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Statistics articles from across Nature Portfolio

Statistics is the application of mathematical concepts to understanding and analysing large collections of data. A central tenet of statistics is to describe the variations in a data set or population using probability distributions. This analysis aids understanding of what underlies these variations and enables predictions of future changes.

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Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

Preventive interventions often require strategies to identify high-risk individuals. Here, the authors illustrate the potential utility of medical history in predicting the onset risk for thousands of diseases across clinical specialties including COVID-19.

  • Jakob Steinfeldt
  • Benjamin Wild
  • Roland Eils

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Unsupervised detection of large-scale weather patterns in the northern hemisphere via Markov State Modelling: from blockings to teleconnections

  • Sebastian Springer
  • Alessandro Laio
  • Valerio Lucarini

Modified correlated measurement errors model for estimation of population mean utilizing auxiliary information

  • Housila P. Singh

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Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment

  • Doaa A. Abdel Hady
  • Omar M. Mabrouk
  • Tarek Abd El-Hafeez

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Unexpected HCHO transnational transport: influence on the temporal and spatial distribution of HCHO in Tibet from 2013 to 2021 based on satellite

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Towards optimal model evaluation: enhancing active testing with actively improved estimators

  • JooChul Lee
  • Likhitha Kolla

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Efficient learning of many-body systems

The Hamiltonian describing a quantum many-body system can be learned using measurements in thermal equilibrium. Now, a learning algorithm applicable to many natural systems has been found that requires exponentially fewer measurements than existing methods.

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Fudging the volcano-plot without dredging the data

Selecting omic biomarkers using both their effect size and their differential status significance ( i.e. , selecting the “volcano-plot outer spray”) has long been equally biologically relevant and statistically troublesome. However, recent proposals are paving the way to resolving this dilemma.

  • Thomas Burger

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Disentangling truth from bias in naturally occurring data

A technique that leverages duplicate records in crowdsourcing data could help to mitigate the effects of biases in research and services that are dependent on government records.

  • Daniel T. O’Brien

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Sciama’s argument on life in a random universe and distinguishing apples from oranges

Dennis Sciama has argued that the existence of life depends on many quantities—the fundamental constants—so in a random universe life should be highly unlikely. However, without full knowledge of these constants, his argument implies a universe that could appear to be ‘intelligently designed’.

  • Zhi-Wei Wang
  • Samuel L. Braunstein

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A method for generating constrained surrogate power laws

A paper in Physical Review X presents a method for numerically generating data sequences that are as likely to be observed under a power law as a given observed dataset.

  • Zoe Budrikis

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Connected climate tipping elements

Tipping elements are regions that are vulnerable to climate change and capable of sudden drastic changes. Now research establishes long-distance linkages between tipping elements, with the network analysis offering insights into their interactions on a global scale.

  • Valerie N. Livina

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Articles on Statistics

Displaying 1 - 20 of 254 articles.

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Vegan dog food has been hailed as the healthiest – our study shows the reality is more complicated

Alexander German , University of Liverpool and Richard Barrett-Jolley , University of Liverpool

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The luck of the puck in the Stanley Cup – why chance plays such a big role in hockey

Mark Robert Rank , Arts & Sciences at Washington University in St. Louis

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South Africa is short of academic statisticians: why and what can be done

Inger Fabris-Rotelli , University of Pretoria ; Ansie Smit , University of Pretoria ; Danielle Jade Roberts , University of KwaZulu-Natal ; Daniel Maposa , University of Limpopo ; Fabio Mathias Correa , University of the Free State ; Michael Johan von Maltitz , University of the Free State , and Sonali Das , University of Pretoria

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School results, smoking rates, shop closures? New statistics tool helps you compare local areas in the UK

Richard Harris , University of Bristol

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For over a century, baseball’s scouts have been the backbone of America’s pastime – do they have a future?

H. James Gilmore , Flagler College and Tracy Halcomb , Flagler College

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Social media apps have billions of ‘active users’. But what does that really mean?

Milovan Savic , Swinburne University of Technology

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The ‘average’ revolutionized scientific research, but overreliance on it has led to discrimination and injury

Zachary del Rosario , Olin College of Engineering

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Here’s why you should (almost) never use a pie chart for your data

Adrian Barnett , Queensland University of Technology and Victor Oguoma , The University of Queensland

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20 people, 2.4 quintillion possibilities: the baffling statistics of Secret Santa

Stephen Woodcock , University of Technology Sydney

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South Africa’s 2022 census missed 31% of people - big data could help in future

David Everatt , University of the Witwatersrand

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From stock markets to brain scans, new research harmonises hundreds of scientific methods to understand complex systems

Ben Fulcher , University of Sydney

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Tests that diagnose diseases are less reliable than you’d expect. Here’s why

Adrian Barnett , Queensland University of Technology and Nicole White , Queensland University of Technology

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The order in which you acquire diseases could affect your life expectancy – new research

Rhiannon Owen , Swansea University

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Bazball by the numbers: what the stats say about English cricket’s ambitious but risky change of pace

Tim Newans , Griffith University and Christopher Drovandi , Queensland University of Technology

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If 1% of COVID-19 cases result in death, does that mean you have a 1% chance of dying if you catch it? A mathematician explains the difference between a population statistic and your personal risk

Joseph Stover , Gonzaga University

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Best time to play Tim Hortons’ Roll up to Win? The middle of the night dramatically increases your odds

Michael Wallace , University of Waterloo

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Declines in math readiness underscore the urgency of math awareness

Manil Suri , University of Maryland, Baltimore County

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Robodebt not only broke the laws of the land – it also broke laws of mathematics

Noel Cressie , University of Wollongong

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Australia’s new pay equality law risks failing women – unless we make this simple fix

Mark Humphery-Jenner , UNSW Sydney

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A brief history of statistics in football: why actual goals remain king in predicting who will win

Laurence Shaw , Nottingham Trent University

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JAMA Guide to Statistics and Methods

Explore this JAMA essay series that explains the basics of statistical techniques used in clinical research, to help clinicians interpret and critically appraise the medical literature.

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This JAMA Guide to Statistics and Methods article explains effect score analyses, an approach for evaluating the heterogeneity of treatment effects, and examines its use in a study of oxygen-saturation targets in critically ill patients.

This JAMA Guide to Statistics and Methods explains the use of historical controls—persons who had received a specific control treatment in a previous study—when randomizing participants to that control treatment in a subsequent trial may not be practical or ethical.

This JAMA Guide to Statistics and Methods discusses the early stopping of clinical trials for futility due to lack of evidence supporting the desired benefit, evidence of harm, or practical issues that make successful completion unlikely.

This JAMA Guide to Statistics and Methods explains sequential, multiple assignment, randomized trial (SMART) study designs, in which some or all participants are randomized at 2 or more decision points depending on the participant’s response to prior treatment.

This JAMA Guide to Statistics and Methods article examines conditional power, calculated while a trial is ongoing and based on both the currently observed data and an assumed treatment effect for future patients.

This Guide to Statistics and Methods describes the use of target trial emulation to design an observational study so it preserves the advantages of a randomized clinical trial, points out the limitations of the method, and provides an example of its use.

This Guide to Statistics and Methods provides an overview of the use of adjustment for baseline characteristics in the analysis of randomized clinical trials and emphasizes several important considerations.

This Guide to Statistics and Methods provides an overview of regression models for ordinal outcomes, including an explanation of why they are used and their limitations.

This Guide to Statistics and Methods provides an overview of patient-reported outcome measures for clinical research, emphasizes several important considerations when using them, and points out their limitations.

This JAMA Guide to Statistics and Methods discusses instrumental variable analysis, a method designed to reduce or eliminate unobserved confounding in observational studies, with the goal of achieving unbiased estimation of treatment effects.

This JAMA Guide to Statistics and Methods describes collider bias, illustrates examples in directed acyclic graphs, and explains how it can threaten the internal validity of a study and the accurate estimation of causal relationships in randomized clinical trials and observational studies.

This JAMA Guide to Statistics and Methods discusses the CONSERVE guidelines, which address how to report extenuating circumstances that lead to a modification in trial design, conduct, or analysis.

This JAMA Guide to Statistics and Methods discusses the basics of causal directed acyclic graphs, which are useful tools for communicating researchers’ understanding of the potential interplay among variables and are commonly used for mediation analysis.

