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.

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.

Total Covid cases, end of month in New Zealand, March, 2023 The most recent value is 2206394 total Covid cases as of March 2023, an increase compared to the previous value of 2166470 total Covid cases. Historically, the average for New Zealand from February 2020 to March 2023 is 570900 total Covid cases. The minimum of 1 total Covid cases was recorded in February 2020, while the maximum of 2206394 total Covid cases was reached in March 2023. | TheGlobalEconomy.com

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.

Total Covid cases per million people in New Zealand, March, 2023 The most recent value is 425510 cases per million as of March 2023, an increase compared to the previous value of 417811 cases per million. Historically, the average for New Zealand from February 2020 to March 2023 is 110100 cases per million. The minimum of 0 cases per million was recorded in February 2020, while the maximum of 425510 cases per million was reached in March 2023. | TheGlobalEconomy.com

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

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.

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 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.

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.

2019 Novel Coronavirus COVID-19 (2019-nCoV) Visual Dashboard and Map:
https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6

• Confirmed Cases by Country/Region/Sovereignty
• Confirmed Cases by Province/State/Dependency
• Deaths
• Recovered

Downloadable data:
https://github.com/CSSEGISandData/COVID-19

Additional Information about the Visual Dashboard:
https://systems.jhu.edu/research/public-health/ncov

