According to an endpoint resilience report, COVID-19 witness a surge in the installation of collaboration tools on enterprise endpoint devices. There was a more than 100 percent increase in number of enterprise devices intalled with collaboration apps at the end of March, 2020 compared to pre-COVID level, and a *** percent increase in mid May, 2020.

Infections averted in the general population with 5-day testing and one-time testing of students compared to a policy of no routine asymptomatic testing (symptom-based surveillance and contact tracing only).

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

As of November 14, 2021, all S&P 500 sector indices had recovered to levels above those of January 2020, prior to full economic effects of the global coronavirus (COVID-19) pandemic taking hold. However, different sectors recovered at different rates to sit at widely different levels above their pre-pandemic levels. This suggests that the effect of the coronavirus on financial markets in the United States is directly affected by how the virus has impacted various parts of the underlying economy. Which industry performed the best during the coronavirus pandemic? Companies operating in the information technology (IT) sector have been the clear winners from the pandemic, with the IT S&P 500 sector index sitting at almost ** percent above early 2020 levels as of November 2021. This is perhaps not surprising given this industry includes some of the companies who benefitted the most from the pandemic such as ************** and *******. The reason for these companies’ success is clear – as shops were shuttered and social gatherings heavily restricted due to the pandemic, online services such shopping and video streaming were in high demand. The success of the IT sector is also reflected in the performance of global share markets during the coronavirus pandemic, with tech-heavy NASDAQ being the best performing major market worldwide. Which industry performed the worst during the pandemic? Conversely, energy companies fared the worst during the pandemic, with the S&P 500 sector index value sitting below its early 2020 value as late as July 2021. Since then it has somewhat recovered, and was around ** percent above January 2020 levels as of October 2021. This reflects the fact that many oil companies were among the share prices suffering the largest declines over 2020. A primary driver for this was falling demand for fuel in line with the reduction in tourism and commuting caused by lockdowns all over the world. However, as increasing COVID-19 vaccination rates throughout 2021 led to lockdowns being lifted and global tourism reopening, demand has again risen - reflected by the recent increase in the S&P 500 energy index.

2.1. Participants and procedure

The participants were patients with DM from nine primary health care areas corresponding to four Cuban provinces belonging to different regions of the country (Pinar del Río, Havana, Ciego de Ávila and Santiago de Cuba), selected by means of non-probabilistic sampling. The inclusion criteria included: 1) having type 2 diabetes mellitus according to the criteria of the World Health Organization 2) being ≥18 years of age 3) being attended in the previously mentioned health areas where their clinical histories were located and 4) being willing to participate in the research study and answer the survey after signing the informed consent form. Patients with severe mental illness or cognitive deficits (dementia, psychosis or mental disabilities) or any other apparent condition that compromised their ability to understand and complete the questionnaire were not included in the study. The sample size was calculated with the Soper software [29], which indicated a number of 200 participants. For this we considered the number of observed variables (6 items), latent variables of the model to be evaluated (concern for COVID-19 contagion), the anticipated effect size (λ = 0.3), the probability (α = 0.05) and the statistical power (1 - β = 0.95).

Finally, 219 people with type 2 DM were surveyed. The application of the survey was carried out between the months of January and April 2021, while the patients attended consultation or in their own homes by the researchers trained for the task and complying with strict COVID-19 prevention protocols. The Cuban panorama in the fight against COVID-19 during the period of data collection was not favorable, as the country was in a phase of resurgence characterized by high numbers of people infected with the virus, much higher compared to the diagnoses at a similar point during the first stage of the disease, in 2020. Although government health measures were strengthened to contain the pandemic, the population's perception of risk was on the rise. During those dates, more than 64,414 positive diagnoses and 384 deaths were reported. Participation in the study was voluntary and no financial compensation was provided. All participants signed informed consent and were allowed to withdraw at any time from the study without having to justify their decision. In addition, the data were guaranteed to be confidential and anonymous. The study received approval from the ethics committee of the Universidad Privada del Norte in Peru (registration number: 20213002).

