Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

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The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

• sex: 1 for female and 2 for male.
• age: of the patient.
• classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
• patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
• pneumonia: whether the patient already have air sacs inflammation or not.
• pregnancy: whether the patient is pregnant or not.
• diabetes: whether the patient has diabetes or not.
• copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
• asthma: whether the patient has asthma or not.
• inmsupr: whether the patient is immunosuppressed or not.
• hypertension: whether the patient has hypertension or not.
• cardiovascular: whether the patient has heart or blood vessels related disease.
• renal chronic: whether the patient has chronic renal disease or not.
• other disease: whether the patient has other disease or not.
• obesity: whether the patient is obese or not.
• tobacco: whether the patient is a tobacco user.
• usmr: Indicates whether the patient treated medical units of the first, second or third level.
• medical unit: type of institution of the National Health System that provided the care.
• intubed: whether the patient was connected to the ventilator.
• icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
• date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.

Provisional death counts of diabetes, coronavirus disease 2019 (COVID-19) and other select causes of death, by month, sex, and age.

According to a survey conducted in Europe in 2020, ** percent of people living with diabetes reported they were less active during the COVID-19 pandemic and subsequent lockdown. Furthermore, around a third of diabetes sufferers reported gaining weight during the lockdown.

This dataset provides COVID-19 mortality data with details on age groups, sex, and pre-existing conditions such as diabetes and hypertensive diseases. It includes the date of death, COVID-19 diagnosis, and comorbidities, helping to analyze the impact of COVID-19 on different demographics and health conditions. The dataset is valuable for epidemiological research, healthcare policy planning, and understanding the role of comorbidities in COVID-19-related deaths.

Association of COVID-19 diagnosis with incidence of T1D among patients in Cerner.

Demographic and clinical characteristics of patients previously diagnosed with T1D (overall and stratified by COVID-19 diagnosis).

BackgroundDiabetes mellitus (DM) is one of the most frequent comorbidities in patients suffering from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with a higher rate of severe course of coronavirus disease (COVID-19). However, data about post-COVID-19 syndrome (PCS) in patients with DM are limited.MethodsThis multicenter, propensity score-matched study compared long-term follow-up data about cardiovascular, neuropsychiatric, respiratory, gastrointestinal, and other symptoms in 8,719 patients with DM to those without DM. The 1:1 propensity score matching (PSM) according to age and sex resulted in 1,548 matched pairs.ResultsDiabetics and nondiabetics had a mean age of 72.6 ± 12.7 years old. At follow-up, cardiovascular symptoms such as dyspnea and increased resting heart rate occurred less in patients with DM (13.2% vs. 16.4%; p = 0.01) than those without DM (2.8% vs. 5.6%; p = 0.05), respectively. The incidence of newly diagnosed arterial hypertension was slightly lower in DM patients as compared to non-DM patients (0.5% vs. 1.6%; p = 0.18). Abnormal spirometry was observed more in patients with DM than those without DM (18.8% vs. 13; p = 0.24). Paranoia was diagnosed more frequently in patients with DM than in non-DM patients at follow-up time (4% vs. 1.2%; p = 0.009). The incidence of newly diagnosed renal insufficiency was higher in patients suffering from DM as compared to patients without DM (4.8% vs. 2.6%; p = 0.09). The rate of readmission was comparable in patients with and without DM (19.7% vs. 18.3%; p = 0.61). The reinfection rate with COVID-19 was comparable in both groups (2.9% in diabetics vs. 2.3% in nondiabetics; p = 0.55). Long-term mortality was higher in DM patients than in non-DM patients (33.9% vs. 29.1%; p = 0.005).ConclusionsThe mortality rate was higher in patients with DM type II as compared to those without DM. Readmission and reinfection rates with COVID-19 were comparable in both groups. The incidence of cardiovascular symptoms was higher in patients without DM.

