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This paper presents a comprehensive analysis of flood frequency and a spatio-temporal characterization of historical torrential floods in the Šumadija region using water discharge datasets and documented events. A chronology of 344 recorded torrential flood events, spanning from 1929 to 2020, illustrates the region's vulnerability, with a death toll exceeding 43. The study defines the intra-annual primary and secondary peaks of torrential flood occurrences and explains their spatial distribution. Furthermore, the identification of suitable probability distribution functions underscores the necessity of tailored approaches for effective flood risk management in this diverse geographical environment. The study employed Flood Frequency Analysis (FFA) and goodness-of-fit tests, including the Kolmogorov–Smirnov (K-S) and Cramér–von Mises (CvM) tests, to assess the frequency and magnitude of flood events and evaluate diverse distribution functions. The main results include the identification of suitable probability distribution functions for each river within the region, emphasizing the need for tailored approaches in flood risk management. Additionally, discharge values for various return periods offer crucial insights for informed decision-making in flood risk management and infrastructure planning. [ABSTRACT FROM AUTHOR]

BackgroundThe COVID-19 pandemic has proved deadly all over the globe; however, one of the most lethal outbreaks occurred in Ecuador.AimsThis study aims to highlight the pandemic’s impact on the most affected countries worldwide in terms of excess deaths per capita and per day.MethodsAn ecological study of all-cause mortality recorded in Ecuador was performed. To calculate the excess deaths relative to the historical average for the same dates in 2017, 2018, and 2019, we developed a bootstrap method based on the central tendency measure of mean. A Poisson fitting analysis was used to identify trends on officially recorded all-cause deaths and COVID-19 deaths. A bootstrapping technique was used to emulate the sampling distribution of our expected deaths estimator μ⌢deaths by simulating the data generation and model fitting processes daily since the first confirmed case.ResultsIn Ecuador, during 2020, 115,070 deaths were reported and 42,453 were cataloged as excess mortality when compared to 2017–2019 period. Ecuador is the country with the highest recorded excess mortality in the world within the shortest timespan. In one single day, Ecuador recorded 1,120 deaths (6/100,000), which represents an additional 408% of the expected fatalities.ConclusionAdjusting for population size and time, the hardest-hit country due to the COVID-19 pandemic was Ecuador. The mortality excess rate shows that the SARS-CoV-2 virus spread rapidly in Ecuador, especially in the coastal region. Our results and the proposed new methodology could help to address the real situation of the number of deaths during the initial phase of pandemics.

BackgroundAs of May 9, 2021, the United States had 32.7 million confirmed cases of COVID-19 (20.7% of confirmed cases worldwide) and 580,000 deaths (17.7% of deaths worldwide). Early on in the pandemic, widespread social, financial, and mental insecurities led to extreme and irrational coping behaviors, such as panic buying. However, despite the consistent spread of COVID-19 transmission, the public began to violate public safety measures as the pandemic got worse.ObjectiveIn this work, we examine the effect of fear-inducing news articles on people's expression of anxiety on Twitter. Additionally, we investigate desensitization to fear-inducing health news over time, despite the steadily rising COVID-19 death toll.MethodsThis study examined the anxiety levels in news articles (n=1465) and corresponding user tweets containing "COVID," "COVID-19," "pandemic," and "coronavirus" over 11 months, then correlated that information with the death toll of COVID-19 in the United States.ResultsOverall, tweets that shared links to anxious articles were more likely to be anxious (odds ratio [OR] 2.65, 95% CI 1.58-4.43, P

