In 2023, organizations all around the world detected 317.59 million ransomware attempts. Overall, this number decreased significantly between the third and fourth quarters of 2022, going from around 102 million to nearly 155 million cases, respectively. Ransomware attacks usually target organizations that collect large amounts of data and are critically important. In case of an attack, these organizations prefer paying the ransom to restore stolen data rather than to report the attack immediately. The incidents of data loss also damage companies’ reputation, which is one of the reasons why ransomware attacks are not reported. Most targeted industries and regions As a part of critical infrastructure, the manufacturing industry is usually targeted by ransomware attacks. In 2022, manufacturing organizations worldwide saw 437 such attacks. The food and beverage industry ranked second, with over 50 ransomware attacks. By the share of ransomware attacks on critical infrastructure, North America ranked first among other worldwide regions, followed by Europe. Healthcare and public health sector organizations filed the highest number of complaints to the U.S. law enforcement in 2022 about ransomware attacks. Ransomware as a service (RaaS) The Ransomware as a Service (RaaS) business model has existed for over a decade. The model involves hackers and affiliates. Hackers develop ransomware attack models and sell them to affiliates. The latter then use them independently to attack targets. According to the business model, the hacker who created the RaaS receives a service fee per collected ransom. In the first quarter of 2022, there were 31 Ransomware as a Service (RaaS) extortion groups worldwide, compared to the 19 such groups in the same quarter of 2021.

Ransomware is considered as a significant threat for most enterprises since past few years. In scenarios wherein users can access all files on a shared server, one infected host is capable of locking the access to all shared files. In the article related to this repository, we detect ransomware infection based on file-sharing traffic analysis, even in the case of encrypted traffic. We compare three machine learning models and choose the best for validation. We train and test the detection model using more than 70 ransomware binaries from 26 different families and more than 2500 h of ‘not infected’ traffic from real users. The results reveal that the proposed tool can detect all ransomware binaries, including those not used in the training phase (zero-days). This paper provides a validation of the algorithm by studying the false positive rate and the amount of information from user files that the ransomware could encrypt before being detected.

This dataset directory contains the 'infected' and 'not infected' samples and the models used for each T configuration, each one in a separated folder.

The folders are named NxSy where x is the number of 1-second interval per sample and y the sliding step in seconds.

Each folder (for example N10S10/) contains: - tree.py -> Python script with the Tree model. - ensemble.json -> JSON file with the information about the Ensemble model. - NN_XhiddenLayer.json -> JSON file with the information about the NN model with X hidden layers (1, 2 or 3). - N10S10.csv -> All samples used for training each model in this folder. It is in csv format for using in bigML application. - zeroDays.csv -> All zero-day samples used for testing each model in this folder. It is in csv format for using in bigML application. - userSamples_test -> All samples used for validating each model in this folder. It is in csv format for using in bigML application. - userSamples_train -> User samples used for training the models. - ransomware_train -> Ransomware samples used for training the models - scaler.scaler -> Standard Scaler from python library used for scale the samples. - zeroDays_notFiltered -> Folder with the zeroDay samples.

In the case of N30S30 folder, there is an additional folder (SMBv2SMBv3NFS) with the samples extracted from the SMBv2, SMBv3 and NFS traffic traces. There are more binaries than the ones presented in the article, but it is because some of them are not "unseen" binaries (the families are present in the training set).

The files containing samples (NxSy.csv, zeroDays.csv and userSamples_test.csv) are structured as follows: - Each line is one sample. - Each sample has 3*T features and the label (1 if it is 'infected' sample and 0 if it is not). - The features are separated by ',' because it is a csv file. - The last column is the label of the sample.

Additionally we have placed two pcap files in root directory. There are the traces used for compare both versions of SMB.

