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From the beginning of the Iraq war, in March of 2003, to the present day, controversy has swirled around the death toll of the war. This paper narrows down the range of uncertainty for the numbers and trends in violent deaths in the war. I assemble and appraise all primary sources that cover the period from March of 2003 onwards—six sample surveys plus a casualty recording project (Iraq Body Count [IBC]). Data permitting, I present cumulative monthly figures with, for the surveys, 95% bootstrapped uncertainty intervals. The analysis uncovers a core of high-quality mainstream sources that are highly consistent with each another. In addition, there are three outlier surveys that are compromised by serious flaws and produce estimates far outside the mainstream. Discarding the outlying and flawed surveys reveals a clear picture of the violent death toll from the Iraq war. IBC figures, extended to include combatants, occupy a central position within the mainstream range of estimates. The strong consistency across the high-quality sources provides a rare validation of three war-death-measurement methodologies—household-based surveys, sibling-based surveys, and casualty recording. Methodological success notwithstanding, we must transcend the numbers to truly comprehend the human costs of the war. [ABSTRACT FROM AUTHOR]

During the 2022 summer, Europe experienced heatwaves with record temperatures, and a study has argued that they caused about 62,000 deaths between 30 May and 4 September. The total number of excess deaths during the same period was about 137,000, indicating that the heatwaves were a substantial contributor. Not ruling out that explanation entirely, this paper argues that it was unlikely a strong cause. First, if the heatwaves were a strong cause of numerous deaths, one would assume that the older and deprived were relatively likely to die. However, during the 2022 summer heatwaves in England, which were claimed to have caused about 2900 deaths, the oldest age cohort did not have a higher excess death rate than the middle age cohort, and the excess death rate actually decreased with deprivation status. Moreover, Iceland had among Europe's highest excess death rates during the summer, which cannot be attributed to heatwaves. During June, July, and August 2022, comparable southern hemisphere countries furthermore had high excess death rates, which cannot be attributed to heatwaves either, as it was during their winter. Also, Europe's excess death rate was higher during the 2022–2023 winter than during the 2022 summer, and intuitively not attributed to heatwaves, but neither to cold weather, as that winter was abnormally mild. Finally, the paper discusses the puzzling issue that about 56% more women than men, relative to the population, presumably died from the heatwaves. [ABSTRACT FROM AUTHOR]

Severe traumatic brain injury (sTBI) is a silent epidemic, causing approximately 300,000 intensive care unit (ICU) admissions annually, with a 30% mortality rate. Despite worldwide efforts to optimize the management of patients and improve outcomes, the level of evidence for the treatment of these patients remains low. The concomitant occurrence of thromboembolic events, particularly pulmonary embolism (PE), remains a challenge for intensivists due to the risks of anticoagulation to the injured brain. We performed a literature review on sTBI and concomitant PE to identify and report the most recent advances on this topic. We searched PubMed and Scopus for papers published in the last five years that included the terms "pulmonary embolism" and "traumatic brain injury" in their title or abstract. Exclusion criteria were papers referring to children, non-sTBI populations, and post-acute care. Our search revealed 75 papers, of which 38 are included in this review. The main topics covered include the prevalence of and risk factors for pulmonary embolism, the challenges of timely diagnosis in the ICU, the timing of pharmacological prophylaxis, and the treatment of diagnosed PE. [ABSTRACT FROM AUTHOR]

Background: A nanny state imposes restrictions on people's liberty and freedom of action in order to advance their interest and welfare. The extent to which this is desirable, or even ethically acceptable, is debated in the literature. This paper formulates and tests the following hypothesis: the more of a nanny a state has been in the past, the more likely it is that the incumbent government will respond to a new, unknown threat with interventions of a paternalist nature, irrespective of other factors that might contribute to shaping government's response. This hypothesis is then taken to the data using the first wave of COVID-19 as an empirical test. Methods: Data are collected from secondary sources for a sample of 99 countries. Nanny statism is measured by the number of paternalist laws and regulations adopted by a country in the past. The response to COVID is proxied by the time of adoption of control and containment measures and their stringency. The public health outcome is measured by the COVID-19 death toll at the end of June 2020. These variables, plus several controls, are then used to estimate a set of linear and probit regressions and a proportional hazard model of the timing of adoption of control and containment measures. Results: An increase in nanny statism by 0.1 (on a scale from 0 to 10) on average increases the probability of adoption of control and containment measures by 0.077 (i.e. 7.7 percentage points). The central tenement of the hypothesis is therefore consistent with the empirical evidence. The linear and probit regressions also show that there is no evidence of a significant effect of nanny statism on the stringency of the measures adopted. Irrespective of stringency, however, early adoption of control and containment measures is found to reduce the death toll of COVID-19 in the first half of 2020: an increase in nanny statism by 0.1 reduces the COVID death toll by approximately 7%. Conclusions: A tradition of nanny statism potentially leads to a more timely and effective public policy response to a new, unknown crisis. Further tests of the hypothesis should look at the relationship between nanny statism and public health outcomes from natural disasters. [ABSTRACT FROM AUTHOR]

