A novel methodology for epidemic risk assessment of COVID-19 outbreak

Spatial spread of COVID-19 during the early pandemic phase in Italy

Quantifying the potential spatial spread of an infectious pathogen is key to defining effective containment and control strategies. The aim of this study is to estimate the risk of SARS-CoV-2 transmission at different distances in Italy before the first regional lockdown was imposed, identifying important sources of national spreading. To do this, we leverage on a probabilistic model applied to daily symptomatic cases retrospectively ascertained in each Italian municipality with symptom onset between January 28 and March 7, 2020. Results are validated using a multi-patch dynamic transmission model reproducing the spatiotemporal distribution of identified cases. Our results show that the contribution of short-distance ( ≤ 10 k m ) transmission increased from less than 40% in the last week of January to more than 80% in the first week of March 2020. On March 7, 2020, that is the day before the first regional lockdown was imposed, more than 200 local transmission foci were contributing to the spread of SARS-CoV-2 in Italy. At the time, isolation measures imposed only on municipalities with at least ten ascertained cases would have left uncontrolled more than 75% of spillover transmission from the already affected municipalities. In early March, national-wide restrictions were required to curb short-distance transmission of SARS-CoV-2 in Italy.

COVID-19 Pandemic Risk Assessment: Systematic Review

Background

The COVID-19 pandemic presents the possibility of future large-scale infectious disease outbreaks. In response, we conducted a systematic review of COVID-19 pandemic risk assessment to provide insights into countries' pandemic surveillance and preparedness for potential pandemic events in the post-COVID-19 era.

Objective

We aim to systematically identify relevant articles and synthesize pandemic risk assessment findings to facilitate government officials and public health experts in crisis planning.

Methods

This study followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and included over 620,000 records from the World Health Organization COVID-19 Research Database. Articles related to pandemic risk assessment were identified based on a set of inclusion and exclusion criteria. Relevant articles were characterized based on study location, variable types, data-visualization techniques, research objectives, and methodologies. Findings were presented using tables and charts.

Results

Sixty-two articles satisfying both the inclusion and exclusion criteria were identified. Among the articles, 32.3% focused on local areas, while another 32.3% had a global coverage. Epidemic data were the most commonly used variables (74.2% of articles), with over half of them (51.6%) employing two or more variable types. The research objectives covered various aspects of the COVID-19 pandemic, with risk exposure assessment and identification of risk factors being the most common theme (35.5%). No dominant research methodology for risk assessment emerged from these articles.

Conclusion

Our synthesized findings support proactive planning and development of prevention and control measures in anticipation of future public health threats.

Sustainable planning in Wuhan City during COVID-19: an analysis of influential factors, risk profiles, and clustered patterns

The outbreak of novel coronavirus pneumonia (COVID-19) is closely related to the intra-urban environment. It is important to understand the influence mechanism and risk characteristics of urban environment on infectious diseases from the perspective of urban environment composition. In this study, we used python to collect Sina Weibo help data as well as urban multivariate big data, and The random forest model was used to measure the contribution of each influential factor within to the COVID-19 outbreak. A comprehensive risk evaluation system from the perspective of urban environment was constructed, and the entropy weighting method was used to produce the weights of various types of risks, generate the specific values of the four types of risks, and obtain the four levels of comprehensive risk zones through the K-MEANS clustering of Wuhan's central urban area for zoning planning. Based on the results, we found: ①the five most significant indicators contributing to the risk of the Wuhan COVID-19 outbreak were Road Network Density, Shopping Mall Density, Public Transport Density, Educational Facility Density, Bank Density. Floor Area Ration, Poi Functional Mix ②After streamlining five indicators such as Proportion of Aged Population, Tertiary Hospital Density, Open Space Density, Night-time Light Intensity, Number of Beds Available in Designated Hospitals, the prediction accuracy of the random forest model was the highest. ③The spatial characteristics of the four categories of new crown epidemic risk, namely transmission risk, exposure risk, susceptibility risk and Risk of Scarcity of Medical Resources, were highly differentiated, and a four-level integrated risk zone was obtained by K-MEANS clustering. Its distribution pattern was in the form of "multicenter-periphery" gradient diffusion. For the risk composition of the four-level comprehensive zones combined with the internal characteristics of the urban environment in specific zones to develop differentiated control strategies. Targeted policies were then devised for each partition, offering a practical advantage over singular COVID-19 impact factor analyses. This methodology, beneficial for future public health crises, enables the swift identification of unique risk profiles in different partitions, streamlining the formulation of precise policies. The overarching goal is to maintain regular social development, harmonizing preventive measures and economic efforts.

A rapid review of best practices in the development of risk registers for public health emergency management

Introduction

Public health organizations (PHO) must prepare to respond to a range of emergencies. This represents an ongoing challenge in an increasingly connected world, where the scope, complexity, and diversity of public health threats (PHT) have expanded, as exemplified by the COVID-19 pandemic. Risk registers (RR) offer a framework for identifying and managing threats, which can be employed by PHOs to better identify and characterize health threats. The aim of this review is to establish best practices (BP) for the development of RRs within Public Health Emergency Management (PHEM).

Methods

In partnership with a librarian from Health Canada (HC), and guided by the Cochrane Rapid Review Guideline, journal articles were retrieved through MEDLINE, and a comprehensive search strategy was applied to obtain grey literature through various databases. Articles were limited to those that met the following criteria: published on or after January 1, 2010, published in the English language and published within an Organisation for Economic Co-operation and Development setting.

Results

57 articles were included for synthesis. 41 papers specifically discussed the design of RRs. The review identified several guidelines to establish RRs in PHEM, including forward-looking, multidisciplinary, transparent, fit-for-purpose, and utilizing a systems approach to analyze and prioritize threats. Expert consultations, literature reviews, and prioritization methods such as multi-criteria-decision-analysis (MCDA) are often used to support the development of RRs. A minimum five-year-outlook is applied to assess PHTs, which are revisited yearly, and iteratively revised as new knowledge arises.

