key: cord-0852130-25jb7o8c
authors: Rovetta, A.; Castaldo, L.
title: Relations between demographic, geographic, and environmental statistics, and the spread of novel coronavirus disease (COVID-19) in Italy
date: 2020-09-18
journal: nan
DOI: 10.1101/2020.09.18.20196980
sha: 105f59bb657e6b3ee4bdedb79d7a5a5b4db8c4ab
doc_id: 852130
cord_uid: 25jb7o8c

Background: From January 2020, the COVID-19 pandemic has raged around the world, causing nearly a million deaths and hundreds of severe economic crises. In this terrible scenario, Italy was one of the most affected countries. ------------ Objective: The aim of this study is to look for significant correlations between COVID-19 cases and demographic, geographical, and environmental statistics of each Italian region from February 26 to August 12, 2020. Finally, we further investigated the link between SARS-CoV-2 spread and particulate matter 2.5 and 10 concentrations before the lockdown in Lombardy. ------------ Methods: All demographic data were taken from the AdminStat Italia website, while the geographic data from the Il Meteo website. The collection frequency was 1 week. Data on PM2.5 and PM10 average daily concentrations were collected from previously published articles. We used Pearson's coefficients to correlate quantities that followed a normal distribution, and Spearman's coefficient to correlate quantities that did not follow a normal distribution. To evaluate this, we used the kurtosis (k) and skewness (s) coefficients according to the following scheme: we considered data compatible with a normal distribution only when t_k=k(24/n)^(-1/2) [≤] 1.5 and t_s=s(6/n)^(-1/2) [≤] 1.5; here, the Pearson correlation index was deemed more reliable. When t_k in ]1.5,3],t_s [≤] 3 or t_k [≤] 3, t_s in ]1.5,3], we considered it appropriate to evaluate both correlations. Finally, when t_k,t_s > 3, we judged the Spearman correlation index more appropriate. When the linear correlations were significant, we interpolated the data linearly. We reported in round brackets () the week in which the correlation approached the threshold of statistical significance e.g. Abruzzo (4). The chosen p-value threshold was =.05. ------------ Results: We found significant strong correlations between COVID-19 cases and population density in 60.0% of regions, such as Calabria (5), Campania (1), Lazio (1), Liguria (2), Lombardy (4), Piedmont (2), Sardinia (3), Sicily (1), and Veneto (4) (R_best=.935, 95% CI: .830 -1.000,p_best=.046,95% CI:.006-.040). The average of the angular coefficients resulting from the linear interpolations of the pairs (COVID-19 cases, population number) is b=.0037 (95% CI .0009-.0065). We found a significant strong correlation between the angular coefficients b of the various regions and their latitude. This data shows the dependence of COVID-19 on geographical and/or climatic factors (R=.926,p=.001,r=.886,p=.003). in particular, we found a significant correlation with the historical averages (last 30 years) of the minimum temperatures of the Italian regions (R=-.849,p=.008,r=-.940,p=.005 for March, R=-.923,p=.001,r=-.872,p = .005 for February). We found a significant strong correlation between the number of COVID-19 cases until August 12 and the average daily concentrations of PM2.5 in Lombardy until February 29, 2020 (r=.76,p=.004). No significant correlation with PM10 was found in the same periods. Until February 26, 2020, we found both a correlation with PM2.5 (r=.63,p=.029) and PM10 (r=.72,p=.009). In the second week of March, the correlation with PM10 disappeared while that with PM2.5 continued to exist until nowadays. We found that 40 g/m^3 for PM2.5 and 50 g/m^3 for PM10 are plausible thresholds beyond which particulate pollution clearly favors the spread of SARS-CoV-2. ------------ Conclusion: Since SARS-CoV-2 is correlated with historical minimum temperatures and particulate matter 10 and 2.5, health authorities are urged to monitor pollution levels and to invest in precautions for the arrival of autumn. Furthermore, we suggest creating awareness campaigns for the recirculation of air in closed places and to avoid exposure to cold.

