Special Issue: Demography and COVID-19: risks, responses and impacts. (Special Issue: Demography and COVID-19: risks, responses and impacts.)

This special issue contains 11 articles that discuss substantive empirical analyses, theoretical works, applied research and contributions to methodology, focusing on demographic issues alongside COVID-19 risks, responses and impacts.

Detection of Pneumonia Based on ResNet Improved by Attention Mechanism

In recent years, Covid-19 is one of the major health challenges facing the human population. Due to the highly infectious nature of Covid-19 and the difficulty of detecting symptoms in the early stages, it is definitely necessary to combine X-ray for the diagnosis of pneumonia. Using traditional neural networks such as VGG, ResNet, and DenseNet to diagnose pneumonia based on X-ray images faces a number of difficulties. These models have insufficient spatial information extraction capability and are prone to overfitting on the training set. The attention mechanism is a means to improve model performance by helping the model better extract channel and spatial features from the feature maps. To identify pneumonia more accurately, we combined the ResNet network and CBAM attention mechanism to design the ResNet101-cbam model with a series of data augmentation methods as well as training strategies. We used the same approach to add attention mechanisms to ResNet50, ResNet101 and ResNet152 and tested their performance. The results show that ResNet101-cbam is the best performing model overall. It achieved a recall of 0.8205, a precision of 0.822, and an accuracy of 0.8285 on the test set, while the original pretrained ResNet101 had a precision of 0.7280 and an accuracy of 0.7644. Its performance were better than the more complex model: ResNet152-cbam, a little bit, but the training speed is improved by more than 25%. More importantly, the model with the added attention mechanism effectively overcomes the effects of positive and negative sample imbalance. The ResNet101-cbam model can be used as a medical aid, which can improve diagnostic efficiency and help us better deal with large-scale pneumonia epidemics. © 2023 IEEE.

Cough Audio Signal-Based Clinical Emergency Classification of Corona Variant Infected Patients Using Multiclass SVM

The variants of coronavirus both delta and omicron are much more contagious and affecting greater percentage of human population. In this research, an attempt is made to predict classification of clinical emergency treatment of corona variant infected patients using their recorded cough sound file. Cough audio signal features such as zero crossing and mel-frequency cepstral coefficients (MFCC), chromo gram (chroma_stft), spectral centroid, spectral roll off, spectral-bandwidth are to be extracted and stored along with patient ID, date, and timings. Digital signal processing of recorded cough audio file obtained needs to be cleaned and pre-processed and normalized to get a training dataset in order to build intelligent ML model using multiclass classifier SVM for predicting the class labels with maximum accuracy. The model proposed in this research paper helps to systematically plan and handle emergency treatment of the patients by classifying their severity based on the cough audio signal using SVM. The built model predicts and classifies the emergency treatment level as low, medium, and high with 96% accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis of SARS-CoV-2 Temporal Molecular Networks Using Global and Local Topological Characteristics

The global COVID-19 pandemic continues to have a devastating impact on human population health. In an effort to fully characterize the virus, a significant volume of SARS-CoV-2 genomes have been collected from infected individuals and sequenced. Comprehensive application of this molecular data toward epidemiological analysis in large parts has employed methods arising from phylogenetics. While undeniably valuable, phylogenetic methods have their limitations. For instance, due to their rooted structure, outgroup samples are often needed to contextualize genetic relationships inferred by branching. In this paper we describe an alternative: global and local topological characterization of neighborhood graphs relating viral genomes collected from samples in longitudinal studies. The applicability of our approach is demonstrated by constructing and analyzing such graphs using two distinct datasets from Israel and France, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Assessing pandemic era stadium events and infections using mobile phone based population mobility data: An exploratory study from Ireland, 2021

Mass gathering events (MGEs) attracting local, national, or international crowds presented particular challenges in the context of the coronavirus disease 2019 (COVID-19) pandemic. Sporting, religious, music and other cultural events held during the early months of the pandemic, without social distancing or other safeguards, have been regarded as so-called ‘super spreader’ events. By the summer of 2020, MGEs were generally banned or subject to severe restrictions. Regular European sporting fixtures such as England’s Football Association and Germany’s Bundesliga matches began to return in the autumn with protective measures in place, such as matches initially held behind closed doors, and later with sub-capacity crowd limits and mandatory social distancing [1, 2, 3, 4, 5].With protective measures in place, and proof of COVID-19 vaccination or recovery required for entry, a series of six sporting MGEs, ‘the All-Ireland Finals’ were held in the Republic of Ireland’s largest stadium, Croke Park in Dublin, during August-September 2021. This study draws on a high-resolution human population mobility dataset to quantify journeys to/from the stadium area on MGE days by destination. The anonymised, aggregated, data used is based on mobile phone usage, and consists of a series of fine-grained geographical origin-destination matrices presenting daily estimates of area to area journey numbers. With mobility from the stadium area serving as a proxy for MGE attendance, this study explores associations between MGE attendance numbers and local COVID-19 infections over subsequent five week periods. No evidence was found of association between attendance at any of the six 2021 All-Ireland MGEs and COVID-19 infections over subsequent five week periods. This finding contrasts with studies of comparable MGEs in 2020, such as English Association Football matches held during spring 2020, and German Bundesliga football matches held during autumn 2020. These differing outcomes may point to the effectiveness of transmission mitigation policies and behaviours. [ FROM AUTHOR]