Discussion

Based upon this review, RRs offer a systems approach to PHEM that can be expanded to facilitate the analysis of disparate threats. These approaches should factor in the multidimensionality of threats, need for multi-sectoral inputs, and use of vulnerability analyses that consider inherent drivers. Further research is needed to understand how drivers modify threats. The BPs and recommendations highlighted in our research can be adopted in the practice of PHEM to characterize the public health (PH) risk environment at a given point in time and support PHOs policy and decision-making.

Exogenous Chemicals Impact Virus Receptor Gene Transcription: Insights from Deep Learning

Despite the fact that coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been disrupting human life and health worldwide since the outbreak in late 2019, the impact of exogenous substance exposure on the viral infection remains unclear. It is well-known that, during viral infection, organism receptors play a significant role in mediating the entry of viruses to enter host cells. A major receptor of SARS-CoV-2 is the angiotensin-converting enzyme 2 (ACE2). This study proposes a deep learning model based on the graph convolutional network (GCN) that enables, for the first time, the prediction of exogenous substances that affect the transcriptional expression of the ACE2 gene. It outperforms other machine learning models, achieving an area under receiver operating characteristic curve (AUROC) of 0.712 and 0.703 on the validation and internal test set, respectively. In addition, quantitative polymerase chain reaction (qPCR) experiments provided additional supporting evidence for indoor air pollutants identified by the GCN model. More broadly, the proposed methodology can be applied to predict the effect of environmental chemicals on the gene transcription of other virus receptors as well. In contrast to typical deep learning models that are of black box nature, we further highlight the interpretability of the proposed GCN model and how it facilitates deeper understanding of gene change at the structural level.

Simulating exposure-related human mobility behavior at the neighborhood-level under COVID-19 in Porto Alegre, Brazil

Modeling experts have been continually researching the interplay of human mobility and COVID-19 transmission since the outbreak of the pandemic. They tried to address this problem and support the control of the pandemic spreading at the national or regional levels. However, these modeling approaches had little success in producing empirically verifiable results at the neighborhood level due to a lack of data and limited representation of low spatial scales in the models. To fill this gap, this research aims to present an agent-based model to simulate human mobility choices in the context of COVID-19, based on social activities of individuals in the neighborhood. We apply the VIABLE model to the decision-making process of heterogeneous agents, who populate the system's environment. The agents adapt their mobility and activities autonomously at each iteration to improve their well-being and respond to exposure risks. The study reveals significant temporal variations in mobility choices between the groups of agents with different vulnerability levels under the Covid-19 pandemic. Agents from the same group with similar economic backgrounds tend to select the same mobility patterns and activities leading to segregation at this low scale. We calibrated the model with a focus on Porto Alegre in Brazil.

Nexus between drivers of COVID-19 and country risks

COVID-19 has disrupted all spheres of life, including country risk regarding the exposure of economies to multi-dimensional risk drivers. However, it remains unexplored how COVID-19 has impacted different drivers of country risk in a probabilistic network setting. This paper uses two datasets on country-level COVID-19 and country risks to explore dependencies among associated drivers using a Bayesian Belief Network model. The drivers of COVID-19 risk, considered in this paper, are hazard and exposure, vulnerability and lack of coping capacity, whereas country risk drivers are economic, financing, political, business environment and commercial risks. The results show that business environment risk is significantly influenced by COVID-19 risk, whereas commercial risk (demand disruptions) is the least important factor driving COVID-19 and country risks. Further, country risk is mainly influenced by financing, political and economic risks. The contribution of this study is to explore the impact of various drivers associated with the country-level COVID-19 and country risks in a unified probabilistic network setting, which can help policy-makers prioritize drivers for managing the two risks.

Anti-social behaviour and economic decision-making: Panel experimental evidence in the wake of COVID-19

We systematically examine the acute impact of exposure to a public health crisis on anti-social behaviour and economic decision-making using unique experimental panel data from China, collected just before the outbreak of COVID-19 and immediately after the first wave was overcome. Exploiting plausibly exogenous geographical variation in virus exposure coupled with a dataset of longitudinal experiments, we show that participants who were more intensely exposed to the virus outbreak became more anti-social than those with lower exposure, while other aspects of economic and social preferences remain largely stable. The finding is robust to multiple hypothesis testing and a similar, yet less pronounced pattern emerges when using alternative measures of virus exposure, reflecting societal concern and sentiment, constructed using social media data. The anti-social response is particularly pronounced for individuals who experienced an increase in depression or negative affect, which highlights the important role of psychological health as a potential mechanism through which the virus outbreak affected behaviour.

Population genetic models for the spatial spread of adaptive variants: A review in light of SARS-CoV-2 evolution

Theoretical population genetics has long studied the arrival and geographic spread of adaptive variants through the analysis of mathematical models of dispersal and natural selection. These models take on a renewed interest in the context of the COVID-19 pandemic, especially given the consequences that novel adaptive variants have had on the course of the pandemic as they have spread through global populations. Here, we review theoretical models for the spatial spread of adaptive variants and identify areas to be improved in future work, toward a better understanding of variants of concern in Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) evolution and other contemporary applications. As we describe, characteristics of pandemics such as COVID-19-such as the impact of long-distance travel patterns and the overdispersion of lineages due to superspreading events-suggest new directions for improving upon existing population genetic models.

Assessment of community vulnerability during the COVID-19 pandemic: Hong Kong as a case study

The COVID-19 pandemic continues to threaten global public health. Reliable assessment of community vulnerability is therefore essential to fighting and mitigating the pandemic. This study presents a framework that considers the roles of internal and external factors, including the components of social vulnerability, exposure, and sensitivity, to comprehensively and accurately assess community vulnerability to the pandemic. With respect to internal factors, we summarized the inherent social characteristics of people groups using census data and explored the roles of both overall and four major thematic social vulnerabilities in shaping community infection by COVID-19. We then designed two external factors to characterize exposure and sensitivity and implemented an aggregation by multiplying them with the internal social vulnerability to achieve a comprehensive vulnerability assessment. The role of the estimated vulnerability in shaping community infection was evaluated by statistical and spatial analysis as well as by risk factor classification using defined rules. This case study of Hong Kong demonstrated the value of our framework in vulnerability assessment and revealed the role of vulnerability in shaping community infection by COVID-19.

