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COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: • How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavirus cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.

Debris flows cause significant damage and fatalities throughout the world. This study addresses the overall impacts of debris flows on a global scale from 1950 to 2011. Two hundred and thirteen events with 77,779 fatalities have been recorded from academic publications, newspapers, and personal correspondence. Spatial, temporal, and physical characteristics have been documented and evaluated. In addition, multiple socioeconomic indicators have been reviewed and statistically analyzed to evaluate whether vulnerable populations are disproportionately affected by debris flows. This research provides evidence that higher levels of fatalities tend to occur in developing countries, characterized by significant poverty, more corrupt governments, and weaker healthcare systems. The median number of fatalities per recorded deadly debris flow in developing countries is 23, while in advanced countries, this value is only 6 fatalities per flow. The analysis also indicates that the most common trigger for fatal events is extreme precipitation, particularly in the form of large seasonal storms such as cyclones and monsoon storms. Rainfall caused or triggered 143 of the 213 fatal debris flows within the database. However, it is the more uncommon and catastrophic triggers, such as earthquakes and landslide dam bursts, that tend to create debris flows with the highest number of fatalities. These events have a median fatality count >500, while rainfall-induced debris flows have a median fatality rate of only 9 per event.

As 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. In 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. This 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. Overall, tweets that shared links to anxious articles were more likely to be anxious (odds ratio [OR] 2.65, 95% CI 1.58-4.43, <.001). These odds decreased (OR 0.41, 95% CI 0.2-0.83, =.01) when the death toll reached the third quartile and fourth quartile (OR 0.42, 95% CI 0.21-0.85, =.01). However, user tweet anxiety rose rapidly with articles when the death toll was low and then decreased in the third quartile of deaths (OR 0.61, 95% CI 0.37-1.01, =.06). As predicted, in addition to the increasing death toll being matched by a lower level of article anxiety, the extent to which article anxiety elicited user tweet anxiety decreased when the death count reached the second quartile. The level of anxiety in users' tweets increased sharply in response to article anxiety early on in the COVID-19 pandemic, but as the casualty count climbed, news articles seemingly lost their ability to elicit anxiety among readers. Desensitization offers an explanation for why the increased threat is not eliciting widespread behavioral compliance with guidance from public health officials. This work investigated how individuals' emotional reactions to news of the COVID-19 pandemic manifest as the death toll increases. Findings suggest individuals became desensitized to the increased COVID-19 threat and their emotional responses were blunted over time.

BACKGROUND In the absence of high-quality data, the death tolls of epidemics, conflicts, and disasters are often estimated using ad hoc methods. One understudied class of methods, which we term the growth rate discontinuity method (GRDM), estimates death tolls by projecting pre-crisis and post-crisis total population estimates using crude growth rates and then subtracting the results. Despite, or perhaps due to, its simplicity, this method is the source of prominent death toll estimates for the Black Death, the 1918 influenza pandemic, the Great Chinese Famine, and the Rwandan genocide, among others. OBJECTIVE In this article, we review the influence and validity of GRDM and its applications. METHODS Using statistical simulation and comparison with better-validated demographic methods, we assess the accuracy, precision, and biases of this method for estimating mortality in absolute and relative terms. RESULTSWe find that existing GRDM estimates often misestimate death tolls by large, unpredictable margins. Simulations suggest this is because GRDM requires precision in its inputs to an extent rarely possible in the contexts of interest. CONCLUSIONS If there is sufficient data to specify GRDM well, it is probably possible to also use a more reliable method; if there is not sufficient data, GRDM estimates are so sensitive to their assumptions that they cannot be considered informative. CONTRIBUTION These findings question the empirical suitability of existing demographic and econometric work that has used GRDM to analyse mortality crises. They also underscore the need for improved data collection in crisis settings and the utility of qualitative methods in contexts where quantification using better-validated methods is not possible.

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.

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.

COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2. As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world. The United States recorded more than 1.9 million new infections in July, which is nearly 36 percent of the more than 5.4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020. In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days. This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases. We analyzed and compared the modeling results to two recent models based on several model selection criteria. The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer. The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020. The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model. The proposed model predicted that the total number of U.S. deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020. The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths. The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs. The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020. It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths. The proposed model also predicted that the global death toll could reach 1.47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19. We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States.

In a given country, the cumulative death toll of the first wave of the COVID-19 epidemic follows a sigmoid curve as a function of time. In most cases, the curve is well described by the Gompertz function, which is characterized by two essential parameters, the initial growth rate and the decay rate as the first epidemic wave subsides. These parameters are determined by socioeconomic factors and the countermeasures to halt the epidemic. The Gompertz model implies that the total death toll depends exponentially, and hence very sensitively, on the ratio between these rates. The remarkably different epidemic curves for the first epidemic wave in Sweden and Norway and many other countries are classified and discussed in this framework, and their usefulness for the planning of mitigation strategies is discussed.

Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R2) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response.

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. This study aimed to measure the impact of hospital avoidance on excess non-COVID-19 deaths in public hospitals in Hong Kong. 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. 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. 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.