This dataset talks about the rates of positive covid test done in all over India and compiled it according to the district. The dataset includes the positivity rates came on the Rapid Antigen Test (RAT) and Real Time Polymerase Chain Reaction (RT-PCR).

State - Name of the Indian States District - Name of the district % Contribution of Testing by RAT - % of test done through Rapid Antigen Test % Contribution of Testing by RTPCR - % of test done through RT-PCR Positivity - The percentage of test which shows positive result

Hello all, this notebook consists of the patients suffering from corona virus from various states of India. This pandemic started from Kerala and it spread all over. If you will try to analyze the dataset, you will come to know that Maharashtra state have large number of positive results, also the recovery rate is high over there. This notebook clearly categorizes the positive result, death rates and the recovery rates of different states. Data visualization is done here which makes the case study more attractive and informative.

This Zenodo resource contains the data used to perform analysis in the article "Sex-disaggregated Analysis of Risk Factors of COVID-19 Mortality Rates in India".

Data

The data is organized in the form of tables.

hypothesis-test-data

This table contains data used to perform the two tailed hypothesis test on gender mortality in different regions.

* Region
* Male_Deaths - Number of male COVID-19 deaths in region.  
* Female_Deaths - Number of female COVID-19 deaths in region.  
* Male_cases - Number of male COVID-19 positive in region.
* Female_cases - Number of female COVID-19 positive in region.

lasso-covid19India

This table contains data used for analysis on cases throughout India.

Columns from COVID-19 India data

* State_Code  
* State  
* District  
* Confirmed  
* Active  
* Recovered  
* Deceased

Columns taken from NFHS data

* Sex_ratio_of_the_total_population_females_per_1000_males  
* Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm214_  
* Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm2_  
* Women_who_are_overweight_or_obese_BMI_250_kgm214_  
* Men_who_are_overweight_or_obese_BMI_250_kgm2_  
* All_women_age_1549_years_who_are_anaemic_  
* Men_age_1549_years_who_are_anaemic_130_gdl_  
* Women_Blood_sugar_level_high_140_mgdl_  
* Men_Blood_sugar_level_high_140_mgdl_  
* Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_  
* Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_

lasso-KA+TN-bulletin

This table contains data used for analysis on the sub-cohort of Karnataka and Tamil Nadu.

Data from Media Bulletin

* District    
* Total_Positives  
* total_deaths
* male_deaths  
* female_deaths  
* Male_cases_in_data
* Female_cases_in_data

Calculated Data

* Estimated_Male_cases - Estimated male cases using total positives column and existing case data
* Estimated_Female_Cases - Estimated female cases using total positives column and existing case data  
* Male_Mortality - Estimated Male Cases / male_deaths
* Female_Mortality - Estimated Female Cases / female_deaths

Columns taken from NFHS data

* Sex_Ratio_females_every_1000_males
* State  Women_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm214_  
* Men_whose_Body_Mass_Index_BMI_is_below_normal_BMI_185_kgm2_  
* Women_who_are_overweight_or_obese_BMI_250_kgm214_  
* Men_who_are_overweight_or_obese_BMI_250_kgm2_  
* All_women_age_1549_years_who_are_anaemic_  
* Men_age_1549_years_who_are_anaemic_130_gdl_  
* Women_Blood_sugar_level_high_140_mgdl_  
* Men_Blood_sugar_level_high_140_mgdl_  
* Women_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_  
* Men_Very_high_Systolic_180_mm_of_Hg_andor_Diastolic_110_mm_of_Hg_

Code

The code is available at this Github Repository.

Context

Covid-19 Data collected from various sources on the internet. This dataset has daily level information on the number of affected cases, deaths, and recovery from the 2019 novel coronavirus. Please note that this is time-series data and so the number of cases on any given day is the cumulative number.