Connected in health: Place-to-place commuting networks and COVID-19 spillovers

Biweekly county COVID-19 data were linked with Longitudinal Employer-Household Dynamics data to analyze population risk exposures enabled by pre-pandemic, country-wide commuter networks. Results from fixed-effects, spatial, and computational statistical approaches showed that commuting network exposure to COVID-19 predicted an area's COVID-19 cases and deaths, indicating spillovers. Commuting spillovers between counties were independent from geographic contiguity, pandemic-time mobility, or social media ties. Results suggest that commuting connections form enduring social linkages with effects on health that can withstand mobility disruptions. Findings contribute to a growing relational view of health and place, with implications for neighborhood effects research and place-based policies.

Pandemic policy assessment by artificial intelligence

Mobility-control policy is a controversial nonpharmacological approach to pandemic control due to its restriction on people's liberty and economic impacts. Due to the computational complexity of mobility control, it is challenging to assess or compare alternative policies. Here, we develop a pandemic policy assessment system that employs artificial intelligence (AI) to evaluate and analyze mobility-control policies. The system includes three components: (1) a general simulation framework that models different policies to comparable network-flow control problems; (2) a reinforcement-learning (RL) oracle to explore the upper-bound execution results of policies; and (3) comprehensive protocols for converting the RL results to policy-assessment measures, including execution complexity, effectiveness, cost and benefit, and risk. We applied the system to real-world metropolitan data and evaluated three popular policies: city lockdown, community quarantine, and route management. For each policy, we generated mobility-pandemic trade-off frontiers. The results manifest that the smartest policies, such as route management, have high execution complexity but limited additional gain from mobility retention. In contrast, a moderate-level intelligent policy such as community quarantine has acceptable execution complexity but can effectively suppress infections and largely mitigate mobility interventions. The frontiers also show one or two turning points, reflecting the safe threshold of mobility retention when considering policy-execution errors. In addition, we simulated different policy environments and found inspirations for the current policy debates on the zero-COVID policy, vaccination policy, and relaxing restrictions.

Were anxiety, depression and psychological distress associated with local mortality rates during COVID-19 outbreak in Italy? Findings from the COMET study

BACKGROUND

The mental health of the Italian population declined at the onset of the COVID-19 pandemic. However, nationwide population prevalence estimates may not effectively reproduce the heterogeneity in distress responses to the pandemic. In particular, contextual determinants specific to COVID-19 pandemic need to be considered. We thus aimed to explore the association between local COVID-19 mortality rates and mental health response among the general population.

METHODS

We capitalised on data (N = 17,628) from a large, cross-sectional, national survey, the COMET study, run between March and May 2020. While psychological distress was measured by General Health Questionnaire-12 (GHQ-12), the Depression, Anxiety and Stress Scale-21 Items (DASS-21) was used to assess relevant domains. In addition, a Covid-19 mortality ratio was built to compare single regional mortality rates to the national estimate and official statistics were used to control for other area-level determinants.

RESULTS

Adjusted ordered regression analyses showed an association between mortality ratio and moderate (OR = 1.10, 95%CI 1.03-1.18) and severe (OR = 1.11, 95%CI 1.03-1.21) DASS-21 anxiety levels. No effects of mortality ratio on GHQ-12 scores and DASS-21 depression and stress levels, uniformly high across the country, were estimated.

CONCLUSIONS

Although we could not find any association between regional COVID-19 mortality ratio and depression or psychological distress, anxiety levels were significantly increased among subjects from areas with the highest mortality rates. Local mortality rate seems a meaningful driver for anxiety among the general population. Considering the potentially long-lasting scenario, local public health authorities should provide neighbouring communities with preventive interventions reducing psychological isolation and anxiety levels.

Are We Sure We Fully Understand What an Infodemic Is? A Global Perspective on Infodemiological Problems

Infodemic is defined as an information epidemic that can lead to engaging in dangerous behavior. Although the most striking manifestations of the latter occurred on social media, some studies show that dismisinformation is significantly influenced by numerous additional factors, both web-based and offline. These include social context, age, education, personal knowledge and beliefs, mood, psychological defense mechanisms, media resonance, and how news and information are presented to the public. Moreover, various incorrect scientific practices related to disclosure, publication, and training can also fuel such a phenomenon. Therefore, in this opinion article, we seek to provide a comprehensive overview of the issues that need to be addressed to bridge the gap between science and the public and build resilience to the infodemic. In particular, we stress that the infodemic cannot be curbed by simply disproving every single false or misleading information since the belief system and the cultural or educational background are chief factors regarding the success of fake news. For this reason, we believe that the process of forming a critical sense should begin with children in schools (ie, when the mind is more receptive to new ways of learning). Furthermore, we also believe that themes such as scientific method and evidence should be at the heart of the university education of a future scientist. Indeed, both the public and scientists must be educated on the concepts of evidence and validity of sources, as well as learning how to dialogue appropriately with each other. Finally, we believe that the scientific publishing process could be greatly improved by paying reviewers for their work and by ceasing to pursue academic success at all costs.

An analysis of the dynamic spatial spread of COVID-19 across South Korea

The first case of coronavirus disease 2019 (COVID-19) in South Korea was confirmed on January 20, 2020, approximately three weeks after the report of the first COVID-19 case in Wuhan, China. By September 15, 2021, the number of cases in South Korea had increased to 277,989. Thus, it is important to better understand geographical transmission and design effective local-level pandemic plans across the country over the long term. We conducted a spatiotemporal analysis of weekly COVID-19 cases in South Korea from February 1, 2020, to May 30, 2021, in each administrative region. For the spatial domain, we first covered the entire country and then focused on metropolitan areas, including Seoul, Gyeonggi-do, and Incheon. Moran's I and spatial scan statistics were used for spatial analysis. The temporal variation and dynamics of COVID-19 cases were investigated with various statistical visualization methods. We found time-varying clusters of COVID-19 in South Korea using a range of statistical methods. In the early stage, the spatial hotspots were focused in Daegu and Gyeongsangbuk-do. Then, metropolitan areas were detected as hotspots in December 2020. In our study, we conducted a time-varying spatial analysis of COVID-19 across the entirety of South Korea over a long-term period and found a powerful approach to demonstrating the current dynamics of spatial clustering and understanding the dynamic effects of policies on COVID-19 across South Korea. Additionally, the proposed spatiotemporal methods are very useful for understanding the spatial dynamics of COVID-19 in South Korea.

