In most US states, mortality rates grew more slowly between April 5, 2020 and April 12, 2020 than they did in prior weeks. However, “slower” does not mean “slow”—during that week, mortality rates doubled or more in 37 states.

COVID-19 has spread to most regions and territories around the world. As of May 2, 2023, the number of confirmed cases had reached roughly 687 million.

COVID-19 in the Americas The Americas is one of the regions most impacted by COVID-19. The number of coronavirus cases and deaths are particularly high in the United States and Brazil. The pandemic has had a devastating impact on Latin America, and several nations have recorded a resurgence in cases, highlighting the complexity of easing restrictions while the virus is still a threat. However, mass vaccination programs have been launched in countries including Argentina, Chile, and Panama.

The role of face masks in the prevention of COVID-19 There has been much discussion about the effectiveness of face masks in slowing the spread of the COVID-19 disease. Many governments around the world made it mandatory to wear a form of face mask, particularly in shops and on public transport. Masks alone will not halt the spread of the disease, and they should be used alongside other measures such as social distancing.

After entering Italy, the coronavirus (COVID-19) spread fast. The strict lockdown implemented by the government during the Spring 2020 helped to slow down the outbreak. However, in the following months the country had to face four new harsh waves of contagion. As of January 1, 2025, 198,638 deaths caused by COVID-19 were reported by the authorities, of which approximately 48.7 thousand in the region of Lombardy, 20.1 thousand in the region of Emilia-Romagna, and roughly 17.6 thousand in Veneto, the regions mostly hit. The total number of cases reported in the country reached over 26.9 million. The north of the country was mostly hit, and the region with the highest number of cases was Lombardy, which registered almost 4.4 million of them. The north-eastern region of Veneto counted about 2.9 million cases. Italy's death toll was one of the most tragic in the world. In the last months, however, the country saw the end to this terrible situation: as of November 2023, 85 percent of the total Italian population was fully vaccinated. For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

This dataset is updated till 15th September 2022.

Introduction

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus.

Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illnesses. Anyone can get sick with COVID-19 and become seriously ill or die at any age.

The best way to prevent and slow transmission is to be well informed about the disease and how the virus spreads. Protect yourself and others from infection by staying at least 1 meter apart from others, wearing a properly fitted mask, and washing your hands or using an alcohol-based rub frequently. Get vaccinated when it’s your turn and follow local guidance.

The virus can spread from an infected person’s mouth or nose in small liquid particles when they cough, sneeze, speak, sing or breathe. These particles range from larger respiratory droplets to smaller aerosols. It is essential to practice respiratory etiquette, for example by coughing into a flexed elbow, and to stay home and self-isolate until you recover if you feel unwell.

Symptoms

COVID-19 affects different people in different ways. Most infected people will develop mild to moderate illness and recover without hospitalization.

Most common symptoms:

fever cough tiredness loss of taste or smell.

Less common symptoms:

sore throat headache aches and pains diarrhea a rash on the skin, or discoloration of fingers or toes red or irritated eyes.

Serious symptoms:

difficulty breathing or shortness of breath loss of speech or mobility, or confusion chest pain. Seek immediate medical attention if you have serious symptoms. Always call before visiting your doctor or health facility.

People with mild symptoms who are otherwise healthy should manage their symptoms at home. On average it takes 5–6 days from when someone is infected with the virus for symptoms to show, however it can take up to 14 days.

