Context

In the time of epidemics, what is the status of HIV AIDS across the world, where does each country stands, is it getting any better. The data set should be helpful to explore much more about above mentioned factors.

Content

The data set contains data on

1. No. of people living with HIV AIDS
2. No. of deaths due to HIV AIDS
3. No. of cases among adults (19-45)
4. Prevention of mother-to-child transmission estimates
5. ART (Anti Retro-viral Therapy) coverage among people living with HIV estimates
6. ART (Anti Retro-viral Therapy) coverage among children estimates

Acknowledgements / Data Source

• WHO : https://www.who.int/hiv/data/en/
• UNESCO : https://data.unicef.org/resources/dataset/hiv-aids-statistical-tables/

Collection methodology

https://github.com/imdevskp/hiv_aids_who_unesco_data_cleaning

Cover Photo

Photo by Anna Shvets from Pexels https://www.pexels.com/photo/red-ribbon-on-white-surface-3900425/

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

COVID-19 deaths among health care workers, clients and family members due to transmission during access to HIV services and HIV-related deaths that could be averted by these services per 10,000 clients.

The Risks and Benefits of Providing HIV Services during the COVID-19 Pandemic

Introduction

The COVID-19 pandemic has caused widespread disruptions including to health services. In the early response to the pandemic many countries restricted population movements and some health services were suspended or limited. In late 2020 and early 2021 some countries re-imposed restrictions. Health authorities need to balance the potential harms of additional SARS-CoV-2 transmission due to contacts associated with health services against the benefits of those services, including fewer new HIV infections and deaths. This paper examines these trade-offs for select HIV services.

Methods

We used four HIV simulation models (Goals, HIV Synthesis, Optima HIV and EMOD) to estimate the benefits of continuing HIV services in terms of fewer new HIV infections and deaths. We used three COVID-19 transmission models (Covasim, Cooper/Smith and a simple contact model) to estimate the additional deaths due to SARS-CoV-2 transmission among health workers and clients. We examined four HIV services: voluntary medical male circumcision, HIV diagnostic testing, viral load testing and programs to prevent mother-to-child transmission. We compared COVID-19 deaths in 2020 and 2021 with HIV deaths occurring now and over the next 50 years discounted to present value. The models were applied to countries with a range of HIV and COVID-19 epidemics.

Results

Maintaining these HIV services could lead to additional COVID-19 deaths of 0.002 to 0.15 per 10,000 clients. HIV-related deaths averted are estimated to be much larger, 19 - 146 discounted deaths per 10,000 clients.

Discussion

While there is some additional short-term risk of SARS-CoV-2 transmission associated with providing HIV services, the risk of additional COVID-19 deaths is at least 100 times less than the HIV deaths averted by those services. Ministries of Health need to take into account many factors in deciding when and how to offer essential health services during the COVID-19 pandemic. This work shows that the benefits of continuing key HIV services are far larger than the risks of additional SARS-CoV-2 transmission.

How did Brazil become a global epicenter of the outbreak? After seeming to ease, is the virus making a comeback?

A world leader in infections and deaths.

Latin America became an epicenter of the coronavirus pandemic in May, driven by Brazil’s ballooning caseload. Ten months after its first known case, Brazil has had more than 7.9 million cases and over 200,000 deaths.

In early June, Brazil began averaging about 1,000 deaths per day from Covid-19, joining the United States — and later India — as the countries with the world’s largest death tolls.

This dataset contains information about COVID-19 in Brazil extracted on the date 16/06/2021. It is the most updated dataset available about Covid in Brazil

Features:

🔍 date: date that the data was collected. format YYYY-MM-DD.
🔍 state: Abbreviation for States. Example: SP
🔍 city: Name of the city (if the value is NaN, they are referring to the State, not the city)
🔍 place_type: Can be City or State
🔍 order_for_place: Number that identifies the registering order for this location. The line that refers to the first log is going to be shown as 1, and the following information will start the count as an index.
🔍 is_last: Show if the line was the last update from that place, can be True or False
🔍 city_ibge_code: IBGE Code from the location
🔍confirmed: Number of confirmed cases.
🔍deaths: Number of deaths.
🔍estimated_population: Estimated population for this city/state in 2020. Data from IBGE
🔍estimated_population_2019: Estimated population for this city/state in 2019. Data from IBGE.
🔍confirmed_per_100k_inhabitants: Number of confirmed cases per 100.000 habitants (based on estimated_population).
🔍death_rate: Death rate (deaths / confirmed cases).