COVID-19 Detection Using Raw Chest X-Ray Images

COVID-19 has had a lasting effect on the human population around the globe. originating from Wuhan, China, in December 2019, the virus managed to spread worldwide in a short time. Huge waiting time between the detection of symptoms and clinical confirmation of the virus being present in the body has made the virus more fatal;thus, rapid screening of large numbers of suspected patients is essential. Due to inefficiency in pathological testing, alternate ways must be devised to combat these issues. Due to advancements in CAD, integrating radiological images with Artificial Intelligence (AI) can detect the disease accurately. This study proposes a deep learning model for automatic COVID-19 detection using raw Chest X-ray (CXR) images. With 17 convolutional layers, the proposed model is trained to diagnose COVID-19 with an 96.67% accuracy. The model can be used to help the world in numerous ways. © 2022 IEEE.

Why Lockdowns Are Futile and Face Masks Effective in Slowing down COVID-19-Like Pandemic — Quantitative Analysis with Compartmental Model

There exist a number of complex and often nonlinear phenomena in physical and biophysical systems that can be efficiently approached by systems of differential equations. Even though the deterministic character of the solutions is typically not adequate to describe the actual behaviour of given systems, this approach is very well suited to describe the influence of the well-defined factors on the evolution of the systems described by properly chosen dynamical models. For example, a compartmental model with three groups of people (susceptible, infected, and recovered) is able to capture some of the general principles related to the dynamics of a pandemic in a biophysical system such as the human population. Here, motivated by the ongoing COVID-19 pandemic, with the help of a proper generalisation of the simple model, we analyse influence and efficacy of commonly invoked counter-pandemic actions — lockdowns and mandatory face masks — in reducing the number of fatalities. To reach this goal, our model takes into account the number of hospitalised persons and the fraction of those hospitalised who need special treatment in intensive care units. We show that even if there is an optimal time for introduced lockdowns to be effective, it is impossible to reach in practice due to the limited capacity of the health system. The calculations indicate that wearing face masks decreases the number of hospitalised people and the total death toll. Half of the population appropriately wearing masks, even the home-made ones (with an efficacy of only about 60%), would halve the peak value of those needing intensive medical treatment. Our study indicates a slightly greater effectiveness of masks worn by healthy people, which is related to the fact that ill people do not protect themselves. © 2022 Polish Academy of Sciences. All rights reserved.

A Proposed Quantitative Model for the Computation and Analysis of Waning Immunity for COVID-19 Virus among Human Population

Waning Immunity is an important and relevant concept during these days as the COVID-19 pandemic is expected to become endemic in the coming months. By definition, Waning Immunity is the loss of protective antibodies over time and hence necessitates booster shots at regular intervals of time. This quantitative study is on proposition of a model for computing a newly defined metric called Waning Immunity Index (WII). The model takes into account the three group of people namely, susceptible, infected and recovered individuals from the COVID-19 infections. The required data can be collected from the Kaggle repository that contains information on infections, recovery, vaccination and booster doses given on the human population while considering a geographical location. The proposed model and its implementation have thrown light on the spread, control and effect of COVID-19 virus. Results of the proposed model and the measurement can help health officials to seamlessly plan the duration of booster doses administered on vaccinated population. A sample data has been prepared for testing the model and the application of the proposed metrics. Based on the results, it is found that vulnerability of the Waning Immunity increases steeply at some duration and gradually steadies in time. © 2022 IEEE.