Given the lack of potential vaccines and effective medications, non-pharmaceutical interventions are the major option to curtail the spread of COVID-19. An accurate estimate of the potential impact of different non-pharmaceutical measures on containing, and identify risk factors influencing the spread of COVID-19 is crucial for planning the most effective interventions to curb the spread of COVID-19 and to reduce the deaths. Additive model-based bivariate causal discovery for scalar factors and multivariate Granger causality tests for time series factors are applied to the surveillance data of lab-confirmed Covid-19 cases in the US, University of Maryland Data (UMD) data, and Google mobility data from March 5, 2020 to August 25, 2020 in order to evaluate the contributions of social-biological factors, economics, the Google mobility indexes, and the rate of the virus test to the number of the new cases and number of deaths from COVID-19. We found that active cases/1,000 people, workplaces, tests done/1,000 people, imported COVID-19 cases, unemployment rate and unemployment claims/1,000 people, mobility trends for places of residence (residential), retail and test capacity were the popular significant risk factor for the new cases of COVID-19, and that active cases/1,000 people, workplaces, residential, unemployment rate, imported COVID cases, unemployment claims/1,000 people, transit stations, mobility trends (transit), tests done/1,000 people, grocery, testing capacity, retail, percentage of change in consumption, percentage of working from home were the popular significant risk factor for the deaths of COVID-19. We observed that no metrics showed significant evidence in mitigating the COVID-19 epidemic in FL and only a few metrics showed evidence in reducing the number of new cases of COVID-19 in AZ, NY and TX. Our results showed that the majority of non-pharmaceutical interventions had a large effect on slowing the transmission and reducing deaths, and that health interventions were still needed to contain COVID-19.

Context: The human development territories have been severely constrained under the Covid-19 pandemic. A common dynamics has been observed, but its propagation has not been homogeneous over each continent. We aimed at characterizing the non-viral parameters that were most associated with death rate.Methods: We tested major indices from five domains (demography, public health, economy, politics, environment) and their potential associations with Covid-19 mortality during the first 8 months of 2020, through a Principal Component Analysis and a correlation matrix with a Pearson correlation test. Data of all countries, or states in federal countries, showing at least 10 fatality cases, were retrieved from official public sites. For countries that have not yet finished the first epidemic phase, a prospective model has been computed to provide options of death rates evolution.Results: Higher Covid death rates are observed in the [25/65°] latitude and in the [−35/−125°] longitude ranges. The national criteria most associated with death rate are life expectancy and its slowdown, public health context (metabolic and non-communicable diseases (NCD) burden vs. infectious diseases prevalence), economy (growth national product, financial support), and environment (temperature, ultra-violet index). Stringency of the measures settled to fight pandemia, including lockdown, did not appear to be linked with death rate.Conclusion: Countries that already experienced a stagnation or regression of life expectancy, with high income and NCD rates, had the highest price to pay. This burden was not alleviated by more stringent public decisions. Inherent factors have predetermined the Covid-19 mortality: understanding them may improve prevention strategies by increasing population resilience through better physical fitness and immunity.

As of March 1, 2023, the total number of coronavirus (COVID-19) cases in Algeria reached 271,448. Overall, the growth in the number of cases slowed down considerably from March 2022, also thanks to successful vaccination efforts in the country.

The pandemic in Algeria

The first case of COVID-19 in Algeria was confirmed in February 2020, when a man coming from Italy tested positive for the virus. Afterward, the virus spread rapidly in the country, causing the first deaths in March 2020. Overall, Algeria recorded a total of around 7,000 deaths, one of the highest mortality registered in Africa. Similar to the rest of the world, the Algerian government adopted measures to prevent the spread of the virus. These included partial and total lockdown, the closure of gyms, recreational areas, and beaches.

Vaccination campaign

Algeria officially started the vaccination campaign at the end of January 2021. After a slow start, the total number of vaccine doses administered began to grow considerably. Despite the acceleration of the campaign, the vaccination rate remained below the African average. The country obtained COVID-19 vaccines from different sources, namely from bilateral agreements as well as through the United Nations-led COVAX, an initiative aiming at delivering vaccines against COVID-19 to all countries worldwide.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. 100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent one third of case days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 63 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 6-21 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 6 to 21 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 6-21 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 6-21 days and less than past 2 days indicates slight positive trend, but likely still within peak trend timeframe.Past five days is less than the past 6-21 days. This means a downward trend. This would be an important trend for any administrative area in an epidemic trend that the rate of spread is slowing.If less than the past 2 days, but not the last 6-21 days, this is still positive, but is not indicating a passage out of the peak timeframe of the daily new cases curve.Past 5 days has only one or two new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 6 to 21 days. Most recent 6-21 days: Represents the full tail of the curve and provides context for the past 2- and 5-day trends.If this is greater than both the 2- and 5-day trends, then a short-term downward trend has begun. Mean of Recent Tail NCD in the context of the Mean of All NCD, and raw counts of cases:Mean of Recent NCD is less than 0.5 cases per 100,000 = high level of controlMean of Recent NCD is less than 1.0 and fewer than 30 cases indicate continued emergent trend.3. Mean of Recent NCD is less than 1.0 and greater than 30 cases indicate a change from emergent to spreading trend.Mean of All NCD less than 2.0 per 100,000, and areas that have been in epidemic trends have Mean of Recent NCD of less than 5.0 per 100,000 is a significant indicator of changing trends from epidemic to spreading, now going in the direction of controlled trend.Similarly, in the context of Mean of All NCD greater than 2.0

