The Most Vulnerable Hispanic Immigrants in New York City: Structural Racism and Gendered Differences in COVID-19 Deaths.

This paper explores the structural and group-specific factors explaining the excess death rates experienced by the Hispanic population in New York City during the peak years of the coronavirus pandemic. Neighborhood-level analysis of Census data allows an exploration of the relation between Hispanic COVID-19 deaths and spatial concentration, conceived in this study as a proxy for structural racism. This analysis also provides a more detailed exploration of the role of gender in understanding the effects of spatial segregation among different Hispanic subgroups, as gender has emerged as a significant variable in explaining the structural and social effects of COVID-19. Our results show a positive correlation between COVID-19 death rates and the share of Hispanic neighborhood residents. However, for men, this correlation cannot be explained by the characteristics of the neighborhood, as it is for women. In sum, we find: (a) differences in mortality risks between Hispanic men and women; (b) that weathering effects increase mortality risks the longer Hispanic immigrant groups reside in the U.S.; (c) that Hispanic males experience greater contagion and mortality risks associated with the workplace; and (d) we find evidence corroborating the importance of access to health insurance and citizenship status in reducing mortality risks. The findings propose revisiting the Hispanic health paradox with the use of structural racism and gendered frameworks.

Predicting COVID-19 Mortalities for Patients with Special Health Conditions Using an Agent-Based Model

The spread of COVID-19 has thrown the world into a panic. We are constantly learning more about the virus every day, from how it spreads to who is more susceptible to becoming infected by different variants. Those with underlying respiratory conditions and other immunocompromised individuals need to be extra cautious regarding the virus. Many researchers have created COVID-19 trackers to detect the spread of COVID-19 around the world and show hot spots where COVID-19 cases are more prevalent. Previous work lacks the consideration of comorbidity as a factor of death rate. This work aims to create an agent-based model to predict comorbidity death rate caused by a health condition in addition to COVID-19. The model is evaluated using the symmetric mean absolute percentage error metric and proved to be very efficient. © 2023 IEEE.

Forecasting the Temporal Evolution of COVID-19

COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated. © 2022 IEEE.

Forecasting the Temporal Evolution of COVID-19

COVID-19 epidemic has resulted in severe chaos across the globe. Complex frameworks can be investigated and studied using mathematical models, which are reliable and efficient. The objective of this research is to scrutinize the progression and prediction of parameters that evaluate the emergence and transmission of COVID-19 in the two most affected nations, i.e., the USA and India. Five models including the standard and hybrid epidemic models, viz, SIR (Susceptible-Infectious-Removed), SIRD (Susceptible-Infectious-Recovered-Death), SIRD with vaccination, SIRD with vital dynamics (i.e., including birth rate and death rate) and, SIRD with vital dynamics and vaccination have been developed. Worldwide statistics have been observed utilizing graphical layouts. Model evaluation measures such as Mean Absolute error (MAE), Mean-square error (MSE), and Root Mean Square Error (RMSE) for different parameters namely infection rate, recovery rate, and death rate have been estimated. © 2022 IEEE.

Social Distance Alerting System Using Machine Learning

The increase into the Corona virus pandemic led to a higher death rate globally. The best way to prevent getting sick is to keep yourself physically or socially far. Our project provides an approach for physical isolation revealing using machine knowledge toward indicate the necessary space to be maintained to decrease the collision of the corona virus contagious widespread spread. By analyzing a videotape provide for from the camera, the detect apparatus be fashioned in the direction of notify individuals toward maintain a out of harm's way aloofness on or after one an additional. The open-source person recognition pretrained model, YOLO3 algorithm, was utilized to recognize people using the video frame from the camera as input. YOLO3 has the benefit of mortal a lot quicker than further algorithms, at a halt maintain exactness and meets the real-time requirements for person detection. In order to calculate distance from the 2D plane, the video frames are afterwards transformed into top-down views. Estimated distance between individuals and any non-compliant pair of individuals within the display is indicate by means of a red colour edge and stripe, the moderate distance is represented with orange colour and the safe distance is represented by green colour frame. The suggested technique was examined lying on a pre record videotape as well as on the live video feed of persons walking on the road. Additionally an alarm sound is provided to notify the persons. The outcome show that the planned strategy is ready toward sees the societal separation trial among many populaces withinthe videotape. © 2022 IEEE.

