key: cord-0763407-f8sknla0 authors: Pansini, R.; Fornacca, D. title: Higher virulence of COVID-19 in the air-polluted regions of eight severely affected countries date: 2020-05-05 journal: nan DOI: 10.1101/2020.04.30.20086496 sha: 75fd892bc9dad35f658ab69d24365b9ed5a65b7c doc_id: 763407 cord_uid: f8sknla0 COVID-19 has spread in all continents in a span of just over three months, escalating into a pandemic that poses several humanitarian as well as scientific challenges. We here investigated the geographical character of the infection and correlate it with several annual satellite and ground indexes of air quality in China, Iran, Italy, Spain, France, Germany, U.K. and U.S.A. Adjusting for population size, we find more viral infections in those areas afflicted by high PM 2.5 and Nitrogen Dioxide values. Higher mortality was also correlated with relatively poor air quality. In Italy, the correspondence between the Po valley pollution and SARS-CoV-2 infections and induced mortality was the starkest, originating right in the most polluted European area. Air pollution appears to be for this disease a risk factor similar to smoking. This suggests the detrimental impact climate change will have on the trajectory of future respiratory epidemics. From the first detected outbreak of a new member of the coronavirus (CoV) family 1 in Wuhan, Hubei Province, China 2-4 , SARS-CoV-2 5 has rapidly spread around the world 6 , with governments and institutions showing mixed results in its effective containment 7 . Certain regions have been much more adversely impacted in terms of the rate of infections and mortality rates than others, and the full reasons for this are not yet clear. This paper shows preliminary, yet compelling evidence of a correlation between air pollution and incidence of COVID-19 in eight countries. Air pollution is notoriously known to cause health problems and, in particular, respiratory diseases, to individuals exposed for longer than several days per year [8] [9] [10] [11] [12] . Moreover, pollutants in the air are significant underlying contributors to the emergence of respiratory viral infections 13 . In particular, PM 10 and PM 2.5 have been linked to respiratory disease hospitalisations for pneumonia and chronic pulmonary diseases [14] [15] [16] [17] [18] [19] [20] . There is some further experimental evidence that emissions from diesel and coal affect the lungs, causing pathological immune response and inflammations 21, 22 , wiping away past disputes 23 that only high concentrations of these gasses were needed to cause pathologies. Airborne microorganisms can directly infect other people's mucosae or travel further into the air and onto surfaces causing delayed infections. The particles of several pollutants such as PMs and NO2 can act as a vector for the spread and extended survival in the air of bioaerosols [24] [25] [26] [27] [28] [29] including viruses [30] [31] [32] [33] [34] . A first hypothesis in this direction has arisen for COVID-19 in Northern Italy 35,36granted that the viral load in a flying aggregate can be enough to cause morbidity. The strong containment measures adopted firstly by the Chinese government have necessarily biased the natural virus spread 37-39 , not allowing the virus to distribute evenly to polluted and non-polluted areas of the country. What shall be noted, though, is that its appearance was recorded in a Chinese area affected by some of the highest air pollution in the world, and it showed a relatively high virulence there. In the case that, as in Italy 40 , the onset of the infection went undetected for weeks before the outbreaks became apparent, air pollution might have played a more relevant role in the exacerbation of the virus. A number of personal risk factors (discussed in S.I.) including male gender and being a smoker have been associated with higher morbidity and mortality of COVID-19 41 . A high population density boosts the virus spread, but by itself it should not be a reliable predictor for a higher virulence and a higher mortality 42 . Another evident predictor variable is transportation. The surrounding areas of transport hubs such as airports and large train stations should witness the appearance of the virus earlier than other zones increasing its transmission [43] [44] [45] 37, 46 . One factor that has so far been overlooked is the role of air pollution in the spread and mortality rates of COVID-19. Air pollution has been shown to be strongly associated with a high incidence of other respiratory infections 14, 15, 8, 16, 17, 13, 12, [18] [19] [20] and higher mortality rates 10, 11 . Here we investigate whether there is a correlation between air pollution and air-borne SARS-CoV-2 causing respiratory diseases in China, Iran, Italy 47 , the United States, Spain, France, Germany, and the United Kingdom. Our hypotheses were: Hyp 1: Is there a higher incidence of COVID-19 in areas with poorer air quality? Hyp 2: