Daily Number Of Deaths Research Articles

Functional cluster and canonical correlation analysis of EU countries by number of daily deaths and stringency index during Covid-19 pandemic

The danger of a global pandemic, such as the new Coronavirus (Covid-19),is obvious. This study aims to investigate the behavior and relationship of thenumber of daily new conrmed deaths per million and the stringency indexof twenty-seven European Union (EU) countries by utilizing functional clusteranalysis and functional canonical correlation analysis. Functional clusteranalysis was used to observe how countries cluster together according to dailydeaths during the time interval between March and July 2020. Functionalcanonical correlation analysis was also utilized to measure the correlationbetween the frequency index and daily deaths, and also to determine therelative positions of countries concerning their respective variability structure.The data is obtained from OWID. Here, it is seen that Italy, Spain,Belgium, and France are particularly aected by the pandemic during thetime interval within the EU countries, and the course of daily deaths is in adierent position compared to other EU countries. At the same time, a veryhigh relationship emerged between the stringency index and daily deaths asexpected.

One Year of Coronavirus Disease 2019 (COVID-19) in Brazil: A Political and Social Overview.

Background:Coronavirus Disease 2019 (COVID-19) became the deadliest pandemic of the new millennium. One year after it became a pandemic, the current COVID-19 situation in Brazil is an example of how the impacts of a pandemic are beyond health outcomes and how health, social, and political actions are intertwined.Objectives:We aimed to provide an overview of the first year of the COVID-19 pandemic in Brazil, from a social and political point of view, and to discuss the perspectives from now on.Methods:This is a narrative review using official, scientific (PubMed, Medline, and SciELO databases) and publicly available data. Press articles were also used that contain important information not found in these databases.Findings:We address the impacts of COVID-19 in different regions of Brazil, on indigenous populations, health care workers, and how internal social contrasts impacted the pandemics advance across the country. We also discuss key points that culminated in the countrys failed management of the COVID-19 spread, such as poor management of the public health care system, disparities between public and private health care infrastructure, lack of mass testing and viral spread tracking, lack of preparedness and planning to implement strict isolation and social distancing measures, and, most importantly, political instability, a deteriorating Health Ministry and sabotaging attitudes of the countrys president, including anti-scientific actions, underplaying COVID-19 severity, spreading and powering fake news about the pandemic, promoting knowingly inefficient medications for COVID-19 treatment, and interference in collective health policies, including the countrys vaccination plan.Conclusions:After one year of COVID-19 and a disastrous management of the disease, Brazil has more than 11 million cases, 270,000 deaths, and the highest number of daily deaths due to COVID-19 in the world, most of which could have been avoided and can be credited to negligence of municipal, state, and federal authorities, especially President Jair Messias Bolsonaro. Unfortunately, the country is an example of what not to do in a pandemic setting.Key Points: One year after COVID-19 was declared a pandemic, Brazil had the second higher number of cases and deaths, and the highest number of daily deaths due to the disease. Lack of massive testing, non-stringent and ineffective collective health policies, poor management of the public health care system, and political instability were the main drivers of the countrys flawed management of the COVID-19 advancement. Anti-science and sabotaging actions by government had a pivotal role in the countrys current situation. Brazil has a large territory and is marked by social contrasts among different regions and states, which showed contrasting data regarding the impact caused by COVID-19. COVID-19 databases and data sharing are important to provide an overview of epidemiological aspects of the disease; however, Brazil lacks standardization in these datasets.

The Challenging but Imperative Path to Monoclonal Antibody Use Against COVID-19 for a Small Overseas Military Hospital.

ABSTRACTUpon the U.S. FDA approval in early November for a monoclonal antibody proven to potentially mitigate adverse outcomes from coronavirus disease 2019 (COVID-19) infections, our small overseas community hospital U.S. Naval Hospital Rota, Spain (USNH Rota) requested and received a limited number of doses. Concurrently, our host nation, which previously had reported the highest number of daily deaths from COVID-19, was deep within a second wave of infections, increasing hospital admissions, near intensive care unit capacity, and deaths. As USNH Rota was not normally equipped for the complex infusion center required to effectively deliver the monoclonal antibody, we coordinated a multi-directorate and multidisciplinary effort in order to set up an infusion room that could be dedicated to help with our fight against COVID. Identifying a physician team lead, with subject matter experts from nursing, pharmacy, facilities, and enlisted corpsmen, our team carefully ensured that all requisite steps were set up in advance in order to be able to identify the appropriate patients proactively and treat them safely with the infusion that has been clinically proven to decrease hospital admissions and mortality. Additional benefits included the establishment of an additional negative pressure room near our emergency room for both COVID-19 patients and, when needed, the monoclonal antibody infusion. In mid-January, a COVID-19-positive patient meeting the clinical criteria for monoclonal antibody infusion was safely administered this potentially life-saving medication, a first for small overseas hospitals. Here, we describe the preparation, challenges, obstacles, lessons learned, and successful outcomes toward effectively using the monoclonal antibody overseas.

