As of May 2, 2023, the outbreak of the coronavirus disease (COVID-19) had been confirmed in almost every country in the world. The virus had infected over 687 million people worldwide, and the number of deaths had reached almost 6.87 million. The most severely affected countries include the U.S., India, and Brazil.

COVID-19: background information COVID-19 is a novel coronavirus that had not previously been identified in humans. The first case was detected in the Hubei province of China at the end of December 2019. The virus is highly transmissible and coughing and sneezing are the most common forms of transmission, which is similar to the outbreak of the SARS coronavirus that began in 2002 and was thought to have spread via cough and sneeze droplets expelled into the air by infected persons.

Naming the coronavirus disease Coronaviruses are a group of viruses that can be transmitted between animals and people, causing illnesses that may range from the common cold to more severe respiratory syndromes. In February 2020, the International Committee on Taxonomy of Viruses and the World Health Organization announced official names for both the virus and the disease it causes: SARS-CoV-2 and COVID-19, respectively. The name of the disease is derived from the words corona, virus, and disease, while the number 19 represents the year that it emerged.

This graph represents the distribution of the dwellings where French people live the lockdown of March 17 due to coronavirus (COVID-19) in March 2020, by surface area in square meters. At that time 34 percent of respondents were confined in dwellings with a surface area varying between 80 and 109 square meters.

For more information on the coronavirus pandemic (COVID-19), please see our page: facts and figures about COVID-19 coronavirus.

"COVID-19 mortality correlation with cloudiness, sunlight, latitude in European countries"

Dataset for article titled "COVID-19 mortality: positive correlation with cloudiness, sunlight and no correlation with latitude in Europe"

by SECIL OMER, ADRIAN IFTIME, VICTOR BURCEA

Corresponding author: A. Iftime, University of Medicine and Pharmacy "Carol Davila", Biophysics Department, 8 Blvd. Eroii Sanitari, 050474 Bucharest, Romania. Email address: adrian.iftime [at] umfcd.ro.

Preprint corresponding to this dataset: https://doi.org/10.1101/2021.01.27.21250658

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Dataset file: 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

Dataset graphical preview: 1.0.0.INFOGRAFIC_CloudFraction_vs_COVID-19_mortality_Europe_March-August_2020.png

DATASET fields: "Country" : Country name; 37 European countries included.

"Date": Date stamp at the collection time. Data collection was performed in the last day of every month. Date format: YYYY-MM-DD

"Month_Key" : Date stamp at the collection time, formatted for easier monthly time series analysis. Date format: YYYY-MM

"Month_Fct2020" Date stamp at the collection time,formatted for easier graphing, as a string with names of the months (in English).

"Deaths_per_1Mpop" : Monthly mortality from COVID-19 raported in the country, reported as number of COVID-19 deaths per 1 million population of the country, in that particular month / country. NB: it is reported as million population, not patients.

"LogDeaths_per_1Mpop" : Log10 transformation of "Deaths_per_1Mpop"

"Insolation_Average" : Insolation average (solar irradiance at ground level), in that particular month / country. It is expressed in Watt / square meter of the ground surface. Data derived from data avaialble at NASA Langley Research Center, NASA’s Earth Observatory, CERES / FLASHFlux team, 2020, https://neo.sci.gsfc.nasa.gov/view.php?datasetId=CERES_INSOL_M

"Cloud_Fraction" : Cloudiness (also known as cloud fraction, cloud cover, cloud amount or sky cover), as decimal fraction of the sky obscured by clouds, in that particular month / country. Data derived from NASA Goddard Space Flight Center, NASA’s Earth Observatory, MODIS Atmosphere Science Team, 2020, https://neo.sci.gsfc.nasa.gov/view.php?datasetId=MODAL2_M_CLD_FR

"CENTR_latitude" and "CENTR_longitude" : Latitude and Longitude of the country centroid, for each country. Data derived from Google LLC, "Dataset publishing language: country centroids", https://developers.google.com/public-data/docs/canonical/countries_csv
NOTE: This is identical in every month (obviuously); it is redundantly included for easier monthly sectional analysis of the data.

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Versioning: 1.0.0.COVID-19_Mortality_Cloudiness_Insolation_EUROPE_March_August_2020.csv

MAJOR: changes yearly; 1 = 2020 MINOR: changes if new monthly data is added in that particular year. PATCH: Changes only if errors or minor edits were performed.

DOI for this version: 10.5281/zenodo.4266758

Dataset file source for this version (internal analysis source file): db_covid_all-ANALYSIS.2020-09-22_r10.csv

This graph illustrates the average surface area of the dwellings in which French people live during the containment of March 17 due to the coronavirus (COVID-19) in March 2020, by region and in square meters. At that time in the region of Bourgogne-Franche-Comté, French people were confined in dwellings with an average surface area of 108 square meters.