Acknowledgements

This dataset was downloaded from the URL bello. Thanks, Brasil.IO! Their main goal is to make all Brazilian data available to the public DATASET URL: https://brasil.io/dataset/covid19/files/ Cities map file https://geoftp.ibge.gov.br/organizacao_do_territorio/malhas_territoriais/malhas_municipais/municipio_2020/Brasil/BR/

Similar Datasets

COVID-19 - https://www.kaggle.com/rafaelherrero/covid19-brazil-full-cases-17062021 COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019 Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

Method

The dataset contains several confirmed COVID-19 cases, number of deaths, and death rate in six regions. The objective of the study is to compare the number of confirmed cases in Africa to other regions.

Death rate = Total number of deaths from COVID-19 divided by the Total Number of infected patients.

The study provides evidence for the country-level in six regions by the World Health Organisation's classification.

Findings

Based on the descriptive data provided above, we conclude that the lack of tourism is one of the key reasons why COVID-19 reported cases are low in Africa compared to other regions. We also justified this claim by providing evidence from the economic freedom index, which indicates that the vast majority of African countries recorded a low index for a business environment. On the other hand, we conclude that the death rate is higher in the African region compared to other regions. This points to issues concerning health-care expenditure, low capacity for testing for COVID-19, and poor infrastructure in the region.

Apart from COVID-19, there are significant pre-existing diseases, namely; Malaria, Flu, HIV/AIDS, and Ebola in the continent. This study, therefore, invites the leaders to invest massively in the health-care system, infrastructure, and human capital in order to provide a sustainable environment for today and future generations. Lastly, policy uncertainty has been a major issue in determining a sustainable development goal on the continent. This uncertainty has differentiated Africa to other regions in terms of stepping up in the time of global crisis.

T-cell reduction is an important characteristic of coronavirus disease 2019 (COVID-19), and its immunopathology is a subject of debate. It may be due to the direct effect of the virus on T-cell exhaustion or indirectly due to T cells redistributing to the lungs. HIV/AIDS naturally served as a T-cell exhaustion disease model for recognizing how the immune system works in the course of COVID-19. In this study, we collected the clinical charts, T-lymphocyte analysis, and chest CT of HIV patients with laboratory-confirmed COVID-19 infection who were admitted to Jin Yin-tan Hospital (Wuhan, China). The median age of the 21 patients was 47 years [interquartile range (IQR) = 40–50 years] and the median CD4 T-cell count was 183 cells/μl (IQR = 96–289 cells/μl). Eleven HIV patients were in the non-AIDS stage and 10 were in the AIDS stage. Nine patients received antiretroviral treatment (ART) and 12 patients did not receive any treatment. Compared to the reported mortality rate (nearly 4%–10%) and severity rate (up to 20%–40%) among COVID-19 patients in hospital, a benign duration with 0% severity and mortality rates was shown by 21 HIV/AIDS patients. The severity rates of COVID-19 were comparable between non-AIDS (median CD4 = 287 cells/μl) and AIDS (median CD4 = 97 cells/μl) patients, despite some of the AIDS patients having baseline lung injury stimulated by HIV: 7 patients (33%) were mild (five in the non-AIDS group and two in the AIDS group) and 14 patients (67%) were moderate (six in the non-AIDS group and eight in the AIDS group). More importantly, we found that a reduction in T-cell number positively correlates with the serum levels of interleukin 6 (IL-6) and C-reactive protein (CRP), which is contrary to the reported findings on the immune response of COVID-19 patients (lower CD4 T-cell counts with higher levels of IL-6 and CRP). In HIV/AIDS, a compromised immune system with lower CD4 T-cell counts might waive the clinical symptoms and inflammatory responses, which suggests lymphocyte redistribution as an immunopathology leading to lymphopenia in COVID-19.

Context

The 2009 swine flu pandemic was an influenza pandemic that lasted for about 19 months, from January 2009 to August 2010, and the second of two pandemics involving H1N1 influenza virus.

Content

1. data.csv - contains day by day country wise no. of cases & deaths from 4th April to 6th July 2009

Note

• Although the pandemic went on for more than 2 years the data is only from 24th April 2009 to 6th July 2009.
• Because the countries were no longer required to test and report individual cases from 6th July 2009.
• So that day by day data from 6th July 2009 is not available.

