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.

content

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.

Please see FAQ for latest information on COVID-19 Data Hub data flows: https://covid-19.geohive.ie/pages/helpfaqs.Notice:See the Technical Data Issues section in the FAQ for information about issues in data: https://covid-19.geohive.ie/pages/helpfaqs.Deaths: From 16th May 2022 onwards, reporting of Notified Deaths will be weekly (each Wednesday) with deaths notified since the previous Wednesday reported. This is based on the date on which a death was notified on CIDR, not the date on which the death occurred. Data on deaths by date of death is available on the new HPSC Epidemiology of COVID-19 Data Hub https://epi-covid-19-hpscireland.hub.arcgis.com/.Notice:

Please be advised that on 29th April 2021, the 'Aged65up' and 'HospitalisedAged65up' fields were removed from this table. The three fields 'Aged65to74', 'Aged75to84', and 'Aged85up' replace the 'Aged65up' field.The three fields 'HospitalisedAged65to74', 'HospitalisedAged75to84' and 'HospitalisedAged85up' replace the 'HospitalisedAged65up' field.Please be advised that on the week beginning 1st March 2021, the values in the following fields in this table were set to zero: 'CommunityTransmission' , 'CloseContact', 'TravelAbroad' and ‘ClustersNotified’. ----------------------------------------------------------------------This feature service contains the up to date Covid-19 Daily Statistics as well as the Profile of Covid-19 Daily Statistics for Ireland, as reported by the Health Protection Surveillance Centre.The Covid-19 Daily Statistics are updated once a week, each Wednesday, which includes data for the full time series. Data on deaths is updated once a week, each Wednesday, which includes data for the full time series.The further breakdown of these counts (age, gender, transmission, etc.) is part of a Daily Statistics Profile of Covid-19, to help identify patterns and trends.The primary Date applies to the following fields:ConfirmedCovidCases, TotalConfirmedCovidCases, ConfirmedCovidDeaths, TotalCovidDeaths, ConfirmedCovidRecovered,SevenDayAverageCases.The StatisticProfileDate applies to the following fields:CovidCasesConfirmed, HospitalisedCovidCases, RequiringICUCovidCases, HealthcareWorkersCovidCases,Clusters Notified,HospitalisedAged5,HospitalisedAged5to14,HospitalisedAged15to24,HospitalisedAged25to34,HospitalisedAged35to44,HospitalisedAged45to54,HospitalisedAged55to64,HospitalisedAged65to74,HospitalisedAged75to84,HospitalisedAged85up,Male, Female, Unknown,Aged1to4, Aged5to14, Aged15to24, Aged25to34, Aged35to44, Aged45to54, Aged55to64, Aged65to74,Aged75to84,Aged85up,MedianAgeCommunityTransmission, CloseContact, TravelAbroad, Total Deaths by Date of Death,Deaths by Date of Death.

Note: Note: Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. As of April 27, 2023 updates changed from daily to weekly. Summary The cumulative number of probable COVID-19 deaths among Maryland residents by gender: Female; Male; Unknown. Description The MD COVID-19 - Probable Deaths by Gender Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by gender. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Confirmed Deaths by Gender Distribution data layer. Terms of Use The Spatial Data, and the information therein, (collectively the "Data") is provided "as is" without warranty of any kind, either expressed, implied, or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted, nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct, indirect, incidental, consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data, nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.

BackgroundThe differential effect of comorbidities on COVID-19 severe outcomes by sex has not been fully evaluated.ObjectiveTo examine the association of major comorbidities and COVID-19 mortality in men and women separately.MethodsWe performed a retrospective cohort analysis using a large electronic health record (EHR) database in the U.S. We included adult patients with a clinical diagnosis of COVID-19 who also had necessary information on demographics and comorbidities from January 1, 2016 to October 31, 2021. We defined comorbidities by the Charlson Comorbidity Index (CCI) using ICD-10 codes at or before the COVID-19 diagnosis. We conducted logistic regressions to compare the risk of death associated with comorbidities stratifying by sex.ResultsA total of 121,342 patients were included in the final analysis. We found significant sex differences in the association between comorbidities and COVID-19 death. Specifically, moderate/severe liver disease, dementia, metastatic solid tumor, and heart failure and the increased number of comorbidities appeared to confer a greater magnitude of mortality risk in women compared to men.ConclusionsOur study suggests sex differences in the effect of comorbidities on COVID-19 mortality and highlights the importance of implementing sex-specific preventive or treatment approaches in patients with COVID-19.

