According to a survey performed in the United Kingdom (UK) in March 2020, 24 percent of respondents stated their workplace was offering sanitization products eg. hand sanitizer, wipes to help protect employees against coronavirus (COVID-19), while an additional 19 percent reported receiving regular communication about the virus at their workplace. However, 21 percent of respondents mention that nothing had changed in their workplace policy to manage the spread of coronavirus and business was running as usual. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Question Paper Solutions of chapter Measures of Dispersion of Fundamentals of Statistics, 1st Semester , Bachelor of Business Administration

As with the first three regulations imposed by the National Committee for Special Emergency Situations, the next measures taken by the Romanian authorities against the spread of coronavirus (COVID-19) in Romania benefited from a high percentage of approval from the population and were considered to be qualitative. The only measure that was received with a slight disagreement involved helping the Romanian citizens who returned to Romania from abroad. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Data from simulations of COVID-19 spread in Sweden under different public-health measures. Results from individual-based models.

Question Paper Solutions of chapter Measures of Dispersion of Basic Mathematics & Statistics, 2nd Semester , Bachelor in Business Administration (Hons.) 2023-2024

Graph and download economic data for Leading Indicators OECD: Component Series: Interest Rate Spread: Normalised for United States (USALOCOSINOSTSAM) from Jan 1960 to Dec 2023 about leading indicator and spread.

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.

In 2022, around 55 percent of adults in the United States stated that COVID-19 vaccination had been extremely or very effective at limiting the spread of coronavirus, while only around 34 percent of adults stated the same for staying at least 6 feet apart from other people indoors. This statistic illustrates the percentage of adults in the United States in 2022 who believed select measures have been effective at limiting the spread of COVID-19.

Dataset description This dataset contains background data and supplementary material for Sönning (forthcoming), a study that looks at the behavior of dispersion measures when applied to text-level frequency data. For the literature survey reported in that study, which examines how dispersion measures are used in corpus-based work, it includes tabular files listing the 730 research articles that were examined as well as annotations for those studies that measured dispersion in the corpus-linguistic (and lexicographic) sense. As for the corpus data that were used to train the statistical model parameters underlying the simulation study reported in that paper, the dataset contains a term-document matrix for the 49,604 unique word forms (after conversion to lower-case) that occur in the Brown Corpus. Further, R scripts are included that document in detail how the Brown Corpus XML files, which are available from the Natural Language Toolkit (Bird et al. 2009; https://www.nltk.org/), were processed to produce this data arrangement. Abstract: Related publication This paper offers a survey of recent corpus-based work, which shows that dispersion is typically measured across the text files in a corpus. Systematic insights into the behavior of measures in such distributional settings are currently lacking, however. After a thorough discussion of six prominent indices, we investigate their behavior on relevant frequency distributions, which are designed to mimic actual corpus data. Our evaluation considers different distributional settings, i.e. various combinations of frequency and dispersion values. The primary focus is on the response of measures to relatively high and low sub-frequencies, i.e. texts in which the item or structure of interest is over- or underrepresented (if not absent). We develop a simple method for constructing sensitivity profiles, which allow us to draw instructive comparisons among measures. We observe that these profiles vary considerably across distributional settings. While D and DP appear to show the most balanced response contours, our findings suggest that much work remains to be done to understand the performance of measures on items with normalized frequencies below 100 per million words.

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.

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.

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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Yearly citation counts for the publication titled "Field Probes Performance for the Measurement of Spread-Spectrum Radio Signals".

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

Based on the results of a survey, 70 percent of Indian respondents stated that the government should follow up and track the health of all those who arrived in India from China and Singapore in the month preceding the survey to prevent the spread of the novel coronavirus. On the other hand, around two percent stated that they think precautionary measures were not needed as the virus is still a minor risk in India.

The country went into lockdown on March 25, 2020, the largest in the world, restricting 1.3 billion people.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

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.

