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Effect of timing of implementation of containment measures on Covid-19 epidemic. The case of the first wave in Italy
DOI 10.1371/journal.pone.0245656 , Volume: 16 , Issue: 1
Article Type: research-article, Article History
Abstract

There is evidence that adoption of non-pharmaceutical containment measures (NPMs) may have had a major impact on Covid-19 epidemic dynamics, and mitigated its effect on healthcare system. Optimal timing of implementation of these measures however is not known. In Italy, a national lockdown was decided on March 11th 2020 and ended 4th of May. At that time, cumulative incidence (CI) was different in Italian regions which ranged from <5 cases/100,000 to >11 cases/100,000 inhabitants. In this paper, we aim to evaluate how level of incidence in different regions at the time of implementation of NPMs affected CI and had an impact on the healthcare system in terms of ICU bed occupancy and mortality rates. We used regional daily new COVID-19 diagnosed cases as well number of people hospitalized in ICU and number of deaths for period February 24-May 11 from all the 19 Italian regions and two autonomous provinces. For each region we calculated: temporal daily trend of cumulative cases of Covid-19/100,000 inhabitants, daily trend of ICU bed occupancy and mortality rate at the end of period. We found that the epidemic curves show similar trends for all regions and all tend to flatten between 11–32 days. However, after 2 months, regions with lower CI at lockdown remained at substantially lower CI (<265 cases/100,000), had a peak of percentage of cases hospitalized in ICU which did not exceed 79.4% and a mortality<0.27/1,000. On the other hand, in regions with higher incidence at lockdown, CI reached 382–921 cases/100,000, the peak of percentage of cases hospitalized in ICU and mortality rate reached 270%, and 1.5/1,000, respectively. Our data suggests that level of CI at the moment of lockdown is important to control the subsequent spread of infection so NPMs should be adopted very early during the course of Covid-19 epidemic, in order to mitigate the impact on the healthcare system and to reduce related mortality.

Timelli, Girardi, and Lolli: Effect of timing of implementation of containment measures on Covid-19 epidemic. The case of the first wave in Italy

Background

From February 2020, Covid-19, the disease caused by the infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread across the world [1]. In response to the growing numbers of cases and deaths due to this disease, countries have implemented measures to control their epidemics, and to preserve healthcare systems. These interventions, also referred to as no pharmacologic measures (NPMs) include: i) personal protective measures (e.g., masks and hand hygiene); ii) environmental measures (e.g., disinfection and ventilation); iii) social distancing measures (e.g., school and workplace closures); and iv) travel related measures (e.g., travel restrictions) [2, 3]. There is evidence that the adoption of NPMs may have had a major impact on Covid-19 epidemic dynamics, slowing the spread of the virus and may have mitigated its effect on the healthcare systems [46]. It has been suggested that the timing of implementation of NPMs may have an important effect on the number of cases, and consequently on the burden on the health care system [7]; the data on this issue is however limited.

Italy was the first European country to be severely hit by the Covid-19 epidemic. In February 2020, local SarsCov2 transmission clusters were identified in Northern Italy; in the following month the number of cases rapidly grew in northern Italian regions and the epidemic progressively spread in regions of central and the southern Italy [8]. Nationwide school closure was ordered on March 5th, public events were banned, social distancing encouraged, self-isolation if ill and quarantine if tested positive on March 9th. On March 11th 2020 a national lockdown was ordered, meaning closure of all public places and requirements for residents to stay within their home and travel restrictions, this ended the 4th of May (Table 1).

