scholarly journals S2M: A Stochastic Simulation Model of Poliovirus Genetic State Transition

2016 ◽  
Vol 10 ◽  
pp. BBI.S38194 ◽  
Author(s):  
Carol L. Ecale Zhou
Keyword(s):  
Simulation Model ◽  
Stochastic Simulation ◽  
State Transition ◽  
Stochastic Simulation Model

Stochastic simulation for determining the design flood of cascade reservoir systems

2012 ◽  
Vol 43 (1-2) ◽  
pp. 54-63 ◽  
Author(s):  
Baohong Lu ◽  
Huanghe Gu ◽  
Ziyin Xie ◽  
Jiufu Liu ◽  
Lejun Ma ◽  
...  
Keyword(s):  
Simulation Model ◽  
Stochastic Simulation ◽  
Southern China ◽  
Flow Simulation ◽  
Simulation Method ◽  
Simulation Approach ◽  
Design Flood ◽  
Design Values ◽  
Stochastic Simulation Model ◽  
The Impact

Stochastic simulation is widely applied for estimating the design flood of various hydrosystems. The design flood at a reservoir site should consider the impact of upstream reservoirs, along with any development of hydropower. This paper investigates and applies a stochastic simulation approach for determining the design flood of a complex cascade of reservoirs in the Longtan watershed, southern China. The magnitude of the design flood when the impact of the upstream reservoirs is considered is less than that without considering them. In particular, the stochastic simulation model takes into account both systematic and historical flood records. As the reliability of the frequency analysis increases with more representative samples, it is desirable to incorporate historical flood records, if available, into the stochastic simulation model. This study shows that the design values from the stochastic simulation method with historical flood records are higher than those without historical flood records. The paper demonstrates the advantages of adopting a stochastic flow simulation approach to address design-flood-related issues for a complex cascade reservoir system.

scholarly journals Estimating the economic impact of subclinical ketosis in dairy cattle using a dynamic stochastic simulation model

animal ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 145-154 ◽  
Author(s):  
P.F. Mostert ◽  
E.A.M. Bokkers ◽  
C.E. van Middelaar ◽  
H. Hogeveen ◽  
I.J.M. de Boer
Keyword(s):  
Dairy Cattle ◽  
Simulation Model ◽  
Economic Impact ◽  
Stochastic Simulation ◽  
Subclinical Ketosis ◽  
Dynamic Stochastic ◽  
Stochastic Simulation Model

scholarly journals Random-Walk, Agent-Level Pandemic Simulation (RAW-ALPS) for Analyzing Effects of Different Lockdown Measures

2021 ◽  
Vol 7 ◽  
Author(s):  
Anuj Srivastava
Keyword(s):  
Random Walk ◽  
Simulation Model ◽  
Random Walks ◽  
Stochastic Simulation ◽  
Preventive Measures ◽  
Infection Rates ◽  
Physical Proximity ◽  
Stochastic Simulation Model ◽  
Restrictive Measures ◽  
Effective Use

This article develops an agent-level stochastic simulation model, termed RAW-ALPS, for simulating the spread of an epidemic in a community. The mechanism of transmission is agent-to-agent contact, using parameters reported for the COVID-19 pandemic. When unconstrained, the agents follow independent random walks and catch infections due to physical proximity with infected agents. Under lockdown, an infected agent can only infect a coinhabitant, leading to a reduction in the spread. The main goal of the RAW-ALPS simulation is to help quantify the effects of preventive measures—timing and durations of lockdowns—on infections, fatalities, and recoveries. The model helps measure changes in infection rates and casualties due to the imposition and maintenance of restrictive measures. It considers three types of lockdowns: 1) whole population (except the essential workers), 2) only the infected agents, and 3) only the symptomatic agents. The results show that the most effective use of lockdown measures is when all infected agents, including both symptomatic and asymptomatic, are quarantined, while the uninfected agents are allowed to move freely. This result calls for regular and extensive testing of a population to isolate and restrict all infected agents.

scholarly journals Estimating US dairy clinical disease costs with a stochastic simulation model

2017 ◽  
Vol 100 (2) ◽  
pp. 1472-1486 ◽  
Author(s):  
D. Liang ◽  
L.M. Arnold ◽  
C.J. Stowe ◽  
R.J. Harmon ◽  
J.M. Bewley
Keyword(s):  
Simulation Model ◽  
Stochastic Simulation ◽  
Clinical Disease ◽  
Disease Costs ◽  
Stochastic Simulation Model
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