Statistical data on the number of violators of precautionary and preventive measures to limit the spread of the coronavirus in Qatar, categorized by nationality, gender, and type of crime.

Negative values mean that spatial aggregation estimates for peak measures were smaller than spatial aggregation differences for onset measures. Bolded values denote mean estimates that we interpret to have statistical significance; that is, the 95% credible intervals did not overlap with zero.

Since February 25, 2020 Switzerland has been affected by COVID-19. Modelling predictions show that this pandemic will not stop on its own and that stringent migitation strategies are needed. Switzerland has implemented a series of measures both at cantonal and federal level. On March 16, 2020 the Federal Council of Switzerland declared “extraordinary situation” and introduced a series of stringent measures. This includes the closure of schools, restaurants, bars, businesses with close contact (e.g. hair dressers), entertainment or leisure facilities. Incoming cross-border mobility from specific countries is also restricted to Swiss citizens, residency holders or work commuters. As of March 20, 2020 mass gatherings of more than five people are also banned. Already in early March various cantons had started to ban events of various sizes and have restricted or banned access to short- and long-term care facilites and day care centers.

The aim of this project is to collect and categorize these control measures implemented and provide a continously updated data set, which can be used for modelling or visualization purposes. Please use the newest version available.

We collect the date/duration and level of the most important measures taken in response to COVID-19 from official cantonal and federal press releases. A description of the measures, the levels as well as the newest version of data dataset can be found here.

The guidance identifies core personal and community-based public health measures to mitigate the transmission of coronavirus disease (COVID-19).

Context

There's a story behind every dataset and here's your opportunity to share yours.

The COVID-19 Government Measures Dataset puts together all the measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The researched information available falls into five categories:

Social distancing Movement restrictions Public health measures Social and economic measures Lockdowns

Content

Updated last 10/12/2020 The #COVID19 Government Measures Dataset puts together all the measures implemented by governments worldwide in response to the Coronavirus pandemic. Data collection includes secondary data review. The researched information available falls into five categories: - Social distancing - Movement restrictions - Public health measures - Social and economic measures - Lockdowns Each category is broken down into several types of measures.

ID ISO COUNTRY REGION ADMIN_LEVEL_NAME PCODE LOG_TYPE CATEGORY MEASURE_TYPE TARGETED_POP_GROUP COMMENTS NON_COMPLIANCE DATE_IMPLEMENTED SOURCE SOURCE_TYPE LINK ENTRY_DATE ALTERNATIVE SOURCE

Acknowledgements

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Inspiration

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This interactive chart tracks the daily TED Spread (3 Month LIBOR / 3 Month Treasury Bill) as a measure of the perceived credit risk in the U.S. economy. LIBOR measures the interbank lending rate so as the spread between LIBOR and the T-bill rate increases, it shows an accelerating lack of trust between banks and a corresponding tightening of credit for all other counterparties.

Understanding how epidemics spread in a system is a crucial step to prevent and control outbreaks, with broad implications on the system’s functioning, health, and associated costs. This can be achieved by identifying the elements at higher risk of infection and implementing targeted surveillance and control measures. One important ingredient to consider is the pattern of disease-transmission contacts among the elements, however lack of data or delays in providing updated records may hinder its use, especially for time-varying patterns. Here we explore to what extent it is possible to use past temporal data of a system’s pattern of contacts to predict the risk of infection of its elements during an emerging outbreak, in absence of updated data. We focus on two real-world temporal systems; a livestock displacements trade network among animal holdings, and a network of sexual encounters in high-end prostitution. We define the node’s loyalty as a local measure of its tendency to maintain contacts with the same elements over time, and uncover important non-trivial correlations with the node’s epidemic risk. We show that a risk assessment analysis incorporating this knowledge and based on past structural and temporal pattern properties provides accurate predictions for both systems. Its generalizability is tested by introducing a theoretical model for generating synthetic temporal networks. High accuracy of our predictions is recovered across different settings, while the amount of possible predictions is system-specific. The proposed method can provide crucial information for the setup of targeted intervention strategies.

This dataset provides economic indicators used to monitor Iowa's economy and forecast future direction of economic activity in Iowa.

Abstract

The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding.

The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine.

The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators.

The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well.

The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables.

MAIN RESULTS

• Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level.

• Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time.

• Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion.

• Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively).

• HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing.

• Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis.

• Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care.

• Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women.

• Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

Geographic coverage

The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The 27 administrative regions were grouped for this survey into five geographic regions: North, Central, East, South and West. The five geographic regions are the five study domains of the survey. The estimates obtained from the 2007 UDHS are presented for the country as a whole, for urban and rural areas, and for each of the five geographic regions.

Analysis unit

• Household
• Women age 15-49
• Men age 15-49

Universe

The population covered by the 2007 UDHS is defined as the universe of all women and men age 15-49 in Ukraine.

Kind of data

Sample survey data

Sampling procedure

The 2007 Ukraine Demographic and Health Survey (UDHS) was the first survey of its kind carried out in Ukraine. The survey was a nationally representative sample survey of 15,000 households, with an expected yield of about 7,900 completed interviews of women age 15-49. It was designed to provide estimates on fertility, infant and child mortality, use of contraception and family planning, knowledge and attitudes toward HIV/AIDS and other sexually transmitted infections (STI), and other family welfare and health indicators. Ukraine is made up of 24 oblasts, the Autonomous Republic of Crimea, and two special cities (Kyiv and Sevastopol), which together make up 27 administrative regions, each subdivided into lower-level administrative units. The 27 administrative regions were grouped for this survey into five geographic regions: North, Central, East, South and West. The five geographic regions are the five study domains of the survey. The estimates obtained from the 2007 UDHS are presented for the country as a whole, for urban and rural areas, and for each of the five geographic regions.

A men's survey was conducted at the same time as the women's survey, in a subsample consisting of one household in every two selected for the female survey. All men age 15-49 living in the selected households were eligible for the men's survey. The survey collected information on men's use of contraception and family planning and their knowledge and attitudes toward HIV/AIDS and other sexually transmitted infections (STI).

SAMPLING FRAME

The sampling frame used for the 2007 UDHS was the Ukraine Population Census conducted in 2001 (SSC, 2003a), provided by the State Statistical Committee (SSC) of Ukraine. The sampling frame consisted of about 38 thousand enumeration areas (EAs) with an average of 400-500 households per EA. Each EA is subdivided into 4-5 enumeration units (EUs) with an average of 100 households per EU. An EA is a city block in urban areas; in rural areas, an EA is either a village or part of a large village, or a group of small villages (possibly plus a part of a large village). An EU is a list of addresses (in a neighborhood) that was used as a convenient counting unit for the census. Both EAs and EUs include information about the location, type of residence, address of each structure in it, and the number of households in each structure.

Census maps were available for most of the EAs with marked boundaries. In urban areas, the census maps have marked boundaries/locations of the EUs. In rural areas, the EUs are defined by detailed descriptions available at the SSC local office. Therefore, either the EA or the EU could be used as the primary sampling unit (PSU) for the 2007 UDHS. Because the EAs in urban areas are large (an average of 500 households), using

Due to measures introduced to curb the spread of the coronavirus (COVID-19) in India, supermarket and pharmacy mobility saw an increase of ** percent in November 2021 compared to the baseline. Retail and recreation had the lowest shares that month compared to other categories with a decline of * percent. Due to the easing restrictions, workplaces saw a rise in mobility.

♾️ Mekong Open Development

This paper seeks to address the policy issue of the usefulness of financial spreads as indicators of future inflation and output growth in the countries of the European Union, placing a particular focus on out-of-sample forecasting performance. Such analysis is of considerable relevance to monetary authorities, given the breakdown of the money/income relation in a number of countries and following increased emphasis of domestic monetary policy on control of inflation following the broadening of the ERM bands. The results confirm that for some countries, financial spread variables do contain some information about future output growth and inflation, with the yield curve and the reverse yield gap performing best. However, the relatively poor out-of-sample forecasting performance and/or parameter instability suggests that the need for caution in using spread variables for forecasting in EU countries. Only a small number of spreads contain information, and improve forecasting in a manner which is stable over time.

