The outbreak of COVID-19, also known as novel coronavirus, is impacting almost all industries and sectors worldwide. Two of the most impacted sectors are manufacturing and travel & transportation. Both sectors are set to be severely impacted by coronavirus pandemic.

The impact is ranked on a 5-point scale from minor impact to severe impact:

1 - minor impact

2 - moderate impact

3 - significant impact

4- major impact

5 - severe impact

In a survey conducted in Chile at the end of March 2020, nine out of ten company executives thought that the tourism and hospitality sector would be one of the most affected by the coronavirus (COVID-19) pandemic. In turn, respondents' answers showed that the public sector, along with telecommunications and technology, would be likely spared from this crisis' negative effects. According to the same survey, over two thirds of Chilean respondents expected the country's GDP to fall in 2020.

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

The outbreak of coronavirus in Poland will significantly reduce labor demand. According the source, bankruptcies of companies, dismissals of employees, the need to take care of children due to closed educational institutions, and limited possibilities of remote work in some sectors have a direct impact on the labor market during the pandemic. In total, nearly 4.2 million people work in industries strongly exposed to the economic consequences of the lockdown. Of this figure, three million are employed, and just over one million are business owners and co-owners. More than half of the jobs at risk are in the trade.

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

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

According to recent estimates, the most affected sectors by the coronavirus pandemic in Latin America would be wholesale and retail trade as well as services in general, such as tourism, foodservice, transport, and communications. In 2020, this group of most affected sectors was forecasted to represent more than 16 percent of Brazil’s gross domestic product (GDP). Among the countries shown in this graph, Brazil is the nation where sectors moderately affected by the pandemic could represent the highest contribution to GDP (75.8 percent).

Which Latin American economies were most vulnerable to the pandemic? In 2020, the economic sectors most affected by the coronavirus pandemic - wholesale and retail, hotels and restaurants, transport and services in general - were forecasted to account for 35.5 percent of Panama’s GDP. In addition, the moderately and most affected economic segments were estimated to contribute the most to Panama’s GDP (a combined 97.6 percent) than any other country in this region. A similar scenario was projected in Mexico, where the sectors that would least suffer the pandemic's negative effects would account for only 3.4 percent of GDP.

Did the pandemic put a stop to economic growth in Latin America? Economic growth changed dramatically after the COVID-19 outbreak. Most of the largest economies in Latin America fell under recession in 2020. Estimates predict a more optimistic scenario for 2021, with countries such as Mexico, Colombia, and Argentina growing their GDP at least five percent.

The coronavirus (SARS-CoV-2) outbreak, dubbed COVID-19, is first and foremost a human tragedy, affecting millions of people globally. The contagious coronavirus, which broke out at the close of 2019, has led to a medical emergency across the world, with the World Health Organization officially declaring the novel coronavirus a pandemic on March 11, 2020. Read More

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

The “KOMPAKK index of economic sectors closure during the first wave of COVID-19” is a dataset on the German federal state-specific sector closures compiled from the original state decrees (March/April 2020). A large and growing number of studies shows the severe social and economic consequences of the governmental measures introduced to reduce the spread of the Covid-19 virus in March and April 2020 in Germany. However, we still lack a systematic analysis of intra-German differences in regulations and outcomes. The German federalist system leaves decisions over the implementation of decrees by the federal government to the federal states. This meant that the 16 states issued individual decrees over economic sector closure and social distancing measures during the course of the pandemic. We retrieved all decrees issued from 15.03.2020 to 17.04.2020 from the official website of each of the 16 federal states of Germany. All decrees used for generating the dataset are also available in the file “KOMPAKK_federalstatesdecrees.zip”.

