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Table 3 Design of IBM studies on vaccine-preventable childhood diseases, excluding influenza

From: Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

Reference Topic, setting Purpose State variables Population Time horizon, step size Realiza-tions Platform Reason IBM Terminology
Grais et al. [91] Measles, Niger Reactive vaccination Age, social mixing patterns, location 346.254 people 1 year, per day 1.000x - Spatio-temporal interventions and coverage IBM
Perez and Dragicevic [24] Measles, Canada Dynamics in spatial context Social mixing patterns 1.000 people 60 days, per hour 1x RepastS Spatio-temporal analysis ABM
Liu et al. [80] Measles, USA Reactive vaccination and contact tracing Age, social mixing patterns, compliance 118.261 people 1 year, per day 256x C++ (FRED) Contact tracing and clustering ABM
Marguta and Parisi [84] Measles, UK Dynamics using detailed mobility patterns Preferred locations “British Isles” 60 years, per day 100x - Mobility patterns IBM
Thompson and Kisjes [92] Measles, USA Amish Outbreak response in connected under-vaccinated subpopulations Age, gender, social mixing patterns, conservatism, compliance 280.000 people (dynamic) 1 year, per 30 min 1000x Netlogo Clustering IBM
Martinez et al. [85] Meningococcus, Grid Dynamics with immunity Network location 1.000 people (static) 60 days, per day 1x Mathe-matica Spatio-temporal analysis CA
Pérez-Breva et al. [99] Meningococcus, Spain Vaccination Age, serotype 1 million people (dynamic) 36 years, per month - - Serotype dynamics with spatial analysis ABM
Poore and Bauch [101] Meningococcus, Canada Vaccination and serotype groups Age, serotype - (dynamic) 300 years, per month 50x - Serotype dynamics with spatial analysis ABM
Monteiro et al. [86] Varicella, USA Dynamics and parameter fitting Network position, neighbors 1 million people 11 years, per month - - Spatio-temporal analysis CA
Silhol and Boëlle [88] Varicella, Corsica Dynamics and parameter fitting Age, social mixing patterns 35.000 children (dynamic) 100 years, per day 500x - Spatio-temporal analysis with clustering ABM, IBM
Ogunjimi et al. [87] Varicella, Belgium Dynamics and parameter fitting Age, cellular mediated immunity 998.400 people (dynamic) 320 years, per week 3x MATLAB Within-host cellular immunity IBM
Ajelli and Merler [103] Hepatitis A, Italy Household dynamics, vaccination, NPI Age, social mixing patterns 5.701.931 people (dynamic) 50 years, per week - - Clustering and assessment of real-world interventions IBM
Karlsson et al. [102] Pneumococcus, Sweden NPI Age, social mixing patterns 25.000 people 15 years, per week 100x MATLAB Spatio-temporal analysis IBM
Saito et al. [83] Pneumococcus, Grid Dynamics with antibiotics Network location, social mixing behavior 2.500 people 400 days, per day 100x - Spatio-temporal analysis CA
Choi et al. [98] Pneumococcus, UK Vaccination Serotype 48 million people (dynamic) 20 years, per week 10x MATLAB Serotype dynamics with spatial analysis IBM
Flasche et al. [97] Pneumococcus, UK Vaccination Age, serotype 243.792 people (dynamic) 30 years, per day - C++ (on request) Serotype dynamics with spatial analysis IBM
Nurhonen et al. [100] Pneumococcus, Finland Vaccination and serotype replacement Age, social mixing patterns, serotype 100.000 people (dynamic) 100 years, per day 50x C++ Serotype dynamics with spatial analysis ABM, IBM, micro-simulation
Rahmandad et al. [89] Polio, Low-income country Dynamics Age, social mixing patterns 100.000 people (dynamic) 2000 days, per day 1000x AnyLogic Spatio-temporal analysis ABM, IBM
Kisjes et al. [23] Polio, USA Amish Dynamics in connected under-vaccinated subpopulations Age, gender, social mixing patterns 276.000 people (dynamic) 3 years, per 30 min 1000x Netlogo Clustering IBM
Wagner et al. [93] Polio, Nigeria Expanded age group vaccination programs Age, gender, risk factors 300.000 people (dynamic) 40 years, event-driven - C++ (EMOD) Spatio-temporal risk factors IBM
Kim and Rho [96] Polio (vaccine-derived), Grid Dynamics with immunity and vaccine-related side effects Network location 100.000 people 25 years, per day 100x - Spatio-temporal analysis IBM
Greer and Fisman [94] Pertussis, USA Booster vaccination programs in hospital setting Social mixing behavior 38 patients with health care workers and family 3 month, per day 1000x AnyLogic Spatio-temporal analysis ABM
de Vries et al. [95] Pertussis, The Netherlands Universal booster vaccination programs Age 150.000 people (dynamic) 25 years, event-driven 20x Arena Individual stochastic disease burden IBM
Sanstead et al. [90] Pertussis, USA Dynamics Age 400.000 people -, per day 100x NetLogo Within-host dynamics with spatial analysis ABM
  1. Studies are listed by topic. The state variables express the individual heterogeneity next to health-states (e.g., SIR, SIRV,... etc.). If multiple experiments are described, the maximal time horizon, minimal step size and maximum number of realizations are presented. A “dynamic population” considers next to health state also socio-demographical changes over time, such as aging and household alterations. NPI: all non-pharmaceutical intervention strategies, “-”: unknown