Diffusion Of Coronavirus Disease 2019 Research Articles

Influencing Factors and Clustering Characteristics of COVID-19: A Global Analysis

The unprecedented coronavirus disease 2019 (COVID-19) pandemic is still raging (in year 2021) in many countries worldwide. Various response strategies to study the characteristics and distributions of the virus in various regions of the world have been developed to assist in the prevention and control of this epidemic. Descriptive statistics and regression analysis on COVID-19 data from different countries were conducted in this study to compare and evaluate various regression models. Results showed that the extreme random forest regression (ERFR) model had the best performance, and factors such as population density, ozone, median age, life expectancy, and Human Development Index (HDI) were relatively influential on the spread and diffusion of COVID-19 in the ERFR model. In addition, the epidemic clustering characteristics were analyzed through the spectral clustering algorithm. The visualization results of spectral clustering showed that the geographical distribution of global COVID-19 pandemic spread formation was highly clustered, and its clustering characteristics and influencing factors also exhibited some consistency in distribution. This study aims to deepen the understanding of the international community regarding the global COVID-19 pandemic to develop measures for countries worldwide to mitigate potential large-scale outbreaks and improve the ability to respond to such public health emergencies.

Impact of pre-pandemic travel mobility patterns on the spatial diffusion of COVID-19 in South Korea

IntroductionPhysical mobility is critical for the spread of infectious diseases in humans. However, few studies have conducted empirical investigations on the impact of pre-pandemic travel mobility patterns on the diffusion of coronavirus disease 2019 (COVID-19). Therefore, this study examines its impact at the city-county level on the diffusion by the wave period during the two-year pandemic in South Korea. MethodsThis study first employs factor analysis by using the travel origin-destination data by travel mode at the county level as of 2019 to derive pre-pandemic travel mobility patterns. Next, the study identifies how they had affected the diffusion of COVID-19 over time by employing the negative binomial regression models on confirmed COVID-19 cases for each wave, including the entire pandemic period. ResultsThe study derived five pre-pandemic mobility patterns: 1) rail-oriented mobility, 2) intra-county bus-oriented mobility, 3) road-oriented mobility, 4) high-speed rail-oriented mobility, and 5) inter-county bus-oriented mobility. Among them, the biggest risk to the diffusion of COVID-19 was the rail-oriented mobility before the pandemic if controlling such measures as accessibility, physical environment, and demographic and socioeconomic indicators. In addition, the order of the magnitudes for the impact of pre-pandemic travel mobility factors on its spatial diffusion had not changed during experiencing the three different wave periods during the two-year pandemic in South Korea. ConclusionsThe study concludes that the rail-oriented travel mobility pattern before the pandemic could pose the greatest threat factor to the spatial spread of COVID-19 at any scale and time. Policymakers should develop strategies to prevent the spatial spread of COVID-19 by reducing human mobility for daily living in areas with strong rail mobility patterns formed before the pandemic.

International trade as critical parameter of COVID-19 spread that outclasses demographic, economic, environmental, and pollution factors

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the Coronavirus Disease 2019 (COVID-19), generating high numbers of COVID-19 related infected individuals and deaths, is still circulating in 2021 with new variants of the coronavirus, such that the state of emergency remains in manifold countries. Currently, there is still a lack of a full understanding of the factors determining the COVID-19 diffusion that clarify the causes of the variability of infections across different provinces and regions within countries. The main goal of this study is to explain new and main determinants underlying the diffusion of COVID-19 in society. This study focuses on international trade because this factor, in a globalized world, can synthetize different drivers of virus spread, such as mobility patterns, economic potentialities, and social interactions of an investigated areas. A case study research is performed on 107 provinces of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. Statistical analyses from March 2020 to February 2021 suggest that total import and export of provinces has a high association with confirmed cases over time (average r > 0.78, p-value <.001). Overall, then, this study suggests total import and export as complex indicator of COVID-19 transmission dynamics that outclasses other common parameters used to justify the COVID-19 spread, given by economic, demographic, environmental, and climate factors. In addition, this study proposes, for the first time, a time-dependent correlation analysis between trade data and COVID-19 infection cases to explain the relation between confirmed cases and social interactions that are a main source of the diffusion of SARS-CoV-2 and subsequent negative impact in society. These novel findings have main theoretical and practical implications directed to include a new parameter in modelling of the diffusion of COVID-19 pandemic to support effective policy responses of crisis management directed to constrain the impact of COVID-19 pandemic and similar infectious diseases in society.