Context

What Is COVID-19?

A coronavirus is a kind of common virus that causes an infection in your nose, sinuses, or upper throat. Most coronaviruses aren't dangerous.

COVID-19 is a disease that can cause what doctors call a respiratory tract infection. It can affect your upper respiratory tract (sinuses, nose, and throat) or lower respiratory tract (windpipe and lungs). It's caused by a coronavirus named SARS-CoV-2.

It spreads the same way other coronaviruses do, mainly through person-to-person contact. Infections range from mild to serious.

SARS-CoV-2 is one of seven types of coronavirus, including the ones that cause severe diseases like Middle East respiratory syndrome (MERS) and sudden acute respiratory syndrome (SARS). The other coronaviruses cause most of the colds that affect us during the year but aren’t a serious threat for otherwise healthy people.

In early 2020, after a December 2019 outbreak in China, the World Health Organization identified SARS-CoV-2 as a new type of coronavirus. The outbreak quickly spread around the world.

Is there more than one strain of SARS-CoV-2?

It’s normal for a virus to change, or mutate, as it infects people. A Chinese study of 103 COVID-19 cases suggests the virus that causes it has done just that. They found two strains, which they named L and S. The S type is older, but the L type was more common in early stages of the outbreak. They think one may cause more cases of the disease than the other, but they’re still working on what it all means.

How long will the coronavirus last?

It’s too soon to tell how long the pandemic will continue. It depends on many things, including researchers’ work to learn more about the virus, their search for a treatment and a vaccine, and the public’s efforts to slow the spread.

Dozens of vaccine candidates are in various stages of development and testing. This process usually takes years. Researchers are speeding it up as much as they can, but it still might take 12 to 18 months to find a vaccine that works and is safe.

Symptoms of COVID-19

The main symptoms include:

• Fever
• Coughing
• Shortness of breath
• Fatigue
• Chills, sometimes with shaking
• Body aches
• Headache
• Sore throat
• Loss of smell or taste
• Nausea
• Diarrhea

The virus can lead to pneumonia, respiratory failure, septic shock, and death. Many COVID-19 complications may be caused by a condition known as cytokine release syndrome or a cytokine storm. This is when an infection triggers your immune system to flood your bloodstream with inflammatory proteins called cytokines. They can kill tissue and damage your organs.

STAY HOME. STAY SAFE !

Content

ALL DATASETS HAVE BEEN CLEANED FOR DIRECT USE.

Total_World_covid-19.csv : This dataset contains the worldwide data country-wise such as total cases , total active, deaths, etc. along with testing data.

Total_India_covid-19.csv : This dataset contains India level data statewise such as confirmed cases , active cases, deaths, etc.

Total_US_covid-19.csv : This dataset contains India level data statewise such as confirmed cases , active cases, deaths, etc.

Daily_States_India.csv : This dataset contains daily statewise data of India such as daily confirmed , daily active , daily deaths and daily recovered.

Total_Maharshtra_covid-19.csv : This dataset contains Maharashtra's district wise data such as confirmed cases , active cases, deaths, etc.

Acknowledgements

1. World and US data has been collected from Worldometer . Thanks a lot.

2. India and State level along with Maharashtra district data has been collected from Covid19India. Special thanks to them for providing updated and such wonderful data .

Inspiration

1) What has been the Covid-19 trend across the world, Is it declining? Is it increasing? 2) Which countries have been able to sustain and control the virus spread? 3) How is India coping up with the virus? Have they been able to control it at the given cost of 2 months nationwide lockdown?