Acknowledgements / Data Source

https://www.who.int/csr/disease/swineflu/updates/en/

Collection methodology

https://github.com/imdevskp/h1n1_data_webscrapping

Cover Photo

Photo from CDC Blog https://blogs.cdc.gov/publichealthmatters/2019/04/h1n1/

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

Key characteristics of the COVID-19 models.

Context

• Severe acute respiratory syndrome (SARS) is a viral respiratory disease of zoonotic origin caused by the SARS coronavirus (SARS-CoV).
• Between November 2002 and July 2003, an outbreak of SARS in southern China caused an eventual
• 8,098 cases, resulting in 774 deaths reported in
• 17 countries (9.6% fatality rate), with the majority of cases in mainland China and Hong Kong.
• No cases of SARS have been reported worldwide since 2004.
• In late 2017, Chinese scientists traced the virus through the intermediary of civets to cave-dwelling horseshoe bats in Yunnan province.
• More information https://en.wikipedia.org/wiki/Severe_acute_respiratory_syndrome

Content

• sars_2003_complete_dataset_clean.csv - The file contains day by day no. from March to July 2003 across the world.
• summary_data_clean.csv - Final no.s from across the world

Acknowledgements / Data Source

https://www.who.int/csr/sars/country/en/

Collection methodology

https://github.com/imdevskp/sars-2003-outbreak-data-webscraping-code

Cover Photo

Photo from CDC website https://www.cdc.gov/dotw/sars/index.html#

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

Context

• The Western African Ebola virus epidemic (2013–2016) was the most widespread outbreak of Ebola virus disease (EVD) in history
• Causing major loss of life and socioeconomic disruption in the region, mainly in Guinea, Liberia, and Sierra Leone.
• The ** first cases** were recorded in Guinea in December 2013;
• Later, the disease spread to neighboring Liberia and Sierra Leone, with minor outbreaks occurring elsewhere.
• It caused significant mortality, with the case fatality rate reported which was initially considered, while the rate among hospitalized patients was 57–59%
• The final numbers 28,616 people, including 11,310 deaths, for a case-fatality rate of 40%.

Content

Each row contains a report from each region/location for each day Each column represents the number of cases reported from each country/region

Inspiration

To see how the epidemic spread worldwide in such a short time

Acknowledgements / Data Source

https://www.who.int/csr/don/archive/disease/ebola/en/ https://data.humdata.org/dataset/ebola-cases-2014

Collection methodology

https://github.com/imdevskp/ebola_outbreak_dataset

Cover Photo

Photo from CDC website https://www.cdc.gov/vhf/ebola/index.html

Similar Datasets

• COVID-19 - https://www.kaggle.com/imdevskp/corona-virus-report
• MERS - https://www.kaggle.com/imdevskp/mers-outbreak-dataset-20122019
• Ebola Western Africa 2014 Outbreak - https://www.kaggle.com/imdevskp/ebola-outbreak-20142016-complete-dataset
• H1N1 | Swine Flu 2009 Pandemic Dataset - https://www.kaggle.com/imdevskp/h1n1-swine-flu-2009-pandemic-dataset
• SARS 2003 Pandemic - https://www.kaggle.com/imdevskp/sars-outbreak-2003-complete-dataset
• HIV AIDS - https://www.kaggle.com/imdevskp/hiv-aids-dataset

