In Italy, 5 million people are ready to buy 5G subscriptions as early as 2021

In Italy, 5 million people are ready to buy 5G subscriptions as early as 2021

In Italy

This is Ericsson's estimate of the largest study in the world involving the pioneers of fifth generation networks, among consumers in possession of smartphones

5G (Getty Images) What is the impact that 5G is having on the first smartphone users around the world? This question was answered by the largest study ever carried out on the pioneers of fifth generation networks, carried out by Ericsson among consumers in 26 markets including the United States, China, United Kingdom and Italy. In our country, 5G has a potential market of 5 million smartphone users who could switch to this new connection already in the current year, according to Ericsson's analysis carried out on the data obtained from the answers of one thousand respondents, of which 215 early user, as part of the Five ways to better 5G research, between last December and February.

The share of consumers who want to switch to 5G in Italy is 27%, higher than in other European countries such as Germany , France or the United Kingdom. A fraction of less than 32% recorded in 2019: according to the Swedish multinational, the slow implementation of 5G, the financial uncertainty induced by the pandemic and the disinformation campaign affected the decline. On a global level, the trend was actually reversed: two out of five smartphone users (39%) expressed their intention to switch to 5G in 2020, compared to 37% in 2019. 2021 therefore depends on 12% of consumers who would strongly like to switch to 5G by the end of 2021. This percentage also rises to 21% globally.

Ericsson, committed to building radio systems for 5G all over the world, highlights five emerging trends in Italy, among the first users of the new networks, starting from the need to fill the knowledge gap, without which , 23% more smartphone users would have adopted 5G by the end of 2020. It also emerged that 5G users spend an average of 2.5 hours more per week on augmented reality apps and an extra hour in the cloud. gaming, compared to 4G users, changing consumption habits.

In addition, 17% of users in Italy have decreased the use of wifi after switching to 5G, so much so that network coverage in closed spaces is considered more important than speed and battery consumption. in terms of 5G user satisfaction. Furthermore, if 44% of early adopters in Italy are satisfied with the speed, 71% would like more innovative apps and services. Consumers would be willing to pay 22% more for 5G subscriptions including digital services. However, two thirds of all use cases are still in the demonstration or development stage.

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Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic

Figure 1 shows the counterfactual predictions for all regions in Italy in 2020. We plot predictions from both, SCM (yellow) and CGP (green). For comparison, we also show the historical 2015–2019 data and their mean (gray), as well as the mortality in 2020 after accounting for the reported COVID-19 deaths (black), i.e., the total reported mortality minus the reported COVID death count. We note that the SCM and CGP methods both trace the pre-pandemic data closely (the latter method is designed to match the pre-pandemic data exactly as detailed in the Methods section) while the historical mean estimates are generally higher. The mortality in Italy has been below-average in the first 2 months of 2020, probably owing to a milder than usual flu season. This discrepancy demonstrates why SCM and CGP provide better counterfactuals than the simple historical mean estimate: they take into account this year’s pre-pandemic mortality and exploit the time-correlations in the mortality rates allowing to make more accurate and precise predictions, forgiven the different assumptions made by these methods hold true. Where our predicted excess mortality is lower than the reported COVID-19 fatalities, we will use the latter for our estimate of COVID-19 deaths. This selection only makes a statistically significant difference for the region of Lazio and age groups below 30 years of age in few other regions, but otherwise does not affect our conclusions with any significance. Figure 1 shows a clear excess in mortality over the counterfactual predictions after the week ending on 22 February, when the first COVID-19-related deaths were reported in Italy. This excess is primarily seen in the Northern regions, which are the hardest hit. In the remainder of this work, we focus on these regions and the province of Bergamo (see also ref. 4 for an earlier analysis).

Fig. 1: Validating counterfactuals for the pre-pandemic data.

we show the observed weekly mortality due to all causes for the period of 1 January to June 27 (black) in all 20 regions in Italy, and our prediction for the expected mortality in the absence of COVID-19 (conditional Gaussian Process (CGP) with its 1 and 2−σ error from the variance of the Gaussian model, i.e., 68% and 95% confidence interval, respectively, in green and synthetic controls method (SCM) in orange). The first reported COVID-19 mortality occurred in the week ending on February 22 (thin red vertical line). The historical data from 2015 to 2019 (blue) and corresponding historical mean (gray) is shown for comparison and are not a good fit to the observed pre-pandemic data. In the dashed-black line, we also show the observed mortality after removing reported COVID-19 deaths.