Is there a higher COVID-19 mortality rate in highly polluted areas? We selected eight countries particularly affected by the virus and evaluated the potential correlation between air quality metrics and infections at the finest granularity available. Results for each country were analysed separately. (More on this in S.I.) The COVID-19 dataset was compiled at the second-level administrative subdivision for six out of eight countries (U.S. counties equivalent); however, a few exceptions occurred and, in some cases, adaptations were required. Both infections and deaths due to COVID-19 were collected and normalised by population size (100,000 residents). Air quality information was retrieved mainly from satellite observations and for the U.S.A., China and Italy, we included ground measures from different sources. Satellite data hold several advantages over ground station data, such as regular and continuous data acquisition, quasiglobal coverage, and spatially consistent measurement methodologies. On the other hand, ground stations offer real measures of single pollutants instead of deriving it from spectral information (satellites), however, they require more or less arbitrary estimations (such as interpolation) to fill spatial gaps. Table S1 in S.I. summarises the datasets used. Exploratory analysis of the variables was conducted with a focus on evaluating the air pollution distributions within each country. Due to the highly skewed distributions of both population-adjusted dependent variables, namely COVID-19 infections / 100,000 inhabitants and COVID-19 deaths / 100.000 inhabitants, the assumption of normality was not met. We therefore opted for the Kendall tau correlation coefficient for all statistical tests. Finally, thematic maps were produced for visual interpretation to better highlight the potential air quality and COVID-19 distributions within the eight assessed countries. patterns. The spread of COVID-19 and its related fatalities in Spain could not be explained by differences in air pollution; however, population size and density were negatively correlated with the virus. In a distinct manner, population density explained weakly COVID-19 cases and deaths in Germany while the distribution of fine particulate matter was negatively correlated weakly. Among the different pollutant analysed, O3 and SO2 measures from ground stations in China and the United States did not show significant correlation with COVID-19 or were negatively correlated, in contrast with the other pollutants. Table 2 Correlation coefficients between COVID-19 deaths per 100,000 inhabitants and air quality variables. Significant correlations (p-value < 0.05) are shown in bold; blue and red colour highlights indicate positive and negative correlations, respectively Correlational plots Graph 1 reports correlational plots of COVID-19 fatalities with the satellite-based PM 2.5 concentrations for seven countries except the missing fatalities of Iran. . CC-BY-NC-ND 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) The copyright holder for this preprint this version posted May 5, 2020. . https://doi.org/10.1101/2020.04.30.20086496 doi: medRxiv preprint Graph 1 -Correlational plots of COVID-19 fatalities with the satellite-based PM 2.5 concentrations for seven countries except the missing fatalities of Iran. The countries should not be compared in respect to mortality rate due to different administration sizes. Figure 1 reports comparison maps of COVID-19 distributions with the satellite-based PM 2.5 concentrations for the eight analysed countries. These graphical representations also allow a rapid assessment of the air pollution pattern in each country (basic descriptive statistics for each pollutant can be found in S.I., Table S2 ). While the PM 2.5 maps are continuous surfaces drawn following the same classification scheme across countries, the COVID-19 infections and deaths maps required ad-hoc classification adaptations due to different population profiles and infection dynamics. In China, due to the very large population and an apparently effective policy for the containment of the virus, the number of cases per 100,000 residents were relatively low and highly concentrated in the epicentre of the outbreak (Wuhan and the Hubei province). A visual correlation between the two maps can be perceived, especially between the eastern and western parts of the country. In the United States, due to a higher correlation with PM 2.5, we chose to represent COVID-19 deaths instead of cases. Here the virus noticeably appears spread over several areas. PM 2.5 differences are not large, but their distribution looks adequately coincident with the fatalities. The high correlation results found for Italy are clearly visible. The polluted areas of the Po valley are those heavily affected by COVID-19 . CC-BY-NC-ND 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) The copyright holder for this preprint