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

The relation between meteorological factors and COVID-19 spread remains uncertain, particularly with regard to the role of temperature, relative humidity and solar ultraviolet (UV) radiation. To assess this relation, we investigated disease spread within Italy during 2020. The pandemic had a large and early impact in Italy, and during 2020 the effects of vaccination and viral variants had not yet complicated the dynamics. We used non-linear, spline-based Poisson regression of modeled temperature, UV and relative humidity, adjusting for mobility patterns and additional confounders, to estimate daily rates of COVID-19 new cases, hospital and intensive care unit admissions, and deaths during the two waves of the pandemic in Italy during 2020. We found little association between relative humidity and COVID-19 endpoints in both waves, whereas UV radiation above 40 kJ/m2 showed a weak inverse association with hospital and ICU admissions in the first wave, and a stronger relation with all COVID-19 endpoints in the second wave. Temperature above 283 K (10 °C/50 °F) showed a strong non-linear negative relation with COVID-19 endpoints, with inconsistent relations below this cutpoint in the two waves. Given the biological plausibility of a relation between temperature and COVID-19, these data add support to the proposition that temperature above 283 K, and possibly high levels of solar UV radiation, reduced COVID-19 spread.

Towards effective COVID­19 vaccines: Updates, perspectives and challenges (Review).

In the current context of the pandemic triggered by SARS-COV-2, the immunization of the population through vaccination is recognized as a public health priority. In the case of SARS­COV­2, the genetic sequencing was done quickly, in one month. Since then, worldwide research has focused on obtaining a vaccine. This has a major economic impact because new technological platforms and advanced genetic engineering procedures are required to obtain a COVID­19 vaccine. The most difficult scientific challenge for this future vaccine obtained in the laboratory is the proof of clinical safety and efficacy. The biggest challenge of manufacturing is the construction and validation of production platforms capable of making the vaccine on a large scale.

A deep learning approach for Spatio-Temporal forecasting of new cases and new hospital admissions of COVID-19 spread in Reggio Emilia, Northern Italy.

BACKGROUND: Since February 2020, the COVID-19 epidemic has rapidly spread throughout Italy. Some studies showed an association of environmental factors, such as PM10, PM2.5, NO2, temperature, relative humidity, wind speed, solar radiation and mobility with the spread of the epidemic. In this work, we aimed to predict via Deep Learning the real-time transmission of SARS-CoV-2 in the province of Reggio Emilia, Northern Italy, in a grid with a small resolution (12 km × 12 km), including satellite information. METHODS: We focused on the Province of Reggio Emilia, which was severely hit by the first wave of the epidemic. The outcomes included new SARS-CoV-2 infections and COVID-19 hospital admissions. Pollution, meteorological and mobility data were analyzed. The spatial simulation domain included the Province of Reggio Emilia in a grid of 40 cells of (12 km)2. We implemented a ConvLSTM, which is a spatio-temporal deep learning approach, to perform a 7-day moving average to forecast the 7th day after. We used as training and validation set the new daily infections and hospital admissions from August 2020 to March 2021. Finally, we assessed the models in terms of Mean Absolute Error (MAE) compared with Mean Observed Value (MOV) and Root Mean Squared Error (RMSE) on data from April to September 2021. We tested the performance of different combinations of input variables to find the best forecast model. FINDINGS: Daily new cases of infection, mobility and wind speed resulted in being strongly predictive of new COVID-19 hospital admissions (MAE = 2.72 in the Province of Reggio Emilia; MAE = 0.62 in Reggio Emilia city), whereas daily new cases, mobility, solar radiation and PM2.5 turned out to be the best predictors to forecast new infections, with appropriate time lags. INTERPRETATION: ConvLSTM achieved good performances in forecasting new SARS-CoV-2 infections and new COVID-19 hospital admissions. The spatio-temporal representation allows borrowing strength from data neighboring to forecast at the level of the square cell (12 km)2, getting accurate predictions also at the county level, which is paramount to help optimise the real-time allocation of health care resources during an epidemic emergency.

Substantial impact of mobility restrictions on reducing COVID-19 incidence in Italy in 2020.

