In past 24 hours, New Zealand, Australia-Oceania had N/A new cases, N/A deaths and N/A recoveries.

As of September 5, 2022, the number of 30 to 39 year olds diagnosed with COVID-19 in New Zealand had reached over three hundred thousand people. At the time, the over 90 age group had the least number of active cases.

New Zealand recorded 2282861 Coronavirus Cases since the epidemic began, according to the World Health Organization (WHO). In addition, New Zealand reported 2792 Coronavirus Deaths. This dataset includes a chart with historical data for New Zealand Coronavirus Cases.

Context

With the arrival of the COVID19 virus in New Zealand, the ministry of health is tracking new cases and releasing daily updates on the situation on their webpage: https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases and https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases/covid-19-current-cases-details. Much of the information given in these updates are not in a machine-friendly format. The objective of this dataset is to provide NZ Minstry of Health COVID19 data in easy-to-use format.

Content

All data in this dataset has been acquired from the New Zealand Minstry of Health's 'COVID19 current cases' webpage, located here: https://www.health.govt.nz/our-work/diseases-and-conditions/covid-19-novel-coronavirus/covid-19-current-cases. The Ministry of Health updates their page daily, that will be the targeted update frequency for this dataset for the Daily Count of Cases dataset. The Case Details dataset which includes travel details on each case will be updated weekly.

Limitations of this dataset

The mission of this project is to reliably convey data that the Ministry of Health has reported in the most digestable format. Enrichment of data is currently out of scope.

Help improve this dataset

If you find any discrepancies between the Ministry of Health's data and this dataset, please provide your feedback as an issue on the git repo for this dataset: https://github.com/2kruman/COVID19-NZ-known-cases/issues.

WHO: COVID-2019: Number of Patients: Death: To-Date: New Zealand data was reported at 3,596.000 Person in 24 Dec 2023. This stayed constant from the previous number of 3,596.000 Person for 23 Dec 2023. WHO: COVID-2019: Number of Patients: Death: To-Date: New Zealand data is updated daily, averaging 52.000 Person from Feb 2020 (Median) to 24 Dec 2023, with 1396 observations. The data reached an all-time high of 3,596.000 Person in 24 Dec 2023 and a record low of 0.000 Person in 28 Mar 2020. WHO: COVID-2019: Number of Patients: Death: To-Date: New Zealand data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: Coronavirus Disease 2019 (COVID-2019): by Country and Region (Discontinued).

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.

The number of COVID-19 vaccination doses administered in New Zealand rose to 12052315 as of Oct 27 2023. This dataset includes a chart with historical data for New Zealand Coronavirus Vaccination Total.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

Summary statistics for the New Zealand epidemic by age and type of case.

WHO: COVID-2019: Number of Patients: Confirmed: To-Date: New Zealand data was reported at 2,440,375.000 Person in 24 Dec 2023. This stayed constant from the previous number of 2,440,375.000 Person for 23 Dec 2023. WHO: COVID-2019: Number of Patients: Confirmed: To-Date: New Zealand data is updated daily, averaging 15,417.500 Person from Feb 2020 (Median) to 24 Dec 2023, with 1396 observations. The data reached an all-time high of 2,440,375.000 Person in 24 Dec 2023 and a record low of 1.000 Person in 03 Mar 2020. WHO: COVID-2019: Number of Patients: Confirmed: To-Date: New Zealand data remains active status in CEIC and is reported by World Health Organization. The data is categorized under High Frequency Database’s Disease Outbreaks – Table WHO.D002: World Health Organization: Coronavirus Disease 2019 (COVID-2019): by Country and Region (Discontinued).

Context

2020 has been the year of events (probably for the wrong reasons!). Covid has affected a large number of things and one of them is TRADE.

Content

This dataset contains the ffects of COVID-19 on trade is a weekly update on New Zealand’s daily goods trade with the world from 1 February 2020. Comparing the values with previous years shows the potential impacts of COVID-19.

Acknowledgements

STATS NZ

Inspiration

Curious to know the potential impact of COVID19 on New Zealand's Trade from 1 Feb to 12 Aug 2020 compared to previous years.

License

Creative Commons Attribution 4.0 International

Multilingual (EN, KO, IN, ES) corpus acquired from website (https://covid19.govt.nz/) of the New Zealand Government. It contains 250 TUs in total.

