scholarly journals Using Uncertainty and Sensitivity Analyses in Socioecological Agent-Based Models to Improve Their Analytical Performance and Policy Relevance

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109779 ◽  
Author(s):  
Arika Ligmann-Zielinska ◽  
Daniel B. Kramer ◽  
Kendra Spence Cheruvelil ◽  
Patricia A. Soranno
Keyword(s):  
Analytical Performance ◽  
Sensitivity Analyses ◽  
Agent Based Models ◽  
Agent Based ◽  
Policy Relevance

scholarly journals Sensitivity analysis of agent-based models: a new protocol

2022 ◽  
Author(s):  
Emanuele Borgonovo ◽  
Marco Pangallo ◽  
Jan Rivkin ◽  
Leonardo Rizzo ◽  
Nicolaj Siggelkow
Keyword(s):  
Sensitivity Analysis ◽  
Factorial Design ◽  
Conditional Expectation ◽  
Systematic Approach ◽  
Sensitivity Analyses ◽  
Agent Based Models ◽  
Agent Based ◽  
Garbage Can Model ◽  
Stochastic Nature ◽  
Common Goals

AbstractAgent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such critiques, we propose a systematic approach to conducting sensitivity analyses of ABMs. Our approach deals with a feature that can complicate sensitivity analyses: most ABMs include important non-parametric elements, while most sensitivity analysis methods are designed for parametric elements only. The approach moves from charting out the elements of an ABM through identifying the goal of the sensitivity analysis to specifying a method for the analysis. We focus on four common goals of sensitivity analysis: determining whether results are robust, which elements have the greatest impact on outcomes, how elements interact to shape outcomes, and which direction outcomes move when elements change. For the first three goals, we suggest a combination of randomized finite change indices calculation through a factorial design. For direction of change, we propose a modification of individual conditional expectation (ICE) plots to account for the stochastic nature of the ABM response. We illustrate our approach using the Garbage Can Model, a classic ABM that examines how organizations make decisions.

scholarly journals Public health impact of delaying second dose of BNT162b2 or mRNA-1273 covid-19 vaccine: simulation agent based modeling study

BMJ ◽  
2021 ◽  
pp. n1087
Author(s):  
Santiago Romero-Brufau ◽  
Ayush Chopra ◽  
Alex J Ryu ◽  
Esma Gel ◽  
Ramesh Raskar ◽  
...  
Keyword(s):  
Hospital Admissions ◽  
Vaccination Strategy ◽  
Population Based ◽  
Agent Based Modeling ◽  
Sensitivity Analyses ◽  
Agent Based ◽  
Cumulative Mortality ◽  
Vaccination Rates ◽  
Dose Strategy ◽  
Modeling Study

AbstractObjectiveTo estimate population health outcomes with delayed second dose versus standard schedule of SARS-CoV-2 mRNA vaccination.DesignSimulation agent based modeling study.SettingSimulated population based on real world US county.ParticipantsThe simulation included 100 000 agents, with a representative distribution of demographics and occupations. Networks of contacts were established to simulate potentially infectious interactions though occupation, household, and random interactions.InterventionsSimulation of standard covid-19 vaccination versus delayed second dose vaccination prioritizing the first dose. The simulation runs were replicated 10 times. Sensitivity analyses included first dose vaccine efficacy of 50%, 60%, 70%, 80%, and 90% after day 12 post-vaccination; vaccination rate of 0.1%, 0.3%, and 1% of population per day; assuming the vaccine prevents only symptoms but not asymptomatic spread (that is, non-sterilizing vaccine); and an alternative vaccination strategy that implements delayed second dose for people under 65 years of age, but not until all those above this age have been vaccinated.Main outcome measuresCumulative covid-19 mortality, cumulative SARS-CoV-2 infections, and cumulative hospital admissions due to covid-19 over 180 days.ResultsOver all simulation replications, the median cumulative mortality per 100 000 for standard dosing versus delayed second dose was 226 v 179, 233 v 207, and 235 v 236 for 90%, 80%, and 70% first dose efficacy, respectively. The delayed second dose strategy was optimal for vaccine efficacies at or above 80% and vaccination rates at or below 0.3% of the population per day, under both sterilizing and non-sterilizing vaccine assumptions, resulting in absolute cumulative mortality reductions between 26 and 47 per 100 000. The delayed second dose strategy for people under 65 performed consistently well under all vaccination rates tested.ConclusionsA delayed second dose vaccination strategy, at least for people aged under 65, could result in reduced cumulative mortality under certain conditions.

scholarly journals Agent Based Models of Polymicrobial Biofilms and the Microbiome—A Review

2021 ◽  
Vol 9 (2) ◽  
pp. 417
Author(s):  
Sherli Koshy-Chenthittayil ◽  
Linda Archambault ◽  
Dhananjai Senthilkumar ◽  
Reinhard Laubenbacher ◽  
Pedro Mendes ◽  
...  
Keyword(s):  
Species Interactions ◽  
Computational Models ◽  
Human Microbiome ◽  
Agent Based Modeling ◽  
Autonomous Agent ◽  
Agent Based Models ◽  
Agent Based ◽  
Complex Interactions ◽  
Mucosal Surfaces ◽  
Living Organisms

The human microbiome has been a focus of intense study in recent years. Most of the living organisms comprising the microbiome exist in the form of biofilms on mucosal surfaces lining our digestive, respiratory, and genito-urinary tracts. While health-associated microbiota contribute to digestion, provide essential nutrients, and protect us from pathogens, disturbances due to illness or medical interventions contribute to infections, some that can be fatal. Myriad biological processes influence the make-up of the microbiota, for example: growth, division, death, and production of extracellular polymers (EPS), and metabolites. Inter-species interactions include competition, inhibition, and symbiosis. Computational models are becoming widely used to better understand these interactions. Agent-based modeling is a particularly useful computational approach to implement the various complex interactions in microbial communities when appropriately combined with an experimental approach. In these models, each cell is represented as an autonomous agent with its own set of rules, with different rules for each species. In this review, we will discuss innovations in agent-based modeling of biofilms and the microbiota in the past five years from the biological and mathematical perspectives and discuss how agent-based models can be further utilized to enhance our comprehension of the complex world of polymicrobial biofilms and the microbiome.

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