BACKGROUND: Italy was the first country after China to be severely affected by the COVID-19 pandemic, in early 2020. The country responded swiftly to the outbreak with a nationwide two-step lockdown, the first one light and the second one tight. By analyzing 2020 national mobile phone movements, we assessed how lockdown compliance influenced its efficacy. METHODS: We measured individual mobility during the first epidemic wave with mobile phone movements tracked through carrier networks, and related this mobility to daily new SARS-CoV-2 infections, hospital admissions, intensive care admissions and deaths attributed to COVID-19, taking into account reason for travel (work-related or not) and the means of transport. RESULTS: The tight lockdown resulted in an 82% reduction in mobility for the entire country and was effective in swiftly curbing the outbreak as indicated by a shorter time-to-peak of all health outcomes, particularly for provinces with the highest mobility reductions and the most intense COVID-19 spread. Reduction of work-related mobility was accompanied by a nearly linear benefit in outbreak containment; work-unrelated movements had a similar effect only for restrictions exceeding 50%. Reduction in mobility by car and by airplane was nearly linearly associated with a decrease in most COVID-19 health outcomes, while for train travel reductions exceeding 55% had no additional beneficial effects. The absence of viral variants and vaccine availability during the study period eliminated confounding from these two sources. CONCLUSIONS: Adherence to the COVID-19 tight lockdown during the first wave in Italy was high and effective in curtailing the outbreak. Any work-related mobility reduction was effective, but only high reductions in work-unrelated mobility restrictions were effective. For train travel, there was a threshold above which no further benefit occurred. These findings could be particular to the spread of SARS-CoV-2, but might also apply to other communicable infections with comparable transmission dynamics.

Seroprevalence Survey of Anti-SARS-CoV-2 Antibodies in a Population of Emilia-Romagna Region, Northern Italy.

Italy was the first Western European country to be severely hit by the COVID-19 pandemic. Variations in seroprevalence rates were reported according to geographical and temporal differences of previous surveys, as well as depending on demographic and occupational factors. In this cross-sectional study, we evaluated the prevalence of anti-SARS-CoV-2 antibodies in a population of the Emilia-Romagna region in Northern Italy after the first wave in the period from 26 September 2020-26 March 2021. We included 5128 subjects who voluntarily underwent serological tests to determine anti-SARS-CoV-2 antibody positivity, including both self-referred individuals (24.2%) and workers adhering to company screening programs (76.8%). Overall, seroprevalence was 11.3%, higher in self-referred (13.8%) than employed-referred (10.5%) individuals. A slightly higher seroprevalence emerged in women compared to men (12.3% and 10.7%), as well as in the extreme age categories (18.6% for 60-69 years, 18.0% for ≥70 years, and 17.1% for <20 years compared to 7.6% for 20-39 years). Healthcare professionals showed the highest prevalence of seropositivity (22.9%), followed by workers in direct contact with customers, such as the communication, finance, and tourism sectors (15.7%). Overall subgroups seroprevalence increased compared to the first wave data but the trends agreed between the first and subsequent waves, except for an increase in the younger age group and in the sector in direct contact with customers. Among the occupational categories, our study confirms that healthcare workers and workers in the sports sector were at high risk of exposure to SARS-CoV-2.

Zinc and selenium supplementation in COVID-19 prevention and treatment: a systematic review of the experimental studies.

BACKGROUND AND AIM: The COVID-19 pandemic has severely affected the world's population in the last two years. Along with non-pharmacological public health interventions, major efforts have also been made to identify effective drugs or active substances for COVID-19 prevention and treatment. These include, among many others, the trace elements zinc and selenium, based on laboratory studies and some observational human studies. However, both of these study designs are not adequate to identify and approve treatments in human medicine, and experimental studies in the form of randomized controlled trials are needed to demonstrate the effectiveness and the safety of any interventions. METHODS: We undertook a systematic review in which we searched for published and unpublished clinical trials using zinc or selenium supplementation to treat or prevent COVID-19 in the Pubmed, Scopus and ClinicalTrials databases up to 10 January 2022. RESULTS: Amongst the published studies, we did not find any trial with selenium, whereas we retrieved four eligible randomized clinical trials using zinc supplementation, only one of which was double-blind. One of these trials looked at the effect of the intervention on the rate of new SARS-CoV-2 infections, and three at the COVID-19 clinical outcome in already infected individuals. The study populations of the four trials were very heterogeneous, ranging from uninfected individuals to those hospitalized for COVID-19. Only two studies investigated zinc alone in the intervention arm with no differences in the endpoints. The other two studies examined zinc in association with one or more drugs and supplements in the intervention arm, therefore making it impossible to disentangle any specific effects of the element. In addition, we identified 22 unpublished ongoing clinical trials, 19 on zinc, one on selenium and two on both elements. CONCLUSION: No trials investigated the effect of selenium supplementation on COVID-19, while the very few studies on the effects of zinc supplementation did not confirm efficacy. Therefore, preventive or therapeutic interventions against COVID-19 based on zinc or selenium supplementation are currently unjustified, although when the results of the on-going studies are published, this may change our conclusion.