The majority of the participants were women (66.2%) with a mean age of 58.5 years old (SD = 18.2). Thirty-two point nine percent had higher education. Of the total participants, 37.9% were retired and 32% were state workers; while 43.4 had more than 10 years with the disease. The majority (68.9%) had no associated chronic complications and were receiving treatment for diabetes (98.2%). More details of the sociodemographic variables can be seen in Table 1.

Table 1. Characteristics of the participants (n = 219).

Characteristic n (%) Age 58.5 (18.2)a Sex Female 145 (66.2) Male 74 (33.8) Level of education University 72 (32.9) Pre-university 63 (28.8) Mid-level technical 39 (17.8) Secondary 25 (11.4) Primary 17 (7.8) No schooling 3 (1.4) Occupation Retired/pensioned 83 (37.9) State employee 70 (32.0) Self-employed 37 (17.0) Housewife 17 (7.8) Student 10 (4.6) Unemployed 2 (0.9) Time of evolution of diabetes (years) Less than 5 52 (23.7) From 5 to 10 72 (32.9) More than 10 95 (43.4) Associated chronic complications b None 151 (68.9) Diabetic foot 31 (14.2) Polyneuropathy 20 (9.1) Retinopathy 15 (6.8) Nephropathy 7 (3.2) Other 2 (0.9) Treatment of diabetes Yes 215 (98.2) No 4 (1.8) Comorbidities Yes 141 (64.4) No 78 (35.6) Family member or friend infected by COVID-19 Yes 110 (50.2) No 109 (49.8) Family member or friend deceased due to COVID-19 No 210 (95.9) Yes 9 (4.1) a: mean and standard deviation; b: a patient may have more than one complication.

2.2. Instruments Scale of Worry for Contagion of COVID-19 (PRE-COVID-19). The scale is comprised of 6 items that assess concern about becoming infected with COVID-19 and its impact on people's daily functioning, specifically on their mood and their ability to perform their daily activities. Each item presented 4 Likert-type response options (from 1 = never or rarely to 4 = almost all the time), with higher scores indicating greater concern about COVID-19 infection.

Generalized Anxiety Disorder Scale-2 (GAD-2) [30]. The GAD-2 consists of 2 items that measure an emotional (feeling nervous) and cognitive (worry) symptom of generalized anxiety in the past 2 weeks. The 2 items have 4 response options using a Likert-type scale (from 0 = not at all to 3 = almost every day), where a higher score indicates a higher level of generalized anxiety.

2.3. Data analysis Confirmatory Factor Analysis (CFA) was performed using the Diagonally Weighted Least Squares with Mean and Variance corrected (WLSMV) estimator since the items are ordinal in nature. The chi-square test (χ2), the RMSEA index and the SRMR index were used to evaluate the model fit. In the case of the latter two indices, values less than 0.05 indicate good fit, and between 0.05 and 0.08 is considered acceptable. In addition, the CFI and TLI indices were used, where values greater than 0.95 indicate good fit and greater than 0.90 an acceptable fit. To assess validity based on the relationship with other constructs, structural equation modeling (SEM) was employed to assess the latent relationship between concern for being infected with COVID-19 and anxiety. The above fit indices, and their respective cutoff points, were used to assess the adequacy of the model. Cronbach's alpha coefficient and the omega coefficient were used to assess the internal consistency of the scale, where a value greater than 0.70 is adequate.

As for the use of Item Response Theory (IRT), a Graded Response Model (GRM) [35] was employed, specifically an extension of the 2-parameter logistic model (2-PLM) for ordered polytomous items. For each item, two types of parameters were estimated: discrimination (a) and difficulty (b). The a parameter determines the slope at which item responses change as a function of the level in the latent trait and the b parameters determine how much of the latent trait the item requires to be answered in a given way. Since the scale has four response categories, there are three estimates of difficulty, one per threshold. The estimates for these three thresholds indicate the level of the latent variable at which an individual has a 50% chance of scoring at or above a particular response category. Item information curves (IIC) and the test information curve (TIC) were also calculated.

