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.

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 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

These datasets contain a set of news articles in English, French and Spanish extracted from Medisys (i‧e. advanced search) according the following criteria: (1) Keywords (at least): COVID-19, ncov2019, cov2019, coronavirus; (2) Keywords (all words): masque (French), mask (English), máscara (Spanish) (3) Periods: March 2020, May 2020, July 2020; (4) Countries: UK (English), Spain (Spanish), France (French). A corpus by country has been manually collected (copy/paste) from Medisys. For each country, 100 snippets by period (the 1st, 10th, 15th, 20th for each month) are built. The datasets are composed of: (1) A corpus preprocessed for the BioTex tool - https://gitlab.irstea.fr/jacques.fize/biotex_python (.txt) [~ 900 texts]; (2) The same corpus preprocessed for the Weka tool - https://www.cs.waikato.ac.nz/ml/weka/ (.arff); (3) Terms extracted with BioTex according spatio-temporal criteria (*.csv) [~ 9000 terms]. Other corpora can be collected with this same method. The code in Perl in order to preprocess textual data for terminology extraction (with BioTex) and classification (with Weka) tasks is available. A new version of this dataset (December 2020) includes additional data: - Python preprocessing and BioTex code [Execution_BioTex‧tgz]. - Terms extracted with different ranking measures (i‧e. C-Value, F-TFIDF-C_M) and methods (i‧e. extraction of words and multi-word terms) with the online version of BioTex [Terminology_with_BioTex_online_dec2020.tgz],

Dataset for following paper.

Introduction

New Zealand’s early response to the novel coronavirus pandemic included a strict lockdown which eliminated community transmission of COVID-19. However, this success was not without cost, both economic and social. In our study, we examined the psychological wellbeing of New Zealanders during the COVID-19 lockdown when restrictions reduced social contact, limited recreation opportunities, and resulted in job losses and financial insecurity.

Methods

We conducted an online panel survey of a demographically representative sample of 2010 adult New Zealanders. The survey contained three standardised measures – the Kessler Psychological Distress Scale (K10), the GAD-7, and the Well-Being Index (WHO-5) – as well as questions designed specifically to measure family violence, suicidal ideation, and alcohol consumption. It also included items assessing positive aspects of the lockdown.

Results

Thirty percent of respondents reported moderate to severe psychological distress (K10), 16% moderate to high levels of anxiety, and 39% low wellbeing; well above baseline measures. Poorer outcomes were seen among young people and those who had lost jobs or had less work, those with poor health status, and who had past diagnoses of mental illness. Suicidal ideation was reported by 6%, with 2% reporting making plans for suicide and 2% reporting suicide attempts. Suicidality was highest in those aged 18–34. Just under 10% of participants had directly experienced some form of family harm over the lockdown period. However, not all consequences of the lockdown were negative, with 64% reporting ‘silver linings’, which included enjoying working from home, spending more time with family, and a quieter, less polluted environment.

Conclusions

New Zealand’s lockdown successfully eliminated COVID-19 from the community, but our results show this achievement brought a significant psychological toll. Although much of the debate about lockdown measures has focused on their economic effects, our findings emphasise the need to pay equal attention to their effects on psychological wellbeing.

Methods Methods of data collection are in the published paper (open-access).

Briefly:

Respondents were survey participants from an internet Panel survey firm.

Data have been cleaned and processed: this was mostly simplifying/collapsing response options to fewer options for reporting.