For more information on the coronavirus pandemic (COVID-19), please see our page: Facts and figures about COVID-19 coronavirus

The size and share of the market is categorized based on Application (Industrial Use, Commercial Use) and Product (Distribution Management Systems (DMS), Demand Response Management Systems (DRM), Meter Data Management Systems (MDMS), Supervisory Control and Data Acquisition (SCADA), Outage Management Systems (OMS), Smart Meter) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

In April 2020, the Sakha (Yakutiya) Republic recorded the most significant price drop in real estate prices in Russia with a roughly five percent price fall per square meter. In the Moscow and Leningrad Regions, the price of residential properties dropped by 3.2 and 3 percentage points per square meter over the given period, respectively.

Because of the outbreak of the coronavirus (COVID-19), many countries around the world were put into lockdown. In February, average levels of PM2.5 pollution in the Chinese city of Wuhan were 35.1 micrograms per cubic meter. This was a reduction of approximately 44 percent when compared to the same period in 2019.

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

The dataset contains temperature measurements on the surface of the face taken on 109 people. Each patient (identified by Patient ID in the dataset) acclimatized in a room with a temperature of 22-24 degrees Celsius. Then the person completed a survey, during which they provided their: • age (column Survey - age [years]), • gender (column Survey - Gender), • temperature measurement using a pyrometer thermometer (column Survey - temperature [°C]), • and pulse measurement using a pulse oximeter (column Survey - measured pulse [BPM]). After that, the examined person stood in front of the contactless temperature measurement system (using a thermal camera), which was continuously calibrated to the black body at a distance of 1.5-3 meters (column Distance between camera and patient [m]). Then, several hundred temperature measurements were taken on each person in the following ways: • Median temperature on face [°C] • Median temperature on face, 1% of pixels with max temperature [°C] • Median temperature on face, 5% of pixels with max temperature [°C] • Median temperature on face, 10% of pixels with max temperature [°C] • Median temperature in the center of the eyes (3x3 pixels) [°C] • Median temperature measured at the corners of the eyes (3x3 pixels) [°C] Additionally, the system automatically estimated: • the age of the examined person (column Estimated Age [years]), • the pulse of the examined person (column Estimated Pulse [BPM]), • and gender (Estimated Gender). According to [1], the measured temperature on the surface of the face is influenced by the age of the measured person. As part of the project, a Binary Regression Tree was developed, which considers (estimated) age when calculating the temperature on the surface of the face (column Temperature calculated by Binary Tree Regression algorithm [°C]). [1] Cheung, Ming & Chan, Lung & Lauder, I & Kumana, Cyrus. (2012). Detection of body temperature with infrared thermography: accuracy in detection of fever. Hong Kong medical journal = Xianggang yi xue za zhi / Hong Kong Academy of Medicine. 18 Suppl 3. 31-4.

The dataset has three parts; quantitative data, transcripts of Online FGDs and Photovoice Group Discussions, and Photovoice Photographs. Quantitative data includes the outcome variable which consists of nine measures: 1) maintaining a 1-meter distance, 2) avoiding handshakes, 3) avoiding hugs, 4) avoiding public transportation, 5) working/studying from home, 6) avoiding gatherings and crowds, 7) postponing meetings, 8) avoiding visiting elderly people, and 9) praying at home. In addition, other variables in this data set are sociodemographic characteristics; COVID-19-related variables such as COVID-19 testing, knowledge of COVID-19, etc.; and religious and tradition-related activities such as breaking fast during Ramadan, joining Mudik tradition, etc. Qualitative data includes Online FGDs and Photovoice Group Discussions transcripts and Photovoice Photographs. Five Online FGDs transcripts and 10 transcripts for Photovoice. 29 Photographs of Photovoice are also available in a list.

The COVID-19 pandemic lockdown worldwide provided a unique research opportunity for ecologists to investigate the human-wildlife relationship under abrupt changes in human mobility, also known as Anthropause. Here we chose 15 common non-migratory bird species with different levels of synanthrope and we aimed to compare how human mobility changes could influence the occupancy of fully synanthropic species such as House Sparrow (Passer domesticus) versus casual to tangential synanthropic species such as White-breasted Nuthatch (Sitta carolinensis). We extracted data from the eBird citizen science project during three study periods in the spring and summer of 2020 when human mobility changed unevenly across different counties in North Carolina. We used the COVID-19 Community Mobility reports from Google to examine how community mobility changes towards workplaces, an indicator of overall human movements at the county level, could influence bird occupancy., The data source we used for bird data was eBird, a global citizen science project run by the Cornell Lab of Ornithology. We used the COVID-19 Community Mobility Reports by Google to represent the pause of human activities at the county level in North Carolina. These data are publicly available and were last updated on 10/15/2022. We used forest land cover data from NC One Map that has a high resolution (1-meter pixel) raster data from 2016 imagery to represent canopy cover at each eBird checklist location. We also used the raster data of the 2019 National Land Cover Database to represent the degree of development/impervious surface at each eBird checklist location. All three measurements were used for the highest resolution that was available to use. We downloaded the eBird Basic Dataset (EBD) that contains the 15 study species from February to June 2020. We also downloaded the sampling event data that contains the checklist efforts information. First, we used the R package Auk (versio..., , # Processed data for the analysis of human mobility changes on bird occupancy in NC