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit the following sources: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.-- Esri COVID-19 Trend Report for 3-9-2023 --0 Countries have Emergent trend with more than 10 days of cases: (name : # of active cases) 41 Countries have Spreading trend with over 21 days in new cases curve tail: (name : # of active cases)Monaco : 13, Andorra : 25, Marshall Islands : 52, Kyrgyzstan : 79, Cuba : 82, Saint Lucia : 127, Cote d'Ivoire : 148, Albania : 155, Bosnia and Herzegovina : 172, Iceland : 196, Mali : 198, Suriname : 246, Botswana : 247, Barbados : 274, Dominican Republic : 304, Malta : 306, Venezuela : 334, Micronesia : 346, Uzbekistan : 356, Afghanistan : 371, Jamaica : 390, Latvia : 402, Mozambique : 406, Kosovo : 412, Azerbaijan : 427, Tunisia : 528, Armenia : 594, Kuwait : 716, Thailand : 746, Norway : 768, Croatia : 847, Honduras : 1002, Zimbabwe : 1067, Saudi Arabia : 1098, Bulgaria : 1148, Zambia : 1166, Panama : 1300, Uruguay : 1483, Kazakhstan : 1671, Paraguay : 2080, Ecuador : 53320 Countries may have Spreading trend with under 21 days in new cases curve tail: (name : # of active cases)61 Countries have Epidemic trend with over 21 days in new cases curve tail: (name : # of active cases)Liechtenstein : 48, San Marino : 111, Mauritius : 742, Estonia : 761, Trinidad and Tobago : 1296, Montenegro : 1486, Luxembourg : 1540, Qatar : 1541, Philippines : 1915, Ireland : 1946, Brunei : 2010, United Arab Emirates : 2013, Denmark : 2111, Sweden : 2149, Finland : 2154, Hungary : 2169, Lebanon : 2208, Bolivia : 2838, Colombia : 3250, Switzerland : 3321, Peru : 3328, Slovakia : 3556, Malaysia : 3608, Indonesia : 3793, Portugal : 4049, Cyprus : 4279, Argentina : 5050, Iran : 5135, Lithuania : 5323, Guatemala : 5516, Slovenia : 5689, South Africa : 6604, Georgia : 7938, Moldova : 8082, Israel : 8746, Bahrain : 8932, Netherlands : 9710, Romania : 12375, Costa Rica : 12625, Singapore : 13816, Serbia : 14093, Czechia : 14897, Spain : 17399, Ukraine : 19568, Canada : 24913, New Zealand : 25136, Belgium : 30599, Poland : 38894, Chile : 41055, Australia : 50192, Mexico : 65453, United Kingdom : 65697, France : 68318, Italy : 70391, Austria : 90483, Brazil : 134279, Korea - South : 209145, Russia : 214935, Germany : 257248, Japan : 361884, US : 6440500 Countries may have Epidemic trend with under 21 days in new cases curve tail: (name : # of active cases) 54 Countries have Controlled trend: (name : # of active cases)Palau : 3, Saint Kitts and Nevis : 4, Guinea-Bissau : 7, Cabo Verde : 8, Mongolia : 8, Benin : 9, Maldives : 10, Comoros : 10, Gambia : 12, Bhutan : 14, Cambodia : 14, Syria : 14, Seychelles : 15, Senegal : 16, Libya : 16, Laos : 17, Sri Lanka : 19, Congo (Brazzaville) : 19, Tonga : 21, Liberia : 24, Chad : 25, Fiji : 26, Nepal : 27, Togo : 30, Nicaragua : 32, Madagascar : 37, Sudan : 38, Papua New Guinea : 38, Belize : 59, Egypt : 60, Algeria : 64, Burma : 65, Ghana : 72, Haiti : 74, Eswatini : 75, Guyana : 79, Rwanda : 83, Uganda : 88, Kenya : 92, Burundi : 94, Angola : 98, Congo (Kinshasa) : 125, Morocco : 125, Bangladesh : 127, Tanzania : 128, Nigeria : 135, Malawi : 148, Ethiopia : 248, Vietnam : 269, Namibia : 422, Cameroon : 462, Pakistan : 660, India : 4290 41 Countries have End Stage trend: (name : # of active cases)Sao Tome and Principe : 1, Saint Vincent and the Grenadines : 2, Somalia : 2, Timor-Leste : 2, Kiribati : 8, Mauritania : 12, Oman : 14, Equatorial Guinea : 20, Guinea : 28, Burkina Faso : 32, North Macedonia : 351, Nauru : 479, Samoa : 554, China : 2897, Taiwan* : 249634 -- SPIKING OF NEW CASE COUNTS --20 countries are currently experiencing spikes in new confirmed cases:Armenia, Barbados, Belgium, Brunei, Chile, Costa Rica, Georgia, India, Indonesia, Ireland, Israel, Kuwait, Luxembourg, Malaysia, Mauritius, Portugal, Sweden, Ukraine, United Kingdom, Uzbekistan 20 countries experienced a spike in new confirmed cases 3 to 5 days ago: Argentina, Bulgaria, Croatia, Czechia, Denmark, Estonia, France, Korea - South, Lithuania, Mozambique, New Zealand, Panama, Poland, Qatar, Romania, Slovakia, Slovenia, Switzerland, Trinidad and Tobago, United Arab Emirates 47 countries experienced a spike in new confirmed cases 5 to 14 days ago: Australia, Austria, Bahrain, Bolivia, Brazil, Canada, Colombia, Congo (Kinshasa), Cyprus, Dominican Republic, Ecuador, Finland, Germany, Guatemala, Honduras, Hungary, Iran, Italy, Jamaica, Japan, Kazakhstan, Lebanon, Malta, Mexico, Micronesia, Moldova, Montenegro, Netherlands, Nigeria, Pakistan, Paraguay, Peru, Philippines, Russia, Saint Lucia, Saudi Arabia, Serbia, Singapore, South Africa, Spain, Suriname, Thailand, Tunisia, US, Uruguay, Zambia, Zimbabwe 194 countries experienced a spike in new confirmed cases over 14 days ago: Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan, Bolivia, Bosnia and Herzegovina, Botswana, Brazil, Brunei, Bulgaria, Burkina Faso, Burma, Burundi, Cabo Verde, Cambodia, Cameroon, Canada, Central African Republic, Chad, Chile, China, Colombia, Comoros, Congo (Brazzaville), Congo (Kinshasa), Costa Rica, Cote d'Ivoire, Croatia, Cuba, Cyprus, Czechia, Denmark, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Estonia, Eswatini, Ethiopia, Fiji, Finland, France, Gabon, Gambia, Georgia, Germany, Ghana, Greece, Grenada, Guatemala, Guinea, Guinea-Bissau, Guyana, Haiti, Honduras, Hungary, Iceland, India, Indonesia, Iran, Iraq, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kazakhstan, Kenya, Kiribati, Korea - South, Kosovo, Kuwait, Kyrgyzstan, Laos, Latvia, Lebanon, Lesotho, Liberia, Libya, Liechtenstein, Lithuania, Luxembourg, Madagascar, Malawi, Malaysia, Maldives, Mali, Malta, Marshall Islands, Mauritania, Mauritius, Mexico, Micronesia, Moldova, Monaco, Mongolia, Montenegro, Morocco, Mozambique, Namibia, Nauru, Nepal, Netherlands, New Zealand, Nicaragua, Niger, Nigeria, North Macedonia, Norway, Oman, Pakistan, Palau, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Rwanda, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Samoa, San Marino, Sao Tome and Principe, Saudi Arabia, Senegal, Serbia, Seychelles, Sierra Leone, Singapore, Slovakia, Slovenia, Solomon Islands, Somalia, South Africa, South Sudan, Spain, Sri Lanka, Sudan, Suriname, Sweden, Switzerland, Syria, Taiwan*, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Tonga, Trinidad and Tobago, Tunisia, Turkey, Tuvalu, US, Uganda, Ukraine, United Arab Emirates, United Kingdom, Uruguay, Uzbekistan, Vanuatu, Venezuela, Vietnam, West Bank and Gaza, Yemen, Zambia, Zimbabwe Strongest spike in past two days was in US at 64,861 new cases.Strongest spike in past five days was in US at 64,861 new cases.Strongest spike in outbreak was 424 days ago in US at 1,354,505 new cases. Global Total Confirmed COVID-19 Case Rate of 8620.91 per 100,000Global Active Confirmed COVID-19 Case Rate of 37.24 per 100,000Global COVID-19 Mortality Rate of 87.69 per 100,000 21 countries with over 200 per 100,000 active cases.5 countries with over 500 per 100,000 active cases.3 countries with over 1,000 per 100,000 active cases.1 country with over 2,000 per 100,000 active cases.Nauru is worst at 4,354.54 per 100,000.

