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Mortality statistics is underlay for public health measures and action and consequently it is one of the major indicator in measures of Covid-19 impact on population. This study aim to explore excess mor-tality during the Covid-19 pandemic in Serbia. Excess mortality compares expected and observed num-ber of deaths during the given period. Analysis in this paper was based on excess deaths and excess mortality rate. Data was downloaded from the national COVID-19 database and obtained from a rele-vant source from the Statistical Office of the Republic of Serbia. In order to provide better understand-ing of excess death, the excess mortality rate was calculated for the period January 2015-June 2022. For the period January 2015-February 2020, 38 months were observed without excess deaths, while in months with excess deaths, almost in all months excess mortality rate was below 12%. Since March 2020, the excess mortality rate has increased significantly, with highest values in December 2020 (91.4%), October (84.3) and November (67.8) 2021.
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COVID-19 has significant fatality rate since its appearance in December 2019 as a respiratory ailment that is extremely contagious. As the number of cases in reduction zones rises, highly health officials are control that authorized treatment centers may become overrun with corona virus patients. Artificial neural networks (ANNs) are machine coding that can be used to find complicate relationships between datasets. They enable the detection of category in complicated biological datasets that would be impossible to identify with traditional linear statistical analysis. To study the survival characteristics of patients, several computational techniques are used. Men and older age groups had greater mortality rates than women, according to this study. COVID-19 patients discharge times were predicted;also, utilizing various machine learning and statistical tools applied technically. In medical research, survival analysis is a regularly used technique for identifying relevant predictors of adverse outcomes and developing therapy guidelines for patients. Historically, demographic statistics have been used to predict outcomes in such patients. These projections, on the other hand, have little meaning for the individual patient. We present the training of neural networks to predict outcomes for individual patients at one institution, as well as their predictive performance using data from another institution in a different region. The research output show that the Gradient boosting longevity model beats the all other different models, also in this research study for predicting patient longevity. This study aims to assist health officials in making more informed decisions during the outbreak. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Beginning in 2020, Covid has increased as a result of a burst put on by a respiratory infection with a substantial peaking fatality rate. The unforeseen occurrence and unchecked global spread of the COVID-19 illness highlight the limitations of current healthcare systems in responding to emergencies affecting public wellness. In these conditions, innovative developments like public blockchain and intelligent systems (AI) have emerged as possible treatments for the covid epidemic. In particular, block chain may help with early identification to combat pandemics. With the measures put in place to prevent infection by wearing masks, social seclusion with a 6m radius, routine testing, and two vaccine doses. This system includes mask measurement, people identification, temp sensors, information tracking, in-person interaction locating, and the current state of a user's medical chart. With the development of technology and increased smartphone usage, illnesses may be tracked and their spread controlled. Considering that the expansion of the business sector's rehabilitation and its continued broad distribution of Covid, it is more crucial to adhere to the instructions to avoid contamination. © 2022 IEEE.
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The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The goal of the study here is to analyze and assess whether strict containment policies to cope with Coronavirus Disease 2019 (COVID-19) pandemic crisis are effective interventions to reduce high numbers of infections and deaths. A homogenous sample of 31 countries is categorized in two sets: countries with high or low strictness of public policy to cope with COVID-19 pandemic crisis. The findings here suggest that countries with a low intensity of strictness have average confirmed cases and fatality rates related to COVID-19 lower than countries with high strictness in containment policies (confirmed cases are 24.69% vs. 26.06% and fatality rates are 74.33% vs. 76.38%, respectively, in countries with low and high strictness of COVID-19 public policies of containment). What this study adds is that high levels of strict restriction policies may not be useful measures of control in containing the spread and negative impact of pandemics similar to COVID-19 and additionally a high strictness in containment policies generates substantial social and economic costs. These findings can be explained with manifold socioeconomic and environmental factors that support transmission dynamics and circulation of COVID-19 pandemic. Hence, high levels of strictness in public policy (and also a high share of administering new vaccines) seem to have low effectiveness to stop pandemics similar to COVID-19 driven by mutant viral agents. These results here suggest that the design of effective health policies for prevention and preparedness of future pandemics should be underpinned in a good governance of countries and adoption of new technology, rather than strict and generalized health polices having ambiguous effects of containment in society.