Overlapping Delta and Omicron Outbreaks During the COVID-19 Pandemic: Dynamic Panel Data Estimates

BACKGROUND

The Omicron variant of SARS-CoV-2 is more transmissible than prior variants of concern (VOCs). It has caused the largest outbreaks in the pandemic, with increases in mortality and hospitalizations. Early data on the spread of Omicron were captured in countries with relatively low case counts, so it was unclear how the arrival of Omicron would impact the trajectory of the pandemic in countries already experiencing high levels of community transmission of Delta.

OBJECTIVE

The objective of this study is to quantify and explain the impact of Omicron on pandemic trajectories and how they differ between countries that were or were not in a Delta outbreak at the time Omicron occurred.

METHODS

We used SARS-CoV-2 surveillance and genetic sequence data to classify countries into 2 groups: those that were in a Delta outbreak (defined by at least 10 novel daily transmissions per 100,000 population) when Omicron was first sequenced in the country and those that were not. We used trend analysis, survival curves, and dynamic panel regression models to compare outbreaks in the 2 groups over the period from November 1, 2021, to February 11, 2022. We summarized the outbreaks in terms of their peak rate of SARS-CoV-2 infections and the duration of time the outbreaks took to reach the peak rate.

RESULTS

Countries that were already in an outbreak with predominantly Delta lineages when Omicron arrived took longer to reach their peak rate and saw greater than a twofold increase (2.04) in the average apex of the Omicron outbreak compared to countries that were not yet in an outbreak.

CONCLUSIONS

These results suggest that high community transmission of Delta at the time of the first detection of Omicron was not protective, but rather preluded larger outbreaks in those countries. Outbreak status may reflect a generally susceptible population, due to overlapping factors, including climate, policy, and individual behavior. In the absence of strong mitigation measures, arrival of a new, more transmissible variant in these countries is therefore more likely to lead to larger outbreaks. Alternately, countries with enhanced surveillance programs and incentives may be more likely to both exist in an outbreak status and detect more cases during an outbreak, resulting in a spurious relationship. Either way, these data argue against herd immunity mitigating future outbreaks with variants that have undergone significant antigenic shifts.

Exploring the impact of mobility restrictions on the COVID-19 spreading through an agent-based approach

BACKGROUND

The recent health emergency caused by the COVID-19 pandemic forced people to change their mobility habits, with the reduction of non-essential travels and the promotion online activities. During the first phase of the emergency in 2020, governments considered several mobility restrictions to avoid the pandemic diffusion. However, it is difficult to quantify the actual effects of these restrictions on the virus spreading, especially due to the biased data available. Notwithstanding the big role of data analysis to understand the pandemic phenomenon, it is also important to have more general models capable of predicting the impact of different policy scenarios, including territorial parameters, independently from the available infection data. In this respect, this paper proposes an agent-based model to simulate the impact of mobility restrictions on the spreading of the COVID-19 at a large scale level, by considering different factors that can be attributed to the diffusion and lethality of the virus and population mobility patterns.

METHODS

The first step of the method includes a zonation of the study area, according to administrative boundaries. A risk index is calculated for each zone considering indicators which can influence the virus spreading and people lethality: mean winter temperature, housing concentration, healthcare density, population mobility, air pollution and the percentage of population over 60 years old. The agent-based model associates the risk index to the agents and determines their "status" ("susceptible", "infected", "isolated", "recovered" or "dead") by combining the risk index with the mean infection duration, using a SIR-based approach (i.e. susceptible-infective-removed).

RESULTS

The study is applied to Italy. Several scenarios based on different mobility restrictions have been simulated, including the one based on the official data (status quo). The main results show that characterizing zones with a risk index allows to adopt local policies with almost the same effectiveness as in the case of restrictions extended to the full study area; scenario simulations return an increase in terms of infected (+20%) and deaths (+25%) with respect to the status quo. These results underline the importance of finding a trade-off between socio-economic benefits and health impact.

CONCLUSIONS

The reproducibility of the proposed methodology and its scalability allow to apply it to different contexts and at a different administrative level, from the urban scale to a national one. Moreover, the model is able to provide a decision-support tool for the design of strategic plans to contrast pandemics based on respiratory diseases.

COVID-19 transmission risk factors

We analyze risk factors correlated with the initial transmission growth rate of the recent COVID-19 pandemic in different countries. The number of cases follows in its early stages an almost exponential expansion; we chose as a starting point in each country the first day di with 30 cases and we fitted for 12 days, capturing thus the early exponential growth. We looked then for linear correlations of the exponents α with other variables, for a sample of 126 countries. We find a positive correlation, i.e. faster spread of COVID-19, with high confidence level with the following variables, with respective p-value: low Temperature (4⋅10-7), high ratio of old vs. working-age people (3⋅10-6), life expectancy (8⋅10-6), number of international tourists (1⋅10-5), earlier epidemic starting date di (2⋅10-5), high level of physical contact in greeting habits (6⋅10-5), lung cancer prevalence (6⋅10-5), obesity in males (1⋅10-4), share of population in urban areas (2⋅10-4), cancer prevalence (3⋅10-4), alcohol consumption (0.0019), daily smoking prevalence (0.0036), and UV index (0.004, 73 countries). We also find a correlation with low Vitamin D serum levels (0.002-0.006), but on a smaller sample, ∼50 countries, to be confirmed on a larger sample. There is highly significant correlation also with blood types: positive correlation with types RH- (3⋅10-5) and A+ (3⋅10-3), negative correlation with B+ (2⋅10-4). We also find positive correlation with moderate confidence level (p-value of 0.02∼0.03) with: CO₂/SO emissions, type-1 diabetes in children, low vaccination coverage for Tuberculosis (BCG). Several of the above variables are correlated with each other, and so they are likely to have common interpretations. We thus performed a Principal Component Analysis, to find the significant independent linear combinations of such variables. The variables with loadings of at least 0.3 on the significant PCA are: greeting habits, urbanization, epidemic starting date, number of international tourists, temperature, lung cancer, smoking, and obesity in males. We also analyzed the possible existence of a bias: countries with low GDP-per capita might have less intense testing, and we discuss correlation with the above variables.