A database of lightning-related fatalities and injuries in Turkey was constructed by collecting data from the Turkish State Meteorological Service, newspaper archives, European Severe Weather Database, and the internet. The database covers January 1930 to June 2014. In total, 742 lightning incidents causing human fatalities and injuries were found. Within these 742 incidents, there were 895 fatalities, 149 serious injuries, and 535 other injuries. Most of the incidents (89%) occurred during April through September, with a peak in May and June (26 and 28%) followed by July (14%). Lightningrelated fatalities and injuries were most frequent in the afternoon. Most of the incidents (86%) occurred in the rural areas, with only 14% in the urban areas. Approximately, two thirds of the victims with known gender were male. Because of the unrepresentativeness of the historical data, determining an average mortality rate over a long period is not possible. Nevertheless, there were 31 fatalities (0.42 per million) in 2012, 26 fatalities (0.35 per million) in 2013, and 25 fatalities (0.34 per million) in 2014 (as of June). There were 36 injuries (0.49 per million) in each of 2012 and 2013, and 62 injuries (0.84 per million) in 2014 (as of June). [ABSTRACT FROM AUTHOR]

In early 2021, the United Kingdom (UK) had the highest per capita death rate from Covid-19 of any large country. Yet it had previously been ranked as one of the best prepared countries for a future pandemic. This gap between preparedness and performance has been the subject of intense debate, including as part of the UK Covid-19 Inquiry. In this paper, we contribute to this ongoing process of reflection by identifying the imagined public(s) within the UK's scientific advice system. Drawing on scholarship in Science and Technology Studies (STS) that critiques framings of a singular or homogeneous 'public', we review meeting minutes and media briefings to reveal two imagined publics, co-constructed by the UK's science advisors and policymakers in early 2020: first, a 'freedom-loving' public resistant to stringent policy interventions; and second, a public that was—in an echo of wartime rhetoric— 'all in it together'. These imagined publics reflect a series of framing assumptions that help to make sense of the UK's pandemic response. We focus particularly on the tensions between the homogeneous and multi-faceted imagined public, and the compound health and social inequalities that predated the pandemic but became starker and more visible as it unfolded. Our paper charts these tensions and demonstrates how these imagined publics went through stages of cohesion and fracture in the fraught early months of the pandemic. We conclude by considering the implications of this analysis for understanding the UK's response to Covid-19, and for the future of scientific advice and emergency preparedness. Why does this matter? Studies of scientific advice reveal that how scientists and decision makers imagine the public and their concerns affect the communication of scientific advice, and the construction and value placed on relevant knowledge. Advisory scientists frame their models and their advice in terms of what they regard as politically possible. [ABSTRACT FROM AUTHOR]

The goal of this paper is to understand the interplay between seasonality and diapause on the population dynamics of ticks. To analyse this challenge, we study a mathematical model in which the population is divided into two groups: individuals with regular development and individuals undergoing diapause. A key ingredient in the model is that the common birth and death rates depend on time in order to describe the seasonal fluctuations of the environment. From a mathematical point of view, this paper offers a novel methodology to derive criteria of global attraction. In some classical models, we give delay-dependent conditions that cover the best delay-independent criteria of global attraction. We discuss how our approach improves existing results in relevant literature. Moreover, our results are simple and versatile enough to match experimental observations. To illustrate this potential, we analyse our model with parameters coming from real observations. A message of this paper is that seasonality plays a critical role in the population dynamics of ticks. [ABSTRACT FROM AUTHOR]

As a no-disturbance integrated-retrofitting technique, an external rocking frame was widely used on reinforced concrete (RC) structures. Yet, with the increasing demand for seismic strengthening of existing buildings, it has become a concern to evaluate the seismic strengthening schemes based on seismic resilience. Firstly, the dynamic equation of the structural system was derived, and the deformation control mechanism was revealed; thus, the corresponding design method was put forward for the rocking frame reinforcement. Secondly, after soft-first-floor structures were reinforced by rocking frames, the evaluation method of the reinforcement scheme was investigated based on seismic resilience. Finally, the feasibility of the assessment method was verified by a soft-first-floor frame structure, and a comparison was made between the method proposed in this paper and the conventional method. The results find that the soft-first-floor structure reinforced by the rocking frame increased by 10% in the inter-layer displacement and improved by 55.6% and 63.0% in the injury and mortality rates, compared to the buckling-restrained brace scheme. This indicates that the reinforcement scheme of soft layer structures with rocking frames is feasible and effective, and the reinforcement evaluation method proposed in this paper can quantitatively reflect the improvement in seismic performance. [ABSTRACT FROM AUTHOR]