The outbreak of the COVID-19 pandemic has prompted the German government and the 16 German federal states to announce a variety of public health measures in order to suppress the spread of the coronavirus. These non-pharmaceutical measures intended to curb transmission rates by increasing social distancing (i.e., diminishing interpersonal contacts) which restricts a range of individual behaviors. These measures span moderate recommendations such as physical distancing, up to the closures of shops and bans of gatherings and demonstrations. The implementation of these measures are not only a research goal for themselves but have implications for behavioral research conducted in this time (e.g., in form of potential confounder biases). Hence, longitudinal data that represent the measures can be a fruitful data source. The presented data set contains data on 14 governmental measures across the 16 German federal states. In comparison to existing datasets, the data set at hand is a fine-grained daily time series tracking the effective calendar date, introduction, extension, or phase-out of each respective measure. Based on self-regulation theory, measures were coded whether they did not restrict, partially restricted or fully restricted the respective behavioral pattern. The time frame comprises March 08, 2020 until May 15, 2020. The project is an open-source, ongoing project with planned continued updates in regular (approximately monthly) intervals. New variables include restrictions on travel and gastronomy. The variable trvl (travel) comprises the following categories: fully restricted (=2) reflecting a potential general ban to travel within Germany (except for sound reasons like health or business); partially restricted (=1): travels are allowed but may be restricted through prohibition of accommodation or entry ban for certain groups (e.g. people from risk areas); free (=0): no travel and accommodation restrictions in place). The variable gastr (gastronomy) comprises: fully restricted (=2): closure of restaurants or bars; partially restricted (=1): Only take-away or food delivery services are allowed; free (=0): restaurants are allowed to open without restrictions). Further, the variables msk (recommendations to wear a mask) and zoo (restrictions of zoo visits) have been adjusted.:

Graph and download economic data for Leading Indicators OECD: Component Series: Interest Rate Spread: Original Series for Germany (DEULOCOSIORSTM) from Jan 1960 to Dec 2023 about origination, leading indicator, spread, and Germany.

This study aims to examine the development trend of COVID-19 in China and propose a model to assess the impacts of various prevention and control measures in combating the COVID-19 pandemic. Using COVID-19 cases reported by the National Health Commission of China from January 2, 2020, to January 2, 2022, we established a Susceptible-Exposed-Infected-Asymptomatic-Quarantined-Vaccinated-Hospitalized-Removed (SEIAQVHR) model to calculate the COVID-19 transmission rate and Rt effective reproduction number, and assess prevention and control measures. Additionally, we built a stochastic model to explore the development of the COVID-19 epidemic. We modeled the incidence trends in five outbreaks between 2020 and 2022. Some important features of the COVID-19 epidemic are mirrored in the estimates based on our SEIAQVHR model. Our model indicates that an infected index case entering the community has a 50%–60% chance to cause a COVID-19 outbreak. Wearing masks and getting vaccinated were the most effective measures among all the prevention and control measures. Specifically targeting asymptomatic individuals had no significant impact on the spread of COVID-19. By adjusting prevention and control parameters, we suggest that increasing the rates of effective vaccination and mask-wearing can significantly reduce COVID-19 cases in China. Our stochastic model analysis provides a useful tool for understanding the COVID-19 epidemic in China.

We employ a cellular-automata to reconstruct the land use patterns of cities that we characterize by two measures of spatial heterogeneity: (a) a variant of spatial entropy, which measures the spread of residential, business, and industrial activity sectors, and (b) an index of dissimilarity, which quantifies the degree of spatial mixing of these land use activity parcels. A minimalist and bottom-up approach is adopted that utilizes a limited set of three parameters which represent the forces which determine the extent to which each of these sectors spatially aggregate into clusters. The dispersion degrees of the land uses are governed by a fixed pre-specified power-law distribution based on empirical observations in other cities. Our method is then used to reconstruct land use patterns for the city state of Singapore and a selection of North American cities. We demonstrate the emergence of land use patterns that exhibit comparable visual features to the actual city maps defining our case studies whilst sharing similar spatial characteristics. Our work provides a complementary approach to other measures of urban spatial structure that differentiate cities by their land use patterns resulting from bottom-up dispersion and aggregation processes.