The impact of COVID-19 on the stock market crash risk in China

This study investigates the impact of the COVID-19 pandemic on the stock market crash risk in China. For this purpose, we first estimated the conditional skewness of the return distribution from a GARCH with skewness (GARCH-S) model as the proxy for the equity market crash risk of the Shanghai Stock Exchange. We then constructed a fear index for COVID-19 using data from the Baidu Index. Based on the findings, conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the pandemic increases stock market crash risk. Moreover, the fear sentiment exacerbates such risk, especially with regard to the impact of COVID-19. In other words, when the fear sentiment is high, the stock market crash risk is more strongly affected by the pandemic. Our evidence is robust for the number of daily deaths and global cases.

Evaluation of the Ugandan Government’s Communication Strategies of the COVID-19 Pandemic

This study focuses on the communication strategies employed by the Ugandan government in addressing the COVID-19 pandemic. The study used a mixed method approach, including interviews, questionnaires and content analysis. A sample size of 400 was drawn for the survey, five community leaders and health workers were interviewed and 270 COVID-19 media reports were used for the content analysis. The findings showed that the government of Uganda did not engage in dialogue at the grassroots level. Secondly, it was found that the media generally reported on the spread of the virus and the number of daily deaths. However, the findings also showed that the media largely supported and complemented the government’s efforts. Finally, little or no effort was made to engage with communities before major preventive and protective measures were announced and enforced by the government. It was suggested that grassroots dialogue and community engagement must be carried out before major decisions are made by the government if they are to be effective in the future.

Anxiety Levels of Healthcare Professionals’ Relatives During COVID-19 Pandemic: A Longitudinal Study

Aim: Health workers are the most important risk groups in the last century outbreaks. This situation creates anxiety in the relatives of healthcare workers as well as in healthcare workers. In this study, it was aimed to evaluate the anxiety levels of the relatives of healthcare workers during the COVID-19 epidemic. Methods: The study was designed as a longitudinal study in quantitative research design. In the study, data were collected regularly every day for 20 days between 20.03.2020-08.04.2020, when the coronavirus pandemic continued, and these data were associated with the data of the Ministry of Health. Results: One thousand one hundres and eighty participants' opinions were collected for the study. In the study process, it was observed that the anxiety level at 20.03.2020 was 8.25±5.36 and it increased to 17.21±17.07 on 08.04.2020. The relationship between the anxiety level of the participants and the total number of cases the total number of deaths were weak. There was a moderate correlation between the number of deaths per day and the number of patients recovering per day. In the study, it was shown that there is a moderate correlation between the anxiety levels and the level of knowledge of the opinion makers. In this upward trend, it has been observed that there are 3 breaking points: 23-24.03.2020, 28-29.03.2020 and 05-06.04.2020. Conclusion: In our study described the first cases of anxiety in the vicinity of 20 health workers in Turkey after the first day (twenty) were found to correlate with increased number of deaths in days. It was observed that anxiety level correlated weakly with the total number of cases / deaths, but moderately with the number of daily deaths / recovered patients in the relatives of the health studies. This situation was interpreted as that although the anxiety of healthcare workers was affected by the process, it was more affected by daily changes. Relatives as well as healthcare professionals also experienced a manageable anxiety during the COVID-19 outbreak. We believe that information and rehabilitation programs should be developed for the relatives of healthcare professionals in order to maintain the resilience / resiliency of healthcare professionals. Keywords: anxiety, coronavirus, pandemics, Family Medicine

ABO/Rh Blood Groups and COVID-19: Temporal Analyses Point Out Rh-negative with the Greatest Correlations