Earthquake death tolls are a basic statistical measure of the capability of a country to manage seismic risk. The extremely high Turkish death toll of 50,000 from the Kahramanmaras earthquake doublet of 6 February 2023 is the product of a cascade of detrimental factors. These need to be explained if lessons from this disaster are to be learned. This is the purpose and objective of this paper, which is a contribution to the interdisciplinary Frontiers research topic on integrated perspectives on the 2023 Turkey and Syria earthquakes: advancing understanding and preparedness across earth sciences, engineering and public health. This paper covers these three disciplines by focusing on casualties, and identifying crucial aspects of earth sciences and engineering which contributed to the high death toll. First, there was a surprising combination of multiple fault segment ruptures, and a high level of ground motion relative to the risk-based Turkish code, indicative of the under-representation of the M7.5+ earthquake doublet event in the national probabilistic seismic hazard model. This combination of fault segment ruptures was missing from all seismic source models. Furthermore, the capability of buildings to cope with strong ground motion was much reduced by informal construction methods, which eroded the margin of safety needed to avoid building collapse. The extent of building code non-compliance was widely underestimated in seismic risk models. Noncompliance is often hard to identify, but construction amnesties make noncompliance more transparent and trackable. The disastrous outcome of the Kahramanmaras earthquake doublet of 6 February 2023 has drawn global attention to systemic building code non-compliance, and the open official acceptance of informal housing. To demonstrate that this key systemic risk is far from being just a Turkish problem, notably in Istanbul, the challenge of Italian informal housing is highlighted within the context of international building code non-compliance. [ABSTRACT FROM AUTHOR]

In this paper, we consider the influence of a nonlinear contact rate caused by multiple contacts in classical SIR model. In this paper, we unversal unfolding a nilpotent cusp singularity in such systems through normal form theory, we reveal that the system undergoes a Bogdanov-Takens bifurcation with codimension 2. During the bifurcation process, numerous lower codimension bifurcations may emerge simultaneously, such as saddle-node and Hopf bifurcations with codimension 1. Finally, employing the Matcont and Phase Plane software, we construct bifurcation diagrams and topological phase portraits. Additionally, we emphasize the role of symmetry in our analysis. By considering the inherent symmetries in the system, we provide a more comprehensive understanding of the dynamical behavior. Our findings suggest that if this occurrence rate is applied to the SIR model, it would yield different dynamical phenomena compared to those obtained by reducing a 3-dimensional dynamical model to a planar system by neglecting the disease mortality rate, which results in a stable nilpotent cusp singularity with codimension 2. We found that in SIR models with the same occurrence rate, both stable and unstable Bogdanov-Takens bifurcations occur, meaning both stable and unstable limit cycles appear in this system. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) have the trouble of limited battery power, and wireless charging provides a promising solution to this problem, which is not easily affected by the external environment. In this paper, we study the recharging of sensors in wireless rechargeable sensor networks (WRSNs) by scheduling two mobile chargers (MCs) to collaboratively charge sensors. We first formulate a novel sensor charging scheduling problem with the objective of maximizing the number of surviving sensors, and further propose a collaborative charging scheduling algorithm(CCSA) forWRSNs. In the scheme, the sensors are divided into important sensors and ordinary sensors. TwoMCs can adaptively collaboratively charge the sensors based on the energy limit of MCs and the energy demand of sensors. Finally, we conducted comparative simulations. The simulation results showthat the proposed algorithm can effectively reduce the death rate of the sensor. The proposed algorithm provides a solution to the uncertainty of node charging tasks and the collaborative challenges posed by multiple MCs in practical scenarios. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on modeling and simulation in disaster and emergency management. The COVID-19 pandemic of 2020 has wrought damage globally to a degree that is totally unprecedented in human history.Within three months that the first known death from the illness caused by the so-called novel Coronavirus was reported by state media in China on the 11th of January 2020, close to 1.8 million people worldwide had tested positive, with more than 100,000 deaths reported. By the time the pandemic will have been declared to be under control, millions more are expected to have been infected and tens (possibly hundreds) of thousands more will have perished as a result of the disease. Not only are the numbers of COVID-19 infected people and deaths staggering. Economies throughout the world have suffered tremendously, with many companies forced to shut down, or at the very least significantly scale down, their production of goods and services as a result of the need for social distancing and self-isolation required to prevent the unmitigated transmission of the disease. All this has led to hundreds of millions worldwide losing their employment or other sources of livelihood. Governments of all nations, large and small, developed and underdeveloped, have needed to pump into their economy and onto their struggling citizens whatever financial resources they could manage to generate. [ABSTRACT FROM AUTHOR]

Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts. [ABSTRACT FROM AUTHOR]

This paper considers the distribution of mortality across social groups classified by caste, gender and sector of origin in India in the mid-2010s: as such, the essay is intended to be both a methodological/measurement-oriented study and a substantive empirical assessment of an important dimension of human functioning in India. The analysis is carried out employing micro-data on the age-distributions of population and death-rates available in the National Family Health Survey of 2015–16 (NFHS-4). Mortality in the paper is measured in terms of the crude death rate, an indicator of "inefficiency" in the age-distribution of deaths, and an "age-adjusted" death rate which takes account of both the mean and the dispersion of a distribution. The last-mentioned indicator is taken to be the preferred measure of mortality. The analysis in the paper suggests that mortality outcomes across castes replicate the caste hierarchy and that there is a sharp rural-urban divide in the distribution of death. Mortality sex-ratios are found to be relatively more favourable for the lower than the higher castes. The results presented in the paper are not unexpected, but they provide quantitative confirmation of one's worst suspicions regarding the skewed distribution of mortality across social groups in India. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to assess the economic impact of uniform COVID-controlling policies that were implemented by the US government in 2020 and compare it with hypothetical targeted policies that consider the heterogenous effect of COVID-19 on different age groups. Design/methodology/approach: The author began by showing that the adjusted SEQIHR model is a good fit to the US COVID-induced daily death data in that it can capture the nonlinearities of the data very well. Then, he used this model with extra parameters to evaluate the economic effects of COVID-19 through its impact on the job market. Findings: The results show that targeted COVID-controlling policies could reduce the US death rate and GDP loss to 0.03% and 2%, respectively. By comparing these results with uniform COVID-controlling policies, which led to a 0.1% death rate and 3.5% GDP loss, we could conclude that the death rate reduction is 0.07%. Approximately 378,000 Americans died because of COVID-19 during 2020, therefore, reducing the death rate to 0.03% means saving a significant proportion of the COVID-19 casualties, around 280,000 lives. Originality/value: To the best of the author's knowledge, this paper is the first study to assess the economic impacts of COVID-controlling policies by using the multirisk SEQIHR model. [ABSTRACT FROM AUTHOR]

In this paper, with the method of epidemic dynamics, we assess the spread and prevalence of COVID-19 after the policy adjustment of prevention and control measure in December 2022 in Taiyuan City in China, and estimate the excess population deaths caused by COVID-19. Based on the transmission mechanism of COVID-19 among individuals, a dynamic model with heterogeneous contacts is established to describe the change of control measures and the population's social behavior in Taiyuan city. The model is verified and simulated by basing on reported case data from November 8th to December 5th, 2022 in Taiyuan city and the statistical data of the questionnaire survey from December 1st to 23rd, 2022 in Neijiang city. Combining with reported numbers of permanent residents and deaths from 2017 to 2021 in Taiyuan city, we apply the dynamic model to estimate theoretical population of 2022 under the assumption that there is no effect of COVID-19. In addition, we carry out sensitivity analysis to determine the propagation character of the Omicron strain and the effect of the control measures. As a result of the study, it is concluded that after adjusting the epidemic policy on December 6th, 2022, three peaks of infection in Taiyuan are estimated to be from December 22nd to 31st, 2022, from May 10th to June 1st, 2023, and from September 5th to October 13th, 2023, and the corresponding daily peaks of new cases can reach 400 000, 44 000 and 22 000, respectively. By the end of 2022, excess deaths can range from 887 to 4887, and excess mortality rate can range from 3.06% to 14.82%. The threshold of the infectivity of the COVID-19 variant is estimated 0.0353, that is if the strain infectivity is above it, the epidemic cannot be control with the previous normalization measures. [ABSTRACT FROM AUTHOR]

COVID-19 has become one of the critical health issues globally, which surfaced first in latter part of the year 2019. It is the topmost concern for many nations’ governments as the contagious virus started mushrooming over adjacent regions of infected areas. In 1980, a vaccine called Bacillus Calmette-Guérin (BCG) was introduced for preventing tuberculosis and lung cancer. Countries that have made the BCG vaccine mandatory have witnessed a lesser COVID-19 fatality rate than the countries that have not made it compulsory. This paper’s initial research shows that the countries with a longtermcompulsory BCGvaccination system are less affected by COVID-19 than those without a BCG vaccination system. This paper discusses analytical data patterns for medical applications regarding COVID-19 impact on countries with mandatory BCG status on fatality rates. The paper has tackled numerous analytical challenges to realize the full potential of heterogeneous data. An analogy is drawn to demonstrate how other factors can affect fatality and infection rates other than BCG vaccination only, such as age groups affected, other diseases, and stringency index. The data of Spain, Portugal, and Germany have been taken for a case study of BCG impact analysis. [ABSTRACT FROM AUTHOR]

