key: cord-331858-oz4mvyw8
authors: Kifer, D.; Bugada, D.; Villar-Garcia, J.; Gudelj, I.; Menni, C.; Sudre, C. H.; Vuckovic, F.; Ugrina, I.; Lorini, L. F.; Bettinelli, S.; Ughi, N.; Maloberti, A.; Epis, O.; Giannattasio, C.; Rossetti, C.; Kalogjera, L.; Persec, J.; Ollivere, L.; Ollivere, B.; Yan, H.; Cai, T.; Aithal, G.; Steves, C.; Kantele, A.; Kajova, M.; Vapalahti, O.; Sajantila, A.; Wojtowicz, R.; Wierzba, W.; Krol, Z.; Zaczynski, A.; Zycinska, K.; Postula, M.; Luksic, I.; Civljak, R.; Markotic, A.; Brachmann, J.; Murray, B.; Ourselin, S.; Pascual, J.; Valdes, A. M.; Posso, M.; Horcajada, J.; Castells, X.; Allegri, M.; Prim,
title: Effects of environmental factors on severity and mortality of COVID-19
date: 2020-07-14
journal: nan
DOI: 10.1101/2020.07.11.20147157
sha: 
doc_id: 331858
cord_uid: oz4mvyw8

Background Most respiratory viruses show pronounced seasonality, but for SARS-CoV-2 this still needs to be documented. Methods We examined the disease progression of COVID-19 in 6,911 patients admitted to hospitals in Europe and China. In addition, we evaluated progress of disease symptoms in 37,187 individuals reporting symptoms into the COVID Symptom Study application. Findings Meta-analysis of the mortality risk in eight European hospitals estimated odds ratios per one day increase in the admission date to be 0.981 (0.973-0.988, p<0.001) and per increase in ambient temperature of one degree Celsius to be 0.854 (0.773-0.944, p=0.007). Statistically significant decreases of comparable magnitude in median hospital stay, probability of transfer to Intensive Care Unit and need for mechanical ventilation were also observed in most, but not all hospitals. The analysis of individually reported symptoms of 37,187 individuals in the UK also showed the decrease in symptom duration and disease severity with time. Interpretation Severity of COVID-19 in Europe decreased significantly between March and May and the seasonality of COVID-19 is the most likely explanation. Mucosal barrier and mucociliary clearance can significantly decrease viral load and disease progression, and their inactivation by low relative humidity of indoor air might significantly contribute to severity of the disease.

Over 500,000 COVID-19 related deaths have been reported so far, but a significant number of people (over 80% in some populations) infected with SARS-CoV-2 manage to contain infection in their upper respiratory tract and despite being PCR positive for the viral RNA do not develop any visible symptoms 1 . So far, very little attention has been given to the effects of environmental conditions on the individual course of the diseases.

The first study of the environmental effects on the COVID-19 infection rate in 30 Chinese provinces found significant negative associations with temperature and relative humidity in Hubei province with the decrease of cases by 36%-57% for every 1 °C and 11%-22% for every 1% increase in relative humidity, these associations were inconsistent in other provinces 2 . Negative effects on COVID-19 transmission with warmer temperatures were also observed in Turkey 3 , Mexico 4 , Brazil 5 and United States 6 , while similar association with humidity was reported in Australia, but with temperature having no effect on the virus transimission 7 . The study from Brazil observed flattening of the temperature effect on the virus transmission at 25.8°C thus suggesting that warmer weather will not cause the transmission decline which is in accordance with the studies from Iran and Spain where they observed no changes in transmission rates under different temperatures and humidity. 8, 9 These studies are inconsistent and do not give clear evidence as to whether there is an association between the temperature, humidity and virus transmission, the global view seems to give a clearer conclusion; all the three studies which conducted analysis at the global level found an association between higher humidity, warmer temperatures and lower transmission rate 10 . However, climate-dependent epidemic modelling suggested that the absence of population immunity is a much stronger factor in viral transmission and that summer weather will not substantially limit the spread of COVID-19 pandemics 11 . This is consistent with high numbers of infected individuals in tropical countries and the increase of cases in the south of United States in the second half of June 2020.