In this work, the correlations between ABO/Rh blood groups and the number of deaths from COVID-19 were studied through Pearson's correlation coefficients, while considering the distribution of blood groups in populations of 88 countries for a period of 166 days and the daily cumulative deaths since the fifth death. The current study stems from the need to understand associations between ABO/Rh blood groups and COVID-19 in a temporal analysis. The preliminary results indicated that correlations evolve statistically as the pandemic extends and the time of exposure to SARS-CoV-2 increases, making them more reliable. For the analysis in the set of 88 countries, strong maximum correlations were obtained for A-, O- and Rh-negative (not distinguished ABO blood groups), and close moderate maximum correlations for A and O blood groups were found. The A blood group correlations were higher than those of the O blood group during most of the study period. The correlations between Rh-negative blood type and cumulative deaths remained greater than those of Rh-positive throughout the 166-day period. With these studies, it was possible to interpret the pandemic temporal evolution from the correlation coefficients perspective, here modeled by degree 11 polynomials using the least squares method. In addition, studies on the total number of daily deaths were suggested from the temporal analyses behavior, so COVID-19 pandemic establishment rates and characterization parameters for the study period were proposed.

Changes of Circulatory and Nervous Diseases Mortality Patterns during Periods of Exceptional Solar Events

A statistical analysis of the relation between exceptional solar events and daily numbers of deaths in the Czech Republic is presented. In particular, we concentrate on diseases of the nervous system (group VI from ICD-10—International Statistical Classification of Diseases and Related Health Problems 10th Revision) and diseases of the circulatory system (group IX from ICD-10). We demonstrate that the neurological diseases exhibit greater instability during the period of rising and falling solar activity. We study the daily numbers of deaths, separately for both sexes and two age groups (under 39 and 40+), during the Solar Cycles No. 23 and No. 24. We focus on exceptionally strong solar events in this period, such as the “Bastille Day event” on 14 July 2000, “Halloween solar storm” on 28 October 2003, and events on 7 January 1997, 2 April 2000, and on 7 September 2005. Special attention is paid to “St. Patrick’s Day storm” on 17 March 2015, the strongest geomagnetic storm of the Solar Cycle No. 24 that occurred following a coronal mass ejection (CME). We investigate the changes in the daily numbers of deaths during 1 month before and 1 month after these exceptional solar events. We take specific storm dynamics of their geophysical parameters into consideration. It has been verified that, for diseases of the nervous system, women are generally more sensitive than men. On the contrary, this differences between men and women have not been found for diseases of the circulatory system. Our findings suggest that the impact of the hazardous space weather conditions on human health depends on the specific dynamic and strength of the solar storm.

Evaluating the Impact of New York's Executive Order on Face Mask Use on COVID-19 Cases and Mortality: a Comparative Interrupted Times Series Study.

BackgroundOn April 17, 2020, the State of New York (NY) implemented an Executive Order that requires all people in NY to wear a face mask or covering in public settings where social distancing cannot be maintained. Although the Centers for Disease Control and Prevention recommended face mask use by the general public, there is a lack of evidence on the effect of face mask policies on the spread of COVID-19 at the state level.ObjectiveTo assess the impact of the Executive Order on face mask use on COVID-19 cases and mortality in NY.DesignA comparative interrupted time series analysis was used to assess the impact of the Executive Order in NY with Massachusetts (MA) as a comparison state.ParticipantsWe analyzed data on COVID-19 in NY and MA from March 25 to May 6, 2020.InterventionThe Executive Order on face mask use in NY.Main MeasuresDaily numbers of COVID-19 confirmed cases and deaths.Key ResultsThe average daily number of confirmed cases in NY decreased from 8549 to 5085 after the Executive Order took effect, with a trend change of 341 (95% CI, 187–496) cases per day. The average daily number of deaths decreased from 521 to 384 during the same two time periods, with a trend change of 52 (95% CI, 44–60) deaths per day. Compared to MA, the decreasing trend in NY was significantly greater for both daily numbers of confirmed cases (P = 0.003) and deaths (P < 0.001).ConclusionsThe Executive Order on face mask use in NY led to a significant decrease in both daily numbers of COVID-19 confirmed cases and deaths. Findings from this study provide important evidence to support state-level policies that require face mask use by the general public.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11606-020-06476-9.

Modeling and forecasting the spread of COVID-19 with stochastic and deterministic approaches: Africa and Europe

Using the existing collected data from European and African countries, we present a statistical analysis of forecast of the future number of daily deaths and infections up to 10 September 2020. We presented numerous statistical analyses of collected data from both continents using numerous existing statistical theories. Our predictions show the possibility of the second wave of spread in Europe in the worse scenario and an exponential growth in the number of infections in Africa. The projection of statistical analysis leads us to introducing an extended version of the well-blancmange function to further capture the spread with fractal properties. A mathematical model depicting the spread with nine sub-classes is considered, first converted to a stochastic system, where the existence and uniqueness are presented. Then the model is extended to the concept of nonlocal operators; due to nonlinearity, a modified numerical scheme is suggested and used to present numerical simulations. The suggested mathematical model is able to predict two to three waves of the spread in the near future.

