Predicting when peaks will occur, ex ante. Insights from the COVID-19 Pandemic in Italy and Belgium

In this paper we advance a set of heuristics which allow to predict ex ante the peak of diffusion curves and apply these heuristics on the COVID-19 pandemic (casualties). The heuristics build on innovation diffusion models and combine an extensive grid search with a loss function. The grid search is designed such that multi-finality (different end states) can unfold; the loss function takes into account the fit with a limited set of available observations. No assumptions are made ex ante in terms of the timing of inflection points. As such, these heuristics combine scenario thinking with forecasting algorithms (scenario driven forecasting). The heuristics have been applied for both Italy and Belgium as a whole as well as for decomposed time series based on policy relevant cohorts (regions (IT); hospital / residential care centers (BE)). While actually observed peaks (including Black Friday in Italy) are consistently falling into the predicted time range, we also observe that the predictive validity increases when decomposed time series – coinciding with cohorts displaying different, but policy relevant, diffusion dynamics– are being introduced. As the heuristics implied are agnostic in terms of epidemiological parameters – which are unknown in the case of a novel pathogen – they provide a decision-making space which is highly informative in situations characterized by levels of Knightian uncertainty. As such, scenario driven forecasting might become a valuable alternative and complement both for more qualitative approaches as well as for hope (and guesses) in decision making contexts characterized by such profound uncertainty. Acknowledgment: This contribution benefited from useful input and reflections by Jorge Ricardo Blanco Nova (KU Leuven), Sien Luyten (Flanders Business School), Xiaoyan Song (KU Leuven) and Stijn Kelchtermans (KU Leuven) and feedback from our students at Flanders Business School (MBA; Panther Program) and KU Leuven (Strategy and Innovation; Technology Trends and Opportunities). We want to express our gratitude to the Rega Institute and the Institute for the Future for providing a context to validate our models, and to EURO POOL GROUP for funding part of the research reported here.