Table 1
Non-pharmacologic measures for Covid-19 mitigation and dates of issuance in Italy.
MeasureDateSource
Nationwide school closures March 5https://www.gazzettaufficiale.it/eli/id/2020/03/01/20A01381/sg
Public events banned March 9https://www.gazzettaufficiale.it/eli/id/2020/03/08/20A01522/sg
Social distancing encouragedA distance of more than 1 meter has to be kept and any other form of alternative aggregation is to be excludedMarch 9https://www.gazzettaufficiale.it/eli/id/2020/03/08/20A01522/sg
Case-based measuresStrong recommendation to stay at home and limit social contacts as much as possible if ill (fever greater than 37.5°C) and quarantine (absolute prohibition of mobility from one's home o residence) if tested positiveMarch 9https://www.gazzettaufficiale.it/eli/id/2020/03/08/20A01522/sg
National LockdownClosure of all public places, people have to stay at home except for essential travelMarch 11https://www.gazzettaufficiale.it/atto/stampa/serie_generale/originario

The degree of spread of Covid-19 in Italy when NPMs were adopted at national level was extremely heterogeneous by geographical area with Northern Italy being impacted more compared to Central and Southern part [9]. This gives the opportunity to retrospectively evaluate how the different implementation timing affected the evolution of the epidemic in different areas of the country. We, therefore aimed to evaluate how the level of cumulative incidence (CI) in different Italian regions at the time of implementation of mitigation measures, affected the CI and the impact on the healthcare system during the initial phase of the epidemic.

Methods

Italy is administratively divided in 19 regions and 2 autonomous provinces (PA). Since the 24th of February2020, the Italian Ministry of Health, started the daily collection of new Covid-19 cases, where each Region and PA [10] provided the number of deaths, number of hospitalized cases, and number of cases in intensive care unit (ICU). A Covid-19 case was defined as a positive person with microbiologically confirmed SARS Cov-2 evaluated by Rt-PCR on oral swab samples [9].

This study was retrospective and we used regional daily new COVID-19 diagnosed cases as well number of people hospitalized in ICU and number of deaths for the period February 24-May 11 from all the 19 Italian regions and the two autonomous provinces (hereinafter all referred to as regions) (NUTS 2) [10]. February 24th was the date of starting data collection, May 11th was the date one week after the end of the National lockdown. We extended the observation period one week more to reduce bias due to notification delays.

Statistical analysis

For each region we calculated: 1) the temporal daily trend, on a logarithmic scale of cumulative incidence, i.e., number of cumulative cases of Covid-19 per 100,000 inhabitants [11] reported within each specific date; 2) the daily trend of percentage of ICU bed occupancy, i.e., number of patients in ICU out of the regional bed ICU availability at the beginning of the epidemic (pre Covid-19) [12] and the maximum ICU bed occupancy rate, i.e., maximum number of ICU beds occupied per 100,000 inhabitants; the mortality rate at the end of period, i.e. number of deaths per 1,000 inhabitants.

We also calculated the trend of daily reported new cases of COVID-19 by region and overall Italy using 7-day moving averages to smooth out fluctuations.

Then, for each region, we calculated the number of days (hereinafter referred to as delay) between the date on which each region reached the lowest regional CI (i.e., 1.0 per 100,000 inhabitants at the date of National lockdown, LCI) and the date of National lockdown. To explore the relationship between the CI at the end of the observation period and the delay of declaration of National lockdown, a scatterplot was then visualized pairing the delay and CI at the end of the period for each Region and a correlation coefficient was calculated. In an analogous way, delay was paired with mortality, with the highest percentage of ICU bed occupancy and with the maximum ICU bed occupancy rate reached during the period.

Data was analyzed using Stata version 16.

Results

At the date of National lockdown, CI was lower than 5/100,000 inhabitants for eight regions, between 5 and 11/100,000 for five and higher than 11/100,000 for ten regions. (Table 2). It was noted that most of the Northern Regions had a high CI and, on the other hand, the Central-Southern regions had a low CI.