Simulation of Spatiotemporal Relationship between COVID-19 Propagation and Regional Economic Development in China

Coronavirus disease 2019 (COVID-19) propagation in 2019 posed serious threats and challenges to human public health and safety. Currently, COVID-19 is still not effectively controlled in certain countries and regions. This study combines the traditional susceptible-exposed-infectious-recovered (SEIR) model, system dynamics (SD) model, and cellular automata (CA) model to construct a spatiotemporal dynamics pandemic model (SDPM). The SDPM is used to dynamically depict the spatiotemporal diffusion and outbreak of COVID-19 through research on the relationship between epidemic spread and regional development. The results show that: (1) There is a positive correlation between regional development and epidemic spread. The more developed the regional economy, especially in areas with short-range population migration from Hubei Province, the more severe the epidemic spread; and (2) The spatial isolation and control measures adopted by the government can effectively prevent the COVID-19 spread. The results explore the relationship between COVID-19 spread and regional economic development by studying the spatial and temporal transmission characteristics of COVID-19, and provide a scientific reference for the government to formulate reasonable response measures.

How Schools Affected the COVID-19 Pandemic in Italy: Data Analysis for Lombardy Region, Campania Region, and Emilia Region

Background: Coronavirus Disease 2019 (COVID-19) is the main discussed topic worldwide in 2020 and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. Objectives: In this paper, a data analytics study on the diffusion of COVID-19 in Lombardy Region and Campania Region is developed in order to identify the driver that sparked the second wave in Italy. Methods: Starting from all the available official data collected about the diffusion of COVID-19, we analyzed Google mobility data, school data and infection data for two big regions in Italy: Lombardy Region and Campania Region, which adopted two different approaches in opening and closing schools. To reinforce our findings, we also extended the analysis to the Emilia Romagna Region. Results: The paper shows how different policies adopted in school opening/closing may have had an impact on the COVID-19 spread, while other factors related to citizen mobility did not affect the second Italian wave. Conclusions: The paper shows that a clear correlation exists between the school contagion and the subsequent temporal overall contagion in a geographical area. Moreover, it is clear that highly populated provinces have the greatest spread of the virus.

Relative Concentrations and Diffusion of COVID-19 across the United States in 2020

Les auteurs étudient la propagation de la COVID-19 (maladie à coronavirus 2019) en termes de cas et de décès dans l’ensemble des États-Unis, à l’aide des données relatives aux comtés à différents niveaux de comparaison. Ils utilisent l’indice de Hoover et l’indice de différentiel géographique pour analyser l’évolution de la concentration de COVID-19 à l’échelle des grandes zones économiques ( city systems), de mai 2020 à décembre 2020. Les auteurs cartographient également l’indice du différentiel géographique (DG) des différents comtés afin de montrer la distribution des cas de COVID-19 et des décès qui lui sont attribuables dans les comtés par rapport aux moyennes nationales. Ils constatent que la maladie s’est rapidement répandue et qu’elle s’est déconcentrée dans l’ensemble des zones économiques pour ce qui est des indices de Hoover, et qu’en décembre 2020, de nombreuses zones économiques affichant des niveaux très élevés de concentration de COVID-19 étaient passées à des taux de dispersion supérieurs aux taux observés dans la population en général. Cette évolution semble indiquer un degré appréciable de propagation rurale dans les derniers mois de 2020. Les indices de différentiel géographique pour les mêmes zones économiques révèlent aussi une convergence des géographies de la population et des décès liés à la COVID-19. Les statistiques des différents comtés mettent en relief l’existence de grappes de grands nombres et de faibles nombres de décès comparativement aux normes nationales, qui se sont déplacées du nord-est au sud-est et qui, en décembre, sont aussi apparues dans le Midwest et la portion centrale du pays, entre autres régions.

