COVID-19 Diffusion Research Articles

A tale of two cities: London and New York City during Covid-19.

Using publicly available data, this paper investigates the diffusion of COVID-19 across neighborhoods in two major cities, London and New York. We link neighborhood demographics to incidence, and we investigate patterns of change over time in conjunction with changing policy responses to the pandemic. By comparing and contrasting these two cities, we are able to exploit surveillance and policy differences, demonstrating how each contributes information to the other. We conclude that better coordination can be translated into improved health policy.

COVID-19 and stroke in women: impact on clinical, psychosocial and research aspects.

Despite the growing interest in gender medicine, the influence of sex and gender on human diseases, including stroke, continues to be underestimated and understudied. The COVID-19 pandemic has overall impacted not only the occurrence and management of stroke but has also exacerbated sex and gender disparities among both patients and healthcare providers. This paper aims to provide an updated overview on the influence of sex and gender in stroke pathophysiology and care during COVID-19 pandemic, through biological, clinical, psychosocial and research perspectives. Gender equity and awareness of the importance of sexual differences are sorely needed, especially in times of health crisis but have not yet been achieved to date. To this purpose, the sudden yet worldwide diffusion of COVID-19 represents a unique learning experience that highlights critical unmet needs also in gender medicine. The failures of this recent past should be kept as food for thought to inspire proper strategies reducing inequalities and to address women's health and wellbeing issues, particularly in case of future pandemics.

Urban Air Pollution Exposure Impact on COVID-19 Transmission in a Few Metropolitan Regions

Based on outdoor air pollution and meteorological daily time series observational and in-situ monitoring data, this study investigated the impacts of environmental factors under different urban climates on COVID-19 transmission in four hotspot European metropolises (Berlin, London, Madrid, and Paris) from March 2020 to March 2022. Through applied statistical methods and cross-correlation tests involving multiple datasets pertaining to the main air pollutants (inhalable particulate matter PM2.5 and PM10, nitrogen dioxide (NO2), and ozone (O3)) and climate parameters (air temperature at 2 m height, relative humidity, wind speed intensity and direction, planetary boundary layer height, and surface solar irradiance), a direct positive impact of aerosol loading (PM2.5, PM10, and aerosol optical depth (AOD)) on COVID-19 spreading and severity was revealed. Despite some urban differences existing between the selected cities, particularly for the spring–summer periods, we have observed negative correlations between daily new COVID-19 cases and deaths and daily average ground-level ozone concentration, air temperature at 2 m height, planetary boundary layer height, and surface solar irradiance. Air relative humidity and urban population density have a direct impact on COVID-19 diffusion in large metropolitan areas, and the findings of this study highlight the crucial role of air pollution, in synergy with climate variability, in viral pathogens dispersion in COVID-19 transmission in large urban areas. This information can be used by decision-makers to develop targeted interventions during epidemic periods to reduce the potential risks associated with air pollution exposure and to promote the sustainable development of urban economies.

Latent event history models for quasi-reaction systems

Various processes, such as cell differentiation and disease spreading, can be modelled as quasi-reaction systems of particles using stochastic differential equations. The existing Local Linear Approximation (LLA) method infers the parameters driving these systems from measurements of particle abundances over time. While dense observations of the process in time should in theory improve parameter estimation, LLA fails in these situations due to numerical instability. Defining a latent event history model of the underlying quasi-reaction system resolves this problem. A computationally efficient Expectation-Maximization algorithm is proposed for parameter estimation, incorporating an extended Kalman filter for evaluating the latent reactions. A simulation study demonstrates the method's performance and highlights the settings where it is particularly advantageous compared to the existing LLA approaches. An illustration of the method applied to the diffusion of COVID-19 in Italy is presented.

Statistical learning models to measure the impact of COVID-19 on financial fragility.

This paper investigates the effects of the economic shock produced by the COVID-19 outbreak and diffusion on households'. Through a survey administered to Italian households, without loss of generality, we investigate changes in financial and economic decisions and the households' ability to cope with daily purchases, repay their debt obligations and face unexpected expenses. The paper also applies a statistical learning model through a synthetic indicator for the financial vulnerability of households, integrating the relevant information on the financial literacy and education of the surveyed individuals.