this version posted May 5, 2020. . https://doi.org/10.1101/2020.04.30.20086496 doi: medRxiv preprint infections. While in Iran and France the correlations are only lightly perceivable, the maps of Spain confirm the results in Table 1 and 2, going against our general hypotheses. Moreover, PM 2.5 levels in Spain are minimal. The U.K. map of PM 2.5 shows well the higher concentrations around urban areas and the overall south-eastern area where COVID-19 cases are mostly found too. However, also a few counties/NHS in the centre are particularly affected by the virus infections. Finally, COVID-19 cases in Germany are concentrated in the southern and western districts, while PM 2.5 concentrations are higher in the eastern districts, giving a negative correlation. . CC-BY-NC-ND 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 preprint this version posted May 5, 2020. . https://doi.org/10.1101/2020.04.30.20086496 doi: medRxiv preprint . CC-BY-NC-ND 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 preprint this version posted May 5, 2020. This study is the first to investigate air pollution for eight countries as a potential risk factor for the incidence and mortality rates of COVID-19. It provides preliminary evidence that SARS-CoV-2 infections are most often found in highly polluted areas regardless of population density. In addition, in these areas affected by low air quality, the virus kills more often than elsewhere. The interpretation of these findings has to be necessary cautious, as the virus spread in most of the countries is still ongoing 48 and is being contained 40 . There are also confounding factors such as how the virus infection was determined in patients by different countries. However, the larger the geographical areas are affected by the pandemic, the lower these elements play a role. Accounting for these preliminary caveats, by controlling for the number of infections per 100,000 inhabitants, we found statistically significant, positive correlations between COVID-19 infections and low air quality in six out of eight assessed countries, despite varying population densities. Infected people were more likely to die in the Chinese, Italian, American, Iranian, French, and British areas with poor air quality. The absence of correlation found in Spain may be attributed to the minimal difference of average PM 2.5 and NO2 levels among its provinces (S. I. Table S1 ). Despite these small variations, air pollution appears to be correlated with population density (S. I. Table S2 ), while COVID-19 had a higher incidence in less populated areas (Table 2 and 3). One hypothesis of this particular configuration could be an effect of the people's reaction to the spread of the virus. In facts, it was reported that a high number of residents of big cities quickly moved to the countryside areas, favouring the spread of the virus in rural areas. In China, in Hubei province, brand new time analyses give preliminary evidence of a correlation between high levels of NO2 and 12day delayed virus outbreaks 49 and other PM covariates 50,51 . In the U.S.A., levels of PM2.5 have just been found responsible for 20-time higher mortality rate by COVID-19, a rate much higher than other demographic co-variables 52 . We found that in Italy, the correspondence between poor air quality and SARS-CoV-2 appearance and its induced mortality was the starkest. The area with the largest number of infections and deaths in Italy is the Po Valley, which is also the foremost place of polluted air in Europe 53 . This result was also confirmed by another preliminary study which included other demographic co-variables 54 . It should be noted that the Italian higher mortality than the one predicted from mathematical modelling is unlikely caused by genetic mutations of the virus 55 . Therefore, other factors must be attributed to such a stronger virulence. We run these analyses considering eight countries in their second-order administrations' level. If controlling for several other predictors like demographic variables is something advisable to perform at a single-country level, including them at an international scale poses an apparent technical limitation. National or federal health systems have different capacities and provide care in their distinct ways. This in turn influences case detections, intensive care capacity and fatality rates. Cofactors such as earliest location of pathogen, population mobility and patient socioeconomic status or ethnicity may not be accounted reliably between such diverse countries like China and the United States, even when included as random factors as in a comprehensive generalized mixed model, because they are interdependent and only in part nested within countries or administrations. Yet the epidemics, which has turned into a pandemic, might have catered for this limitation; the larger its extent, the more prominent a common factor like air pollution may become, while