In Fig. 2, we show the excess deaths over the expected counterfactual for every week of reported data. We focus on the few regions that were hardest hit by the pandemic and which lead to the most statistically significant conclusions. Figure 2a shows that the excess weekly mortality is significantly higher than the official COVID-19 deaths in all regions, as the beginning of the pandemic. We only have access to reported COVID-19 deaths in Bergamo up to 1 May 2020 (shown in a dashed pink vertical line) and beyond that, we extrapolate it in the same proportion as Lombardia, the region that the province of Bergamo resides in. Since May 2020, the estimated excess is less than reported COVID-19 fatalities for some regions, though it is mostly still consistent with the 1−σ (68%) confidence interval of our predictions (except in Piemonte and Toscana). This is to be expected, as we move away from the intervention (the start of the pandemic), which happened in February, the counterfactual prediction becomes less accurate and will be more heavily influenced by the global mean. For these weeks and regions, whenever our predicted excess is less than the reported COVID-19 deaths, we will use the latter for estimating fatality rates and infection ratios (IRs). Figure 2b shows the cumulative excess in mortality compared with the total reported COVID-19 deaths at the end of each week. All regions see a consistent rise in excess deaths until early May 2020. If we attribute these deaths to COVID-19 infections, this implies that the worst affected regions such as Lombardia and Emilia-Romagna have likely underestimated the mortality by factors of 1.5, whereas other regions like Piemonte and Toscana have underestimated mortality by a factor of 2. For most regions, the number of deaths has decreased significantly since May 2020.

Fig. 2: Excess mortality compared with reported COVID-19 deaths in regions of Northern Italy and the province of Bergamo.

(a) Excess weekly deaths, and (b) cumulative excess deaths, over the predicted counterfactual in comparison to the reported COVID-19 deaths (in pink) for the period since February 23rd (available COVID-19 data). Estimates from both the synthetic controls method (SCM, orange) and conditional Gaussian Process (CGP, green) counterfactuals agree. We show 1 and 2−σ error (68% and 95% confidence interval) from the variance of the Gaussian model. We find that COVID-19 deaths are under-reported by multiple factors for every period and every region. We extrapolate the data excess beyond June 27th, which is the last week with available total mortality data (dashed-black line), with dashed-lines. To do this, we make the conservative assumption after June 27 that the reported COVID-19 deaths are accurate and account for the excess mortality over predicted trends.

In Figs. 2 and 3, we have extrapolated our estimated excess from 27 June 5 September. Since the number of estimated excess deaths is consistent with reported COVID-19 deaths for the last 8 weeks for all regions (except Piemonte and Toscana), we assume that the weekly excess mortality is the same as the reported COVID-19 deaths for extrapolation after 27 June. Based on this, we estimate that the number of COVID-19 deaths in Italy is between 59,000−62,000 as of 9 September 2020, more than a factor of 1.5× higher than the official number. In the remainder of the paper, we will use this extrapolation to estimate the age-dependent and population fatality rates and IRs.

In Fig. 3, we show the excess mortality for different age groups in intervals of 10 years above the age of 40. We find some agreement between the estimated excess and the reported COVID-19 deaths below the age of 70, but observe a significant and increasing discrepancy for higher age groups. This seems to suggest that testing and consequently probably also treatment has been more complete for lower age groups.

Fig. 3: Age distribution of excess mortalities.

Same as Fig. 2b but for different age groups. We find a statistically significant excess over the reported COVID-19 deaths that is increasing with age.

Attributing excess deaths to COVID-19

To back the assumption that excess deaths are a consequence of the pandemic, we establish a correlation between the daily excess deaths over the counterfactual and the official COVID-19 deaths by means of regression analysis: we perform a two-parameter fit to the excess deaths by allowing the official deaths to be scaled and shifted. We infer the time-lag and amplitude of this fit by minimizing χ2. We find that best fits are obtained for time-lags of −6 days for Lombardia, −7 days for Emilia-Romagna, −8 days for Piemonte, and −6 days for Marche. The inferred amplitudes range between 1.2 and 1.6. We provide figures and details for this analysis in the supplemental material. Given that both data sets report the day of death not the day of report, the inferred time-lags suggest that the official COVID-19 mortality lags behind the total mortality. One possible reasons for this could be that hospital treatment postpones death on average by several days. A ramping up of testing with time could also cause this behavior.

