The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave

Balboni, Erica; Filippini, Tommaso; Rothman, Kenneth J.; Costanzini, Sofia; Bellino, Stefania; Pezzotti, Patrizio; Brusaferro, Silvio; Ferrari, Fabrizio; Orsini, Nicola; Teggi, Sergio

doi:10.1016/j.envres.2023.115796

Cited by 9 publications

(9 citation statements)

References 51 publications

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“…Among others, the Pearson correlation was used to investigate the correlation of COVID-19 related outcomes with climate/weather variables [ 33 , 59 , 97 ], elevation and geographic location [ 29 , 47 ]. The Poisson regression was instead applied to study the correlation between the independent socioeconomic and demographic variables with COVID-19 related outcomes [e.g., 123 , 142 , 147 ] but this method is also suitable for investigating the relation with meteorological factors [e.g., 148 ].…”

Section: Resultsmentioning

confidence: 99%

Conditioning factors in the spreading of Covid-19 – Does geography matter?

Vandelli,

Palandri,

Coratza

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

Conditioning factors in the spreading of Covid-19 – Does geography matter?

Vandelli,

Palandri,

Coratza

et al. 2024

Heliyon

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“…The four waves are timed as follows: the first wave occurred from February to May 2020; the second wave occurred from October to December 2020; the third wave occurred from January to May 2021; the fourth wave occurred from November 2021 to March 2022 [ 13 ]. All the waves’ peaks coincide with the coldest seasons in Italy; in fact, SARS-CoV-2 virulence has been found to be maximum below 10 °C and 40 kJ/m 2 [ 9 ]. This behavior towards environmental temperature and solar ultraviolet radiation is quite similar to that of other viral respiratory infections, in primis influenza [ 14 ].…”

Section: Discussionmentioning

confidence: 99%

“…Like all respiratory viruses, COVID-19 is influenced by environmental temperature and solar ultraviolet radiation; in fact, the virulence of the SARS Coronavirus 2 (SARS-CoV-2), its etiological agent, has been found to be maximum below 10 °C and 40 kJ/m 2 , respectively [ 8 , 9 ]. In Italy, these meteorological conditions are typical of the autumn–winter periods; therefore, we have focused our real-world nationwide study on these seasons.…”

Section: Introductionmentioning

confidence: 99%

A Real-World Nationwide Study on COVID-19 Trend in Italy during the Autumn–Winter Season of 2020 (before Mass Vaccination) and 2021 (after Mass Vaccination) Integrated with a Retrospective Analysis of the Mortality Burden per Year

Roncati,

Galeazzi,

Bartolacelli

et al. 2024

Microorganisms

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SARS-CoV-2 virulence is known to increase with lowering of environmental temperature and solar ultraviolet radiation; therefore, we have focused our real-world nationwide study concerning with COVID-19 trend and dynamics on the coldest seasons of the year in Italy, the Western country hardest hit at the onset of the pandemic, comparing the autumn–winter of 2020 (before mass vaccination but when the emergency machinery was fully operative in terms of tracing and swabs) with the autumn–winter of 2021 (after mass vaccination), and analyzing the mortality burden by age groups and life stages in the years 2019 (pre-COVID-19), 2020 (before mass vaccination), and 2021 (after mass vaccination). Methods: During the state of national health emergency, the Civil Defense Department released the aggregate data coming from the Higher Institute of Health, the Ministry of Health, the Italian Regions, and the Independent Provinces, to inform the population about the pandemic situation, daily. Among these data, there were the number of contagions, performed swabs, hospitalizations in Intensive Care Units (ICU), non-ICU patients, and deaths. By means of a team effort, we have collected and elaborated all these data, comparing the COVID-19 pandemic in Italy during the autumn–winter of 2020 with the autumn–winter of 2021. Moreover, we have extracted from the database of the National Institute of Statistics the total number of annual deaths in Italy during the years 2019, 2020, and 2021, comparing them to each other in order to evaluate the mortality burden attributable to COVID-19. Results: From the autumn–winter of 2020 to the autumn–winter of 2021, the contagions increased by ≈285%, against a ≈290% increase in the performed swabs; therefore, the mean positivity rate passed from 8.74% before mass vaccination to 8.59% after mass vaccination. The unprecedent vaccination campaign allowed a ≈251% abatement in COVID-19 deaths, and a reduction of ≈224% and ≈228% in daily ICU and non-ICU hospitalizations due to COVID-19, respectively. Regarding COVID-19 deaths, in 2020, there was a mortality excess of ≈14.3% quantifiable in 105,900 more deaths compared to 2019, the pre-COVID-19 year; 103,183 out of 105,900 deaths occurred in older adults (≥60 years), which is equivalent to ≈97.4%, while in adults over 50, the segment of population just below older adults, in 2020, there were 2807 more deaths than in 2019. Surprisingly, from the analysis of our data, it is emerged that in people under the age of 40 in the years 2019, 2020, and 2021, there were 7103, 6808, and 7165 deaths, respectively. This means that in subjects under 40 during 2020, there were 295 fewer deaths than in 2019, while during 2021, there were 357 more deaths than in 2020, equivalent to ≈5.2% more. Conclusions: COVID-19 is a potential life-threatening disease mainly in older adults, as they are the most vulnerable due to inherent immunosenescence and inflammaging. Extensive vaccination in this segment of population with up-to-date vaccines is the means to reduce deaths, hospitalizations, and ICU pressure in the public interest. In the event of future threats, a new mass vaccination campaign should not be implemented without taking into account the individual age; it should primarily be aimed at people over 60 and at patients of any age with immune deficits, and secondly at people over 50. COVID-19 vaccination shows a favorable benefit–risk ratio in older adults, while the balance steps down under the age of 40; this younger segment of the population should be therefore exempt from any mandatory vaccination.