ObjectiveStudies have shown an increased incidence of pediatric type 1 diabetes during the COVID-19 pandemic, but the detailed role of SARS-CoV-2 infection in the incidence increase in type 1 diabetes remains unclear. We investigated the spatiotemporal association of pediatric type 1 diabetes and COVID-19 incidence at the district level in Germany.MethodsFor the period from March 2020 to June 2022, nationwide data on incident type 1 diabetes among children and adolescents aged <20 years and daily documented COVID-19 infections in the total population were obtained from the German Diabetes Prospective Follow-up Registry and the Robert Koch Institute, respectively. Data were aggregated at district level and seven time periods related to COVID-19 pandemic waves. Spatiotemporal associations between indirectly standardized incidence rates of type 1 diabetes and COVID-19 were analyzed by Spearman correlation and Bayesian spatiotemporal conditional autoregressive Poisson models.ResultsStandardized incidence ratios of type 1 diabetes and COVID-19 in the pandemic period were not significantly correlated across districts and time periods. A doubling of the COVID-19 incidence rate was not associated with a significant increase in the incidence rate of type 1 diabetes (relative risk 1.006, 95% CI 0.987; 1.019).ConclusionOur findings based on data from the pandemic period indirectly indicate that a causal relationship between SARS-COV-2 infection and type 1 diabetes among children and adolescents is unlikely.

With the onset of COVID-19, hospitals statewide saw a sharp drop in inpatient discharges, emergency department utilization, and ambulatory surgeries. These datasets contain monthly counts of encounters and in-hospital mortalities in those three settings and are also broken down by the following common health conditions/categories: anxiety, asthma, behavioral syndromes, cancer, cardiac arrest, chronic obstructive pulmonary disease (COPD), COVID-19, depression, diabetes, homeless, hypertension, mood disorders (excluding depression), non-mood psychotic disorders, nonpsychotic disorders (excluding anxiety), obesity, pneumonia, respiratory arrest/failure, sepsis, stroke, substance use disorders, and unspecified mental disorders.

According to a survey conducted in Europe in 2020, ** percent of diabetes sufferers reported it was not at all difficult to obtain their medication prior to the COVID-19 pandemic, although this dropped to below ** percent during the pandemic. Furthermore, ** percent of people living with diabetes said it was difficult to access medication during the pandemic.

It was estimated that around 30 percent of those aged 80 years and older who had COVID-19 in the United States from January 22 to May 30, 2020 died from the disease. Deaths due to COVID-19 are much higher among those with underlying health conditions such as cardiovascular disease, chronic lung disease, or diabetes. This statistic shows the percentage of people in the U.S. who had COVID-19 from January 22 to May 30, 2020 who died, by age.

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

Global Covid-19 Data

Global Covid-19 data on cases, deaths, vaccinations, and more

By Valtteri Kurkela [source]

About this dataset

The dataset is constantly updated and synced hourly to ensure up-to-date information. With over several columns available for analysis and exploration purposes, users can extract valuable insights from this extensive dataset.

Some of the key metrics covered in the dataset include:

1. Vaccinations: The dataset covers total vaccinations administered worldwide as well as breakdowns of people vaccinated per hundred people and fully vaccinated individuals per hundred people.

2. Testing & Positivity: Information on total tests conducted along with new tests conducted per thousand people is provided. Additionally, details on positive rate (percentage of positive Covid-19 tests out of all conducted) are included.

3. Hospital & ICU: Data on ICU patients and hospital patients are available along with corresponding figures normalized per million people. Weekly admissions to intensive care units and hospitals are also provided.

4. Confirmed Cases: The number of confirmed Covid-19 cases globally is captured in both absolute numbers as well as normalized values representing cases per million people.

5.Confirmed Deaths: Total confirmed deaths due to Covid-19 worldwide are provided with figures adjusted for population size (total deaths per million).

6.Reproduction Rate: The estimated reproduction rate (R) indicates the contagiousness of the virus within a particular country or region.

7.Policy Responses: Besides healthcare-related metrics, this comprehensive dataset includes policy responses implemented by countries or regions such as lockdown measures or travel restrictions.

8.Other Variables of InterestThe data encompasses various socioeconomic factors that may influence Covid-19 outcomes including population density,membership in a continent,gross domestic product(GDP)per capita;

For demographic factors: -Age Structure : percentage populations aged 65 and older,aged (70)older,median age -Gender-specific factors: Percentage of female smokers -Lifestyle-related factors: Diabetes prevalence rate and extreme poverty rate

1. Excess Mortality: The dataset further provides insights into excess mortality rates, indicating the percentage increase in deaths above the expected number based on historical data.

The dataset consists of numerous columns providing specific information for analysis, such as ISO code for countries/regions, location names,and units of measurement for different parameters.

Overall,this dataset serves as a valuable resource for researchers, analysts, and policymakers seeking to explore various aspects related to Covid-19

How to use the dataset

Introduction:

• Understanding the Basic Structure:

  • The dataset consists of various columns containing different data related to vaccinations, testing, hospitalization, cases, deaths, policy responses, and other key variables.
  • Each row represents data for a specific country or region at a certain point in time.