Trust based COVID-19 vaccine distribution using blockchain technology

The COVID-19 outbreak has impacted huge number of individuals all around the world and has caused a great economic loss all over the world. Vaccination is most effective solution to prevent this disease. It helps in protecting the whole community. It improves the human immune system and fights against corona virus reducing the death rate. This paper deals with the different types of COVID-19 vaccine and their related distribution, it includes measures to ensure safe and secured distribution of the vaccine through block chain technology with the help of supply chain. Any malfunction in the chain is identified by the trust value of the function point method and the value of the Markov Chain. © 2023 - IOS Press. All rights reserved.

The contribution of population age-sex structure to the excess mortality estimates of 2020-2021 in Denmark, Finland, Iceland, Norway, and Sweden.

The Nordic countries offer an ideal case study of the COVID-19 pandemic due to their comparability, high data quality, and variable mitigations. We investigated the age- and sex-specific mortality patterns during 2020-2021 for the five Nordic countries and analysed the total age- and sex-adjusted excess deaths, ratios of actual to expected death rates, and age-standardized excess death estimates. We assessed excess deaths using several time periods and sensitivity tests, and 42 sex and age groups. Declining pre-pandemic age-specific death rates reflected improving health demographics. These affect the expected death estimates and should be accounted for in excess mortality models. Denmark had the highest death rates both before and during the pandemic, whereas in 2020 Sweden had the largest mortality increase. The age-standardized mortality of Denmark, Iceland and Norway was lowest in 2020. 2021 was one of the lowest mortality years for all Nordic countries. The total excess deaths in 2020-2021 were dominated by 70-89-year-olds, were not identified in children, and were more pronounced among men than women. Sweden had more excess deaths in 2020 than in 2021, whereas Finland, Norway and Denmark had the opposite. Our study provides new details on Nordic sex- and age-specific mortality during the first two years of the pandemic and shows that several metrics are important to enable a full understanding and comparison of the pandemic mortality.

Tracking Covid-19 cases and deaths in the United States: metrics of pandemic progression derived from a queueing framework.

We analyze the progression of COVID-19 in the United States over a nearly one-year period beginning March 1, 2020 with a novel metric motivated by queueing models, tracking partial-average day-of-event and cumulative probability distributions for events, where events are points in time when new cases and new deaths are reported. The partial average represents the average day of all events preceding a point of time, and is an indicator as to whether the pandemic is accelerating or decelerating in the context of the entire history of the pandemic. The measure supplements traditional metrics, and also enables direct comparisons of case and death histories on a common scale. We also compare methods for estimating actual infections and deaths to assess the timing and dynamics of the pandemic by location. Three example states are graphically compared as functions of date, as well as Hong Kong as an example that experienced a pronounced recent wave of the pandemic. In addition, statistics are compared for all 50 states. Over the period studied, average case day and average death day varied by two to five months among the 50 states, depending on data source, with the earliest averages in New York and surrounding states, as well as Louisiana.

Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data.

Tracking the progress of an infectious disease is critical during a pandemic. However, the incubation period, diagnosis, and treatment most often cause uncertainties in the reporting of both cases and deaths, leading in turn to unreliable death rates. Moreover, even if the reported counts were accurate, the "crude" estimates of death rates which simply divide country-wise reported deaths by case numbers may still be poor or even non-computable in the presence of small (or zero) counts. We present a novel methodological contribution which describes the problem of analyzing COVID-19 data by two nested Poisson models: (i) an "upper model" for the cases infected by COVID-19 with an offset of population size, and (ii) a "lower" model for deaths of COVID-19 with the cases infected by COVID-19 as an offset, each equipped with their own random effect. This approach generates robustness in both the numerator as well as the denominator of the estimated death rates to the presence of small or zero counts, by "borrowing" information from other countries in the overall dataset, and guarantees positivity of both the numerator and denominator. The estimation will be carried out through non-parametric maximum likelihood which approximates the random effect distribution through a discrete mixture. An added advantage of this approach is that it allows for the detection of latent subpopulations or subgroups of countries sharing similar behavior in terms of their death rates.