This JAMA Guide to Statistics and Methods discusses cardinality matching, a method for finding the largest possible number of matched pairs in an observational data set, with the goal of balanced and representative samples of study participants between groups.

This Guide to Statistics and Methods discusses the various approaches to estimating variability in treatment effects, including heterogeneity of treatment effect, which was used to assess the association between surgery to close patent foramen ovale and risk of recurrent stroke in patients who presented with a stroke in a related JAMA article.

This Guide to Statistics and Methods describes how confidence intervals can be used to help in the interpretation of nonsignificant findings across all study designs.

This JAMA Guide to Statistics and Methods describes why interim analyses are performed during group sequential trials, provides examples of the limitations of interim analyses, and provides guidance on interpreting the results of interim analyses performed during group sequential trials.

This JAMA Guide to Statistics and Methods describes how ACC/AHA guidelines are formatted to rate class (denoting strength of a recommendation) and level (indicating the level of evidence on which a recommendation is based) and summarizes the strengths and benefits of this rating system in comparison with other commonly used ones.

This JAMA Guide to Statistics and Methods takes a look at estimands, estimators, and estimates in the context of randomized clinical trials and suggests several qualities that make for good estimands, including their scope, ability to summarize treatment effects, external validity, and ability to provide good estimates.

This JAMA Guide to Statistics and Methods describes how intention-to-treat, per-protocol, and as-treated approaches to analysis differ with regard to the patient population and treatment assignments and their implications for interpretation of treatment effects in clinical trials.

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HIV estimation using population-based surveys with non-response: A partial identification approach – lay abstract

17th may 2024.

The lay abstract featured today (for HIV estimation using population-based surveys with non-response: A partial identification approach by Oyelola A. Adegboye, Tomoki Fujii, Denis Heng-Yan Leung,... Read More

Assessing efficacy in non-inferiority trials with non-adherence to interventions: Are intention-to-treat and per-protocol analyses fit for purpose? – lay abstract

11th april 2024.

The lay abstract featured today (for Assessing efficacy in non-inferiority trials with non-adherence to interventions: Are intention-to-treat and per-protocol analyses fit for purpose? by Matthew... Read More

Bayesian Federated Inference for estimating statistical models based on non-shared multicenter data sets – lay abstract

10th april 2024.

The lay abstract featured today (for Bayesian Federated Inference for estimating statistical models based on non-shared multicenter data sets by Marianne A Jonker, Hassan Pazira and Anthony CC... Read More

How could a pooled testing policy have performed in managing the early stages of the COVID-19 pandemic? Results from a simulation study – lay abstract

3rd april 2024.

The lay abstract featured today (for How could a pooled testing policy have performed in managing the early stages of the COVID-19 pandemic? Results from a simulation study by Bethany Heath, Sofía... Read More

A class of computational methods to reduce selection bias when designing Phase 3 clinical trials – lay abstract

13th march 2024.

The lay abstract featured today (for A class of computational methods to reduce selection bias when designing Phase 3 clinical trials by Tianyu Zhan) is from Statistics in Medicine with the full... Read More

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Control charts for threshold correlated count data in disease infection number monitoring – lay abstract

The lay abstract featured today (for Control charts for threshold correlated count data in disease infection number monitoring by Nannan Li, Cong Li and Jing Wan) is from Quality and Reliability... Read More

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What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing – lay abstract

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The lay abstract featured today (for What they forgot to tell you about machine learning with an application to pharmaceutical manufacturing by Kjell Johnson and Max Kuhn) is from Pharmaceutical... Read More

Statistical Plasmode Simulations: Potentials, Challenges and Recommendations – lay abstract

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The lay abstract featured today (for the Tutorial in Biostatistics on Statistical plasmode simulations–Potentials, challenges and recommendations by Nicholas Schreck, Alla Slynko, Maral Saadati... Read More

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Sampling design methods for making improved lake management decisions – lay abstract

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Publishing an applied statistics paper: Guidance and advice from editors – lay abstract

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  • Signs and Symptoms
  • Risk Factors
  • Stroke Facts
  • Stroke Communications Toolkit
  • About Heart Disease
  • About High Blood Pressure
  • About Cholesterol

What to know

  • Stroke risk increases with age, but strokes can—and do—occur at any age.
  • Early action is important for stroke.
  • Stroke statistics vary by race and ethnicity.

Stroke Death Rates for 2018 through 2020 for Adults Aged 35 Years and Older by County.

  • In 2021, 1 in 6 deaths from cardiovascular disease was due to stroke. 1
  • The death rate for stroke increased from 38.8 per 100,000 in 2020 to 41.1 per 100,000 in 2021. 1
  • Every 40 seconds, someone in the United States has a stroke. 2 Every 3 minutes and 14 seconds, someone dies of stroke. 1
  • Every year, more than 795,000 people in the United States have a stroke. 2
  • About 610,000 of these are first or new strokes. 2
  • About 185,000 strokes— nearly 1 in 4 —are in people who have had a previous stroke. 2
  • About 87% of all strokes are ischemic strokes, in which blood flow to the brain is blocked. 2
  • Stroke-related costs in the United States came to nearly $56.5 billion between 2018 and 2019. 2
  • Costs include the cost of health care services, medicines to treat stroke, and missed days of work.
  • Stroke is a leading cause of serious long-term disability. 2
  • Stroke reduces mobility in more than half of stroke survivors age 65 and older. 2

Stroke Death Rates for 2018 through 2020 for Adults Aged 35 Years and Older by County. The map shows that concentrations of counties with the highest stroke death rates - meaning the top quintile - are located primarily in Guam, the Northern Mariana Island, Mississippi, Louisiana, Arkansas, Texas, Kentucky, Tennessee, Alabama, Georgia, Florida, South Carolina, North Carolina, Ohio, and Michigan., and. Pockets of high-rate counties also were found in Virginia, Indiana, Illinois, Missouri, Nebraska, California, Oregon, South Dakota, and North Dakota.

Stroke statistics by race and ethnicity

  • Stroke is a leading cause of death for Americans.
  • The risk of having a stroke varies with race and ethnicity.
  • Risk of having a first stroke is nearly twice as high for non-Hispanic Black adults as for White adults. 2
  • Non-Hispanic Black adults and Pacific Islander adults have the highest rates of death due to stroke. 1

Stroke risk varies by age

  • In 2014, 38% of people hospitalized for stroke were less than 65 years old . 3

Early action is important for stroke

Know the warning signs and symptoms of stroke so that you can act fast if you or someone you know might be having a stroke. The chances of survival are greater when emergency treatment begins quickly.

  • In one survey, 93% of respondents recognized sudden numbness on one side as a symptom of stroke.
  • Only 38% were aware of all major symptoms and knew to call 9-1-1 when someone was having a stroke. 4
  • Patients who arrive at the emergency room within 3 hours of their first symptoms often have less disability 3 months after a stroke than those who receive delayed care. 4

Populations

Americans at risk for stroke.

High blood pressure, high cholesterol, smoking, obesity, and diabetes are leading causes of stroke. One in three US adults has at least one of these conditions or risk factors. 2

Learn how to take steps to prevent stroke .

What CDC is doing

CDC and its partners are leading national initiatives and programs to reduce rates of death and disability caused by stroke and to help people live longer, healthier lives.

CDC's Division for Heart Disease and Stroke Prevention (DHDSP) provides resources to all 50 states to address heart disease and stroke, as well as helping lead or support the following:

  • The WISEWOMAN program screens women aged 35 to 64 who with low incomes and little or no insurance for chronic disease risk factors and refers them to lifestyle programs in an effort to prevent heart disease and strokes.
  • The Paul Coverdell National Acute Stroke Program funds states to measure, track, and improve the quality of care for stroke patients. The program works to reduce death and disability from stroke.
  • The Million Hearts ® initiative, which is co-led by CDC and the Centers for Medicare & Medicaid Services, works with other federal agencies and private-sector partners to raise awareness about heart disease and stroke and prevent heart attacks and strokes.
  • Signs and Symptoms of Stroke
  • Facts About Hypertension
  • High Cholesterol Facts
  • Smoking & Tobacco Use
  • Diabetes Fast Facts
  • Adult Obesity Facts

Other organizations

  • National Institute of Neurological Disorders and Stroke (NINDS): Stroke Information Page
  • NINDS Know Stroke Campaign
  • National Institutes of Health: Mind Your Risks ®
  • MedlinePlus: Stroke
  • Brain Attack Coalition
  • Centers for Disease Control and Prevention. Provisional Multiple Cause of Death Data. Accessed February 2, 2023. https://wonder.cdc.gov/mcd.html
  • Tsao CW, Aday AW, Almarzooq ZI, et al. Heart disease and stroke statistics—2023 update: a report from the American Heart Association. Circulation. 2023;147:e93–e621.
  • Jackson G, Chari K. National hospital care survey demonstration projects: stroke inpatient hospitalizations . Natl Health Stat Report. 2019 Nov;(132):1–11.
  • Fang J, Keenan NL, Ayala C, Dai S, Merritt R, Denny CH. Awareness of stroke warning symptoms—13 states and the District of Columbia, 2005. MMWR Morb Mortal Wkly Rep . 2008;57(18):481–485.