This paper presents a comprehensive analysis of flood frequency and a spatio-temporal characterization of historical torrential floods in the Šumadija region using water discharge datasets and documented events. A chronology of 344 recorded torrential flood events, spanning from 1929 to 2020, illustrates the region’s vulnerability, with a death toll exceeding 43. The study defines the intra-annual primary and secondary peaks of torrential flood occurrences and explains their spatial distribution. Furthermore, the identification of suitable probability distribution functions underscores the necessity of tailored approaches for effective flood risk management in this diverse geographical environment. The study employed Flood Frequency Analysis (FFA) and goodness-of-fit tests, including the Kolmogorov–Smirnov (K-S) and Cramér–von Mises (CvM) tests, to assess the frequency and magnitude of flood events and evaluate diverse distribution functions. The main results include the identification of suitable probability distribution functions for each river within the region, emphasizing the need for tailored approaches in flood risk management. Additionally, discharge values for various return periods offer crucial insights for informed decision-making in flood risk management and infrastructure planning.

In order to explore the characteristics and laws of coal mine safety accidents in a more in-depth and objective way, the number of coal mine accidents and deaths in 2019 and 2020 published by the State Administration of Coal Supervision and the Coal Mine Safety Network in China were statistically analyzed from different perspectives, and the situation of coal mine safety production in China was analyzed by means of pie chart, bar chart and broken line chart combined with the current domestic situation of China. The analysis shows that: on the whole, the number of coal mine accidents and deaths in China has been at a relatively low level; in terms of accident types, roof, transportation and gas accidents are the main accident types that affect coal mine safety production in China. In terms of spatial distribution, the number of coal mine accidents in China is generally more in the north than in the south. The differences of geological structure and production capacity in the north and the south lead to different causes of the high occurrence of coal mine accidents. In terms of time distribution, the number of accidents in February and March is relatively small, while the number of accidents in June, July and August is frequent and concentrated. From the point of view of the specific time period, downhole work shifts are more prone to accidents.

There is a rich body of literature on numbers as tools of governance. But the attention of the corpus in question is almost entirely on the rational properties of quantification. This article shows that government by numbers is also, and inseparably, a government by feelings. The Covid-19 pandemic was also a datademic in the sense that numbers populated and spread through the public sphere. We focus on three cases. Death tolls were associated with fear, immunization rates were linked to hope, and the threshold of 100,000 deaths was credited with symbolic significance. This article, based on the French case, examines how data like these, frequently perceived as objective evidence, can at the same time be a source of emotional engagement and, as such, be used to inform modes of public governance in times of crises.

COVID-19 is a severe acute respiratory syndrome caused by the new Coronavirus. COVID-19 outbreak is a Public Health Emergency of International Concern, declared by WHO, that killed more than 2 million people worldwide. Since there are no specific drugs available and vaccination campaigns are in the initial phase, or even have not begun in some countries, the main way to fight the outbreak worldwide is still based on non-pharmacological strategies, such as the use of protective equipment, social isolation and mass testing. Modeling of the disease epidemics have gained pivotal importance to guide health authorities on the decision making and applying of those strategies. Here, we present the use of the Weibull distribution to model predictions of the COVID-19 outbreak based on daily new cases and deaths data, by non-linear regression using Metropolis-Markov Chain Monte Carlo simulations. It was possible to predict the evolution of daily new cases and deaths of COVID-19 in many countries as well as the overall number of cases and deaths in the future. Modeling predictions of COVID-19 pandemic may be of importance on the evaluation of governments and health authorities mitigation procedures, since it allows one to extract parameters that may help to guide those decisions and measures, slowing down the spread of the disease. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has proved deadly all over the globe; however, one of the most lethal outbreaks occurred in Ecuador., Aims: This study aims to highlight the pandemic's impact on the most affected countries worldwide in terms of excess deaths per capita and per day., Methods: An ecological study of all-cause mortality recorded in Ecuador was performed. To calculate the excess deaths relative to the historical average for the same dates in 2017, 2018, and 2019, we developed a bootstrap method based on the central tendency measure of mean. A Poisson fitting analysis was used to identify trends on officially recorded all-cause deaths and COVID-19 deaths. A bootstrapping technique was used to emulate the sampling distribution of our expected deaths estimator μ ⌢ d e a t h s by simulating the data generation and model fitting processes daily since the first confirmed case., Results: In Ecuador, during 2020, 115,070 deaths were reported and 42,453 were cataloged as excess mortality when compared to 2017-2019 period. Ecuador is the country with the highest recorded excess mortality in the world within the shortest timespan. In one single day, Ecuador recorded 1,120 deaths (6/100,000), which represents an additional 408% of the expected fatalities., Conclusion: Adjusting for population size and time, the hardest-hit country due to the COVID-19 pandemic was Ecuador. The mortality excess rate shows that the SARS-CoV-2 virus spread rapidly in Ecuador, especially in the coastal region. Our results and the proposed new methodology could help to address the real situation of the number of deaths during the initial phase of pandemics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Fernandez-Naranjo, Vasconez-Gonzalez, Izquierdo-Condoy, Landazuri, Castillo and Ortiz-Prado.)