Content

The dataset includes 28 files scrapped from various data sources mainly the John Hopkins GitHub repository, the ministry of health affairs India, worldometer, and Our World in Data website. The details of the files are as follows

• countries-aggregated.csv A simple and cleaned data with 5 columns with self-explanatory names. -covid-19-daily-tests-vs-daily-new-confirmed-cases-per-million.csv A time-series data of daily test conducted v/s daily new confirmed case per million. Entity column represents Country name while code represents ISO code of the country. -covid-contact-tracing.csv Data depicting government policies adopted in case of contact tracing. 0 -> No tracing, 1-> limited tracing, 2-> Comprehensive tracing. -covid-stringency-index.csv The nine metrics used to calculate the Stringency Index are school closures; workplace closures; cancellation of public events; restrictions on public gatherings; closures of public transport; stay-at-home requirements; public information campaigns; restrictions on internal movements; and international travel controls. The index on any given day is calculated as the mean score of the nine metrics, each taking a value between 0 and 100. A higher score indicates a stricter response (i.e. 100 = strictest response). -covid-vaccination-doses-per-capita.csv A total number of vaccination doses administered per 100 people in the total population. This is counted as a single dose, and may not equal the total number of people vaccinated, depending on the specific dose regime (e.g. people receive multiple doses). -covid-vaccine-willingness-and-people-vaccinated-by-country.csv Survey who have not received a COVID vaccine and who are willing vs. unwilling vs. uncertain if they would get a vaccine this week if it was available to them. -covid_india.csv India specific data containing the total number of active cases, recovered and deaths statewide. -cumulative-deaths-and-cases-covid-19.csv A cumulative data containing death and daily confirmed cases in the world. -current-covid-patients-hospital.csv Time series data containing a count of covid patients hospitalized in a country -daily-tests-per-thousand-people-smoothed-7-day.csv Daily test conducted per 1000 people in a running week average. -face-covering-policies-covid.csv Countries are grouped into five categories: 1->No policy 2->Recommended 3->Required in some specified shared/public spaces outside the home with other people present, or some situations when social distancing not possible 4->Required in all shared/public spaces outside the home with other people present or all situations when social distancing not possible 5->Required outside the home at all times regardless of location or presence of other people -full-list-cumulative-total-tests-per-thousand-map.csv Full list of total tests conducted per 1000 people. -income-support-covid.csv Income support captures if the government is covering the salaries or providing direct cash payments, universal basic income, or similar, of people who lose their jobs or cannot work. 0->No income support, 1->covers less than 50% of lost salary, 2-> covers more than 50% of the lost salary. -internal-movement-covid.csv Showing government policies in restricting internal movements. Ranges from 0 to 2 where 2 represents the strictest. -international-travel-covid.csv Showing government policies in restricting international movements. Ranges from 0 to 2 where 2 represents the strictest. -people-fully-vaccinated-covid.csv Contains the count of fully vaccinated people in different countries. -people-vaccinated-covid.csv Contains the total count of vaccinated people in different countries. -positive-rate-daily-smoothed.csv Contains the positivity rate of various countries in a week running average. -public-gathering-rules-covid.csv Restrictions are given based on the size of public gatherings as follows: 0->No restrictions 1 ->Restrictions on very large gatherings (the limit is above 1000 people) 2 -> gatherings between 100-1000 people 3 -> gatherings between 10-100 people 4 -> gatherings of less than 10 people -school-closures-covid.csv School closure during Covid. -share-people-fully-vaccinated-covid.csv Share of people that are fully vaccinated. -stay-at-home-covid.csv Countries are grouped into four categories: 0->No measures 1->Recommended not to leave the house 2->Required to not leave the house with exceptions for daily exercise, grocery shopping, and ‘essent...

Context

Covid-19 is spreading in India at a very high rate. Recently, India witnessed the most number of positive cases in a day. We must do what we can to understand and defeat this deadly virus. Here is the data set I gathered from official 'Indian Ministry of Health' website updated on 15 June, 2020. I hope you find it useful. I will keep updating the data set on a regular basis.

Acknowledgements

https://www.mohfw.gov.in/

PC: Photo by Fusion Medical Animation on Unsplash

Column Description

State - Name of the State/ Union Territory Active Cases - Number of active cases in the State Cured/Migrated - Number of Cases Cured/ Migrated from the State Deaths - Number of deaths in the State due to Covid19 Total Confirmed Cases - Total number of confirmed cases in the State (Active + Cured + Deaths)

The southern Indian state of Kerala had almost 8,417 people under observation due to the coronavirus (COVID-19) as of April 10, 2022. Of these, over eight thousand were confined to home or institutions, while over 150 patients were quarantined in designated isolation facilities. India recorded over 62 thousand active cases of the virus as September 1, 2022. The regions of Kerala , Karnataka and Maharashtra had the highest number of confirmed cases in the same time period.