Data from influenza A virus (IAV) infected ferrets (Mustela putorius furo) provides invaluable information towards the study of novel and emerging viruses that pose a threat to human health. This gold standard animal model can recapitulate many clinical signs of infection present in IAV-infected humans, supports virus replication of human and zoonotic strains without prior adaptation, and permits evaluation of virus transmissibility by multiple modes. While ferrets have been employed in risk assessment settings for >20 years, results from this work are typically reported in discrete stand-alone publications, making aggregation of raw data from this work over time nearly impossible. Here, we describe a dataset of 333 ferrets inoculated with 107 unique IAV, conducted by a single research group (NCIRD/ID/IPB/Pathogenesis Laboratory Team) under a uniform experimental protocol. This collection of ferret tissue viral titer data on a per-individual ferret level represents a companion dataset to ‘An aggregated dataset of serially collected influenza A virus morbidity and titer measurements from virus-infected ferrets’. However, care must be taken when combining datasets at the level of individual animals (see PMID 40245007 for guidance in best practices for comparing datasets comprised of serially-collected and fixed-timepoint in vivo-generated data). See publications using and describing data for more information: Kieran TJ, Sun X, Tumpey TM, Maines TR, Belser JA. 202X. Spatial variation of infectious virus load in aggregated day 3 post-inoculation respiratory tract tissues from influenza A virus-infected ferrets. Under peer review. Kieran TJ, Sun X, Maines TR, Belser JA. 2025. Predictive models of influenza A virus lethal disease: insights from ferret respiratory tract and brain tissues. Scientific Reports, in press. Bullock TA, Pappas C, Uyeki TM, Brock N, Kieran TJ, Olsen SJ, Davis CD, Tumpey TM, Maines TR, Belser JA. 2025. The (digestive) path less traveled: influenza A virus and the gastrointestinal tract. mBio, in press. Kieran TJ, Sun X, Maines TR, Beauchemin CAA, Belser JA. 2024. Exploring associations between viral titer measurements and disease outcomes in ferrets inoculated with 125 contemporary influenza A viruses. J Virol98: e01661-23. https://doi.org/10.1038/s41597-024-03256-6 Related dataset: Kieran TJ, Sun X, Creager HM, Tumpey TM, Maine TR, Belser JA. 2025. An aggregated dataset of serial morbidity and titer measurements from influenza A virus-infected ferrets. Sci Data, 11(1):510. https://doi.org/10.1038/s41597-024-03256-6 https://data.cdc.gov/National-Center-for-Immunization-and-Respiratory-D/An-aggregated-dataset-of-serially-collected-influe/cr56-k9wj/about_data Other relevant publications for best practices on data handling and interpretation: Kieran TJ, Maines TR, Belser JA. 2025. Eleven quick tips to unlock the power of in vivo data science. PLoS Comput Biol, 21(4):e1012947. https://doi.org/10.1371/journal.pcbi.1012947 Kieran TJ, Maines TR, Belser JA. 2025. Data alchemy, from lab to insight: Transforming in vivo experiments into data science gold. PLoS Pathog, 20(8):e1012460. https://doi.org/10.1371/journal.ppat.1012460

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

Originally sourced from https://ourworldindata.org/coronavirus-source-data

Synced daily

Update 12/04/2020

The data sources have been updated to use JHU data:

From OWID:

​> On 30 November 2020, we changed our source for confirmed cases and deaths to the COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. Our previous source for confirmed cases and deaths, the European Centre for Disease Prevention and Control (ECDC), had announced in November 2020 that it would switch from a daily to a weekly reporting schedule from December. Our World in Data therefore had to transition away from the ECDC as a source to continue to provide daily updates of confirmed cases and deaths. The data last sourced from the ECDC remains available as an archive in the ecdc folder. The format (variable names and types) of our complete COVID-19 dataset remains the same.

Data is from the California Department of Public Health (CDPH) Respiratory Virus Weekly Report.

The report is updated each Friday.

Laboratory surveillance data: California laboratories report SARS-CoV-2 test results to CDPH through electronic laboratory reporting. Los Angeles County SARS-CoV-2 lab data has a 7-day reporting lag. Test positivity is calculated using SARS-CoV-2 lab tests that has a specimen collection date reported during a given week.

Laboratory surveillance for influenza, respiratory syncytial virus (RSV), and other respiratory viruses (parainfluenza types 1-4, human metapneumovirus, non-SARS-CoV-2 coronaviruses, adenovirus, enterovirus/rhinovirus) involves the use of data from clinical sentinel laboratories (hospital, academic or private) located throughout California. Specimens for testing are collected from patients in healthcare settings and do not reflect all testing for influenza, respiratory syncytial virus, and other respiratory viruses in California. These laboratories report the number of laboratory-confirmed influenza, respiratory syncytial virus, and other respiratory virus detections and isolations, and the total number of specimens tested by virus type on a weekly basis.

Test positivity for a given week is calculated by dividing the number of positive COVID-19, influenza, RSV, or other respiratory virus results by the total number of specimens tested for that virus. Weekly laboratory surveillance data are defined as Sunday through Saturday.

Hospitalization data: Data on COVID-19 and influenza hospital admissions are from Centers for Disease Control and Prevention’s (CDC) National Healthcare Safety Network (NHSN) Hospitalization dataset. The requirement to report COVID-19 and influenza-associated hospitalizations was effective November 1, 2024. CDPH pulls NHSN data from the CDC on the Wednesday prior to the publication of the report. Results may differ depending on which day data are pulled. Admission rates are calculated using population estimates from the P-3: Complete State and County Projections Dataset provided by the State of California Department of Finance (https://dof.ca.gov/forecasting/demographics/projections/). Reported weekly admission rates for the entire season use the population estimates for the year the season started. For more information on NHSN data including the protocol and data collection information, see the CDC NHSN webpage (https://www.cdc.gov/nhsn/index.html).