However, correlation is not causation and attributing the excess death rate to COVID-19 is still a strong assumption. Hence, we discuss possible caveats. COVID-19 has put enormous pressure on Italy’s medical system and social services. This could have led to fatalities that could otherwise be averted, causing us to overestimate the COVID-19 deaths. However, the pressure on the medical system is regional and likely sustainable for regions with a low number of official COVID-19 deaths, like Piemonte and Liguria. Instead, we consistently find a similar and very large excess in mortality over the official counts in many regions in Italy, which appears to be independent of how hard the region was hit (see Fig. 2b).

The temporal trend also lends a similar argument: the societal and medical systems should function normally in the earliest stages of the pandemic and get increasingly stressed as the number of infections increases. We see that the fraction of deaths missed by the reported COVID-19 fatalities is the highest in the early stage, and decreases as the number of reported infections increases. The reported COVID-19 fatalities finally catch up with the estimated excess fatalities by the end of April 2020. We show this in Fig. 4 where we compare the fraction of deaths missed every week with the number of COVID-19 reported hospitalizations (normalized with the maximum number of hospitalizations up to 18 April 2020).

Fig. 4: Fraction of missed deaths over time.

For the period of the pandemic, we show per week the fraction of missed deaths (green) with corresponding 1−σ (68% CI) estimated from the variance of the Gaussian model, the number of hospitalizations (normalized with the maximum weekly hospitalization up to 18 April 2020, in orange) and the number of tests conducted (normalized as a fraction of tests conducted in the week of 11–18 April 2020, in blue). We find that the missed fraction goes down as the number of tests increases while the hospitalizations have remained consistently high in the last 4 weeks.

Our hypothesis is that the excess deaths over official COVID-19 deaths are primarily due to the lack of testing in the initial stages of the pandemic. In Fig. 4, we also show the number of tests conducted every week as the fraction of tests conducted in the week of 11 April 2020. The trend supports our assertion that with an increase in testing as the pandemic evolves, the reported fatalities due to COVID-19 slowly catch up with the true current mortality and the increased pressure on medical systems did not have a statistically significant effect on the mortality.

There are also arguments that suggest we may have underestimated the COVID-19 death rate. Italy has been under lockdown since 9 March 2020, which may have reduced fatalities due to other common causes such as road and workplace accidents, or criminal activities. This can be studied by observing the death rate correlations with the lockdown data in regions with little or no infection, such as South Italy. There are several regions that do not show an excess death rate, but none of them show a deficit death rate post 9 March 2020, so we assume that this effect is negligible, especially for age groups above working age.

Fatality and IRs

Having established that the observed excess deaths can reasonably be attributed to COVID-19, we can use our estimates and uncertainties of the excess mortality from the CGP and SCM counterfactuals to calculate the fatality rates and infection fractions for Italian regions. The left panel of Fig. 5 shows the PFR in different age groups, the total number of excess mortality deaths attributable to COVID-19 as a fraction of the population. We find a steep age dependence of PFR: in Bergamo province, 1.89%, 4.84%, and 11.06% of the entire population in the age groups 70–79, 80–89, and 90+, respectively, died. For the entire population, the PFR is 0.57% (and similarly 0.29% in Lombardia). Since the PFR corresponds to the IFR if the infection fraction is 1 (maximum possible), we expect these numbers to be the most conservative lower limits on the (age dependent) IFR (Table 2).

Fig. 5: Fatality rates for different age groups and regions.

(Left) Population fatality rate (PFR) from the cumulative estimates divided by the regional population. (Center) Lower bounds on infection fatality rate (IFR) using the maximum test positive rate (TPR) as an upper bound on infection fraction. (Right) Estimates of the true IFR when normalizing the age 70–89 group to the Diamond Princess IFR (in shaded blue, with the corresponding Poisson error estimate). We also show estimates from Verity et al.5 with corresponding (68% CI). in magenta, which gives less steep age dependence. In the center and right panel, the gray lines are weighted mean estimates for IFR with 1 sigma weighted standard deviation bands. The horizontal lines are the age-averaged IFR for the entire population. Error bars for all the regions and age groups are 1−σ (68% CI) error from the variance of the Gaussian model combined with Poisson errors based on number of deaths that differs for every region and age group. In all panels, we have staggered the points horizontally for every age group for better visibility.