Botswana registered its first coronavirus (COVID-19) death on the last day of March 2020. Since that date, the total cumulative number of casualties recorded in Botswana due to the pandemic increased slowly, with ***** lives lost as of August 8, 2021. As of the same day, the total number of registered infections in the country reached approximately ***** thousand, while roughly ***** thousand civilians recovered from the disease.

As of June 6, 2022, the novel coronavirus SARS-CoV-2 that originated in Wuhan, the capital of Hubei province in China, had infected over 2.1 million people and killed 14,612 in the country. Hong Kong is currently the region with the highest active cases in China. From Wuhan to the rest of China In late December 2019, health authorities in Wuhan detected several pneumonia cases of unknown cause. Most of these patients had links to the Huanan Seafood Market. With Chinese New Year approaching, millions of Chinese migrant workers travelled back to their hometowns for the celebration. Before the start of the travel ban on January 23, around five million people had left Wuhan. By the end of January, the number of infections had surged to over ten thousand. The death toll from the virus exceeded that of the SARS outbreak a few days later. On February 12, thousands more cases were confirmed in Wuhan after an improvement to the diagnosis method, resulting in another sudden surge of confirmed cases. On March 31, 2020, the National Health Commission (NHC) in China announced that it would begin reporting the infection number of symptom-free individuals who tested positive for coronavirus. On April 17, 2020, health authorities in Wuhan revised its death toll, adding 50 percent more fatalities. After quarantine measures were implemented, the country reported no new local coronavirus COVID-19 transmissions for the first time on March 18, 2020. The overloaded healthcare system In Wuhan, 28 hospitals were designated to treat coronavirus patients, but the outbreak continued to test China’s disease control system and most of the hospitals were soon fully occupied. To combat the virus, the government announced plans to build a new hospital swiftly. On February 3, 2020, Huoshenshan Hospital was opened to provide an additional 1,300 beds. Due to an extreme shortage of health-care professionals in Wuhan, thousands of medical staff from all over China came voluntarily to the epicenter to offer their support. After no new deaths reported for first time, China lifted ten-week lockdown on Wuhan on April 8, 2020. Daily life was returning slowly back to normal in the country.

Italy experienced a sharp rise in the number of positive infections shortly after confirming its 100th coronavirus case. In the space of just 17 days, the number of cases in Italy had soared to more than 12,000. In comparison, the spread of the virus was much slower in Japan.

The COVID-19 outbreak in Italy Italy was the first European nation to be severely impacted by COVID-19. There had been approximately 35,400 coronavirus-related deaths recorded in the country as of August 17, 2020. Following a two-month lockdown period, restrictions in Italy were eased in early May, and citizens are now permitted to travel between regions and abroad. However, the risk of a resurgence remains, and the country’s state of emergency has been extended until October 15, 2020. It is looking increasingly likely that restrictions will not be completely lifted until a vaccine for the disease is discovered.

Pfizer confident of vaccine success Pfizer and BioNTech are jointly developing one candidate vaccine that is under clinical evaluation. In July 2020, the two companies announced an agreement with the U.S. government that will bring millions of doses to the American people. The BNT162 mRNA-based vaccine is currently being produced even though it has not received regulatory approval from the FDA. This is a risky approach and is one that could cost the companies millions of dollars should the vaccine be rejected. However, if regulatory approval is received, the safe and effective vaccine can be shipped quickly.