The evolution of COVID-19: A discontinuous approach

The evolution of the COVID-19 disease is monitored on the basis of the daily number of infected patients and the daily number of deaths provided from national health agencies. The variation of such parameters with time parallels that described for the growth/decay of historic transportation systems revealing the appearance of discontinuities. The evolution of the pandemic disease is represented in terms of two nominally equivalent formulations: a logistic model with sharp changes in its rate parameters, and in topological terms resulting in 2nd order phase transitions in the infected patients/time space.

COVID-19 lethality in Brazilian States using information theory quantifiers

In this paper, we presented an overview diagnosis consider the time series of daily deaths by COVID-19 in the Brazilian States using Bandt & Pompe method (BPM) to estimate the Information Theory quantifiers, more specifically the Permutation entropy (H s ) and the Fisher information measure (F s ). Based on the Information Theory quantifiers, we build up the Shannon-Fisher causality plane (SFCP) to promote insights into the COVID-19 temporal evolution inherent in the phenomenology associated with the number of daily deaths well as their respective locations along the SFCP. Moreover, we apply H s and F s to elaborate on the rank of the Brazilian States’ real situation, considering the number of daily death due to COVID-19 based on the complexity hierarchy. The Brazilian States that are located in the middle region of the two-dimensional plane (H s x F s ), such as Amapá (AP), Roraima (RO), Acre (AC), and Tocantins (TO) are characterized by a less entropic and low disorder, which implies in high predictability of the COVID-19 lethality. While, the Brazilian States that are located in the lower-right region, such as Ceará (CE), Bahia (BA), Pernambuco (PE), and Rio de Janeiro (RJ), are characterized by high entropy and high disorder, which leads to low predictability of the COVID-19 lethality. Given this, our results provide empirical evidence that the permutation entropy is a powerful approach to predicting infectious diseases. Dynamic monitoring of permutation entropy can help policymakers to take more or less restrictive measures to combat COVID-19.

Modeling the relationship between temperature and mortality : a case study in SENEGAL

In this paper, we have used the Generalized Additive Model (GAM) to investigate the relationships between high temperature and daily number of deaths in Niakhar, a Sehalian-Sudanese climate in central Senegal. Daily data on number of deaths and meteorological variables over the period of 1983-2013 were considered. Descriptive statistics show that, over the study period, the total of non-accidental deaths were 12,798, among which we notice that 490 persons (3.83%) died of cardiovascular disease, 1,015 persons (7.93%) died of respiratory disease, 3,970 persons (31.02%) died of certain infectious and parasitic diseases, and 224 persons (1.75%) died of nervous system disease From the GAM model, we observe that high temperature significantly increased the relative risk (RR)Indeed, relative risk of deaths due to cardiovascular disease is 1.034 with a 95% confidence intervals (CI) 1.025 to 1.044, while it is 1.030 with a 95% CI 1.026 to 1.033 for certain infectious and parasitic disease. For respiratory disease, the RR is 1.012 with a 95% CI 1.007 to 1.017, and for nervous system disease, the relative risk is 1.034 with 95% CI 1.026 to 1.043.

Mathematical Modeling and Analysis of COVID-19 Epidemic Situation Based on Markov Chain

Based on the statistical data of COVID-19 from January 22, 2020 to June 7, 2020 in Hubei, China, the states are divided according to the standard deviation, and based on the Markov chain theory, the mathematical analysis model of daily confirmed, dead and cured number of COVID-19 in Hubei, China was established, and the probability of steady distribution of each state and the average return time were used to analyze the prevention and control of COVID-19 epidemic situation in Hubei, China. The analysis shows that by June 2020, the daily confirmed number of COVID-19 in Hubei, China is basically in state Ⅰ, the number of daily deaths is basically in state Ⅰ-Ⅱ, and the number of people cured per day is basically in state Ⅰ-Ⅱ.