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

As of January 23, 2021, Vermont had the highest Rt value of any U.S. state. The Rt value indicates the average number of people that one person with COVID-19 is expected to infect. A number higher than one means each infected person is passing the virus to more than one other person.

Which are the hardest-hit states? The U.S. reported its first confirmed coronavirus case toward the end of January 2020. More than 28 million positive cases have since been recorded as of February 24, 2021 – California and Texas are the states with the highest number of coronavirus cases in the United States. When figures are adjusted to reflect each state’s population, North Dakota has the highest rate of coronavirus cases. The vaccine rollout has provided Americans with a significant morale boost, and California is the state with the highest number of COVID-19 vaccine doses administered.

How have other nations responded? Countries around the world have responded to the pandemic in varied ways. The United Kingdom has approved three vaccines for emergency use and ranks among the countries with the highest number of COVID-19 vaccine doses administered worldwide. In the Asia-Pacific region, the outbreak has been brought under control in New Zealand, and the country’s response to the pandemic has been widely praised.

Context

This dataset was collected from data received via this APi.

Content

“[Recovered cases are a] more important metric to track than Confirmed cases.”— Researchers for the University of Virginia’s COVID-19 dashboard

If the number of total cases were accurately known for every country then the number of cases per million people would be a good indicator as to how well various countries are handling the pandemic.