Social Media Sentiment Analysis Related to COVID-19 Vaccines: Case Studies in English and Greek Language

SARS-CoV-2 and its mutations are spreading around the world, threatening the human population with millions of infections and deaths. Vaccines are considered the main available weapon at hand to mitigate the spread. As a result, the development of efficient systems to understand and supervise the information dissemination, as well as the evolution of sentiments towards vaccines is critical. The goal of this research was to build and apply a supervised learning approach to monitor the dynamics of public opinion on COVID-19 vaccines using Twitter data. 1,394,535 and 61,077 tweets about COVID-19 vaccines, respectively in English and Greek, were collected, classified based on sentiment polarity and analyzed over time to gain insights into sentiment trends. Our findings reveal that overall negative, neutral, and positive sentiments were at 36.5%, 39.9%, and 23.6% in the English language dataset, respectively, whereas overall negative and non-negative sentiments were at 60.1% and 39.9% in the Greek language dataset. Policymakers and health experts could take into consideration social media sentiment analysis alongside other ways of evaluating public sentiment. Social media users are actively seeking and sharing information about pandemic-related topics, allowing governments to use social media to develop effective crisis management strategies, better inform the public with accurate and reliable news, and alleviate disease-specific concerns. © 2022, IFIP International Federation for Information Processing.

Science-based environmental conservation to answer the risk of pandemic, with a focus on the Republic of Korea

The pandemic resulting from COVID-19 infections had short-term positive impacts on the environment such as improvement in air and water quality. However, long term changes still have disastrous effects in terms of loosening of conservation policies and an increase in 'post-COVID-19' development subsidies to boost the economy at the expense of the environment. The prevention of habitat loss and zoonoses will avert future pandemics and measures to protect the local environment should be taken. The Republic of Korea follows the global trend in the weakness of long-term environmental answer to the pandemic and other on-going zoonoses, such as the avian influenza and African swine fever. Some of the current activities may even increase the risks of pandemic as mass culling of animals is widespread despite known risks. Instead, environmental protection and decreased encroachment may be the only safe way to proactively prevent the emergence of further pandemics.

Attitudes towards Online Community Support Initiatives during the COVID-19 pandemic: A Survey in the UK

The COVID-19 pandemic has pushed unexpected hardship on the health, environment, economic, and social-political governance of the entire human population. Local communities have adopted new ways of communicating and connecting to support each other. This paper reports people's attitudes towards online community support initiatives (OCSIs) during the COVID-19 pandemic based on a survey conducted in the UK. Our analysis of responses from 699 participants suggests the increased use of social media sites and OCSI engagement since the pandemic, and that people had positive attitudes towards the OCSIs, but improvements were still required. We suggest four design implications to alleviate the challenges of using OCSIs. © 2022 ACM.

Why Urban Ecology Matters in Ethiopia

Three concurrent global environmental trends are particularly apparent: human population growth, urbanization, and climate change. Especially in countries such as Ethiopia in the Global South, all three are impacted by, and in turn have bearing upon, social justice and equity. Combined, these spatial and social factors reduce wellbeing, leading to increasing urgency to create urban environments that are more livable, resilient, and adaptive. However, the impacts on, and of, non-human urban residents, particularly on the ecosystem services they provide, are often neglected. We review the literature using the One Health theoretical framework and focusing on Ethiopia as a case-study. We argue for specific urban strategies that benefit humans and also have spillover effects that benefit other species, and vice versa. For example, urban trees provide shade, clean the air, help combat climate change, create more livable neighborhoods, and offer habitat for many species. Similarly, urban neighborhoods that attract wildlife have characteristics that also make them more desirable for humans, resulting in improved health outcomes, higher livability, and enhanced real-estate values. After summarizing the present state of knowledge about urban ecology, we emphasize components relevant to the developing world in general and pre- COVID-19 pandemic Ethiopia in particular, then expand the discussion to include social justice and equity concerns in the built environment. Prior to the ongoing civil war, Ethiopia was beginning to invest in more sustainable urbanization and serve as a model. Especially in light of the conflict and pandemic, much more will need to be done. Copyright © 2022 Perry, Gebresenbet, DaPra, Branco, Whibesilassie, Jelacic and Eyob.

Stochastic Epidemic Model of Covid-19 via the Reservoir-People Transmission Network

The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network.When faced with a potential outbreak, decision-makers need to be able to trust mathematical models for their decision-making processes. One of the most considerable characteristics of COVID-19 is its different behaviors in various countries and regions, or even in different individuals, which can be a sign of uncertain and accidental behavior in the disease outbreak. This trait reflects the existence of the capacity of transmitting perturbations across its domains. We construct a stochastic environment because of parameters random essence and introduce a stochastic version of theReservoir-Peoplemodel. Then we prove the uniqueness and existence of the solution on the stochastic model. Moreover, the equilibria of the system are considered. Also, we establish the extinction of the disease under some suitable conditions. Finally, some numerical simulation and comparison are carried out to validate the theoretical results and the possibility of comparability of the stochastic model with the deterministic model. © 2022 Tech Science Press. All rights reserved.