Recent studies report increasing numbers of SARS-Cov2 positive asymptomatic individuals 1 , but it is not clear whether the apparent increase in people with mild or no symptoms is due to the change in the extent of testing, or some other characteristic of the SARS-CoV-2 virus. Aiming to evaluate the association of humidity, and ambient temperature with the severity of the COVID-19 disease, we analysed individual-patient data for 6,914 patients with COVID-19 admitted to hospitals in Bergamo, Italy: Barcelona, Spain; Coburg, Germany; Helsinki, Finland; Milan, Italy; Nottingham, United Kingdom; Warsaw, Poland; Zagreb, Croatia and Zhejiang province, China since the beginning of the pandemics and compared it to environmental temperature and calculated indoor humidity. Furthermore, we analysed information about COVID-19 severity from the COVID Symptom Study application that is collecting information of 37,187 individuals in the UK.

We collected information about hospital admission, discharge dates, admission to intensive care unit (ICU), need for mechanical ventilation and type of discharge (alive or dead) for 5229 successive patients hospitalized for COVID-19 in six European Hospitals and 13 hospitals in Zhejiang province, China since the beginning of the pandemics (Table 1) . We included patients with confirmed diagnosis of COVID-19 at the time of admission. We confirmed that patients had a positive result on polymerase chain reaction testing of a nasopharyngeal sample and/or a clinically/radiologically diagnosis of COVID-19. Patients were not followed after discharge, but COVID-19 related early readmissions were considered as part of the COVID-19 course. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki. In Zhejiang hospitals, ASST Papa Giovanni XXIII° Hospital in Bergamo, Hospital del Mar in Barcelona and Helsinki University Hospital local ethics committees approved this retrospective study of COVID-19 patient data. For REGIOMED Hospital in Coburg, Ethics committee of the Bavarian state physician´s association approved the study. In Nottingham University Hospital's trust, ASST GOM Niguarda, Warsaw and Zagreb this information was released as public statistical information. 

The COVID Symptom Study app 12 developed by Zoe with scientific input from researchers and clinicians at King's College London and Massachusetts General Hospital, (https://covid.joinzoe.com/) was launched in the UK on Tuesday the 24th March 2020, and in 3 months reached more than 3.9 million subscribers. It enables capture of self-reported information related to COVID-19 infections, as reported previously 12 . Importantly, . CC-BY 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 July 14, 2020. . https://doi.org/10.1101/2020.07.11.20147157 doi: medRxiv preprint participants enrolled in ongoing epidemiologic studies, clinical cohorts, or clinical trials, can provide informed consent to link data collected through the app in a HIPPA and GDPRcompliant manner with extant study data they have previously provided or may provide in the future. The Ethics for the app has been approved by King's College London ethics Committee (REMAS ID 18210, review reference LRS-19/20-18210) and all users provided consent for non-commercial use. For this work we included participants from the United Kingdom who started reporting with a healthy status and subsequently developed symptoms leading to suspect Covid disease following the disease score presented in Menni et al. 12 In order to get an estimate of disease duration, the time for disease end corresponded to either the last day of report before stopping using the app, or the first healthy day when followed by 6 consecutive days of healthy reporting. To avoid censoring, only participants with a disease duration of less than 30 days and with a disease onset occurring before the 17 th May were included in the analysis (37,187 individuals). Severity score was calculated as a weighted average of symptoms at disease peak using as weight the normalised ratio in symptom frequency at disease peak between people reporting hospital visit after disease onset and those that did not.