Table 2
Cumulative Incidence (CI) /100,000 of COVID-19 at date of National lockdown (March 11th, 2020) and after 2 months (May 11th, 2020), highest percentage Intensive Care Unit (ICU) bed occupancy, maximum ICU bed occupancy rate and mortality rate (May 11th, 2020) in Italy.
Region (NUTS 2)Geographical areaDelay (days)CI at March 11th (per 100,000)CI at May 11th (per 100,000)Highest percentage bed ICU occupancyMaximum ICU bed occupancy rateMortality rate (per 1,000)
CalabriaSouth00.9858.2415.751.180.05
BasilicataSouth21.4268.5838.783.380.05
PugliaSouth31.91107.4052.303.950.11
SardegnaSouth32.2681.9123.131.890.07
AbruzzoSouth42.90236.8961.795.790.28
SiciliaSouth41.6666.7819.141.600.05
CampaniaSouth52.6579.3254.033.120.07
LazioCenter52.55122.3035.553.450.10
P.A. BolzanoNorth514.12484.21175.6812.240.55
MoliseSouth75.24125.3230.002.940.07
P.A. TrentoNorth714.23794.13253.1314.970.82
Valle d'AostaNorth715.92921.49270.0021.491.11
ToscanaCenter88.58262.4179.417.960.25
UmbriaCenter85.22160.0968.575.440.08
Friuli Venezia GiuliaNorth910.37258.2250.835.020.26
MarcheCenter1131.40428.97146.9611.080.63
PiemonteNorth1111.50660.54138.5310.400.78
LiguriaNorth1412.51569.5799.4411.540.83
Emilia-RomagnaNorth1539.00602.6783.528.410.87
VenetoNorth1520.85382.01108.877.260.34
LombardiaNorth1772.36813.78160.3913.731.50
Delay are days between the date on which each the region reached the cumulative incidence equal to the lowest regional CI at time of lockdown and the date of lockdown; highest percentage bed ICU occupancy is the maximum number of patients in ICU out of the regional bed ICU availability (before Covid-19) in the reference period; maximum ICU bed occupancy rate is the maximum number of ICU beds occupied per 100,000 inhabitants; mortality rate is expressed as number of Covid-19 deaths per 1,000 inhabitants. Regions are reported by ascending order of delay.

As shown in Figs 1 and 2, the incidence of cases was rapidly increasing in all regions, and then flattened in all regions after 21 days, on average, with a minimum of 11 and a maximum of 32 days after the lockdown (Figs 1 and 2). The trends were also characterized by daily fluctuations likely due to variability on the reporting. For this reason they are shown as weekly moving average values.

Cumulative incidence of Covid-19 by region in Italy, February 19- May 13, 2020.
Fig 1
The vertical blue line is the National lockdown declaration; the green line is the end of our observation period; the red dash lines show the time range in which the number of new cases started to decrease.Cumulative incidence of Covid-19 by region in Italy, February 19- May 13, 2020.
Daily reported new cases of COVID-19 by region and overall Italy, February 19-May 13, 2020.
Fig 2
The vertical green line is the time when new cases started to decrease.Daily reported new cases of COVID-19 by region and overall Italy, February 19-May 13, 2020.

Compared to the region with the lowest cumulative incidence at the date of lockdown, the median delay in implementation of NPIs was 2 days (IQR: 2–4) in the 7 regions with low CI, 7 days (IQR: 7–9) in regions with intermediate CI and 15 days (IQR: 14.5–16) in regions with high CI.

Fig 3, shows a scatterplot by Region of the relationship of CI two months after the date of lockdown (i.e. May 11th) and the delay. Overall, regions with shorter delay remained at substantial lower incidence during the considered period compared to regions with longer delay, and we observe CI increased as delay was increasing with a correlation of 0.64 (95%CI 0.41–0.87).

Delay of lockdown declaration and cumulative incidence of Covid-19 at May 11th by region-Italy.
Fig 3
The horizontal axis is the number of days between the date on which each the region reached the cumulative incidence equal to the lowest regional CI at time of lockdown and the date of lockdown; the vertical is the cumulative incidence of Covid-19 (May 11th) per 100k inhabitants; the size of bubble is proportional to the total number of diagnosed positives (May, 11th).Delay of lockdown declaration and cumulative incidence of Covid-19 at May 11th by region-Italy.

