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

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.

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.

As of November 16, 2020, 43 percent of COVID-19 infection cases in New Zealand were contracted through contact with a person who had recently travelled overseas. Less than five percent of cases were attributed to locally acquired cases where the infection source was unknown.

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.

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

The COVID-19 pandemic impacted several New Zealand industries. From ************* to **********, the insurance industry recognized a reduction in their monthly cost per click from **** U.S. dollars to **** U.S. dollars. Elsewhere, the internet and telecommunication industries saw a drop in cost per click rates from **** U.S. dollars to **** U.S. dollars.

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.

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

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.

In 2021, ** percent of respondents who participated in a survey on the COVID-19 response in New Zealand rated the government's response to COVID-19 as excellent. Around * percent of respondents rated the government's response to the outbreak as terrible.

In 2021, ten percent of respondents who participated in a survey on COVID-19 response in New Zealand rated the National Party response to COVID-19 as excellent. Around ** percent of respondents rated the national party response to the outbreak as terrible.

Learning outcomes:Students will gain an understanding of the global patterns of COVID-19 cases, and how this information changes over time.Students will also compare the COVID-19 data with the World Health Organisations health statistics.Other New Zealand GeoInquiry instructional material freely available at https://arcg.is/1GPDXe

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

No. of Current CIRP Recipients data was reported at 342.000 Number in Jan 2021. This records a decrease from the previous number of 2,649.000 Number for Dec 2020. No. of Current CIRP Recipients data is updated monthly, averaging 9,786.000 Number from Jun 2020 (Median) to Jan 2021, with 8 observations. The data reached an all-time high of 24,813.000 Number in Aug 2020 and a record low of 342.000 Number in Jan 2021. 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.G094: Monthly Benefit and Supplementary Assistance: COVID-19 Income Relief Payment Recipients (Discontinued).

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.

New Zealand No. of Current CIRP Recipients: 19 years & Under data was reported at 6.000 Number in Jan 2021. This records a decrease from the previous number of 39.000 Number for Dec 2020. New Zealand No. of Current CIRP Recipients: 19 years & Under data is updated monthly, averaging 306.000 Number from Jun 2020 (Median) to Jan 2021, with 8 observations. The data reached an all-time high of 921.000 Number in Aug 2020 and a record low of 6.000 Number in Jan 2021. New Zealand No. of Current CIRP Recipients: 19 years & Under 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).

Near-term forecasting of infectious disease incidence and consequent demand for acute healthcare services can support capacity planning and public health responses. Despite well-developed scenario modelling to support the Covid-19 response, Aotearoa New Zealand lacks advanced infectious disease forecasting capacity. We develop a model using Aotearoa New Zealand’s unique Covid-19 data streams to predict reported Covid-19 cases, hospital admissions and hospital occupancy. The method combines a semi-mechanistic model for disease transmission to predict cases with Gaussian process regression models to predict the fraction of reported cases that will require hospital treatment. We evaluate forecast performance against out-of-sample data over the period from 2 October 2022 to 23 July 2023. Our results show that forecast performance is reasonably good over a 1-3 week time horizon, although generally deteriorates as the time horizon is lengthened. The model has been operationalised to provide weekly national and regional forecasts in real-time. This study is an important step towards development of more sophisticated situational awareness and infectious disease forecasting tools in Aotearoa New Zealand.

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.

According to a 2021 survey of millennials and members of Generation Z in New Zealand, ** percent of Gen Z members reported regularly wearing a facemask in public during the COVID-19 pandemic. In comparison, ** percent of the older millennial generation respondents reported having done so.