The Veneto region and the Piemonte region followed two different testing policies. In order to estimate the impact of a more wide testing policy we compared these two regions.
In particular we tried to apply to the Piemonte region the model embraced by the Veneto region.

Figure 1 shows that, based on the number of confirmed cases, the two regions should register more or less the same number of hospitalizations: it the figure here below this is represented by the green and the red curve, which are almost overlapping.
Still, if we take a look at the real data we can see that the Piemonte region (red dots) actually registers a much higher number of hospitalizations then the Veneto region.

If we include in the model the number of swabs performed throughout time, as shown in Figure 2, we can see that the Veneto model can predict well the number of hospitalizations in the Piemonte region, hence explaining the difference observed in Figure 1.

We used a Poisson model with a natural spline of degree 3 on the days and a swabs x days interaction. We used the resident population as an offset.

The National Institute of Statistics (Istat) made available on its website (https://www.istat.it/it/archivio/240401) the mortality data of 1084 Italian municipalities, with data updated to the 21st of March 2020. 1.¹

As it is possible to see on the Istat website, the municipalities that take part in this analysis are the ones that counted at least 10 deaths in the period 1 January 2020 to 28 March 2020 and that registered a rise in mortality of at least 20 % in the first 21 or 28 days of March 2020. The selection criteria introduced by ISTAT causes an overestimate of mortality. For this reason the numbers we present here must be seen as the highest forseeable values.

Overall mortality is a strong indicator as it has low susceptibility to errors or discrepancies in assessments and it accounts for both the mortality caused directly by a specific pathology and the mortality caused in an indirect way, as for example by difficulties for people who suffer from different pathologies in accessing the hospital services.

Moreover, the overall mortality is not affected by diagnostic questions or difficulties in coding cause of death and is therefore a useful foundation on which we can build an accurate estimate of the effects of the COVID-19 epidemics.

Data is made available by Istat in different tables, which can be accessed and downloaded from the official website. The tables allow an immediate reading and can also be used for further analysis. We hence used the data in order to make some descriptive analysis, that are essentially presented in the form of graphs, in order to illustrate the trend in overall mortality by geographic area, sex, age and time period.

These are some preliminary analyses that aim at sharing information during times of emergencies, that will be improved and explored further in the coming weeks. In particular the current goal is only to give a reasoned presentation of the absolute values and the change percentages. Further analyses will be conducted in order to reach a better modelling of the trend and to improve the indices of the confidence intervals.

These analyses want to answer to the following questions:

• What is the entity of the observed mortality change if we compare the period from the 1st to the 21st of March 2019 to the period from the 1st to the 21st of March 2020?
• How the mortality change distributed by sex, age and province of residency?
• If we also consider previous years, starting from 2015, can we observe relevant change throughout the different years? And again, what is the distribution by sex, age and province of residency?
• Starting from which week of the year is it possible to observe change of the overall mortality?

Note on aggregation and numerosity of data: some variables were grouped into wider categories, as indicated in the analyses results.

How much did the overall mortality change from last year (1-21 March 2019 vs 1-21 March 2020)? How is the mortality change distributed by sex, age, and province of residency?

The percentage change in mortality (1-21 March 2019 vs 1-21 March 2020) was estimated by region, sex and age aggregated data. In this case, aggregation by province only includes the aforementioned municipalities made available by Istat. Data was categorized as in the table provided by Istat (https://www.istat.it/it/files//2020/03/Tavola-sintetica-decessi.xlsx). Age categories are: 65-74, 75-84, over 85.

The percentage change is defined as:

change_% = 100 * ( deaths₂₀₂₀ -- deaths₂₀₁₉ ) / deaths₂₀₁₉

This index appears in the original table, computed for each municipality. In order to reduce statistical variability given by random fluctuations, which is rather high in those municipalities with a low number of inhabitants, we computed the percentage change on a regional level. Total deaths by provincies and percentage change (from 2019 to 2020) are shown in the table.²

The analysis was also conducted separately by age class and sex, and results are presented in the following graphs (Figure 1 and Figure 2)

For a correct reading of the percentage change it is necessary to remember that number of total deaths is very different from one province to another as the sample size can change quite a lot. In some provincies the mortality change seems quite important, but it is actually given by a small difference in terms of number of deaths (Table 1).

Considering the data on mortality starting from 2015, what is the entity of the change registered throughout the years by age and province of residency?

The data provided by Istat allows to analyse the mortality trend starting from 2015. Data can be found at https://www.istat.it/it/files//2020/03/dati-comunali-settimanali-ANPR-1.zip. Further analyses will be conducted in the coming weeks to better explore mortality between 2015 and 2020.

Deaths in all municipalities in the Istat database belonging to the same province were summed together in order to obtain the number of deaths by province. The graph here below (Figure 3) shows the number of total deaths by province from 2015 to 2020.

The graphs here below (Figure 4) show how mortality changed from 2015 to 2020 by province and age. Deaths in all municipalities in the Istat database belonging to the same province were summed together in order to obtain the number of deaths by province. Age classes were defined as follows: under 64 (putting together the classes 0-14 and 15-64 of the original table), 65-74, over 75.