Ambient temperature data was obtained from the Climate Data Online (National Centers for Environmental Information (NCEI) database): https://www.ncdc.noaa.gov/cdo-web/

The data collated from 7 cohorts are summarised in Table 1 . Patients without information about outcome were excluded from the analysis. Logistic regression was used to estimate the effect of admission date and local ambiental temperature on mortality change. The following patient characteristics, and hospitalization episode co-variates were explored: Died/discharged outcome was used as dependent variable and admission as independent variable along with age (in years) and gender (female/male). We them used the same approach for estimating the effect of ambient temperature on need for admission to ICU, and for mechanical ventilation therapy. A linear model was then used to estimate the effect of ambient temperature on the hospital stay length (in days) as dependent variable, and admission date as independent variable along with age and gender. Prior the analysis data transformation was undertaken with hospital length of stay increased by 1 (due to zeros) and log10 transformed (Zero days in hospital stay correspond to hospitalization with a length lower than 24 hours). For each dependent variable, raw data were presented with bar plots (death, ICU and mechanical ventilation) or box-and-whiskers plots (hospital length of stay) for patients in twoweek groups. Fill of bars and boxes reflects the number of patients admitted to hospital in particular two-week group. With groups of less than 5 patients individual data points were plotted.

Coefficients estimated in logistic regressions and linear regression were combined using an inverse variance-weighted meta analyses methods where given the heterogenity of cohorts random effects models were used (R package "metaphor").

Results of the meta-analysis were presented as forest plots, created using R package "ggplot2". All statistical analyses were performed in R programming software (version 3.6.3), . CC-BY 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 July 14, 2020. . https://doi.org/10.1101/2020.07.11.20147157 doi: medRxiv preprint with exception of logistic and linear regressions on Milano cohort data which are performed in Stata Statistical Software (version 12).

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Aiming to evaluate seasonal nature of COVID-19, we evaluated disease course in 6,914 individuals from nine cohorts admitted to hospitals in Europe and China (Table 1) . To avoid sampling bias, all hospitalizations that resulted in either death, or medical discharge were included in the analysis. Actual numbers of patients who died and patients who recovered (grouped in two-week intervals) since the beginning of the epidemics, until the final follow up date for reliable data capture reporting final outcome was available are presented in Figure  1A for each of the hospitals. Meta-analysis of the effect of admission date on the mortality is presented in Figure 1B . The most significant change was observed in Barcelona, where mortality odds decreased by 4.1% per day (p<0.001). Weighted average decrease in mortality odds across all studied hospitals was 1.9% per day (p<0.001). Our model included age as a covariate, so this change is unlikely to be accounted for by change in age of patients. To further confirm that age was not underlying the observed changes we analysed age of patients admitted to hospitals in different periods and demonstrated that change in the age of patients was not a factor that could explain the observed decrease in mortality (Supplementary Figure 1 ).

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The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07.11.20147157 doi: medRxiv preprint Figure 1 . Mortality in people admitted in hospitals with COVID-19. A -hospitalization outcome (death/discharge) depending on the admission date (grouped in two-week intervals) since the beginning of the pandemics; B -Meta-analysis of the effects of admission date on the mortality (presented as odds ratios per one day increase in admission date). In Helsinki there were only 2 deaths and in Zhenjiang hospitals 4 deaths, so they were not included in the meta-analysis. OR -odds ratio, CI -confidence interval. Odds ratio (with 95% CI)

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Since there is no standard measure or classification of COVID-19 severity used across all hospitals, to further evaluate disease severity, we analysed secondary outcomes. We compared the duration of hospitalization, need for intensive care unit (ICU) and separately mechanical ventilation. Strong and statistically significant decrease in the duration of hospitalization was observed in Barcelona, Coburg, Milano, Nottingham and Zagreb. In Helsinki, Warsaw and Zhenjiang the change was in the same direction but was not statistically significant. The only outlier was Bergamo, where the change was in the opposite direction, but the change was not statistically significant (Supplementary Figure 2) . In meta-analysis the decrease in lengths of hospitalization was statistically significant (10^b=0.995; CI=0.991,-0.998); p=0.007). The odds to need of intensive care decreased in all hospitals in Europe and was individually statistically significant in all hospitals beside Bergamo, Helsinki and Zagreb (Supplementary Figure 3) . Meta-analysis of European hospitals estimated that the odds to need the intensive care decreased by 2.2% per day of change in the admission date (OR=0.978; CI=0.962-0.993; p=0.008) and the odds to need mechanical ventilation decreased 2.1% (OR=0.979; CI=0.964-0.994; p=0.008) per day of change in the admission date (Supplementary Figure 4) .

