Context

In the shadows of the Covid-19 pandemic, there is another global health crisis that has gone largely unnoticed. This is the Noncommunicable Disease (NCD) pandemic.

The WHO website describes NCDs as follows:

Noncommunicable diseases (NCDs), also known as chronic diseases, tend to be of long duration and are the result of a combination of genetic, physiological, environmental and behaviours factors.

The main types of NCDs are cardiovascular diseases (like heart attacks and stroke), cancers, chronic respiratory diseases (such as chronic obstructive pulmonary disease and asthma) and diabetes.

NCDs disproportionately affect people in low- and middle-income countries where more than three quarters of global NCD deaths – 32million – occur.

Key facts:

• Noncommunicable diseases (NCDs) kill 41 million people each year, equivalent to 71% of all deaths globally.
• Each year, 15 million people die from a NCD between the ages of 30 and 69 years; over 85% of these "premature" deaths occur in low- and middle-income > * countries.
• Cardiovascular diseases account for most NCD deaths, or 17.9 million people annually, followed by cancers (9.0 million), respiratory diseases (3.9million), and diabetes (1.6 million).
• These 4 groups of diseases account for over 80% of all premature NCD deaths.
• Tobacco use, physical inactivity, the harmful use of alcohol and unhealthy diets all increase the risk of dying from a NCD.
• Detection, screening and treatment of NCDs, as well as palliative care, are key components of the response to NCDs.

Content

This data repository consists of 3 CSV files: WHO-cause-of-death-by-NCD.csv is the main dataset, which provides the percentage of deaths caused by NCDs out of all causes of death, for each nation globally. Metadata_Country.csv and Metadata_Indicator.csv provide additional metadata which is helpful for interpreting the main CSV.

The data collected spans a period from 2000 to 2016. The main CSV has columns for every year from 1960 to 2019. It is advisable to drop all redundant columns where no data was collected.

Furthermore, it is advisable to merge Metadata_Country.csv with the main CSV as it provides valuable additional information, particularly on the economic situation of each nation.

Acknowledgements

This dataset has been extracted from The World Bank 'Cause of death, by non-communicable diseases (% of total)' Dataset, derived based on the data from WHO's Global Health Estimates. It is freely provided under a Creative Commons Attribution 4.0 International License (CC BY 4.0), with the additional terms as stated on the World Bank website: World Bank Terms of Use for Datasets.

Inspiration

I would be interested to see some good data wrangling (dropping redundant columns), as well as kernels interpreting additional information in 'SpecialNotes' column in Metadata_country.csv

It would also be great to see what different factors influence NCDs: most of all, the geopolitical factors. Would be great to see some choropleth visualisations to get an idea of which regions are most affected by NCDs.

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.

About Dataset

Context:

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs).

DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content:

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise):

1. Country/Territory - Name of the Country/Territory
2. Code - Country/Territory Code
3. Year - Year of the Incident
4. Meningitis - No. of People died from Meningitis
5. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
6. Parkinson's Disease - No. of People died from Parkinson's Disease
7. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
8. Malaria - No. of People died from Malaria
9. Drowning - No. of People died from Drowning
10. Interpersonal Violence - No. of People died from Interpersonal Violence
11. Maternal Disorders - No. of People died from Maternal Disorders
12. Drug Use Disorders - No. of People died from Drug Use Disorders
13. Tuberculosis - No. of People died from Tuberculosis
14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
16. Neonatal Disorders - No. of People died from Neonatal Disorders
17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
18. Self-harm - No. of People died from Self-harm
19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
22. Neoplasms - No. of People died from Neoplasms
23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
26. Poisonings - No. of People died from Poisoning
27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
30. Digestive Diseases - No. of People died from Digestive Diseases
31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
32. Acute Hepatitis - No. of People died from Acute Hepatitis Structure of the Dataset

Acknowledgement:

This Dataset is created from Our World in Data. This Dataset falls under open access under the Creative Commons BY license. You can check the FAQ for more informa...