Here we investigated whether the dengue fever pandemic of 2019-2020 may have influenced COVID-19 incidence and spread around the world. In Brazil, the geographic distribution of dengue fever was highly complementary to that of COVID-19. This was accompanied by an inverse correlation between COVID-19 and dengue fever incidence that could not be explained by socioeconomic factors. This inverse correlation was observed for 5,016 Brazilian municipalities reporting COVID-19 cases, 558 micro- and 137 meso-regions, 27 states and 5 regions. Brazilian states with high population levels of dengue IgM in 2020 exhibited: (i) lower COVID-19 case and death incidence, (ii) slower infection growth rates, and (iii) took longer to accumulate COVID-19 cases. No such inverse correlations were observed for the chikungunya virus, which is also transmitted by the Aedes aegypti mosquito. The same inverse correlation between COVID-19 and dengue fever incidence was observed for 145 locations (66 countries and the 64 states of Mexico and Colombia) in Latin America, the Caribbean, and Asia. Countries with high dengue incidence took longer to accumulate COVID-19 cases than those without dengue. Although the dataset considered has quality and availability limitations, these findings raise the possibility of an immunological cross-reaction between dengue virus serotypes and SARS-CoV-2, which could have led to partial immunological protection for COVID-19 in dengue infected communities. However, further studies are necessary to better test this hypothesis. Methods COVID-19 incidence in Brazil was obtained from Brasil.io (https://brasil.io/covid19/), which compiles data from all the Brazilian state health agencies and was accessed on 2020-10-06. The period considered in the analysis was from the first COVID-19 case to the 26th epidemiological week of 2020 (which ends on the 27 th of June 2020). The COVID-19 incidence in countries around the world was collected from Coronavirus COVID-19 Global Cases by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) (https://coronavirus.jhu.edu/).

State-level data were considered for Colombia and Mexico, and a country-level was considered for the other countries under investigation. Dengue epidemiological and serological data was compiled from data published regularly in the official epidemiological bulletins during 2019 and 2020 by the Brazilian Ministry of Health (Ministério da Saúde, 2020a and 2020b). The incidence available via DATASUS (2020) considered the period from the 27th epidemiological week of 2019 to the 26th epidemiological week of 2020. This incidence for Latin American countries was collected from the Pan American Health Organization (www.paho.org), which also provides dengue incidence data on a state level for Mexico. For Colombian states data was collected from bulletins made available by the Colombian Health Ministry (https://www.minsalud.gov.co). For other countries considered data was collected from disease threat reports provided by the European Centre for Disease Prevention and Control - (ECDC - www.ecdc.europa.eu).

At the onset of COVID-19, U.S. labor-force participation dropped by about 3 percentage points and remained below pre-pandemic levels three years later. Recovery varied across states, with slower rebounds in those more affected by the pre-pandemic opioid crisis, as measured by age-adjusted opioid overdose death rates. An event study shows that a one-standard-deviation increase in pre-COVID opioid death rates corresponds to a 0.9 percentage point decline in post-COVID labor participation. The result is not driven by differences in overall health between states. The effect of prior opioid exposure had a more significant impact on individuals without a college degree. The slow recovery in states with more opioid exposure was characterized by an increase in individuals who are not in the labor force due to disability.

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Context - World Health Organization (WHO)

• Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus (2019-nCoV).
• At this time, there are no specific vaccines or treatments for COVID-19. However, there are many ongoing clinical trials evaluating potential treatments.
• The best way to prevent and slow down transmission is to be well informed about the COVID-19 virus, the disease it causes, and how it spreads.
• As of 28 April 2020, 71.886 cases have been confirmed in Brazil.

Content

1. This dataset has information on the number of confirmed cases, deaths, and recoveries (by neighborhood) in the city of Rio de Janeiro, Brazil.
2. Please note that this is a time-series data and so the number of cases on any given day is a cumulative number.
3. The number of new cases can be obtained by the difference between current and previous days.
4. The data is available from 21 April 2020 and the dataset is updated on a daily basis.

Acknowledgements

• Rio de Janeiro City Hall, Municipal Health Secretariat & Others - Painel COVID-19 Rio
• Raphael Fontes @unanimad - Dataset Inspiration

Inspiration

• Changes in the number of confirmed cases, deaths, and recoveries by neighborhood over time.
• Changes in the number of confirmed cases, deaths, and recoveries at the city level.
• Spread of the disease in the city.