Effects of the Spread of COVID-19 on Public Health of Polluted Cities: Results of the First Wave for Explaining the Dejà Vu in the Second Wave of COVID-19

The pandemic of coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, is generating a high number of deaths worldwide. One of the current questions in the field of environmental science is to explain how air pollution can affect the impact of COVID-19 on public health. The research here focuses on a case study of Italy. Results suggest that the diffusion of COVID-19 in cities with high levels of air pollution is generating higher numbers of COVID-19 related infected individuals and deaths. In particular, results reveal that, among Italian cities, the number of infected people was higher in cities with more than 100 days per year exceeding limits set for PM10 or ozone, cities located in hinterland zones (i.e. away from the coast), cities having a low average speed of wind and cities with a lower average temperature. In hinterland cities (mostly those bordering large urban conurbations) having a high level of air pollution, coupled with low wind speed, the average number of infected people in April 2020during the first wave of the COVID-19 is more than tripled compared to cities that low levels of air pollution. In addition, results show that more than 75% of infected individuals and about 81% of deaths of the first wave of COVID-19 in Italy are in industrialized regions with high levels of air pollution. Although these vital results of the first wave of the COVID-19 from February to August 2020, policymakers have had a low organizational capacity to plan effective policy responses for crisis management to cope with recurring COVID-19 pandemic that is generating a new wave with again negative effects, déjà vu, on public health and of course economic systems.

Effects of the spread of COVID-19 on public health of polluted cities: results of the first wave forexplaining the dejà vu in the second wave of COVID-19pandemic and epidemics of future vital agents.

The pandemic of coronavirus disease 2019 (COVID-19), caused by the novel coronavirus SARS-CoV-2, is generating a high number of deaths worldwide. One of the current questions in the field of environmental science is to explain how air pollution can affect the impact of COVID-19 pandemic on public health. The research here focuses on a case study of Italy. Results suggest that the diffusion of COVID-19 in cities with high levels of air pollution is generating higher numbers of COVID-19 related infected individuals and deaths. In particular, results reveal that the number of infected people was higher in cities with more than 100 days per year exceeding limits set for PM10 or ozone, cities located in hinterland zones (i.e. away from the coast), cities having a low average speed of wind and cities with a lower average temperature. In hinterland cities having a high level of air pollution, coupled with low wind speed, the average number of infected people in April 2020—during the first wave of the COVID-19 pandemic—is more than tripled compared to cities with low levels of air pollution. In addition, results show that more than 75% of infected individuals and about 81% of deaths of the first wave of COVID-19 pandemic in Italy are in industrialized regions with high levels of air pollution. Although these vital results of the first wave of the COVID-19 from February to August 2020, policymakers have had a low organizational capacity to plan effective policy responses for crisis management to cope with COVID-19 pandemic that is generating recurring waves with again negative effects, déjà vu, on public health and of course economic systems.

Juggling the COVID-19 pandemic: A cytopathology point of view.

Since its first identification in China at the end of 2019, severe acute respiratory syndrome coronavirus 2 has rapidly spread all over the world, becoming an international healthcare emergency. In the era of coronavirus disease-2019 (COVID-19), several aspects of normal life, including those related to the medical activities, have been radically changed. Extraordinary measures have been adopted by different nations to cope with the rapid diffusion of COVID-19 all over the world. In hospitals, careful attention has been paid to manage infected patients with a possible detrimental effect for patients affected by other diseases. As with other medical fields, cytopathology laboratories have also drastically modified their activities to cope with the COVID-19 healthcare emergency. Here, the main effects of COVID-19 pandemic on the routine practice of cytopathology are summarised, focusing on the prioritisation policy adopted by cytopathologists worldwide.

How (Un)sustainable Environments Are Related to the Diffusion of COVID-19: The Relation between Coronavirus Disease 2019, Air Pollution, Wind Resource and Energy

The pandemic caused by novel coronavirus disease 2019 (COVID-19) is generating a high number of cases and deaths, with negative effects on public health and economic systems. One of the current questions in the contemporary environmental and sustainability debate is how high air pollution and reduced use of renewable energy can affect the diffusion of COVID-19. This study endeavors to explain the relation between days of air pollution, wind resources and energy, and the diffusion of COVID-19 to provide insights into sustainable policy to prevent future epidemics. The statistical analysis here focuses on a case study of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. The results reveal two main findings: (1) cities with high wind speed and high wind energy production have a lower number of cases of COVID-19 in the context of a more sustainable environment; (2) cities located in hinterland zones with high air pollution, low wind speed and less wind energy production have a greater number of cases and total deaths. The results presented here suggest that the pandemic caused by novel coronavirus (SARS-CoV-2) and future epidemics similar to COVID-19 cannot be solved only with research in medicine but the solution also needs advanced capabilities and technologies for supporting sustainable development based on the reduction of air pollution and increase of production in renewable energy to improve air quality and as a consequence public health.