Modeling Early Phases of COVID-19 Pandemic in Northern Italy and Its Implication for Outbreak Diffusion.

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as "dynamic causal modeling" (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.

The association between first and second wave COVID-19 mortality in Italy.

BACKGROUND: The relation between the magnitude of successive waves of the COVID-19 outbreak within the same communities could be useful in predicting the scope of new outbreaks. METHODS: We investigated the extent to which COVID-19 mortality in Italy during the second wave was related to first wave mortality within the same provinces. We compared data on province-specific COVID-19 2020 mortality in two time periods, corresponding to the first wave (February 24-June 30, 2020) and to the second wave (September 1-December 31, 2020), using cubic spline regression. RESULTS: For provinces with the lowest crude mortality rate in the first wave (February-June), i.e. < 22 cases/100,000/month, mortality in the second wave (September-December) was positively associated with mortality during the first wave. In provinces with mortality greater than 22/100,000/month during the first wave, higher mortality in the first wave was associated with a lower second wave mortality. Results were similar when the analysis was censored at October 2020, before the implementation of region-specific measures against the outbreak. Neither vaccination nor variant spread had any role during the study period. CONCLUSIONS: These findings indicate that provinces with the most severe initial COVID-19 outbreaks, as assessed through mortality data, faced milder second waves.

An assessment of case-fatality and infection-fatality rates of first and second COVID-19 waves in Italy.

BACKGROUND AND AIM: The exact COVID-19 severity is still not well defined and it is hotly debated due to the a few methodological issues such as the uncertainties about the spread of the SARS-CoV-2 infection. METHODS: We investigated COVID-19 case-fatality rate and infection-fatality rate in 2020 in Italy, a country severely affected by the pandemic, basing our assessment on publicly available data, and calculating such measures during the first and second waves. RESULTS: We found that province-specific crude case-fatality rate in the first wave (February-July 2020) had a median value of 12.0%. Data about infection-fatality rate was more difficult to compute, due to large underestimation of SARS-CoV-2 infection during the first wave when asymptomatic individuals were very rarely tested. However, when using as a reference population-based seroprevalence data for anti-SARS-CoV-2 antibodies collected in May-July 2020, we computed an infection-fatality rate of 2.2%. During the second wave (Sep-Dec 2020), when SARS-CoV-2 testing was greatly increased and extended to many asymptomatic individuals, we could only compute a 'hybrid' case/infection-fatality rate with a value of 2.2%, similar to the infection-fatality rate of the first wave. CONCLUSIONS: Overall, this study allowed to assess the COVID-19 case- and infection-fatality rates in Italy before of variant spread and vaccine availability, confirming their high values compared with other airborne infections like influenza. Our findings for Italy were similar to those characterizing other Western European countries.

Characteristics and risk factors of isolated and quarantined children and adolescents during the first wave of SARS-CoV-2 pandemic: A cross-sectional study in Modena, Northern Italy.