This dataset contains numbers of COVID-19 outbreaks and associated cases, categorized by setting, reported to CDPH since January 1, 2021. AB 685 (Chapter 84, Statutes of 2020) and the Cal/OSHA COVID-19 Emergency Temporary Standards (Title 8, Subchapter 7, Sections 3205-3205.4) required non-healthcare employers in California to report workplace COVID-19 outbreaks to their local health department (LHD) between January 1, 2021 – December 31, 2022. Beginning January 1, 2023, non-healthcare employer reporting of COVID-19 outbreaks to local health departments is voluntary, unless a local order is in place. More recent data collected without mandated reporting may therefore be less representative of all outbreaks that have occurred, compared to earlier data collected during mandated reporting. Licensed health facilities continue to be mandated to report outbreaks to LHDs. LHDs report confirmed outbreaks to the California Department of Public Health (CDPH) via the California Reportable Disease Information Exchange (CalREDIE), the California Connected (CalCONNECT) system, or other established processes. Data are compiled and categorized by setting by CDPH. Settings are categorized by U.S. Census industry codes. Total outbreaks and cases are included for individual industries as well as for broader industrial sectors. The first dataset includes numbers of outbreaks in each setting by month of onset, for outbreaks reported to CDPH since January 1, 2021. This dataset includes some outbreaks with onset prior to January 1 that were reported to CDPH after January 1; these outbreaks are denoted with month of onset “Before Jan 2021.” The second dataset includes cumulative numbers of COVID-19 outbreaks with onset after January 1, 2021, categorized by setting. Due to reporting delays, the reported numbers may not reflect all outbreaks that have occurred as of the reporting date; additional outbreaks may have occurred that have not yet been reported to CDPH. While many of these settings are workplaces, cases may have occurred among workers, other community members who visited the setting, or both. Accordingly, these data do not distinguish between outbreaks involving only workers, outbreaks involving only residents or patrons, or outbreaks involving both. Several additional data limitations should be kept in mind: Outbreaks are classified as “Insufficient information” for outbreaks where not enough information was available for CDPH to assign an industry code. Some sectors, particularly congregate residential settings, may have increased testing and therefore increased likelihood of outbreak recognition and reporting. As a result, in congregate residential settings, the number of outbreak-associated cases may be more accurate. However, in most settings, outbreak and case counts are likely underestimates. For most cases, it is not possible to identify the source of exposure, as many cases have multiple possible exposures. Because some settings have been at times been closed or open with capacity restrictions, numbers of outbreak reports in those settings do not reflect COVID-19 transmission risk. The number of outbreaks in different settings will depend on the number of different workplaces in each setting. More outbreaks would be expected in settings with many workplaces compared to settings with few workplaces.

This report analyzes the impact of COVID-19 across industry sectors. It provides side-by-side analysis of alternative datasets to present you with unique quantitative analysis of the effects of COVID-19 and how these differ across sectors. We also provide qualitative analysis of each sector and analyse COVID-19’s impact on leading companies. Read More

According to a study in mid-March 2020, around **** percent of jobs in the leisure and hospitality industry in the United States are at risk from the global coronavirus pandemic (COVID-19). This amounts to around **** million jobs nationwide.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

Full France data by activity (according to the aggregated classification of 88 NAF posts) relating to the carryovers of contributions of employers affiliated to the general scheme in the context of the COVID-19 health crisis (dues from 15 March to 15 December 2020). Situations at the end of March to December 2020.

In order to take into account the impact of the coronavirus outbreak on economic activity, the Urssaf network triggered exceptional measures to support companies with serious cash flow difficulties as of the deadline of 15 March. In the event of major difficulties, companies could postpone, first without prior request and then on request, all or part of the payment of employee and employer contributions.

This dataset describes all the amounts carried over, regardless of whether they are part of a mechanism allowing the carry-over or not. The amounts of carry-overs therefore correspond to the “rests to be recovered”.

The data are declined by payment deadline: the 5th or 15th of the month. Contributions must in principle be paid during the month following the period of paid employment:

• no later than the 5th of that month for employers with at least 50 employees whose pay is paid in the same month as the period of work;

• no later than the 15th of this month in other cases.