All statistical analyses were performed using the "lavaan" package for the CFA and the "ltm" package for the GRM. In all cases, the RStudio environment was used for R.

As of February 2021, reports of burnout increased during the COVID-19 pandemic among all surveyed employees regardless of generation. While Millennials previously reported much higher burnout rates than other generations, in February 2021, Gen Z and Gen X reported similar rates. This statistic displays the percentage of U.S. employees feeling burnout pre-COVID Jan. 2020 vs Feb. 2021, by generation.

In 2023, it was estimated that over 161 million Americans were in some form of employment, while 3.64 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

Levels of engagement in various activities ‘during’ compared with ‘before’ the COVID-19 pandemic by participants (N = 811).

Association between coping and levels of engagement in various activities during and ‘before’ the COVID-19 pandemic by participants.

Pre-pandemic (data of 2019) epidemiologic and demographic data have shown that some parameters such as cancer, Alzheimer's disease, advanced age, and alcohol intake levels are positively correlated to Covid-19 mortality, instead, birth and fertility rates are negatively correlated to Covid-19 mortality. A stepwise multiple regression analysis of the above parameters against Covid-19 mortality in 32 countries from Asia, America, Africa, and Europe has generated two main predictors of Covid-19 mortality: alcohol consumption and birth/mortality ratio. A first-order equation correlated alcohol intake to Covid-19 mortality as follows; Covid-19 mortality= 0.1057 x (liters of alcohol intake) + 0.2214 (Coefficient of determination = 0.750, F value = 38.63 , P-value = 7.64x10-7). A second equation correlated (birth rate/mortality rate) to Covid-19 mortality as follows; Covid-19 mortality= - 0.3129 x (birth rate/mortality) ratio +1.638 (coefficient of determination = 0.799, F value = 51.2, P-value = 7.09x10-8). Thus, pre-pandemic alcohol consumption is a high predictor of Covid-19 mortality that should be taken into account as a serious risk factor for future safety measures against SARS-CoV-2 infection.

The global solar PV market is anticipated to expand at robust CAGR during the forecast period 2020-2027. The growth of the market is attributed to rising depletion of fossil reserves all around the world. Furthermore, favorable government subsidies and regulations are anticipated to play a major role in the market growth.
Solar photovoltaics (PVs) directly convert the sunlight energy into electricity, with the help of technology based on photovoltaic effect. Silicon is the most common element utilized for PV cells. These cells are collated together to form solar panels. Over the years, the PV panels has been developed in various sizes and shapes to serve various power applications. According to a report by the Solar Energies Industries Association (SEIA), the US solar installation is anticipated to increase by 43% year-on-year growth.

Market Trends, Drivers, Restraints, and Opportunities

• Rapid growth of the solar energy market owing to low-cost energy generation and environment friendliness are anticipated to propel the growth of the market.
• Government initiatives to reduce the carbon footprint is expected to boost the market growth in the forecast period.
• According to the report published by Abu Dhabi-headquartered Renewable Energy Agency (IRENA), the solar PV cost has been reduced by 80% since 2009. Reducing cost of solar PVs owing to the increasing production of the same is speculated to positively influence the market growth.
• Growing technological advancements to develop sustainable and secure energy generation systems is one of the key drivers of the market in the forecast period.
• The COVID-19 pandemic had disrupted the semiconductor industry owing to the imposed restrictions in the manufacturing facilities. However, the market is anticipated to recover and reach the pre-COVID levels soon.
• Rising awareness about solar energy system benefits in the low and middle-level countries are predicted to create remunerative opportunities and open up new market avenues for the industry players in the coming years.