Stroke is a leading cause of death in the United States and is a major cause of serious disability for adults. It is also preventable and treatable.

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70 years after brown v. board of education, new research shows rise in school segregation.

Kids getting onto a school bus

As the nation prepares to mark the 70th anniversary of the landmark U.S. Supreme Court ruling in Brown v. Board of Education , a new report from researchers at Stanford and USC shows that racial and economic segregation among schools has grown steadily in large school districts over the past three decades — an increase that appears to be driven in part by policies favoring school choice over integration.

Analyzing data from U.S. public schools going back to 1967, the researchers found that segregation between white and Black students has increased by 64 percent since 1988 in the 100 largest districts, and segregation by economic status has increased by about 50 percent since 1991.

The report also provides new evidence about the forces driving recent trends in school segregation, showing that the expansion of charter schools has played a major role.  

The findings were released on May 6 with the launch of the Segregation Explorer , a new interactive website from the Educational Opportunity Project at Stanford University. The website provides searchable data on racial and economic school segregation in U.S. states, counties, metropolitan areas, and school districts from 1991 to 2022. 

“School segregation levels are not at pre- Brown levels, but they are high and have been rising steadily since the late 1980s,” said Sean Reardon , the Professor of Poverty and Inequality in Education at Stanford Graduate School of Education and faculty director of the Educational Opportunity Project. “In most large districts, school segregation has increased while residential segregation and racial economic inequality have declined, and our findings indicate that policy choices – not demographic changes – are driving the increase.” 

“There’s a tendency to attribute segregation in schools to segregation in neighborhoods,” said Ann Owens , a professor of sociology and public policy at USC. “But we’re finding that the story is more complicated than that.”

Assessing the rise

In the Brown v. Board decision issued on May 17, 1954, the U.S. Supreme Court ruled that racially segregated public schools violated the Equal Protection Clause of the Fourteenth Amendment and established that “separate but equal” schools were not only inherently unequal but unconstitutional. The ruling paved the way for future decisions that led to rapid school desegregation in many school districts in the late 1960s and early 1970s.

Though segregation in most school districts is much lower than it was 60 years ago, the researchers found that over the past three decades, both racial and economic segregation in large districts increased. Much of the increase in economic segregation since 1991, measured by segregation between students eligible and ineligible for free lunch, occurred in the last 15 years.

White-Hispanic and white-Asian segregation, while lower on average than white-Black segregation, have both more than doubled in large school districts since the 1980s. 

Racial-economic segregation – specifically the difference in the proportion of free-lunch-eligible students between the average white and Black or Hispanic student’s schools – has increased by 70 percent since 1991. 

School segregation is strongly associated with achievement gaps between racial and ethnic groups, especially the rate at which achievement gaps widen during school, the researchers said.  

“Segregation appears to shape educational outcomes because it concentrates Black and Hispanic students in higher-poverty schools, which results in unequal learning opportunities,” said Reardon, who is also a senior fellow at the Stanford Institute for Economic Policy Research and a faculty affiliate of the Stanford Accelerator for Learning . 

Policies shaping recent trends 

The recent rise in school segregation appears to be the direct result of educational policy and legal decisions, the researchers said. 

Both residential segregation and racial disparities in income declined between 1990 and 2020 in most large school districts. “Had nothing else changed, that trend would have led to lower school segregation,” said Owens. 

But since 1991, roughly two-thirds of districts that were under court-ordered desegregation have been released from court oversight. Meanwhile, since 1998, the charter sector – a form of expanded school choice – has grown.

Expanding school choice could influence segregation levels in different ways: If families sought schools that were more diverse than the ones available in their neighborhood, it could reduce segregation. But the researchers found that in districts where the charter sector expanded most rapidly in the 2000s and 2010s, segregation grew the most. 

The researchers’ analysis also quantified the extent to which the release from court orders accounted for the rise in school segregation. They found that, together, the release from court oversight and the expansion of choice accounted entirely for the rise in school segregation from 2000 to 2019.

The researchers noted enrollment policies that school districts can implement to mitigate segregation, such as voluntary integration programs, socioeconomic-based student assignment policies, and school choice policies that affirmatively promote integration. 

“School segregation levels are high, troubling, and rising in large districts,” said Reardon. “These findings should sound an alarm for educators and policymakers.”

Additional collaborators on the project include Demetra Kalogrides, Thalia Tom, and Heewon Jang. This research, including the development of the Segregation Explorer data and website, was supported by the Russell Sage Foundation, the Robert Wood Johnson Foundation, and the Bill and Melinda Gates Foundation.   

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Understanding epidemic data and statistics: A case study of COVID‐19

Amirhoshang hoseinpour dehkordi.

1 School of Computer Science, Institute for Research in Fundamental Sciences, Tehran Iran

Majid Alizadeh

2 School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran Iran

Pegah Derakhshan

3 School of Medicine, Iran University of Medical Sciences, Tehran Iran

Peyman Babazadeh

4 School of Engineering, Islamic Azad University Central Tehran Branch, Tehran Iran

Arash Jahandideh

5 Adhesive and Resin Department, Iran Polymer and Petrochemical Institute (IPPI), Tehran Iran

The 2019 novel‐coronavirus (COVID‐19) has affected 181 countries with approximately 1197405 confirmed cases (by 5th April). Understanding the transmission dynamics of the infection in each country which got affected on a daily basis and evaluating the effectiveness of control policies are critical for our further actions. To date, the statistics of COVID‐19 reported cases show that more than 80% of infected are mild cases of disease, around 14% of infected have severe complications, and about 5% are categorized as critical disease victims. Today's report (5th April 2020; daily updates in the prepared website) shows that the confirmed cases of COVID‐19 in the United States, Spain, Italy, and Germany are 308850, 126168, 124632, and 96092, respectively. Calculating the total case fatality rate (CFR) of Italy (4th April 2020), about 13.3% of confirmed cases have passed away. Compared with South Korea's rate of 1.8% (seven times lower than Italy) and China's 4% (69% lower than Italy), the CFR of Italy is too high. Some effective policies that yielded significant changes in the trend of cases were the lockdown policy in China, Italy, and Spain (the effect observed after some days), the shutdown of all nonessential companies in Hubei (the effect observed after 5 days), combined policy in South Korea, and reducing working hours in Iran.

  • Due to the very recent outbreak of COVID‐19, our knowledge is expanding as new data is presented.
  • Pandemic's outbreak behavior and the the effectiveness of the corresponding policies by the countries analyzed in this study.
  • A new procedure based on regression approach to calculate the case fatality rate during pandemics.
  • A reference of patterns and behaviors to help future pandemics' analysis.