Source: ACOSS-Urssaf, extraction early May 2021

Indicators:

• Number of institutions at maturity (*)
• Amount of contributions due
• Number of establishments that have carried over (*)
• Amount of carryovers

(*) WARNING: information on the number of establishments should be interpreted with caution. Indeed, as institutions are counted at each maturity, the selection of a period covering more than one month leads to the same institutions being counted several times (an institution is likely to report each month). Thus, in order to have the total number of institutions without double accounts, it is necessary to select two maturities of the same month.

Methodological clarifications:

• these data take into account the social contribution exemption and payment aid schemes introduced by Article 65 of Law No 2020-935 of 30 July 2020 to support the companies most affected by the crisis. The contributions due are indeed amounts after application of the exemptions. And carry-overs are amounts after the payment aid has been charged, which reduces the amounts to be paid by the undertakings concerned.
• an institution is counted as deferred if the amount of contributions not paid at maturity exceeds EUR 44
• the sector of activity “nca not elsewhere” includes the agricultural sector (AZ) for the general scheme (most of the AZ sector falls under the agricultural scheme, out of field here), extraterritorial activities (UZ) and unknown activities.

DATAVIZ: putting in perspective

GlobalData expects the construction industry to contract by 6.5% in 2020, with a further downward revision likely if activity in the short-term is more severely disrupted than currently anticipated. Read More

With the Coronavirus Job Retention Scheme having drawn to a close on 30 September 2021, we've looked at which regions and sectors were the biggest users of the scheme.

The “KOMPAKK index of economic sectors closure during the first wave of COVID-19” is a dataset on the German federal state-specific sector closures compiled from the original state decrees (March/April 2020). A large and growing number of studies shows the severe social and economic consequences of the governmental measures introduced to reduce the spread of the Covid-19 virus in March and April 2020 in Germany. However, we still lack a systematic analysis of intra-German differences in regulations and outcomes. The German federalist system leaves decisions over the implementation of decrees by the federal government to the federal states. This meant that the 16 states issued individual decrees over economic sector closure and social distancing measures during the course of the pandemic. We retrieved all decrees issued from 15.03.2020 to 17.04.2020 from the official website of each of the 16 federal states of Germany. All decrees used for generating the dataset are also available in the file “KOMPAKK_federalstatesdecrees.zip”.

Report on the impact COVID-19 has had on the Apparel market as it pertains to the sports industry. Read More

Understanding the dynamic link between the development of COVID-19 pandemic and industry sector risk spillovers is crucial to explore the underlying mechanisms by which major public health events affect economic systems. This paper applies ElasticNet method proposed by Diebold and Yilmaz (2009, 2012, 2014) to estimate the dynamic risk spillover indicators of 20 industrial sectors in China from 2016 to 2022, and systematically examines the impact of industry risk network fluctuations and the transmission path caused by COVID-19 shock. The findings reveal that risk spillovers of Chinese industries show a dynamic change of "decline-fluctuation-rebound" with the three phases of COVID-19 epidemic. At the beginning of the epidemic, machinery and equipment, paper and printing, tourism and hotels, media and information services, and agriculture were the exporters of epidemic risk, while materials, transportation equipment, commercial trade, health care, and environmental protection were the importers of epidemic risk; However, as the epidemic developed further, the direction and effect of risk transmission in the industry was reversed. Examining the network characteristics of the pair sectors, we found that under the epidemic shock, the positive risk spillover from tourism and hotels, culture, education and sports to consumer goods, finance, and energy industries was significantly increased, and finance and real estate industries were affected by the risk impact of more industries, while the number of industries affected by information technology and computer industry was significantly reduced. This paper shows that there is inter-industry risk transmission of the COVID-19 epidemic shock, and the risk transmission feeds back in a cycle between industries as the epidemic develops, driving the economy into a vicious circle. The role of the service sector in blocking the spread of negative shocks from the epidemic should be emphasized and brought into play to avoid increasing the overall economic vulnerability. This study will help to deepen the understanding of scholars and policy makers on the network transmission effects of the epidemic.