COVID-19 Detection Using CNN-ResNet-50 Model

COVID-19 is a deadly virus that originated in 2019 and could be easily transmitted from one geographical area to another. It affected the integral world, resulting in severe mortality due to its contagious effect on human life. The infection rate is continuously growing and it is becoming unmanageable since the virus moves easily from one human to another. Once we detect the COVID-19 virus in its early stages, we can easily reduce the death rate. The most common and widely used method of diagnosing COVID is through reverse transcription polymerase chain (RT-PCR). But the RT-PCR test is time consuming, inaccurate, and expensive. In this situation, the time period for the detection of viruses is valuable. Keeping these limitations in mind, we use an X-ray image of the chest to identify the COVID-19 infected patient. This procedure is achieved by using convolution neural network (CNN) in deep learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Hybrid Approach to Predict the Death Rate of COVID-19 Patients

From closedown of December 2019, coronavirus has directly exhibited a lofty rate of transmission, coercing the World Health Organization to contend in the month of March 2020 that this unbeknownst coronavirus can be depicted as a pandemic. COVID-19 epidemic has guided to an operatic misplacement of deathly life over the public and presents an unbeknownst complaint to public fitness. It also affects the food systems of the person and the world of work. Once the person is infected by COVID, the metabolic exertion of vulnerable cells in his or her body is enhanced, similar as the one driven by COVID-19. The country's dietary habits are analyzed to predict the particular person's death rate. By using KNN algorithm, the performance metrics such as accuracy, precision, recall, and F1 score are evaluated for the country's dietary habits. In this research, both clustering and classification are combined to increase the accuracy of the prediction of death rate of the person. K-means is used for the clustering of the countries, and KNN is used for classifying the countries. The 170 countries are clustered based on the country's dietary habits, and other disease affected rate using K-means clustering algorithm. Countries are clustered into high and normal death rate countries based on the country's dietary habits and another cluster into high and normal death rate based on the other disease affected rate rather than COVID-19. Using the country's dietary habits and other disease affected clusters, the death rate of the person is predicted. After clustering the data based on the country's dietary habits and other disease affected rate, the KNN algorithm is used to classify and identify the person's death rate. Using clustering and classification algorithms in a combined way, an accuracy of 79% is achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Susceptible-Infected-Removed Epidemic Model On Network: A Migration Flow Study

An epidemic Susceptible-Infected-Removal (SIR) model with vital dynamics of birth and death rates is presented on a network by using graph Laplacian diffusion. Migration parameter has been introduced for controlling the population mobility between different regions. The subsequent waves for the infected occur under some restrictions on the migration parameter. Isolation strategies are investigated for different types of networks. Finally, we estimate important model parameters using the Least-Square method. © 2022 IEEE.

BLOCOVID: A blockchain-based COVID-19 digital vaccination certificate verification system

The global impact of the COVID-19 pandemic has been felt in diverse ways. Although the death rate in Africa has not been as devastating as predicted by the World Health Organization (WHO), its economic and social impact has been fully felt by the African continent. As the world goes through the vaccination process to achieve herd immunity, Africa has not only faced problems like the inability to produce and procure vaccines, but some countries in the west are doubting the authenticity of the vaccination process and even vaccine certificates coming from various countries on the continent. The approach of using centralized systems to validate COVID-19 vaccine certificates makes these systems susceptible to Denial of Service (DoS), modification, and Man-in-The-Middle (MiTM) attacks. To curb this problem, we proposed a blockchain-based digital COVID-19 vaccination certificate verification system called BLOCOVID. The proposed system uses the decentralized approach of distributed ledgers to ensure that vaccine certificates are secured, immutable, and verifiable. Our proposed system stores vaccine serial numbers and their corresponding certificates as hash values. These hash values are stored on the blockchain network as transaction values. The authenticity of a vaccine certificate is determined by the availability of the hash values of the certificate and its corresponding vaccine serial number on the blockchain network. The proposed system was simulated using the BlockSim simulator. To begin with, the simulation results show that the proposed system can ensure system availability, thereby minimizing DoS attacks. Secondly, the proposed system can ensure the integrity of vaccine certificates by allowing third parties to verify the authenticity of these certificates. The simulation results show that even with 10240 nodes, the average transaction time was 137.2ms, with a total transaction rate of 9911.034 transactions per second. © 2022 IEEE.

A Novel Approach to Combat COVID-19 using Smart-ID-Cards

COVID-19, A Pandemic with its increasing pace has spread across the globe. The medical care system was badly hit by it as the number of patients fared the number of available hospital beds and other facilities required to treat patients. To rescue, various Internet of Things (IoT) based devices were proposed to combat COVID-19 by offering a helping hand to the medical care system. The pace at which the death rate was increasing, it became the need to combat the root cause of COVID-19, the root cause being the quick spread. ID-Card though not so famous IoMT (Internet of Medical Things) device can be made to work smart, smart enough to monitor the home isolated patients, to keep a check on a precautionary distance measure and much more. The study aims to explore and discuss the state-of-the-art of various IoT to control the novel Coronavirus (COVID-19) spread by tracing out positive patients and stopping this chain by tracing symptoms just a click away. The IoMT Smart-ID-Card is proposed to easefully detect, monitor, and combat COVID-19. © 2022 IEEE.