CDPH collaborates with Northern California Kaiser Permanente (NCKP) to monitor trends in RSV admissions. The percentage of RSV admissions is calculated by dividing the number of RSV-related admissions by the total number of admissions during the same period. Admissions for pregnancy, labor and delivery, birth, and outpatient procedures are not included in total number of admissions. These admissions serve as a proxy for RSV activity and do not necessarily represent laboratory confirmed hospitalizations for RSV infections; NCKP members are not representative of all Californians.

Weekly hospitalization data are defined as Sunday through Saturday.

Death certificate data: CDPH receives weekly year-to-date dynamic data on deaths occurring in California from the CDPH Center for Health Statistics and Informatics. These data are limited to deaths occurring among California residents and are analyzed to identify influenza, respiratory syncytial virus, and COVID-19-coded deaths. These deaths are not necessarily laboratory-confirmed and are an underestimate of all influenza, respiratory syncytial virus, and COVID-19-associated deaths in California. Weekly death data are defined as Sunday through Saturday.

Wastewater data: This dataset represents statewide weekly SARS-CoV-2 wastewater summary values. SARS-CoV-2 wastewater concentrations from all sites in California are combined into a single, statewide, unit-less summary value for each week, using a method for data transformation and aggregation developed by the CDC National Wastewater Surveillance System (NWSS). Please see the CDC NWSS data methods page for a description of how these summary values are calculated. Weekly wastewater data are defined as Sunday through Saturday.

As of August 2024, internet users worldwide discovered ****** new common IT security vulnerabilities and exposures (CVEs). The highest reported annual figure was recorded in 2023, over ******. Global ransomware threats In the past couple of years, ransomware has become more prominent, becoming the most frequently reported type of cyberattack worldwide in 2023. Additionally, ** percent of organizations worldwide reported experiencing one to three ransomware infections. Among researched markets, France and South Africa were impacted the most. Costly and efficient ransomware families, such as StopCrypt and LockBit, ranked first by detections globally. Additionally, the 2017 WannaCry attack still holds the record as the most impactful ransomware event, causing an estimated **** billion U.S. dollars in damages. Manufacturing and ransomware Manufacturing remains one of the most targeted industries for cyberattacks. In 2023, it was the most vulnerable sector globally to ransomware, experiencing approximately *** incidents worldwide. These attacks were especially prevalent in industrial organizations in North America. Additionally, malware and network or application anomalies were among the most common types of cyber incidents affecting manufacturing organizations.

The Browser Isolation Tool market is experiencing robust growth, driven by the escalating need for enhanced cybersecurity in an increasingly remote and cloud-based work environment. The rising frequency and sophistication of cyberattacks targeting web browsers, coupled with the proliferation of zero-day exploits, are compelling organizations to adopt browser isolation solutions as a critical layer of defense. This technology, which separates web browsing activity from the user's endpoint, significantly mitigates the risk of malware infections and data breaches. The market is segmented by deployment type (cloud-based and on-premises), organization size (small, medium, and large enterprises), and industry vertical (BFSI, healthcare, government, etc.). While the precise market size for 2025 is unavailable, considering a conservative CAGR of 15% (a reasonable estimate given the market's growth trajectory) and a hypothetical 2024 market value of $1.5 billion, we can estimate the 2025 market size to be approximately $1.725 billion. This growth is anticipated to continue, propelled by increasing adoption of advanced browser isolation techniques like application-level and process-level isolation, further reducing the attack surface. The competitive landscape is highly dynamic, with a mix of established cybersecurity vendors and specialized browser isolation providers vying for market share. Key players like Zscaler, Menlo Security, and Proofpoint are leveraging their existing security portfolios to expand their browser isolation offerings, while niche players like Kasm and Authentic8 are focusing on innovation and specific market segments. Geographic expansion, particularly in developing economies with rapidly growing digital infrastructure, presents a significant opportunity for market growth. However, factors such as the complexity of implementation, cost considerations, and integration challenges with existing security infrastructure can act as potential restraints. Despite these challenges, the increasing awareness of browser-borne threats and the growing demand for robust security solutions are expected to fuel the market's expansion throughout the forecast period (2025-2033).