We analyzed trends of admission in ICU by region expressed as percentage of ICU beds available before the start of the pandemic (Fig 4). At implementation of lockdown, this percentage was below 100% in all regions, thereafter, however, in seven regions it would have exceeded 100% (if there had not been a strengthening of ICU beds at the onset of the pandemic), suggesting in any case a substantial overburden on the healthcare system.

Percentage of Intensive Care Unit (ICU) bed occupancy by region-Italy, February 19- May 13, 2020.
Fig 4
The vertical blue line is the National lockdown declaration; the green line is the end of our observation period; the red dash lines show the time range in which the number of new cases started to decrease.Percentage of Intensive Care Unit (ICU) bed occupancy by region-Italy, February 19- May 13, 2020.

The highest percentage bed ICU occupancy varied from 15.75% (Calabria) to 270% (Valle d’Aosta); maximum ICU beds occupancy rate ranged from 1.18 (Calabria) to 21.48 per 100,000 inhabitants (Valle d’Aosta) (Table 2). The correlation with the delay was weak (0.38; 95%CI 0.08–0.68) (Fig 5, Panel A) for the highest percentage of ICU occupancy and moderate (0.50; 95%CI 0.21–0.79) for the maximum ICU beds occupancy rate (Fig 5, Panel B). It is of note that these two ICU measures were strongly correlated (0.95, data not shown).

Scatterplots of highest percentage bed ICU occupancy (A), max ICU bed occupancy rate (B) and mortality rate at May 11th (C) versus delay of lockdown declaration. Numbers represent Regions as follows: Calabria (1), Basilicata (2), Puglia (3), Sardegna (4), Abruzzo (5), Sicilia (6), Campania (7), 8 Lazio (8), P.A. Bolzano (9), Molise (10), P.A. Trento (11), Valle d’Aosta (12), Toscana (13), Umbria (14), Friuli Venezia Giulia (15), Marche (16), Piemonte (17), Liguria (18), Emilia-Romagna (19), Veneto (20), Lombardia (21). The horizontal axis is the number of days between the date on which each the region reached the cumulative incidence equal to the lowest regional CI at time of lockdown and the date of lockdown.
Fig 5
Scatterplots of highest percentage bed ICU occupancy (A), max ICU bed occupancy rate (B) and mortality rate at May 11th (C) versus delay of lockdown declaration. Numbers represent Regions as follows: Calabria (1), Basilicata (2), Puglia (3), Sardegna (4), Abruzzo (5), Sicilia (6), Campania (7), 8 Lazio (8), P.A. Bolzano (9), Molise (10), P.A. Trento (11), Valle d’Aosta (12), Toscana (13), Umbria (14), Friuli Venezia Giulia (15), Marche (16), Piemonte (17), Liguria (18), Emilia-Romagna (19), Veneto (20), Lombardia (21). The horizontal axis is the number of days between the date on which each the region reached the cumulative incidence equal to the lowest regional CI at time of lockdown and the date of lockdown.

Finally, COVID-19 mortality rate during the study period ranged in different regions from 0.05 to 1.5 per 1,000 inhabitants and was correlated at regional level with delay (correlation coefficient 0.70; 95%CI 0.49–0.92) (Fig 5, Panel C).

Discussion

In Italy, strong anti-contagion interventions were implemented at national level in March 2020 when COVID-19 cumulative incidence reached high levels in some northern regions of the country, where the healthcare system was already overstressed by the increasing demand for intensive care. Our retrospective analysis shows that regions for which the lockdown was enacted earlier, when the cumulative incidence was still low, had lower incidence and mortality during the first wave of the epidemic, and experienced a lower burden on the healthcare system as indicated by a manageable demand for intensive care unit beds.

