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Acta Bio Medica : Atenei Parmensis logoLink to Acta Bio Medica : Atenei Parmensis
. 2021 May 12;92(2):e2021171. doi: 10.23750/abm.v92i2.11149

Analysis of the time evolution of COVID-19 lethality during the first epidemic wave in Italy

Nicole Balasco 1, Vincenzo d’Alessandro 2, Pietro Ferrara 3,4,✉, Giovanni Smaldone 5, Luigi Vitagliano 1
PMCID: PMC8182589  PMID: 33988144

Abstract

Background and aim:

While the entire world is still experiencing the dramatic emergency due to SARS-CoV-2, Italy has a prominent position since it has been the locus of the first major outbreak among Western countries. The aim of this study is the evaluation of temporal connection between SARS-CoV-2 positive tests (cases) and deaths in Italy in the first wave of the epidemic.

Methods:

A temporal link between cases and deaths was determined by comparing their daily/weekly trends using surveillance data of the period March 2–June 2020.

Results:

The monitoring of the cases/deaths evolution during the first wave of the outbreak highlights a striking correlation between infections of a certain week and deaths of the following one. We defined a weekly lethality rate that is virtually unchanged over the entire months of April and May until the first week of June (≈13.6%). Due to the rather low number of cases/deaths, this parameter starts to fluctuate in the following three weeks.

Conclusions:

The analysis indicates that the weekly lethality rate is virtually unchanged over the entire first wave of the epidemic, despite the progressive increase of the testing. As observed for the overall lethality, this parameter uniformly presents rather high values. The definition of a temporal link between cases and deaths will likely represent a useful tool for highlighting analogies and differences between the first and the second wave of the pandemic and for evaluating the effectiveness, even if partial, of the strategies applied during the ongoing outbreak. (www.actabiomedica.it)

Keywords: COVID-19, Italy, lethality rate, mortality burden, SARS-CoV-2, weekly lethality rate

Introduction

After the detection of the first case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019, the spread of the novel coronavirus disease 2019 (COVID-2019) has posed an enormous challenge to the entire world, involving more than 200 countries with over 27 million infected individuals and 1.7 million deaths in one year (1,2).

Italy was the first European country that experienced the dramatic consequences of a rapid COVID-19 diffusion, with hospital overload, high shortage of healthcare resources and professionals, as well as a massive death toll (3–5). Here, a total of 240,331 cases (confirmed infections) and 34,892 deaths from pneumonia were registered as of June 28th, 2020, identifiable as the end of the first wave of the Italian outbreak (6). The integrated surveillance data of the Italian National Institute of Health (Istituto Superiore di Sanità [ISS]) indicated that subjects who tested positive were on average 58 years old, while patients who died of COVID-19 had a median age of 82 years, being mainly men with pre-existing comorbidities (7,8).

As for other novel emerging infectious diseases, one of the most relevant epidemiologic measure to be determined is the proportion of cases who eventually die from the disease (9). During the pandemic months, several attempts to quantify the case fatality ratio (CFR) of SARS-CoV-2 have been proposed, but were considerably weakened by intrinsic barriers. First, the demographic characteristics of the population from one country to another pose important challenges in drawing firm conclusions. Second, general consensus is growing in support of the hypothesis that the CFR variability was likely attributable to the underestimated number of people who are infected with SARS-CoV-2 – mostly asymptomatic and pauci-symptomatic individuals (9,10). Specific literature underlined that CFR estimations of COVID-19 according to either the calendar date or the days since the first confirmed case may be affected from wide variation (11,12). Thus, several outstanding methodological issues prevent from providing reliable death estimates from the perspective of longitudinal time-series analysis of COVID-19 lethality, also due to the static nature of the traditional cumulative CFR in describing the extent of a dynamic event (11).