COVID-19 transmission risk factors

We analyze risk factors correlated with the initial transmission growth rate of the recent COVID-19 pandemic in different countries. The number of cases follows in its early stages an almost exponential expansion; we chose as a starting point in each country the first day d i with 30 cases and we fitted for 12 days, capturing thus the early exponential growth. We looked then for linear correlations of the exponents α with other variables, for a sample of 126 countries. We find a positive correlation, i.e. faster spread of COVID-19, with high confidence level with the following variables, with respective p -value: low Temperature (4 ⋅ 10 - 7 ), high ratio of old vs. working-age people (3 ⋅ 10 - 6 ), life expectancy (8 ⋅ 10 - 6 ), number of international tourists (1 ⋅ 10 - 5 ), earlier epidemic starting date d i (2 ⋅ 10 - 5 ), high level of physical contact in greeting habits (6 ⋅ 10 - 5 ), lung cancer prevalence (6 ⋅ 10 - 5 ), obesity in males (1 ⋅ 10 - 4 ), share of population in urban areas (2 ⋅ 10 - 4 ), cancer prevalence (3 ⋅ 10 - 4 ), alcohol consumption (0.0019 ), daily smoking prevalence (0.0036 ), and UV index (0.004 , 73 countries). We also find a correlation with low Vitamin D serum levels (0.002 - 0.006 ), but on a smaller sample, ∼ 50 countries, to be confirmed on a larger sample. There is highly significant correlation also with blood types: positive correlation with types RH- (3 ⋅ 10 - 5 ) and A+ (3 ⋅ 10 - 3 ), negative correlation with B+ (2 ⋅ 10 - 4 ). We also find positive correlation with moderate confidence level (p -value of 0.02 ∼ 0.03 ) with: CO2/SO emissions, type-1 diabetes in children, low vaccination coverage for Tuberculosis (BCG). Several of the above variables are correlated with each other, and so they are likely to have common interpretations. We thus performed a Principal Component Analysis, to find the significant independent linear combinations of such variables. The variables with loadings of at least 0.3 on the significant PCA are: greeting habits, urbanization, epidemic starting date, number of international tourists, temperature, lung cancer, smoking, and obesity in males. We also analyzed the possible existence of a bias: countries with low GDP-per capita might have less intense testing, and we discuss correlation with the above variables.

The Impact of COVID-19 on Mortality in Italy: Retrospective Analysis of Epidemiological Trends

BACKGROUND

Despite the available evidence on its severity, COVID-19 has often been compared with seasonal flu by some conspirators and even scientists. Various public discussions arose about the noncausal correlation between COVID-19 and the observed deaths during the pandemic period in Italy.

OBJECTIVE

This paper aimed to search for endogenous reasons for the mortality increase recorded in Italy during 2020 to test this controversial hypothesis. Furthermore, we provide a framework for epidemiological analyses of time series.

METHODS

We analyzed deaths by age, sex, region, and cause of death in Italy from 2011 to 2019. Ordinary least squares (OLS) linear regression analyses and autoregressive integrated moving average (ARIMA) were used to predict the best value for 2020. A Grubbs 1-sided test was used to assess the significance of the difference between predicted and observed 2020 deaths/mortality. Finally, a 1-sample t test was used to compare the population of regional excess deaths to a null mean. The relationship between mortality and predictive variables was assessed using OLS multiple regression models. Since there is no uniform opinion on multicomparison adjustment and false negatives imply great epidemiological risk, the less-conservative Siegel approach and more-conservative Holm-Bonferroni approach were employed. By doing so, we provided the reader with the means to carry out an independent analysis.

RESULTS

Both ARIMA and OLS linear regression models predicted the number of deaths in Italy during 2020 to be between 640,000 and 660,000 (range of 95% CIs: 620,000-695,000) against the observed value of above 750,000. We found strong evidence supporting that the death increase in all regions (average excess=12.2%) was not due to chance (t21=7.2; adjusted P<.001). Male and female national mortality excesses were 18.4% (P<.001; adjusted P=.006) and 14.1% (P=.005; adjusted P=.12), respectively. However, we found limited significance when comparing male and female mortality residuals' using the Mann-Whitney U test (P=.27; adjusted P=.99). Finally, mortality was strongly and positively correlated with latitude (R=0.82; adjusted P<.001). In this regard, the significance of the mortality increases during 2020 varied greatly from region to region. Lombardy recorded the highest mortality increase (38% for men, adjusted P<.001; 31% for women, P<.001; adjusted P=.006).

CONCLUSIONS

Our findings support the absence of historical endogenous reasons capable of justifying the mortality increase observed in Italy during 2020. Together with the current knowledge on SARS-CoV-2, these results provide decisive evidence on the devastating impact of COVID-19. We suggest that this research be leveraged by government, health, and information authorities to furnish proof against conspiracy hypotheses that minimize COVID-19-related risks. Finally, given the marked concordance between ARIMA and OLS regression, we suggest that these models be exploited for public health surveillance. Specifically, meaningful information can be deduced by comparing predicted and observed epidemiological trends.

A new infodemiological approach through Google Trends: longitudinal analysis of COVID-19 scientific and infodemic names in Italy

The scientific community has classified COVID-19 as the worst pandemic in human history. The damage caused by the new disease was direct (e.g., deaths) and indirect (e.g., closure of economic activities). Within the latter category, we find infodemic phenomena such as the adoption of generic and stigmatizing names used to identify COVID-19 and the related novel coronavirus 2019 variants. These monikers have fostered the spread of health disinformation and misinformation and fomented racism and segregation towards the Chinese population. In this regard, we present a comprehensive infodemiological picture of Italy from the epidemic outbreak in December 2019 until September 2021. In particular, we propose a new procedure to examine in detail the web interest of users in scientific and infodemic monikers linked to the identification of COVID-19. To do this, we exploited the online tool Google Trends. Our findings reveal the widespread use of multiple COVID-19-related names not considered in the previous literature, as well as a persistent trend in the adoption of stigmatizing and generic terms. Inappropriate names for cataloging novel coronavirus 2019 variants of concern have even been adopted by national health agencies. Furthermore, we also showed that early denominations influenced user behavior for a long time and were difficult to replace. For these reasons, we suggest that the assignments of scientific names to new diseases are more timely and advise against mass media and international health authorities using terms linked to the geographical origin of the novel coronavirus 2019 variants.