other secondary predictors will level out across places. Satellite NO2 and PM 2.5 on their own are suitable representatives for general air quality 56 , since they are more consistent than ground station data, providing finer detail, consistency, accuracy, with virtually no errors of counterfeits. Despite limited by cloud cover, they are to a certain extent less prone to biases from other conditions 57 of wind and greenhouse effect of temperature inversion, in turn also related to air pollution. In spite of this, we found some consistency between ground and satellite data for those countries for which we could obtain fine-grained ground measures. The satellite databases employed in this study represent annual means which we further averaged over the full time-series. This only partially represents the real emission of pollutants during the year and do not make evident seasonal variations and other fluctuations. However, our aim was to highlight differences in air quality within a country's region and show the correlation with . CC-BY-NC-ND 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 preprint this version posted May 5, 2020. . https://doi.org/10.1101/2020.04.30.20086496 doi: medRxiv preprint the virus. Therefore, threshold values of air pollution cannot be inferred from this study. Since there is now some first evidence that the cross of the virus from animals to humans may have happened years earlier than the end of 2019 in the Chinese city of Wuhan 58 , we can speculate that air pollution could have played a role in gradually exacerbating morbidity and mortality, mutating the virus from an initial evolutionary stage not causing any more serious morbidity than a cold, to becoming so threatening to humans. Also, its detection in Italy could be the result of an exacerbation of the European import which drifted for some time 59 before expressing its strong viral lineages in the most polluted region of the continent. In the unlikely case that the figures provided by the states in relation to the number of infections and deaths are inaccurate 60 , our analyses and conclusions would not need to be reframed. If that is the case, this error will most likely be concentrated in just one or very few administrations and it would not affect our very large correlational dataset. States have responded by trying to mitigate the spread of the virus through imposing widespread lockdowns. This has led to a decrease in air pollution, which in China likely prevented the deaths of 4,000 children under 5 and 73,000 adults over 70 61 . However, the winter months and low temperature caused people to keep at least domestic heating systems on, maintaining a certain amount of emissions. In Europe 62 and in China 63 a consistent reduction in air pollution was recorded by satellites due to reduced anthropogenic activities during the lockdowns, although it occurred gradually 64, 65 , also due to weather conditions unfavourable to air quality. The quarantines certainly decreased the role that commuting has in the virus spread. Nonetheless, reduced anthropogenic activities and reduced mobility lose correlational significance over time, after the first stages of the infection 37 . Instead, the correlation we found with low air quality remains significant throughout the different epidemic stages. This pandemic has not ended yet, so our conclusions are necessarily restricted to the stage of infection of those eight countries. Further research in the field of physics should be endorsed to investigate the capacity of air pollutants to act as viral vectors. Air pollutants may in fact act as a medium for the aerial transport of SARS-CoV-2 35,36 , potentially broadening the harm done in the contagions. Our results inform epidemiologists on how to prevent future, possibly more lethal viral outbreaks by curbing air pollution and climate change. Institutions need to endorse such interventions more seriously 66 together with endorsing collateral and more comprehensive measures playing a role in reducing these epidemics, such as impeding biodiversity loss 67 , ending wars, and alleviating poverty 68 . is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 5, 2020. Setti, L., Passarini, F., de Gennaro, G., Di Gilio, A., Palmisani, J., Buono, P., Fornari, G., . CC-BY-NC-ND 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 preprint this version posted May 5, 2020. is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 5, 2020. Several risk factors have been implicated with the fast spread of the virus, including super spread events 1,2 . Its further spread to different countries has been attributed to air travellers [3] [4] [5] [6] [7] [8] [9] [10] [11] . A number of personal risk factors have further been implicated with higher morbidity and mortality rates of Covid-19, including male gender and smoking status. In particular, smoking has been associated with a higher morbidity and mortality of