BackgroundThe clinical outcome of COVID-19 pneumonia is highly variable. Few biological predictive factors have been identified. Genetic and immunological studies suggest that type 1 interferons (IFN) are essential to control SARS-CoV-2 infection.ObjectiveTo study the link between change in blood IFN-α2 level and plasma SARS-Cov2 viral load over time and subsequent death in patients with severe and critical COVID-19.MethodsOne hundred and forty patients from the CORIMUNO-19 cohort hospitalized with severe or critical COVID-19 pneumonia, all requiring oxygen or ventilation, were prospectively studied. Blood IFN-α2 was evaluated using the Single Molecule Array technology. Anti-IFN-α2 auto-Abs were determined with a reporter luciferase activity. Plasma SARS-Cov2 viral load was measured using droplet digital PCR targeting the Nucleocapsid gene of the SARS-CoV-2 positive-strand RNA genome.ResultsAlthough the percentage of plasmacytoid dendritic cells was low, the blood IFN-α2 level was higher in patients than in healthy controls and was correlated to SARS-CoV-2 plasma viral load at entry. Neutralizing anti-IFN-α2 auto-antibodies were detected in 5% of patients, associated with a lower baseline level of blood IFN-α2. A longitudinal analysis found that a more rapid decline of blood IFN-α2 was observed in fatal versus surviving patients: mortality HR=3.15 (95% CI 1.14–8.66) in rapid versus slow decliners. Likewise, a high level of plasma SARS-CoV-2 RNA was associated with death risk in patients with severe COVID-19.ConclusionThese findings could suggest an interest in evaluating type 1 IFN treatment in patients with severe COVID-19 and type 1 IFN decline, eventually combined with anti-inflammatory drugs.Clinical trial registrationhttps://clinicaltrials.gov, identifiers NCT04324073, NCT04331808, NCT04341584.

Objective: To identify the prevalence of EEG abnormalities in patients with COVID-19 with neurologic changes, their associated neuroimaging abnormalities and rates of mortality.

Methods: A retrospective case series of 192 adult COVID-19 positive inpatients with EEG performed between March and June 2020 at 4 hospitals: 161 undergoing continuous, 24 routine, and 7 reduced-montage EEG. Study indication, epilepsy history, intubation status, administration of sedatives or antiseizure medications, metabolic abnormalities, neuroimaging pathology associated with epileptiform abnormalities, and in-hospital mortality were analyzed.

Results: EEG indications included encephalopathy (54.7%), seizure (18.2%), coma (17.2%), focal deficit (5.2%), and abnormal movements (4.6%). Epileptiform abnormalities occurred in 39.6% of patients: focal intermittent epileptiform discharges in 25.0%, lateralized periodic discharges in 6.3%, and generalized periodic discharges in 19.3%. Seizures were recorded in 8 patients, 3 with status epilepticus. Antiseizure medication administration, epilepsy history, and older age were associated with epileptiform abnormalities. Only 26.3% of patients with any epileptiform abnormality, 37.5% with electrographic seizures, and 25.7% patients with clinical seizures had known epilepsy. Background findings included generalized slowing (88.5%), focal slowing (15.6%), burst suppression (3.6%), attenuation (3.1%), and normal EEG (3.1%). Neuroimaging pathology was identified in 67.1% of patients with epileptiform abnormalities, over two-thirds acute. In-hospital mortality was 39.5% for patients with epileptiform abnormalities, 36.2% for those without. Risk factors for mortality were coma and ventilator support at time of EEG.

Significance: This article highlights the range of EEG abnormalities frequently associated with acute neuroimaging abnormalities in COVID-19. Mortality rates were high, particularly for patients in coma requiring mechanical ventilation. These findings may guide the prognosis and management of patients with COVID-19 and neurologic changes.

Methods Retrospective chart review, Redcap.

Context

On December 31, 2019, Chinese officials informed the first case of COVID-19 in Wuhan (China). Around the end of January, 2020, many countries (the U.S., the UK, South Korea, etc.), including Vietnam reported their first COVID-19 cases.

Since then, each country has their own specific strategy to contain the outbreak. Most of the countries have now shifted from the containment (early tracking, isolating the infection sources) to serious mitigation (tactics to reduce transmission) paradigms. Although loosing some F0 cases, Vietnam still has remained safely in the containment stage.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3439828%2Fbe8a17529fc1b48e3c44be94afe75529%2FVietnam_trend.png?generation=1588195825303050&alt=media" alt="">

Vietnam currently has only 270 COVID-19 confirmed cases in total with NO FATALITIES. And now, Vietnam is on its 13 straight days with no new local transmitted cases and 5 straight days without any imported cases (Updated on April 29, 2020). This leave us so many question to ask.