). In the second week of March, the correlation with PM10 disappeared while that with PM2.5 continued to exist until nowadays. We found that Conclusion: Since SARS-CoV-2 is correlated with historical minimum temperatures and particulate matter 10 and 2.5, health authorities are urged to monitor pollution levels and to invest in precautions for the arrival of autumn. Furthermore, we suggest creating awareness campaigns for the recirculation of air in closed places and to avoid exposure to cold.

The COVID-19 pandemic has been declared by the World Health Organization (WHO) chief as the most severe pandemic in recent human history [1] . To date, over 200 countries have been involved, for a total of over 29 million cases and 900,000 deaths [2] . Between February and April 2020, Italy was the most affected nation both for the number of new cases and new deaths [3] . The severity of the emergency was so important that, despite a drastic drop in infections during the summer months, it is still among the top 20 nations afflicted by the novel coronavirus [4] . On January 23, two Chinese tourists tested positive for COVID-19 near Rome [5] . However, the patients appeared to have been readily isolated, averting extensive contagion. Towards the end of February, the situation began to precipitate and fall outside the control of the institutions. Starting from February 21, to counter the spread of the virus, the Italian government declared various lockdowns that extended around the outbreak of Lodi, in the region of Lombardy. On March 10, the lockdown went into effect nationwide [6] . For these reasons, we believe Italy to be one of the main sources of information useful for fully understanding the behavior of SARS-CoV-2. This is the first study that provides a complete and detailed history of the correlations between the SARS-CoV-2 spread and the demographic, geographic, and environmental statistics in Italy. From the analysis of our results, it was possible to highlight anomalous and/or local properties and behaviors of the virus, as well as test the statistical significance of the hypotheses and scenarios proposed by other papers.

We have collected data on Italian demographic statistics and pollution from the AdminStat Italia website and a previously published article [7, 8] . We looked for significant Pearson (ܴ) and

Spearman ‫)ݎ(‬ correlations between COVID-19 cases per province in each region from February 26 until August 12, 2020, and birth rate, median age, population density, death rate, old age index, population number, % of unmarried, family members, growth rate, % of divorcees, % of foreigners, foreigners growth rate, % of widowers. The frequency of data collection was 1 week. For each correlation found, we calculated the angular coefficient of the interpolating line and correlated the latter with geographical characteristics such as regions' latitude and minimum temperatures in the months of February and March (last 30 years historical data). We have collected geographic data from the Il Meteo website [9] . Below, we list the regions on which the survey was carried out: 

We found significant strong correlations between COVID-19 cases and population number in . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

The copyright holder for this this version posted September 18, 2020. ).

We have identified a strong significant correlation between the number of COVID-19 cases until 

We found significant strong correlations between COVID-19 cases and population density in

of regions, such as Abruzzo (4) ); however, we highlighted discrepancies between the onset of the above in Sicily and in Veneto.

. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 18, 2020. . https://doi.org/10.1101/2020.09.18.20196980 doi: medRxiv preprint

The results found are compatible with the following scenarios:

per province in Campania, Lazio, Sicily, Liguria, and Piedmont, strongly indicate that SARS-CoV-2 was in circulation for a long time before the first confirmed case. Observing the Lombardy trend, we deduced the virus seems to have taken 4 weeks to correlate with the demographic dimension; therefore, it is plausible that COVID-19 spread in Italy from January 2020 (or earlier). 

(particularly in Lombardy). This agrees with the possibility that COVID-19 was spread nationwide, mistaken for flu or common colds until the aforementioned mutation occurred.