Further, in this study, evaluating the impact of the first Covid-19 epidemic wave in Italy, we observed it was needed on average, 21 days (range 11–32), regardless of the delay in their implementation, from National lockdown to NPMs effectiveness (i.e., a decrease on the incidence of diagnosed cases). This result is an agreement with a recent study evaluating the change of reproduction number (R) that finding that national lockdown of March 11 brought R below 1 in most Italian regions and provinces within 2 weeks [13].

Previous studies have shown that the reproduction numbers of the epidemics were similar in all Italian regions during the first decade of March 2020 [14]. Accordingly, our analysis also found that all regions had similar epidemic trends during this period, suggesting a potential for similar growth of the epidemic in absence of NPIs. Thus, this study provides empirical support to previous modelling studies, suggesting that the timing of implementation of control measures is critical to achieve effective control of the epidemic—at least during its first wave. For example, Dehning et al. [15] estimated that a five-day delay in implementation of strict control measures in Germany would have resulted in a three-fold difference in cumulative cases. Similarly, Pei et al. [16] estimated that 61.6% of SARS-Cov2 infections reported in the USA as of May 3, 2020 could have been avoided if control measures had been implemented one week earlier. Moreover, in China, the size of the epidemic was limited more efficiently in cities that implemented control measures in the first week of their outbreaks compared with cities that started control later [17].

This work has several limitations that should be taken into account. First, the data used was aggregated the regional reported daily new cases of confirmed SARS-Cov-2 infections. Such data does not permit a more in depth analysis which would allow to standardize the CI and the other indicators here, used at least for age and sex population structure of each region. Furthermore, people microbiologically diagnosed with SARS-Cov-2, using RT-PCR in rhino-pharyngeal oral swab samples, are only part of the real infections occurred and this proportion of people diagnosed could vary by Region. A National serological survey evaluating the presence of IgG antibodies to SARS-Cov-2 estimated that, overall, around 1 out of 6 persons infected with SARS-Cov-2 were reported to the surveillance system [18], suggesting that the real impact measured by this data is limited. However, this data was substantially similar among the regions and thus not strongly biased with respect at the objective of this analysis. Analogous results, in terms of representativeness were also found in another study comparing the excess mortality observed in the period February-April in the Italian regions with deaths associated to Covid-19 reported to the Italian Surveillance system of Covid-19 [19]. Moreover, we did not considered in the analysis factors such as territorial conformation, level of urban and industrial development, territorial interconnections, etc. [20] who may influence the course of the epidemic and may vary among regions.

In conclusion, the present study highlight that early timing of implementation of containment measures on Covid-19 epidemic may have had a relevant impact on reducing the outbreak magnitude, on playing less pressure on ICU and on impacting the mortality associated to Covid-19 in Italian regions that where less impacted at the start of the epidemics. Interventions that may slow the spread of COVID 19, limit associated mortality and reduce its impact on the healthcare system are essentially based on limiting human mobility and reducing human activity including social and economic activity [21]. It is no surprise that these interventions have a dramatic socioeconomic impact [22] and that populations and governments may be hesitant in implementing these interventions. Nonetheless, our analysis supports the notion that non-pharmaceutical containment measures should be adopted very early during the course of Covid-19 epidemic in order to mitigate the impact on the healthcare system and to reduce related mortality.

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10 Dec 2020

PONE-D-20-34437

Effect of timing of implementation of containment measures on Covid-19 epidemic. The case of Italy.

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Reviewer #1: The authors present a study that shows the impact of the timing of implementation of non-pharmaceutical containment measures (NPMs) on Covid-19 epidemic. They focus on the italian national lockdown, which started on March 11th 2020 and ended on May 4th 2020. The impact of NPMs on healthcare system has been evaluated in 21 italian local administrations (19 regions+2 autonomous provinces). Three indices have been used in order to assess this impact: cumulative incidence (CI), intensive care units occupancy (percentage on the total amount of ICUs), mortality rate. Timing of implementation of NPMs has been calculated as the number of days between the date on which each region reached a CI equal to 1.0 per 100k inhabitants, and the date of National Lockdown (it has been called delay). In the following lines some comments about the manuscript are reported:

1) The impact of the delay on healthcare system in terms of ICU occupancy and mortality rates is not sufficiently assessed. Two scatterplots (as Fig. 3) should be added: one between delay and highest ICU occupancy, the other one between delay and mortality rates (at May 11th). Related correlations should also be reported.

