COVID-19 Simulation Model
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A COVID-19 Simulation Model is an Infectious Disease model for COVID-19.
- Example(s):
- CoSim19,
- COVID-19 Mobility Mdeling,
- COVID-19 Simulator,
- COVID-19 Surge,
- CovidSim (Imperial College London and MRC Centre for Global Infectious Disease Analysis),
- CovidSim (Research project by Munich University of Applied Sciences),
- COVIDSim (Ng and Gui),
- CovidSIM.eu,
- CovRadar,
- Event Horizon - COVID-19,
- OpenCOVID,
- …
- Counter-Example(s):
- See: Real-Time Data, Mathematical Modelling of Infectious Disease, COVID-19, COVID-19 App, Digital Contact Tracing.
References
2023
- (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/List_of_COVID-19_simulation_models Retrieved:2023-6-22.
- COVID-19 simulation models are mathematical infectious disease models for the spread of COVID-19.[1] The list should not be confused with COVID-19 apps used mainly for digital contact tracing.
Note that some of the applications listed are website-only models or simulators, and some of those rely on (or use) real-time data from other sources.
- COVID-19 simulation models are mathematical infectious disease models for the spread of COVID-19.[1] The list should not be confused with COVID-19 apps used mainly for digital contact tracing.
- ↑ Adam D (April 2020). "Special report: The simulations driving the world's response to COVID-19". Nature. 580 (7803): 316–318. Bibcode:2020Natur.580..316A. doi:10.1038/d41586-020-01003-6. PMID 32242115.
2022
- (Ioannidis et al., 2022) ⇒ John P.A. Ioannidis, Sally Cripps, and Martin A. Tanner. (2022). “Forecasting for COVID-19 Has Failed.” In: International Journal of Forecasting, 38(2). DOI:10.1016%2Fj.ijforecast.2020.08.004
- ABSTRACT: Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, consideration of only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects, and selective reporting are some of the causes of these failures. Nevertheless, epidemic forecasting is unlikely to be abandoned. Some (but not all) of these problems can be fixed. Careful modeling of predictive distributions rather than focusing on point estimates, considering multiple dimensions of impact, and continuously reappraising models based on their validated performance may help. If extreme values are considered, extremes should be considered for the consequences of multiple dimensions of impact so as to continuously calibrate predictive insights and decision-making. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence.
- KEYWORDS: Forecasting, COVID-19, Mortality, Hospital bed utilization, Bayesian models, SIR models, Bias, Validation
- QUOTE: COVID-19 is a major acute crisis with unpredictable consequences. Many scientists have struggled to make forecasts about its impact (Holmdahl & Buckee, 2020). However, despite involving many excellent modelers, best intentions, and highly sophisticated tools, forecasting efforts have largely failed.