Prioritizing Multidimensional Interdependent Factors Influencing COVID-19 Risk

COVID-19 has significantly affected various industries and domains worldwide. Since such pandemics are considered as rare events, risks associated with pandemics are generally managed through reactive approaches, which involve seeking more information about the severity of the pandemic over time and adopting suitable strategies accordingly. However, policy-makers at a national level must devise proactive strategies to minimize the harmful impacts of such pandemics. In this article, we use a country-level data-set related to humanitarian crises and disasters to explore critical factors influencing COVID-19 related hazard and exposure, vulnerability, lack of coping capacity, and the overall risk for individual countries. The main contribution is to establish the relative importance of multidimensional factors associated with COVID-19 risk in a probabilistic network setting. This study provides unique insights to policy-makers regarding the identification of critical factors influencing COVID-19 risk and their relative importance in a network setting.

Human mobility and infection from Covid-19 in the Osaka metropolitan area

Controlling human mobility is thought to be an effective measure to prevent the spread of the COVID-19 pandemic. This study aims to clarify the human mobility types that impacted the number of COVID-19 cases during the medium-term COVID-19 pandemic in the Osaka metropolitan area. The method used in this study was analysis of the statistical relationship between human mobility changes and the total number of COVID-19 cases after two weeks. In conclusion, the results indicate that it is essential to control the human mobility of groceries/pharmacies to between −5 and 5% and that of parks to more than −20%. The most significant finding for urban sustainability is that urban transit was not found to be a source of infection. Hence governments in cities around the world may be able to encourage communities to return to transit mobility, if they are able to follow the kind of hygiene processes conducted in Osaka.

The role of European health system characteristics in affecting Covid 19 lethality during the early days of the pandemic

This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic.

Exposure to PM2.5 and PM10 and COVID-19 Infection Rates and Mortality: a one-year observational study in Poland

Background

Atmospheric contamination, especially particulate matter (PM), can be associated viral infections connected with respiratory failure. Literature data indicates that intensity of SARS-CoV-2 infections worldwide can be associated with PM pollution levels.

Objectives

The aim of the study was to examine the relationship between atmospheric contamination, measured as PM2.5 and PM10 levels, and the number of COVID-19 cases and related deaths in Poland in a one-year observation study.

Methods

Number and geographical distribution of COVID-19 incidents and related deaths, as well as PM2.5 and PM10 exposure levels in Poland were obtained from publicly accessible databases. Average monthly values of these parameters for individual provinces were calculated. Multiple regression analysis was performed for the period between March 2020 and February 2021, taking into account average monthly exposure to PM2.5 and PM10, monthly COVID-19 incidence and mortality rates per 100,000 inhabitants and the population density across Polish provinces.

Results

Only December 2020 the number of new infections was significantly related to the three analyzed factors: PM2.5, population density and the number of laboratory COVID-19 tests (R² = 0.882). For COVID-19 mortality, a model with all three significant factors: PM10, population density and number of tests was obtained as significant only in November 2020 (R² = 0.468).

Conclusion

The distribution of COVID-19 incidents across Poland was independent from annual levels of particulate matter concentration in provinces. Exposure to PM2.5 and PM10 was associated with COVID-19 incidence and mortality in different provinces only in certain months. Other cofactors such as population density and the number of performed COVID-19 tests also corresponded with both COVID-19-related infections and deaths only in certain months. Particulate matter should not be treated as the sole determinant of the spread and severity of the COVID-19 pandemic but its importance in the incidence of infectious diseases should not be forgotten.

Analysis and monitoring of post-COVID mobility demand in Rome resulting from the adoption of sustainable mobility measures

The paper describes research activities of monitoring, modeling, and planning of people mobility in Rome during the Covid-19 epidemic period from March to June 2020. The results of data collection for different transport modes (walking, bicycle, car, and transit) are presented and analyzed. A specific focus is provided for the subway mass transit, where 1 m interpersonal distancing is required to prevent the risks for Covid-19 contagion together with the use of masks and gloves. A transport system model has been calibrated on the data collected during the lockdown period -when people's behavior significantly changed because of smart-working adoption and contagion fear- and was applied to predict future mobility scenarios under different assumptions on economic activities restarting. Based on the estimations of passenger loading, a timing policy that differentiates the opening hours of the shops depending on their commercial category was implemented, and an additional bus transit service was introduced to avoid incompatible loads of the subway lines with the required interpersonal distancing.

Functional data analysis characterizes the shapes of the first COVID-19 epidemic wave in Italy

We investigate patterns of COVID-19 mortality across 20 Italian regions and their association with mobility, positivity, and socio-demographic, infrastructural and environmental covariates. Notwithstanding limitations in accuracy and resolution of the data available from public sources, we pinpoint significant trends exploiting information in curves and shapes with Functional Data Analysis techniques. These depict two starkly different epidemics; an "exponential" one unfolding in Lombardia and the worst hit areas of the north, and a milder, "flat(tened)" one in the rest of the country-including Veneto, where cases appeared concurrently with Lombardia but aggressive testing was implemented early on. We find that mobility and positivity can predict COVID-19 mortality, also when controlling for relevant covariates. Among the latter, primary care appears to mitigate mortality, and contacts in hospitals, schools and workplaces to aggravate it. The techniques we describe could capture additional and potentially sharper signals if applied to richer data.

Impact of vaccine supplies and delays on optimal control of the COVID-19 pandemic: mapping interventions for the Philippines

BACKGROUND

Around the world, controlling the COVID-19 pandemic requires national coordination of multiple intervention strategies. As vaccinations are globally introduced into the repertoire of available interventions, it is important to consider how changes in the local supply of vaccines, including delays in administration, may be addressed through existing policy levers. This study aims to identify the optimal level of interventions for COVID-19 from 2021 to 2022 in the Philippines, which as a developing country is particularly vulnerable to shifting assumptions around vaccine availability. Furthermore, we explore optimal strategies in scenarios featuring delays in vaccine administration, expansions of vaccine supply, and limited combinations of interventions.