Between April and May 2020, due to measures introduced to curb the spread of coronavirus (COVID-19), there was a 15 percent decrease in mobility trends for places like restaurants, shopping centers, museums, libraries, and movie theaters in Hungary. Mobility trends for workplaces also decreased by 33 percent.For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In this paper, we investigate the spread of COVID-19 and the impact of government measures at the early stage of the pandemic (before the introduction of the vaccines) in the Netherlands. We build a multiple linear regression model to predict the effective reproduction rate using key factors and measures and integrate it with a system dynamics model to predict the spread and the impact of measures against COVID-19. Data from February to November 2020 is used to train the model and data until December 2020 is used to validate the model. We use data about the key factors, e.g., disease specific such as basic reproduction rate and incubation period, weather related factors such as temperature, and controllable factors such as testing capacity. We consider particularly the following measures taken by the government: wearing facemasks, event allowance, school closure, catering services closure, and self-quarantine. Studying the strategy of the Dutch government, we control these measures by following four main policies: doing nothing, mitigation, curbing, elimination. We develop a systems dynamic model to simulate the effect of policies. Based on our numerical experiments, we develop the following main insights: It is more effective to implement strict, sharp measures earlier but for a shorter duration than to introduce measures gradually for a longer duration. This way, we can prevent a quick rise in the number of infected cases but also to reduce the number of days under measures. Combining the measures with a high testing capacity and with effective self-quarantine can significantly reduce the spread of COVID-19.

Decision No. 111 on the continuous administrative measures for the geographical designation in Phnom Penh clearly delineates to separate two zones in the following Orange and Yellow zone defining as moderate and low levels of COVID-19 transmission respectively. There are only Orange zones with attachment files in appendices.

‘SQ’ denotes the squared returns measure of volatility, ‘GK’ denotes the Garman-Klass measure while ‘RS’ denotes the Rogers-Satchell measure.

This dataset provides daily fire weather indices for interior Alaska during the active fire seasons from 2001 to 2010. Data are gridded at 60-m resolution. The active fire season is defined as May 24-September 18 (days of the year 144-261) in this dataset. Fire weather is the use of meteorological parameters such as relative humidity, wind speed and direction, cloud cover, mixing heights, and soil moisture to determine whether conditions are favorable for fire growth and smoke dispersion. The six indices provided in this dataset are defined and produced following the methodology of the Canadian Forest Fire Weather Index System: Fine Fuel Moisture Code, Duff Moisture Code, Drought Code, Initial Spread Index, Buildup Index, Fire Weather Index. The dataset was developed following point source data interpolation from weather station observations.

The coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented global challenges. A zero-COVID strategy is needed to end the crisis, but there is a lack of biological evidence. In the present study, we collected available data on SARS, MERS, and COVID-19 to perform a comprehensive comparative analysis and visualization. The study results revealed that the fatality rate of COVID-19 is low, whereas its death toll is high compared to SARS and MERS. Moreover, COVID-19 had a higher asymptomatic rate. In particular, COVID-19 exhibited unique asymptomatic transmissibility. Further, we developed a foolproof operating software in Python language to simulate COVID-19 spread in Wuhan, showing that the cumulative cases of existing asymptomatic spread would be over 100 times higher than that of only symptomatic spread. This confirmed the essential role of asymptomatic transmissibility in the uncontrolled global spread of COVID-19, which enables the necessity of implementing the zero-COVID policy. In conclusion, we revealed the triggering role of the asymptomatic transmissibility of COVID-19 in this unprecedented global crisis, which offers support to the zero-COVID strategy against the recurring COVID-19 spread.

🇩🇪 독일

Australia’s first coronavirus case was discovered on ****************. The infected person was a man from the coronavirus epicenter, Wuhan, who had flown into Melbourne on the **** of January. Although some of the first infections in Australia can be attributed to travelers from China, by ********, infections attributed to people who had visited the United States and Italy had overtaken China.

Travel restrictions

With the rate of infection in China climbing steadily in early *************, the Australian government began to implement measures to slow the spread of the coronavirus. These measures involved social distancing and broad travel restrictions, including the closing of boarders to all foreign nationals arriving from China on **********. Overall, the number of travelers moving through airports across Australia had already begun to drop noticeably and Chinese students were one of the largest groups to be affected. By March, well after the 2020 school year had begun, over ** percent of Chinese university students with visas to study in Australia had not entered the country. This also added to economic concerns, with Chinese students representing just over ** billion Australian dollars in education export income in 2019.

Cruise ships

During the COVID-19 pandemic a number of cruise ships were hit by the virus, which spread amongst passengers and staff in the closed environments. The Diamond Princess, which was quarantined in Yokohama, Japan, had around *** Australians on board, of which at least a quarter contracted the coronavirus. The Ruby Princess was another cruise ship attributed to the spread of COVID-19 within Australia. On **************, the ship docked in Sydney harbor and ***** passengers disembarked. By ******** it was confirmed that *** passengers had contracted COVID-19 on the Ruby Princess.