1. What has happened in Vietnam? Was the number of COVID-19 cases reported by Vietnamese officials undercounted? Did testing work well in Vietnam?

2. Did the Vietnam government suppressed information about their local COVID-19 pandemic? And if not, with such the 'real' low number of cases and no death, how did Vietnam contain the virus?

3. What did we know about the Vietnam COVID-19 patients? Is there characteristics of the patients that helps slow down the infection rate in Vietnam?

One remarkable thing about Vietnam health care system is the fact that privacy laws are not as stringent as in the US, Canada or the EU. Therefore, COVID-19 patient data in Vietnam is publicly available. For some cases, detail gets seriously down to their names, their personal contacts, daily activities and even their habits.

To help answer some of the above questions, I decided to collect the Vietnam data and study it independently using all the information available on the internet. I hope this dataset will provide some insights into the COVID-19 pandemic at the specific country level.

Collection methodology

• Data was acquired by web scrapping with manually curated from the Vietnam Ministry of Health's website (https://ncov.moh.gov.vn/) and other mainstream media in Vietnam (which were cited specifically in each data row).
• The world data was obtained from the data repository for the 2019 Novel Coronavirus Visual Dashboard operated by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). https://github.com/CSSEGISandData/COVID-19

Disclaimer:

• This is my personal work with no link to any organization. Although this analysis is data-driven, my comments reflect my personal perspectives.
• My results are based on the data collected from the Vietnam Health Ministry website and the mainstream media in Vietnam. The data might be biased and reflects what is publicly available on the internet. However, it can served as a good reference for someone who are curious about the COVID-19 pandemic in Vietnam. Some information conflicts (about the data) would be explained in detail in the exploration notebook that goes together with this dataset.

A full report and visualization for this dataset can be found in my Medium site.

Dataset was updated until May 10, 2020.

As of March 16, 2022, there was a total of approximately 5.9 million confirmed cases of coronavirus disease (COVID-19) in Japan, with around 529 thousand people needing inpatient treatment.

Development of cases in Japan Generally, the increase of new COVID-19 cases recorded from January to March 2020 in Japan followed a slower trajectory as compared to, for example, China, Europe, or the United States of America. The first reported case of COVID-19 in Japan was confirmed on January 16, 2020, when a man that had returned from Wuhan city, China, was tested positive. The first transmission within Japan was recorded on January 28. The number of new cases then increased tenfold in February. April saw a further acceleration of the infection rate. Consequently, the Japanese government declared a nationwide state of emergency that month. The government announced a state of emergency for the second time in January 2021, the third time in April 2021, and the forth time in the July 2021.

Vaccine rollout The Japanese government started the distribution of COVID-19 vaccination in February 2021, mainly for medical professionals. The administration of vaccination for general citizens commenced in April for senior citizens. The vaccine rate of the population was just over 74.7 percent for second doses as of March 2022.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated facts and figure page. 

Public mask use has emerged as a key tool in response to COVID-19. We develop and document a classification of statewide mask mandates that reveals variation in their scope and timing. Some U.S. states quickly mandated the wearing of face coverings in most public spaces, whereas others issued narrow mandates or no mandate at all. We consider how differences in COVID-19 epidemiological indicators and partisan politics affect when states adopted broad mask mandates, starting with the earliest broad public mask mandates in April 2020 and continuing though the end of 2020. The most important predictor is whether a state is led by a Republican governor. These states adopt statewide indoor mask mandates an estimated 98.0 days slower (95% CI: 88.8 to 107.3), if they did so at all (hazard ratio of 6.83, 95\% CI: 2.87 to 16.26). COVID-19 indicators such as confirmed cases or deaths per million are much less important predictors of statewide mask mandates. This finding highlights a key challenge to public efforts to increase mask-wearing, one of the most effective tools for preventing the spread of SARS-CoV-2 while restoring economic activity.