Scope of the Report

In this ongoing project, we examine the short-term consequences of COVID-19 on employment and wages in the United States. Guided by a pre-analysis plan, we document the impact of COVID-19 at the national-level using a simple difference and test whether states with relatively more confirmed cases/deaths were more affected. Our findings suggest that COVID-19 increased the unemployment rate, decreased hours of work and labor force participation and had no significant impacts on wages. The negative impacts on labor market outcomes are larger for men, younger workers, Hispanics and less-educated workers. This suggest that COVID-19 increases labor market inequalities. We also investigate whether the economic consequences of this pandemic were larger for certain occupations. We built three indexes using ACS and O*NET data: workers relatively more exposed to disease, workers that work with proximity to coworkers and workers who can easily work remotely. Our estimates suggest that individuals in occupations working in proximity to others are more affected while occupations able to work remotely are less affected. We also find that occupations classified as more exposed to disease are less affected, possibly due to the large number of essential workers in these occupations.

Throughout the ******** pandemic, the occurrence of depression symptoms during the pandemic roughly doubled compared to pre-pandemic levels in several European countries. While the United Kingdom had the highest share of individuals with depression symptoms during the pandemic, Norway experienced the biggest increase compared to the pre-pandemic period. This statistic depicts the share of people with symptoms of depression in selected European countries before and during the ******** pandemic.

Issuance of visas to foreigners Visas issued to foreigners fall into three categories. Transit visas allow the crossing of a Schengen State to a third country or the passage through the international airport transit zone during an international stopover. Schengen short-stay visas are valid in mainland France and in the other Schengen Member States, with rare exceptions and special conditions for the Overseas Territories. Long-stay visas are valid throughout France. The issuance of visas increased sharply in 2022 (+ 137.1%), with a total of 1,738138 visas issued. This increase, however, puts visa issuances only at about half their pre-COVID level. Long-stay visas, the only ones that have approached their usual level in 2021, continue to rise and record a historical maximum (277 035). Among these long-stay visas, those granted for economic reasons are particularly dynamic (+ 66.6%). India takes first place in terms of the number of visas issued, ahead of Morocco and Algeria. China is back from eighth to fifth place, with an increase of 327%. The number of visas granted to Russian nationals remains much lower but is growing very rapidly (+ 449,2 %).

The global one-off chopsticks market was valued at USD 22.3 Billion in 2019 and is anticipated to reach USD 45 Billion by 2027, expanding at a CAGR of 10% during the forecast period 2020-2027. The growth of the market is attributed to the increasing inclination towards Chinese cuisine. Furthermore, the rising demand for fast-food items is expected to positively influence the market growth.
Chopsticks are the tampered pair of sticks generally made up of wood or bamboo for eating Chinese-related cuisines. They are usually used in Asian communities while are also given as a gift owing to their appealing aesthetics. Some of the benefits of eating with chopsticks include coordinated training, eating slowly, and aids in weight loss, among others. Chopsticks are used as an alternative to the silverware used across the world. They are also available in different colors and materials which confers different significance to them.

Market Trends, Drivers, Restraints, and Opportunities

• Growing demand for disposable one-off chopsticks is anticipated to propel the growth of the market.
• Increasing interest of people to eat or try different cuisines is expected to positively influence the market growth in the forecast period. Moreover, growing awareness about its benefits is speculated to stimulate market growth.
• Stringent government regulations regarding the usage of wood for the one-off chopsticks can hamper the expansion of the market.
• Consumers are shifting towards eco-friendly products owing to environmental concerns. Thus, the rising development of greener or ecofriendly one-off chopsticks products is anticipated to create remunerative opportunities in the coming years.
• Limited usage of this product for the food items can inhibit the market growth. Moreover, the need for learning the etiquettes to eat with chopsticks can limit its usage for the food items.
• The COVID-19 pandemic had harshly impacted the hospitality industry. This, in turn, had impacted the sales of chopsticks across the globe. However, the market is slowly recovering and is expected to reach the pre-COVID levels soon.

Scope of the Report

Bio-Oil Market Outlook 2032

The global Bio-Oil Market size was USD 365.3 Billion in 2023 and is likely to reach USD 543.1 Billion by 2032, expanding at a CAGR of 4.5% during 2024–2032. The market is driven by the growing demand for eco-friendly and sustainable fuel across the industries.