2) Looking at the data on Table 2, correlation between delay and mortality rate is about 0.7, which is close to the correlation between delay and CI. On the other hand, correlation between delay and highest percentage of ICU occupancy is lower (0.38). This probably means that the percentage of ICU occupancy is not the right statistic, and it should be replaced by the number of ICU beds occupied per 100k inhabitants, if these data are available. Percentage in fact is heavily affected by the total amount ICU beds, which is more closely linked to the quality level of the healthcare system than the spread of the epidemic.

3) NPMs effectiveness can be seen after 10 – 31 days (Fig. 1, 2 and 4), regardless of the delay in their implementation. The authors should put a little bit more emphasis on this result, in the abstract and in the Results section.

4) Figure 2 has a very low pixel resolution, it is almost unreadable. Moreover, daily cases have too many fluctuations: it would be better to replace them with weekly averages or moving averages.

5) References are ok.

Reviewer #2: This interesting paper explores how the timing of implementation of NPMs (or NPIs) impacted the cumulative incidence, ICU bed occupancy and mortality related to COVID-19 in Italy. I found the work clear and sufficiently explicative, providing support to modelling studies that suggested that the timing of implementation of control measures is critical to achieving effective control of the epidemic.

Among the strengths, I would mention the clarity of the aims and among the limitations, I would certainly agree that the quality of the data, aggregated, did not allow for in-depth analysis such as age and sex standardization.

The manuscript is very linear and coherent. Moreover, it is technically sound, and the data support the conclusions. Moreover, general conclusions are acceptable and in line with recent literature reports. I particularly appreciated the mention of the data coming from the Italian sero-survey and the report on excess mortality in the limitations section, as it gives a realistic picture of the reality of the pandemic in Italy while acknowledging the struggle of gathering complete data on new cases and deaths.

I believe the statistical analysis has been performed appropriately and rigorously. However, I would add a few details in the Methods/statistical analysis section, both technical, such as the retrospective nature of the study, and on aspects such as the specific software used to perform the analysis,

In Table 1 I would better explain the case-based measures, which do not seem clear.

The authors made all data underlying the findings in their manuscript fully available.

The manuscript is presented in an intelligible fashion and written in standard English, however, I would proofread it as a few sentences might be less clear if some typos are not corrected (e.g. figure 3 “shows a scatterplot by Region of the relationship of CI two months later the date of lockdown”, I believe it should be 2 months “after”), as well as some past tense forms or the s for the third person form of some verbs.

The aims, the discussion and the conclusions are coherent and sound, and ultimately support the adoption of early containment measures in order to counteract the epidemic surge and control its magnitude.

**********

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Reviewer #1: No

Reviewer #2: No

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30 Dec 2020

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Answer: we checked /changed the file naming according to PLOS ONE's style requirements

2. In your Methods section, please give the sources for the following information:

i) COVID-19 cases

Answer: we inserted the web link to the data as reported below:

https://github.com/pcm-dpc/COVID-19 (download 01/07/2020)

ii) ICU bed occupancy

Answer: we inserted the web link to the data as reported below: https://www.trovanorme.salute.gov.it/norme/renderNormsanPdf?anno=2020&codLeg=74348&parte=1%20&serie=null

Furthermore, this is a retrospective study; thus, we ask that you revise the text (especially, but no limited to, the aims and Conclusions) to avoid unsupported statements.

