key: cord-0785706-hn7s6on1
authors: Divino, Fabio; Di Loro, Pierfrancesco Alaimo; Farcomeni, Alessio; Jona-Lasinio, Giovanna; Lovison, Gianfranco; Ciccozzi, Massimo; Mingione, Marco; Maruotti, Antonello
title: Decreased severity of the Omicron variant of concern: further evidence from Italy
date: 2022-03-21
journal: Int J Infect Dis
DOI: 10.1016/j.ijid.2022.03.023
sha: d097dd0956a94e9d6063b3b345f563b39c45fc5c
doc_id: 785706
cord_uid: hn7s6on1

nan

We are currently facing an ongoing pandemic wave due to the Omicron variant, owing to its very high transmissibility with a doubling time between 1.5 to 3 days (Pascarella et al., 2021) .

According to IHME forecasts, the massive wave of omicron infections implies that hospital admissions will rise to twice or more the number of COVID-19 hospital admissions of past surges in some countries (IHME, 2022) Luckily, those forecasts were not confirmed, and early studies revealed that Omicron is less severe than other variants, with a risk of hospitalisation ranging from 15% to 80% lower than the Delta Here, we focus on the Italian case and provide useful findingsderived from national level COVID-19 surveillance dataas a reassuring confirmation of early indicators that the omicron variant might lead to less severe disease, and have a reduced effect on deaths and hospital resources, than variants that dominated earlier pandemic waves.

We analyse disease severity by introducing a simple measure and comparing different waves during 2021, based on aggregated data on admissions to intensive care units and deaths.

Data come from the national surveillance system (https://github.com/pcm-dpc/COVID-19) and are on different scales. Thus, to summarise that information, we transform and rescale indicators' time series by the size of the respective range (Divino et al., 2022) . We obtain two comparable series on a standardised, comparable scale at each time point.

Formally, let 1 , . . . , be a set of indicators of the same type (incidence-type or prevalence-type, for instance) observed at time = 1, . . . , . For each indicator , = 1, . . . , , let 1 , . . . , be the observed time series and define the following transformation:

where = { 1 , . . . , } and = { 1 , . . . , } respectively. Furthermore, if denotes the observed range size, that is = − , the transformation in (1) may be written as

where is an offset and the standardised values are the specific proportions of the range size observed at each time point = 1, . . . , . To avoid that the may be null, a proper choice is to consider the modified relation = , that corresponds to = 0 in (2), for every indicator.

Since the scaled values are proportions, in the spirit of the Human Development Index (HDI; UN, 2020), we propose a severity generalised index (SGI) expressed in terms of geometric mean (x100) to summarise the level of severity of the epidemic waves, that is

where , and ℎ, are the ICU admissions and deaths scaled values, respectively. This can be further used to monitor and summarise information about ICU admissions and deaths over time.

When k severity indicators are available, the SGI in (3) becomes as follows = 100 × √∏ =1 . , Keeping aside the summer wave, which shows a minimal severity, also due to seasonal effects, the peak of the SGI for the Alpha-Delta wave is 93.5, while the peak for the Omicron wave is 60.2. The relative ratio is 1.55, i.e. the Alpha-Delta variants severity is about 55% higher than the Omicron one. It is quite possible that reductions in the severity risk for Omicron versus Alpha-Delta can be explained by the immune protection against more severe outcomes of infection, expected to be much higher than those against milder disease. The results are "filling in a blank" about protection against severe diseases.

The use of the SGI allows to compare severity at two different time points, as e.g. the peaks of different waves, but could also be used with monitoring purposes. Its use as a descriptive tool allows us to compare the graphical patterns of several indicators, jointly. It reflects the evolution over time of the severity and may be used as an alert of the increasing pressure on the health system.

Of course, the observed maximum values (or the range sizes) depend on the time points considered.

It is strongly data-driven, which is not a drawback in general, and can be used if aggregated data only are available. 

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