Growing energy demand across the sectors is emphasizing on introducing eco-friendly fuel alternatives to conventional fuels. The bio-oil is emerging as one of the prominent solutions, as it is derived from biowaste materials, such as agricultural waste, plant waste, and animal waste, among others. The rise of sustainable production solutions in different sectors is boosting growth opportunities in the market.

• According to a recent study report published by the International Energy Agency (IEA), in 2022, the demand for biofuel reached 4.3 EJ (170,000 million liters), a record high that surpassed pre-COVID-19 levels. However, the report estimates that bio-oil production must increase significantly to achieve the emission reduction target as per Net Zero Emissions by 2050 (NZE).

Bio-oil or bio-fuel releases a less amount of carbon dioxide (CO2) into the environment, which makes it a clean and less polluting oil. The advancement in technology and improved production techniques are expected to boost the production output of the bio-oil. This is likely to widen the energy transition scope across the industrial operations, making them highly sustainable and nature-friendly.

Impact of Artificial Intelligence (AI) on the Bio-Oil Market

Artificial intelligence is anticipated to benefit the market in the coming years, as it potentially enhances the production and conversion of biological matter. This technology is expected to be utilized in optimizing biomass growth conditions, harvesting methods, and processing techniques.</s

The Latin America and the Caribbean (LAC) region was suffering from a deep learning crisis, before the COVID-19 outbreak, with most students being below minimum proficiency levels for critical foundational competencies in numeracy and literacy, according to the Fourth Regional Comparative and Explanatory Study (ERCE). The pandemic that hit the region in March 2020 led to a massive shutdown of educational systems, placing LAC as the region with the longest duration of school closures in the world. The impact of school closures on education service delivery was significant. The forced move to distance learning negatively impacted attendance in the education process, both when compared to enrollment rates (-10 percent) and with pre-pandemic attendance rates (-12 percent). Most worryingly, one in four students attending the education process during the pandemic confirmed being disengaged from learning activities while at home. The COVID-19 led to a crisis within a crisis, deepening pre-existing inequalities that characterize the LAC region, as the most vulnerable populations were disproportionately affected. A significant increase in drop-out rates and decrease in learning outcomes is expected, especially for these groups and countries which were already not doing well pre-pandemic. There is a sizeable schooling and learning recovery agenda ahead of LAC, where re-enrollment campaigns, standardized and in-classroom assessments, and programs to teach to the right level will be fundamental to determine the exact depth of educational losses and start recovering. The Latin America and the Caribbean (LAC) region was suffering from a deep learning crisis, before the COVID-19 outbreak, with most students being below minimum proficiency levels for critical foundational competencies in numeracy and literacy, according to the Fourth Regional Comparative and Explanatory Study (ERCE). The pandemic that hit the region in March 2020 led to a massive shutdown of educational systems, placing LAC as the region with the longest duration of school closures in the world. The impact of school closures on education service delivery was significant. The forced move to distance learning negatively impacted attendance in the education process, both when compared to enrollment rates (-10 percent) and with pre-pandemic attendance rates (-12 percent). Most worryingly, 1 in 4 students attending the education process during the pandemic confirmed being disengaged from learning activities while at home. The COVID-19 led to a crisis within a crisis, deepening pre-existing inequalities that characterize the LAC region, as the most vulnerable populations were disproportionately affected. A significant increase in drop-out rates and decrease in learning outcomes is expected, especially for these groups and countries which were already not doing well pre-pandemic. There is a sizeable schooling and learning recovery agenda ahead of LAC, where re-enrollment campaigns, standardized and in-classroom assessments, and programs to teach to the right level will be fundamental to determine the exact depth of educational losses and start recovering.

All numerical raw data are combined in a single excel file, “S1_Data.xlsx,” this file consists of several spreadsheets and each contains the data of 1 figure or table. (XLSX)