IntroductionSARS-CoV-2 elicits a hyper-inflammatory response that contributes to increased morbidity and mortality in patients with COVID-19. In the case of HIV infection, despite effective anti-retroviral therapy, people living with HIV (PLWH) experience chronic systemic immune activation, which renders them particularly vulnerable to the life-threatening pulmonary, cardiovascular and other complications of SARS-CoV-2 co-infection. The focus of the study was a comparison of the concentrations of systemic indicators o\f innate immune dysfunction in SARS-CoV-2-PCR-positive patients (n=174) admitted with COVID-19, 37 of whom were co-infected with HIV.MethodsParticipants were recruited from May 2020 to November 2021. Biomarkers included platelet-associated cytokines, chemokines, and growth factors (IL-1β, IL-6, IL-8, MIP-1α, RANTES, PDGF-BB, TGF-β1 and TNF-α) and endothelial associated markers (IL-1β, IL-1Ra, ICAM-1 and VEGF).ResultsPLWH were significantly younger (p=0.002) and more likely to be female (p=0.001); median CD4+ T-cell count was 256 (IQR 115 -388) cells/μL and the median HIV viral load (VL) was 20 (IQR 20 -12,980) copies/mL. Fractional inspired oxygen (FiO2) was high in both groups, but higher in patients without HIV infection (p=0.0165), reflecting a greater need for oxygen supplementation. With the exception of PDGF-BB, the levels of all the biomarkers of innate immune activation were increased in SARS-CoV-2/HIV-co-infected and SARS-CoV-2/HIV-uninfected sub-groups relative to those of a control group of healthy participants. The magnitudes of the increases in the levels of these biomarkers were comparable between the SARS-CoV-2 -infected sub-groups, the one exception being RANTES, which was significantly higher in the sub-group without HIV. After adjusting for age, sex, and diabetes in the multivariable model, only the association between HIV status and VEGF was statistically significant (p=0.034). VEGF was significantly higher in PLWH with a CD4+ T-cell count >200 cells/μL (p=0.040) and those with a suppressed VL (p=0.0077).DiscussionThese findings suggest that HIV co-infection is not associated with increased intensity of the systemic innate inflammatory response during SARS-CoV-2 co-infection, which may underpin the equivalent durations of hospital stay, outcome and mortality rates in the SARS-CoV-2/HIV-infected and -uninfected sub-groups investigated in the current study. The apparent association of increased levels of plasma VEGF with SARS-CoV-2/HIV co-infection does, however, merit further investigation.

Background: In Canada, urban centers have a high prevalence of vulnerable populations with increased risk factors for COVID-19, including homelessness, living in group settings, high-intensity substance use, and infectious diseases, who face many barriers to healthcare services. The uptake, safety, and effectiveness of COVID-19 vaccinations have not been studied in these populations yet. Aims of the CITF funded study: This study aimed to 1) analyze the effectiveness of COVID-19 vaccines among different groups within vulnerable populations, 2) evaluate vaccine uptake and adherence to vaccine protocols, and 3) evaluate vaccines’ length of effectiveness against infection and any potential side effects. Methods: This study enrolled participants in 3 cohorts across Vancouver, British Columbia: individuals from the Vancouver Injection Drug Users Study (VIDUS) who were 18 years or older, HIV negative, and past-30-day injection drug use, individuals from the AIDS Care Cohort (ACCESS) who were 18 years or older, HIV seropositive, and past-30-day injection/non-injection drug use, and individuals from the At-Risk Youth Study (ARYS) who were between 14 and 26 years old, “street-involved” (precariously housed or homeless), and past-30-day injection drug use. All participants were zero, partially, or fully vaccinated against COVID-19. Participants from each cohort answered a questionnaire and provided blood samples via dried blood spot and venipuncture at baseline, and at follow ups 2-months, 4- months, and 6-months post vaccination. Contributed dataset contents: The datasets include 275 participants who completed baseline surveys between June 2021 and September 2021. 101 participants came from the VIDUS cohort, 97 from ACCESS, and 77 from ARYS. All participants gave one or more blood samples at baseline, and approximately 93% gave additional samples at follow ups between September 2021 and March 2022. A total of 1223 samples were collected. Variables include data in the following areas of information: demographics (age, gender, ethnicity, household composition, occupation), general health (weight and height, smoking, chronic conditions, flu vaccine), longitudinal follow-up for COVID infection (dates of positive tests, symptoms, hospitalizations), SARS-CoV-2 vaccination, and serology (IgG against SARS-CoV-2 RBD, nucleocapsid, and spike proteins).

