omari ◽

Adel Settati ◽

Aadil Lahrouz

Keyword(s):

Numerical Simulations ◽

Stationary Distribution ◽

Stochastic Perturbations ◽

Sir Epidemic ◽

Spread Model ◽

Stochastic Sir ◽

Theoretical Results

We investigate the conditions that control the extinction and the existence of a unique stationary distribution of a nonlinear mathematical spread model with stochastic perturbations in a population of varying size with relapse. Numerical simulations are carried out to illustrate the theoretical results.

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Comparison of Amyloid β and Tau Spread Models in Alzheimer’s Disease

Cerebral Cortex ◽

10.1093/cercor/bhy311 ◽

2018 ◽

Vol 29 (10) ◽

pp. 4291-4302 ◽

Cited By ~ 1

Author(s):

Hang-Rai Kim ◽

Peter Lee ◽

Sang Won Seo ◽

Jee Hoon Roh ◽

Minyoung Oh ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Large Scale ◽

Amyloid Β ◽

Brain Network ◽

Amyloid Positron Emission Tomography ◽

Local Spread ◽

Positron Emission ◽

Spread Model ◽

Graph Theoretical Approach

Abstract Tau and amyloid β (Aβ), 2 key pathogenic proteins in Alzheimer’s disease (AD), reportedly spread throughout the brain as the disease progresses. Models of how these pathogenic proteins spread from affected to unaffected areas had been proposed based on the observation that these proteins could transmit to other regions either through neural fibers (transneuronal spread model) or through extracellular space (local spread model). In this study, we modeled the spread of tau and Aβ using a graph theoretical approach based on resting-state functional magnetic resonance imaging. We tested whether these models predict the distribution of tau and Aβ in the brains of AD spectrum patients. To assess the models’ performance, we calculated spatial correlation between the model-predicted map and the actual map from tau and amyloid positron emission tomography. The transneuronal spread model predicted the distribution of tau and Aβ deposition with significantly higher accuracy than the local spread model. Compared with tau, the local spread model also predicted a comparable portion of Aβ deposition. These findings provide evidence of transneuronal spread of AD pathogenic proteins in a large-scale brain network and furthermore suggest different contributions of spread models for tau and Aβ in AD.

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Research on risk spread model of industrial chain

Grey Systems Theory and Application ◽

10.1108/gs-05-2014-0014 ◽

2014 ◽

Vol 4 (2) ◽

pp. 328-338 ◽

Cited By ~ 1

Author(s):

Yanfeng Chu ◽

Mei-Mei Dai

Keyword(s):

Risk Communication ◽

External Environment ◽

Chain Complex ◽

Propagation Model ◽

Normal Operation ◽

Design Algorithm ◽

Content Type ◽

Industry Chain ◽

Industrial Chain ◽

Spread Model

Purpose – The industrial chain network is a complex system consisted by many members of the enterprise, and the complex relationship and the interaction with the external environment among the node enterprises and the existence of various uncertainty all increase the risk of the industry chain. The risk of some individual node enterprises will not only affect the normal operation but also spread the risk to other enterprises by network relationship because of their own mismanagement or deterioration of the external environment. The purpose of this paper is to make an attempt to establish the risk spread model of the industrial chain based on complex networks. Design/methodology/approach – By improving Lobos disaster diffuse model, the paper introduces two indexes: the risk spread range and the risk propagation velocity to measure of industrial chain risk communication effects, and design algorithm for industrial chain complex network structure. The risk spread range can be used to measure the coverage of the risk communication influence produced by the propagation enterprises in the industry chain and to analyse the risk spread breadth on the industrial chain network .The speed index of risk communication represents the total numbers of infection enterprises in unit simulation time. Findings – This paper proposes the universal industrial chain risk propagation model. Originality/value – Through proposed algorithm constructs industrial chain network, and enterprise class divide, the importance of the product chain enterprises in the industry chain is strengthened.

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Lies, Gosh Darn Lies, and Not Enough Good Statistics: Why Epidemic Model Parameter Estimation Fails

10.1101/2020.04.20.20071928 ◽

2020 ◽

Author(s):

Daniel E. Platt ◽

Laxmi Parida ◽

Pierre Zalloua

Keyword(s):

Genetic Association ◽

Transmission Rate ◽

Association Studies ◽

Genetic Association Studies ◽

Personal Space ◽

Model Parameters ◽

Spread Model ◽

Positive Growth ◽

Positive Growth Rate ◽

Rate Limiting

AbstractAn opportunity exists in exploring epidemic modeling as a novel way to determine physiological and demic parameters for genetic association studies on a population/environmental (quasi) epidemiological study level. First, the spread of SARS-COV-2 has produced population specific lineages; second, epidemic spread model parameters are tied directly to these physiological and demic rates (e. g. incubation time, recovery time, transmission rate); and third, these parameters may serve as novel phenotypes to associate with region-specific genetic mutations as well as demic characteristics (e. g. age structure, cultural observance of personal space, crowdedness). Therefore, we sought to understand whether the parameters of epidemic models could be determined from the trajectory of infections, recovery, and hospitalizations prior to peak, and also to evaluate the quality and comparability of data between jurisdictions reporting their statistics necessary for the analysis of model parameters across populations. We found that, analytically, the pre-peak growth of an epidemic is limited by a subset of the model variates, and that the rate limiting variables are dominated by the expanding eigenmode of their equations. The variates quickly converge to the ratio of eigenvector components of the positive growth rate, which determines the doubling time. There are 9 parameters and 4 independent components in the eigenmode, leaving 5 undetermined parameters. Those parameters can be strikingly population dependent, and can have significant impact on estimates of hospital loads downstream. Without a sound framework, measurements of infection rates and other parameters are highly corrupted by uneven testing rates to uneven counting and reporting of relevant values. From the standpoint of phenotype parameters, this means that structured experiments must be performed to estimate these parameters in order to perform genetic association studies, or to construct viable models that accurately predict critical quantities such as hospitalization loads.

