scholarly journals Simulating Market Entry Rewards for Antibiotics Development

2018 ◽  
Vol 46 (S1) ◽  
pp. 32-42 ◽  
Author(s):  
Christopher Okhravi ◽  
Simone Callegari ◽  
Steve McKeever ◽  
Carl Kronlid ◽  
Enrico Baraldi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Research And Development ◽  
Market Entry ◽  
Monte Carlo Model ◽  
Clinical Development ◽  
Policy Intervention ◽  
Venture Capitalists ◽  
Agent Based ◽  
Reducing Costs

We design an agent based Monte Carlo model of antibiotics research and development (R&D) to explore the effects of the policy intervention known as Market Entry Reward (MER) on the likelihood that an antibiotic entering pre-clinical development reaches the market. By means of sensitivity analysis we explore the interaction between the MER and four key parameters: projected net revenues, R&D costs, venture capitalists discount rates, and large pharmaceutical organizations' financial thresholds. We show that improving revenues may be more efficient than reducing costs, and thus confirm that this pull-based policy intervention effectively stimulates antibiotics R&D.

An on-lattice agent-based Monte Carlo model simulating the growth kinetics of multicellular tumor spheroids

2020 ◽  
Vol 77 ◽  
pp. 194-203
Author(s):  
S. Ruiz-Arrebola ◽  
A.M. Tornero-López ◽  
D. Guirado ◽  
M. Villalobos ◽  
A.M. Lallena
Keyword(s):  
Monte Carlo ◽  
Growth Kinetics ◽  
Monte Carlo Model ◽  
Multicellular Tumor Spheroids ◽  
Tumor Spheroids ◽  
Agent Based ◽  
Kinetics Of

scholarly journals Evaluation of Classical Mathematical Models of Tumor Growth Using an On-Lattice Agent-Based Monte Carlo Model

2021 ◽  
Vol 11 (11) ◽  
pp. 5241
Author(s):  
Samuel Ruiz-Arrebola ◽  
Damián Guirado ◽  
Mercedes Villalobos ◽  
Antonio M. Lallena
Keyword(s):  
Monte Carlo ◽  
Mathematical Models ◽  
Monte Carlo Model ◽  
Growth Curves ◽  
Gompertz Model ◽  
Growth Data ◽  
Multicellular Spheroids ◽  
Agent Based ◽  
Simulation Parameters ◽  
Pseudo Data

Purpose:To analyze the capabilities of different classical mathematical models to describe the growth of multicellular spheroids simulated with an on-lattice agent-based Monte Carlo model that has already been validated. Methods: The exponential, Gompertz, logistic, potential, and Bertalanffy models have been fitted in different situations to volume data generated with a Monte Carlo agent-based model that simulates the spheroid growth. Two samples of pseudo-data, obtained by assuming different variability in the simulation parameters, were considered. The mathematical models were fitted to the whole growth curves and also to parts of them, thus permitting to analyze the predictive power (both prospective and retrospective) of the models. Results: The consideration of the data obtained with a larger variability of the simulation parameters increases the width of the χ2 distributions obtained in the fits. The Gompertz model provided the best fits to the whole growth curves, yielding an average value of the χ2 per degree of freedom of 3.2, an order of magnitude smaller than those found for the other models. Gompertz and Bertalanffy models gave a similar retrospective prediction capability. In what refers to prospective prediction power, the Gompertz model showed by far the best performance. Conclusions: The classical mathematical models that have been analyzed show poor prediction capabilities to reproduce the MTS growth data not used to fit them. Within these poor results, the Gompertz model proves to be the one that better describes the growth data simulated. The simulation of the growth of tumors or multicellular spheroids permits to have follow-up periods longer than in the usual experimental studies and with a much larger number of samples: this has permitted performing the type of analysis presented here.

scholarly journals Sensitivity analysis of Monte Carlo model of a gantry‐mounted passively scattered proton system

2020 ◽  
Vol 21 (2) ◽  
pp. 26-37 ◽  
Author(s):  
Milad Baradaran‐Ghahfarokhi ◽  
Francisco Reynoso ◽  
Michael T. Prusator ◽  
Baozhou Sun ◽  
Tianyu Zhao
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Model ◽  
Scattered Proton

Investigating the reliability of a hedonic travel cost model: a Monte Carlo approach

1994 ◽  
Vol 24 (2) ◽  
pp. 358-363 ◽  
Author(s):  
Michael S. Common ◽  
Daniel W. McKenney
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Cost Model ◽  
Monte Carlo Model ◽  
Consumer Surplus ◽  
Travel Cost ◽  
Management Decision ◽  
Travel Cost Model ◽  
Management Decision Making

The reliability of nonmarket welfare estimates has been examined by analysts in a variety of contexts. Much of the focus of previous work has been on individual, rather than aggregate values. This paper examines the reliability of aggregate consumer surplus estimates via a Monte Carlo model. The basic elements of a hedonic travel cost model are represented in a forest management decision-making context. One result is that what would appear as minor errors in visitor estimates between sites has a significant impact on aggregate consumer surplus estimates. The results serve to emphasize that sensitivity analysis is critical when using nonmarket welfare estimates for decision making.

Sensitivity analysis of a Monte Carlo atmospheric diffusion model

1988 ◽  
Vol 11 (1) ◽  
pp. 13-28 ◽  
Author(s):  
D. Anfossi ◽  
G. Brusasca ◽  
G. Tinarelli
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Diffusion Model ◽  
Atmospheric Diffusion

scholarly journals Drone Ground Impact Footprints with Importance Sampling: Estimation and Sensitivity Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3871
Author(s):  
Jérôme Morio ◽  
Baptiste Levasseur ◽  
Sylvain Bertrand
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Importance Sampling ◽  
Loss Of Control ◽  
Minimum Volume ◽  
Multiple Importance Sampling ◽  
Impact Position ◽  
Engine Failure ◽  
Main Engine ◽  
Ground Impact

This paper addresses the estimation of accurate extreme ground impact footprints and probabilistic maps due to a total loss of control of fixed-wing unmanned aerial vehicles after a main engine failure. In this paper, we focus on the ground impact footprints that contains 95%, 99% and 99.9% of the drone impacts. These regions are defined here with density minimum volume sets and may be estimated by Monte Carlo methods. As Monte Carlo approaches lead to an underestimation of extreme ground impact footprints, we consider in this article multiple importance sampling to evaluate them. Then, we perform a reliability oriented sensitivity analysis, to estimate the most influential uncertain parameters on the ground impact position. We show the results of these estimations on a realistic drone flight scenario.

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