It is commonly recognized that the CFR cannot be evaluated using the number of deaths per number of confirmed cases at the same time because this approach does not take into account the clinical course of the disease (11). In this respect, there is a broad range of estimates for the median time delay from illness onset to death (8,13,14), likely due to disparities in country-based demographics, healthcare access, and treatment options. Additionally, at least in Italy, the adoption of a daily CFR could be biased by the weekday-dependent number of daily laboratory tests, by the way data are transmitted from the local health agencies to the national surveillance system, and by the delay of death notification, which all lead to a marked variation of that value.

Based on these considerations and with the aim of proposing a metric of the magnitude and kinetics of the lethality associated with SARS-CoV-2 that could be also used as a valuable proxy indicator of the COVID-19 control measures and actions, we conducted a population-based retrospective analysis of COVID-19 mortality data in Italy by identifying a temporal link between the number of cases and the number of deceased people taken from epidemiological surveillance data of the first wave of the pandemic.

Materials and Methods

Study Design and Data Source

We carried out a longitudinal retrospective time-series study on the lethality associated with SARS-CoV-2 in Italy, using data collected in the national COVID-19 integrated surveillance system (6). Here, we gathered the daily number of laboratory tests, confirmed cases, and deceased related to SARS-CoV-2 (Supplementary Materials, Table S1). We traced data over 18 weeks (denoted as W0, W1, ..., W17) covering the period from February 24th (the first documented autochthonous infection and the first death date back to February 20th and 21st, respectively) to June 28th (Supplementary Materials, Table S2) that essentially corresponds to the first wave of the epidemic in Italy. Since data became complete and reliable only after some days from the beginning of the outbreak, the analysis was carried out starting from W1 (March 2nd-8th).

Statistical Analysis

Numbers of cases, deaths, and tests (swabs) were grouped in a week-based manner (Supplementary Materials, Table S3). The average daily values of cases and deaths were obtained by dividing the total weekly number by seven. The WLRs for the examined 16 weeks (from W2 to W17) were computed by dividing the average daily number of deaths of a given week (Wi) by the average daily number of cases of the previous week (Wi-1).

To gain further insights into the progression of the pandemic during the first wave, we conducted a post-hoc sensitivity analysis, which can be described as follows: (i) it was observed that the time-trends of the curves were similar and shifted with respect to each other; (ii) the two datasets were normalized to the maximum of each ensemble (Supplementary Materials, Table S3); (iii) the curve of normalized cases was systematically shifted by one day at a time, and the sum of squared residuals (SSR) between the overlaid cases/deaths curves was calculated. The same analysis was performed by evaluating the weekly averages of cases and deaths, and repeating steps (ii) and (iii), where the curve of cases was shifted by one week at a time.

The temporal shift between cases and deaths identified with this approach prompted us calculate the Weekly Lethality Rate (WLR) defined as the ratio between the average number of deaths of a certain week and the average number of cases of the previous one. 95% confidence intervals (95% CI) were calculated according to a Poisson approximation (15).

Data were analyzed with MATLAB R2014b and R statistical software v. 4.0.0 (16,17); results presented in terms of percentage with 95% CIs, and mean and standard deviation (SD).

Results

Comparative analysis of the evolution of cases and deaths

Overall, the whole population of 240,331 cases and 34,892 deaths reported by the Italian surveillance system as of June 28th was considered in the analysis. The curve of cases peaked (6557) on March 21st, while the highest daily number of deaths (919) was reached on March 27th. Fig. 1 displays the daily trends of cases and deaths, along with the lockdown beginning (March 9th) and end (May 18th).

Figure 1.

Figure 1.

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Daily evolutions of (a) cases and (b) deaths. The vertical dashed lines identify the lockdown period (March 9th – May 18th).

Since the visual inspection of the curves suggested a similar temporal evolution of cases and deaths, we systematically shifted the curve of normalized cases with respect to that of the normalized deaths; the best fitting was achieved by applying a six-day shift, which was reached through the evaluation of the SSR between the two curves after each shift (Fig. 2A).

Figure 2.

Figure 2.