It is necessary to keep in mind that the graphs only show absolute numbers, hence differences between provinces are mainly due to different sample sizes.

In which week of the year is it possible to notice change in the overall mortality?

Data regarding the 122 municipalities of the Veneto Region, as presented in the table at https://www.istat.it/it/files//2020/03/dati-comunali-settimanali-ANPR-1.zip for the period of time that goes from the 1st of January to the 21st of March, can be helpful to answer this question. The data in the table is divided into time slots of 7 days, except for the period from the 1st to the 10th of January; this period was therefore excluded from the analysis. The following graphs (Figure 5) present the trend by age and province. The graphs report on the horizontal axis the date that represents the beginning of each time slot.

¹ For further information on data collection see the Istat methodology.
² 2.If the index is equal to 100% it means the mortality has doubled.

Prof. Corrado Magnani
Department of Translational Medicine -- Università del Piemonte Orientale

The National Institute of Statistics (Istat) made available on its website (https://www.istat.it/it/archivio/240401) the mortality data of 1084 Italian municipalities, with data updated to the 21st of March 2020. 1.¹

As it is possible to see on the Istat website, the municipalities that take part in this analysis are the ones that counted at least 10 deaths in the period 1 January 2020 to 28 March 2020 and that registered a rise in mortality of at least 20 % in the first 21 or 28 days of March 2020. The selection criteria introduced by ISTAT causes an overestimate of mortality. For this reason the numbers we present here must be seen as the highest forseeable values.

The data collection includes 6.177.016 men and 6.496.805 women, distributed according to residence. Representation is different in the different regions (Table 1).

Overall mortality is a strong indicator as it has low susceptibility to errors or discrepancies in assessments and it accounts for both the mortality caused directly by a specific pathology and the mortality caused in an indirect way, as for example by difficulties for people who suffer from different pathologies in accessing the hospital services.

Moreover, the overall mortality is not affected by diagnostic questions or difficulties in coding cause of death and is therefore a useful foundation on which we can build an accurate estimate of the effects of the COVID-19 epidemics.

Data is made available by Istat in different tables, which can be accessed and downloaded from the official website. The tables allow an immediate reading and can also be used for further analysis. We hence used the data in order to make some descriptive analysis, that are essentially presented in the form of graphs, in order to illustrate the trend in overall mortality by geographic area, sex, age and time period.

These are some preliminary analyses that aim at sharing information during times of emergencies, that will be improved and explored further in the coming weeks. In particular the current goal is only to give a reasoned presentation of the absolute values and the change percentages. Further analyses will be conducted in order to reach a better modelling of the trend and to improve the indices of the confidence intervals.

The analyses want to answer to the following questions:

• What is the entity of the observed mortality change if we compare the period from the 1st to the 21st of March 2019 to the period from the 1st to the 21st of March 2020?
• How the mortality change distributed by sex, age and region of residency?
• If we also consider previous years, starting from 2015, can we observe relevant change throughout the different years? And again, what is the distribution by sex, age and region of residency?
• Starting from which week of the year is it possible to observe change of the overall mortality?

Note on aggregation and numerosity of data: some variables were grouped into wider categories, as indicated in the analyses results.

How much did the overall mortality change from last year (1-21 March 2019 vs 1-21 March 2020)? How is the mortality change distributed by sex, age, and region of residency?

The percentage change in mortality (1-21 March 2019 vs 1-21 March 2020) was estimated by region, sex and age aggregated data. Remember data only includes the municipalities provided by Istat. Data was categorized as in the table provided by Istat (https://www.istat.it/it/files//2020/03/Tavola-sintetica-decessi.xlsx). Age categories are: 65-74, 75-84, over 85.

The percentage change is defined as:

change_% = 100 * ( deaths₂₀₂₀ -- deaths₂₀₁₉ ) / deaths₂₀₁₉

This index appears in the original table, computed for each municipality. In order to reduce statistical variability given by random fluctuations, which is rather high in those municipalities with a low number of inhabitants, we computed the percentage change on a regional level. Total deaths, number of municipalities included in the measurement for each region and percentage change (from 2019 to 2020) are shown in the table.²

The analysis was also conducted separately by age class and sex, and results are presented in the following graphs (Figure 1 and Figure 2)

All regions show increased mortality for the two oldest age classes. Moreover, the majority of regions also present an increased mortality between 65 and 74 years. For a correct reading of the percentage change it is necessary to remember that number of total deaths is very different from one region to another as the sample size can change quite a lot. In some regions the mortality change seems quite important, but it is actually given by a small difference in terms of number of deaths.

Considering the data on mortality starting from 2015, what is the entity of the change registered throughout the years by sex, age and region of residency?