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PurposeThis study aims to highlight the critical role case fatality rates (CFR) have played in the emergence and the management of particularly the early phases of the current coronavirus crisis.Design/methodology/approachThe study presents a contrastive map of CFR for the coronavirus (SARS-CoV-2) and influenza (H1N1 and H2N2).FindingsThe mapped data shows that current CFR of SARS-CoV-2 are considerably lower than, or similar to those, of hospitalised patients in the UK, Spain, Germany or international samples. The authors therefore infer a possible risk that the virulence of the coronavirus is considerably overestimated because of sampling biases, and that increased testing might reduce the general CFR of SARS-CoV-2 to rates similar to, or lower than, of the common seasonal influenza.Originality/valueThis study concludes that governments, health corporations and health researchers must prepare for scenarios in which the affected populations cease to believe in the statistical foundations of the current coronavirus crisis and interventions.
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The world is witnessing a pandemic of SARS-CoV2 infection since the first quarter of the twenty-first century. Ever since the first case was reported in Wuhan city of China in December 2019, the virus has spread over 223 countries. Understanding and predicting the dynamics of COVID-19 spread through data analysis will empower our administrations with insights for better planning and response against the burden inflicted on our health care infrastructure and economy. The aim of the study was to analyze and predict COVID-19 spread in Ernakulam district of Kerala. Data was extracted from lab data management system (LDMS), a government portal to enter all the COVID-19 testing details. Using the EpiModel package of R-mathematical modeling of infectious disease dynamics, the predictive analysis for hospitalization rate, percentage of patients requiring oxygen and ICU admission, percentage of patients getting admitted, duration of hospital stay, case fatality rate, age group and gender-wise fatality rate, and hospitalization rate were computed. While calculating the above-said variables, the percentage of vaccinated population, breakthrough infections, and percentage of hospitalization among the vaccinated was also taken into consideration. The time trend of patients in ICU showed men outnumbered women. Positive cases were more among 20–30 years, while 61–70 years age group had more risk for ICU admission. An increase in CFR with advancing age and also a higher CFR among males were seen. Conclusions: Analyzing and predicting the trend of COVID-19 would help the governments to better utilize their limited healthcare resources and adopt timely measures to contain the virus. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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We estimate the case fatality rate from COVID-19 with our method by age groups for three waves - September 2020 to January 2021 (wild type), February 2021 to May 2021 (alpha), and July 2021 to January 2022 (delta). We use linear regression with optimal lag with 21 days moving averaging to correct for reporting delays. We take the coefficient from the regression as the case fatality ratio. We unite the lower age groups into one to achieve a good correlation. We have new cases by age group and deaths by age group and sex. Our results indicate that the delta variant is more severe than alpha, and this is enough to outweigh any improvements in treatment since the first major wave, 14.08.2020–01.01.2021. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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The coronavirus disease 2019 (Covid-19) has devastated the entire globe in a short period of time and was declared a global pandemic by the World Health Organization (WHO) on March 11th, 2020. It rapidly increased in fatality rate and has become an international public health crisis, culminating in social and economic calamity. However, mobile applications are being introduced globally to minimize the coronavirus's continuous spread by tracing people's circulation or mobility using digital software and smartphones. There is no digitally equipped tool that monitors the movement of the people, particularly in the public places. This work on Smarter Movement Control Application (SMCApp) aims to develop methods that will assist Malaysian people to move around with the aid of mobile tracing application. Therefore, the SMCApp was designed and developed as a mobile application software which stands to improve compliance with the mandated SOP measures across the country, as well as to provide digital support to those who wish to travel to various parts of the country. As a result, it is concluded that the usage of Smarter Movement Control Application (SMCApp) is hoped to bring about safe and effective movement of people throughout these two regions, as it will constantly alert individuals of any suspicious close-contact or the state of the location. Once the application is in effect, with at least 90% of users installing the App on their smartphones, it is projected to increase tranquility and elevate compliance with SOP measures. © 2022 IEEE.