Context

A straightforward way to assess the health status of a population is to focus on mortality – or concepts like child mortality or life expectancy, which are based on mortality estimates. A focus on mortality, however, does not take into account that the burden of diseases is not only that they kill people, but that they cause suffering to people who live with them. Assessing health outcomes by both mortality and morbidity (the prevalent diseases) provides a more encompassing view on health outcomes. This is the topic of this entry. The sum of mortality and morbidity is referred to as the ‘burden of disease’ and can be measured by a metric called ‘Disability Adjusted Life Years‘ (DALYs). DALYs are measuring lost health and are a standardized metric that allow for direct comparisons of disease burdens of different diseases across countries, between different populations, and over time. Conceptually, one DALY is the equivalent of losing one year in good health because of either premature death or disease or disability. One DALY represents one lost year of healthy life. The first ‘Global Burden of Disease’ (GBD) was GBD 1990 and the DALY metric was prominently featured in the World Bank’s 1993 World Development Report. Today it is published by both the researchers at the Institute of Health Metrics and Evaluation (IHME) and the ‘Disease Burden Unit’ at the World Health Organization (WHO), which was created in 1998. The IHME continues the work that was started in the early 1990s and publishes the Global Burden of Disease study.

Content

In this Dataset, we have Historical Data of different cause of deaths for all ages around the World. The key features of this Dataset are: Meningitis, Alzheimer's Disease and Other Dementias, Parkinson's Disease, Nutritional Deficiencies, Malaria, Drowning, Interpersonal Violence, Maternal Disorders, HIV/AIDS, Drug Use Disorders, Tuberculosis, Cardiovascular Diseases, Lower Respiratory Infections, Neonatal Disorders, Alcohol Use Disorders, Self-harm, Exposure to Forces of Nature, Diarrheal Diseases, Environmental Heat and Cold Exposure, Neoplasms, Conflict and Terrorism, Diabetes Mellitus, Chronic Kidney Disease, Poisonings, Protein-Energy Malnutrition, Road Injuries, Chronic Respiratory Diseases, Cirrhosis and Other Chronic Liver Diseases, Digestive Diseases, Fire, Heat, and Hot Substances, Acute Hepatitis.

Dataset Glossary (Column-wise)

• 01. Country/Territory - Name of the Country/Territory
• 02. Code - Country/Territory Code
• 03. Year - Year of the Incident
• 04. Meningitis - No. of People died from Meningitis
• 05. Alzheimer's Disease and Other Dementias - No. of People died from Alzheimer's Disease and Other Dementias
• 06. Parkinson's Disease - No. of People died from Parkinson's Disease
• 07. Nutritional Deficiencies - No. of People died from Nutritional Deficiencies
• 08. Malaria - No. of People died from Malaria
• 09. Drowning - No. of People died from Drowning
• 10. Interpersonal Violence - No. of People died from Interpersonal Violence
• 11. Maternal Disorders - No. of People died from Maternal Disorders
• 12. Drug Use Disorders - No. of People died from Drug Use Disorders
• 13. Tuberculosis - No. of People died from Tuberculosis
• 14. Cardiovascular Diseases - No. of People died from Cardiovascular Diseases
• 15. Lower Respiratory Infections - No. of People died from Lower Respiratory Infections
• 16. Neonatal Disorders - No. of People died from Neonatal Disorders
• 17. Alcohol Use Disorders - No. of People died from Alcohol Use Disorders
• 18. Self-harm - No. of People died from Self-harm
• 19. Exposure to Forces of Nature - No. of People died from Exposure to Forces of Nature
• 20. Diarrheal Diseases - No. of People died from Diarrheal Diseases
• 21. Environmental Heat and Cold Exposure - No. of People died from Environmental Heat and Cold Exposure
• 22. Neoplasms - No. of People died from Neoplasms
• 23. Conflict and Terrorism - No. of People died from Conflict and Terrorism
• 24. Diabetes Mellitus - No. of People died from Diabetes Mellitus
• 25. Chronic Kidney Disease - No. of People died from Chronic Kidney Disease
• 26. Poisonings - No. of People died from Poisoning
• 27. Protein-Energy Malnutrition - No. of People died from Protein-Energy Malnutrition
• 28. Chronic Respiratory Diseases - No. of People died from Chronic Respiratory Diseases
• 29. Cirrhosis and Other Chronic Liver Diseases - No. of People died from Cirrhosis and Other Chronic Liver Diseases
• 30. Digestive Diseases - No. of People died from Digestive Diseases
• 31. Fire, Heat, and Hot Substances - No. of People died from Fire or Heat or any Hot Substances
• ...

Tuberculosis is one of the most common causes of death globally.

By Saloni Dattani, Fiona Spooner, Hannah Ritchie and Max Roser

Data description:

In richer countries, the impact of tuberculosis has been reduced significantly over history, but in poorer parts of our world, it continues to be a major challenge even today: it causes an estimated 1.2 million deaths annually.