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“…In the early stages of the pandemic, Sfica et al [9] found evidence for an inverse relationship between the spread of COVID-19 and UV radiation. Balboni et al [103], analysing the first two waves of the COVID-19 pandemic in Italy, also found some evidence that COVID-19 cases were inversely related to UV radiation levels. However, this correlation was much weaker than with temperature, with a strong mitigat-ing effect against COVID-19 transmission observed when outdoor temperatures exceeded 10 C [103].…”

Section: Effect Of the Weather On The Survivability And Infectivity O...mentioning

confidence: 96%

“…Balboni et al [103], analysing the first two waves of the COVID-19 pandemic in Italy, also found some evidence that COVID-19 cases were inversely related to UV radiation levels. However, this correlation was much weaker than with temperature, with a strong mitigat-ing effect against COVID-19 transmission observed when outdoor temperatures exceeded 10 C [103]. Furthermore, Burra et al [98] suggested that UV radiation might induce an immunosuppression response, potentially leading to an increase in COVID-19 cases.…”

Section: Effect Of the Weather On The Survivability And Infectivity O...mentioning

confidence: 96%

Airborne Transmission of SARS-CoV-2: The Contrast between Indoors and Outdoors

Beggs,

Abid,

Motallebi

et al. 2024

Fluids

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COVID-19 is an airborne disease, with the vast majority of infections occurring indoors. In comparison, little transmission occurs outdoors. Here, we investigate the airborne transmission pathways that differentiate the indoors from outdoors and conclude that profound differences exist, which help to explain why SARS-CoV-2 transmission is much more prevalent indoors. Near- and far-field transmission pathways are discussed along with factors that affect infection risk, with aerosol concentration, air entrainment, thermal plumes, and occupancy duration all identified as being influential. In particular, we present the fundamental equations that underpin the Wells–Riley model and show the mathematical relationship between inhaled virus particles and quanta of infection. A simple model is also presented for assessing infection risk in spaces with incomplete air mixing. Transmission risk is assessed in terms of aerosol concentration using simple 1D equations, followed by a description of thermal plume–ceiling interactions. With respect to this, we present new experimental results using Schlieren visualisation and computational fluid dynamics (CFD) based on the Eulerian–Lagrangian approach. Pathways of airborne infection are discussed, with the key differences identified between indoors and outdoors. In particular, the contribution of thermal and exhalation plumes is evaluated, and the presence of a near-field/far-field feedback loop is postulated, which is absent outdoors.

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The influence of meteorological factors on COVID-19 spread in Italy during the first and second wave

Cited by 9 publications

References 51 publications

Conditioning factors in the spreading of Covid-19 – Does geography matter?

Conditioning factors in the spreading of Covid-19 – Does geography matter?

A Real-World Nationwide Study on COVID-19 Trend in Italy during the Autumn–Winter Season of 2020 (before Mass Vaccination) and 2021 (after Mass Vaccination) Integrated with a Retrospective Analysis of the Mortality Burden per Year

Airborne Transmission of SARS-CoV-2: The Contrast between Indoors and Outdoors

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