Kerala’s links to Wuhan

On February 7, 2020, three Indians from Kerala were tested positive for COVID-19 after returning to India from Wuhan- the epicenter of the virus that has infected over 90 thousand people. Wuhan has been a popular destination among Keralites for its quality and affordable medical education. After conducting test swabs on all returnees, the Kerala government swung into immediate action by advising home quarantines for the people suspected to have been in contact with this coronavirus.

A state known for its healthcare performance

Kerala’s last major health scare was the Nipah virus in 1998, that returned in 2018, killing 17 people, along with almost six million cases of acute respiratory infections in 2016. Even then, Kerala is known to be India’s leading state for healthcare and medical literacy compared to the rest of the country. The southern state’s health department was reported to have been strictly following the protocols given by the World Health Organization to combat COVID-19. This preparedness seems to have borne good results so far with a high rate of recovery and containment of the virus.

India experienced a second wave of COVID-19 infection with an unprecedented upsurge in the number of cases. We have analyzed the effect of different restrictive measures implemented in six Indian states. Further, based on available national and international data on disease transmission and clinical presentation, we have proposed a decision-making matrix for planning adequate resources to combat the future waves of COVID-19. We conclude that pragmatic and well calibrated localized restrictions, tailored as per specific needs may achieve a decline in disease transmission comparable to drastic steps like national lockdowns. Additionally, we have underscored the critical need for countries to generate local epidemiological, clinical and laboratory data alongwith community perception and uptake of various non-pharmaceutical interventions, for effective planning and policy making.

Analysis of ‘Covid_cases_in_India’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/luvharishkhati/covid-cases-in-india on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Hello all, this notebook consists of the patients suffering from corona virus from various states of India. This pandemic started from Kerala and it spread all over. If you will try to analyze the dataset, you will come to know that Maharashtra state have large number of positive results, also the recovery rate is high over there. This notebook clearly categorizes the positive result, death rates and the recovery rates of different states. Data visualization is done here which makes the case study more attractive and informative.

--- Original source retains full ownership of the source dataset ---

Contact positivity among COVID-19 cases US Mission India, March 2020 - July 2021 (n = 627).

Context

Coronavirus is a family of viruses that can cause illness, which can vary from common cold and cough to sometimes more severe disease. SARS-CoV-2 (n-coronavirus) is the new virus of the coronavirus family, which first discovered in 2019, which has not been identified in humans before. It is a contiguous virus which started from Wuhan in December 2019. Which later declared as Pandemic by WHO due to high rate spreads throughout the world. Currently (on date 27 March 2020), this leads to a total of 24K+ Deaths across the globe, including 16K+ deaths alone in Europe.Pandemic is spreading all over the world; it becomes more important to understand about this spread.

The number of new cases are increasing day by day around the world. This dataset has information from the states and union territories of India at daily level.

State Wise data fetched from Ministry of Health & Family Welfare ICMR Testing Data comes from Indian Council of Medical Research

Content

COVID-19 cases at daily level is present in covid_19_india.csv file

COVID-19 State and Union Territory data with latitude and longitude is present in state_wise_data.csv

COVID-19 cases at daily level is present in data_wise_data.csv and perday_new_cases.csv file

Number of COVID-19 tests and positive cases at daily level in ICMR_Testing_Data.csv file

Acknowledgements

Thanks to Ministry of Health & Family Welfare for making the data available to general public.

This work is highly inspired from few other kaggle kernels , github sources and other data science resources. Any traces of replications, which may appear , is purely co-incidental. Due respect & credit to all my fellow kagglers.

Inspiration

Together we can do this. Help the world to make a better place and with this fight against COVID-19.

Content

District-wise Covid-19 data of Maharashtra, a state in India as on April 29, 2022. The data include number of positive cases, active cases, recovered, deceased cases, recovery rate and fatality rate.