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BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has disrupted lives across all countries and communities. It significantly reduced the global economic output and dealt health systems across the world a serious blow. There is growing evidence showing the progression of the COVID-19 pandemic and the impact it has on health systems, which should help to draw lessons for further consolidating and realizing universal health coverage (UHC) in all countries, complemented by more substantial government commitment and good governance, and continued full implementation of crucial policies and plans to avert COVID-19 and similar pandemic threats in the future. Therefore, the objective of the study was to assess the impact of good governance, economic growth and UHC on the COVID-19 infection rate and case fatality rate (CFR) among African countries. METHODS: We employed an analytical ecological study design to assess the association between COVID-19 CFR and infection rate as dependent variables, and governance, economic development and UHC as independent variables. We extracted data from publicly available databases (i.e., Worldometer, Worldwide Governance Indicators, Our World in Data and WHO Global Health Observatory Repository). We employed a multivariable linear regression model to examine the association between the dependent variables and the set of explanatory variables. STATA version 14 software was used for data analysis. RESULTS: All 54 African countries were covered by this study. The median observed COVID-19 CFR and infection rate were 1.65% and 233.46%, respectively. Results of multiple regression analysis for predicting COVID-19 infection rate indicated that COVID-19 government response stringency index (ß = 0.038; 95% CI 0.001, 0.076; P = 0.046), per capita gross domestic product (GDP) (ß = 0.514; 95% CI 0.158, 0.87; P = 0.006) and infectious disease components of UHC (ß = 0.025; 95% CI 0.005, 0.045; P = 0.016) were associated with COVID-19 infection rates, while noncommunicable disease components of UHC (ß = -0.064; 95% CI -0.114; -0.015; P = 0.012), prevalence of obesity among adults (ß = 0.112; 95% CI 0.044; 0.18; P = 0.002) and per capita GDP (ß = -0.918; 95% CI -1.583; -0.254; P = 0.008) were associated with COVID-19 CFR. CONCLUSIONS: The findings indicate that good governance practices, favourable economic indicators and UHC have a bearing on COVID-19 infection rate and CFR. Effective health system response through a primary healthcare approach and progressively taking measures to grow their economy and increase funding to the health sector to mitigate the risk of similar future pandemics would require African countries to move towards UHC, improve governance practices and ensure economic growth in order to reduce the impact of pandemics on populations.
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COVID-19 , Universal Health Insurance , Humans , Economic Development , Pandemics , Gross Domestic ProductABSTRACT
Aim: COVID-19 has exerted distress on virtually every aspect of human life with disproportionate mortality burdens on older individuals and those with underlying medical conditions. Variations in COVID-19 incidence and case fatality rates (CFRs) across countries have incited a growing research interest regarding the effect of social factors on COVID-19 case-loads and fatality rates. We investigated the effect of population median age, inequalities in human development, healthcare capacity, and pandemic mitigation indicators on country-specific COVID-19 CFRs across countries and regions. Subject and methods: Using population secondary data from multiple sources, we conducted a cross-sectional study and used regional analysis to compare regional differences in COVID-19 CFRs as influenced by the selected indicators. Results: The analysis revealed wide variations in COVID-19 CFRs and the selected indicators across countries and regions. Mean CFR was highest for South America at 1.973% (± 0.742) and lowest for Oceania at 0.264% (± 0.107), while the Africa sub-region recorded the lowest scores for pandemic preparedness, vaccination rate, and other indicators. Population Median Age [0.073 (0.033 0.113)], Vaccination Rate [-3.3389 (-5.570.033 -1.208)], and Inequality-Adjusted Human Development Index (IHDI) [-0.014 (-0.023 -0.004)] emerged as statistically significant predictors of COVID-19 CFR, with directions indicating increasing Population Median Age, higher inequalities in human development and low vaccination rate are predictive of higher fatalities from COVID-19. Conclusion: Regional differences in COVID-19 CFR may be influenced by underlying differences in sociodemographic and pandemic mitigation indicators. Populations with wide social inequalities, increased population Median Age and low vaccination rates are more likely to suffer higher fatalities from COVID-19.
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INTRODUCTION: In Japan, as of December 31, 2021, more than 1.73 million laboratory-confirmed cases have been reported. However, the actual number of infections is likely to be under-ascertained due to the epidemiological characteristics such as mild and subclinical infections and limited testing availability in the early days of the pandemic. In this study, we infer the true number of infections in Japan between January 16, 2020, and December 31, 2021, using a statistical modelling framework that combines data on reported cases and fatalities. METHODS: We used reported COVID-19 deaths and age-specific infection fatality ratios (IFR) to impute the true number of infections. Estimates of IFR were informed from published studies and were adjusted to reflect the effects of pharmaceutical interventions, mass vaccination, and evolving variants. To account for the uncertainty in IFR, we sampled values from relevant distributions. RESULTS: We estimated that as of December 31, 2021, 3.07 million (CrI: 2.05-4.24 million) people had been infected in Japan, which is 1.77 times higher than the 1.73 million reported cases. Our meta-analysis confirmed that these findings were consistent with the intermittent seroprevalence studies conducted in Japan. CONCLUSIONS: We have estimated that a substantial number of COVID-19 infections in Japan were unreported, particularly in adults. Our approach provides a more realistic assessment of the true underlying burden of COVID-19. The results of this study can be used as fundamental components to strengthen population health control and surveillance measures.