Skin cancer is the predominant form of cancer worldwide, including 75% of all cancer cases. This study aims to evaluate the effectiveness of the spectrum-aided visual enhancer (SAVE) in detecting skin cancer. This paper presents the development of a novel algorithm for snapshot hyperspectral conversion, capable of converting RGB images into hyperspectral images (HSI). The integration of band selection with HSI has facilitated the identification of a set of narrow band images (NBI) from the RGB images. This study utilizes various iterations of the You Only Look Once (YOLO) machine learning (ML) framework to assess the precision, recall, and mean average precision in the detection of skin cancer. YOLO is commonly preferred in medical diagnostics due to its real-time processing speed and accuracy, which are essential for delivering effective and efficient patient care. The precision, recall, and mean average precision (mAP) of the SAVE images show a notable enhancement in comparison to the RGB images. This work has the potential to greatly enhance the efficiency of skin cancer detection, as well as improve early detection rates and diagnostic accuracy. Consequently, it may lead to a reduction in both morbidity and mortality rates. [ABSTRACT FROM AUTHOR]

In one of the first papers on the impact of early-life conditions on individuals' health in older age, Barker and Osmond [Lancet, 327, 1077-1081 (1986)] show a strong positive relationship between infant mortality rates in the 1920s and ischemic heart disease in the 1970s. We merge historical data on infant mortality rates to 370,000 individual records in the UK Biobank using information on local area and year of birth. We replicate the association between the early-life infant mortality rate and laterlife ischemic heart disease in our sample. We then go "beyond Barker," by showing considerable genetic heterogeneity in this association that is robust to within-area as well as within-family analyses. We find no association between the polygenic index and heart disease in areas with the lowest infant mortality rates, but a strong positive relationship in areas characterized by high infant mortality. These findings suggest that advantageous environments can cushion one's genetic disease risk. [ABSTRACT FROM AUTHOR]

This paper combines data on GDP and unemployment and from Google's COVID-19 Community Mobility Reports with data on deaths from COVID-19 to study the macroeconomic outcomes of the pandemic. We present results from an international perspective using data at the country level as well as results for individual US states and key cities throughout the world. The data from these different levels of geographic aggregation offer a remarkably similar view of the pandemic despite the substantial heterogeneity in outcomes. Countries like South Korea, Japan, Germany, and Norway and cities such as Tokyo and Seoul have comparatively few deaths and low macroeconomic losses. At the other extreme, New York City, Lombardy, the United Kingdom, and Madrid have many deaths and large macroeconomic losses. There are fewer locations that seem to succeed on one dimension but suffer on the other, but these include California and Sweden. The variety of cases potentially offers useful policy lessons regarding how to use non-pharmaceutical interventions to support good economic and health outcomes. [ABSTRACT FROM AUTHOR]

India is a densely populated country where traffic is a major issue in our country. We can see traffic accidents are increased day by day and becoming more pathetic nowadays. Even though the government are running so many campaigns to raise awareness in public but still we can't able to reduce the death rate due to road accident are not decreased at all. Main cause of the accident is a high speed, rash driving and mental pressure Some of the papers deals with how to track accidents before it could happen and alert the driver to maintain some speeds limit so that person can avoid an accident by taking early precaution to avoid the accident but this paper focuses on how to rescue person life by immediately admitted to a nearby hospital after meeting with an accident. the paper deals with analysing an image uploaded by the end-users once the image is uploaded into the module. once it is uploaded it should satisfy many criteria if these criteria are satisfied only then it continues the process otherwise it terminates the process .criteria to be satisfied to continue the execution one it is a recently uploaded image or it should not be downloaded image, second it is real or fake, third it should be major injury if it is a minor injury then it will terminate the process .these all things happen in the second module i.e. image processing. once the image processing is done it will track the exact location where the accident has met through GPSs send the exact address and ping to the ambulance to arrive at that exact location. [ABSTRACT FROM AUTHOR]

A balanced ecosystem with coexisting constituent species is often perturbed by different natural events that persist only for a finite duration of time. What becomes important is whether, in the aftermath, the ecosystem recovers its balance or not. Here we study the fate of an ecosystem by monitoring the dynamics of a particular species that encounters a sudden increase in death rate. For exploration of the fate of the species, we use Monte-Carlo simulation on a three-species cyclic rock-paper-scissor model. The density of the affected (by perturbation) species is found to drop exponentially immediately after the pulse is applied. In spite of showing this exponential decay as a short-time behavior, there exists a region in parameter space where this species surprisingly remains as a single survivor, wiping out the other two which had not been directly affected by the perturbation. Numerical simulations using stochastic differential equations of the species give consistency to our results. [ABSTRACT FROM AUTHOR]