A Computational Approach for Predicting the Termination of COVID-19

In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen's life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field. © 2022 IEEE.

Susceptible-Infected-Removed Epidemic Model On Network: A Migration Flow Study

An epidemic Susceptible-Infected-Removal (SIR) model with vital dynamics of birth and death rates is presented on a network by using graph Laplacian diffusion. Migration parameter has been introduced for controlling the population mobility between different regions. The subsequent waves for the infected occur under some restrictions on the migration parameter. Isolation strategies are investigated for different types of networks. Finally, we estimate important model parameters using the Least-Square method. © 2022 IEEE.

Differences in mortality in Switzerland by citizenship during the first and second COVID-19 waves: Analysis of death statistics.

Background: Early in the COVID-19 pandemic, it became apparent that members of marginalized populations and immigrants were also at risk of being hospitalized and dying more frequently from COVID-19. To examine how the pandemic affected underserved and marginalized populations, we analyzed data on changes in the number of deaths among people with and without Swiss citizenship during the first and second SARS-CoV-2 waves. Method: We analyzed the annual number of deaths from the Swiss Federal Statistical Office from 2015 to 2020, and weekly data from January 2020 to May 2021 on deaths of permanent residents with and without Swiss citizenship, and we differentiated the data through subdivision into age groups. Results: People without Swiss citizenship show a higher increase in the number of deaths in 2020 than those who were Swiss citizens. The increase in deaths compared to the previous year was almost twice as high for people without Swiss citizenship (21.8%) as for those with it (11.4%). The breakdown by age group indicates that among people between the ages of 64 and 75, those without Swiss citizenship exhibited an increase in mortality (21.6%) that was four times higher than that for people with Swiss citizenship (4.7%). Conclusion: This study confirms that a highly specialized health care system, as is found in Switzerland, does not sufficiently guarantee that all parts of the population will be equally protected in a health crisis such as COVID-19.

COVID-19 Data Analysis and Appropriate Vaccine Prediction using Machine Learning

COVID-19 highly contagious virus, it has wreaked devastation on the earth. To help the world overcome this challenging situation, scientists and professionals across many disciplines are working relentlessly to develop vaccines and prevention measures. Many people are getting sick because they don't know which of the discovered coronavirus vaccines are beneficial for the human body. The appropriate vaccine has been predicted by analyzing the types of diseases that people have in our data set and the types of diseases that people get after giving the first dose and second dose. From these data, we can predict what kind of vaccine will be appropriate for any disease and there will be no side effects in the first doses and no side effects in the second doses. Here three algorithms such as SVM, Random Forest, and Bagging Classifier of machine learning are used to get the appropriate vaccine. Finally, we can say that the vaccine made by machine learning will help reduce the death rate of the coronavirus and increase the immunity of our body. © 2022 IEEE.

COVID-19 Detection Using CNN-ResNet-50 Model

COVID-19 is a deadly virus that originated in 2019 and could be easily transmitted from one geographical area to another. It affected the integral world, resulting in severe mortality due to its contagious effect on human life. The infection rate is continuously growing and it is becoming unmanageable since the virus moves easily from one human to another. Once we detect the COVID-19 virus in its early stages, we can easily reduce the death rate. The most common and widely used method of diagnosing COVID is through reverse transcription polymerase chain (RT-PCR). But the RT-PCR test is time consuming, inaccurate, and expensive. In this situation, the time period for the detection of viruses is valuable. Keeping these limitations in mind, we use an X-ray image of the chest to identify the COVID-19 infected patient. This procedure is achieved by using convolution neural network (CNN) in deep learning. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An age-dependent immuno-epidemiological model with distributed recovery and death rates.

The work is devoted to a new immuno-epidemiological model with distributed recovery and death rates considered as functions of time after the infection onset. Disease transmission rate depends on the intra-subject viral load determined from the immunological submodel. The age-dependent model includes the viral load, recovery and death rates as functions of age considered as a continuous variable. Equations for susceptible, infected, recovered and dead compartments are expressed in terms of the number of newly infected cases. The analysis of the model includes the proof of the existence and uniqueness of solution. Furthermore, it is shown how the model can be reduced to age-dependent SIR or delay model under certain assumptions on recovery and death distributions. Basic reproduction number and final size of epidemic are determined for the reduced models. The model is validated with a COVID-19 case data. Modelling results show that proportion of young age groups can influence the epidemic progression since disease transmission rate for them is higher than for other age groups.