Social and economic variables explain COVID-19 diffusion in European regions

At the beginning of 2020, Italy was the country with the highest number of COVID-19 cases, not only in Europe, but also in the rest of the world, and Lombardy was the most heavily hit region of Italy. The objective of this research is to understand which variables have determined the prevalence of cases in Lombardy and in other highly-affected European regions. We consider the first and second waves of the COVID-19 pandemic, using a set of 22 variables related to economy, population, healthcare and education. Regions with a high prevalence of cases are extracted by means of binary classifiers, then the most relevant variables for the classification are determined, and the robustness of the analysis is assessed. Our results show that the most meaningful features to identify high-prevalence regions include high number of hours spent in work environments, high life expectancy, and low number of people leaving from education and neither employed nor educated or trained.

Differences in lethality and diffusion of Covid-19 and different kinds of vaccines: Correspondence.

Differences in lethality and diffusion of Covid-19 and different kinds of vaccines: Correspondence.

How COVID-19 affects user interaction with online streaming service providers on twitter

The worldwide diffusion of COVID-19, declared pandemic in March 2020, has led to significant changes in people’s lifestyles and behavior, especially when it comes to the consumption of media and entertainment. Indeed, during this period, online streaming platforms have become the preferred providers of recreational content, whereas Online Social Networks proved to be the favorite place to find social connections while adhering to distancing measures. In the meantime, from the online Streaming Service Providers’ point of view, Online Social Networks have gained more and more importance both as valuable data sources for business intelligence and as connected and co-viewing platforms. This study starts from these considerations to explore the impact of COVID-19 on user interaction with Streaming Service Providers in Online Social Networks. In particular, our investigation focuses on the Twitter platform; by comparing several large datasets referring to different periods (i.e., before, during, and after COVID-19 emergence), we investigate interesting patterns and dynamics leveraging both Natural Language Processing and sentiment analysis techniques. Our data science campaign, and the main findings derived, adopts a peculiar perspective focusing on the different categories of users and Streaming Service Providers. The main objective of the analysis is to uncover the dynamics underlying the evolution of the interaction between people and businesses during the COVID-19 outbreak.

Maritime transportation and people mobility in the early diffusion of COVID-19 in Croatia.

The outbreak of COVID-19 in Europe began in early 2020, leading to the emergence of several waves of infection with varying timings across European countries. The largest wave of infection occurred in August-September. Croatia, known for being a hotspot of tourism in the Mediterranean region, raised concerns that it might have played a role in incubating the pandemic during the summer of 2020. To investigate this possibility, we conducted a data-driven study to examine the potential influence of passenger mobility to and within Croatia, utilizing various modes of transportation. To achieve this, we integrated observational datasets into the "epidemic Renormalization Group" modeling framework. By comparing the models with epidemiological data, we found that in the case of Croatia in 2020, neither maritime nor train transportation played a prominent role in propagating the infection. Instead, our analysis highlighted the leading role of both road and airborne mobility in the transmission of the virus. The proposed framework serves to test hypotheses concerning the causation of infectious waves, offering the capacity to rule out unrelated factors from consideration.

Correlations of mobility and Covid-19 transmission in global data.

Assessing the contribution of mobility declines to the control of Covid-19 diffusion is an urgent challenge of global import. We analyze the temporal correlation between transmission rates and societal mobility levels using daily mobility data from Google and Apple in an international panel of 99 countries during the period of March-December 2020. Reduced form regression estimates that flexibly control for time trends suggest that globally, a 10 percentage point reduction in mobility is associated with a 0.05-0.07 reduction in the value of the effective reproduction number, R(t). However, the strength of the association varies substantially across world regions and over time, being initially positive and strong in most world regions during the 2020 spring period, but becoming weaker over the summer, especially in Europe and Asia. We further find evidence that the strength of the association between mobility and transmission rates is reduced where facial coverings rules were implemented.

The Impact of COVID-19 Vaccination on Inflammatory Skin Disorders and Other Cutaneous Diseases: A Review of the Published Literature

Background: Four vaccines have been authorized by the European Medicines Agency (EMA): viral vector-based vaccines (AstraZeneca; AZD1222 and Johnson & Johnson; Ad26.COV2. and 2 mRNA-based vaccines (Pfizer/BioNTech; BNT162b2 and Moderna; mRNA-1273). Adverse events (AEs) related to vaccination have been described in the literature. The main aim of the dermatological practice was to avoid the diffusion of COVID-19, allowing the continuity of care for patients. Objective: The aim of this review article is to investigate current literature regarding cutaneous reactions following COVID-19 vaccination, mainly inflammatory dermatological diseases. Materials and methods: Investigated manuscripts included metanalyses, reviews, letters to the editor, real-life studies, case series, and reports. Results: We selected a total of 234 articles involving more than 550 patients. We have divided the results section into various sub-sections to ensure greater understanding for readers. Conclusions: Clinicians should keep in mind the possibility of new onsets or the worsening of several dermatoses following vaccination in order to promptly recognize and treat these AEs. Certainly, vaccination should not be discouraged.

