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Back Forecasts, scenarios, projections: how to predict the pandemic’s performance

Rome, 18 August 2021

 

 

Estimate, with relative uncertainty (forecast range), of the future trend of the epidemic (e.g. in terms of number of cases, hospital admissions, and expected deaths over time).

In addition to knowing the parameters of the pathogen’s natural history, a forecast also requires knowing in advance and in quantitative terms, all the events that may affect the future dynamics of the epidemic. In addition to many others, what might turn out to be particularly relevant are the social distancing measures that will be adopted (which and when), the vaccination coverage that will be progressively reached, the citizens’ individual behaviour. These factors can be more or less decisive, depending on the epidemic that is being analysed. Indeed, the predictability of an epidemic greatly depends on the severity of the infection.  

In the case of SARS-CoV-2, these are the reasons that currently make it extremely complex to make predictions on the progress of the epidemic. Instead, a forecast can be made in the case of a virus whose behaviour is known, for example the mosquito-transmitted West Nile virus, which can reasonably be forecast to peak in the summer, or the flu, which performs similarly every year (except for the 2020-2021 season, when social distancing considerably affected the infection rate curve, as well as in the 2009-2010 pandemic season).   

 

Fig 1. Forecast of the autumn trend of the H1N1 pandemic in 2009 in Italy [1].

 

Scenario Analyses

Estimate, with relative uncertainty, of the future trend of the epidemic, under the condition that the events that define the scenario occur.

As it is impossible to know in advance all the events that can affect the future dynamics of the epidemic, we can nonetheless formulate different hypotheses (to define the scenario) and analyse the corresponding dynamics.

Scenario analyses, associated for example to the different measures that can be adopted (both mandated and restrictive), or the different level of vaccination coverage targeted, are particularly useful in making an estimate of the risk.

For example, in the event several intervention options are being debated, scenario analyses enable us to single out the interventions capable of maintaining the epidemic under control and of very probably providing estimates of the epidemic risk (e.g., number of cases, hospital admissions, and expected deaths over time), associated with actions that do not assure epidemic control.

In general, when retrospectively evaluating the quality of scenario analyses, it is best not to confuse them with forecasts. First, it is necessary to make an in-depth evaluation of the actions that were actually performed (e.g., in terms of interventions), then verify if there were scenarios that envisioned what actually happened (again in terms of the same interventions) and, if so, compare what actually observed with the simulated epidemics in that specific scenario. It is quite clear that there can only be one scenario compatible with what is observed.

Several scenario analyses relative to SARS-CoV-2 were conducted in April 2020, ahead of lifting the lockdown. On that occasion, an estimate was made of the possible impact this would have on several parameters (hospitalizations, deaths) according to the different degrees to which the restrictions were eased. The scenario to be compared against the epidemic observed is the one in which it is assumed: i) that schools would not reopen; ii) that working activities would gradually reopen as of the 4th of May (almost completely as of the 18th of May), while maintaining a high percentage of telework, especially in basic services; iii) that restrictions on social gatherings would be maintained. The results of this scenario are extremely consistent with the epidemic observed in the summer of 2020. Obviously, in the scenarios that envisioned early or more permissive reopenings in terms of social contacts (e.g., fully reopening activities as before the epidemic) the impact on the healthcare system was estimated to be much higher the one observed.

 

Fig.2 Scenario analyses on the lifting of the lockdown in April 2020 [2].

 

 

Projection

Estimate, with relative uncertainty, of the future trend of the epidemic, under the condition that the parameters that regulate the epidemic (e.g. R number) do not change in the future.

A projection is a particular scenario analysis (status quo scenario) and is a useful risk indicator.

Projections are usually a useful tool in the short run. When the analysis period is particularly short (and therefore it is not reasonable to expect substantial changes in the parameters during that period), a projection can also be considered to be a short-term forecast.

An example of a projection is that on the hospital bed occupancy (in medical wards and intensive care units) which is usually reported in the weekly monitoring of the epidemic, in which it is assumed that the R number remains constant and that there are no substantial changes in the other parameters. The calculation method, based on the renewal equation and the epidemiological estimates on admissions in the medical wards and intensive care units, is described in [3,4]. These 30-day projections cannot be interpreted as short-term forecasts.

Projections of the SARS-CoV-2 outbreak were also made in the summer of 2020 to assess the possible course of the outbreak in the fall. The mitigation measures, and in particular the DPCM of November 3, 2020 which established the yellow, orange and red risk zones, made it possible to greatly reduce the impact of the epidemic [4].

 

Fig.3 Projections (scenario status quo, without mitigating actions) of the SARS-CoV-2 epidemic in the 2020 fall season [2].

 

 

 

 

[1] M. Ajelli, S. Merler, A. Pugliese, C. Rizzo. Model predictions and evaluation of possible control strategies for the 2009 A/H1N1v influenza pandemic in Italy. Epidemiology & Infection, 139: 68 – 79, 2011.

[2] V. Marziano, G. Guzzetta, BM Rondinone, F. Boccuni, F. Riccardo, A. Bella, P. Poletti, F. Trentini, P. Pezzotti, S. Brusaferro, G. Rezza, S. Iavicoli, M. Ajelli, S. Merler. Retrospective analysis of the Italian exit strategy from COVID-19 lockdown. PNAS, 118: e2019617118, 2021.

[3] G. Guzzetta, S. Merler. Stime della trasmissibilità di SARS-CoV-2 in Italia. Disponibile online https://www.epicentro.iss.it/coronavirus/open-data/rt.pdf

[4] M. Manica, G. Guzzetta, F. Riccardo, A. Valenti, P. Poletti, V. Marziano, F. Trentini, .X Andrianou, A. Mateo-Urdiales, M. del Manso, M. Fabiani, M.F. Vescio, M. Spuri, D. Petrone, A. Bella, S. Iavicoli, M. Ajelli, S. Brusaferro, P. Pezzotti, S. Merler. Impact of tiered restrictions on human activities and the epidemiology of the second wave of COVID-19 in Italy. Nature Communications, 12: 4570, 2021.


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