Mapping Spatiotemporal Diffusion of COVID-19 in Lombardy (Italy) on the Base of Emergency Medical Services Activities

The epidemic of coronavirus-disease-2019 (COVID-19) started in Italy with the first official diagnosis on 21 February 2020; hence, it is now known how many cases were already present in earlier days and weeks, thus limiting the possibilities of conducting any retrospective analysis. We hypothesized that an unbiased representation of COVID-19 diffusion in these early phases could be inferred by the georeferenced calls to the emergency number relevant to respiratory problems and by the following emergency medical services (EMS) interventions. Accordingly, the aim of this study was to identify the beginning of anomalous trends (change in the data morphology) in emergency calls and EMS ambulances dispatches and reconstruct COVID-19 spatiotemporal evolution on the territory of Lombardy region. Accordingly, a signal processing method, previously used to find morphological features on the electrocardiographic signal, was applied on a time series representative of territorial clusters of about 100,000 citizens. Both emergency calls and age- and gender-weighted ambulance dispatches resulted strongly correlated to COVID-19 casualties on a provincial level, and the identified local starting days anticipated the official diagnoses and casualties, thus demonstrating how these parameters could be effectively used as early indicators for the spatiotemporal evolution of the epidemic on a certain territory.

Influence of Wind Speed on COVID-19 Transmission in Polluted Cities

The coronavirus disease 2019 (COVID-19) can spread through droplets of infected humans but this transmission dynamics is affected by air circulation in polluted cities. This study endeavors to explain the relation between wind speed and the diffusion of COVID-19 in polluted cities to explain viral airborne transmission. Results reveal that in Italy, polluted cities with low wind speed have higher numbers of COVID-19 related infected individuals. This finding seems to suggest that high concentrations of particulate matter in the air, in the presence of little wind can promote a longer permanence of the viral particles in polluted air, thus favoring COVID-19 airborne transmission in environments. Because of complex real-world environmental ventilation conditions, environmental policies should be directed to reduce air pollution in polluted cities with low wind speed, that added to measures of social distancing and wearing masks for both infected and healthy humans, can to reduce airborne transmission of the SARS-CoV-2 and other viral agents.

How do low wind speeds and high levels of air pollution support the spread of COVID-19?

The pandemic of coronavirus disease 2019 (COVID-19) is generating a high number of infected individuals and deaths. One of the current questions is how climatological factors and environmental pollution can affect the diffusion of COVID-19 in human society. This study endeavours to explain the relation between wind speed, air pollution and the diffusion of COVID-19 to provide insights to constrain and/or prevent future pandemics and epidemics. The statistical analysis here focuses on case study of Italy and reveals two main findings: 1) cities with high wind speed have lower numbers of COVID-19 related infected individuals; 2) cities located in hinterland zones (mostly those bordering large urban conurbations) with little wind speed and frequently high levels of air pollution had higher numbers of COVID-19 related infected individuals. Results here suggest that high concentrations of air pollutants, associated with low wind speeds, may promote a longer permanence of viral particles in polluted air of cities, thus favouring an indirect means of diffusion of the novel coronavirus (SARS-CoV-2), in addition to the direct diffusion with human-to-human transmission dynamics.

The Bass Model: a parsimonious and accurate approach to forecasting mortality caused by COVID-19