1. INTRODUCTION

Human coronaviruses (HCoV) which cause gastrointestinal and respiratory tract infections were first introduced by the discovery of HCoV‐229E and HCoV‐OC43, from the nasal cavities of human patients with the common cold, in the 1960s. 1 , 2 Other discovered human coronaviruses, which have caused serious respiratory tract infections, include SARS‐CoV (in 2003), HCoV NL63 (in 2004), HKU1 (in 2005), MERS‐CoV (in 2012), and the latest one SARS‐CoV‐2 (in 2019) resulting in coronavirus disease (COVID‐19). 3 , 4 The name originates from the morphology of the virus when viewed under 2D transmission electron microscopy (large pleomorphic spherical particles with the bulbous surface) and stems from the Latin word "corona," meaning "crown." 5 Concerning the risk factor, HCoVs vary significantly from the relatively harmless ones (ie, the common cold) to the most lethal ones (MERS‐CoV, with more than 30% mortality rate in the infected). 6 CoVs spread during cold seasons and cause colds with major symptoms, that is, fever, sore throat, and less commonly pneumonia and bronchitis for the more aggressive strains. To date, there are no vaccines or antiviral drugs capable of preventing or treating HCoV infections. 6 , 7 , 8

To date, several outbreaks of coronavirus‐related diseases have been reported. Severe acute respiratory syndrome (SARS) was the first coronavirus‐related outbreak that started in Guangdong, China, in November 2002, and spread to a total of 29 territories, including Hong Kong, Taiwan, Canada, Singapore, Vietnam, and the United States, within 9 months. It infected a total of 8098 people and killed 774 worldwide. 9 The second coronavirus‐related outbreak happened in the Middle East in April 2012, officially named Middle East respiratory syndrome (MERS). This virus was first identified in a patient from Saudi Arabia, and later, MERS affected several other countries, including Saudi Arabia, South Korea, the United Arab Emirates, Jordan, Qatar, and Oman. Overall, the virus affected 24 countries, with over 1000 cases and over 400 deaths. 10 The outbreak of MERS happened again in South Korea, supposedly from a traveler from the Middle East. It happened during May and July 2015 and infected a total of 186 individuals, with a death toll of 36. 11 After 3 years in August 2018, the next MERS outbreak happened in countries of the Arabian Peninsula and resulted in almost 147 infected people and the death of 47. The MERS outbreak had been reported in Saudi Arabia, the United Kingdom, and South Korea.

In December 2019, a pneumonia outbreak was reported in Wuhan, China, and on 31st December, it was attributed to a new strain of HCoV, first named as 2019‐nCoV by the World Health Organization (WHO) and later renamed to SARS‐CoV‐2 by the International Committee on Taxonomy of Viruses. Almost 2 weeks later, on 11th January 2020, Chinese state media reported the first fatality from the newly discovered virus, which led to the infection of dozens more. Until 20th January, multiple countries reported their first cases, including Japan, South Korea, and Thailand. The first confirmed case in the United States came the very next day in the Washington State. As the spread continued, coronavirus presence was confirmed throughout the month of February in the Philippines (2nd February), France (14th February), Iran (21st February), and as reports started in Italy on 23rd February; many more European countries followed the suit, reporting their first confirmed cases. To date, the coronavirus has affected 181 (by 5th April) countries with more than 1100000 confirmed cases and around 65000 people have lost their lives. With the United States, Spain, Italy, and Germany experiencing the worst cases of outbreaks and showing no sign of alleviation, the 2019‐2020 outbreak of COVID‐19 is now officially recognized as a pandemic by WHO. An outbreak or epidemic often refers to a sudden increase in the occurrence of infectious disease, in a particular time and place. Pandemics are near‐global epidemic outbreaks, where multiple countries across the world are involved. 12

The mentioned rapid trend of spread prompts a lot of concerns and questions such as "How fast is the virus spreading?," "Which policies or efforts could control the disease better?," and "What is the main difference of COVID‐19 outbreak with pervious epidemics?" Fortunately, the daily case detection changes are available and can be tracked almost in real time on the website provided by authors ( http://iuwa.ir/corona/ ). The aim of this study is to provide the transmission trend from China to other countries and to report the daily confirmed cases, fatality causes, and surveillance in every country from the first day of the outbreak until 5th April and, also, to evaluate the effect of each government policy in controlling the outbreak of COVID‐19.

2.1. Basic statistics

COVID‐19 has currently spread to 181 countries and most national authorities have failed to keep its rapid spread contained. 13 WHO reports that it began in Wuhan city, located in Hubei province of China, first reported on 21st January. 14 COVID‐19 categorizes in three distinctions concerning it is infected host's severity of disease. 15 , 16 To date, the statistics of its reported cases show more than 80% of infected had a mild case of disease, whereas around 14% of infected experienced a severe one, suffering from breathlessness and pneumonia. And about 5% are categorized as critical disease patients, their symptoms include septic shock, respiratory failure, and the failure of more than one organ.

Reports on 5th April 2020 show that the United States, Italy, and China have the most confirmed fatal and also recovered cases. The order of confirmed cases after the United States is followed by Spain, Italy, and Germany, which can be seen in Table  1 . Confirmed death cases caused by COVID‐19 are also observed in 140 different countries (by 5th April), lead in numbers by Italy, Spain, the United States, and France. About 24% of death cases, 26% of confirmed cases, and 32% of recovered cases located in Italy, the United States, and China, respectively, are also shown in Table  1 . The overall statistics since 5th April state that there are 1197405 confirmed, 64606 deaths, and 243572 recovered cases, overall. Figure  1 also shows that the COVID‐19 spread exists in all continents.

Top 10 total confirmed, deaths, and recovered cases for 5th April

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Transmission of coronavirus disease 2019‐2020 (COVID‐19); blue nodes represent regions with confirmed COVID‐19 cases, and red nodes represent the regions with COVID‐19 causes deaths

2.2. Finding linear relations

There is not much known at the moment about COVID‐19, so there is a small amount of data about its comprehensive effects and behaviors. In this study, relations are assumed to be linear, when, initially the drawn plot shows obvious linear relations, and later, the fitted linear regression line shows a small enough error to preserve the values given and the linear regression results can be interpreted with relative ease. Besides, fitting regression lines with higher order causes overfitting, resulting from the amount of data. There is no evidence yet about the relationship of other conditions with the outbreak and its case fatality rate (CFR), so by using linear regression line, policies and behaviors can be compared. In the prediction cases, by using linear regression, we can compare future trends of countries in earlier stages, with the ones in later stages. By considering the above‐mentioned statements, we will find the best linear relation between arrays of data. In some cases, the linear relation can be observed but it may exhibit linear relation with some date shift of others (ie, death cases should have a linear relation with earlier values of confirmed cases, given the fact that it should take time from confirmation to death).

CFR could be calculated by the following formula:

where D eath and C onfirmed functions calculate the value of death cases and confirmed cases at that date, T is the date we want to inspect the CFR, and dt is the mean duration of confirmed to death.

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2.3. Global daily statistics

Figure  2A shows the global confirmed deaths and recovered cases' trend for COVID‐19 from 22nd January to 5th April 2020. Death cases are excessively lower than the confirmed ones, so we normalized (by dividing the value of confirmed deaths and recovered cases to their maximum respectively) it in Figure  2C to investigate all three trends of cases. For the confirmed cases, there is a huge increase since 11th February, the increased tones down from 11th February to the next day. Furthermore, on 13th February, another sharp increase is reported. It can be observed in Figure  2B which shows new cases for each day (and normalized in Figure  2D ). The most reliable speculation for this jump is that on that day, China (the country with the most confirmed cases), for the first time, reported the clinically diagnosed cases in addition to laboratory‐confirmed cases, 17 in which 13332 clinically diagnosed cases were added to 1148 laboratory‐confirmed ones. Since then, China has kept the same reporting method for the confirmed cases. On 23rd and 24th February, confirmed new cases started to increase again. As shown in Figure  2A , the reduction trend is continued (approximately) and the cause of the increase was other countries' growing numbers. So, for more accurate analysis, each country will be investigated separately.

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COVID‐19 global epidemic data and statistics of (A) confirmed, recovered, and death COVID‐19 cases, (B) normalized confirmed, recovered, and death COVID‐19 cases, (C) new confirmed, recovered, and death COVID‐19 cases, and (D) normalized new confirmed, recovered, and death COVID‐19 cases

3.1. China: The mainland

Wuhan city located in Hubei province is reported to be the origin of COVID‐19. On 23rd January 2020, a lockdown in Wuhan and other cities in Hubei was implemented to control the outbreak of the COVID‐19. A total of 12 other cities in Hubei, consisting of Huangshi, Jingzhou, Yichang, Xiaogan, Jingmen, Suizhou, Xianning, Qianjiang, Xiantao, Shiyan, Tianmen, and Enshi, restricted any form of transportation by the end of 24th January. These decisions were made to prevent the further expansion of COVID‐19.

3.1.1. Confirmed cases

As measured by Backer et al, 18 the incubation period for Wuhan travelers estimated from 2.1 to 11.1 days (the mean incubation period was estimated to be 6.4 days), and also generally the mean incubation period was estimated at 5.2 days which distributed in intervals of 4.1 to 7.0 with 95% confidence. 19 By adding these two values, 11.6 days after 24th January (4th and 5th February), the effects should be manifesting.