This 20MB download is a zip file containing 1 docx document and 2 xlsx spreadsheets.Waka Kotahi has been running an ongoing study across New Zealand for the effects of COVID-19 on transport choices. The study started on 3 April 2020 and runs for 28 waves, with the final wave scheduled to take place in late 2021. This market research analysis was conducted by Ipsos, with the results data kept in the Harmoni application by Infotools, an external vendor.

We have created summarised tables from this data, in the form of an Excel spreadsheet, for release as open data. The data records how New Zealanders felt, behaved and travelled under the different COVID-19 alert levels.The data tables from the study, to allow you to do your own analysis. We have already made analysed data from this study available as reports on the Waka Kotahi website.Read 'covid-19 impacts on transport' reportsComplete open dataset: click on the download button for a .zip file containing this item.

Watch our video about the impacts of COVID-19 on New Zealanders' transport choices

Data reuse caveats: as per license. Additionally, this data is from research currently being undertaken by Ipsos on behalf of Waka Kotahi NZ Transport Agency. While Waka Kotahi provided investment, the research was undertaken independently, and the resulting findings should not be regarded as being the opinion, responsibility or policy of Waka Kotahi or indeed of any NZ Government agency. We have removed the data for sample sizes of fewer than 60 people, to protect privacy. Data quality statement: high level of confidence.

Data quality caveats: none known.

Other metadata: technical report - click on the download button for a .zip file containing this itemquestionnaire changes tracking log - click on the download button for a .zip file containing this item.

New Zealand Total No. of Current CIRP Recipients data was reported at 342.000 Number in 29 Jan 2021. This records a decrease from the previous number of 660.000 Number for 22 Jan 2021. New Zealand Total No. of Current CIRP Recipients data is updated weekly, averaging 9,756.000 Number from Jun 2020 (Median) to 29 Jan 2021, with 34 observations. The data reached an all-time high of 24,810.000 Number in 28 Aug 2020 and a record low of 342.000 Number in 29 Jan 2021. New Zealand Total No. of Current CIRP Recipients data remains active status in CEIC and is reported by Ministry of Social Development. The data is categorized under Global Database’s New Zealand – Table NZ.G093: Weekly Benefit and Supplementary Assistance: COVID-19 Income Relief Payment Recipients (Discontinued). The last CIRP payments were on the week ending 5 February 2021, this payment has now finished.

New Zealand No. of Current CIRP Recipients: Couple data was reported at 177.000 Number in Jan 2021. This records a decrease from the previous number of 1,263.000 Number for Dec 2020. New Zealand No. of Current CIRP Recipients: Couple data is updated monthly, averaging 4,078.500 Number from Jun 2020 (Median) to Jan 2021, with 8 observations. The data reached an all-time high of 10,053.000 Number in Aug 2020 and a record low of 177.000 Number in Jan 2021. New Zealand No. of Current CIRP Recipients: Couple data remains active status in CEIC and is reported by Ministry of Social Development. The data is categorized under Global Database’s New Zealand – Table NZ.G094: Monthly Benefit and Supplementary Assistance: COVID-19 Income Relief Payment Recipients (Discontinued).

Bilingual (EN-KO) corpus acquired from website (https://covid19.govt.nz/) of the New Zealand Government

The purpose of this project is to write a large and in sync dataset focused patient characteristics for identify the Risk groups and characteristics human-level that impact on infection, Complication and Death as a result of the disease

for more detail about the data:

https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

last date for update 06.12.2020

4535323 rows

Version 5:

A version that includes cleaning the data and engineering new features for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

Version 6:

Machine-ready version of machine learning model Consists only of INT and FLOAT for more detail : https://docs.google.com/spreadsheets/d/1awEY-04UK8wibkbZ1qfV6a-Q9YKScfP7qiAtWDsp9Jw/edit?usp=sharing

problem with dataset

• There may be duplicate cases (which come from different data systems) Focusing on countries: France, Korea, Indonesia, Tunisia, Japan, canada, new_zealand, singapore, guatemala, philippines, india, vietnam, hong kong , Toronto, Mexico.

