Modelling lockdown-induced 2 nd COVID waves in France
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Abstract
As with the Spanish Flu a century ago, authorities have responded to the current COVID-19 pandemic with extraordinary public health measures. In particular, lockdown and related social distancing policies are motivated in some countries by the need to slow virus propagation—so that the primary wave of patients suffering from severe forms of COVID infection do not exceed the capacity of intensive care units. But unlocking poses a critical issue because relaxing social distancing may, in principle, generate secondary waves. Ironically however, the dynamic repertoire of established epidemiological models that support this kind of reasoning is limited to single epidemic outbreaks. In turn, predictions regarding secondary waves are tautologically derived from imposing assumptions about changes in the so-called “effective reproduction number”. In this work, we depart from this approach and extend the LIST (Location-Infection-Symptom-Testing) model of the COVID pandemic with realistic nonlinear feedback mechanisms that under certain conditions, cause lockdown-induced secondary outbreaks. The original LIST model captures adaptive social distancing, i . e . the transient reduction of the number of person-to-person contacts (and hence the rate of virus transmission), as a societal response to salient public health risks. Here, we consider the possibility that such pruning of socio-geographical networks may also temporarily isolate subsets of local populations from the virus. Crucially however, such unreachable people will become susceptible again when adaptive social distancing relaxes and the density of contacts within socio-geographical networks increases again. Taken together, adaptive social distancing and network unreachability thus close a nonlinear feedback loop that endows the LIST model with a mechanism that can generate autonomous (lockdown-induced) secondary waves. However, whether and how secondary waves arise depend upon the interaction with other nonlinear mechanisms that capture other forms of transmission heterogeneity. We apply the ensuing LIST model to numerical simulations and exhaustive analyses of regional French epidemiological data. In brief, we find evidence for this kind of nonlinear feedback mechanism in the empirical dynamics of the pandemic in France. However, rather than generating catastrophic secondary outbreaks (as is typically assumed), the model predicts that the impact of lockdown-induced variations in population susceptibility and transmission may eventually reduce to a steady-state endemic equilibrium with a low but stable infection rate.
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SciScore for 10.1101/2020.06.24.20139444: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:At this point, we acknowledge a few weaknesses of our approach. First, we derived our predictions from model-based analyses of available epidemiological French data: namely, daily death rates, positive case rates, ICU occupancy and remission rates. These time series are already more informative than most data available from …
SciScore for 10.1101/2020.06.24.20139444: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:At this point, we acknowledge a few weaknesses of our approach. First, we derived our predictions from model-based analyses of available epidemiological French data: namely, daily death rates, positive case rates, ICU occupancy and remission rates. These time series are already more informative than most data available from international data repositories (which only report death and positive case rates). Nevertheless, currently available data are noisy (cf. weekly artefactual drops, heterogeneous sampling by testing laboratories, etc.), preliminary (from the perspective of the global time course of the pandemic), and scarce (they do not relate to all hidden factors of the LIST model). In particular, should time-resolved data regarding population adherence to social distancing and/or immunity levels be available, the reliability of model-based predictions will be considerably strengthened (Daunizeau et al., 2020). Organizing systematic reporting of these types of data during this or future epidemics should be a matter of high priority for quantitative epidemiology. Second, we did not evaluate the sensitivity of model-based predictions to prior constraints on unknown model parameters. This, in fact, is directly related to the previous comment, because if the data were sufficiently informative, then the inference would not depend upon priors. Having said this, we do not claim that our predictions are more (or less) reliable than those derived from established epidemiologic mode...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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