• Selecting Desired Columns:

  • Identify the specific columns that are relevant to your analysis or research needs.
  • Some important columns include population, total cases, total deaths, new cases per million people, and vaccination-related metrics.

• Filtering Data:

  • Use filters based on specific conditions such as date ranges or continents to focus on relevant subsets of data.
  • This can help you analyze trends over time or compare data between different regions.

• Analyzing Vaccination Metrics:

  • Explore variables like total_vaccinations, people_vaccinated, and people_fully_vaccinated to assess vaccination coverage in different countries.
  • Calculate metrics such as people_vaccinated_per_hundred or total_boosters_per_hundred for standardized comparisons across populations.

• Investigating Testing Information:

  • Examine columns such as total_tests, new_tests, and tests_per_case to understand testing efforts in various countries.
  • Calculate rates like tests_per_case to assess testing efficiency or identify changes in testing strategies over time.

• Exploring Hospitalization and ICU Data:

  • Analyze variables like hosp_patients, icu_patients, and hospital_beds_per_thousand to understand healthcare systems' strain.
  • Calculate rates like icu_patients_per_million or hosp_patients_per_million for cross-country comparisons.

• Assessing Covid-19 Cases and Deaths:

  • Analyze variables like total_cases, new_ca...

2.1. Participants and procedure The participants were patients with DM from nine primary health care areas corresponding to four Cuban provinces belonging to different regions of the country (Pinar del Río, Havana, Ciego de Ávila and Santiago de Cuba), selected by means of non-probabilistic sampling. The inclusion criteria included: 1) having type 2 diabetes mellitus according to the criteria of the World Health Organization 2) being ≥18 years of age 3) being attended in the previously mentioned health areas where their clinical histories were located and 4) being willing to participate in the research study and answer the survey after signing the informed consent form. Patients with severe mental illness or cognitive deficits (dementia, psychosis or mental disabilities) or any other apparent condition that compromised their ability to understand and complete the questionnaire were not included in the study. The sample size was calculated with the Soper software [29], which indicated a number of 200 participants. For this we considered the number of observed variables (6 items), latent variables of the model to be evaluated (concern for COVID-19 contagion), the anticipated effect size (λ = 0.3), the probability (α = 0.05) and the statistical power (1 - β = 0.95). Finally, 219 people with type 2 DM were surveyed. The application of the survey was carried out between the months of January and April 2021, while the patients attended consultation or in their own homes by the researchers trained for the task and complying with strict COVID-19 prevention protocols. The Cuban panorama in the fight against COVID-19 during the period of data collection was not favorable, as the country was in a phase of resurgence characterized by high numbers of people infected with the virus, much higher compared to the diagnoses at a similar point during the first stage of the disease, in 2020. Although government health measures were strengthened to contain the pandemic, the population's perception of risk was on the rise. During those dates, more than 64,414 positive diagnoses and 384 deaths were reported. Participation in the study was voluntary and no financial compensation was provided. All participants signed informed consent and were allowed to withdraw at any time from the study without having to justify their decision. In addition, the data were guaranteed to be confidential and anonymous. The study received approval from the ethics committee of the Universidad Privada del Norte in Peru (registration number: 20213002). The majority of the participants were women (66.2%) with a mean age of 58.5 years old (SD = 18.2). Thirty-two point nine percent had higher education. Of the total participants, 37.9% were retired and 32% were state workers; while 43.4 had more than 10 years with the disease. The majority (68.9%) had no associated chronic complications and were receiving treatment for diabetes (98.2%). More details of the sociodemographic variables can be seen in Table 1. Table 1. Characteristics of the participants (n = 219). Characteristic n (%) Age 58.5 (18.2)a Sex Female 145 (66.2) Male 74 (33.8) Level of education University 72 (32.9) Pre-university 63 (28.8) Mid-level technical 39 (17.8) Secondary 25 (11.4) Primary 17 (7.8) No schooling 3 (1.4) Occupation Retired/pensioned 83 (37.9) State employee 70 (32.0) Self-employed 37 (17.0) Housewife 17 (7.8) Student 10 (4.6) Unemployed 2 (0.9) Time of evolution of diabetes (years) Less than 5 52 (23.7) From 5 to 10 72 (32.9) More than 10 95 (43.4) Associated chronic complications b None 151 (68.9) Diabetic foot 31 (14.2) Polyneuropathy 20 (9.1) Retinopathy 15 (6.8) Nephropathy 7 (3.2) Other 2 (0.9) Treatment of diabetes Yes 215 (98.2) No 4 (1.8) Comorbidities Yes 141 (64.4) No 78 (35.6) Family member or friend infected by COVID-19 Yes 110 (50.2) No 109 (49.8) Family member or friend deceased due to COVID-19 No 210 (95.9) Yes 9 (4.1) a: mean and standard deviation; b: a patient may have more than one complication. 2.2. Instruments Scale of Worry for Contagion of COVID-19 (PRE-COVID-19). The scale is comprised of 6 items that assess concern about becoming infected with COVID-19 and its impact on people's daily functioning, specifically on their mood and their ability to perform their daily activities. Each item presented 4 Likert-type response options (from 1 = never or rarely to 4 = almost all the time), with higher scores indicating greater concern about COVID-19 infection. Generalized Anxiety Disorder Scale-2 (GAD-2) [30]. The GAD-2 consists of 2 items that measure an emotional (feeling nervous) and cognitive (worry) symptom of generalized anxiety in the past 2 weeks. The 2 items have 4 response options using a Likert-type scale (from 0 = not at all to 3 = almost every day), where a higher score indicates a higher level of generalized anxiety. 2.3. Data analysis Confirmatory Factor Analysis (CFA) was performed using the Diagonally Weighted Least Squares with Mean and...