Answer: text was revised to take into account of this comment

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found; the link provided states data not found.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Answer: as reported above, used data are available at the links provided:

Covid-19 cases � https://github.com/pcm-dpc/COVID-19 (downloaded 01/07/2020)

ICU bed � https://www.trovanorme.salute.gov.it/norme/renderNormsanPdf?anno=2020&codLeg=74348&parte=1%20&serie=null (Table 1)

Regional population at 01/01/2020 � www.demo.istat.it

4. Please upload a new copy of Figure 2 as the detail is not clear.

Answer: The new figure 2 is now provided with high pixel resolution

5. Please include your tables as part of your main manuscript and remove the individual files. Please note that supplementary tables should be uploaded as separate "supporting information" files.

Answer: we included the tables as part of the manuscript.

Reviewer #1: The authors present a study that shows the impact of the timing of implementation of non-pharmaceutical containment measures (NPMs) on Covid-19 epidemic. They focus on the italian national lockdown, which started on March 11th 2020 and ended on May 4th 2020. The impact of NPMs on healthcare system has been evaluated in 21 italian local administrations (19 regions+2 autonomous provinces). Three indices have been used in order to assess this impact: cumulative incidence (CI), intensive care units occupancy (percentage on the total amount of ICUs), mortality rate. Timing of implementation of NPMs has been calculated as the number of days between the date on which each region reached a CI equal to 1.0 per 100k inhabitants, and the date of National Lockdown (it has been called delay). In the following lines some comments about the manuscript are reported:

1) The impact of the delay on healthcare system in terms of ICU occupancy and mortality rates is not sufficiently assessed. Two scatterplots (as Fig. 3) should be added: one between delay and highest ICU occupancy, the other one between delay and mortality rates (at May 11th). Related correlations should also be reported.

Answer: we followed your suggestion and added two scatterplots, one between delay and highest ICU occupancy, the other one between delay and mortality rates (at May 11th) and relative correlations. Following also your comment number 2, we added the scatterplot between delay and the number of ICU beds occupied per 100k inhabitants (hereinafter referred to as maximum ICU beds occupancy rate) and relative correlation. We included in the revised version another figure (Figure 5) combining these new scatterplots in three panels. Also Table 1 was updated adding a column with maximum ICU beds occupancy rate. We also revised methods, results and discussion sections to take into account the new results.

2) Looking at the data on Table 2, correlation between delay and mortality rate is about 0.7, which is close to the correlation between delay and CI. On the other hand, correlation between delay and highest percentage of ICU occupancy is lower (0.38). This probably means that the percentage of ICU occupancy is not the right statistic, and it should be replaced by the number of ICU beds occupied per 100k inhabitants, if these data are available. Percentage in fact is heavily affected by the total amount ICU beds, which is more closely linked to the quality level of the healthcare system than the spread of the epidemic.

Answer: Thanks for the advice. We calculated also the maximum ICU bed occupancy rate and relative correlation. We decided to maintain also the previous statistic as a measure both of quality level of the healthcare system and the impact of the spread on HS. It is of note that the correlation between the highest percentage of ICU occupancy maximum ICU bed occupancy rate is high (0.95). Below we show the scatterplot. We also revised methods, results and discussion sections to take into account the new results.

3) NPMs effectiveness can be seen after 10 – 31 days (Fig. 1, 2 and 4), regardless of the delay in their implementation. The authors should put a little bit more emphasis on this result, in the abstract and in the Results section.

Answer: thanks for the suggestion. This is now highlighted in the revised version.

4) Figure 2 has a very low pixel resolution, it is almost unreadable. Moreover, daily cases have too many fluctuations: it would be better to replace them with weekly averages or moving averages.

Answer: The new figure 2 is now with high pixel resolution; we also used weekly moving average, as suggested.

5) References are ok.

Answer: Thanks for the comment.