This is a metadata record for a continuously updated dataset of SARS-CoV-2 RNA data in wastewater in Uppsala. The dataset is part of a research study led by associate professor Anna J. Székely (SLU, Swedish University of Agricultural Sciences) and her research groups in collaboration with Uppsala Vatten. The research group is part of the Environmental Virus Profiling Research Area of the SciLifeLab National COVID-19 Research Program. The data is generated by weekly SARS-CoV-2, influenza A and influenza B virus measurements in wastewater samples from Sweden. The monitoring started in Uppsala in August 2020, while other places joined the program later. For all places, raw, untreated wastewater samples representative of a single day (24 hours) are collected by flow compensated samplers. All measurements represent only 1 day except for Uppsala, where since week 16, 2021 the measurements represent 1 week as samples are collected each day and then combined flow-proportionally into 1 composite weekly sample. The samples are processed according to standard methods. Briefly, the viral genomic material is concentrated and extracted by the direct capture method using the Maxwell RSC Enviro TNA kit (Promega) and the copy number of SARS-CoV-2 genomes is quantified by RT-qPCR using the CDC RUO nCOV N1 assay (IDT DNA). To correct for variations in population size and wastewater flow, we also quantify the pepper mild mottle virus (PMMoV) which is the most abundant RNA virus in human feces and serves as an estimator of human fecal content (Symonds et al., 2019). For more about the evaluation of this normalization method, please consult the corresponding publication: Isaksson et al. (2022). The data is presented on the graph as the ratio of the copy numbers measured by the N1 and PMMoV-assays multiplied by 10^4. As N1 copy number is a proxy for SARS-CoV-2 virus content in the wastewater and PMMoV is a proxy of the fecal content, which is related to the contributing population, this ratio can be considered as proxy of the prevalence of infections in the population of the wastewater catchment area. The dataset is available as part of the Environmental Virus Profiling data section "The amount of SARS-CoV-2 virus in wastewater across Sweden". It can be found here and downloaded under the heading "Dataset". Note that the dataset is preliminary. The team is still conducting method efficiency checks that might slightly affect the final results.

The dataset shows the 7-day median of the RNA copies of the specified virus per day and 100’000 people in the wastewater treatment plant (ARA) Basel as well as the 7-day median of the corresponding case numbers. The data set is usually updated on Tuesdays with the data until the previous Sunday. ProRheno AG (operator of ARA Basel) takes a 24h sample of the raw waste water, which is examined for RNA of the specified viruses by the Cantonal Laboratory Basel-Stadt (KL BS). The measurement methodology has not been changed since the beginning of the monitoring: see publication https://smw.ch/index.php/smw/article/view/3226. The plausibility of the values is continuously checked against internal quality parameters. The study area comprises the catchment area of the ARA Basel, which consists mainly of the canton of Basel-Stadt as well as the municipalities of Allschwil, Binningen, Birsfelden, Bottmingen, Oberwil and Schönenbuch (all Canton Baselland). Until the end of June 2023, the measured values of the KL BS were also presented on the wastewater dashboard of the BAG Covid-19 Switzerland | Coronavirus | Dashboard (https://www.covid19.admin.ch/de/epidemiologic/waste-water?wasteWaterFacility=270101). As of July 2023, the measured values of the EAWAG SARS-CoV2 in wastewater – Eawag (https://www.eawag.ch/de/abteilung/sww/projekte/sars-cov2-im-abwasser/) will be published on this page, which also examines the raw wastewater of ARA Basel. The examination methods used by KL BS and EAWAG are very similar but not identical.Case figures correspond to the number of confirmed and reported cases of infections in the catchment area of ARA Basel.Interpretation of curvesThe monitoring of viruses in wastewater is primarily about identifying trends (in particular, of course, the increase of a circulating virus). It is not possible to derive a certain number of cases or the severity of an infection. A comparison of the curve rash (height of peaks) at different times is hardly possible, because different virus variants lead to different amounts of virus per case. Different virus variants can also affect the symptoms, so that, for example, infections in humans run without a trace, but nevertheless viruses are released into the wastewater.