While all hospitals in Europe were basically displaying the same trend of decreasing COVID-19 severity with time, in Zhenjiang hospitals there was either no change, or the changes trended non-significantly in opposite direction to European centres. The most notable difference between COVID-19 pandemics in Europe and in China was that while in China the epidemic was entirely during winter, in Europe it covered both winter and spring periods. To evaluate whether weather was an important factor, we correlated the observed changes with local ambient temperature. Minimal and maximal local temperatures for all hospitals a presented in Supplementary Figure 5 . To evaluate whether the change in temperature may have been responsible for the observed changes in disease severity, we modelled mortality with ambient temperature instead of admission date. The results presented in Figure 2 suggest strong effect of ambient temperature on the mortality risk (OR=0.854 per one-degree Celsius; CI=0.773-0.944; p=0.007).

To further verify the change of COVID-19 with time we analysed individual symptom data for 37,187 participants of the Covid Symptom study app. Although there is also a sampling bias in that study, it is a different from bias in hospitalization, so it was reassuring to observe a gradual decrease in duration of symptoms and COVID-19 severity in April and May ( Figure 3 )

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The copyright holder for this preprint this version posted July 14, 2020. . https://doi.org/10.1101/2020.07.11.20147157 doi: medRxiv preprint Figure 2 . Meta-analysis of the effects of temperature on mortality (presented as odds ratios per one-degree Celsius increase in average daily temperature during hospitalization). In Helsinki there were only 2 deaths and in Zhenjiang hospitals 4 deaths, so they were not included in the meta-analysis. OR -odds ratio, CI -confidence interval. . CC-BY 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.

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By analysing hospital records of 6,914 patients admitted to eight European hospitals we observed strong and statistically significant decrease in COVID-19 mortality and severity with time. Possible change in the average age of patients in different stages of the pandemic is the first obvious explanation for the decreased severity, since age is the strongest predictor of COVID-19 severity (with up to 100-fold difference in mortality risk 13 ). However, age was included in our model as a co-variate and furthermore the average age of patients did not change with time ( Supplementary Figure 2) , so we excluded this hypothesis. An alternative explanation could be that there was change in policies for admission and/or release of COVID-19 patients during the evaluated period -possibly due to 'overwhelming' of medical facilities. This might have been particularly relevant in the situation of limited hospital capacity, when hospitalization may have been preceded with a triage process to identify patients who might benefit from hospitalization, admission to ICU or mechanical ventilation. However, the only hospital in our cohort that reached full capacity was Bergamo, while all others operated well below the maximal capacity for either hospitalization, or ICU, which suggests that changes in hospital admission policy were not a major driver behind the observed change in COVID-19 mortality and severity. This conclusion is further supported by concurrent decrease in duration and severity of symptoms of non-hospitalized individuals reporting symptoms in the COVID-Symptom Study Application (Figure 3 ). Change in COVID-19 management, also, could have resulted in decreased severity. However, all these changes were hospital-specific and the current most effective therapy report is that dexamethasone reduced mortality from 24.6% to 21.6% 14 and is considered to be a major breakthrough. It is hard to imagine that minor modifications in patient management could have significantly contributed to the observed decrease in the disease mortality and severity.