METHODS

Embedding our work within the local policy landscape, we apply optimal control theory to the compartmental model of COVID-19 used by the Philippine government's pandemic surveillance platform and introduce four controls: (a) precautionary measures like community quarantines, (b) detection of asymptomatic cases, (c) detection of symptomatic cases, and (d) vaccinations. The model is fitted to local data using an L-BFGS minimization procedure. Optimality conditions are identified using Pontryagin's minimum principle and numerically solved using the forward-backward sweep method.

RESULTS

Simulation results indicate that early and effective implementation of both precautionary measures and symptomatic case detection is vital for averting the most infections at an efficient cost, resulting in [Formula: see text] reduction of infections compared to the no-control scenario. Expanding vaccine administration capacity to 440,000 full immunizations daily will reduce the overall cost of optimal strategy by [Formula: see text], while allowing for a faster relaxation of more resource-intensive interventions. Furthermore, delays in vaccine administration require compensatory increases in the remaining policy levers to maintain a minimal number of infections. For example, delaying the vaccines by 180 days (6 months) will result in an [Formula: see text] increase in the cost of the optimal strategy.

CONCLUSION

We conclude with practical insights regarding policy priorities particularly attuned to the Philippine context, but also applicable more broadly in similar resource-constrained settings. We emphasize three key takeaways of (a) sustaining efficient case detection, isolation, and treatment strategies; (b) expanding not only vaccine supply but also the capacity to administer them, and; (c) timeliness and consistency in adopting policy measures.

Early Spread of COVID-19 in the Air-Polluted Regions of Eight Severely Affected Countries

COVID-19 escalated into a pandemic posing several humanitarian as well as scientific challenges. We here investigated the geographical character of the early spread of the infection and correlated it with several annual satellite and ground indexes of air quality in China, the United States, Italy, Iran, France, Spain, Germany, and the United Kingdom. The time of the analysis corresponded with the end of the first wave infection in China, namely June 2020. We found more viral infections in those areas afflicted by high PM 2.5 and nitrogen dioxide values. Higher mortality was also correlated with relatively poor air quality. In Italy, the correspondence between the Po Valley pollution and SARS-CoV-2 infections and induced mortality was the starkest, originating right in the most polluted European area. Spain and Germany did not present a noticeable gradient of pollution levels causing non-significant correlations. Densely populated areas were often hotspots of lower air quality levels but were not always correlated with a higher viral incidence. Air pollution has long been recognised as a high risk factor for several respiratory-related diseases and conditions, and it now appears to be a risk factor for COVID-19 as well. As such, air pollution should always be included as a factor for the study of airborne epidemics and further included in public health policies.

Analyzing the Risk to COVID-19 Infection using Remote Sensing and GIS

Globally, the COVID-19 pandemic has become a threat to humans and to the socioeconomic systems they have developed since the industrial revolution. Hence, governments and stakeholders call for strategies to help restore normalcy while dealing with this pandemic effectively. Since till now, the disease is yet to have a cure; therefore, only risk-based decision making can help governments achieve a sustainable solution in the long term. To help the decisionmakers explore viable actions, we propose a risk-based assessment framework for analyzing COVID-19 risk to areas, using integrated hazard and vulnerability components associated with this pandemic for effective risk mitigation. The study is carried on a region administrated by Jaipur municipal corporation (JMC), India. Based on the current understanding of this disease, we hypothesized different COVID-19 risk indices (C19Ri) of the wards of JMC such as proximity to hotspots, total population, population density, availability of clean water, and associated land use/land cover, are related with COVID-19 contagion and calculated them in a GIS-based multicriteria risk reduction method. The results showed disparateness in COVID-19 risk areas with a higher risk in north-eastern and south-eastern zone wards within the boundary of JMC. We proposed prioritizing wards under higher risk zones for intelligent decision making regarding COVID-19 risk reduction through appropriate management of resources-related policy consequences. This study aims to serve as a baseline study to be replicated in other parts of the country or world to eradicate the threat of COVID-19 effectively.

Particulate Matter Short-Term Exposition, Mobility Trips and COVID-19 Diffusion: A Correlation Analyses for the Italian Case Study at Urban Scale

The conjecture discussed in this paper was that the daily number of certified cases of COVID-19 is direct correlated to the average particular matter (PM) concentrations observed several days before when the contagions occurred (short-term effect), and this correlation is higher for areas with a higher average seasonal PM concentration, as a measure of prolonged exposure to a polluted environment (long-term effect). Furthermore, the correlations between the daily COVID-19 new cases and the mobility trips and those between the daily PM concentrations and mobility trips were also investigated. Correlation analyses were performed for the application case study consisting in 13 of the main Italian cities, through the national air quality and mobility monitoring systems. Data analyses showed that the mobility restrictions performed during the lockdown produced a significant improvement in air quality with an average PM concentrations reduction of about 15%, with maximum variations ranging between 25% and 42%. Estimation results showed a positive correlation (stronger for the more highly polluted cities) between the daily COVID-19 cases and both the daily PM concentrations and mobility trips measured about three weeks before, when probably the contagion occurred. The obtained results are original, and if confirmed in other studies, it would lay the groundwork for the definition of the main context variables which influenced the COVID-19 spread. The findings highlighted in this research also supported by the evidence in the literature and allow concluding that PM concentrations and mobility habits could be considered as potential early indicators of COVID-19 circulation in outdoor environments. However, the obtained results pose significant ethical questions about the proper urban and transportation planning; the most polluted cities have not only worst welfare for their citizens but, as highlighted in this research, could lead to a likely greater spread of current and future respiratory and/or pulmonary health emergencies. The lesson to be learned by this global pandemic will help planners to better preserve the air quality of our cities in the post-COVID-19 era.