Attribute Information

Cumulative Cases by Districts

1. Districts - Name of districts in Maharashtra, India
2. Positive Cases - Number of positive cases
3. Active Cases - Number of active cases
4. Recovered - Number of recovered cases
5. Deceased - Number of deaths
6. Recovery Rate (%) - Ratio of number of recovered cases to positive cases
7. Fatality Rate (%) - Ratio of number of deaths to positive cases

Source

Link : https://www.covid19maharashtragov.in/mh-covid/dashboard

Other Updated Covid19 Datasets

Link : https://www.kaggle.com/anandhuh/datasets

If you find it useful, please support by upvoting 👍

Thank You

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

India's quarterly GDP was estimated to grow by 8.4 percent in the second quarter of financial year 2022 compared to the same quarter in the previous fiscal year. While continuing to be a positive change, it was a significant reduction from the performance during the first quarter of fiscal year 2022 when GDP growth peaked by 20 percent.

Cost of the pandemic

As a result of the various lockdowns enforced since the onset of the coronavirus pandemic in 2020, the Indian economy has been reeling from a multibillion dollar setback. The GDP contribution as well as the employment rate among most major sectors, especially services and trade, had taken a hit. The agriculture sector was an exception, having experienced positive changes on both these fronts.

A slowly recovering economy

With the outbreak of the second wave of the pandemic in March 2021, the government redirected financial support to boost India’s vaccination campaign. As of February 2022, over a billion vaccine doses had been administered across the country. Furthermore, inflation within the country was expected to decline 2021 onwards. However, the stagnation of employment continued to remain a matter of concern with protests erupting across different states in 2022.

-COVID-19 raw and derived epidemiological data -Updated daily, timeseries data since beginning of pandemic -National and statewise data

Following epidemiological indicators in the dataset: Effective reproduction number (Rt), Positivity rate (daily and cumulative), Crude CFR, Lag adjusted CFR, Outcome adjusted CFR, Mobility index (Google mobility).

Latest updated data and detailed information for use available at https://github.com/CovidToday/indicator-dataset

Live dashboard and visualisation at www.covidtoday.in (open sourced project) at https://github.com/CovidToday

Contact us at covidtodayindia@gmail.com

Aims and objectives: The purpose of this study is to detect the presence of SAR-CoV-2 viral RNA in conjunctival secretions of COVID-19 patients and to compare the RT-PCR positivity rate for SARS-CoV-2 in conjunctival and nasopharyngeal swabs. Materials and method: Eighty hospitalised COVID-19 patients whose nasopharyngeal swab tested positive for SARS-CoV-2 by RT-PCR were included in the study. Conjunctival swab was collected from the eyes of these patients and sent for detection of SARS-CoV-2 by RT-PCR method. Results: Among the eighty patients, 51 (63.7%) were males and 29 (36.3%) were females. The mean age of the patients was 55.93 ± 16.59. Six patients had ocular manifestations. Eleven (13.75%) patients tested positive on conjunctival swab for SARS-CoV-2 viral RNA, and only one of them had ocular manifestations out of the eleven. Conclusion: In our study, the presence of SARS-CoV-2 in conjunctival secretions of COVID-19 patients was detected and this was not dependent on the presenc...

Studies on host-pathogen interaction have identified human ACE2 as a host cell receptor responsible for mediating infection by coronavirus (COVID-19). Subsequent studies have shown striking difference of allele frequency among Europeans and Asians for a polymorphism rs2285666, present in ACE2. It has been revealed that the alternate allele (TT-plus strand or AA-minus strand) of rs2285666 elevate the expression level of this gene upto 50%, hence may play a significant role in SARS-CoV-2 susceptibility. Therefore, we have first looked the phylogenetic structure of rs2285666 derived haplotypes in worldwide populations and compared the spatial frequency of this particular allele with respect to the COVID-19 infection as well as case-fatality rate in India. For the first time, we ascertained a significant positive correlation for alternate allele (T or A) of rs2285666, with the lower infection as well as case-fatality rate among Indian populations. We trust that this information will be useful to understand the role of ACE2 in COVID-19 susceptibility.

Petroleum products were the most affected commodities in terms of exports from India, with a decline of about ** percent in ************, compared to the same month in the previous year. Other cereals and oil meals witnessed a highly positive change rate.

Global economic impact The outbreak of COVID-19 caused a massive economic recession, with *** out of the ***** largest economies showing a massive GDP loss in the third quarter of 2020. A slump in demand and changing consumption patterns shook international trade worldwide. Since **********, lockdowns became a global necessity, and the Indian subcontinent was no exception, announcing its first nation-wide lockdown by the end of March. Aimed at getting hold of the infectious chains, the lockdown resulted in a massive decrease in mobility, but also meant that livelihoods were disproportionately impacted. This was especially true for those with daily or hourly wages across the country.