After excluding these three causes for a Europe-wide decrease in disease severity and mortality in the period from March to June, the change in season surfaced as the most probable explanation since in all studied locations ambient temperature increased considerably in that period (Supplementary Figure 5) . Exchanging hospital admission date with local temperature (Figure 2 ) showed that temperature strongly correlated with decrease in COVID-19 mortality. Since reverse causation is not possible, it is reasonable to conclude that COVID-19 as a disease has a strong seasonal nature. Despite the fact that most human coronaviruses are highly seasonal 15 , the seasonal nature of COVID-19 is frequently challenged with the fact that numerous cases have been reported in tropical countries and that virus evidently can also be efficiently transmitted in hot and humid climates. However, in all these countries the disease mortality and severity are very low (e.g. Singapore reported 26 deaths and over 44,000 confirmed infections), which actually suggests that there may be seasonal or climate related differences in severity of COVID-19. It is possible that the same is the case for other respiratory viruses that show strong seasonality, but asymptomatic people are never tested for the presence of viral RNA in the nose, thus viral transmission, outside of their season, was not observed.

It is very difficult to prove causality in an observational study, in particular when many correlated factors are changed in the same time, but the observed decrease in COVID-19 severity with the end of winter fits very well with the known effects of outside temperature on indoor humidity and consequential restoration of mucosal barrier function, which is often impaired by dry air during the heating season 16 . Most respiratory viruses peak in winter and fluctuation of temperature and humidity have been proposed as the most potent drivers of . CC-BY 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 July 14, 2020. . https://doi.org/10.1101/2020.07.11.20147157 doi: medRxiv preprint seasonality, especially in the context of the epidemics in the winter season 15 . However, the peak of infection and the severity of the disease are not always full aligned. For example although infection rates of rhinoviruses peak in spring and fall, the disease severity increases in winter 17 . Seasonal appearance of respiratory viruses is often attributed to seasonal indoor crowding and effects of temperature and humidity on stability of viral particles 18 , with effect of low air humidity on the mucosal barrier often neglected.

While often considered to be a physical barrier, mucus is actually an active biological barrier that crosslinks viruses and bacteria to mucins, a group of highly glycosylated proteins that are secreted to our mucosal barriers. Mucins mimic cell surface glycosylation and by acting as a decoy for viral lectins trap viral particles, which are then transported out of airways by mucociliary clearance 19 . Furthermore, since all envelope viruses are highly glycosylated, a number of lectins like trefoil factors (TFF) are secreted to mucous where they crosslink viruses by binding to glycans on both viruses and mucins. 20 However, this barrier is functional only if it is well hydrated to both maintain its structural integrity and enable constant flow of mucus that remove viruses and other pathogens from our airways 19 . If exposed to dry air, these barriers dry out and cannot perform their protective functions 21 .

Animal experiments demonstrated the importance of humidity for both transfection of respiratory viruses and disease severity [22] [23] [24] , while population-level studies in the United States indicated the importance of humidity for influenza transmission 25 . One of these studies demonstrated that increasing relative humidity from 20% to 50% can significantly decrease mortality from influenza infections 24 . In another study humidification of air in obstructive sleep apnea patients reduced nasal symptoms by 60% 26 , which all suggest that protective effects of humidity on mucosal barrier may be a dominant molecular mechanism behind seasonality of respiratory viruses.

A large part of human inter-individual differences are glycan-based and glycan diversity represent one of the main defences of all higher organisms against pathogens 27 . Glycans (which are covalently attached to most proteins) are chemical structures that are being inherited as complex traits, which enables diversity and significant inter-individual differences 28 . SARS-CoV-2 spike glycoprotein is heavily glycosylated 29 and it was reported to bind to glycosaminoglycans 30 and sialylated glycans 31 . ABO blood antigens are also glycans and are probably the best known example of glycan diversity; interestingly, people with blood type A and, thus, having one N-acetylgalactosamine more than type O are more susceptible to COVID-19. 32 All this suggest that, like most other viruses, SARS-CoV-2 is also dependent on glycans for transmission, which further support the importance of mucins and functional mucosal barrier in COVID-19. 24 . Dry air inhalation significantly decreases nasal mucociliary transition time (NMTT) in heathy individuals 33 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 July 14, 2020. . https://doi.org/10.1101/2020.07.11.20147157 doi: medRxiv preprint of epithelial barrier function. 37 Recent studies on the interaction between climate changes and respiratory barrier dysfunction, indicated not only higher incidence of viral infection but also higher vulnerability of nasal mucosa through increased incidence of nosebleed in the emergency departments in the conditions of low temperature and low humidity 38 .