Context

Understanding gender is essential to understanding the risk factors of poor health, early death and health inequities. The COVID-19 outbreak is no different. At this point in the pandemic, we are unable to provide a clear answer to the question of the extent to which sex and gender are influencing the health outcomes of people diagnosed with COVID-19. However, experience and evidence thus far tell us that both sex and gender are important drivers of risk and response to infection and disease.

http://globalhealth5050.org/covid19 https://data.humdata.org/dataset/covid-19-sex-disaggregated-data-tracker

Content

In order to understand the role gender is playing in the COVID-19 outbreak, countries urgently need to begin both collecting and publicly reporting sex-disaggregated data. At a minimum, this should include the number of cases and deaths in men and women.

In collaboration with CNN, Global Health 50/50 began compiling publicly available sex-disaggregated data reported by national governments to date and is exploring how gender may be driving the higher proportion of reported deaths in men among confirmed cases so far.

Acknowledgements

http://globalhealth5050.org/covid19 https://data.humdata.org/dataset/covid-19-sex-disaggregated-data-tracker

Photo by Nick Fewings on Unsplash

Inspiration

Covid-19 Pandemic.

Outcomes of male vs female adults with COVID-19.

Number of deaths and age-specific mortality rates for selected grouped causes, by age group and sex, 2000 to most recent year.

https://videnskab.dk/files/styles/columns_12_12_desktop/public/article_media/shutterstock_1779839909.jpg?itok=kYzSroNA%C3%97tamp=1596709364" alt="">

Introduction

Coronavirus disease 2019 (COVID‑19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, Hubei, China, and has resulted in an ongoing pandemic. As of 12 August 2020, more than 20.2 million cases have been reported across 188 countries and territories, resulting in more than 741,000 deaths. More than 12.5 million people have recovered. Most people infected with the 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.

These numbers are sampled exclusively from Denmark between 11th of March 2020 and 9th of August 2020.

Content

This contains 10 data files:

• Cases_by_age.csv: Current number of confirmed cases for each age group.
• Cases_by_sex.csv: Current number of confirmed cases for men and women.
• Deaths_over_time.csv: The death toll for each day.
• Municipality_test_pos.csv: Number of tested and confirmed cases for each Danish municipality.
• Newly_admitted_over_time.csv: Number of newly hospitalised people for each region per day.
• Region_summary.csv: Number of tested and confirmed cases for each Danish region.
• Rt_cases.csv: Reproduction rate each day. A key measure of how fast the virus is growing.
• Rt_hospitalised.csv: Reproduction rate for hospitalised cases.
• Test_pos_over_time.csv: Number of new positive cases over time and total tested.
• Test_regions.csv: Number of tests done in each Danish region.

Wiki about COVID-19 in Denmark: https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Denmark Dashboard with information on COVID-19 in Denmark: https://experience.arcgis.com/experience/aa41b29149f24e20a4007a0c4e13db1d Currentcase count: https://www.worldometers.info/coronavirus/country/denmark/

COVID-19 mortality rates increase rapidly with age, are higher among men than women, and vary across racial/ethnic groups, but this is also true for other natural causes of death. Prior research on COVID-19 mortality rates and racial/ethnic disparities in those rates has not considered to what extent disparities reflect COVID-19-specific factors, versus preexisting health differences. This study examines both questions. We study the COVID-19-related increase in mortality risk and racial/ethnic disparities in COVID-19 mortality, and how both vary with age, gender, and time period. We use a novel measure validated in prior work, the COVID Excess Mortality Percentage (CEMP), defined as the COVID-19 mortality rate (Covid-MR), divided by the non-COVID natural mortality rate during the same time period (non-Covid NMR), converted to a percentage. The CEMP denominator uses Non-COVID NMR to adjust COVID-19 mortality risk for underlying population health. The CEMP measure generates insights which differ from those using two common measures–the COVID-MR and the all-cause excess mortality rate. By studying both CEMP and COVID-MRMR, we can separate the effects of background health from Covid-specific factors affecting COVID-19 mortality. We study how CEMP and COVID-MR vary by age, gender, race/ethnicity, and time period, using data on all adult decedents from natural causes in Indiana and Wisconsin over April 2020-June 2022 and Illinois over April 2020-December 2021. CEMP levels for racial and ethnic minority groups can be very high relative to White levels, especially for Hispanics in 2020 and the first-half of 2021. For example, during 2020, CEMP for Hispanics aged 18–59 was 68.9% versus 7.2% for non-Hispanic Whites; a ratio of 9.57:1. CEMP disparities are substantial but less extreme for other demographic groups. Disparities were generally lower after age 60 and declined over our sample period. Differences in socio-economic status and education explain only a small part of these disparities.