Lower limits on IFR

The central panel of Fig. 5 shows the lower bounds on the IFR. Estimating the IFR from the PFR requires the IR of the population. Here, we use the test positivity rate (TPR)—the fraction of positive to total tests, as an estimate of the fraction of the infected population. Owing to a lack of testing and the criterion of primarily testing people with symptoms, this should be an upper bound on the IR in the early stages of the pandemic. For every region, we use the maximum of the cumulative TPR estimated up to 5 September as our estimate for the IR. This should be an upper limit on the IR and hence give a conservative lower bound on the IFR. We further assume that this ratio is age-independent in every region5. The age-averaged lower bounds on the IFR are shown in Table 1, with the most robust estimate of 0.73 ± 0.08% IFR lower bound from Lombardia, consistent with 0.57% lower bound from Bergamo province.

Table 1 Estimated fatalities, infection rates (IR), and infection fatality rates (IFR).IR and IFR calibrated on the Diamond Princess

The PFR can also be combined with an independent estimate of the IFR to obtain the IR with the relation IR=PFR/IFR. At the time of writing, the only large dataset with complete testing and hence unbiased estimate of the IFR is the Diamond Princess (DP) cruise ship. For our analysis, we assume that the age-dependent IFR is location independent: we account for age differences, but not for other differences between the DP and Italian populations in the same age group such as co-morbities, dose differences, or health-care access.

The last death on the ship was reported on 18 April 2020 and 11 out of 330 DP infections in the age group above 70 had been fatal (a few of the fatalities do not have age information). This results in an IFR for this age group of 3.3% and we assume a Poisson distribution to estimate the errors. The population distribution in this age group on the DP was 80% in 70–79 and 20% above 806. For each region of Italy, we re-weigh the population to match this age distribution and hence match the age-weighted IFR to the DP in the 70–89 age group. Combining this with the corresponding PFR, we are able to estimate IRs for this age group. Then, under the assumption of age-independent IR, we can combine this estimated IR with observed PFR for other age groups to derive IFR for all the other age groups (Table 1). IR range from 4% up to 29% (15–52% 95% CI) in Lombardia and 72% (36–100% 95% CI) in the province of Bergamo. In all cases, the estimated mean IR is below the upper limit set by the maximum TPR.

Age dependence of IFR

The right panel of Fig. 5 shows our estimate of these DP-anchored IFR estimates. As we make the assumption of constant IR for all age groups, we focus on the regions with high IR (>10%) where these assumptions are more likely to hold. The most reliable data come from Lombardia and Bergamo, as they are nearly complete, past the peak, and have a high number of statistics with small errors. The age-dependent IFR range from below 0.04% for ages below 50 years to 2.5%, 7.24%, and 20% for ages 70–79, 80–89, and above 90 years, respectively, (Table 2). This is broadly consistent with the estimates from the Hubei province in China, but suggests a steeper age dependence as shown in Fig. 5 for Verity et al3,5,6,7,8. analysis. Although the overall amplitude of our IFR estimates is anchored to DP, the relative age dependence is not.

Table 2 Age distribution of fatalities and infection fatality ratios (IFR).Crude mortality rate per year traces IFR

In Table 2, we list the crude mortality rate per year (YMR), i.e., the fraction of the population that on average dies within a year for each age group and region. We make an interesting observation that the YMR traces the IFR for ages above 60 within 20% for different regions in Italy. One possible explanation for this trend could be that the YMR takes into account varying prevalences of co-morbidities across different population and ages. Many of the co-morbidities that reduce the general life expectancy might also lead to an increased risk of dying from COVID-19. We find that this observation holds in other places as well, for instance New York City where the population YMR is 0.62% and United Kingdom9. This number is similar to the NYC IFR estimated from combining the age-dependent IFR from Italy data, and consistent with a lower bound of 0.49% IFR estimated independently of Italian data—by combining the NYC COVID-19 PFR of 0.286% as of October 2020, with the maximum TPR of 0.58% which was reached in April 2020. Similarly, as of April 22 2020, with 9900 confirmed deaths, this IFR of 0.62% predicted 19.3% IR. This is in good agreement with the then estimated IR of 23.2% for the NYC population and 16% IR for 65+ years age group from seropositivity tests10. Another interesting observation is that the age-dependent YMR also matches the proportion of deaths in different age groups. The proportion of YMR predicts COVID-19 mortality rate in age groups 45–64, 65–74, and above 75 to be 19%, 18%, and 55%, respectively11. This is in agreement with the current official NYC COVID-19 death fractions of 22%, 24%, and 50%11. These numbers match the higher death fraction among the younger population that is observed in the US compared with some Italian regions like Lombardia where only 8% of fatalities are in the age group <65 years of age.

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