https://doi.org/10.5061/dryad.gb5mkkwxr

There are 3 types of data here including Google Community Mobility data, and processed data (data after extracting spatial covariates and merging with all covariates for the Occupancy Modeling as well as extracted predicted occupancy data that we used to create figures).

Description of the data and file structure

Google Community Mobility data: This is the dataset downloaded from https://www.google.com/covid19/mobility/ that measures the mobility changes throughout the world during the COVID-19 lockdown. Please visit the above website for more information about the data. Please see the "Anthropause_AMCR_02112024" R file (uploaded to Zenodo) for details on how we processed the raw data.

| Dataset name | Dataset description ...

High inflation rates of the Russian ruble, subsequent to the COVID-19 expansion and the cruide oil price drop, promoted a high demand on residential real estate. As a result, an increase in housing prices was recorded in most major cities of the country. After a 1.5 percent growth in March 2020, Moscow led the list with the highest average price for residential real estate, measuring at 211.5 thousand Russian rubles per square meter.

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

Unleash Flow Meters Market Growth Secrets! Discover key trends, CAGR of 6.0%, top applications & players like Honeywell. Download FREE report & gain insights!

In 2020, 30 percent of patients in oncology centers in Poland during the coronavirus epidemic (COVID-19) claimed that the number of patients in the hospital caused a crowd that made it impossible to maintain a distance of 2 meters.

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

According to Cognitive Market Research, the global smart meter system market size is USD 22541.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 36.20% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 9016.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 34.4% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 6762.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 5184.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 38.2% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 1127.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 35.6% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 450.82 million in 2024 and will grow at a compound annual growth rate (CAGR) of 35.9% from 2024 to 2031.
The residential held the highest smart meter system market revenue share in 2024.

Market Dynamics of Smart Meter System Market

Key Drivers for Smart Meter System Market

Rapid Growth in Smart Meter Adoption by Government to Increase the Demand Globally

The rapid growth in smart meter adoption by governments worldwide is driving the expansion of the smart meter system market. Smart meters, which provide real-time data on energy usage, enable better energy management and efficiency. Governments are investing in these technologies to meet sustainability goals, reduce energy consumption, and enhance grid reliability. This trend is fueled by policy mandates, environmental concerns, and advancements in IoT and data analytics. As a result, the smart meter market is experiencing significant growth, with increased deployment in residential, commercial, and industrial sectors?.

Growing Demand for Energy Efficiency to Propel Market Growth

The growing demand for energy efficiency is driving the expansion of the smart meter system market. Smart meters enable precise monitoring and management of energy consumption, aiding consumers and utilities in optimizing usage and reducing waste. Enhanced data collection and real-time analytics provided by these systems support better energy distribution and fault detection. Additionally, regulatory mandates and increasing awareness of environmental sustainability are further propelling market growth. As a result, the smart meter market is poised for significant advancements, offering substantial benefits in energy conservation and cost savings.

Restraint Factor for the Smart Meter System Market

High Deployment Cost to Limit the Sales

High deployment costs in the smart meter system market act as a significant restraint. The installation and maintenance of advanced metering infrastructure require substantial investment in hardware, software, and skilled labor. Smaller utilities and developing regions often struggle with the financial burden, hindering widespread adoption. Additionally, integrating smart meters with existing grid infrastructure can be complex and costly. These financial and logistical challenges slow down the deployment rate, limiting market growth and delaying the benefits of smart grid technologies.

Impact of Covid-19 on the Smart Meter System Market

The COVID-19 pandemic significantly impacted the Smart Meter System market, accelerating its growth. Lockdowns and remote work increased energy consumption monitoring needs, boosting demand for smart meters. Utilities adopted smart meters for real-time data to manage fluctuating energy usage effectively. The pandemic highlighted the importance of efficient energy management, driving investments in smart grid technologies. Despite supply chain disruptions, the market saw a surge due to heightened awareness of energy conservation and the need for advanced metering infrastructure for better energy distribution and management. Introduction of the Smart Meter System Market

A smart meter system is an advanced energy meter that provides real-time monitoring, management, and communication of electricity usage to utilities and consumers. The smart meter system market is growing and is driven by increasing smart city initiatives. Smart meters provide real-time energy consumption data, enabling efficient...