In this paper, we study the impact of the Coronavirus Aid, Relief, and Economic Security (CARES) Act economic impact payments to individuals on the spread of COVID-19 and subsequent mortality. There is a large economic literature linking income shocks and mortality. Using an event study approach and controlling for time-varying stay-at-home directives, we find no statistically significant change in confirmed case rates and no meaningful change to death rates from COVID-19. Our research suggests that distributing the individual stimulus payments at the same time to everyone had no effect on the spread of COVID-19. [ABSTRACT FROM AUTHOR]

During a pandemic outbreak, it is important for health officials to know the proportions of deaths among infected individuals and to understand how these proportions change overtime, to accurately predict the impact of the pandemic and to implement effectively new intervention policies and health protocols and to adjust them accordingly. However, most studies where efforts have been made to estimate accurately the case fatality rates did not address the issue of measuring the dynamics of the pandemic deadliness during its course. Daily data on COVID-19 cases and deaths were collected from selected MENA countries. In this paper, two new measures of the pandemic fatality are developed based on the estimated time it takes hospitalized infected patients to eventually die from the disease. The first measure assigns COVID-19 deaths to its most significant lagged number of cases based on a fixed-effects panel data model. The second fatality measure relates pandemic deaths and cases based on their respective change points. The results find notable variations of the pandemic lethality between the Middle East countries, likely due to the difference in the quality of health care. Although crude case-fatality rate does not identify the pandemic lethality variations during the ongoing of the disease, this paper develops two novel measures for COVID-19 case fatality which can identify the dynamics and the variations of the pandemic deadliness. [ABSTRACT FROM AUTHOR]

The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R-squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people's lives in different sectors were also discussed in this paper. [ABSTRACT FROM AUTHOR]

BACKGROUND Kinship groups can have considerable importance (e.g., generational support, inheritance, and information for key life events). During demographic transitions, kinship networks are reshaped by changes in mortality and fertility rates. OBJECTIVE This paper analyzes consanguineous and female kin and explores the effect on the size and structure of living kin before and after a demographic transition. We compute the kinship network of a female individual with average demographic traits (here called the Focal) at all ages but focus on only demographically dense ages (age 15 to 39). METHODS The analysis uses a time-invariant model (Caswell 2019) to calculate the expected number of living kin using fertility and mortality rates. We use three examples (China, India, and Japan) with fertility and mortality from World Population Prospect 2019, based on empirical data. CONCLUSIONS We highlight two key results. First, at a demographically dense age of the Focal, the maximum expected number of living aunts, sisters, or daughters is approximately the net reproductive rate R0 (linear), while the number of living cousins is approximately R0² (quadratic). Second, such effects on kinship size depend on the magnitude of fertility change and on the age-pattern of changes in mortality. And the effects of fertility and mortality on the number of kin are not additive. CONTRIBUTION This paper shows a simple relationship between demographic transition and kinship size, which makes it possible to estimate kinship size based on the net reproductive rate. The quadratic relationship between the number of certain kin (e.g., cousins, nieces) and the net reproductive rate is informative but not a priori obvious. [ABSTRACT FROM AUTHOR]

OBJECTIVES This paper investigates ethnic and regional disparities in fatality rates in the Russian military in 2022-2023 during the war in Ukraine. METHODS The analysis uses a new crowdsourced dataset comprising the names of over 20,000 Russian soldiers killed in Ukraine between February 2022 and April 2023. This dataset was compiled by a team of volunteers who gathered information from social media and other accessible sources. The dataset is incomplete and therefore the findings reported in this paper are tentative. Mortality rates and relative risks are estimated by ethnic group and region, and a linear model is fitted to assess the correlation between the ethnic composition of the population, socioeconomic factors, and regional fatality rates. RESULTS The study reveals significant disparities in military fatality rates across Russian regions, with the highest mortality observed among soldiers originating from economically disadvantaged areas in Siberia and the Russian Far East and the lowest among soldiers from Moscow and St. Petersburg. Buryats and Tuvans are overrepresented among the fatalities relative to their population share. However, when regional socioeconomic disparities are accounted for, ethnic differences in mortality rates are considerably reduced. CONCLUSIONS The observed regional and ethnic fatality disparities appear to be driven by socioeconomic inequalities between regions. CONTRIBUTION This paper evaluates social inequalities in fatalities in the Russian military in Ukraine and compares these findings with research on US military casualties. [ABSTRACT FROM AUTHOR]