This dataset supports the Biomarker: Vector-Borne Viruses page on the Tempe Wastewater BioIntel Program site.Wastewater collection areas are comprised of merged sewage drainage basins that flow to a shared testing location for the Tempe Wastewater BioIntel Program. The wastewater collection areas represent a geographic area for which virus activity is tested. People infected with a virus excrete the virus in their feces in a process known as “shedding”. The municipal wastewater treatment system (sewage system) collects and aggregates these bathroom contributions across communities. The process begins at sampling site where, over a period of 24 hours, a wastewater sample is collected along the sewer line. After the sample is acquired, it is immediately transferred to a lab where scientists prepare the sample. The laboratory analysis seeks to determine if there is a signal (or detectable presence) of the biomarker in the wastewater. Please see the Tempe Wastewater BioIntel Program site for more information on the wastewater testing process at https://wastewater.tempe.gov/. About the data: These data illustrate a trend of the signal of the weekly average or weekly results of Tempe wastewater biomarker groups. The dashboard and collection area map do not depict the number of individuals infected. Each collection area includes at least one sampling location, which collects wastewater from across the collection area. It does not reflect the specific location where the deposit occurs. While testing can successfully quantify the results, research has not yet determined the relationship between these values and the number of people who are contributing to the signals. The influence of this data on community health decisions in the future is unknown. Data collection is being used to depict overall weekly trends and should not be interpreted without a holistic assessment of public health data. The purpose of this weekly data is to support research as well as to identify overall trends of the genome copies in each liter of wastewater per collection area. We share this information with the public with the disclaimer that only the future can tell how much “diagnostic value” we can and should attribute to the numeric measurements we obtain from the sewer. However, we know what we measure is real and we share that info with our community. Data are shared as the testing results become available. As results may not be released at the same time, testing results for each area may not yet be seen for a given day or week. The dashboard presents the weekly averages. Data are collected from 2-7 days per week. For Collection Area 1, Tempe's wastewater co-mingles with wastewater from a regional sewage line. Tempe's sewage makes up most of Collection Area 1 samples. For Collection Area 3, Tempe's wastewater co-mingles with wastewater from a regional sewage line. For analysis and reporting, Tempe’s wastewater is separated from regional sewage. Week start date represents the starting date of the testing week, which starts on Mondays and ends on Sundays. Additional Information:Source: The Translational Genomics Research Institute (TGen), part of City of Hope, is an Arizona-based, nonprofit medical research institute.Contact: Kimberly SoteloContact email: kimberly_sotelo@tempe.govPreparation Method: Initial values are provided by TGen. Tempe makes additional calculations to determine the weekly averages or weekly results for each biomarker.Publish Frequency: Weekly or as data becomes availablePublish Method: ManualData Dictionary

Context

Corona Virus Statistics in Turkey

Content

This Dataset shows the Corona Virus Test Numbers in Turkey. It includes death, confirmed, test and recovered cases numbers for each day. In Turkey, the test numbers are published after 19 March. Therefore, between 11 March and 19 March, the test numbers are zero. It is updated every day once Minister of Health released the numbers.

This dataset reports the daily reported number of deaths involving COVID-19 by fatality type. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Total number of deaths involving COVID-19 * Number of deaths with “COVID-19 as the underlying cause of death” * Number of deaths with “COVID-19 contributed but not underlying cause” * Number of deaths where the “Cause of death unknown” or “Cause of death missing” ##Additional Notes The method used to count COVID-19 deaths has changed, effective December 1, 2022. Prior to December 1 2022, deaths were counted based on the date the death was updated in the public health unit’s system. Going forward, deaths are counted on the date they occurred. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. As of December 1, 2022, data are based on the date on which the death occurred. This reporting method differs from the prior method which is based on net change in COVID-19 deaths reported day over day. Data are based on net change in COVID-19 deaths for which COVID-19 caused the death reported day over day. Deaths are not reported by the date on which death happened as reporting may include deaths that happened on previous dates. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the number of deaths involving COVID-19 reported. "_Cause of death unknown_" is the category of death for COVID-19 positive individuals with cause of death still under investigation, or for which the public health unit was unable to determine cause of death. The category may change later when the cause of death is confirmed either as “COVID-19 as the underlying cause of death”, “COVID-19 contributed but not underlying cause,” or “COVID-19 unrelated”. "_Cause of death missing_" is the category of death for COVID-19 positive individuals with the cause of death missing in CCM. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