Containment effort reduction and regrowth patterns of the Covid-19 spreading

In all countries the political decisions aim to achieve an almost stable configuration with a small number of new infected individuals per day due to Covid-19. When such a condition is reached, the containment effort is usually reduced in favor of a gradual reopening of the social life and of the various economical sectors. However, in this new phase, the infection spread restarts and, moreover, possible mutations of the virus give rise to a large specific growth rate of the infected people. Therefore, a quantitative analysis of the regrowth pattern is very useful. We discuss a macroscopic approach which, on the basis of the collected data in the first lockdown, after few days from the beginning of the new phase, outlines different scenarios of the Covid-19 diffusion for longer time. The purpose of this paper is a demonstration-of-concept: one takes simple growth models, considers the available data and shows how the future trend of the spread can be obtained. The method applies a time dependent carrying capacity, analogously to many macroscopic growth laws in biology, economics and population dynamics. The illustrative cases of France, Italy and United Kingdom are analyzed.

The Impact of COVID-19 on Conspiracy Hypotheses and Risk Perception in Italy: Infodemiological Survey Study Using Google Trends

BACKGROUND

COVID-19 has caused the worst international crisis since World War II. Italy was one of the countries most affected by both the pandemic and the related infodemic. The success of anti-COVID-19 strategies and future public health policies in Italy cannot separate itself from the containment of fake news and the divulgation of correct information.

OBJECTIVE

The aim of this paper was to analyze the impact of COVID-19 on web interest in conspiracy hypotheses and risk perception of Italian web users.

METHODS

Google Trends was used to monitor users' web interest in specific topics, such as conspiracy hypotheses, vaccine side effects, and pollution and climate change. The keywords adopted to represent these topics were mined from Bufale.net-an Italian website specializing in detecting online hoaxes-and Google Trends suggestions (ie, related topics and related queries). Relative search volumes (RSVs) of the time-lapse periods of 2016-2020 (pre-COVID-19) and 2020-2021 (post-COVID-19) were compared through percentage difference (∆%) and the Welch t test (t). When data series were not stationary, other ad hoc criteria were used. The trend slopes were assessed through Sen slope (SS). The significance thresholds have been indicatively set at P=.05 and t=1.9.

RESULTS

The COVID-19 pandemic drastically increased Italian netizens' interest in conspiracies (∆% ∈ [60, 288], t ∈ [6, 12]). Web interest in conspiracy-related queries across Italian regions increased and became more homogeneous compared to the pre-COVID-19 period (average RSV=80±2.8, t min=1.8, ∆min%=+12.4, min∆SD%=-25.8). In addition, a growing trend in web interest in the infodemic YouTube channel ByoBlu has been highlighted. Web interest in hoaxes has increased more than interest in antihoax services (t 1=11.3 vs t 2=4.5; Δ1%=+157.6 vs Δ2%=+84.7). Equivalently, web interest in vaccine side effects exceeded interest in pollution and climate change (SSvaccines=0.22, P<.001 vs SSpollution=0.05, P<.001; ∆%=+296.4). To date, a significant amount of fake news related to COVID-19 vaccines, unproven remedies, and origin has continued to circulate. In particular, the creation of SARS-CoV-2 in a Chinese laboratory constituted about 0.04% of the entire web interest in the pandemic.

CONCLUSIONS

COVID-19 has given a significant boost to web interest in conspiracy hypotheses and has made it more uniform across regions in Italy. The pandemic accelerated an already-growing trend in users' interest toward some fake news sources, including the 500,000-subscriber YouTube channel ByoBlu, which was removed from the platform by YouTube for disinformation in March 2021. The risk perception related to COVID-19 vaccines has been so distorted that vaccine side effect-related queries outweighed those relating to pollution and climate change, which are much more urgent issues. Moreover, a large amount of fake news has circulated about COVID-19 vaccines, remedies, and origin. Based on these findings, it is recommended that the Italian authorities implement more effective infoveillance systems, and that communication by the mass media be less sensationalistic and more consistent with the available scientific evidence. In this context, Google Trends can be used to monitor users' response to specific infodemiological countermeasures. Further research is needed to understand the psychological mechanisms that regulate risk perception.

The role of transport accessibility within the spread of the Coronavirus pandemic in Italy

The Covid-19 pandemic has caused an unprecedented global crisis and led to a huge number of deaths, economic hardship and the disruption of everyday life. Measures to restrict accessibility adopted by many countries were a swift yet effective response to contain the spread of the virus. Within this topic, this paper aims to support policies and decision makers in defining the most appropriate strategies to manage the Covid-19 crisis. Precisely the correlation between positive Covid-19 cases and transport accessibility of an area was investigated through a multiple linear regression model. Estimation results show that transport accessibility was the variable that better explained the number of Covid-19 infections (about 40% in weight), meaning that the greater is the accessibility of a certain geographical area, the easier the virus reaches its population. Furthermore, other context variables were also significant, i.e. socio-economic, territorial and pollutant variables. Estimated findings show that accessibility, which is often used to measure the wealth of an area, becomes its worst enemy during a pandemic, providing to be the main vehicle of contagion among its citizens. These original results allow the definition of possible policies and/or best practices to better manage mobility restrictions. The quantitative estimates performed show that a possible and probably more sustainable policy for containing social interactions could be to apply lockdowns in proportion to the transport accessibility of the areas concerned, in the sense that the higher the accessibility, the tighter should be the mobility restriction policies adopted.

The κ-statistics approach to epidemiology

A great variety of complex physical, natural and artificial systems are governed by statistical distributions, which often follow a standard exponential function in the bulk, while their tail obeys the Pareto power law. The recently introduced \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ-statistics framework predicts distribution functions with this feature. A growing number of applications in different fields of investigation are beginning to prove the relevance and effectiveness of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ-statistics in fitting empirical data. In this paper, we use \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ-statistics to formulate a statistical approach for epidemiological analysis. We validate the theoretical results by fitting the derived \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ-Weibull distributions with data from the plague pandemic of 1417 in Florence as well as data from the COVID-19 pandemic in China over the entire cycle that concludes in April 16, 2020. As further validation of the proposed approach we present a more systematic analysis of COVID-19 data from countries such as Germany, Italy, Spain and United Kingdom, obtaining very good agreement between theoretical predictions and empirical observations. For these countries we also study the entire first cycle of the pandemic which extends until the end of July 2020. The fact that both the data of the Florence plague and those of the Covid-19 pandemic are successfully described by the same theoretical model, even though the two events are caused by different diseases and they are separated by more than 600 years, is evidence that the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\kappa $$\end{document}κ-Weibull model has universal features.