Abstract
We tried to climb too high the complexity ladder. In this chapter and the following, we’ll understand how far models can go and why. We’ll first explore three different ways of forecasting an epidemic outbreak, to show how complexity can sometimes be tamed. The next chapter explains why meteorological models have succeeded in forecasting the complex evolutions of the atmosphere.
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The individual model for Covid-19 in France: Hoertel, N., Blachier, M., Blanco, C. et al. A stochastic agent-based model of the SARS-CoV-2 epidemic in France. Nat Med 26, 1417–1421 (2020) and for Liberia: Spatiotemporal spread of the 2014 outbreak of Ebola virus disease in Liberia and the effectiveness of non-pharmaceutical interventions: A computational modelling analysis, Lancet Infect. Dis., 15, 2015.
Arguments for formal modeling in epidemiology: “Mathematical models: A key tool for outbreak response”, PNAS, 111 2014, 18095. Unfortunately, epidemic forecasts made during an outbreak are rarely investigated during or after the event for their accuracy. A recent exception is Funk, S. et al. PLoS Comput. Biol. 15, e1006785 (2019) who noted that forecasts made in a 2014–15 Ebola outbreak in Sierra Leone reliably predicted the epidemic’s course one or two weeks ahead of time, but no longer.
A critique of the “black box” flu models developed by Google: http://gking.harvard.edu/files/gking/files/0314policyforumff.pdf
On the competition for flu prediction in the United States: http://www.cdc.gov/flu/news/predict-flu-challenge-winner.htm
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Jensen, P. (2021). Modelling Epidemics. In: Your Life in Numbers: Modeling Society Through Data. Copernicus, Cham. https://doi.org/10.1007/978-3-030-65103-9_9
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DOI: https://doi.org/10.1007/978-3-030-65103-9_9
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