This dataset reports the daily reported number of the 7-day moving average rates of Deaths involving COVID-19 by vaccination status and by age group. Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak. Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool Data includes: * Date on which the death occurred * Age group * 7-day moving average of the last seven days of the death rate per 100,000 for those not fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those fully vaccinated * 7-day moving average of the last seven days of the death rate per 100,000 for those vaccinated with at least one booster ##Additional notes As of June 16, all COVID-19 datasets will be updated weekly on Thursdays by 2pm. As of January 12, 2024, data from the date of January 1, 2024 onwards reflect updated population estimates. This update specifically impacts data for the 'not fully vaccinated' category. On November 30, 2023 the count of COVID-19 deaths was updated to include missing historical deaths from January 15, 2020 to March 31, 2023. CCM is a dynamic disease reporting system which allows ongoing update to data previously entered. As a result, data extracted from CCM represents a snapshot at the time of extraction and may differ from previous or subsequent results. Public Health Units continually clean up COVID-19 data, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes and current totals being different from previously reported cases and deaths. Observed trends over time should be interpreted with caution for the most recent period due to reporting and/or data entry lags. The data does not include vaccination data for people who did not provide consent for vaccination records to be entered into the provincial COVaxON system. This includes individual records as well as records from some Indigenous communities where those communities have not consented to including vaccination information in COVaxON. “Not fully vaccinated” category includes people with no vaccine and one dose of double-dose vaccine. “People with one dose of double-dose vaccine” category has a small and constantly changing number. The combination will stabilize the results. Spikes, negative numbers and other data anomalies: Due to ongoing data entry and data quality assurance activities in Case and Contact Management system (CCM) file, Public Health Units continually clean up COVID-19, correcting for missing or overcounted cases and deaths. These corrections can result in data spikes, negative numbers and current totals being different from previously reported case and death counts. Public Health Units report cause of death in the CCM based on information available to them at the time of reporting and in accordance with definitions provided by Public Health Ontario. The medical certificate of death is the official record and the cause of death could be different. Deaths are defined per the outcome field in CCM marked as “Fatal”. Deaths in COVID-19 cases identified as unrelated to COVID-19 are not included in the Deaths involving COVID-19 reported. Rates for the most recent days are subject to reporting lags All data reflects totals from 8 p.m. the previous day. This dataset is subject to change.

Weekly updates have finished with the June 28th update. Some information may be found here: https://covid.cdc.gov/covid-data-tracker/#maps_new-admissions-rate-state
This dataset contains aggregate COVID-19 case counts and rates by date of first report for all counties in Pennsylvania and for the state as a whole. Counts include both confirmed and probable cases as defined by the Council of State and Territorial Epidemiologists (CSTE). At present, a person is counted as a case only once. Note that case counts by date of report are influenced by a variety of factors, including but not limited to testing availability, test ordering patterns (such as day of week patterns), labs reporting backlogged test results, and mass screenings in nursing homes, workplaces, schools, etc. Case reports received without a patient address are assigned to the county of the ordering provider or facility based on provider zip code. Cases reported with a residential address that does not match to a known postal address per the commonwealth geocoding service are assigned to a county based on the zip code of residence. Many zip codes cross county boundaries so there is some degree of misclassification of county. All counts may change on a daily basis due to reassignment of jurisdiction, removal of duplicate case reports, correction of errors, and other daily data cleaning activities. Downloaded data represents the best information available as of the previous day.
Data will be updated between 11:30 am to 1:30pm each Wednesday.

This dataset supports the Biomarker: Respiratory Viruses page on the Tempe Wastewater BioIntel Program site.Wastewater collection areas are comprised of merged sewage drainage basins that flow to a shared testing location for the Tempe Wastewater BioIntel Program. The wastewater collection areas represent a geographic area for which virus activity is tested. People infected with a virus excrete the virus in their feces in a process known as “shedding”. The municipal wastewater treatment system (sewage system) collects and aggregates these bathroom contributions across communities. The process begins at sampling site where, over a period of 24 hours, a wastewater sample is collected along the sewer line. After the sample is acquired, it is immediately transferred to a lab where scientists prepare the sample. The laboratory analysis seeks to determine if there is a signal (or detectable presence) of the biomarker in the wastewater. Please see the Tempe Wastewater BioIntel Program site for more information on the wastewater testing process at https://wastewater.tempe.gov/. About the data: These data illustrate a trend of the signal of the weekly average or weekly results of Tempe wastewater biomarker groups. The dashboard and collection area map do not depict the number of individuals infected. Each collection area includes at least one sampling location, which collects wastewater from across the collection area. It does not reflect the specific location where the deposit occurs. While testing can successfully quantify the results, research has not yet determined the relationship between these values and the number of people who are contributing to the signals. The influence of this data on community health decisions in the future is unknown. Data collection is being used to depict overall weekly trends and should not be interpreted without a holistic assessment of public health data. The purpose of this weekly data is to support research as well as to identify overall trends of the genome copies in each liter of wastewater per collection area. We share this information with the public with the disclaimer that only the future can tell how much “diagnostic value” we can and should attribute to the numeric measurements we obtain from the sewer. However, we know what we measure is real and we share that info with our community. Data are shared as the testing results become available. As results may not be released at the same time, testing results for each area may not yet be seen for a given day or week. The dashboard presents the weekly averages. Data are collected from 2-7 days per week. For Collection Area 1, Tempe's wastewater co-mingles with wastewater from a regional sewage line. Tempe's sewage makes up most of Collection Area 1 samples. For Collection Area 3, Tempe's wastewater co-mingles with wastewater from a regional sewage line. For analysis and reporting, Tempe’s wastewater is separated from regional sewage. Week start date represents the starting date of the testing week, which starts on Mondays and ends on Sundays. Additional Information:Source: The Translational Genomics Research Institute (TGen), part of City of Hope, is an Arizona-based, nonprofit medical research institute.Contact: Kimberly SoteloContact email: kimberly_sotelo@tempe.govPreparation Method: Initial values are provided by TGen. Tempe makes additional calculations to determine the weekly averages or weekly results for each biomarker.Publish Frequency: Weekly or as data becomes availablePublish Method: ManualData Dictionary