Countries:

Datasets contend data about covid_19 from 232 countries - Afghanistan - Albania - Algeria - Andorra - Angola - Anguilla - Antigua-and-Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia-and-Herzegovina - Botswana - Brazil - British-Virgin-Islands - Brunei - Bulgaria - Burkina-Faso - Burundi - Cabo-Verde - Cambodia - Cameroon - Canada - CAR - Caribbean-Netherlands - Cayman-Islands - Chad - Channel-Islands - Chile - China - Colombia - Comoros - Congo - Cook-Islands - Costa-Rica - Croatia - Cuba - Curaçao - Cyprus - Czechia - Denmark - Diamond-Princess - Diamond-Princess- - Djibouti - Dominica - Dominican-Republic - DRC - Ecuador - Egypt - El-Salvador - Equatorial-Guinea - Eritrea - Estonia - Eswatini - Ethiopia - Faeroe-Islands - Falkland-Islands - Fiji - Finland - France - French-Guiana - French-Polynesia - Gabon - Gambia - Georgia - Germany - Ghana - Gibraltar - Greece - Greenland - Grenada - Guadeloupe - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong-Kong - Hungary - Iceland - India - Indonesia - Iran - Iraq - Ireland - Isle-of-Man - Israel - Italy - Ivory-Coast - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Kuwait - Kyrgyzstan - Laos - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall-Islands - Martinique - Mauritania - Mauritius - Mayotte - Mexico - Micronesia - Moldova - Monaco - Mongolia - Montenegro - Montserrat - Morocco - Mozambique - MS-Zaandam - MS-Zaandam- - Myanmar - Namibia - Nepal - Netherlands - New-Caledonia - New-Zealand - Nicaragua - Niger - Nigeria - Niue - North-Macedonia - Norway - Oman - Pakistan - Palau - Palestine - Panama - Papua-New-Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto-Rico - Qatar - Réunion - Romania - Russia - Rwanda - S-Korea - Saint-Helena - Saint-Kitts-and-Nevis - Saint-Lucia - Saint-Martin - Saint-Pierre-Miquelon - Samoa - San-Marino - Sao-Tome-and-Principe - Saudi-Arabia - Senegal - Serbia - Seychelles - Sierra-Leone - Singapore - Sint-Maarten - Slovakia - Slovenia - Solomon-Islands - Somalia - South-Africa - South-Sudan - Spain - Sri-Lanka - St-Barth - St-Vincent-Grenadines - Sudan - Suriname - Sweden - Switzerland - Syria - Taiwan - Tajikistan - Tanzania - Thailand - Timor-Leste - Togo - Tonga - Trinidad-and-Tobago - Tunisia - Turkey - Turks-and-Caicos - UAE - Uganda - UK - Ukraine - Uruguay - US-Virgin-Islands - USA - Uzbekistan - Vanuatu - Vatican-City - Venezuela - Vietnam - Wallis-and-Futuna - Western-Sahara - Yemen - Zambia - Zimbabw-

The aim of this study was to identify viruses associated with canine infectious respiratory disease syndrome (CIRDS) among a population of New Zealand dogs. Convenience samples of oropharyngeal swabs were collected from 116 dogs, including 56 CIRDS-affected and 60 healthy dogs from various locations in New Zealand between March 2014 and February 2016. Pooled samples from CIRDS-affected (n = 50) and from healthy (n = 50) dogs were tested for the presence of canine respiratory viruses using next generation sequencing (NGS). Individual samples (n = 116) were then tested by quantitative PCR (qPCR) and reverse transcriptase qPCR (RT-qPCR) for specific viruses. Groups were compared using Fisher’s exact or χ2 tests. The effect of explanatory variables (age, sex, type of household, presence of viral infection) on the response variable (CIRDS-affected or not) was tested using RR. Canine pneumovirus (CnPnV), canine respiratory coronavirus (CRCoV), canine herpesvirus-1 (CHV-1), canine picornavirus and influenza C virus sequences were identified by NGS in the pooled sample from CIRDS-affected but not healthy dogs. At least one virus was detected by qPCR/RT-qPCR in 20/56 (36%) samples from CIRDS dogs and in 23/60 (38%) samples from healthy dogs (p = 0.84). CIRDS-affected dogs were most commonly positive for CnPnV (14/56, 25%) followed by canine adenovirus-2 (CAdV-2, 5/56, 9%), canine parainfluenza virus (CpiV) and CHV-1 (2/56, 4% each), and CRCoV (1/56, 2%). Only CnPnV (17/60, 28%) and CAdV-2 (14/60, 23%) were identified in samples from healthy dogs, and CAdV-2 was more likely to be detected healthy than diseased dogs (RR 0.38; 95% CI = 0.15–0.99; p = 0.045) The frequency of detection of viruses traditionally linked to CIRDS (CAdV-2 and CPiV) among diseased dogs was low. This suggests that other pathogens are likely to have contributed to development of CIRDS among sampled dogs. Our data represent the first detection of CnPnV in New Zealand, but the role of this virus in CIRDS remains unclear. On-going monitoring of canine respiratory pathogens by NGS would be beneficial, as it allows rapid detection of novel viruses that may be introduced to the New Zealand canine population in the future. Such monitoring could be done using pooled samples to minimise costs. Testing for novel respiratory viruses such as CnPnV and CRCoV should be considered in all routine laboratory investigations of CIRDS cases, particularly in dogs vaccinated with currently available kennel cough vaccines.