IntroductionBetween 2021 and 2023, a project was funded in order to explore the mortality burden (YLL–Years of Life Lost, excess mortality) of COVID-19 in Southern and Eastern Europe, and Central Asia.MethodsFor each national or sub-national region, data on COVID-19 deaths and population data were collected for the period March 2020 to December 2021. Unstandardized and age-standardised YLL rates were calculated according to standard burden of disease methodology. In addition, all-cause mortality data for the period 2015–2019 were collected and used as a baseline to estimate excess mortality in each national or sub-national region in the years 2020 and 2021.ResultsOn average, 15–30 years of life were lost per death in the various countries and regions. Generally, YLL rates per 100,000 were higher in countries and regions in Southern and Eastern Europe compared to Central Asia. However, there were differences in how countries and regions defined and counted COVID-19 deaths. In most countries and sub-national regions, YLL rates per 100,000 (both age-standardised and unstandardized) were higher in 2021 compared to 2020, and higher amongst men compared to women. Some countries showed high excess mortality rates, suggesting under-diagnosis or under-reporting of COVID-19 deaths, and/or relatively large numbers of deaths due to indirect effects of the pandemic.ConclusionOur results suggest that the COVID-19 mortality burden was greater in many countries and regions in Southern and Eastern Europe compared to Central Asia. However, heterogeneity in the data (differences in the definitions and counting of COVID-19 deaths) may have influenced our results. Understanding possible reasons for the differences was difficult, as many factors are likely to play a role (e.g., differences in the extent of public health and social measures to control the spread of COVID-19, differences in testing strategies and/or vaccination rates). Future cross-country analyses should try to develop structured approaches in an attempt to understand the relative importance of such factors. Furthermore, in order to improve the robustness and comparability of burden of disease indicators, efforts should be made to harmonise case definitions and reporting for COVID-19 deaths across countries.

Notice:Starting October 10th, 2025 this dataset is deprecated and is no longer being updated. Please refer to the Open Data resource at https://data.maryland.gov/Health-and-Human-Services/COVID-Master-Tracker/37gh-4yqf for continued weekly updates. SummaryThe cumulative number of probable COVID-19 deaths among Maryland residents by gender: Female; Male; Unknown.DescriptionThe MD COVID-19 - Probable Deaths by Gender Distribution data layer is a collection of the statewide confirmed and probable COVID-19 related deaths that have been reported each day by the Vital Statistics Administration by gender. A death is classified as probable if the person's death certificate notes COVID-19 to be a probable, suspect or presumed cause or condition. Probable deaths are not yet been confirmed by a laboratory test. Some data on deaths may be unavailable due to the time lag between the death, typically reported by a hospital or other facility, and the submission of the complete death certificate. Confirmed deaths are available from the MD COVID-19 - Confirmed Deaths by Gender Distribution data layer.COVID-19 is a disease caused by a respiratory virus first identified in Wuhan, Hubei Province, China in December 2019. COVID-19 is a new virus that hasn't caused illness in humans before. Worldwide, COVID-19 has resulted in thousands of infections, causing illness and in some cases death. Cases have spread to countries throughout the world, with more cases reported daily. The Maryland Department of Health reports daily on COVID-19 cases by county.

Context

The United States have recently become the country with the most reported cases of 2019 Novel Coronavirus (COVID-19). This dataset contains daily updated number of reported cases & deaths in the US on the state and county level, as provided by the Johns Hopkins University. In addition, I provide matching demographic information for US counties.

Content

The dataset consists of two main csv files: covid_us_county.csv and us_county.csv. See the column descriptions below for more detailed information. In addition, I've added US county shape files for geospatial plots: us_county.shp/dbf/prj/shx.

• covid_us_county.csv: COVID-19 cases and deaths which will be updated daily. The data is provided by the Johns Hopkins University through their excellent github repo. I combined the separate "confirmed cases" and "deaths" files into a single table, removed a few (I think to be) redundant geo identifier columns, and reshaped the data into long format with a single date column. The earliest recorded cases are from 2020-01-22.

• us_counties.csv: Demographic information on the US county level based on the (most recent) 2014-18 release of the Amercian Community Survey. Derived via the great tidycensus package.

Column Description

COVID-19 dataset covid_us_county.csv:

• fips: County code in numeric format (i.e. no leading zeros). A small number of cases have NA values here, but can still be used for state-wise aggregation. Currently, this only affect the states of Massachusetts and Missouri.

• county: Name of the US county. This is NA for the (aggregated counts of the) territories of American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and Virgin Islands.