PurposeThe novel coronavirus disease 2019 (COVID-19) pandemic has presented unique challenges in terms of understanding its unique characteristics of transmission and predicting its spread. The purpose of this study is to present a simple, parsimonious and accurate model for forecasting mortality caused by COVID-19.Design/methodology/approachThe presented Bass Model is compared it with several alternative existing models for forecasting the spread of COVID-19. This study calibrates the model for deaths for the period, March 21 to April 30 for the USA as a whole and as the US States of New York, California and West Virginia. The daily data from the COVID-19 Tracking Project has been used, which is a volunteer organization launched from The Atlantic. Every day, data is collected on testing and patient outcomes from all the 50 states, 5 territories and the District of Columbia. This data set is widely used by policymakers and scholars. The fit of the model (F-value and its significance, R-squared value) and the statistical significance of the variables (t-values) for each one of the four estimates are examined. This study also examines the forecast of deaths for a three-day period, May 1 to 3 for each one of the four estimates – US, and States of New York, California and West Virginia. Based on these metrics, the viability of the Bass Model is assessed. The dependent variable is the number of deaths, and the two independent variables are cumulative number of deaths and its squared value.FindingsThe findings of this paper show that compared to other forecasting methods, the Bass Model performs remarkably well. In fact, it may even be argued that the Bass Model does better with its forecast. The calibration of models for deaths in the USA, and States of New York, California and West Virginia are all found to be significant. The F values are large and the significance of the F values is low, that is, the probability that the model is wrong is very miniscule. The fit as measured by R-squared is also robust. Further, each of the two independent variables is highly significant in each of the four model calibrations. These forecasts also approximate the actual numbers reasonably well.Research limitations/implicationsThis study illustrates the applicability of the Bass Model to estimate the diffusion of COVID-19 with some preliminary but important empirical analyses. This study argues that while the more sophisticated models may produce slightly better estimates, the Bass model produces robust and reasonably accurate estimates given the extreme parsimony of the model. Future research may investigate applications of the Bass Model for pandemic management using additional variables and other theoretical lenses.Practical implicationsThe Bass Model offers effective forecasting of mortality resulting from COVID-19 to help understand how the curve can be flattened, how hospital capacity could be overwhelmed and how fatality rates might climb based on time and geography in the upcoming weeks and months.Originality/valueThis paper demonstrates the efficacy of the Bass Model as a parsimonious, accessible and theory-based approach that can predict the mortality rates of COVID-19 with minimal data requirements, simple calibration and accessible decision calculus. For all these reasons, this paper recommends further and continued examination of the Bass Model as an instrument for forecasting COVID-19 (and other epidemic/pandemic) mortality and health resource requirements. As this paper has demonstrated, there is much promise in this model.

Relationship between Influenza Vaccination Coverage Rate and COVID-19 Outbreak: An Italian Ecological Study.

Background: The lack of specific vaccines or drugs against coronavirus disease 2019 (COVID-19) warrants studies focusing on alternative clinical approaches to reduce the spread of this pandemic disease. In this study, we investigated whether anti-influenza vaccination plays a role in minimizing the diffusion of COVID-19 in the Italian population aged 65 and over. Methods: Four COVID-19 outcomes were used: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) seroprevalence, hospitalizations for COVID-19 symptoms, admissions to intensive care units for reasons related to SARS-CoV-2, and deaths attributable to COVID-19. Results: At univariate analyses, the influenza vaccination coverage rates correlated negatively with all COVID-19 outcomes (Beta ranging from −134 to −0.61; all p < 0.01). At multivariable analyses, influenza vaccination coverage rates correlated independently with SARS-CoV-2 seroprevalence (Beta (95% C.I.): −130 (−198, −62); p = 0.001), hospitalizations for COVID-19 symptoms (Beta (95% C.I.): −4.16 (−6.27, −2.05); p = 0.001), admission to intensive care units for reasons related to SARS-CoV-2 (Beta (95% C.I.): −0.58 (−1.05, −0.12); p = 0.017), and number of deaths attributable to COVID-19 (Beta (95% C.I.): −3.29 (−5.66, −0.93); p = 0.010). The R2 observed in the unadjusted analysis increased from 82% to 159% for all the considered outcomes after multivariable analyses. Conclusions: In the Italian population, the coverage rate of the influenza vaccination in people aged 65 and over is associated with a reduced spread and a less severe clinical expression of COVID-19. This finding warrants ad hoc studies to investigate the role of influenza vaccination in preventing the spread of COVID-19.

Internet Public Opinion Evolution in the COVID-19 Event and Coping Strategies.