In 2023, the most common type of cyber crime reported to the United States internet Crime Complaint Center was phishing and spoofing, affecting approximately 298 thousand individuals. In addition, over 55 thousand cases of personal data breaches cases were reported to the IC3 during that year. Dynamic of phishing attacks Over the past few years, phishing attacks have increased significantly. In 2023, almost 300 thousand individuals fell victim to such attacks. The highest number of phishing scam victims since 2018 was recorded in 2021, approximately 324 thousand.Phishing attacks can take many shapes. Bulk phishing, smishing, and business e-mail compromise (BEC) are the most common types. In 2023, 76 percent of the surveyed worldwide organizations reported encountering bulk phishing attacks, while roughly three in four were targeted by smishing scams. Impact of phishing attacks Among the most targeted industries by cybercriminals are healthcare, financial, manufacturing, and education institutions. An observation carried out in the first quarter of 2023 found that social media was most likely to encounter phishing attacks. According to the reports, almost a quarter of them stated being targeted by a phishing scam in the measured period. Very often, phishing e-mails contain a crucial risk for the organization. Almost three in ten worldwide organizations that have experienced phishing attacks suffered from a customer or a client data breach as a consequence. Phishing scams that delivered ransomware infections were also common for the surveyed organizations.

This dataset supports the Biomarker: Other Viruses page on the Tempe Wastewater BioIntel Program site.Wastewater collection areas are comprised of merged sewage drainage basins that flow to a shared testing location for the Tempe Wastewater BioIntel Program. The wastewater collection areas represent a geographic area for which virus activity is tested. People infected with a virus excrete the virus in their feces in a process known as “shedding”. The municipal wastewater treatment system (sewage system) collects and aggregates these bathroom contributions across communities. The process begins at sampling site where, over a period of 24 hours, a wastewater sample is collected along the sewer line. After the sample is acquired, it is immediately transferred to a lab where scientists prepare the sample. The laboratory analysis seeks to determine if there is a signal (or detectable presence) of the biomarker in the wastewater. Please see the Tempe Wastewater BioIntel Program site for more information on the wastewater testing process at https://wastewater.tempe.gov/. About the data: These data illustrate a trend of the signal of the weekly average or weekly results of Tempe wastewater biomarker groups. The dashboard and collection area map do not depict the number of individuals infected. Each collection area includes at least one sampling location, which collects wastewater from across the collection area. It does not reflect the specific location where the deposit occurs. While testing can successfully quantify the results, research has not yet determined the relationship between these values and the number of people who are contributing to the signals. The influence of this data on community health decisions in the future is unknown. Data collection is being used to depict overall weekly trends and should not be interpreted without a holistic assessment of public health data. The purpose of this weekly data is to support research as well as to identify overall trends of the genome copies in each liter of wastewater per collection area. We share this information with the public with the disclaimer that only the future can tell how much “diagnostic value” we can and should attribute to the numeric measurements we obtain from the sewer. However, we know what we measure is real and we share that info with our community. Data are shared as the testing results become available. As results may not be released at the same time, testing results for each area may not yet be seen for a given day or week. The dashboard presents the weekly averages. Data are collected from 2-7 days per week. For Collection Area 1, Tempe's wastewater co-mingles with wastewater from a regional sewage line. Tempe's sewage makes up most of Collection Area 1 samples. For Collection Area 3, Tempe's wastewater co-mingles with wastewater from a regional sewage line. For analysis and reporting, Tempe’s wastewater is separated from regional sewage. Week start date represents the starting date of the testing week, which starts on Mondays and ends on Sundays. Additional Information:Source: The Translational Genomics Research Institute (TGen), part of City of Hope, is an Arizona-based, nonprofit medical research institute.Contact: Kimberly SoteloContact email: kimberly_sotelo@tempe.govPreparation Method: Initial values are provided by TGen. Tempe makes additional calculations to determine the weekly averages or weekly results for each biomarker.Publish Frequency: Weekly or as data becomes availablePublish Method: ManualData Dictionary

According to Cognitive Market Research, the global Data Protection as a Service DPAAS market size will be USD 28241.8 million in 2025. It will expand at a compound annual growth rate (CAGR) of 20.80% from 2025 to 2033.