• state: Name of US state or territory.

• state_code: Two letter abbreviation of US state (e.g. "CA" for "California"). This feature has NA values for the territories listed above.

• lat and long: coordinates of the county or territory.

• date: Reporting date.

• cases & deaths: Cumulative numbers for cases & deaths.

Demographic dataset us_counties.csv:

• fips, county, state, state_code: same as above. The county names are slightly different, but mostly the difference is that this dataset has the word "County" added. I recommend to join on fips.

• male & female: Population numbers for male and female.

• population: Total population for the county. Provided as convenience feature; is always the sum of male + female.

• female_percentage: Another convenience feature: female / population in percent.

• median_age: Overall median age for the county.

Acknowledgements

Data provided for educational and academic research purposes by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE).

Licence

The github repo states that:

This GitHub repo and its contents herein, including all data, mapping, and analysis, copyright 2020 Johns Hopkins University, all rights reserved, is provided to the public strictly for educational and academic research purposes. The Website relies upon publicly available data from multiple sources, that do not always agree. The Johns Hopkins University hereby disclaims any and all representations and warranties with respect to the Website, including accuracy, fitness for use, and merchantability. Reliance on the Website for medical guidance or use of the Website in commerce is strictly prohibited.

Version history

• In version 1, a small number of cases had values of `county == "Unassigned". Those have been superseded.
• Version 5: added US county shape files

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Data is based on a phone survey of 1,545 rural Indian households collected in August 2020 in 20 districts across 6 states (Rajasthan, Uttar Pradesh, Bihar, Jharkhand, Madhya Pradesh, and Maharashtra) in Northern India in August 2020. Households participated in a 20–30 min survey with two parts, a household head module and a female respondent module. In the household head module the household head surveyed about the household’s socioeconomic conditions, household head’s income, the male and female heads’ nutrition, and the number of days the respondent wished for more food for themselves or their children. The nutrition questions were taken from the National Family and Health Survey (NFHS) 2015–16, allowing to use the pre-pandemic responses to the survey from the same district to benchmark nutritional outcomes. After the head module, if the head was male, the head was asked to pass the phone to a female household member (typically the female household head). The female responded to an additional survey asking about her mental health and status within the household, as well as if this had changed since the pandemic. In cases where the respondent to the head module was female, the same respondent answered the female survey. Altogether, this allowed the female module to be conducted with 573 women. To ascertain information on women’s mental health, a selection of questions from the PHQ9 depression diagnostic scale and the GAD7 anxiety scale was asked. For a subset of questions, respondents’ were asked if outcomes have changed due to the pandemic. For example, for each of the mental health questions above (as well as the safety question), respondents were asked a follow-up question about whether their experiences have improved, worsened, or stayed the same since the pandemic. Measuring changes in these outcomes, enables to both assess the aggregate effects of the pandemic and measure the relationship between lockdowns and outcome variables, accounting for pre-pandemic differences across individuals. Additional data on case rates/deaths The phone survey data were supplemented with additional district level data on COVID-19 cases and deaths between the start of the pandemic and the time of the survey. Also hospitalization data from HMIS were used.

Methods: This was a national, population-based, cross-sectional study of routinely-collected mortality and demographic data pertaining to March-August of 2020 (COVID-19 pandemic) compared to the corresponding periods in 2015-2019. ICD-10-coded causes of death of deceased people of any age were obtained from a national mortality registry of death certificates. The G40-41 ICD-10 codes for epilepsy were used to define epilepsy-related deaths, with or without a U07*1-07*2 ICD-10 code for COVID-19 listed as an additional cause. Deaths unrelated to epilepsy were defined as all remaining Scottish deaths without G40-41 ICD-10 codes listed as a cause. We assessed the number of epilepsy-related deaths in 2020 compared to mean year-to-year variation observed in 2015-2019 (overall, men, women). We assessed proportionate mortality and odds ratios (OR) for deaths with COVID-19 listed as the underlying cause in people with epilepsy-related deaths compared to in deaths unrelated to epilepsy, reporting 95% confidence intervals (95% CIs). Sheet 1 contains a key to the remaining dataset.

Greetings everyone! I hope you find this dataset valuable for your COVID-19 models. It is aligned with SRK's Novel Corona Virus dataset. Feel free to upvote if you use it!

This dataset contains what I find as essential demographic information for every country specified in the submission COVID-19 competition file. Moreover, there is additional data which is critical in my point of view in order to predict the infection rate and mortality rate per country such as the number of COVID detection tests, detection date of 'patient zero' and initial restrictions dates. Please look at the columns description for the comprehensive explanation.