A Study of the Impact of COVID-19 on Urban Contact Networks in China Based on Population Flows

The emergence and enduring diffusion of COVID-19 has had a dramatic impact on cities worldwide. The scientific aim of this study was to introduce geospatial thinking to research related to infectious diseases, while the practical aim was to explore the impact on population movements and urban linkages in the longer term following a pandemic outbreak. Therefore, this study took 366 cities in China as the research subjects while exploring the relationship between urban contact and the outbreak of the pandemic from both national and regional perspectives using social network analysis (SNA), Pearson correlation analysis and multi-scale geographically weighted regression (MGWR) modeling. The results revealed that the number of COVID-19 infections in China fluctuated with strain variation over the study period; the urban contact network exhibited a significant trend of recovery. The pandemic had a hindering effect on national urban contact, and this effect weakened progressively. Meanwhile, the effect exhibited significant spatial heterogeneity, with a weakening effect in the eastern region ≈ northeast region > central region > western region, indicating a decreasing phenomenon from coastal to inland areas. Moreover, the four major economic regions in China featured border barrier effects, whereby urban contact networks constituted by cross-regional flows were more sensitive to the development of the pandemic. The geostatistical approach adopted in this study related to infectious disease and urban linkages can be used in other regions, and its findings provide a reference for China and other countries around the world to respond to major public health events.

Did national holidays accelerate COVID-19 diffusion during the first phase of the pandemic in Bangladesh?

Bangladesh registered 20,117,32 confirmed cases of COVID-19 and the death toll crossed the grim milestone of 29,323 across the country as of August 31st, 2022. Despite the enforcement of stringent COVID-19 measures, the country witnessed an accelerated diffusion of coronavirus cases during the national events, inclusive of short festivals, in 2020. The present study aims to examine the association between these national holidays and the COVID-19 trasmission rate in Bangladesh. We employed a mathematical model and calculated the instantaneous reproduction number, Rt, of the 64 districts in Bangladesh to check the dynamics of COVID-19 diffusion. The comprehensive analysis shows a notable escalation of Rt value and thus the enhanced transmission rate in Dhaka and in all industrialized cities during the major events such as, garments reopening and religious holidays in Bangladesh. We further showcase the COVID-19 diffusion explicitly in Dhaka Division at the first phase of the pandemic in Bangladesh. Based on our analysis, a set of measures, including restricted public mobility and the celebration of festivals, alongside improving the public’s awareness of the situation, has been recommended to evade the future pandemic risks while running the national festival activities in Bangladesh.

Analysis of radiation diffusion of COVID-19 driven by social attributes

This paper first estimated the infectious capacity of COVID-19 based on the time series evolution data of confirmed cases in multiple countries. Then, a method to infer the cross-regional spread speed of COVID-19 was introduced in this paper, which took the gross domestic product (GDP) of each region as one of the factors that affect the spread speed of COVID-19 and studied the relationship between the GDP and the infection density of each region (mainland China, the United States, and EU countries). In addition, the geographic distance between regions was also considered in this method and the effect of geographic distance on the spread speed of COVID-19 was studied. Studies have shown that the probability of mutual infection of these two regions decreases with increasing geographic distance. Therefore, this paper proposed an epidemic disease spread index based on GDP and geographic distance to quantify the spread speed of COVID-19 in a region. The analysis results showed a strong correlation between the epidemic disease spread index in a region and the number of confirmed cases. This finding provides reasonable suggestions for the control of epidemics. Strengthening the control measures in regions with higher epidemic disease spread index can effectively control the spread of epidemics.

The impact of COVID-19 on the European short-term rental market.

The diffusion of COVID-19 and related containment measures practically halted tourism flows, which in many countries generate a significant share of GDP. By leveraging Airbnb data covering the main touristic destinations in Europe, we investigate the impact of the pandemic on the market for short-term rentals up to early 2021. We find that the epidemic reduced dramatically both the supply of apartments available for rents and customers' demand, with travellers cancelling many reservations and reducing new bookings even many months in advance. Accommodations relying relatively more on foreign tourism were the hardest hit and reacted by cutting prices. By exploiting the different timing of the epidemic spread across Europe and the timelines and intensity of the measures adopted to contain it, we show that government mandated restrictions had a stronger impact than the spread of the infection itself. All in all, our results point to a persistent and heterogeneous impact of COVID-19 on the accommodation industry.

