No, they can't. Exponentially growing dynamics are intrinsically unpredictable. The time at which the  number of infected individuals  starts decreasing cannot be reliably calculated before it is actually attained . A standard SIR model with confinement shows that infection spread is inhibited only above a threshold . Confinement induces a slow-down in the expansion phase that does not guarantee an eventual control of the epidemic. A Bayesian fit to the on-going COVID-19 pandemic in Spain shows that we can infer neither its peaking time nor whether there is a peak at all. The dispersion of possible trajectories grows extremely fast , yielding a short horizon for reliable prediction. As unpredictability is intrinsic, not due to incomplete or wrong data, our study advocates for a scenario of probabilistic forecasting .
No, they can't. Epidemic spread is characterized by exponentially growing dynamics, which are intrinsically unpredictable. The time at which the growth in the number of infected individuals halts and starts decreasing cannot be calculated with certainty before the turning point is actually attained ; neither can the end of the epidemic after the turning point. An SIR model with confinement (SCIR) illustrates how lockdown measures inhibit infection spread only above a threshold that we calculate. The existence of that threshold has major effects in predictability: A Bayesian fit to the COVID-19 pandemic in Spain shows that a slow-down in the number of newly infected individuals during the expansion phase allows to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that grows extremely fast . The impossibility to predict in the mid-term is not due to wrong or incomplete data, since it persists in error-free, synthetically produced data sets, and does not necessarily improve by using larger data sets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective or phenomenological models, and supports that only probabilities of different outcomes can be confidently given .
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Exponentially growing dynamics
Epidemic spread is characterized by exponentially growing dynamics, which
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growth in the
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halts and
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reliably calculated before it
calculated with certainty before the turning point
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. A standard
; neither can the end of the epidemic after the turning point. An
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shows that infection spread is inhibited
(SCIR) illustrates how lockdown measures inhibit infection spread
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. Confinement induces a slow-down in the expansion phase that does not guarantee an eventual control of the epidemic.
that we calculate. The existence of that threshold has major effects in predictability:
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on-going
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we can infer neither its peaking time nor whether there is a peak at all. The dispersion of possible trajectories
a slow-down in the number of newly infected individuals during the expansion phase allows to infer neither the precise position of the maximum nor whether the measures taken will bring the propagation to the inhibition regime. There is a short horizon for reliable prediction, followed by a dispersion of the possible trajectories that
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, yielding a short horizon for reliable prediction. As unpredictability is intrinsic,
. The impossibility to predict in the mid-term is
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incomplete or wrong data, our study advocates for a scenario of probabilistic forecasting
wrong or incomplete data, since it persists in error-free, synthetically produced data sets, and does not necessarily improve by using larger data sets. Our study warns against precise forecasts of the evolution of epidemics based on mean-field, effective or phenomenological models, and supports that only probabilities of different outcomes can be confidently given