Potential sampling bias is the main limitation of this study. By focusing on individual progression of the disease in already hospitalized patients we excluded effects of the unknown number of true infections on national mortality rates, and we still cannot exclude the possibility that some other unidentified external factors (including confinement and social distancing, improvement and compliance of prevention and environmental hygiene protocols and even decreased air-pollution could have progressively affected the severity of patients arriving to the hospital) were affecting composition of hospitalized patient cohorts and contributing to the decreased COVID-19 severity and mortality. Therefore, it is important that tracking of individual symptoms in 37,187 UK patients are showing the same trend, since these are individuals voluntary reporting symptoms and potential sampling bias there is independent from bias in hospitalization.

Our data suggest that environmental factors play an important role in already infected patients. Since many hospitals have very dry air, providing humidified air to patients in early stages of the disease may be beneficial. Considering the evident detrimental effect of dry air on our mucosal barrier and its role as the first line of defence against infection 39 , in situation of rapidly progressing COVID-19 pandemics it would be essential to actively promote universal humidification of dry air in all public and private heated spaces as well as active nasal hygiene and hydration 40 . Humidity should also be monitored in cooled buildings with limited access to outside air, since air-conditioning is also an effective dehumidification and can result in very dry air. COVID-19 pandemic in Bergamo for their invaluable and brave efforts towards patient's care. We also want to acknowledge the ROCCO Project (Registry Of Coronavirus Complications) who helped with scientific support, and Dr A. Bonetalli (Epidemiology Office -ASST Papa Giovanni XXIII°, Bergamo, Italy) for her help with data collection. We thank all the medical professionals, nurses and technicians of the ASST GOM Niguarda Hospital who worked hard during the COVID 19 emergency months. Furthermore, we want to acknowledge all the member of the Niguarda Covid-19 Research Group. We express our sincere thanks to all the participants of the COVID Symptom Study app. We thank the staff of Zoe Global Limited, the Department of Twin Research, and the Clinical & Translational Epidemiology Unit for their tireless work in contributing to the running of the study and data collection.

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The copyright holder for this preprint this version posted July 14, 2020. . CC-BY 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 July 14, 2020. . https://doi.org/10.1101/2020.07.11.20147157 doi: medRxiv preprint Supplementary Figure 1 . Changes in age of admitted patients with time. A -age boxplots depending on the admission date (grouped in two-week intervals) since the beginning of the pandemics. Lower and upper limits of box present first and third quartile, respectively, and line within the box is median. Whisker lines extend to the minimum and maximum value within 'inner fence' defined as 1.5 times interquartile range bellow 1 st and above 3 rd quartile, respectively. Outliers are presented with dots. If two-week interval had 5 or less values data were presented with dots instead of boxplots; B -Meta-analysis of the effects of admission date on the mortality (presented as odds ratios per one day increase in admission date). In Helsinki there were only 2 deaths and in Zhenjiang hospitals 4 deaths, so they were not included in the meta-analysis. OR -odds ratio, CI -confidence interval. 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 July 14, 2020. . https://doi.org/10.1101/2020.07.11.20147157 doi: medRxiv preprint Supplementary Figure 2 . Hospital stay of subjects admitted in hospitals with COVID-19. Anumber of days stayed in hospital depending on the admission date (grouped in two-week intervals) since the beginning of the pandemics. Lower and upper limits of box present first and third quartile, respectively, and line within the box is median. Whisker lines extend to the minimum and maximum value within 'inner fence' defined as 1.5 times interquartile range bellow 1 st and above 3 rd quartile, respectively. Outliers are presented with dots. If two-week interval had 5 or less values data were presented with dots instead of boxplots; B -Metaanalysis of the effects of admission date on the hospital stay (presented as times change in duration per each one day increase in admission date). Zhenjiang hospital in which all patients were admitted during winter was excluded from the meta-analysis. b -regression coefficient (back transformed), CI -confidence interval. Times change (with 95% CI) B . CC-BY 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 July 14, 2020. 