COVID-19 in men than in women 12 . The temperate-climate latitudes have been identified as the probable areas to be mostly affected by COVID-19 13 due to a limited exposure to UV light in winter. The sole temperature 14, 15 or humidity 16 appear to play less of a role. Indeed, other human coronaviruses (HCoV-229E, HCoV-HKU1, HCoV-NL63, and HCoV-OC43) appear between December and April, and are undetectable in summer months in temperate regions, leading to winter seasonality behaviour. The very first appearance of the virus cannot be directly correlated with one of these predictors, since, like the other SARS coronaviruses, SARS-CoV-2 is alleged to have transferred host from the originating bats to humans 17 . However, it still appeared in a Chinese area affected by some of the highest air pollution in the world, and it showed a relatively high virulence there. We selected the eight countries according to the following elements. China (including Taiwan, Hong Kong and Macau) was chosen because of its large size and now advanced stage of its epidemic, well into the recovery phase. The second choice was Italy, at the time of writing among the most . CC-BY-NC-ND 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 preprint this version posted May 5, 2020. . heavily affected countries of the world, and just passed the peak of contagion. The area with the largest number of infections and deaths in Italy is the Po Valley, which is also the foremost place of polluted air in Europe 18 . The third country investigated was the conterminous United States, which currently has the highest number of cases worldwide, yet is still behind in the pandemic curve due to its later arrival as compared to Asia and Europe. Among the countries where the virus spread earlier, we included Iran which heavily suffers from severe air pollution due to ubiquitous use of gas methane, refineries and heavy traffic. France and Spain were selected because of the high COVID-19 figures, but minor air pollution issues than Italy. Lastly, and the United Kingdom represented suitable candidates to feed into the analysis, because of the relatively reduced containment measures adopted by those states 19 . For Iran, the state made public the data of infections at a first level resolution only, until 22 March 2020. The Chinese dataset includes the 17 April update on a 50% increase in deaths in Wuhan city 20 . Fatalities in Italy were available only at the regional level, therefore two different datasets were is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted May 5, 2020 . CC-BY-NC-ND 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 preprint this version posted May 5, 2020. Table S2 Descriptive statistics for the air pollution variables in the eight countries. . CC-BY-NC-ND 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) The copyright holder for this preprint this version posted May 5, 2020. Table S3 Correlation coefficients between population density and air pollution variables in eight countries. Significant correlations (p-value < 0.05) are shown in bold; blue and red colour highlights indicate positive and negative correlations, respectively. The possibly large proportion of asymptomatic cases has been implied as an important factor in the fast spread of the virus and will necessarily lead to a biased mortality rate. Different government policies with regards to testing have led to vastly different estimates across countries 27, 10 , and a COVID-19 overall mortality rate has not been established yet. Asymptomatic cases could be as high as about 50% of total cases, as estimated by simulations 28 . States have responded by trying to mitigate the spread of the virus through imposing widespread lockdowns. This has led to a decrease in air pollution, which in China likely prevented the deaths of 4,000 children under 5 and 73,000 adults over 70 29 . However, the winter months and low temperature caused people to keep at least domestic heating systems on, maintaining a certain amount of emissions. In Europe 30 and in China 31 a consistent reduction in air pollution was recorded by satellites due to reduced anthropogenic activities during the lockdowns, although it occurred gradually 32, 33 , also due to weather conditions unfavourable to air quality. The quarantines certainly decreased the role that commuting has in the virus spread. Nonetheless, reduced anthropogenic . CC-BY-NC-ND 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) The copyright holder for this preprint this version posted May 5, 2020. . https://doi.org/10.1101/2020.04.30.20086496 doi: medRxiv preprint 7 activities and reduced mobility lose correlational significance over time, after the first stages of the infection 34 . 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Rafael Moreno Ripoll and Mehrdad Samavati helped to obtain the data for Spain and Iran. RP did this work while in selfisolation due to the current pandemic.