Major Insights:

1. I've seen that there are some pretty clear distinctions between female and male mortality rate as men tend to develop more severe symptoms. Therefore, I added some variables which represent the sex ratio (amount of males per female) in each country, with separation by age groups & total. Moreover, I added lung disease data (death rate per 100k people) in each country with separation by sex as well.
2. The average amount of children per woman has a quite high p-value when trying to analyze the trend of the confirmed cases. Especially when it comes in interaction with 'density' and school restrictions.

Citations and Data Gathering

1. https://www.worldometers.info/ - Population, Density, Median Age, Urban Population, Fertility Rate, Patient Zero Detection Date, Confirmed Cases, New Cases, Total Deaths, Total Recovered, Critical Cases.
2. @benhamner 's link (see acknowledgements section below) - Restrictions Initial dates.
3. https://worldpopulationreview.com/countries/smoking-rates-by-country/ - % of smokers by country.
4. https://data.worldbank.org/indicator/SH.MED.BEDS.ZS - Hospital beds per 1000 citizens.
5. https://en.wikipedia.org/wiki/List_of_countries_by_sex_ratio - Sex ratio by age.
6. https://www.worldlifeexpectancy.com/cause-of-death/lung-disease/by-country/ - Lung diseases death rate.
7. https://en.wikipedia.org/wiki/COVID-19_testing - COVID-19 Tests
8. https://www.worldbank.org/ - GDP 2019, Health Expenses (Whatever was missing was filled with information from Wikipedia)
9. https://en.climate-data.org/ - Temperature and Humidity raw data.

Acknowledgements

1. Restrictions are taken from here. Thanks to Ben Hamner for sharing this link!
2. Special thanks to @diamondsnake for the idea of collecting the average temperature and humidity.

Good luck trying to learn more about the virus, feel free to comment and collaborate in order to collect more relevant data!

My

Background: Transmission of COVID-19 in developing countries is expected to surpass that in developed countries; however, information on community perceptions of this new disease is scarce. The aim of the study was to identify possible misconceptions among males and females toward COVID-19 in Uganda using a rapid online survey distributed via social media.Methods: A cross-sectional survey carried out in early April 2020 was conducted with 161 Ugandans, who purposively participated in the online questionnaire that assessed understandings of COVID-19 risk and infection. Sixty-four percent of respondents were male and 36% were female.Results: We found significant divergences of opinion on gendered susceptibility to COVID-19. Most female respondents considered infection risk, symptoms, severe signs, and death to be equally distributed between genders. In contrast, male respondents believed they were more at risk of infection, severe symptoms, severe signs, and death (52.7 vs. 30.6%, RR = 1.79, 95% CI: 1.14–2.8). Most women did not share this perception and disagreed that males were at higher risk of infection (by a factor of three), symptoms (79% disagree), severe signs (71%, disagree), and death (70.2% disagree). Overall, most respondents considered children less vulnerable (OR = 1.12, 95% CI: 0.55–2.2) to COVID-19 than adults, that children present with less symptoms (OR = 1.57, 95% CI: 0.77–3.19), and that there would be less mortality in children (OR = 0.92, 95% CI: 0.41–1.88). Of female respondents, 76.4% considered mortality from COVID-19 to be different between the young and the elderly (RR = 1.7, 95% CI: 1.01–2.92) and 92.7% believed young adults would show fewer signs than the elderly, and 71.4% agreed that elderly COVID-19 patients would show more severe signs than the young (OR = 2.2, 95% CI: 1.4, 4.8). While respondents considered that all races were susceptible to the signs and symptoms of infection as well as death from COVID-19, they considered mortality would be highest among white people from Europe and the USA. Some respondents (mostly male 33/102, 32.4%) considered COVID-19 to be a “disease of whites” (30.2%).Conclusion: The WHO has identified women and children in rural communities as vulnerable persons who should be given more attention in the COVID-19 national response programs across Africa; however, our study has found that men in Uganda perceive themselves to be at greater risk and that these contradictory perceptions (including the association of COVID-19 with “the white” race) suggest an important discrepancy in the communication of who is most vulnerable and why. Further research is urgently needed to validate and expand the results of this small exploratory study.

Sex-ratio of Covid-19 death rates in France and South Africa (Male/Female).

Standardized mortality ratios by male vs. female and overall and specific mortality in the municipality of São Paulo, Brazil, from January to June of 2019 vs. 2020.

Cases, deaths, CFRs (%) (by age group and sex) and male to female CFRRs (%).