In this study, we carried out a text analysis on the information disseminated and discussed among netizens on the Baidu Post Bar (the world's largest Chinese forum) during the coronavirus disease 2019 (COVID-19) epidemic, to create a policy basis for health administrative departments. We used Python tools to search for the relevant data on the Baidu Post Bar. Next, a text analysis was performed on the posts' contents using a combination of latent Dirichlet allocation (LDA), sentiment analysis, and correlation analysis. According to the LDA analysis, the public was highly interested in topics such as COVID-19 prevention, infection symptoms, infection and coping measures, sources of transmission and treatments, community management, and work resumption. The majority of the public had negative emotional values, yet a portion of the public held positive emotional values. We also performed a correlation analysis of the influencing factors was established. Netizens' degree of concern shown in their posts was greatly associated with the spread of COVID-19. With the rise, diffusion, outbreak, and mitigation of COVID-19 in China, netizens have successively created a large number of posts, and the topics of discussion varied over time. Therefore, the media and the government have the responsibility to distribute positive information, to correctly guide the public's emotions to bring some sort of reassurance to the public.

No clear association emerges between intergenerational relationships and COVID-19 fatality rates from macro-level analyses

The severe acute respiratory syndrome coronavirus 2 originated in Wuhan, China at the end of 2019 and rapidly spread in more than 100 countries. Researchers in different fields have been working on finding explanations for the unequal impact of the virus and deaths from the associated coronavirus disease 2019 (COVID-19) across geographical areas. Demographers and other social scientists have hinted at the importance of demographic factors, such as age structure and intergenerational relationships. Our aim is to reflect on the possible link between intergenerational relationships and spread and lethality of COVID-19 in a critical way. We show that with available aggregate data it is not possible to draw robust evidence to support these links. In fact, despite a higher prevalence of intergenerational coresidence and contacts that is broadly positively associated with COVID-19 case fatality rates at the country level, the opposite is generally true at the subnational level. While this inconsistent evidence demonstrates neither the existence nor the absence of a causal link between intergenerational relationships and the severity of COVID-19, we warn against simplistic interpretations of the available data, which suffer from many shortcomings. We conclude by arguing that intergenerational relationships are not only about physical contacts between family members. Theoretically, different forms of intergenerational relationships may have causal effects of opposite sign on the diffusion of COVID-19. Policies should also take into account that intergenerational ties are a source of instrumental and emotional support, which may favor compliance to the lockdown and "phase-2" restrictions and may buffer their negative consequences on mental health.

Diffusion of COVID-19 Outbreaks: The Interaction between Air Pollution-to-Human and Human-to-Human Transmission Dynamics in Hinterland Regions with Cold Weather and Low Average Wind Speed

What is COVID-19? Coronavirus disease 2019 (COVID-19) is viral infection that generates a severe acute respiratory syndrome with serious pneumonia that may result in progressive respiratory failure and death. What are the goals of this investigation? This study explains the geo-environmental determinants of the accelerated diffusion of COVID-19 in Italy that is generating a high level of deaths and suggests general lessons learned for a strategy to cope with future epidemics similar to COVID-19 to reduce viral infectivity and negative impacts in economic systems and society. What are the results of this study? The main results are: o The accelerate and vast diffusion of COVID-19 in North Italy has a high association with air pollution. o Hinterland cities have average days of exceeding the limits set for PM10 (particulate matter 10 micrometers or less in diameter) equal to 80 days, and an average number of infected more than 2,000 individuals as of April 1st, 2020, coastal cities have days of exceeding the limits set for PM10 equal to 60 days and have about 700 infected in average. o Cities that average number of 125 days exceeding the limits set for PM10, last year, they have an average number of infected individual higher than 3,200 units, whereas cities having less than 100 days (average number of 48 days) exceeding the limits set for PM10, they have an average number of about 900 infected individuals. o The results reveal that accelerated transmission dynamics of COVID-19 is due to mainly to mechanisms of air pollution-to-human transmission rather than human-to-human transmission. o The finding here suggests that to minimize future epidemic similar to COVID-19, the max number of days per year in which cities can exceed the limits set for PM10 or for ozone, considering their meteorological condition, is less than 50 days. After this critical threshold, the analytical output here suggests that environmental inconsistencies because of the combination between air pollution and meteorological conditions (with high moisture%, low wind speed and fog) trigger a take-off of viral infectivity (accelerated epidemic diffusion) with damages for health of population, economy and society. What is a socioeconomic strategy to prevent future epidemics similar to COVID-19? Considering the complex interaction between air pollution, meteorological conditions and biological characteristics of viral infectivity, lessons learned for COVID-19 have to be applied for a proactive socioeconomic strategy to cope with future epidemics, especially an environmental policy based on reduction of air pollution mainly in hinterland zones of countries, having low wind speed, high percentage of moisture and fog that create an environment that can damage immune system of people and foster a fast transmission of viral infectivity similar to the COVID-19. This study must conclude that a strategy to prevent future epidemics similar to COVID 19 has also to be designed in environmental and sustainability science and not only in terms of biology.