For further details or analysis code for paper (R format) please contact james.stanley@otago.ac.nz. Data and data dictionaries are included in the uploaded files. The data were collected via two cross-sectional national surveys. For the majority of the analyses undertaken, this datasets from the two surveys was combined and survey weights were not used. However, for comparisons with the New Zealand General Social Survey the two datasets were analysed independently and post-stratification survey weights were used. Post-stratification survey weights are included with the datasets (used to re-scale collected data to the NZ population structure), but should only used if datasets are analysed independently. A total of n=3487 participants are included (meeting eligibility criteria and substantively completed the surveys). Data were originally collected in Qualtrics; cleaned and coded in R; and analysed using R. The "raw + clean" dataset provides both the original variables and the variables as analysed for the paper. Columns with names starting with "Q" are the original responses in the survey The "clean only" dataset provides just the variables analysed for the paper. Additional raw data and code available on request Some additional variables are available on request (with corresponding code) for data sensitivity or to reduce the complexity of the files (e.g. individual item responses on psychological scales). These are detailed below. WHO-5 (scores and categorised variables included; original per-item responses available on request with code for scoring) Age in years (excluded for data confidentiality) Age group (some people were only asked a "screening" question on age group for response quotas, others also provided age at end of survey) Gender (some people were only asked a "screening" question on gender for response quotas, others also confirmed gender at end of survey) Ethnicity (prioritised ethnicity included in file; original recorded ethnicity columns and free text fields available on request along with code for scoring) Alcohol intake before/during lockdown (summarised data included: original free-text columns and recoding step available on request with code for scoring) Cigarettes smoked each day/week (original free-text columns and recoding step available on request) Number of people living within bubble (original free-text column and recoding step available on request) Other main sources of stress (original free-text column available on request) Other prior trauma (original free-text column available on request) Objective: The COVID-19 pandemic and associated restrictions are associated with adverse psychological impacts but an assessment of positive wellbeing is required to understand the overall impacts of the pandemic. Methods: The NZ Lockdown Psychological Distress Survey measured excellent wellbeing categorised by a WHO-Five Well-being Index (WHO-5) score ≥22. The survey also contained demographic and pre-lockdown questions, subjective and objective lockdown experiences, and questions on alcohol use. The proportion of participants with excellent wellbeing is reported with multivariate analysis examining the relative importance of individual factors associated with excellent wellbeing. Results: Approximately 9% of the overall sample reported excellent wellbeing during the New Zealand lockdown. Excellent wellbeing status was associated with older age, male gender, Māori and Asian ethnicity, and lower levels of education. Excellent wellbeing was negatively associated with smoking, poor physical and mental health, and previous trauma. Conclusion: A substantial minority of New Zealanders reported excellent wellbeing during severe COVID-19 pandemic restrictions. Demographic and broader health factors predicted excellent wellbeing status. An understanding of these factors may help to enhance wellbeing during any future lockdowns. Methods of data collection are in the published paper and its parent (both open-access, see related works below). Briefly: Respondents were survey participants from an internet Panel survey firm. Data have been cleaned and processed: this was mostly simplifying/collapsing response options to fewer options for reporting.

MATLAB code to reproduce the results in the paper

New Zealand’s Newspaper Publishing industry has changed significantly as publishers have faced intense competition from new media platforms that have changed how consumers access news. Circulation numbers and readership among major metropolitan and regional newspapers have fallen, which has reduced print advertising revenue for newspaper publishers. Although publishers have grown their digital revenue streams over the past five years, companies like Facebook and Google have dominated the online advertising market, limiting digital revenue. Increased digital revenue has been insufficient to offset the large decrease in print revenue. Industry revenue is expected to fall at an annual average of 0.7% over the five years through 2025-26, to $668.7 million. This decline in advertising revenue was exacerbated in 2020-21 as the COVID-19 pandemic saw consumer confidence plummet. Further impacts to consumer confidence in the face of rampant inflation have driven revenue down further in recent years, including an expected 1.3% drop through the end of 2023-24. The weak demand environment has also weighed on profitability. The industry is highly concentrated, with Stuff Limited and New Zealand Media and Entertainment (NZME) Limited accounting for over 60.0% of industry revenue. Stuff publishes major daily newspapers in Christchurch and Wellington, while NZME publishes the New Zealand Herald, the country's most widely read newspaper. Both firms also publish numerous regional newspapers throughout New Zealand. Their dominance has squeezed out smaller, often regional publishers. Newspaper publishers are projected to experience sluggish growth over the next five years. Ongoing competition from online-only news sources is forecast to encourage further consolidation activities, as newspapers seek to improve efficiencies to remain viable. Cost-cutting measures – like moving some papers to online-only formats – will likely help reduce costs and boost profitability. Industry publishers are also forecast to focus on refining their digital subscription models and paywalls over the period to encourage readers to pay for content, as advertisers continue to shift their spending to digital formats. Overall, industry revenue is projected to improve by an annualised rate of 0.9% over the five years through 2030-31, to $705.3 million.