In South Africa, the Coronavirus Disease 2019 (COVID-19) pandemic is occurring against the backdrop of high Human Immunodeficiency Virus (HIV), tuberculosis and non-communicable disease burdens as well as prevalent herpesviruses infections such as Epstein-Barr virus (EBV) and Kaposi’s sarcoma-associated herpesvirus (KSHV). As part of an observational study of adults admitted to Groote Schuur Hospital, Cape Town, South Africa during the period June–August 2020 and assessed for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, we measured KSHV serology and KSHV and EBV viral load (VL) in peripheral blood in relation to COVID-19 severity and outcome. A total of 104 patients with PCR-confirmed SARS-CoV-2 infection were included in this study. 61% were men and 39% women with a median age of 53 years (range 21–86). 29.8% (95% CI: 21.7–39.1%) of the cohort was HIV positive and 41.1% (95% CI: 31.6–51.1%) were KSHV seropositive. EBV VL was detectable in 84.4% (95% CI: 76.1–84.4%) of the cohort while KSHV DNA was detected in 20.6% (95% CI: 13.6–29.2%), with dual EBV/KSHV infection in 17.7% (95% CI: 11.1–26.2%). On enrollment, 48 [46.2% (95% CI: 36.8–55.7%)] COVID-19 patients were classified as severe on the WHO ordinal scale reflecting oxygen therapy and supportive care requirements and 30 of these patients [28.8% (95% CI: 20.8–38.0%)] later died. In COVID-19 patients, detectable KSHV VL was associated with death after adjusting for age, sex, HIV status and detectable EBV VL [p = 0.036, adjusted OR = 3.17 (95% CI: 1.08–9.32)]. Furthermore, in HIV negative COVID-19 patients, there was a trend indicating that KSHV VL may be related to COVID-19 disease severity [p = 0.054, unstandardized co-efficient 0.86 (95% CI: –0.015–1.74)] in addition to death [p = 0.008, adjusted OR = 7.34 (95% CI: 1.69–31.49)]. While the design of our study cannot distinguish if disease synergy exists between COVID-19 and KSHV nor if either viral infection is indeed fueling the other, these data point to a potential contribution of KSHV infection to COVID-19 outcome, or SARS-CoV-2 infection to KSHV reactivation, particularly in the South African context of high disease burden, that warrants further investigation.

Death rate and survival probability of COVID-19 patients hospitalized at Bokoji Hospital treatment centre, Ethiopia, 2021.

Socio-demographic and clinical characteristics associated with mortality among COVID-19 patients admitted to ICU.

Estimation of treatment cascade in absence of progress on stigma.

Sociodemographic characteristics, COVID-19-related outcomes, and HIV/AIDS characteristics among Malawi Longitudinal Study of Families and Health (MLSFH) respondents.

Frequency distribution of socio-demographic and bio-clinical characteristics of COVID-19 patients admitted in the ICU.

We collected county-level cumulative COVID-19 confirmed cases and death from Mar 25 to Nov 12, 2020, across the contiguous United States from USAFacts (usafacts.org). We considered Mar 25 to Jun 3 as the “1st wave”, Jun 4 to Sep 2 as the “2nd wave”, and Sep 3 to Nov 12 as the “3rd wave” of COVID-19. For the 2nd and 3rd waves, we analyzed the targeted counties in the sunbelt region (including AL, AZ, AR, CA, FL, GA, KS, LA, MS, NV, NM, NC, OK, SC, TX, TN, and UT states) and great plains region (including IA, IL, IN, KS, MI, MO, MN, ND, NE, OH, SD, and WI states), respectively. MIR, as a proxy for survival rate, is calculated by dividing the number of confirmed deaths in each county by the confirmed cases in the same county at the same time-period multiplied by 100. MIR ranges from 0%-100%, 100% indicating the worst situation where all confirmed cases have died.

Thirty-eight potential risk factors (covariates), including county-level MR of comorbidities & disorders, demographics & social factors, and environmental factors, were retrieved from the University of Washington Global Health Data Exchange (http://ghdx.healthdata.org/us-data). Comorbidities and disorders include CVD, cardiomyopathy and myocarditis and myocarditis, hypertensive heart disease, peripheral vascular disease, atrial fibrillation, cerebrovascular disease, diabetes, hepatitis, HIV/AIDS, tuberculosis (TB), lower respiratory infection, interstitial lung disease and pulmonary sarcoidosis, asthma, COPD, ischemia, mesothelioma, tracheal cancer, leukemia, pancreatic cancer, rheumatic disease, drug use disorder, and alcohol use disorder. Demographics & social factors include age, female African American%, female white American%, male African American%, male white American%, Asian%, smokers%, unemployed%, income rate, food insecurity, fair/poor health, and uninsured%. Environmental factors include county population density, air quality index (AQI), temperature, and PM. A descriptive table, including all potential risk factors, is provided in Table S1).

Laboratory results at initial measurements associated with mortality among COVID-19 patients admitted to ICU at Tygerberg Hospital.