The data provided by Istat allows to analyse the mortality trend starting from 2015. Data can be found at https://www.istat.it/it/files//2020/03/dati-comunali-settimanali-ANPR-1.zip. Further analyses will be conducted in the coming weeks to better explore mortality between 2015 and 2020. Anyhow, a descriptive analysis is still useful to notice the low variability of the number of deaths between 2015 and 2019.

Deaths in all municipalities in the Istat database belonging to the same region were summed together in order to obtain the number of deaths by province. The graphs here below (Figure 3) show the number of total deaths by region from 2015 to 2020. Regions were aggregated in two groups in order to enhance the readability of the graphs. The two groups are based on an Istat classification that splits regions in "North Regions" and "South Central Regions and Islands".

The graphs here below (Figure 4) show how mortality changed from 2015 to 2020 by region and age. Age classes were defined as follows: under 64 (putting together the classes 0-14 and 15-64 of the original table), 65-74, over 75.

It is necessary to keep in mind that the graphs only show absolute numbers, hence differences between regions are mainly due to different sample sizes.

In which week of the year is it possible to notice change in the overall mortality?

Data presented in the table at https://www.istat.it/it/files//2020/03/dati-comunali-settimanali-ANPR-1.zip for the period of time that goes from the 1st of January to the 21st of March can be helpful to answer this question. The data in the table is divided into time slots of 7 days, except for the period from the 1st to the 10th of January; this period was therefore excluded from the analysis. The following graphs (Figure 5 and 6) present the trend by age, sex and region. As in the previous analyses, age is divided into classes (same as before) and regions are divided into two groups (same as before).

In this study it is possible to notice increased mortality starting from the week of the 1st of March, especially in those regions that are most affected by the epidemics (especially the Lombardy Region). Remind that the municipalities included in the analysis are the ones made available by Istat.

¹ For further information on data collection see the Istat methodology.
² 2.If the index is equal to 100% it means the mortality has doubled.

Analysis of data related to number of cases, number of tests, number of hospitalizations, number of ICU admissions and number of deaths from the 28th of February to the 30th of March in the Piemonte Region and the Veneto Region.

The two regions took different approaches to the use of the nasopharyngeal swab. The Piemonte Region followed right away the guidelines provided by the Italian Superior Council of Health, testing, during the early stage of the outbreak, only people who both presented clinical signs of Covid-19 infection and satisfied some epidemiological criteria (i.e. came in contact with someone who visited areas considered at risk). The Veneto Region, on its own initiative, decided to act differently: all people that showed symptoms of Covid-19 were tested, as well as people who came in contact with them. This different approach during the early stage of the epidemics (from 28th February to 8th March) drew attention to a higher number of cases in the Veneto Region (Figure 1).

On the 8th on March the so-called "Red Zone" was extended to the whole country placing Italy on lockdown, which led to a progressive reduction of circulation and everyday activities.

Although in the early stage of the outbreak (approximately one week) the Piemonte Region showed a lower number of cases, it is possible to see how Veneto gained in terms of hospitalizations. Looking at the curves that plot the number of hospitalizations in the two regions it is possible to notice a clear bifurcation, showing that the Piemonte region was heavily burdened with a significant increase in hospitalizations (Figure 2). The same observations can be made looking at the number of ICU admissions (Figure 3) and the number of deaths (Figure 4).

The parting of the hospitalization curves of the two regions around the 12th of March (when the Piemonte Region started to grow faster than the Veneto Region, though apparently having less cases) could be the effect of a smaller number of tests performed: people that were identified were way less than the real number, which also led to overestimate the fatality rate (deaths for the disease/people with the disease).The health policies implemented by the Veneto Region paid off. The Region decided to test more, probably motivated by the first outbreak in Vo' Euganeo, increasing, at least at the beginning, the chances of finding and isolating positive cases, hence protecting the most vulnerable age groups and reducing the spread of the virus even before the lockdown of the 8th of March. As the curves plotting the number of cases divided more and more markedly, the deaths curves also did so, with a temporal shift of 5/6 days.

In the current stage of the epidemics, this probably means that:

1. patients who present symptoms but do not test positive are left at home and do not start any therapies right away;
2. When the situations worsens, patients visit the ER where, if they are positive, they are hospitalized and can finally start a therapy;
3. Still, they start therapy too late to prevent ARSD and, probably, the late use of tocilizumab is less likely to succeed;
4. with consequent increase in terms of deaths.

Our analysis suggests that in order to modify this sequence of events, reduce hospitalizations and most importantly optimize ICU admissions it is necessary to increase the number of at-home diagnoses, extending testing to paucisymptomatic patients. This would allow not only to reduce the number of infected people but also to start an anti-inflammatory and/or antiviral at-home therapy under the direct control of trained doctors. The aim would be to prevent or at least mitigate and slow down the disease progression, downsizing the fatality rate of Covid-19. It is our belief that the integrated management of hospital and territory could be the winning strategy.

*The following people contributed to the interpretation of data: Carmen Fava, Giuseppe Saglio (Department of Clinical and Biological Sciences, Università di Torino) and Andrea Ricotti (Department of Public Health and Pedriatic Sciences, Università di Torino