BACKGROUND AND AIM: In early 2020, SARS-CoV-2 was declared a pandemic by the WHO and Italy was one of the first and most severely affected country in Europe. Despite the global interest about COVID-19 pandemic, several aspects of this infection are still unclear, especially in pediatric population. This study aims to investigate the characteristics of the isolated or quarantined children and adolescents followed by the Public Health Department of the Italian province of Modena during the first wave of COVID-19. METHODS: The study population included all non-adult subjects aged 0-18 years who underwent isolation or quarantine during the first wave of SARS-CoV-2 pandemic from February 24 to June 18, 2020 in Modena province, Northern Italy. RESULTS: In Modena province, 1230 children and adolescents were isolated in case of SARS-CoV-2 infection (6.3%), or quarantined due to close contact with confirmed cases (88.7%) or travelling from a high-risk area (5.0%). Among 349 individuals who underwent swab testing, 294 (84.2%) reported close contact with an infected cohabiting relative and 158 (45.3%) were symptomatic. Among all tested subjects, 78 (22.4%) resulted positive, with a higher proportion of symptomatic subjects compared with the SARS-CoV-2-negative (78.2% vs. 35.8%). Fever was mostly present in SARS-CoV-2-positive children (48.7% vs. 12.6%). Both anosmia (58.3% vs. 41.7%) and dysgeusia (54.5% vs. 45.5%) had only slightly higher frequency in SARS-CoV-2-positive. CONCLUSIONS: These findings allow to expand the knowledge regarding characteristics of non-adult subjects isolated or quarantined during the first wave of SARS-CoV-2 pandemic. (www.actabiomedica.it).

Seroprevalence of anti-SARS-CoV-2 antibodies in the Northern Italy population before the COVID-19 second wave.

OBJECTIVES: The COVID-19 pandemic is due to SARS-CoV-2 coronavirus infections. It swept across the world in the spring of 2020, and so far it has caused a huge number of hospitalizations and deaths. In the present study, the authors investigated serum anti-SARS-CoV-2 antibody prevalence in the period of June 1-September 25, 2020, in 7561 subjects in Modena, Northern Italy. MATERIAL AND METHODS: The study population included 5454 workers referred to testing by their companies, and 2107 residents in the Modena area who accessed testing through self-referral. RESULTS: The authors found the overall seroprevalence to be 4.7% (95% confidence interval [CI] 4.2-5.2%), which was higher in women (5.4%, 95% CI: 4.5-6.2%) than in men (4.3%, 95% CI: 3.7-4.9%), and in the oldest age groups (7.3%, 95% CI: 5.2-9.3% for persons aged 60-69 years, and 11.8%, 95% CI: 8.6-15.1%, for persons aged ≥70 years). Among the occupational categories, the highest seroprevalence was found in healthcare workers (8.8%, 95% CI: 7.0-10.5%), dealers and vehicle repairers (5.2%, 95% CI: 2.9-7.6%), and workers in the sports sector (4.0%, 95% CI: 1.8-6.1%), while there was little or no such evidence for those employed in sectors such as transport and storage, accommodation and restaurant services, and the school system. CONCLUSIONS: These results have allowed, for the first time, to assess population seroprevalence in this area of Italy severely hit by the epidemic, while at the same time identifying the subgroups at a higher risk of exposure to SARS-CoV-2. Int J Occup Med Environ Health. 2022;35(1):63-74.

Frequency of Anti-SARS-CoV-2 Antibodies in Various Occupational Sectors in an Industrialized Area of Northern Italy from May to October 2020.

The results of a voluntary screening campaign for the presence of anti-SARS-CoV-2 serum antibodies are presented, performed on workers in the highly industrialized province of Modena in northern Italy in the period 18 May-5 October 2020. The employment activities of the subjects that tested positive for anti-SARS-CoV-2 IgM and/or IgG antibodies were determined and classified using the International Standard Industrial Classification of All Economic Activities (ISIC). The distribution across different sectors was compared to the proportion of workers employed in the same sectors in the province of Modena as a whole. Workers with anti-SARS-CoV-2 serum antibodies were mainly employed in manufacturing (60%), trade (12%), transportation (9%), scientific and technical activities (5%), and arts, entertainment and recreation activities (4.5%). Within the manufacturing sector, a cluster of workers with positive serological tests was observed in the meat processing sector, confirming recent data showing a possible increased risk of SARS-CoV-2 infection in these workers.

SARS-CoV-2 infection incidence during the first and second COVID-19 waves in Italy.