AbstractThe incredible mobilisation to welcome Ukrainian refugees following the start of the Russia-Ukraine war in 2022 demonstrated how housing is an affective sociomaterial infrastructure. In cruel contrast, such mobilisation also exposed the inherently racist and colonial face of accommodating refugees and migrants, and the structural inequalities that racial capitalism continues to reproduce. Drawing reflections across several geographies and temporalities through illustrative vignettes, this paper begs the questions under what conditions do we accept that some people are housed, others not? What kind of power structures allow certain bodies to be welcomed? This paper borrows from postcolonial, black and critical theory to examine refugee housing through the lens of necropolitical infrastructure. It connects Mbembe’s notion of necropower with Power and Mee’s notion of housing as infrastructure of care, and filters this through reflections across Calais, Brescia and Athens, to illustrate the bio- and necropolitical side of the refugee housing infrastructure in Europe. The paper ultimately argues that refugee housing, because it is an infrastructure and because it is part of the broader extractive and exploitative system of (humanitarian) care, is deeply imbricated in the production of racialised subjects. Specifically, it is complicit with the extraction, (re)incorporation, abandonment and slow death of Black and Brown refugee lives. [ABSTRACT FROM AUTHOR]

The growth and fertility rates of past populations can be estimated by analyzing the age-at-death distribution of skeletal samples. The procedure involves regressing growth or fertility rate on the age-at-death ratio, which is a proxy that captures the number of skeletons in two broad age-at-death categories (e.g., D5+/D20+). Galeta and Pankowská (2023, doi: 10.1371/journal.pone.0286580) recently developed a new prediction algorithm. They proposed to estimate growth and fertility rates using a unique prediction formula for each skeletal sample. Each formula is based on a unique reference set of simulated skeletal samples that match the size of the target real skeletal sample. The simulated skeletal samples are generated from populations with similar mortality levels to those assumed in the time period represented by the target skeletal sample. A correct setting of the sample size and the level of mortality increases the accuracy of the estimate. The approach, however, is computationally intensive because it involves generating many simulated reference skeletal samples. In this paper, we present the demrat package, written in the R programming language, which automates the simulation. The functions of the package provide a complete workflow from a real skeletal sample to the prediction of demographic rates. In addition, we offer a web application that allows non-R users to deploy predictions using the demrat package with a user-friendly, point-and-click graphical interface. Although the demrat package allows for estimating demographic rates for a single skeletal sample, we recommend predicting demographic rates in a larger set of skeletal samples and producing smoothed general demographic trends over large areas and/or long periods of time. [ABSTRACT FROM AUTHOR]

By January 2024, the COVID-19 pandemic claimed more than 1.1 million deaths in the United States (U.S.). People in prison are particularly vulnerable to COVID-19 as they have no ability to socially distance, secure masks, disinfect their environment or have as much access to tests or vaccinations as is available in the community. In addition, many of these individuals reside in crowded conditions with little ventilation, which makes the spread of the virus more likely. In this paper, we used data from two projects, including the UCLA Law COVID Behind Bars Data Project and the COVID Prison Project, and supplemented these with publicly available data to examine the number of deaths and infection rates caused by COVID-19 among people in prison and prison staff in the U.S., as reported by the population of those facilities. We found that the incidence of infections and death rates in prisons were affected by crowding, prison security type (maximum, medium, minimum, or mixed) and level of prison (state or federal). People in prison who were less likely to have as much human contact (e.g., maximum-security prisons) were also less likely to be afflicted with COVID-19. People in prison were twice as likely to be infected by COVID-19 but had a similar death rate compared to the general public. Prison overcrowding increased the infection rate. The most effective state health policy was to quarantine people who had close contact with confirmed, positive cases. Further, state prisons demonstrated a higher death rate compared to federal prisons. Greater efforts to ameliorate COVID-19 and similar pathogens should be directed at state prisons with lower-level security and prisons with closer contact with the community. Quarantining close-contacts and restricting movements were the most effective state-level responses to reduce infections in prisons during April 2020 to April 2022. [ABSTRACT FROM AUTHOR]

This paper concerns a diffusive predation model with nonlinear growth, cross-diffusion and protection zone terms. The main purpose is to investigate the effects of nonlinear growth and cross-diffusion on the coexistent solution when protection zone is present. Firstly, a priori estimate and the existence of positive solutions are discussed, including local and global existence. Then, some asymptotic properties of coexistent solutions induced by the mortality rate, nonlinear growth of predator and cross-diffusion are analyzed. It is revealed that there exist critical values related to certain principal eigenvalues such that the nonlinear growth, cross-diffusion and protection zone all have significant effects on the coexistent solutions; as far as the nonlinear growth concerned, we find that it has important influences on the coexistence region of two species undoubtedly. Biologically, this implies that these critical values greatly affect the survival of species. [ABSTRACT FROM AUTHOR]