The death toll of the coronavirus disease 2019 (COVID-19) sparked much controversy since its advent in December 2019. Underestimation because of under testing and deaths happening outside the hospitals were important causes. Bold revisions of the diagnostic criteria leading to dramatic changes in death tolls by different governments were observed in attempts to generate more accurate estimates. On the other hand, the influence, censorship and manipulation on case and death data from top political leaders of some countries could create important impacts on the death toll. Baseline mortality data of previous years may help make more accurate estimates of the actual death toll. The pitfalls and strategies during such processes could become valuable lessons to leaders and policymakers worldwide as more accurate statistics serve to navigate policies to combat this pandemic in the days and months to come. [ABSTRACT FROM AUTHOR]

This is a personal view from London as the Covid-19 pandemic continues to spread here and the situation changes from day to day. As such it can only be a snapshot caught in time; it is not a diary of events. The Coronavirus Act 2020 gives Government enormous powers and was passed by Parliament in one day of debate immediately before it closed early for the Easter break. In March, the government imposed a "lockdown: the closure of all" but "essential" businesses and people other than essential workers must work from home but are allowed out for exercise and food shopping but must maintain 2 m apart, the "social distancing rule". The aim is to suppress the spread of the virus, reduce the death toll and "protect the National Health Service (NHS)" which needed time to empty wards and expand its intensive care unit (ICU) capability to deal with an expected influx of thousands of very sick patients. I discuss whether this strategy is working, how and why it has rapidly been altered to respond to criticism. Why was the Government so slow to seek the help of private laboratories to assist with testing? Why was the personal protective equipment (PPE) guidance altered only after criticism? I look at the impact of the lockdown on the UK economy, the changes to practice of medicine and speeding of scientific research. Cooperating with the lockdown has its price; is it harming the health and mental health of children, people living in households with potentially abusive partners or parents and those who are disabled or financially desperate? Is the cure worse than the disease? The Economy is being devastated by the lockdown and each day of lockdown it is worse. Is litigation being seeded even now by the pandemic? Notwithstanding unprecedented Government financial help many businesses are on the edge of collapse, people will lose their jobs and pensioners income. The winners include pharmacies, supermarkets, online food retailers, Amazon, online apps, providers of video games, services, streaming and scientific research laboratories, manufacturers of testing kits, ventilators, hand sanitisers, coffins, undertakers, etc. The British public is cooperating with lockdown but are we less productive at home? Parents with babies and children often child minders, school, grandparents or paid help which is not now available. Will current reliance on video-conferencing and video calls permanently change the way we work and will we need smaller city offices? Will we travel less? Will medical and legal practice and civil and criminal trials be generally carried out remotely? Will social distancing with self-isolation and job losses and business failures fuel depression? Is Covid-19 comparable to past epidemics like the Plague and Spanish flu? [ABSTRACT FROM AUTHOR]