Data-set of the paper Disentangling Covid-19, Economic Mobility, and Containment Policy Shocks for replication purpose of the Data Editor of AEJMacro. Detailed information on the data-set is in the readme file in the public repository openicpsr-175241 (under review).

We study the dynamic interaction between Covid-19, economic mobility, and containment policy. We use Bayesian panel structural vector autoregressions with daily data for 44 countries, identified through traditional and narrative sign restrictions. We find that incidence shocks and containment shocks have large and persistent effects on mobility, morbidity, and mortality that last for 1-2 months. These shocks are the main drivers of the pandemic, explaining between 20-60% of the average and historical variability in mobility, cases, and deaths worldwide. The policy tradeoff associated to non-pharmaceutical interventions is 1pp less economic mobility per day for 8% fewer deaths after three months.

The panel data-set contains the main data to perform the analysis in the paper. It contains dailiy data for (in sheets) Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Lithuania, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Russia, Saudi Arabia, Slovenia, South Korea, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Arab Emirates, United Kingdom and United States. Included variables are: Confirmed Cases, Total Deaths, Days Last Reported Case, Total Tests, School Closing, Workplace Closing, Cancel Public Events, Restrictions Gatherings, Close Public Transport, Stay at Home Requirements, Restrictions Internal Movement, International Travel Controls, Income Support, Debt/Contract Relief, Fiscal Measures, International Support, Public Information Campaigns, Testing Policy, Contact Tracing, Healthcare Emergency Investment, Investment Vaccines, Stringency Index, Small Cap, Large Cap, Government Benchmarks 3 Month, Government Benchmarks 1 Year, Government Benchmarks 2 Year, Government Benchmarks 5 Year, Government Benchmarks 10 Year, FX Indices Broad, FX Indices Narrow, Mobility Retail Mobility Grocery, Mobility Parks, Mobility Transit Stations Mobility Workplaces, Mobility Residential. Period: 30.12.2016 to 31.08.2020. All data are downloaded from Macrobond.

Collinearity diagnostics test for new death, new cases and vaccine doses.

During the COVID-19 pandemic there have been marked changes in individuals' belief systems (e.g., support for lockdowns) as a result of the threat of COVID-19. In the current study, we investigated whether these belief systems change as a function of changes in the threat of COVID-19. Specifically, we conducted a longitudinal study, with authoritarianism measured at the height of the COVID-19 pandemic in New Zealand and when the threat of COVID-19 was low (i.e., no known COVID-19 cases in the community). A total of 888 participants responded at both timepoints, completing measures of political orientation and distrust of science, in addition to the measure of authoritarianism. We had two hypotheses. First, that liberals would display a more marked reduction in authoritarian submission between Alert Level 4 and Alert Level 1 relative to conservatives. Second, that changes would be mediated by trust in science. Both hypotheses were supported, demonstrating that authoritarianism is sensitive to threat, even for those on the political left, and that trust in science helps to explain these changes. We suggest that fluctuations in authoritarianism may be different across the political spectrum due to underlying belief systems such as a distrust of science.