Reviewer #2: This interesting paper explores how the timing of implementation of NPMs (or NPIs) impacted the cumulative incidence, ICU bed occupancy and mortality related to COVID-19 in Italy. I found the work clear and sufficiently explicative, providing support to modelling studies that suggested that the timing of implementation of control measures is critical to achieving effective control of the epidemic.

Among the strengths, I would mention the clarity of the aims and among the limitations, I would certainly agree that the quality of the data, aggregated, did not allow for in-depth analysis such as age and sex standardization.

The manuscript is very linear and coherent. Moreover, it is technically sound, and the data support the conclusions. Moreover, general conclusions are acceptable and in line with recent literature reports. I particularly appreciated the mention of the data coming from the Italian sero-survey and the report on excess mortality in the limitations section, as it gives a realistic picture of the reality of the pandemic in Italy while acknowledging the struggle of gathering complete data on new cases and deaths.

I believe the statistical analysis has been performed appropriately and rigorously. However, I would add a few details in the Methods/statistical analysis section, both technical, such as the retrospective nature of the study, and on aspects such as the specific software used to perform the analysis

Answer: we thank the reviewer for these positive comments; following your suggestions, we specified that the study is retrospective and reported the software used for the analysis.

In Table 1 I would better explain the case-based measures, which do not seem clear.

Answer: we better specified the case-based measures, as suggested.

The authors made all data underlying the findings in their manuscript fully available.

The manuscript is presented in an intelligible fashion and written in standard English, however, I would proofread it as a few sentences might be less clear if some typos are not corrected (e.g. figure 3 “shows a scatterplot by Region of the relationship of CI two months later the date of lockdown”, I believe it should be 2 months “after”), as well as some past tense forms or the s for the third person form of some verbs.

Answer: the new version was further revised by a mother tongue person aiming to improve the language and to correct the typos.

The aims, the discussion and the conclusions are coherent and sound, and ultimately support the adoption of early containment measures in order to counteract the epidemic surge and control its magnitude.

Answer: thanks for the positive comment.

Submitted filename: Response to Reviewers.pdf

6 Jan 2021

Effect of timing of implementation of containment measures on Covid-19 epidemic. The case of the first wave in Italy.

PONE-D-20-34437R1

Dear Dr. Timelli,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Simone Lolli

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

I am happy to inform you that now the paper is ready for publication. The previously raised issues were addressed by the authors.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No


21 Jan 2021

PONE-D-20-34437R1

Effect of timing of implementation of containment measures on Covid-19 epidemic. The case of the first wave in Italy.

Dear Dr. Timelli:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Dr. Simone Lolli

Academic Editor

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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

https://www.researchpad.co/tools/openurl?pubtype=article&doi=10.1371/journal.pone.0245656&title=Effect of timing of implementation of containment measures on Covid-19 epidemic. The case of the first wave in Italy&author=&keyword=&subject=Research Article,Medicine and Health Sciences,Medical Conditions,Infectious Diseases,Viral Diseases,Covid 19,Medicine and Health Sciences,Health Care,Health Care Facilities,Hospitals,Intensive Care Units,Biology and Life Sciences,Population Biology,Population Metrics,Death Rates,People and places,Geographical locations,Europe,European Union,Italy,Medicine and Health Sciences,Epidemiology,People and Places,Population Groupings,Ethnicities,European People,Italian People,Biology and life sciences,Organisms,Viruses,RNA viruses,Coronaviruses,SARS coronavirus,SARS CoV 2,Biology and life sciences,Microbiology,Medical microbiology,Microbial pathogens,Viral pathogens,Coronaviruses,SARS coronavirus,SARS CoV 2,Medicine and health sciences,Pathology and laboratory medicine,Pathogens,Microbial pathogens,Viral pathogens,Coronaviruses,SARS coronavirus,SARS CoV 2,Biology and life sciences,Organisms,Viruses,Viral pathogens,Coronaviruses,SARS coronavirus,SARS CoV 2,Medicine and Health Sciences,Medical Conditions,Infectious Diseases,Infectious Disease Control,