Prostate cancer remains a prevalent health concern, emphasizing the critical need for early diagnosis and precise treatment strategies to mitigate mortality rates. The accurate prediction of cancer grade is paramount for timely interventions. This paper introduces an approach to prostate cancer grading, framing it as a classification problem. Leveraging ResNet models on multi-scale patch-level digital pathology and the Diagset dataset, the proposed method demonstrates notable success, achieving an accuracy of 0.999 in identifying clinically significant prostate cancer. The study contributes to the evolving landscape of cancer diagnostics, offering a promising avenue for improved grading accuracy and, consequently, more effective treatment planning. By integrating innovative deep learning techniques with comprehensive datasets, our approach represents a step forward in the pursuit of personalized and targeted cancer care. [ABSTRACT FROM AUTHOR]

Reliable estimates of age-specific vital rates are crucial in demographic studies, while ages are, in most cases, commonly grouped in bins of five years. Indeed, public health and national systems require single age-specific data to achieve accurate social planning. This paper introduces a deep learning approach for splitting the abridged death rates, providing a more comprehensive perspective on the indirect age-specific vital rates estimation from grouped data. Additionally, we contribute to the existing literature by introducing a multi-population (countries and genders) approach, providing reliable estimates considering the heterogeneity of longevity dynamics over age, years, and across populations. We also contribute to the state of the art in indirect estimation by introducing, for the first time, a multi-population indirect estimation leveraging subnational data. Our model accurately captures mortality dynamics by age over time and among different populations. We prove the model's ability to estimate reliable predictions of age-specific mortality rates by also studying how the hyperparameters' choice affects the model reliability and analyzing the age-specific relative differences between the real and the estimated mortality rates. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic led to 231,841 deaths and 940,243 hospitalisations in England, by the end of March 2023. This paper calculates the real-time infection hospitalisation risk (IHR) and infection fatality risk (IFR) using the Office for National Statistics Coronavirus Infection Survey (ONS CIS) and the Real-time Assessment of Community Transmission Survey between November 2020 to March 2023. The IHR and the IFR in England peaked in January 2021 at 3.39% (95% Credible Intervals (CrI): 2.79, 3.97) and 0.97% (95% CrI: 0.62, 1.36), respectively. After this time, there was a rapid decline in the severity from infection, with the lowest estimated IHR of 0.32% (95% CrI: 0.27, 0.39) in December 2022 and IFR of 0.06% (95% CrI: 0.04, 0.08) in April 2022. We found infection severity to vary more markedly between regions early in the pandemic however, the absolute heterogeneity has since reduced. The risk from infection of SARS-CoV-2 has changed substantially throughout the COVID-19 pandemic with a decline of 86.03% (80.86, 89.35) and 89.67% (80.18, 93.93) in the IHR and IFR, respectively, since early 2021. From April 2022 until March 2023, the end of the ONS CIS study, we found fluctuating patterns in the severity of infection with the resumption of more normative mixing, resurgent epidemic waves, patterns of waning immunity, and emerging variants that have shown signs of convergent evolution. The severity of SARS-CoV-2 infection varied over the course of the pandemic due to factors such as changes in variant characteristics and population immunity from previous infection or vaccination. Here, the authors estimate infection hospitalisation and infection fatality rates in England over time from the start of the pandemic until March 2023. [ABSTRACT FROM AUTHOR]

Cerebral infarction is a common neurological disease with high rates of morbidity, mortality, and recurrence, posing a great threat to human life and health. Cerebral infarction is the second leading cause of death in the world and the leading cause of long-term disability in humans. The results of the third national retrospective sampling survey on causes of death in 2008 showed that cerebral infarction has become the leading cause of death in China and its mortality rate is 4–5 times that of European and American countries. Therefore, this article proposed a study on the predictive value of Cmmi-MHR combined with thromboelastography parameters that was performed for acute cerebral infarction. This paper mainly proposed a high frame rate imaging technology and analyzed its algorithm. In this article, in the experimental part, an in-depth analysis of the predictive value of the Monocyte-to-high-density lipoprotein cholesterol ratio (MHR) combined with thromboelastography parameters was performed for acute cerebral infarction. The final experimental results showed that HDL (OR = 1.695%, P-trend = 0.049) had a probability of death within 90 days of hospitalization (OR = 0.81, 95% CI = 1.06–3.11, P-trend = 0.523). There were no significant differences in mortality rate after 90 days. Regardless of adjusting for confounders such as age, gender, and NIHSS score, there was no significant difference in the risk of MHR or monocyte count within 90 days of hospitalization. The conclusion indicates that the combination of Cmmi-MHR and thromboelastography parameters provides a new perspective and method for the diagnosis and treatment of cerebral infarction, and provides important support for personalized treatment and management of cerebral infarction. [ABSTRACT FROM AUTHOR]