Figure  3B depicted the new daily confirmed cases of China outside of Hubei. The peak of the plot is located on 13th February and the daily new cases reduce afterward. This reduction shows that lockdown plays a serious role in the further reduction of cases in China (excluding Hubei province). Even though there is no reason to argue the lockdown's positive impact on Hubei itself, the decrease in new confirmed cases (13th February increase's rationale was described in the previous section) shows that emergency circumstances and movement limitations yield positive results in the reduction of confirmed cases from 10th February. In 13th February 2020, the Chinese government issued a shutdown of all nonessential companies, including manufacturing plants, in Hubei province. Five days later, on 18th February, a drop of new cases could be observed (Figure  3B ). Finally, the confirmed new cases in China were negligible from 1st March.

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COVID‐19 epidemic data and statistics of China (mainland), Hubei province, and China excluding Hubei. A‐C, New (confirmed, recovered, and death COVID‐19 cases). D‐F, Normal data (for confirmed, recovered, and death COVID‐19 cases). G‐I, Confirmed and death cases. J‐L, Normal data of confirmed and death cases

3.1.2. Deaths

The number of deaths is far lower than the confirmed cases. So, to investigate the relation of confirmed cases trend with the CFR, the normalized plot will be investigated. By observing normalized Hubei province plot of confirmed deaths and recovered cases in Figure  3K , it can be seen that the CFR trend behaves the same as the confirmation, with a shift (in date). Visually, it could be seen that the value of shift in date varies and increases during this time. In earlier cases, the period of confirmation cases leading to death was shorter. It seems, one reason for this variation is that confirmed cases consist only of just laboratory cases and, by adding clinically diagnosed cases (which existed before but did not count beforehand), the time of confirmation to death increases. In other words, the number of confirmed cases gets closer to the real value, and the cases are announced sooner than they did before. Other possible reasons include the advancements in developing treatments, further delaying fatal cases, and the increase in public awareness, as more people with possible signs of infection come forward to be diagnosed. To estimate the expected value of confirming a case up to the death stage, assuming a linear relationship between the death and confirmed rates, we draw a linear regression line for confirmed and death cases' value, each time increasing the duration and finding the mean absolute error (MAE) of the regression line. Normally, by increasing the duration, following the reduction in investigated points, the MAE is reduced. However, if there is an obvious relation between these two parameters, at the point which they had a correlating relation, MAE will begin to increase (Figure  4A ). Wang et al 20 estimated the time from the appearance of first symptoms to dyspnea was 5.0 days, to hospital admission 7.0 days, and acute respiratory distress syndrome 8.0 days. Another study found the median days from the first symptom to death as 14 (range 6‐41) days. 21 As seen in Figure  4C , this value is about 11 days in China (excluding Hubei province), Figure  4E depicts value 9 for Hubei and Figure  4A depicts value as 9 days for China. Assuming that the hospital admission is on the same day of confirmation (or a day before confirmation), the mean total of 14 days from the first symptom could be approved in. 21 By finding the mean day from confirmation to death, it is possible to find out the CFR in China, Hubei, and China (excluding Hubei province) (Algorithm 1 reports best shifting date value and best linear regression line). To find CFR of 5th April for Hubei, confirmed cases on 5th April should be divided by the death cases 9 days prior (which is 27th March) returning 4.7%. Calculating CFR for China (excluding Hubei) till 5th April follows as confirmed cases on the same day divided by death cases of the previous 11 days that date equals 0.9%. Finally, for China's CFR on 5th April, the confirmed cases dated 5th April should be divided by deceased ones of the previous 9 days (27th March) yield 4%.

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Confirmed death and confirmed recovered regression mean absolute error data for COVID‐19 transmission through (A,B) China (mainland), (C,D) Hubei province, and (E,F) China excluding Hubei

3.1.3. Recovered

Recovered cases are defined as active cases‐patients recovered after a certain amount of time, with its trend seen in Figure  3J‐L . By comparing recovered cases with confirmed ones, a relation is observed after date shifts. Unlike death cases, recovered cases' shifts are initially longer and reduce over time. The assumed reasoning is that as time passes, more medical treatments develop, healthcare providers gain more experience in handling patients' care and as more people are informed, increasing numbers of them get checked in hospitals at the early stages of their disease, resulting in an even more efficient treatment. However, this reduction does not break the linear relation between confirmed cases and recovered ones enough to be significant. To find the mean date shift between confirmed cases and recovered ones, we apply a linear regression line to different dates by shifting them back until a first local minimum MAE is found (Algorithm 1). Hubei province's recovered mean duration value found is 20 days as shown in Figure  4F , the same value for China (excluding Hubei province) is 20 days as shown in Figure  4D , and finally it is 20 days for China in Figure  4B . To find out the ratio of the recovered cases of Hubei province on 5th April, recovered cases of 5th April are divided by confirmed cases of 20 days prior resulting in 94%. The same calculation applies for China (excluding Hubei) 99%, and finally for China 95%.

3.2. South Korea: Fast reaction confirmed cases

First confirmed cases of COVID‐19 were observed on 20th January in South Korea, but the outbreak started around 18th February (29 days later), its death and recovered cases' trend is shown in Figure  5A after normalizing, also the new cases' real‐valued and normal form are also found in Figure  5B . Newly confirmed, the deceased and recovered cases are also depicted in Figure  5C and normalized in Figure  5D . In Figure  5E , first days of four country's outbreak are compared. New confirmed and death cases are also depicted in Figure  5G,H .

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COVID‐19 epidemic data and statistics of South Korea (A‐D), and between counties (South Korea, Iran, Italy, and China). For South Korea: (A) confirmed, recovered, and death COVID‐19 cases, (B) normalized data, (C) new data, and (D) normalized new data. To compare between countries: (E) confirmed cases, (F) death cases, (G) confirmed rate, and (I) new confirmed cases

Being one of the first countries reporting the outbreak of COVID‐19, the first reported case was on 20th January. However, no outbreak was observed by 18th February, after which there was a significant increase in the number of confirmed cases. A comparison between the growth trend in numbers from South Korea from this date with China's is depicted in Figure  5E  with the growth rate of South Korea being more than China's. Followed by a faster reduction in South Korea, patterns are approximately the same between the two. COVID‐19 spreads from humans to humans; 22 therefore, in addition to isolation of people, social avoidance and quarantine policies, and faster detection of infected cases should reduce further growth. Each infected individual, by having contact with others directly or by proxy (in other words by activity), could infect several people, so to reduce the odds of transmission of the virus, faster detection of infected individuals could play a key role alongside lockdown strategies.

On the basis of the Korean Centers for Disease Control and Prevention, as of 8th March 2020, a total number of 188518 cases have been screened and tested for HCoV infection. 23 More than 10000 tests were conducted on 8th March, and in just 2 days, this number reached to 210144 (indicating more than 20000 tests had been taken in 2 days). Until this date, South Korea reached to 4099 taken tests per million people. The magnitude of this number clearly shows the policy of South Korea and attempts to reduce the duration of detection, through a faster detection strategy. 24 Furthermore, the fact that the CFR in the old South Korean affected adults 25 (patients above 70 years) is still lower than that of Italian HCoV‐affected patients (average 47 years old), which better signifies the role of early detection in controlling the epidemic.

3.2.1. Deaths

To determine the CFR of confirmed cases, especially in countries in which the COVID‐19 is spreading, the knowledge of confirmation to death duration is essential. To demonstrate a wrong approach, dividing the death cases by confirmed ones on a specific date yields the wrong answers for the death cases that might have been confirmed someday prior. By assuming that CFR has a linear (or near‐linear) relation with the confirmed rate, the duration of confirmation to death could be evaluated by fitting multiple linear regression lines. The minimum value for MAE appears for 5 days shift (for South Korea), meaning death cases were confirmed 5 days before. To discern the CFR of South Korea, total death cases should be divided by the total confirmed date of 5 days prior (slope of the regression line also shows the rate).

As alluded to before, there are many types of COVID‐19 concerning acuteness of the disease. The CFR varies depending on the level of infected people already confirmed. If a country manages to diagnose and confirm the infection of a patient in an earlier stage and begins curating the infected individuals or tallying those with mild COVID‐19, the CFR would be comparatively lower. Such percussion alludes to the apparent lower CFRs in South Korea (1.8%) vs other countries with comparably large outbreaks.