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(a) Sum of squared residuals (SSR) as a function of shift. (b) Comparison of the evolution of the number of cases (black) and deaths (red) upon normalization of the curves. The normalization was performed by dividing the actual values by the maximum of each ensemble. The curve of cases is six-day shifted ahead.

In particular, as shown in Fig. 2B, the application of this shift produces a very good overlap between the two curves. The same analysis carried out on normalized weekly-averaged data indicated that the optimal fitting is obtained by a one-week shift, with a fairly good matching over the initial weeks and an excellent overlap in the regions beyond the peak (Fig. 3).

Figure 3.

Figure 3.

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Comparison of the evolution of the weekly cases (black) and deaths (red) upon normalization of the curves. The normalization was performed by dividing the actual values by the maximum of each ensemble. The curve of cases is one-week shifted ahead.

Weekly lethality rate

The inspection of the WLR (see the Methods section for the definition) evolution during the first wave of the pandemic (Fig. 4 and Table 1) indicates that this parameter assumes rather high values (range 15-25%) in the first weeks (W2-W4), likely dictated by a marked underestimation of the number of cases in the same period. In W5-W13, the WLR was almost constant with an average value of 13.6% (± 1.2 SD). The parameter starts to fluctuate in the following four weeks while retaining a rather high average value (15.2% ± 5.6 SD).

Figure 4.

Figure 4.

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Weekly lethality rate (WLR) evolution in the first wave of the pandemic.

Table 1.

Weekly lethality rate (WLR) values with 95% confidence intervals (95% CIs).

Week WLR (95% CIs)
W2 25.43 (22.05 - 29.12)
W3 21.11 (19.34 - 23.00)
W4 15.42 (14.35 - 16.57)
W5 13.27 (12.33 - 14.27)
W6 12.83 (11.80 - 13.93)
W7 13.72 (12.58 - 14.92)
W8 13.20 (11.97 - 14.50)
W9 11.98 (10.70 - 13.36)
W10 12.92 (11.31 - 14.64)
W11 16.14 (13.98 - 18.63)
W12 13.78 (11.45 - 16.38)
W13 14.24 (11.45 - 17.50)
W14 15.42 (11.98 - 19.49)
W15 22.26 (17.23 - 28.58)
W16 14.51 (10.32 - 19.52)
W17 8.63 (5.63 - 13.08)
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Discussion

This real-world observational study, based upon the complete epidemiological data of the COVID-19 spread in Italy, allowed straightforwardly evaluating the time evolution of the lethality during the first wave of outbreak, and offered further insights into the SARS-CoV-2 diffusion in the country.

The extremely high WLR values registered in the first three weeks (W2-W4) were most likely affected by a considerable underestimation of the cases in that phase of the infection, when healthcare systems were caught off guard during the rapid diffusion of the virus, and only a selected proportion of individuals underwent COVID-19 testing (3,10). As an overwhelming evidence of this consideration, in the initial weeks a large portion of the swabs resulted positive, with a 25.8% peak at W3, while dropping to less than 1% in the following weeks (Supplementary Materials Table S4, Fig. S1). During the entire months of April and May (W5-W13), the WLR remained almost constant, with a mean value of 13.6% and marginal fluctuations. In this respect, it is important to acknowledge that we based our approach on numbers of cases and deaths, being the first influenced by the number of weekly swabs (Supplementary Materials, Fig. S3); therefore, these differences in testing likely explain the higher precision of WLRs related to the central period (W5-W13), which showed narrower confidence intervals.

Overall, the high lethality values were probably induced by (i) the higher median age of the positive patients (10,18) compared with that registered in other countries (2), (ii) the hospital overload, and (iii) the inadequate number of intensive care units (ICU), which admitted more than 4,000 patients in W5 (Supplementary Materials, Table S1, Fig. S2).