Modelling the roles of visitor flows and returning migrants in the spatial diffusion of COVID-19 from Wuhan city in China

COVID-19 has spread to many cities and countries in the world since the major outbreak in Wuhan city in later 2019. Population flow is the main channel of COVID-19 transmission between different cities and countries. This study recognizes that the flows of different population groups such as visitors and migrants returning to hometown are different in nature due to different length of stay and exposure to infection risks, contributing to the spatial diffusion of COVID-19 differently. To model population flows and the spatial diffusion of COVID-19 more accurately, a population group based SEIR (susceptible-exposed-infectious-recovered) metapopulation model is developed consisting of 32 regions including Wuhan, the rest of Hubei and other 30 provinces in Mainland China. The paper found that, in terms of the total export, Wuhan residents as visitors and Wuhan migrants returned to hometown were the first and second largest contributors in the simulation period. In terms of the net export, Wuhan migrants returned to hometown were the largest contributor, followed by Wuhan residents as visitors.

A simple planning problem for COVID-19 lockdown: a dynamic programming approach

A large number of recent studies consider a compartmental SIR model to study optimal control policies aimed at containing the diffusion of COVID-19 while minimizing the economic costs of preventive measures. Such problems are non-convex and standard results need not to hold. We use a Dynamic Programming approach and prove some continuity properties of the value function of the associated optimization problem. We study the corresponding Hamilton–Jacobi–Bellman equation and show that the value function solves it in the viscosity sense. Finally, we discuss some optimality conditions. Our paper represents a first contribution towards a complete analysis of non-convex dynamic optimization problems, within a Dynamic Programming approach.

The diffusion of COVID-19 across Italian provinces: a spatial dynamic panel data approach with common factors

ABSTRACT This study adopts a spatial dynamic panel data model with common factors and a connectivity matrix based on cross-province population flows to help explain the spread of COVID-19 infections across Italian provinces during the period 2020–21. We find that an increase in the infections in a province has a positive and statistically significant effect on neighbours’ infections, which highlights the relevance of spatial spillover effects. This finding is robust to several robustness checks. Furthermore, we investigate cross-provincial transmission heterogeneity using a heterogeneous spatial dynamic panel, which provides novel insights into the diffusion patterns of the disease.

Global border restrictions in 2020-2021: Adherence and the effectiveness in long-term COVID-19 epidemic control.

Restrictions on international travel were widely applied to contain cross-border COVID-19 diffusion, while such applications varied globally, and little was known about their impacts on the long-term epidemic progression. We explored the global diversity in maintaining border policies classified to four levels (screening, quarantine, ban on regions and total border closure) using data of 185 countries and regions between 01 January 2020 to 31 December 2021. By using Ordinary least squares (OLS) regression and quantile regression (QR) models, we examined the relationship between total COVID-19 incidence and the cumulative duration of each policy level in 2020-2021, and the heterogeneity of such association across different transmission severity countries. Firstly, "ban on regions" was the most durable policy applied in high-income countries, while in low-income countries, less stringent measures of screening and quarantine arrivals were applied the longest. Secondly, the cumulatively longer maintenance of the border quarantine was significantly associated with lower infections (log) in COVID-19 high-prevalent countries (75th QR, coefficient estimates [β]=-0.0038, 95% confidence interval: -0.0066 to -0.0010). By contrast, in medium and high transmission severity countries, those with longer duration of imposing bans on regions showed no suppressing effects but significantly higher COVID-19 incidence (OLS regression, β=0.0028, 95% CI: 0.0009-0.0047; 75th QR, β=0.0039, 95% CI: 0.0014-0.0063). No other significant results were found. From the long-term perspective, inbound quarantine was effective in mitigating severe epidemics. However, in countries with medium or high COVID-19 prevalence, our findings of ban on regions highlighted its ineffectiveness in the long-term epidemic progression.

Opening of hotels and ski facilities: Impact on mobility, spending, and Covid-19 outcomes.

This paper investigates how reopening hotels and ski facilities in Poland impacted tourism spending, mobility, and COVID-19 outcomes. We used administrative data from a government program that subsidizes travel to show that the policy increased the consumption of tourism services in ski resorts. By leveraging geolocation data from Facebook, we showed that ski resorts experienced a significant influx of tourists, increasing the number of local users by up to 50%. Furthermore, we confirmed an increase in the probability of meetings between pairs of users from distanced locations and users from tourist and non-tourist areas. As the policy impacted travel and gatherings, we then analyzed its effect on the diffusion of COVID-19. We found that counties with ski facilities experienced more infections after the reopening. Moreover, counties strongly connected to the ski resorts during the reopening had more subsequent cases than weakly connected counties.