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Integrating psychosocial variables and societal diversity in epidemic models for predicting COVID-19 transmission dynamics

10.1101/2020.08.12.20173252 ◽

2020 ◽

Author(s):

Viktor Jirsa ◽

Spase Petkoski ◽

Huifang Wang ◽

Marmaduke Woodman ◽

Jan Fousek ◽

...

Keyword(s):

Human Mobility ◽

Disease Spread ◽

Vulnerable Groups ◽

Psychosocial Variables ◽

Cross Sectional ◽

Societal Factors ◽

Spread Model ◽

Careful Handling ◽

Diverse Groups ◽

The Impact

During the current COVID-19 pandemic, governments must make decisions based on a variety of information including estimations of infection spread, health care capacity, economic and psychosocial considerations. The disparate validity of current short-term forecasts of these factors is a major challenge to governments. By causally linking an established epidemiological spread model with dynamically evolving psychosocial variables, using Bayesian inference we estimate the strength and direction of these interactions for German and Danish data of disease spread, human mobility, and psychosocial factors based on the serial cross-sectional COVID-19 Snapshot Monitoring (COSMO; N = 16,981). We demonstrate that the strength of cumulative influence of psychosocial variables on infection rates is of a similar magnitude as the influence of physical distancing. We further show that the efficacy of political interventions to contain the disease strongly depends on societal diversity, in particular group-specific sensitivity to affective risk perception. As a consequence, the model may assist in quantifying the effect and timing of interventions, forecasting future scenarios, and differentiating the impact on diverse groups as a function of their societal organization. Importantly, the careful handling of societal factors, including support to the more vulnerable groups, adds another direct instrument to the battery of political interventions fighting epidemic spread.

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Let Trajectories Speak Out the Traffic Bottlenecks

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/3465058 ◽

2022 ◽

Vol 13 (1) ◽

pp. 1-21

Author(s):

Hui Luo ◽

Zhifeng Bao ◽

Gao Cong ◽

J. Shane Culpepper ◽

Nguyen Lu Dang Khoa

Keyword(s):

Traffic Volume ◽

Trajectory Data ◽

Trade Offs ◽

Bottleneck Identification ◽

Spread Model ◽

Efficiency And Effectiveness ◽

Historical Trajectory ◽

Algorithmic Approaches ◽

Compare And Contrast ◽

Road Segments

Traffic bottlenecks are a set of road segments that have an unacceptable level of traffic caused by a poor balance between road capacity and traffic volume. A huge volume of trajectory data which captures realtime traffic conditions in road networks provides promising new opportunities to identify the traffic bottlenecks. In this paper, we define this problem as trajectory-driven traffic bottleneck identification : Given a road network R , a trajectory database T , find a representative set of seed edges of size K of traffic bottlenecks that influence the highest number of road segments not in the seed set. We show that this problem is NP-hard and propose a framework to find the traffic bottlenecks as follows. First, a traffic spread model is defined which represents changes in traffic volume for each road segment over time. Then, the traffic diffusion probability between two connected segments and the residual ratio of traffic volume for each segment can be computed using historical trajectory data. We then propose two different algorithmic approaches to solve the problem. The first one is a best-first algorithm BF , with an approximation ratio of 1-1/ e . To further accelerate the identification process in larger datasets, we also propose a sampling-based greedy algorithm SG . Finally, comprehensive experiments using three different datasets compare and contrast various solutions, and provide insights into important efficiency and effectiveness trade-offs among the respective methods.

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M. cerebralis establishment and spread: A graphical synthesis

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2020-0352 ◽

2021 ◽

Author(s):

Pouria Ramazi ◽

Samuel Matthias Fischer ◽

Julie Alexander ◽

Clayton James ◽

Andrew J. Paul ◽

...

Keyword(s):

Large Scale ◽

Graphical Model ◽

Empirical Studies ◽

Model Development ◽

Ecological Impacts ◽

Surveillance Program ◽

Whirling Disease ◽

Spread Model ◽

Considerable Impact ◽

Economic Constraints

M. cerebralis is the parasite causing whirling disease, which has dramatic ecological impacts due to its potential to cause high mortality in salmonids. The large-scale eﬀorts, necessary to underpin an eﬀective surveillance program, have practical and economic constraints. There is, hence, a clear need for models that can predict the parasite spread. Model development, however, often heavily depends on knowing inﬂuential variables and governing mechanisms. We have developed a graphical model for the establishment and spread of M. cerebralis by synthesizing experts’ opinion and empirical studies. First, we conducted a series of workshops with experts to identify variables believed to impact the establishment and spread of the parasite M. cerebralis and visualized their interactions via a directed acyclic graph. Then we reﬁned the graph by incorporating empirical ﬁndings from the literature. The ﬁnal graph’s nodes correspond to variables whose considerable impact on M. cerebralis establishment and spread is either supported by empirical data or conﬁrmed by experts, and the graph’s directed edges represent direct causality or strong correlation. This graphical model facilitates communication and education of whirling disease and provides an empirically driven framework for constructing future models, especially Bayesian networks.

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