The first two confirmed cases of COVID-19 in Italy were two Chinese tourists, who landed on January 19, 2020 [5] . They could travel freely around the town, making a brief stop in Parma and staying in a Hotel in Rome [11] . In any case, we believe that it is statistically unlikely and morally incorrect to associate the arrival of the novel coronavirus in Italy with them. In particular, the results found in this paper suggest that SARS-CoV-2 has been circulating in Italy since early January, probably mistaken for flu or common cold. In fact, having ascertained that:

• those suffering from COVID-19 were largely asymptomatic [12] • Symptoms associated with COVID-19 are milder in children compared with adults [13] • the incubation period ranges from 2 to 14 days with an average of 5-6 days [14] • 80% of people with COVID-19 have mild symptoms [15] • average mortality is about 1% [16, 17] . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this this version posted September 18, 2020. . https://doi.org/10.1101/2020.09. 18.20196980 doi: medRxiv preprint • the fatality rate is proportional to age and extremely high for patients over 65 [18] the most likely hypothesis is that the virus had been around since before their arrival. All these factors drastically increase the time needed to identify and isolate a COVID-19 infected person since: a) the asymptomatic have infected without their knowledge and the people with whom they have been in contact; b) the children (besides the problem of asymptomaticity) showed milder symptoms than adults, inducing parents and relatives to associate them with normal colds or flu; c) the symptoms were identified up to 14 days after contagion, allowing infected subjects to infect other people unknowingly; d) in the overwhelming majority of cases, symptomatic patients showed mild symptoms not causing concern in work colleagues and relatives; e) the fact that the death risk was extremely concentrated in the high age groups prevented an easy assessment of the extent of the epidemic since these groups were naturally more exposed to fatal phenomena ie, until high numbers have been reached, the collective psychological impact was low. The marked correlation between the virus spreading speed among the population and the minimum temperatures of each region suggests that the late autumn and winter seasons can strongly favor the pandemic. In fact, due to low external temperatures, people more frequently give rise to gatherings without recirculation of air, which create favorable conditions for the proliferation of viruses [19] . Just as the rhinoviruses, adenoviruses, and influenzaviruses, the novel coronavirus may also survive better in colder and drier climates [20] [21] [22] [23] . Furthermore, sudden changes in temperature and cold can lower people's immune defenses [22] . The strong and prolonged correlations we have found with fine particulate matter 2.5 in the Lombardy region, in northern Italy, indicate that this type of pollution played an important role in the epidemy. This may be linked to the fact that fine particles substantially reduce immune defenses as well as increase the severity of symptoms due to damage induced in the respiratory system [24, 25] ; moreover, PM2.5 (and PM10) could act as a virus carrier [26] . In fact, from Figure 1 Finally, considering that, in Lombardy, the correlation between COVID-19 cases and the number of inhabitants per region became significant after 4 weeks, the severity of symptoms was more severe than in other regions, and the basic reproduction number (ܴ0) seems to have been the higher one, we suggest that an evolutionary genetic mutation may have occurred in Lombardy [7] . In fact, . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

The copyright holder for this this version posted September 18, 2020. . https://doi.org/10.1101/2020.09.18.20196980 doi: medRxiv preprint although the genome does not appear to have changed substantially, Zhang et al. showed that even small mutations can cause significant changes in SARS-CoV-2 behavior [28] .

Statistical correlations can provide valid supports for hypotheses and theories as well as fundamental indicators of phenomena to be explored; however, they cannot demonstrate the causal nature of a phenomenon.

In this paper we found significant strong and lasting correlations between the spread of SARS-CoV- March, indicating that this type of pollution also played a role in the spread of the virus, linked to the exceeding of specific daily peaks. Therefore, we suggest to the health authorities to pay particular attention to the arrival of the winter months, not only by investing in adequate precautions but by launching various awareness campaigns on air recirculation indoors and in avoiding exposure to the cold. In addition, it will also be necessary to monitor carefully particulate matter levels 10 and 2.5, even imposing car blocks if necessary.

. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

The copyright holder for this this version posted September 18, 2020. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

The copyright holder for this this version posted September 18, 2020. Table 2 . Geographical and environmental data of the regions in which a correlation was found (including suspicious ones). ܾ = angular coefficient of the straight line that interpolates the COVID-19 cases and the number of inhabitants per region, min = minimum value, T = temperature.

. CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review) preprint

The copyright holder for this this version posted September 18, 2020. . https://doi.org/10.1101/2020.09.18.20196980 doi: medRxiv preprint

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