Synergies Between COVID-19 and Climate Change Impacts and Responses

The COVID-19 pandemic and anthropogenic climate change are global crises. We show how strongly these crises are connected, including the underlying societal inequities and problems of poverty, substandard housing, and infrastructure including clean water supplies. The origins of all these crises are related to modern consumptive industrialisation, including burning of fossil fuels, increasing human population density, and replacement of natural with human dominated ecosystems. Because business as usual is unsustainable on all three fronts, transformative responses are needed. We review the literature on risk management interventions, implications for COVID-19, for climate change risk and for equity associated with biodiversity, water and WaSH, health systems, food systems, urbanization and governance. This paper details the considerable evidence base of observed synergies between actions to reduce pandemic and climate change risks while enhancing social justice and biodiversity conservation. It also highlights constraints imposed by governance that can impede deployment of synergistic solutions. In contrast to the response to the COVID-19 pandemic, governance systems have procrastinated on addressing climate change and biodiversity loss as these are interconnected chronic crises. It is now time to address all three to avoid a multiplication of future crises across health, food, water, nature, and climate systems.

Current and future trends in socio-economic, demographic and governance factors affecting global primate conservation.

Currently, ~65% of extant primate species (ca 512 species) distributed in 91 countries in the Neotropics, mainland Africa, Madagascar, South Asia and Southeast Asia are threatened with extinction and 75% have declining populations as a result of deforestation and habitat loss resulting from increasing global market demands, and land conversion for industrial agriculture, cattle production and natural resource extraction. Other pressures that negatively impact primates are unsustainable bushmeat hunting, the illegal trade of primates as pets and as body parts, expanding road networks in previously isolated areas, zoonotic disease transmission and climate change. Here we examine current and future trends in several socio-economic factors directly or indirectly affecting primates to further our understanding of the interdependent relationship between human well-being, sustainable development, and primate population persistence. We found that between 2001 and 2018 ca 191 Mha of tropical forest (30% canopy cover) were lost as a result of human activities in the five primate range regions. Forty-six percent of this loss was in the Neotropics (Mexico, Central and South America), 30% in Southeast Asia, 21% in mainland Africa, 2% in Madagascar and 1% in South Asia. Countries with the greatest losses (ca 57% of total tree cover loss) were Brazil, Indonesia, DRC, China, and Malaysia. Together these countries harbor almost 50% of all extant primate species. In 2018, the world human population was estimated at ca 8bn people, ca 60% of which were found in primate range countries. Projections to 2050 and to 2100 indicate continued rapid growth of the human populations in these five primate range regions, with Africa surpassing all the other regions and totaling ca 4bn people by the year 2100. Socioeconomic indicators show that, compared to developed nations, most primate range countries are characterized by high levels of poverty and income inequality, low human development, low food security, high levels of corruption and weak governance. Models of Shared Socioeconomic Pathway scenarios (SSPs) projected to 2050 and 2100 showed that whereas practices of increasing inequality (SSP4) or unconstrained growth in economic output and energy use (SSP5) are projected to have dire consequences for human well-being and primate survivorship, practices of sustainability-focused growth and equality (SSP1) are expected to have a positive effect on maintaining biodiversity, protecting environments, and improving the human condition. These results stress that improving the well-being, health, and security of the current and future human populations in primate range countries are of paramount importance if we are to move forward with effective policies to protect the world's primate species and promote biodiversity conservation.

Trends in adaptation of fifteen European countries population to SARS-CoV-2 in March-May 2020: Can Taiwanese experience be adopted?

BACKGROUND: The purpose of the work is to analyze population adaptation to SARS-CoV-2 in Europe in March-May 2020, predict herd immunity formation in the nearest several months on the basis of our SIR modified epidemiological model of the virus spread and elaborate recommendations to governments regarding a second wave of COVID-19 pandemic. METHODS: Outer (1,006,512 RT-PCR tests results for SARS-CoV-2) and proprietary (34,660 respiratory samples) epidemiological data was used. Fifteen European countries were studied. Dates of research: March 2 - May 22, 2020. RESULTS: As of April 21, 2020, the mean population infection rate (PIR) for the European countries considered, was 9.66%. It decreased to 6.85% by May 22, 2020. The model predicted 5.68% PIR, giving accuracy of 79.40%. SARS-CoV-2 basic reproduction number is limited by an extremum that may be observed for closed communities. A concept of effective reproduction number is introduced as a function of r0 with maximum at r0 = 4.671 and value reff. = 0.315 for the full-lockdown mode and r0 = 5.539 and reff. = 0.552 for the no-lockdown mode of SARS-CoV-2 containment. Full-lockdown and no-lockdown modes resulted in the outcomes not strikingly different from each other in terms of herd immunity values. CONCLUSION: In case of a second wave of COVID-19 disease in Europe, it will coincide with seasonal common cold surge, spanning from mid-September 2020 to mid-February 2021, with a median in November-December 2020. Strict epidemiological surveillance must be observed in Europe at that time.