Background: Health care avoidance in the COVID-19 pandemic has been widely reported. Yet few studies have investigated the dynamics of hospital avoidance behavior during pandemic waves and inferred its impact on excess non-COVID-19 deaths., Objective: This study aimed to measure the impact of hospital avoidance on excess non-COVID-19 deaths in public hospitals in Hong Kong., Methods: This was a retrospective cohort study involving 11,966,786 patients examined between January 1, 2016, and December 31, 2021, in Hong Kong. All data were linked to service, treatment, and outcomes. To estimate excess mortality, the 2-stage least squares method was used with daily tallies of emergency department (ED) visits and 28-day mortality. Records for older people were categorized by long-term care (LTC) home status, and comorbidities were used to explain the demographic and clinical attributes of excess 28-day mortality. The primary outcome was actual excess death in 2020 and 2021. The 2-stage least squares method was used to estimate the daily excess 28-day mortality by daily reduced visits., Results: Compared with the prepandemic (2016-2019) average, there was a reduction in total ED visits in 2020 of 25.4% (548,116/2,142,609). During the same period, the 28-day mortality of non-COVID-19 ED deaths increased by 7.82% (2689/34,370) compared with 2016-2019. The actual excess deaths in 2020 and 2021 were 3143 and 4013, respectively. The estimated total excess non-COVID-19 28-day deaths among older people in 2020 to 2021 were 1958 (95% CI 1100-2820; no time lag). Deaths on arrival (DOAs) or deaths before arrival (DBAs) increased by 33.6% (1457/4336) in 2020, while non-DOA/DBAs increased only by a moderate 4.97% (1202/24,204). In both types of deaths, the increases were higher during wave periods than in nonwave periods. Moreover, non-LTC patients saw a greater reduction in ED visits than LTC patients across all waves, by more than 10% (non-LTC: 93,896/363,879, 25.8%; LTC: 7,956/67,090, 11.9%). Most of the comorbidity subsets demonstrated an annualized reduction in visits in 2020. Renal diseases and severe liver diseases saw notable increases in deaths., Conclusions: We demonstrated a statistical method to estimate hospital avoidance behavior during a pandemic and quantified the consequent excess 28-day mortality with a focus on older people, who had high frequencies of ED visits and deaths. This study serves as an informed alert and possible investigational guideline for health care professionals for hospital avoidance behavior and its consequences., (©Abraham Ka-chung Wai, Tsz Fung Yip, Yui Hang Wong, Chun Kit Chu, Teddy Lee, Ken Hung On Yu, Wang Leong So, Janet Y H Wong, Carlos King-ho Wong, Joshua W Ho, Timothy Rainer. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 13.02.2024.)

The Covid-19 pandemic is developing worldwide with common dynamics but also with marked differences between regions and countries. These are not completely understood, but presumably, provide a clue to find ways to mitigate epidemics until strategies leading to its eradication become available. We describe an iteractive monitoring tool available in the internet. It enables inspection of the dynamic state of the epidemic in 187 countries using trajectories that visualize the transmission and removal rates of the epidemic and in this way bridge epi-curve tracking with modelling approaches. Examples were provided which characterize state of epidemic in different regions of the world in terms of fast and slow growing and decaying regimes and estimate associated rate factors. The basic spread of the disease is associated with transmission between two individuals every two-three days on the average. Non-pharmaceutical interventions decrease this value to up to ten days, whereas ‘complete lock down’ measures are required to stop the epidemic. Comparison of trajectories revealed marked differences between the countries regarding efficiency of measures taken against the epidemic. Trajectories also reveal marked country-specific recovery and death rate dynamics. The results presented refer to the pandemic state in May to July 2020 and can serve as ‘working instruction’ for timely monitoring using the interactive monitoring tool as a sort of ‘seismometer’ for the evaluation of the state of epidemic, e.g., the possible effect of measures taken in both, lock-down and lock-up directions. Comparison of trajectories between countries and regions will support developing hypotheses and models to better understand regional differences of dynamics of Covid-19.