Modelling and Predicting the Spatio-Temporal Spread of Coronavirus Disease 2019 (COVID-19) in Italy

Background: Severe acute respiratory syndrome Coronavirus 2019 (COVID-19) has been firstly detected in China at the end of 2019 and it spread in few months all over the world. Italy is the second country in the world for number of cases. In this country, the diffusion of COVID-19 has followed a peculiar spatial pattern. However, the interest of scientific community has been devoted almost exclusively to the prediction of the disease evolution over time so far. Methods: Official freely available data about the number of infected at the finest possible level of spatial areal aggregation (Italian provinces) are used to model the spatio-temporal distribution of COVID-19 infections at local level. Data time horizon ranges from 20 February 2020, which is the date when the first case not directly connected with China has been discovered in northern Italy, to 18 March 2020. An endemic-epidemic multivariate time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon. Previous literature has shown that these class of models provide reliable predictions of infectious diseases in time and space. Findings: Three subcomponents characterize the estimated model. The first is related to the evolution of the disease over time; the second is characterized by transmission of the illness among inhabitants of the same province; the third remarks the effects of spatial neighbourhood and try to capture the contagion effects of nearby areas. Focusing on the aggregated time-series of the daily counts in Italy, the contribution of any of the three subcomponents do not dominate on the others and our predictions are excellent for the whole country, with an error of 3 ‰ compared to the late available data. At local level, instead, interesting distinct patterns emerge. In particular, the provinces first concerned by containment measures are those that are not affected by the effects of spatial neighbours. On the other hand, for the provinces the are currently strongly affected by contagions, the component accounting for the spatial interaction with surrounding areas is prevalent. Moreover, the proposed model provides good forecasts of the number of infections at local level while controlling for delayed reporting. Interpretation: Social and demographic aspects can explain the dramatic involvement of some provinces. A strong evidence is found that strict control measures implemented in some provinces efficiently break contagions and limit the spread to nearby areas. While policies to contain the 1 spreading of the disease may potentially be more effective if planned considering the peculiarities of local territories, the effective and homogeneous enforcement of control measures at national level is imperative to prevent the disease control being delayed or missed as a whole. This may also apply at international level where, as it is for the EU or the USA, the internal border checks among states have largely been abolished. Funding Statement: None. Declaration of Interests: None of the authors of this paper has a financial or personal relationship with other people or organizations that could inappropriately influence or bias the content of the paper. No competing interests are at stake and there is no conflict of interest with other people or organizations that could inappropriately influence or bias the content of the paper. Ethics Approval Statement: Data is publicly available.

How (Un)sustainable Environments are Related to the Diffusion of COVID-19: The Relation between Coronavirus Disease 2019, Air Pollution, Wind Resource and Energy

The pandemic caused by novel coronavirus disease 2019 (COVID-19) is generating a high number of cases and deaths, with negative effects on public health and economic systems. One of the current questions in the contemporary environmental and sustainability debate is how high air pollution and reduced use of renewable energy can affect the diffusion of COVID-19. This study endeavors to explain the relation between days of air pollution, wind resources and energy, and the diffusion of COVID-19 to provide insights into sustainable policy to prevent future epidemics. The statistical analysis here focuses on a case study of Italy, one of the first countries to experience a rapid increase in confirmed cases and deaths. The results reveal two main findings: (1) cities with high wind speed and high wind energy production have a lower number of cases of COVID-19 in the context of a more sustainable environment; (2) cities located in hinterland zones with high air pollution, low wind speed and less wind energy production have a greater number of cases and total deaths. The results presented here suggest that the pandemic caused by novel coronavirus (SARS-CoV-2) and future epidemics similar to COVID-19 cannot be solved only with research in medicine but the solution also needs advanced capabilities and technologies for supporting sustainable development based on the reduction of air pollution and increase of production in renewable energy to improve air quality and as a consequence public health.