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COVID-19 , Adult , COVID-19/epidemiology , Humans , Japan/epidemiology , Pandemics , SARS-CoV-2 , Seroepidemiologic StudiesABSTRACT
This paper proposes a systems engineering perspective to analyze the causes of COVID-19 health disparities impact and interventions to minimize the impact on minorities. The impact of the novel coronavirus has shown to be more intense on minorities. The percentage of COVID-19 case count and fatality rate for minorities is much higher than that of the general population, showing that they are more significantly affected than others. Many different factors influence this impact, ranging from economic to cultural. In this paper, these factors are shown to be connected through a causal model analyzing the effects of each factor, after which, potential interventions are suggested. Many factors are identified, such as high employment in the service industry or lower likelihood to have insurance. From this, a causal model is created showing the impact of each factor. Using this causal model, one can identify the high-impact factors causing a disparate impact as well as suggest possible interventions including making testing and treatment more accessible, reducing healthcare bias, and improving healthcare for immigrants. © 2021 IEEE.
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Along with the development of the Covid-19 pandemic, many responses and news were shared through social media. The new Covid-19 vaccination promoted by the government has raised pros and cons from the public. Public resistance to covid-19 vaccination will lead to a higher fatality rate. This study carried out sentiment analysis about the Covid-19 vaccine using the Support Vector Machine (SVM). This research aims to study the public response to the acceptance of the vaccination program. The research result can be used to determine the direction of government policy. Data collection was taken via Twitter in the year 2021. The data then undergoes the preprocessing methods. Afterward, the data is processed using SVM classification. Finally, the result is evaluated by a confusion matrix. The experimental result shows that SVM produces 56.80% positive, 33.75% neutral, and 9.45% negative. The highest model accuracy was obtained by RBF kernel of 92%, linear and polynomial kernels obtained 90% accuracy, and sigmoid kernel obtained 89% accuracy © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.
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Mucormycosis (MM) is an invasive fungal infection that causes severe systemic infections and poses health risks and threat to life. Despite its impact on human health, MM infection is neglected and underrepresented compared to other infections. Invasiveness of MM severely compromises systemic and metabolic functions of the hepatic system, degeneration of lungs, trachea, and alveoli epithelial cell with increased fatality rate, and associated risk with therapeutic use of glucocorticoids in COVID-19. In Africa, the prevalence rate remains unknown due to late prognosis and non-reportability of the disease. Hence, there is a need to include MM diagnosis in patients with COVID-19, increase surveillance of emerging clonal strains, and regulate the routine glucocorticoids use in COVID-19 infection therapy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Spatial analysis is essential to understand the spreading of the COVID-19 pandemic. Due to numerous factors of multi-disciplines involved, the current pandemic is yet fully known. Hence, the current study aimed to expand the knowledge on the pandemic by exploring the roles of forests and CO2 emission in the COVID-19 case-fatality rate (CFR) at the global level. Data were captured on the forest coverage rate and CO2 emission per capita from 237 countries. Meanwhile, extra demographic and socioeconomic variables were also included to adjust for potential confounding. Associations between the forest coverage rate and CO2 emission per capita and the COVID-19 CFR were assessed using spatial regression analysis, and the results were further stratified by country income levels. Although no distinct association between the COVID-19 CFR and forest coverage rate or CO2 emission per capita was found worldwide, we found that a 10% increase in forest coverage rates was associated with a 2.37‰ (95%CI: 3.12, 1.62) decrease in COVID-19 CFRs in low-income countries;and a 10% increase in CO2 emission per capita was associated with a 0.94‰ (95%CI: 1.46, 0.42) decrease in COVID-19 CFRs in low-middle-income countries. Since a strong correlation was observed between the CO2 emission per capita and GDP per capita (r = 0.89), we replaced CO2 emission with GDP and obtained similar results. Our findings suggest a higher forest coverage may be a protective factor in low-income countries, which may be related to their low urbanization levels and high forest accessibilities. On the other hand, CO2 can be a surrogate of GDP, which may be a critical factor likely to decrease the COVID-19 CFR in lower-middle-income countries. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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Covid19 pandemic is infecting a large community across the globe. Nearly 29 million got affected due to covid in India. The cases are increasing day by day. There are confirmed cases along with recovery and fatality rate. Prediction of the growth /fatality rate is a challengeable one. This paper implements an Artificial Intelligence (AI) strategy for analyzing and predicting the covid cases across the nation on daily basis at various rates. It includes (a) Analysis of growth rate, (b) Prediction of Confirmed rate, (c) Prediction of Deceased Rate, (d) Analysis of Recovery rate, etc. Logistic regression (LR) is a classification problem that performs well on medical data. The proposed work implements logistic regression along with prophet methods for analyzing the time-based covid cases across India. This proposed work analyzes the active cases and perform them effectively with an accuracy of 0.96. © 2021 IEEE.