• I did not check the credibility of the sources

• Concerns of the credibility of the Mexican government's data

• Concerns about the credibility of the data of the Chinese government

Acknowledgements and Sources

india_wiki https://www.kaggle.com/karthikcs1/covid19-coronavirus-patient-list-karnataka-india

philippines https://www.kaggle.com/sundiver/covid19-philippines-edges

france https://www.kaggle.com/lperez/coronavirus-france-dataset

korea https://www.kaggle.com/kimjihoo/coronavirusdataset

indonesia https://www.kaggle.com/ardisragen/indonesia-coronavirus-cases

tunisia https://www.kaggle.com/ghassen1302/coronavirus-tunisia

japan https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan

world https://github.com/beoutbreakprepared/nCoV2019/tree/master/latest_data

canada https://www.kaggle.com/ryanxjhan/coronaviruscovid19-canada

new_zealand https://www.kaggle.com/madhavkru/covid19-nz

singapore https://www.kaggle.com/rhodiumbeng/singapores-covid19-cases

guatemala https://www.kaggle.com/ncovgt2020/covid19-guatemala

colombia https://www.kaggle.com/sebaxtian/covid19co

mexico https://www.kaggle.com/lalish99/covid19-mx

india_data https://www.kaggle.com/samacker77k/covid19india

vietnam https://www.kaggle.com/nh

kerla https://www.kaggle.com/baburajr/covid19inkerala

hong_kong https://www.kaggle.com/teddyteddywu/covid-19-hong-kong-cases

toronto https://www.kaggle.com/divyansh22/toronto-covid19-cases

Determining the severity illness according to WHO: https://www.who.int/publications/i/item/clinical-management-of-covid-19

• Each update contains the information found in the previous version

*Thanks to all sources

*If you have any helpful information or suggestions for improvement, write

Building notebook

• netbook PART A - cleaning and conact the data: https://www.kaggle.com/shirmani/characteristics-of-corona-patient-ds-v4

• netbook PART B- features Engineering: https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-b/edit

• part C data QA https://www.kaggle.com/shirmani/qa-characteristics-corona-patients-part-c

• netbook PART D - format the data to int and float cols (model preparation): https://www.kaggle.com/shirmani/build-characteristics-corona-patients-part-d

The continuous evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses a challenge to determine the optimal updated composition of the coronavirus disease 2019 (COVID-19) vaccine. The present study aimed to investigate the immunogenicity of the Delta monovalent vaccine, the Omicron monovalent vaccine, and the Delta and Omicron BA.1 bivalent vaccine. Three COVID-19 vaccines were designed using the heterologous DNA prime-protein boost strategy, with each vaccine containing either Delta receptor-binding domain (RBD) of the spike protein, Omicron RBD, or both Delta and Omicron antigens. Temporal serum antibody binding titers and neutralizing antibody titers induced by the three vaccines in New Zealand White rabbits were analyzed. To further dissect the vaccine elicited antibodies (mAb) responses at the molecular level, a panel of rabbit monoclonal antibodies (RmAbs) was generated by a high-throughput single B cell sorting and discovery pipeline and further comprehensively characterized. The Omicron monovalent vaccine induced higher antibody binding titers and neutralization activities than the Delta and Omicron bivalent vaccine. Four RmAbs with robust neutralization capacity were isolated from rabbits immunized with the Omicron or Delta monovalent vaccine. Notably, 9E11 isolated from the Omicron monovalent vaccine group neutralized all the Omicron subvariants with an IC50 value ranging from 1.5 to 503.6 ng/mL; thus, this vaccine could serve as a prophylactic and therapeutic intervention. Given the increasing incidence of COVID-19 cases due to the Omicron variant, RBD from the Omicron strain could serve as a candidate immunogen that can induce higher neutralization activities against the SARS-CoV-2 Omicron sublineages.

Context

Its curious to learn more about my country. If covid-19 really did disappear or not for even a brief moment in new zealand.

Content

What's inside is more than just rows and columns. More analytics will have to be done and distribution analysis to find out what parts actually had no covid.

Acknowledgements

We wouldn't be here without https://nzcoviddashboard.esr.cri.nz/#!/ and stats NZ

Inspiration

Did covid actually disappear in parts of new zealand?

This Project Tycho dataset includes a CSV file with COVID-19 data reported in NEW ZEALAND: 2019-12-30 - 2021-07-31. It contains counts of cases and deaths. Data for this Project Tycho dataset comes from: "COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University", "European Centre for Disease Prevention and Control Website", "World Health Organization COVID-19 Dashboard". The data have been pre-processed into the standard Project Tycho data format v1.1.