According to Cognitive Market Research, the global Smart meter data management market size will be USD 1565.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 18.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 626.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 469.56 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 360.00 million in 2024 and will grow at a compound annual growth rate (CAGR) of 20.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 78.26 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 31.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.9% from 2024 to 2031.
The software held the highest Smart meter data management market revenue share in 2024.

Market Dynamics of Smart meter data management Market

Key Drivers for Smart meter data management Market

Utility industry transformations to increase the demand globally

The utility industry is undergoing significant transformations driven by the need for increased efficiency, sustainability, and customer engagement. Innovations in smart grid technologies, data analytics, and renewable energy integration are reshaping how utilities operate. The adoption of smart meters and advanced data management systems enhances real-time monitoring and decision-making, enabling more efficient resource distribution and improved customer service. Regulatory pressures and global sustainability goals further accelerate this shift, pushing utilities towards greener practices and smarter infrastructure. These changes are expanding market opportunities globally, as utilities and consumers alike seek to optimize energy use and reduce environmental impact.

Increased demand for energy efficiency to propel market growth

The growing demand for energy efficiency is significantly propelling market growth for smart meter data management systems. As energy costs rise and environmental concerns intensify, both consumers and utilities are increasingly prioritizing energy-saving measures. Smart meters provide real-time data on energy consumption, enabling more precise management and optimization. This data helps identify inefficiencies, reduce waste, and support targeted conservation efforts. Consequently, the focus on improving energy efficiency drives the adoption of advanced smart metering solutions, which offer enhanced monitoring, analysis, and control capabilities. This heightened awareness and need for efficiency fuel market expansion and innovation in energy management technologies.

Restraint Factor for the Smart meter data management Market

Operational disruptions to limit the sales

Operational disruptions can significantly limit sales in the smart meter data management market. Implementing new technologies often requires extensive system integration and adaptation, which can interrupt existing processes and workflows. These disruptions may lead to temporary inefficiencies, increased costs, and resistance from staff and stakeholders. Additionally, the transition phase might involve steep learning curves and potential technical issues, further complicating deployment. Such challenges can delay or deter organizations from adopting smart meter solutions, impacting overall sales. To mitigate these effects, companies must focus on seamless integration, comprehensive training, and robust support systems to minimize operational disruptions and maintain market momentum.

Impact of Covid-19 on the Smart meter data management Market

The COVID-19 pandemic negatively impacted the smart meter data management market, causing significant disruptions. Lockdowns and social distancing measures slowed down the installation and maintenance of smart meters, leading to delays in project timelines and reduced market activity. Economic uncertainties and budget constraints faced by utilities and businesses resulted in postponed or canceled investments in new technologies. Additionall...

With the third-highest number of confirmed COVID-19 cases worldwide, Brazil was the country that required the largest volume of oxygen in Latin America. As of August 13, 2021, the Portuguese-speaking nation needed nearly 1.5 million cubic meters of oxygen per day to treat its patients. Meanwhile, Mexico needed close to 742 thousand cubic meters of oxygen per day. Most of the countries in the region required less than 200 thousand cubic meters of oxygen per day. A critical situation Medical oxygen is pivotal for treating patients affected by the COVID-19 disease. The virus can cause pneumonia, which can lead to acute respiratory distress syndrome (lung failure) and eventually death. Medical oxygen enables patients to receive the oxygen required for normal bodily function. With more than 206 million cases worldwide, oxygen demand is at an all-time high. As of May 3, 2021, India required the most oxygen at more than 2 million cylinders per day. It is not just oxygen The shortfall in the amount of medical oxygen in Brazil is coupled with a general lack of resources. In 2019, the South American country had only 1.05 intensive care unit (ICU) beds per 100,000 population. In addition, Brazil registered just over 25 ventilators per 100,000 inhabitants that same year. Unfortunately, as one of the most affected countries worldwide, this is not enough to meet the soaring demand.

Hospitality operators around the world have increased their focus on health and hygiene as a result of the coronavirus (COVID-19) pandemic. As of June 2020, a global survey was conducted to determine the share of hospitality operators who spaced their tables and chairs in dining venues at least 1.5 meters apart and frequently disinfected their public areas. The vast majority of respondents, 93 percent, reported having done so, while only seven percent of respondents reported having done otherwise.

Smart Water Metering Market is expected to grow at a high CAGR during the forecast period 2023-2030 | DataM Intelligence

UK and Ireland Heat Meters Market is expected to grow at a high CAGR during the forecast period 2024-2031 | DataM Intelligence

Statistical tests on reported Prop. 47 crime in the vicinity of light rail station locations in Santa Monica, CA, during three successive time periods spanning 2006–2019.