3.3. The United States: The great outbreak

The United States confirmed, the deceased, and recovered COVID‐19 cases trend is depicted in Figure  6A , as well as its normal form in Figure  6B . On 10th March, both the confirmed and death cases of the United States increased (about 2.9 and 2.5 times, respectively). The huge increase in death and confirmed cases lead the regression line algorithm to define confirmed to death duration in 0 days. However, it would not be possible unless most dead cases are confirmed after death. Accepting the 3 days for the United States (see Figure  6G ), the CFR should be about 4%. The high CFR could be alluded to the detection of the infected individuals during the last stages of the illness or reporting on serious cases with higher CFRs exclusively. Compared with China, Iran, Italy, and South Korea, both confirmed and death cases of the United States show higher orders of incrementation. Comparing the increment rate of countries, the United States seems to be in an earlier stage compared with China (outbreak is nearly ended), South Korea (at the ending stage), and Italy (passing the peak of the outbreak). Experimentally, for COVID‐19, most of the countries in which the pandemic happened, (if the strategy does not change many times) two phases of the outbreak could be introduced. The first one is when the virus spreads increasingly (in each day new cases are more than previous), and the second one is the controlling outbreak in which new cases reduced to near zero. The United States is now in the spreading phase (new confirmed and death cases depicted in Figure  6E,F ), the more reduction in new cases would cost more, and a trend with more slope costs more to be flattening. China and South Korea passed the first phase and currently (5th April) it seems they are at the near end of the second stage. The experience of these two countries shows that the second phase will add about 150% of the first stage's cases but it also could vary depending on the strategies undertaken. Considering this calculation, each day in the first phase adds about 2.5 times the new cases on that date in total. Note that, these two phases could be repeated in a country more than once.

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COVID‐19 epidemic data and statistics of the United States: (A) Confirmed, recovered, and death COVID‐19 cases, (B) normalized data for confirmed, recovered, and death COVID‐19 cases, (C) confirmed cases of different countries (the United States, China, South Korea, Italy, and Iran), and (D) death cases of the same countries, (E) new confirmed, recovered, and death COVID‐19 cases, (F) normalized data for new confirmed, recovered, and death COVID‐19 cases, (G) confirmed death regression mean absolute error data for COVID‐19 transmission through the United States

It is advisable to follow the United States in the coming days for more accurate information gathering, considering the latest increment could be caused by wrong data in the previous dates.

3.4. Italy: High CFR confirmed cases

Italy reported its first confirmed cases of COVID‐19 infection on 31st January, later announcing an outbreak around 21 February (21 days later). We have displayed (after normalizing) overall analysis in addition to death and recovered cases' trend in Figure  7B depicting COVID‐19s trend in the country. By normalizing the plot, death and recovered cases' trend could be seen in Figure  7B .

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COVID‐19 epidemic data and statistics of Italy: (A) confirmed, recovered, and death COVID‐19 cases, (B) normalized data, (C) normalized new data. Comparison of COVID‐19 statistics between China and Italy: (D) Confirmed cases, (E) death cases, (F) new confirmed cases, and (G) new death cases

The plot for new cases is also given in Figure  7C in real‐valued and normal form. A comparison between the growth trend in numbers from Italy from this date with China's in the early stages is depicted in Figure  7D as shown, and the growth rate patterns are approximately the same between the two for the first 14 days, showing identical behavior in the increasing numbers of confirmed cases. After day 14, Italy exhibits an incremental increase in confirmed cases juxtaposed to China's. Lombardy, the center of the outbreak in Italy was locked down on 22nd February.

To analyze the stage of the disease in the Italian HCoV‐infected patients (ie, to find out how rapidly the infected people have been screened and diagnosed), the results of the COVID‐19 tests of Italy and South Korea can be compared. South Korea has been considered as a reference as its HCoV‐related CFR is low enough, indicating that both countries are at the same level of HCoV infection. Till 10th March 2020, a total of 60761 HCoV tests of Italians had been undertaken through their screening program, whereas in South Korea, this number exceeds 210144 tests, more than threefolds. However, the number of the daily taken tests in Italy has been increasing, that is, it was 13000 on 11th March. 26

On 9th March, a national quarantine was imposed by the Italian government in which any movement of population has been restricted except necessary ones. Consequently, 11 days later, the increase in new cases in Italy stopped, and it seems Italy is in changing‐phase days.

3.4.1. Deaths

By comparing new cases of Italy and China which is manifested in Figure  7E (and the overall CFR comparison of the two in Figure  7G ), it is clear that new death cases of Italy increased next to China's. Calculating the total CFR of Italy (confirmation to death duration calculated as 4 days), about 14.5% of confirmed cases passed away. Compared with South Korea's rate of 1.8% (eight times than Italy) and China's 4% (72% lower than Italy), the CFR of Italy is too high. Ignoring race and climate as conditions (in which there is no clue of their impact), a strong rationale for this difference should exist. One hypothesis is that some infected individuals are not diagnosed until more serious stages of the disease. This could also explain the increase in confirmed cases, suggesting those infected, remain in contact with others. By comparing hospital beds per 10000 people, the indicator was 115.32 (at 2014), 42 (at 2012), and 34.22 (at 2012) (Iran 15 2014) for South Korea, China, and Italy, respectively. 27 Statistically, it is observed that the CFR of COVID‐19 has had a direct relationship with age (with the average age of death for these three countries, respectively, 28, 38, and 47 years old 28 ) and it also potentially contributes to the increase in CFRs.

3.5. Spain: Italy's follower

Massive levels of COVID‐19 outbreak were reported around 25th February for Spain, as seen by its trend of confirmed, death, and recovered cases demonstrated in Figure  8A . By comparing the early stages of Spain with Iran, Italy, and South Korea in Figure  8C , the trend in 5th April shows Spain has a larger growth rate than other ones mentioned here. The mean age of Spain is 45 year old and hospital beds per person in the country is 29.65 for every 10000 (at 2013); compared with Italy, the mean age of Spain is slightly smaller and hospital beds per person rate is relatively higher, and consequently, it is expected that the CFR should, therefore, be slightly lower. After calculating the confirmed to death cases duration which is 5 days, the CFR of Spain results in 13.5% for Spain, as expected.

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COVID‐19 epidemic data and statistics of Spain: (A) normalized data for confirmed, recovered, and death COVID‐19 cases, (B) new confirmed, recovered, and death COVID‐19 cases, (C) confirmed cases of different countries (Spain, China, South Korea, Italy, and Iran), and (D) death cases of the same countries

Following by 9th March national quarantine in Italy, Spain proposed movement restriction on 16th March. As seen in Figure  8B , the increasing new cases of Spain stopped 10 days later. After Italy, Spain is also at the beginning of the phase‐shifting period.

3.6. Other countries

3.6.1. iran: good recovery rate.

COVID‐19 was first reported in Iran starting with two dead cases on 19th February in Qom (a city near Tehran, Iran's capital), followed immediately by a huge outbreak, displayed in Figure  9A detailing its number of confirmed, deaths, and recovered cases, and normalized in Figure  9B . Officially, Iran has not mandated any city‐wide lockdowns, but recently some provinces are refusing nonlocal travelers. Regarding the experiences in China, lockdown strategy will isolate cities to avoid transmitting the growth rate to other cities. However, such policies entail some negative consequences, and therefore, they were not implemented in the early stages of the outbreak in Iran in contrast to Italy and South Korea, where there was a gap between outbreak and huge outbreak (21 and 27 days). On the 10th day of the outbreak in Iran, the total reported, confirmed, and infected individual cases reached 388.

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COVID‐19 epidemic data and statistics of Iran: (A) confirmed, recovered, and death COVID‐19 cases, (B) normalized data, (C) new data, and (D) normalized new data

To find confirmed to death cases mean duration, as before, we calculate the minimum MAE, assuming there is a linear relationship between these two. The linear regression algorithm shows the date shift should be zero, which means on average, death cases confirmed on the same day. By comparing the death trend with confirmed cases in Figure  9B , it is obvious that the trend of CFR was not linear. The reported death vs confirmed is too excessive in the early days. It is possible that the shortage of COVID‐19 testing kits or/and the lack of clinical diagnosis approach caused such unreliable data. Countries at this stage mostly had about 7 days of confirmation to the dead period, so following this assumption the COVID‐19s CFR in Iran is around 9.4%.