Adjusted associations of COVID-19 diagnosis with incidence of DKA among patients with previous T1D diagnosis.

According to a survey conducted in Europe in 2020, ** percent of diabetes sufferers regarded virtual diabetes consultations as very helpful during the COVID-19 pandemic, while a further ** percent rated the virtual consultations as helpful. On the other hand, **** percent of respondents said they were not helpful at all.

Participants A total of 203 patients with type 2 diabetes mellitus who attended nine primary care areas in four Cuban provinces belonging to different regions of the country (Pinar del Río, La Habana, Ciego de Ávila and Santiago de Cuba) participated in the study. Participants were selected by non-probabilistic sampling based on the following inclusion criteria: 1. have a diagnosis of type 2 DM according to the World Health Organization criteria, 2. be older than 18 years old, 3. be patients of the health care areas mentioned above, and 4. be willing to participate in the study and to sign the informed consent form. Patients with mental illness, cognitive deficit (dementia, psychosis or mental disability) or other apparent condition that prevents understanding and completion of the questionnaire were excluded. Although retrospective data on infection rates in diabetic patients suggest that people with type 1 DM are at higher risk for infectious diseases in general, and death rates are similar to those of people with type 2 DM,this study focused on the latterfortwo main reasons. First, patients with type 1 DM are mostly children and young people and the prevalence of this type of diabetes is lower compared to type 2 DM, which leads to a lower number of patients seen in consultation and primary health care. Second, the study was conducted in the context of the COVID-19 pandemic and patients with type 2 DM were the most accessible population to be surveyed by the research team in primary care areas. The minimum sample size was calculated with the Soper software package for a multiple regression study, according to the desired probability level (α=0.05), the number of predictors in the model (18 predictors), the anticipated effect size (f2=0.15) and the desired statistical power level (1- β=0.80). The software suggested a minimum number of 118 participants; however, the final number was higher than the minimum required. Instruments Socio-demographic and health information A questionnaire was developed specifically for this study, where participants were asked to provide information about their sex, age, educational level, type of work, cohabitation, marital status, presence of chronic complications, presence of comorbidities, family or friends infected with COVID-19, and time since diagnosis with DM. Concern about COVID-19 contagion We used the COVID-19 contagion concern scale (PRECOVID-19) originally developed for the general population, which assesses worry about becoming infected with COVID-19 and its impact on people’s mood and ability to perform daily activities. In this study we used the version validated for Cuban patients with diabetes, which consists of 5 items. All items have 4 Likert-type response options, ranging from 1=never or rarely to 4=almost all the time. The PRE-COVID-19 has a unidimensional structure, where the total score is calculated by adding the scores of each of the 5 items. Higher scores indicate greater concern about becoming infected with COVID19. The reliability of the PRE-COVID-19 for this study was very good (ω=0.91). Blood glucose level Fasting blood glucose values were obtained from the patients’ clinical histories and from blood tests performed in the last three months in laboratories equipped for this purpose. Based on this, poor glycemic control was determined as fasting blood glucose greaterthan or equal to 7 mmol/L (126 mg/dl) in the last three months and good control as figures below this value. The criterion based on glycosylated hemoglobin (HbA1c) could not be used because it is not a test regularly available in the primary health care system where the survey was applied. Other control criteria using continuous glucose monitoring systems were not possible either, as they are not generally available for patients with DM living in Cuba. Procedure The questionnaire was applied by properly trained researchers, who complied with strict COVID-19 prevention health protocols, between the months of January and April 2021. The questionnaire was administered during patients’ visits to primary care centers or in their homes. During this period of time, the fight against COVID-19 in Cuba suffered some setbacks, characterized by an increase in the number of infected people, even higherthan that observed during the first stage of the disease, in 2020. Thus, during those dates, more than 64,414 positive diagnoses and 384 deaths were reported in the country. Participation was voluntary and without any financial compensation. Participants signed the informed consent form and were informed that they could withdraw from the study at any time. Similarly, the reliability of the data was guaranteed. The study protocol was approved by the Ethics Committee of theUniversidad Privada delNorte in Peru (registration number: 20213002). Data Analysis The frequencies and percentages of the categorical variables included in the model were examined. In the case of the outcome variable (concern about...