It is worth mentioning that previous analyses conducted on mortality data suggested that the enormous death toll and the excess mortality registered during the March-May period mainly affected that part of population whose health was already compromised in the highly-impacted areas (4,8,10,19). Further research should therefore explore a possible compensatory harvesting effect on overall mortality during the months after the epidemic phase. It must also be observed that the lethality analyses conducted so far do not provide evidence that supports or corroborates the hypothesis of an altered virus potency claimed by some clinicians and researchers starting from May 2020 (20-22), even though decreasing in viral loads have been admitted in the late phases of the first wave (21,23,24).

Towards the end of the wave, a stating decrease of the WLR can be identified. In this respect, analyses of WLR after the completion of the second epidemic wave should explore the whole WLR trend. On the basis of the lethality rates seen worldwide (2) and of the knowledge so far available, several reasons explain the WLR reduction in the weeks right after the period included in this research. First, the lockdown restrictions and control measures, such as social distancing and use of personal protective equipment imposed by the Italian government and local authorities, profoundly limited the virus circulation and led to a decrease of cases (25,26), especially among vulnerable (e.g., older age) subjects, resulting in a lower proportion of deaths. This also contributed to alleviate the overload of hospitals and ICUs, concurrently with the institution of primary-care medical home service dedicated to COVID-19 patients (10,27-29). Second, the increased number of daily tests (Supplementary Materials, Table S1, Fig. S3) gradually improved the capacity of detecting positive cases.

Thus far, our research provided a robust estimate of magnitude and time evolution of COVID-19-related lethality during the first epidemic months in Italy. The first strength of the study is the inclusion of complete data from national surveillance databases within a universal coverage system of the whole Italian population, providing a comprehensive picture of the mortality burden attributable to the disease in Italy. Second, the use of weekly aggregate counts softened the huge variability due to disparities in the number of daily events (numbers of cases, deaths, and swabs), such as the empirically traceable “weekend effect” in the number of performed tests, thus granting accuracy of the estimates. In this regard, the WLR can be considered a reliable attempt for addressing the limitations related to CFR use which have been described in the introduction. Moreover, the WLR-based analysis is straightforward and easily reproducible elsewhere, allowing for comparison between different contexts or time-periods – namely, different outbreak waves and peaks, different countries or different areas of the same country. Lastly, the study of time evolution of the lethality provides a solid measure of the effectiveness of the public health actions implemented in response to epidemic, informing policymakers on future decisions to be applied.

As the SARS-CoV-2 still keeps spreading internationally, public health is committed in the identification of the reliable health measurements of the real extent of its outbreak, upon which to base the most appropriate actions to contain it. The WLR may serve as population-based metrics to lead towards a deepen knowledge of the evolution of COVID-19-related lethality, which is strongly recognized as a good measure of clinical significance of diseases. Our estimate could be also used in active surveillance programs and all other public health initiatives tending to reveal the true disease burden.

On the other hand, it is important to point out the main limitations of the presented study. First, the analysis only focused on cases and deaths classified as related to COVID-19, with possible missing. This may have affected the death statistics on both geography and completeness of reporting, particularly in the first phase of the epidemic and in those areas of the country where emergency preparedness and response were delayed (3). Second, the research included information gathered from public accessible database where data were provided in aggregated form and without any case stratification; thus, it was not possible to evaluate uncertainty sources and adjust results for potential independent predictors of death. However, some factors (for instance, median age of patients, decrease of virus circulation, etc.) have been considered and discussed in the paper.

Despite these limitations, to the best of our knowledge, this is the first research that provides weekly lethality rates associated with SARS-CoV-2 spread, by virtue of an actionable metric that adds important research information on the study of the COVID-19 pandemic. Moreover, the study was based on an accurate methodology and supported with a reliable sensitivity analysis. In fact, the identified shift, which represents the average delay between the swab outcome and the corresponding death, is compatible with the median shift of eleven days between the insurgence of the symptoms and the fatal outcome reported by the Italian National Health Institute – ISS (18). Finally, the definition of a temporal link between cases and deaths will likely represent a useful tool for highlighting analogies and differences between the first and the second wave of the pandemic. In particular, possible variations in the temporal correlations between cases and deaths may provide an idea about the effectiveness, even if partial, of the strategies and of the actions applied during the ongoing second wave of the pandemic.