By observing the 24th February report, there were 49 active cases (the cases which are not recovered or dead but confirmed as a COVID‐19 infected) reported (at least 14 cases are new) and the value for active cases in the next day was 79 (at least 30 new cases). Two days later, the reported recovered cases was 49. Suppose, no one was treated in less than 2 days, this results in a 100% recovery of 14 new cases in 2 days and also no active cases after 2 days, marking it a great experience which was reported by Iran's officials. Otherwise, some cases were treated in 1 day. This makes Iran's phenomena intriguing for investigating the details and contributing factors for such cases to find a relation between an individual's physical conditions and their treatment period. However, errors in the report could also explain this information. Using the regression line to find the mean recovered duration shows that the recovery duration for Iran should be around 1 day. Also seen in Figure  9C , the rate of recovery in Iran is quite significant at 37%.

On the 20th day of the outbreak, a temporal reduction in new cases could be observed starting on 6th March, persisting until 9th March. Evidently, it is the impact of a nationwide implemented policy reducing working hours 6 days prior (5.4 days is the mean incubation period 18 ). After 4 days, with the arrival of weekends and the following two additional holidays, reports indicate, many people took trips during these dates which are widely assumed as a reason for confirmed cases' rise. Although many companies implemented forms of remote work for their employees (which is estimated to have a positive impact on preventing the growth outbreak), yet many governmental offices working hours returned to their preoutbreak times (naturally assumed to negatively impact the prevention of disease's spread).

3.6.2. Japan

The first confirmed cases of COVID‐19 in Japan returned from Wuhan on 6th January. However, the outbreak in Japan sped up on 15th February. Figure  10A shows the trend of the outbreak after 15th February in Japan, whereas Figure  10B is normalized to compare confirmed, deceased, and recovered cases. The trend shows that the spread of COVID‐19 in Japan behaves at a lower rate than China's first dates. CFR is estimated that it is about 4.1%. The CFR is good considering the mean age in Japan, which is 48. By finding out the hospital per bed value of Japan which is 134 (reported in 2012 by WHO), the relation of death cases compared with hospital per bed is more clear. Although Japan managed the early stage exponential rate of COVID‐19, a reduction in new cases is not observed.

An external file that holds a picture, illustration, etc.
Object name is JMV-92-868-g005.jpg

COVID‐19 epidemic data and statistics of Japan: (A) confirmed, recovered, and death COVID‐19 cases, (B) normalized data for confirmed, recovered, and death COVID‐19 cases

3.6.3. France: Unexpected recent changes

France underwent two increments in its rate of confirmed COVID‐19 cases. The first one was on 25th February and, therefore, chosen as the starting point of the outbreak. The second was from 4th April as shown in Figure  11A . By normalizing data displayed in Figure  11B and calculating the confirmed to death duration which is 7, CFR for France on 5th April results in 19.9%. Considering the mean age as a parameter, which in France is lower than Spain (42 and 45, respectively), in addition to beds per person which is 64.77 (at 2013) 27 (higher than Spain), CFRs are expected to be lower than Spain, but the unexpected growth in death cases from 2nd April increases the CFR of France even more than Spain's. The unexpected change also had an impact on the regression line. By comparing the growth of confirmed cases with China, Iran, Italy, and South Korea as depicted in Figure  11C , France has a very high slope in both confirmed and death. More than 1000 new death cases per day from 2nd April and more than 25000 new confirmed cases on 4th April contribute to such a faster increment.

An external file that holds a picture, illustration, etc.
Object name is JMV-92-868-g006.jpg

COVID‐19 epidemic data and statistics of France: (A) confirmed, recovered, and death COVID‐19 cases, (B) normalized data for confirmed, recovered, and death COVID‐19 cases, (C) confirmed cases of different countries (France, China, South Korea, Italy, and Iran), and (D) death cases of the same countries

3.6.4. Germany: Handled CFR

By observing Figure  12A and comparing the trend with China, Italy, Iran, and South Korea, which is depicted in Figure  12B , Germany had the same COVID‐19 trend as Italy. The total number of deaths in Germany in 17th March is 24. By dividing the deaths of 5th April to the confirmed on 30th March, 2.36% is the CFR, which is too low at considering the mean age of Germany which is 47. The possible reason for this low CRF is earlier confirmation, which leads to earlier hospitalization for the infected.

An external file that holds a picture, illustration, etc.
Object name is JMV-92-868-g007.jpg

COVID‐19 epidemic data and statistics of Germany: (A) confirmed, recovered, and death COVID‐19 cases, and (B) confirmed cases of different countries (Germany, China, South Korea, Italy, and Iran)

4. DISCUSSION

The current outbreak of the novel‐coronavirus (COVID‐19), epicentered in Hubei province of the People's Republic of China, has spread to many other countries. The case detection rate is changing daily and can be tracked in almost real time on the mentioned website.

Reports on 5th April 2020 show that the United States, Italy, and China have the most confirmed, fatal, and recovered cases, respectively, and in terms of confirmed cases, Spain, Italy, Germany, and France are following the United States. Confirmed death cases lead in numbers by Italy and followed by Spain, the United States, and France. The daily statistics showed that lockdown is effective in the reduction of incidence of confirmed cases with COVID‐19 after about 10 days in China, Italy, and Spain. South Korea is one of the first countries reporting the cases after China and the growth pattern of confirmed cases is the same as China's. However, they implement some policies such as in addition to isolation of people, social avoidance, and quarantine policies for infected, and faster detection of infected cases which were effective in a decrease in the new confirmed case and also case fatality. Italy and China have approximately the same growth rate patterns for the first 14 days. The lockdown strategy of Lombardy (the center of outbreak in Italy) seems to have had a positive effect on other municipalities. Unlike China's growth pattern, Iran's incremental trend continued to rise until the 20th day of the outbreak, even though a temporal reduction in new cases could be observed due to a nationwide implemented policy reducing working hours. The CFR in China was 4% (for Hubei was 4.7%). The highest and the lowest CFRs belonged to Italy (14.3%), and South Korea (1.8%), respectively, which could represent the effectiveness of their policies in control of the COVID‐19. The United States has more than a quarter of confirmed cases. It seems the leading country in confirmed cases of COVID‐19 should propose a new policy to reduce new cases and go to the next phase of the pandemic.

Social distancing is one of the most effective policies to control the past epidemic disease by limiting human to human transmission and reducing mortality and morbidity. 29 , 30 However, studies suggest that a combination of multiple policies can boost effectiveness. For instance, New York City Department of Health implemented different policies during the influenza pandemic in 1918‐1919 at the same time and they have the lowest rate of mortality on the eastern seaboard of the United States. 31

During the COVID‐19 outbreak, researchers predicted that the mass movement of people at the end of the Lunar New Year holiday would increase the spreading of disease. Facing this concern, government of China implemented policies which was helpful in controlling the disease such as, extending the holiday so that the holiday would be long enough to shelter the incubation period of COVID‐19, isolation of confirmed cases in hospitals, quarantining mild or asymptomatic persons in different hospitals, home‐based quarantine of people from Hubei province (epicenter of the epidemic), and the most important one was to prevent individuals with asymptomatic infections from spreading the virus. 19 , 22 Iran is facing this concern as an important upcoming event in Iran is Nowruz which is the Iranian New Year, which recommended prompted policies from government.

Hoseinpour Dehkordi A, Alizadeh M, Derakhshan P, Babazadeh P, Jahandideh A. Understanding epidemic data and statistics: A case study of COVID‐19 . J Med Virol . 2020; 92 :868–882. 10.1002/jmv.25885 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

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Why writing by hand beats typing for thinking and learning

Jonathan Lambert

A close-up of a woman's hand writing in a notebook.

If you're like many digitally savvy Americans, it has likely been a while since you've spent much time writing by hand.

The laborious process of tracing out our thoughts, letter by letter, on the page is becoming a relic of the past in our screen-dominated world, where text messages and thumb-typed grocery lists have replaced handwritten letters and sticky notes. Electronic keyboards offer obvious efficiency benefits that have undoubtedly boosted our productivity — imagine having to write all your emails longhand.

To keep up, many schools are introducing computers as early as preschool, meaning some kids may learn the basics of typing before writing by hand.

But giving up this slower, more tactile way of expressing ourselves may come at a significant cost, according to a growing body of research that's uncovering the surprising cognitive benefits of taking pen to paper, or even stylus to iPad — for both children and adults.