According to a survey conducted in Europe in 2020, ** percent of diabetes sufferers reported they encountered a delay of more than *** and less than three months in rescheduling their appointments for the screening of diabetes complications during the COVID-19 pandemic. Furthermore, **** percent of people living with diabetes said their appointment was delayed by more than nine months.

Additional file 1: Table S1. Data at admission, laboratory data and treatment. Table S2. Frquency of hospital permanence. Table S3. Clinical data of COVID-19 patients with diabetes on hospital admission. Table S4. Laboratory data of COVID-19 patients with diabetes on-admission. Table S5. Treatment data during hospitalization of COVID-19 patients with diabetes. Table S6. Clinical data of COVID-19 patients withou diabetes on hospital admission. Table S7. Laboratory data of COVID-19 patients without diabetes on-admission. Table S8. Treatment data during hospitalization of COVID-19 patient withou diabetes. Table S9. Clinical data of COVID-19 patients on hospital admission that went to death. Table S10. Laboratory data of COVID-19 patients that went to death on-admission. Table S11. Treatment data during hospitalization of COVID-19 patients that went to death. Table S12. Frequency of according to age. Table S13. Frequency of death according to body mass index. Table S14. Frquency of death according to glucose levels. Table S15. Multivariate correlations among standard variables obtained at patient admission. Table S16. First two principal components from the inflammation related variables obtained at patient. Table S17. Multivariate correlations among inflammation related variables obtained at patient admission. Table S18. Multivariate correlations among coagulation related variables obtained at patient admission. Table S19. Multivariate correlations among variables related to renal function obtained at patient. Figure S1. Receiver Operator Characteristic (ROC) curves on the outcome death/release of the laboratory data obtained at patient at admission Adm PC 1 (A) and Adm PC2 (B).

As of March 10, 2023, the state with the highest rate of COVID-19 cases was Rhode Island followed by Alaska. Around 103.9 million cases have been reported across the United States, with the states of California, Texas, and Florida reporting the highest numbers of infections.

From an epidemic to a pandemic The World Health Organization declared the COVID-19 outbreak as a pandemic on March 11, 2020. The term pandemic refers to multiple outbreaks of an infectious illness threatening multiple parts of the world at the same time; when the transmission is this widespread, it can no longer be traced back to the country where it originated. The number of COVID-19 cases worldwide is roughly 683 million, and it has affected almost every country in the world.

The symptoms and those who are most at risk Most people who contract the virus will suffer only mild symptoms, such as a cough, a cold, or a high temperature. However, in more severe cases, the infection can cause breathing difficulties and even pneumonia. Those at higher risk include older persons and people with pre-existing medical conditions, including diabetes, heart disease, and lung disease. Those aged 85 years and older have accounted for around 27 percent of all COVID deaths in the United States, although this age group makes up just two percent of the total population

According to a survey conducted in Europe in 2020, ** percent of diabetes sufferers stated they would consider using virtual consultations once the pandemic is over, but only as a part of a mix with face-to-face consultations. Meanwhile, a quarter of respondents would consider having as many virtual consultations as possible in the future.