COVID-19 impact on different sectors Reduced mobility and the unavailability of resources, due to restricted borders caused significant challenges to traditional retailers. The automotive industry, in particular, emerged as one of the worst impacted industries. Simultaneously, petroleum consumption decreased. Other industries such as healthcare or fast-moving consumer goods, were less affected due to their indispensability and local shopper clientele. E-commerce experienced a long-lasting benefit from the pandemic, as most online purchasers consider e-retail as a post-pandemic option.

BackgroundStudies have highlighted a possible influence of gingival and periodontal disease (PD) on COVID-19 risk and severity. However, the evidence is based on hospital-based studies and community-level data are sparse.ObjectivesWe described the epidemiological pattern of SARS-CoV-2 infection in Delhi and evaluated the associations of gingival and PD with incident COVID-19 disease in a regionally representative urban Indian population.MethodsIn a prospective study nested within the Centre for Cardiometabolic Risk Reduction in South-Asia (CARRS) study, participants with clinical gingival and periodontal status available at baseline (2014–16) (n = 1,727) were approached between October 2021 to March 2022. Information on COVID-19 incidence, testing, management, severity was collected as per the WHO case criteria along with COVID-19 vaccination status. Absolute incidence of COVID-19 disease was computed by age, sex, and oral health. Differences in rates were tested using log-rank test. Poisson regression models were used to evaluate independent associations between gingival and PD and incidence of COVID-19, adjusted for socio-demographic and behavioral factors, presence of comorbidity, and medication use.ResultsAmong 1,727 participants, the mean age was 44.0 years, 45.7% were men, 84.5% participants had baseline gingival or PD and 89.4% participants had received at least one dose of COVID-19 vaccine. Overall, 35% (n = 606) participants were tested for COVID-19 and 24% (n = 146/606) tested positive. As per the WHO criteria total number of cases was 210, constituting 12% of the total population. The age and sex-specific rates of COVID-19 were higher among men and older participants, but women aged >60 years had higher rates than men of same age. The incidence rate did not differ significantly between those having gingival or PD and healthy periodontium (19.1 vs. 16.5/1,000 person-years) and there was no difference in risk of COVID-19 by baseline oral disease status.ConclusionGingival and PD were not associated with increased risk of COVID-19.

As of January 1, 2025, the number of active coronavirus (COVID-19) infections in Italy was approximately 218,000. Among these, 42 infected individuals were being treated in intensive care units. Another 1,332 individuals infected with the coronavirus were hospitalized with symptoms, while approximately 217,000 thousand were in isolation at home. The total number of coronavirus cases in Italy reached over 26.9 million (including active cases, individuals who recovered, and individuals who died) as of the same date. The region mostly hit by the spread of the virus was Lombardy, which counted almost 4.4 million cases.For a global overview, visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

Google Trends data have been used to investigate various themes on online information seeking. It was unclear if the population from different parts of the world shared the same amount of attention to different mask types during the COVID-19 pandemic. This study aimed to reveal which types of masks were frequently searched by the public in different countries, and evaluated if public attention to masks could be related to mandatory policy, stringency of the policy, and transmission rate of COVID-19. By referring to an open dataset hosted at the online database Our World in Data, the 10 countries with the highest total number of COVID-19 cases as of 9th of February 2022 were identified. For each of these countries, the weekly new cases per million population, reproduction rate (of COVID-19), stringency index, and face covering policy score were computed from the raw daily data. Google Trends were queried to extract the relative search volume (RSV) for different types of masks from each of these countries. Results found that Google searches for N95 masks were predominant in India, whereas surgical masks were predominant in Russia, FFP2 masks were predominant in Spain, and cloth masks were predominant in both France and United Kingdom. The United States, Brazil, Germany, and Turkey had two predominant types of mask. The online searching behavior for masks markedly varied across countries. For most of the surveyed countries, the online searching for masks peaked during the first wave of COVID-19 pandemic before the government implemented mandatory mask wearing. The search for masks positively correlated with the government response stringency index but not with the COVID-19 reproduction rate or the new cases per million.