Conclusions

This study documented the lethality evolution during the first wave of COVID-19 spread in Italy through the introduction of an easily-calculable parameter – referred to as WLR – suited to provide a robust estimate of the proportion of cases who died from the disease. Additionally, it offered a clear overview on the effectiveness of the public health measures and can also be exploited to minimize the disease impact. Finally, the present approach may be useful in unraveling interesting analogies and differences between time-periods and contexts in the pandemic development and in data reporting.

Appendix - Supplementary Materials

Table S1.

Daily cases, deaths, and tests (swabs) collected from the Reports of the Italian National Institute of Health (ISS).

Date Daily cases Daily deaths Daily tests
20-Feb 0 0
21-Feb 17 0
22-Feb 47 1
23-Feb 90 2
24-Feb 72 4 4324
25-Feb 94 4 4299
26-Feb 147 1 964
27-Feb 185 5 2427
28-Feb 234 4 3681
29-Feb 239 8 2966
01-Mar 573 12 2466
02-Mar 335 11 2218
03-Mar 466 27 2511
04-Mar 587 28 3981
05-Mar 769 41 2525
06-Mar 778 49 3997
07-Mar 1247 36 5703
08-Mar 1492 133 7875
09-Mar 1797 97 3889
10-Mar 1577 168 6935
11-Mar 1713 196 12393
12-Mar 2651 189 12857
13-Mar 2547 250 11477
14-Mar 3497 175 11682
15-Mar 3590 368 15729
16-Mar 3385 349 13063
17-Mar 3374 345 10695
18-Mar 4207 475 16884
19-Mar 5322 427 17236
20-Mar 5986 627 24109
21-Mar 6557 793 26336
22-Mar 5560 651 25180
23-Mar 4790 601 17066
24-Mar 5249 743 21496
25-Mar 5210 683 27481
26-Mar 6153 712 36615
27-Mar 5909 919 33019
28-Mar 5974 889 35447
29-Mar 5217 756 24504
30-Mar 4050 812 23329
31-Mar 4053 837 29609
01-Apr 4782 727 34455
02-Apr 4668 760 39809
03-Apr 4585 766 38617
04-Apr 4805 681 37375
05-Apr 4316 525 34237
06-Apr 3599 636 30271
07-Apr 3039 604 33713
08-Apr 3836 542 51680
09-Apr 4204 610 46244
10-Apr 3951 570 53495
11-Apr 4694 619 56609
12-Apr 4092 431 46720
13-Apr 3153 566 36717
14-Apr 2972 602 26779
15-Apr 2667 578 43715
16-Apr 3786 525 60999
17-Apr 3493 575 65705
18-Apr 3491 482 61725
19-Apr 3047 433 50708
20-Apr 2256 454 41483
21-Apr 2729 570 52126
22-Apr 3370 401 63101
23-Apr 2646 464 66658
24-Apr 3021 420 62447
25-Apr 2357 415 65387
26-Apr 2324 260 49916
27-Apr 1739 333 32003
28-Apr 2019 382 57272
29-Apr 2086 323 63827
30-Apr 1872 285 68456
01-May 1965 269 74208
02-May 1900 474 55412
03-May 1389 174 44935
04-May 1221 195 37631
05-May 1075 236 55263
06-May 1444 369 64263
07-May 1401 274 70359
08-May 1327 243 63775
09-May 1083 194 69171
10-May 802 165 51678
11-May 744 179 40740
12-May 1402 172 67003
13-May 888 195 61973
14-May 992 262 71876
15-May 789 242 68176
16-May 875 153 69179
17-May 675 145 60101
18-May 451 99 36406
19-May 813 162 63158
20-May 665 161 67195
21-May 642 156 71679
22-May 652 130 75380
23-May 669 119 72410
24-May 531 50 55824
25-May 300 92 35241
26-May 397 78 57674
27-May 584 117 67324
28-May 593 70 75893
29-May 516 87 72135
30-May 416 111 69342
31-May 333 75 54118
01-Jun 200 60 31394
02-Jun 319 55 52159
03-Jun 322 71 37299
04-Jun 177 88 49953
05-Jun 519 85 65028
06-Jun 270 72 72485
07-Jun 197 53 49478
08-Jun 280 65 27112
09-Jun 283 79 55003
10-Jun 202 71 62699
11-Jun 380 53 62472
12-Jun 163 56 70620
13-Jun 347 78 49750
14-Jun 337 44 56527
15-Jun 301 26 28107
16-Jun 210 34 46882
17-Jun 329 43 77701
18-Jun 332 66 58154
19-Jun 251 47 57541
20-Jun 264 49 54722
21-Jun 224 24 40545
22-Jun 221 23 28972
23-Jun 113 18 40485
24-Jun 190 30 53266
25-Jun 296 34 56061
26-Jun 255 30 52768
27-Jun 175 8 61351
28-Jun 174 22 37346
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Table S2.

