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