We assessed the relation between COVID-19 waves in Italy, which was severely affected during the pandemic. We evaluated the hypothesis that a larger impact from the first wave (February-May 2020) predicts a smaller peak during the second wave (September-October 2020), in the absence of local changes in public health interventions and area-specific differences in time trends of environmental parameters. Based on publicly available data on province-specific SARS-CoV-2 infections and both crude and multivariable cubic spline regression models, we found that for provinces with the lowest incidence rates in the first wave, the incidence in the second wave increased roughly in proportion with the incidence in the first wave until an incidence of about 500-600 cases/100,000 in the first wave. Above that value, provinces with higher incidences in the first wave experienced lower incidences in the second wave. It appears that a comparatively high cumulative incidence of infection, even if far below theoretical thresholds required for herd immunity, may provide noticeable protection during the second wave. We speculate that, if real, the mechanism for this pattern could be depletion of most susceptible individuals and of superspreaders in the first wave. A population learning effect regarding cautious behavior could have also contributed. Since no area-specific variation of the national policy against the SARS-CoV-2 outbreak was allowed until early November 2020, neither individual behaviors nor established or purported environmental risk factors of COVID-19, such as air pollution and meteorological factors, are likely to have confounded the inverse trends we observed in infection incidence over time.

Back to basics in COVID-19: Antigens and antibodies-Completing the puzzle.

The outbreak of the coronavirus disease 2019 (COVID-19) has gathered 1 year of scientific/clinical information. This informational asset should be thoroughly and wisely used in the coming year colliding in a global task force to control this infection. Epidemiology of this infection shows that the available estimates of SARS-CoV-2 infection prevalence largely depended on the availability of molecular testing and the extent of tested population. Within molecular diagnosis, the viability and infectiousness of the virus in the tested samples should be further investigated. Moreover, SARS-CoV-2 has a genetic normal evolution that is a dynamic process. The immune system participates to the counterattack of the viral infection by pathogen elimination, cellular homoeostasis, tissue repair and generation of memory cells that would be reactivated upon a second encounter with the same virus. In all these stages, we still have knowledge to be gathered regarding antibody persistence, protective effects and immunological memory. Moreover, information regarding the intense pro-inflammatory action in severe cases still lacks and this is important in stratifying patients for difficult to treat cases. Without being exhaustive, the review will cover these important issues to be acknowledged to further advance in the battle against the current pandemia.

Associations between mortality from COVID-19 in two Italian regions and outdoor air pollution as assessed through tropospheric nitrogen dioxide.

After the appearance of COVID-19 in China last December 2019, Italy was the first European country to be severely affected by the outbreak. The first diagnosis in Italy was on February 20, 2020, followed by the establishment of a light and a tight lockdown on February 23 and on March 8, 2020, respectively. The virus spread rapidly, particularly in the North of the country in the 'Padan Plain' area, known as one of the most polluted regions in Europe. Air pollution has been recently hypothesized to enhance the clinical severity of SARS-CoV-2 infection, acting through adverse effects on immunity, induction of respiratory and other chronic disease, upregulation of viral receptor ACE-2, and possible pathogen transportation as a virus carrier. We investigated the association between air pollution and subsequent COVID-19 mortality rates within two Italian regions (Veneto and Emilia-Romagna). We estimated ground-level nitrogen dioxide through its tropospheric levels using data available from the Sentinel-5P satellites of the European Space Agency Copernicus Earth Observation Programme before the lockdown. We then examined COVID-19 mortality rates in relation to the nitrogen dioxide levels at three 14-day lag points after the lockdown, namely March 8, 22 and April 5, 2020. Using a multivariable negative binomial regression model, we found an association between nitrogen dioxide and COVID-19 mortality. Although ecological data provide only weak evidence, these findings indicate an association between air pollution levels and COVID-19 severity.

A new threat from an old enemy: Re­emergence of coronavirus (Review).

The new outbreak of coronavirus from December 2019 has brought attention to an old viral enemy and has raised concerns as to the ability of current protection measures and the healthcare system to handle such a threat. It has been known since the 1960s that coronaviruses can cause respiratory infections in humans; however, their epidemic potential was understood only during the past two decades. In the present review, we address current knowledge on coronaviruses from a short history to epidemiology, pathogenesis, clinical manifestation of the disease, as well as treatment and prevention strategies. Although a great amount of research and efforts have been made worldwide to prevent further outbreaks of coronavirus­associated disease, the spread and lethality of the 2019 outbreak (COVID­19) is proving to be higher than previous epidemics on account of international travel density and immune naivety of the population. Only strong, joint and coordinated efforts of worldwide healthcare systems, researchers, and pharmaceutical companies and receptive national leaders will succeed in suppressing an outbreak of this scale.

Lockdown timing and efficacy in controlling COVID-19 using mobile phone tracking.