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In kids, studies show that tracing out ABCs, as opposed to typing them, leads to better and longer-lasting recognition and understanding of letters. Writing by hand also improves memory and recall of words, laying down the foundations of literacy and learning. In adults, taking notes by hand during a lecture, instead of typing, can lead to better conceptual understanding of material.

"There's actually some very important things going on during the embodied experience of writing by hand," says Ramesh Balasubramaniam , a neuroscientist at the University of California, Merced. "It has important cognitive benefits."

While those benefits have long been recognized by some (for instance, many authors, including Jennifer Egan and Neil Gaiman , draft their stories by hand to stoke creativity), scientists have only recently started investigating why writing by hand has these effects.

A slew of recent brain imaging research suggests handwriting's power stems from the relative complexity of the process and how it forces different brain systems to work together to reproduce the shapes of letters in our heads onto the page.

Your brain on handwriting

Both handwriting and typing involve moving our hands and fingers to create words on a page. But handwriting, it turns out, requires a lot more fine-tuned coordination between the motor and visual systems. This seems to more deeply engage the brain in ways that support learning.

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"Handwriting is probably among the most complex motor skills that the brain is capable of," says Marieke Longcamp , a cognitive neuroscientist at Aix-Marseille Université.

Gripping a pen nimbly enough to write is a complicated task, as it requires your brain to continuously monitor the pressure that each finger exerts on the pen. Then, your motor system has to delicately modify that pressure to re-create each letter of the words in your head on the page.

"Your fingers have to each do something different to produce a recognizable letter," says Sophia Vinci-Booher , an educational neuroscientist at Vanderbilt University. Adding to the complexity, your visual system must continuously process that letter as it's formed. With each stroke, your brain compares the unfolding script with mental models of the letters and words, making adjustments to fingers in real time to create the letters' shapes, says Vinci-Booher.

That's not true for typing.

To type "tap" your fingers don't have to trace out the form of the letters — they just make three relatively simple and uniform movements. In comparison, it takes a lot more brainpower, as well as cross-talk between brain areas, to write than type.

Recent brain imaging studies bolster this idea. A study published in January found that when students write by hand, brain areas involved in motor and visual information processing " sync up " with areas crucial to memory formation, firing at frequencies associated with learning.

"We don't see that [synchronized activity] in typewriting at all," says Audrey van der Meer , a psychologist and study co-author at the Norwegian University of Science and Technology. She suggests that writing by hand is a neurobiologically richer process and that this richness may confer some cognitive benefits.

Other experts agree. "There seems to be something fundamental about engaging your body to produce these shapes," says Robert Wiley , a cognitive psychologist at the University of North Carolina, Greensboro. "It lets you make associations between your body and what you're seeing and hearing," he says, which might give the mind more footholds for accessing a given concept or idea.

Those extra footholds are especially important for learning in kids, but they may give adults a leg up too. Wiley and others worry that ditching handwriting for typing could have serious consequences for how we all learn and think.

What might be lost as handwriting wanes

The clearest consequence of screens and keyboards replacing pen and paper might be on kids' ability to learn the building blocks of literacy — letters.

"Letter recognition in early childhood is actually one of the best predictors of later reading and math attainment," says Vinci-Booher. Her work suggests the process of learning to write letters by hand is crucial for learning to read them.

"When kids write letters, they're just messy," she says. As kids practice writing "A," each iteration is different, and that variability helps solidify their conceptual understanding of the letter.

Research suggests kids learn to recognize letters better when seeing variable handwritten examples, compared with uniform typed examples.

This helps develop areas of the brain used during reading in older children and adults, Vinci-Booher found.

"This could be one of the ways that early experiences actually translate to long-term life outcomes," she says. "These visually demanding, fine motor actions bake in neural communication patterns that are really important for learning later on."

Ditching handwriting instruction could mean that those skills don't get developed as well, which could impair kids' ability to learn down the road.

"If young children are not receiving any handwriting training, which is very good brain stimulation, then their brains simply won't reach their full potential," says van der Meer. "It's scary to think of the potential consequences."

Many states are trying to avoid these risks by mandating cursive instruction. This year, California started requiring elementary school students to learn cursive , and similar bills are moving through state legislatures in several states, including Indiana, Kentucky, South Carolina and Wisconsin. (So far, evidence suggests that it's the writing by hand that matters, not whether it's print or cursive.)

Slowing down and processing information

For adults, one of the main benefits of writing by hand is that it simply forces us to slow down.

During a meeting or lecture, it's possible to type what you're hearing verbatim. But often, "you're not actually processing that information — you're just typing in the blind," says van der Meer. "If you take notes by hand, you can't write everything down," she says.

The relative slowness of the medium forces you to process the information, writing key words or phrases and using drawing or arrows to work through ideas, she says. "You make the information your own," she says, which helps it stick in the brain.

Such connections and integration are still possible when typing, but they need to be made more intentionally. And sometimes, efficiency wins out. "When you're writing a long essay, it's obviously much more practical to use a keyboard," says van der Meer.

Still, given our long history of using our hands to mark meaning in the world, some scientists worry about the more diffuse consequences of offloading our thinking to computers.

"We're foisting a lot of our knowledge, extending our cognition, to other devices, so it's only natural that we've started using these other agents to do our writing for us," says Balasubramaniam.

It's possible that this might free up our minds to do other kinds of hard thinking, he says. Or we might be sacrificing a fundamental process that's crucial for the kinds of immersive cognitive experiences that enable us to learn and think at our full potential.

Balasubramaniam stresses, however, that we don't have to ditch digital tools to harness the power of handwriting. So far, research suggests that scribbling with a stylus on a screen activates the same brain pathways as etching ink on paper. It's the movement that counts, he says, not its final form.

Jonathan Lambert is a Washington, D.C.-based freelance journalist who covers science, health and policy.

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US set to impose 100% tariff on Chinese electric vehicle imports

Electric vehicles of Chinese car manufacturer BYD leave the car carrier ship BYD Explorer No. 1 at the port of Bremerhaven, Germany

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The Biden administration plans to raise tariffs on Chinese electric vehicle imports from 25 per cent to 100 per cent, as it intensifies efforts ahead of the US election to protect American industry.

The administration is expected to announce the move, and other tariffs on clean energy imports, on Tuesday, according to people familiar with the situation.

The sharp rise in the levies comes amid mounting concern that China could flood the US market with cheap EVs , threatening the American car industry. President Joe Biden has taken several actions in recent months to convince union members in swing states that he will protect jobs.

The Biden administration has for three years been reviewing the tariffs that then president Donald Trump put on imports from China as part of the trade war he launched in 2018. The new EV tariffs will be announced alongside the conclusion of the review, led by the US Trade Representative.

During a visit last month to Pennsylvania — a swing state in November’s election — Biden said he wanted the agency to triple tariffs on Chinese steel and aluminium. USTR also recently opened an investigation into unfair practices in the Chinese shipbuilding industry following a petition from the United Steelworkers union.

But the decision to increase tariffs on EVs comes as the administration becomes particularly concerned that China is moving far ahead in the green industrial sector, including in the production of solar panels.

“The Biden administration is trying to get ahead of the curve and ensure that the US car industry does not suffer the same fate as the US solar industry, which was virtually decimated by unfairly traded Chinese imports,” said Wendy Cutler, a former trade official and vice-president of the Asia Society Policy Institute. 

Cutler said Chinese carmakers had been prepared to swallow the cost of the existing tariffs in an effort to “cripple” their US competitors, but the higher tariffs would make that much harder.

“A quadrupling of this tariff rate, however, would more effectively shield US auto manufacturers from unfairly traded Chinese vehicles before they can gain a foothold in the US market,” Cutler said.

The Biden administration has poured billions of dollars into subsidies for EV and battery production in the US — an effort to spur investment in a domestic clean tech sector as part of a strategy to reindustrialise the rust-belt, slash carbon emissions and break dependence on Chinese supply chains.

In February, Biden also ordered an investigation into whether Chinese “connected vehicles” — a growing category of vehicles connected to the internet that includes EVs — posed a national security risk to the US.

The tariffs are the latest action by the administration that show how Biden is continuing to impose costs on China at the same time that Beijing and Washington pursue efforts to stabilise relations following a summit between the US president and Chinese President Xi Jinping last year.

News of the tariff increase comes after the US and China, the world’s two biggest emitters, said this week they would “ intensify ” co-operation on climate-related issues, including the rollout of green energy.

The decision to increase tariffs was first reported by Bloomberg.

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