North America held the major market share for more than 40% of the global revenue with a market size of USD 10449.47 million in 2025 and will grow at a compound annual growth rate (CAGR) of 18.6% from 2025 to 2033.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 8190.12 million.
APAC held a market share of around 23% of the global revenue with a market size of USD 6778.03 million in 2025 and will grow at a compound annual growth rate (CAGR) of 22.8% from 2025 to 2033.
South America has a market share of more than 5% of the global revenue with a market size of USD 1073.19 million in 2025 and will grow at a compound annual growth rate (CAGR) of 19.8% from 2025 to 2033.
Middle East had a market share of around 2% of the global revenue and was estimated at a market size of USD 1129.67 million in 2025 and will grow at a compound annual growth rate (CAGR) of 20.1% from 2025 to 2033.
Africa had a market share of around 1% of the global revenue and was estimated at a market size of USD 621.32 million in 2025 and will grow at a compound annual growth rate (CAGR) of 20.5% from 2025 to 2033.
Payment Processing category is the fastest growing segment of the Data Protection as a Service DPAAS industry

Market Dynamics of Data Protection as a Service DPAAS Market

Key Drivers for Data Protection as a Service DPAAS Market

Escalating Cybersecurity Threats and Data Breaches to Boost Market Growth

The rising frequency and complexity of cyberattacks have significantly intensified concerns around data security. Organizations are increasingly grappling with threats such as ransomware, data breaches, and phishing attacks, which can result in severe financial losses and reputational harm. For example, in 2023, the U.S. reported 2,365 data breaches impacting approximately 343.3 million individuals—a staggering 72% increase compared to 2021. In the UK, half of all businesses (50%) and nearly a third of charities (32%) reported experiencing some form of cybersecurity breach or attack in the past year. The figures are even higher among medium-sized businesses (70%), large enterprises (74%), and high-income charities with annual revenues over £500,000 (66%). Phishing remains the most prevalent type of attack, affecting 84% of businesses and 83% of charities. This is followed by impersonation attacks via email or online platforms (35% of businesses and 37% of charities) and malware infections (17% of businesses and 14% of charities). This escalating threat landscape highlights the critical need for robust data protection strategies, driving demand for Data Protection as a Service (DPaaS) solution. These services offer advanced security features such as data encryption, multi-factor authentication, and real-time monitoring to help organizations safeguard their sensitive information.

https://www.gov.uk/government/statistics/cyber-security-breaches-survey-2024/cyber-security-breaches-survey-2024//./

Increasing Data Volumes from Digital Transformation and IoT to Boost Market Growth

The rapid surge in data generation—driven by digital transformation initiatives and the widespread adoption of Internet of Things (IoT) devices—has created an urgent need for efficient storage, backup, and recovery solutions. Global data volume skyrocketed from 2 zettabytes (ZB) in 2010 to an astounding 64.2 ZB by 2020, surpassing even the number of observable stars in the universe. This figure is projected to reach 181 ZB by 2025. Despite this explosive growth, only about 2% of the data created in 2020 was actually saved and stored by 2021. On a daily basis, the world produces around 2.5 quintillion bytes of data, with 90% of all existing data generated in just the past two years. Additionally, over 40% of internet data in 2020 was generated by machines. In this context, Data Protection as a Service (DPaaS) emerges as a vital solution, offering scalable, secure, and cost-effective means to protect this ever-expanding volume of data. DPaaS ensures data availability, security, and compliance with increasingly stringent regulatory requirements.

https://spacelift.io/blog/how-much-data-is-generated-every-day./

Restraint Factor for the Da...

This dataset contains aggregate COVID-19 case counts and rates by date of first report for all counties in Pennsylvania and for the state as a whole. Counts include both confirmed and probable cases as defined by the Council of State and Territorial Epidemiologists (CSTE). At present, a person is counted as a case only once. Note that case counts by date of report are influenced by a variety of factors, including but not limited to testing availability, test ordering patterns (such as day of week patterns), labs reporting backlogged test results, and mass screenings in nursing homes, workplaces, schools, etc. Case reports received without a patient address are assigned to the county of the ordering provider or facility based on provider zip code. Cases reported with a residential address that does not match to a known postal address per the commonwealth geocoding service are assigned to a county based on the zip code of residence. Many zip codes cross county boundaries so there is some degree of misclassification of county. All counts may change on a daily basis due to reassignment of jurisdiction, removal of duplicate case reports, correction of errors, and other daily data cleaning activities. Downloaded data represents the best information available as of the previous day.
Data will be updated between 9-11 am every day.