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The copyright holder for this preprint this version posted July 14, 2020. Odds ratio (with 95% CI) B . CC-BY 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 July 14, 2020. CC-BY 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)

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Prevalence of Asymptomatic SARS-CoV-2 Infection

COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis

Impact of weather on COVID-19 pandemic in Turkey

The temperature and regional climate effects on communitarian COVID-19 contagion in Mexico throughout phase 1. Sci Total Environ

Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil

Maximum Daily Temperature, Precipitation, Ultra-Violet Light and Rates of Transmission of SARS-Cov-2 in the United States

The Role of Climate During the COVID-19 epidemic in New South Wales

A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain

The sensitivity and specificity analyses of ambient temperature and population size on the transmission rate of the novel coronavirus (COVID-19) in different provinces of Iran

Effects of temperature and humidity on the daily new cases and new deaths of COVID-19 in 166 countries

Susceptible supply limits the role of climate in the early SARS-CoV-2 pandemic

Real-time tracking of self-reported symptoms to predict potential COVID-19

Factors associated with COVID-19-related hospital death in the linked electronic health records of 17 million adult NHS patients

Effect of Dexamethasone in Hospitalized Patients with COVID-19: Preliminary Report. medRxiv

Seasonality of Respiratory Viral Infections

Fighting COVID-19 with water

Coincidence of COVID-19 epidemic and olfactory dysfunction outbreak

Update of the scientific evidence for specifying lower limit relative humidity levels for comfort, health, and indoor environmental quality in occupied spaces (RP-1630). Sci Technol Built Environ

Mucin glycans attenuate the virulence of Pseudomonas aeruginosa in infection

Trefoil factors share a lectin activity that defines their role in mucus

Physiological impairments at low indoor air humidity

Roles of Humidity and Temperature in Shaping Influenza Seasonality

Absolute humidity modulates influenza survival, transmission, and seasonality

Low ambient humidity impairs barrier function and innate resistance against influenza infection

Absolute humidity and the seasonal onset of influenza in the continental United States

Effects of heated humidification and topical steroids on compliance, nasal symptoms, and quality of life in patients with obstructive sleep apnea syndrome using nasal continuous positive airway pressure

Variability, heritability and environmental determinants of human plasma n-glycome

Profiling and genetic control of the murine immunoglobulin G glycome

Site-specific glycan analysis of the SARS-CoV-2 spike

Glycosaminoglycans induce conformational change in the SARS-CoV-2 Spike S1 Receptor Binding Domain. bioRxiv

The SARS-COV-2 Spike Protein Binds Sialic Acids, and Enables Rapid Detection in a Lateral Flow Point of Care Diagnostic Device

Testing the association between blood type and COVID-19 infection, intubation, and death

Nasal mucociliary transport in healthy subjects is slower when breathing dry air

SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes

Mechanical ventilation (ventilation risk) in people admitted in hospitals with COVID-19. A -Proportion of people who needed mechanical ventilation depending on the admission date

Time period of Zhenjiang did not include both cold and warm weather and because of that results were not included in meta-analysis. OR -odds ratio

 Supplementary Figure 3 . Admission to the Intensive care unit (ICU risk) in people admitted in hospitals with COVID-19. A -proportion of people who were ever admitted to ICU depending on the admission date (grouped in two-week intervals) since the beginning of the pandemics; B -Meta-analysis of the effects of admission date on the ICU admission (presented as odds ratios per one day increase in admission date). Effect of the admission date was not calculated for Warsaw because all subjects were all in ICU. Time period of Zhenjiang did not include both cold and warm weather and because of that results were not included in meta-analysis. ORodds ratio, CI -confidence interval.