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Since the beginning of the 21st century the world had to face several outbreaks ofviral diseases like Chikungunya, Ebola, Nipah, H7H9 Bird flu, H1N1, SARS, MERS and above all the Coronavirus or SARS-CoV-2 which has brought the whole world to a standstill, as of now it as infected around 200 million people and has caused over 4 million deaths. The countries of USA, India, Brazil and Russia have been the most seriously affected countries of all. outbreak of Covid-19 a 'Public Health Emergency of International Concern' on 30th January 2020 and a 'pandemic' on 11th march 2020. Although the SARS-CoV-2 (Severe acute respiratory syndrome Coronavirus 2) or the Coronavirus has a very low fatality rate i.e. 2.9%, but the most problematic aspect of the Coronavirus is its unusually high infectivity rate. Such unique attributes of the virus called for more stringent and advanced methods of investigation, research and analysis and this is where Artificial Intelligence (AI) comes in;the scientific community strongly believe that AI and data science can be used to fight against the Coronavirus and can fill in the blanks still left by science. In current study of approach, we have emphasis the role of AI during the pandemic and implemented the study. Further, a trend analysis is conducted from year 2020 till 2021 to analyze the pattern for future forecasting. © 2022 IEEE.
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The public health interventions to mitigate coronavirus disease 2019 (COVID-19) could also potentially reduce the global activity of influenza. However, this strategy's impact on other common infectious diseases is unknown. We collected data of 10 respiratory infectious (RI) diseases, influenza-like illnesses (ILIs), and seven gastrointestinal infectious (GI) diseases during 2015-2020 in China and applied two proportional tests to check the differences in the yearly incidence and mortality, and case-fatality rates (CFRs) over the years 2015-2020. The results showed that the overall RI activity decreased by 7.47%, from 181.64 in 2015-2019 to 168.08 per 100 000 in 2020 (p < 0.001); however, the incidence of influenza was seen to have a 16.08% escalation (p < 0.001). In contrast, the average weekly ILI percentage and positive influenza virus rate decreased by 6.25% and 61.94%, respectively, in 2020 compared to the previous 5 years (all p < 0.001). The overall incidence of GI decreased by 45.28%, from 253.73 in 2015-2019 to 138.84 in 2020 per 100 000 (p < 0.001), and with the greatest decline seen in hand, foot, and mouth disease (HFMD) (64.66%; p < 0.001). The mortality and CFRs from RI increased by 128.49% and 146.95%, respectively, in 2020, compared to 2015-2019 (p < 0.001). However, the mortality rates and CFRs of seven GI decreased by 70.56% and 46.12%, respectively (p < 0.001). In conclusion, China's COVID-19 elimination/containment strategy is very effective in reducing the incidence rates of RI and GI, and ILI activity, as well as the mortality and CFRs of GI diseases.
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COVID-19 , Communicable Diseases , Influenza, Human , COVID-19/epidemiology , COVID-19/prevention & control , China/epidemiology , Communicable Diseases/epidemiology , Humans , Influenza, Human/epidemiology , Influenza, Human/prevention & control , Public Health , SARS-CoV-2ABSTRACT
As the world struggles with the SARS-CoV2 pandemic, public health officials and governments continue to refine the key metrics that are used to capture and compare the state of the pandemic and the effects of responses within and between countries and regions. This work presents a novel fatality metric, the COVID-19 burden, which normalises SARS-CoV2 fatalities with respect to historical mortality rates over the same period of time. We argue that this measure provides an improved basis for comparing fatality rates between countries, and we present an analysis of this measure across 174 countries, using data up to November 15, 2020, to better understand the impact of the virus in different countries and regions. © 2020 ACM.