Modeling the relationship between temperature and mortality : a case study in SENEGAL

In this paper, we have used the Generalized Additive Model (GAM) to investigate the relationships between high temperature and daily number of deaths in Niakhar, a Sehalian-Sudanese climate in central Senegal. Daily data on number of deaths and meteorological variables over the period of 1983-2013 were considered. Descriptive statistics show that, over the study period, the total of non-accidental deaths were 12,798, among which we notice that 490 persons (3.83%) died of cardiovascular disease, 1,015 persons (7.93%) died of respiratory disease, 3,970 persons (31.02%) died of certain infectious and parasitic diseases, and 224 persons (1.75%) died of nervous system disease From the GAM model, we observe that high temperature significantly increased the relative risk (RR)Indeed, relative risk of deaths due to cardiovascular disease is 1.034 with a 95% confidence intervals (CI) 1.025 to 1.044, while it is 1.030 with a 95% CI 1.026 to 1.033 for certain infectious and parasitic disease. For respiratory disease, the RR is 1.012 with a 95% CI 1.007 to 1.017, and for nervous system disease, the relative risk is 1.034 with 95% CI 1.026 to 1.043.

Prediction Regions for Poisson and Over-Dispersed Poisson Regression Models with Applications in Forecasting the Number of Deaths during the COVID-19 Pandemic

AbstractMotivated by the Coronavirus Disease (COVID-19) pandemic, which is due to the SARS-CoV-2 virus, and the important problem of forecasting the number of daily deaths and the number of cumulative deaths, this paper examines the construction of prediction regions or intervals under the no-covariate or intercept-only Poisson model, the Poisson regression model, and a new over-dispersed Poisson regression model. These models are useful for settings with events of interest that are rare. For the no-covariate Poisson and the Poisson regression model, several prediction regions are developed and their performances are compared through simulation studies. The methods are applied to the problem of forecasting the number of daily deaths and the number of cumulative deaths in the United States (US) due to COVID-19. To examine their predictive accuracy in light of what actually happened, daily deaths data until May 15, 2020 were used to forecast cumulative deaths by June 1, 2020. It was observed that there is over-dispersion in the observed data relative to the Poisson regression model. A novel over-dispersed Poisson regression model is therefore proposed. This new model, which is distinct from the negative binomial regression (NBR) model, builds on frailty ideas in Survival Analysis and over-dispersion is quantified through an additional parameter. It has the flavor of a discrete measurement error model and with a viable physical interpretation in contrast to the NBR model. The Poisson regression model is a hidden model in this over-dispersed Poisson regression model, obtained as a limiting case when the over-dispersion parameter increases to infinity. A prediction region for the cumulative number of US deaths due to COVID-19 by October 1, 2020, given the data until September 1, 2020, is presented. Realized daily and cumulative deaths values from September 1st until September 25th are compared to the prediction region limits. Finally, the paper discusses limitations of the proposed procedures and mentions open research problems. It also pinpoints dangers and pitfalls when forecasting on a long horizon, especially during a pandemic where events, both foreseen and unforeseen, could impact point predictions and prediction regions.

Air pollution and the number of daily deaths due to respiratory causes in Tehran

Air pollution is a major environmental and health issue around the world in the past decades. The main objective of this project was to evaluate the association between the number of daily respiratory deaths and air pollution concentrations among residents of Tehran. Air pollution parameters such as particulate matter (PM), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3), and nitrogen dioxide (NO2) were obtained from the Tehran air quality control company. We recognized the number of daily respiratory mortality data from Tehran's cemetery registration from 2014 to 2019. We employed a Poisson regression model with natural spline functions to adjust air pollution and daily respiratory mortality data with temperature, long-term, and seasonality. The mean concentrations of PM2.5, PM10, O3, CO, SO2, and NO2 were 29.8 μg/m3, 78.7 μg/m3, 220.4 part per billion (ppb), 2.3 part per million (ppm), 9.7 ppb, and 49.6 ppb, respectively. The percent change and 95% confidence intervals (CIs) for mortality from respiratory causes due to 10-μg/m3 increment in PM2.5 and PM10 concentrations were 4.5% (95% CI: 0.3–9.5%) and 2.5% (95% CI: 0.1–3.5%) in the adjusted model, respectively. The percent change of respiratory death was significant for SO2 (2.8% per 1 ppb increment, 95% CI: 0.5–19.5%), NO2 (1.5% per 1 ppb increment, 95% CI: 0.8–15%) and O3 (0.6% per 1 ppb increment, 95% CI: 0.1–1.1%) exposure in adjusted model. Stronger associations obtained among elderly and women subjects. Conclusively, the findings of the present study suggest that the risk of respiratory death was associated with PM2.5, PM10, SO2, NO2, and O3 exposure.