BACKGROUND: Italy's severe COVID-19 outbreak was addressed by a lockdown that gradually increased in space, time and intensity. The effectiveness of the lockdown has not been precisely assessed with respect to the intensity of mobility restriction and the time until the outbreak receded. METHODS: We used processed mobile phone tracking data to measure mobility restriction, and related those data to the number of new SARS-CoV-2 positive cases detected on a daily base in the three most affected Italian regions, Lombardy, Veneto and Emilia-Romagna, from February 1 through April 6, 2020, when two subsequent lockdowns with increasing intensity were implemented by the Italian government. FINDINGS: During the study period, mobility restriction was inversely related to the daily number of newly diagnosed SARS-CoV-2 positive cases only after the second, more effective lockdown, with a peak in the curve of diagnosed cases of infection occurring 14 to 18 days from lockdown in the three regions and 9 to 25 days in the included provinces. An effective reduction in transmission must have occurred nearly immediately after the tighter lockdown, given the lag time of around 10 days from asymptomatic infection to diagnosis. The period from lockdown to peak was shorter in the areas with the highest prevalence of the infection. This effect was seen within slightly more than one week in the most severely affected areas. INTERPRETATION: It appears that the less rigid lockdown led to an insufficient decrease in mobility to reverse an outbreak such as COVID-19. With a tighter lockdown, mobility decreased enough to bring down transmission promptly below the level needed to sustain the epidemic. FUNDING: No funding sources have been used for this work.

Satellite-detected tropospheric nitrogen dioxide and spread of SARS-CoV-2 infection in Northern Italy.

Following the outbreak of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) last December 2019 in China, Italy was the first European country to be severely affected, with the first local case diagnosed on 20 February 2020. The virus spread quickly, particularly in the North of Italy, with three regions (Lombardy, Veneto and Emilia-Romagna) being the most severely affected. These three regions accounted for >80% of SARS-CoV-2 positive cases when the tight lockdown was established (March 8). These regions include one of Europe's areas of heaviest air pollution, the Po valley. Air pollution has been recently proposed as a possible risk factor of SARS-CoV-2 infection, due to its adverse effect on immunity and to the possibility that polluted air may even carry the virus. We investigated the association between air pollution and subsequent spread of the SARS-CoV-2 infection within these regions. We collected NO2 tropospheric levels using satellite data available at the European Space Agency before the lockdown. Using a multivariable restricted cubic spline regression model, we compared NO2 levels with SARS-CoV-2 infection prevalence rate at different time points after the lockdown, namely March 8, 22 and April 5, in the 28 provinces of Lombardy, Veneto and Emilia-Romagna. We found little association of NO2 levels with SARS-CoV-2 prevalence up to about 130 µmol/m2, while a positive association was evident at higher levels at each time point. Notwithstanding the limitations of the use of aggregated data, these findings lend some support to the hypothesis that high levels of air pollution may favor the spread of the SARS-CoV-2 infection.

COVID-19, an opportunity to reevaluate the correlation between long-term effects of anthropogenic pollutants on viral epidemic/pandemic events and prevalence.

Occupational, residential, dietary and environmental exposures to mixtures of synthetic anthropogenic chemicals after World War II have a strong relationship with the increase of chronic diseases, health cost and environmental pollution. The link between environment and immunity is particularly intriguing as it is known that chemicals and drugs can cause immunotoxicity (e.g., allergies and autoimmune diseases). In this review, we emphasize the relationship between long-term exposure to xenobiotic mixtures and immune deficiency inherent to chronic diseases and epidemics/pandemics. We also address the immunotoxicologic risk of vulnerable groups, taking into account biochemical and biophysical properties of SARS-CoV-2 and its immunopathological implications. We particularly underline the common mechanisms by which xenobiotics and SARS-CoV-2 act at the cellular and molecular level. We discuss how long-term exposure to thousand chemicals in mixtures, mostly fossil fuel derivatives, exposure toparticle matters, metals, ultraviolet (UV)-B radiation, ionizing radiation and lifestyle contribute to immunodeficiency observed in the contemporary pandemic, such as COVID-19, and thus threaten global public health, human prosperity and achievements, and global economy. Finally, we propose metrics which are needed to address the diverse health effects of anthropogenic COVID-19 crisis at present and those required to prevent similar future pandemics.