Week definition with starting and ending date.

Week Starting Date Ending Date
W0 24-Feb 01-Mar
W1 02-Mar 08-Mar
W2 09-Mar 15-Mar
W3 16-Mar 22-Mar
W4 23-Mar 29-Mar
W5 30-Mar 05-Apr
W6 06-Apr 12-Apr
W7 13-Apr 19-Apr
W8 20-Apr 26-Apr
W9 27-Apr 03-May
W10 04-May 10-May
W11 11-May 17-May
W12 18-May 24-May
W13 25-May 31-May
W14 01-Jun 07-Jun
W15 08-Jun 14-Jun
W16 15-Jun 21-Jun
W17 22-Jun 28-Jun
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Table S3.

Cases and deaths per week. Average daily values were obtained dividing the total weekly number of cases/deaths by seven. The normalization was performed by dividing the actual values by the maximum of each ensemble.

Week Average number of cases Average number of deaths Normalized number of cases Normalized number of deaths
W0 221 5 0.0401 0.0072
W1 811 46 0.1474 0.0613
W2 2482 206 0.4512 0.2721
W3 4913 524 0.8932 0.6915
W4 5500 758 1.0000 1.0000
W5 4466 730 0.8119 0.9632
W6 3916 573 0.7120 0.7566
W7 3230 537 0.5872 0.7092
W8 2672 426 0.4858 0.5627
W9 1853 320 0.3369 0.4224
W10 1193 239 0.2169 0.3160
W11 909 193 0.1653 0.2542
W12 632 125 0.1149 0.1654
W13 448 90 0.0815 0.1188
W14 286 69 0.0520 0.0913
W15 285 64 0.0517 0.0841
W16 273 41 0.0496 0.0545
W17 203 24 0.0370 0.0311
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Table S4.

Positive tests over swabs.

Week Positive tests over swabs [%]
W1 19.69
W2 23.17
W3 25.76
W4 19.68
W5 13.17
W6 8.60
W7 6.53
W8 4.66
W9 3.27
W10 2.03
W11 1.45
W12 1.00
W13 0.73
W14 0.56
W15 0.52
W16 0.53
W17 0.43
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Figure S1.

Figure S1.

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Time evolution of the percentage of weekly positive tests over total swabs.

Figure S2.

Figure S2.

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Daily evolution of the number of intensive care patients. The vertical dashed lines identify the lockdown period (March 9th – May 18th).

Figure S3.

Figure S3.

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Daily evolution of the number of tests. The vertical dashed lines identify the lockdown period (March 9th – May 18th).

Funding:

This research was funded by Regione Campania project “RicErCa e sviluppO VERsus COVID19 in Campania RECOVER-COVID19” (POR FESR CAMPANIA 2014-2020 - Asse III Obiettivo Specifico 1.3 - Azione 1.3.1).

Conflicts of Interest:

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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