The epidemiological characteristics of deaths with COVID-19 in the early stage of epidemic in Wuhan, China

ObjectivesTo analyze the epidemiological characteristics of COVID-19 related deaths in Wuhan, China and comprehend the changing trends of this epidemic along with analyzing the prevention and control measures in Wuhan.MethodsThrough the China’s Infectious Disease Information System, we collected information about COVID-19 associated deaths from December 15, 2019 to February 24, 2020 in Wuhan. We analyzed the patient’s demographic characteristics, drew epidemiological curve and made geographic distribution maps of the death toll in each district over time, etc. ArcGIS was used to plot the numbers of daily deaths on maps. Statistical analyses were performed using SPSS and @Risk software.ResultsAs of February 24, 2020, a total of 1833 deaths were included. Among the deaths with COVID-19, mild type accounted for the most (37.2%), followed by severe type (30.1%). The median age was 70.0 (inter quartile range: 63.0–79.0) years. Most of the deaths were distributed in 50–89 age group, whereas no deaths occurred in 0–9 age group. Additionally, the male to female ratio was 1.95:1. A total of 65.7% of the deaths in Wuhan combined with underlying diseases, and was more pronounced among males. Most of the underlying diseases included hypertension, diabetes and cardiovascular diseases. The peak of daily deaths appeared on February 14 and then declined. The median interval from symptom onset to diagnosis was 10.0 (6.0–14.0) days; the interval from onset to diagnosis gradually shortened. The median intervals from diagnosis to death and symptom onset to deaths were 6.0 (2.0–11.0), 17.0 (12.0–22.0) days, respectively. Most of the disease was centralized in central urban area with highest death rate in Jianghan District.ConclusionCOVID-19 poses a greater threat to the elderly people and men with more devastating effects, particularly in the presence of underlying diseases. The geographical distributions show that the epidemic in the central area of Wuhan is more serious than that in the surrounding areas. Analysis of deaths as of February 24 indicates that a tremendous improvement of COVID-19 epidemic in Wuhan has achieved by effective control measures taken by Wuhan Government.

A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths

The paper presents a phenomenological epidemiological model for the description and prediction of the time trends of COVID-19 deaths worldwide. A bimodal distribution function—defined as the mixture of two lognormal distributions—is assumed to model the time distribution of deaths in a country. The asymmetric lognormal distribution enables better data fitting with respect to symmetric distribution functions. Besides, the presence of a second mode allows the model to also describe second waves of the epidemic. For each country, the model has six parameters, which are determined by fitting the available data through a nonlinear least-squares procedure. The fitted curves can then be extrapolated to predict the future trends of the total and daily number of deaths. Results for the six continents and the World are obtained by summing those computed for the 210 countries in the Our World in Data (OWID) dataset. To assess the accuracy of predictions, a validation study is first conducted. Then, based on data available as of 30 September 2020, the future trends are extrapolated until the end of year 2020.

Methods for forecasting the effect of exogenous risks on stock markets

Markets are subjected to both endogenous and exogenous risks that have caused disruptions to financial and economic markets around the globe, leading eventually to fast stock market declines. In the past, markets have recovered after any economic disruption. On this basis, we focus on the outbreak of COVID-19 as a case study of an exogenous risk and analyze its impact on the Standard and Poor’s 500 (S&P500) index. We assumed that the S&P500 index reaches a minimum before rising again in the not-too-distant future. Here we present a forecast model of the S&P500 index based on the breaking news and publicly available information. We assumed that the biggest fall of the S&P500 during the COVID-19 outbreak will occur when the largest daily number of deaths was confirmed. We inferred that the peak number of deaths occurs 2-months since the first confirmed case was reported in the USA based on previous COVID-19 situation reports from other countries. We also compare the S&P500 and the DAX market dynamics around the COVID-19 crisis as well as other previous crises, demonstrating that the impact of market news is highly consistent across these multiple market crises. The forecast is a projection of a prediction with stochastic fluctuations described by q-gaussian diffusion process with three spatio-temporal regimes. Our forecast was made on the premise that any market response can be decomposed into an overall deterministic trend and a stochastic term. The prediction was based on the deterministic part and for this case study is approximated by the extrapolation of the S&P500 data trend in the initial stages of the outbreak. The stochastic fluctuations have the same structure as the one derived from the past 24 years. A reasonable forecast was achieved with 85% of accuracy.