Tuberculosis is caused by the bacteria Mycobacterium tuberculosis.

The bacteria spreads through respiratory particles and tends to cause tuberculosis in people with risk factors such as undernourishment, HIV/AIDS, smoking, and existing chronic conditions.

The disease involves symptoms like coughing, fatigue and night sweats, and can damage the lungs, the brain, kidneys and other organs, which can be fatal.

But it is treatable with a combination of specific antibiotics. Without being diagnosed correctly, however, people do not receive the proper treatment. This leaves them vulnerable, and also increases the risk that antibiotic-resistant strains of the bacteria will develop, which are much more difficult and expensive to treat.

With greater effort to tackle its risk factors and improve testing and treatment for the disease, the world can relegate tuberculosis to history — not just in the richer parts of the world, but for everyone.

Data number 1: Tuberculosis is still common in many parts of the world In high-income countries, tuberculosis is largely a disease of the past. Since the beginning of the 20th century, its impact has been significantly reduced with the development of antibiotics and improvements in healthcare and living standards.

Data number 2: Tuberculosis kills over a million people annually, most of whom are adults Tuberculosis kills over a million people each year, as you can see in the chart. The chart shows that most of those who die from tuberculosis are adults.

Data number 3: Many people with tuberculosis are undiagnosed Although tuberculosis is typically a disease of the lungs, the bacteria can affect many organs in the body, and people who are infected don’t always have respiratory symptoms. Instead, they may experience weight loss, breathlessness, fever, or night sweats.

Data number 4: Antibiotic resistance is an important consideration during treatment People with tuberculosis require treatment with a specific combination of antibiotic medications that can kill the bacteria.

Data number 5: HIV increases the risk of developing tuberculosis An HIV infection is a major risk factor for developing tuberculosis.

Good luck

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

Smoking is so common, and feels so familiar, that it can be hard to grasp just how large the impact is. Every year, around 8 million people die prematurely as a result of smoking.1 This means that about one in seven deaths worldwide are due to smoking.2 Millions more live in poor health because of it.

Smoking primarily contributes to early deaths through heart diseases and cancers. Globally, more than one in five cancer deaths are attributed to smoking.

This means tobacco kills more people every day than terrorism kills in a year.

Smoking is a particularly large problem in high-income countries. There, cigarette smoking is the most important cause of preventable disease and death. This is especially true for men: they account for almost three-quarters of deaths from smoking.

The impact of smoking is devastating on the individual level. In case you need some motivation to stop smoking: The life expectancy of those who smoke regularly is about 10 years lower than that of non-smokers.

It’s also devastating on the aggregate level. In the past 30 years more than 200 million have died from smoking. Looking into the future, epidemiologists Prabhat Jha and Richard Peto estimate that “If current smoking patterns persist, tobacco will kill about 1 billion people this century.”

It is on us to prevent this.

A dataset providing GP recorded chronic obstructive pulmonary disease rates. Chronic Obstructive Pulmonary Disease (COPD) is a serious long-term lung disease in which the flow of air into the lungs is gradually reduced by inflammation of the air passages and damage to the lung tissue. Chronic Bronchitis and emphysema are common types of COPD. Chronic Obstructive Pulmonary Disease (COPD) is the fifth biggest killer disease in the UK, killing approximately 25,000 people a year in England. Further information For more information on public health, please visit: http://www.leeds.gov.uk/phrc/Pages/default.aspx

AbstractThe prevalence of disease-driven mass mortality events is increasing, but our understanding of spatial variation in their magnitude, timing, and triggers are often poorly resolved. Here, we use a novel range-wide dataset comprised of 48,810 surveys to quantify how Sea Star Wasting Disease affected Pycnopodia helianthoides, the sunflower sea star, across its range from Baja California, Mexico to the Aleutian Islands, USA. We found that the outbreak occurred more rapidly, killed a greater percentage of the population, and left fewer survivors in the southern half of the species’ range. Pycnopodia now appears to be functionally extinct (> 99.2% declines) from Baja California, Mexico to Cape Flattery, Washington, USA and exhibited severe declines (> 87.8%) from the Salish Sea to the Gulf of Alaska. The importance of temperature in predicting Pycnopodia distribution rose 450% after the outbreak, suggesting these latitudinal gradients may stem from an interaction between disease severity and warmer waters. We found no evidence of population recovery in the years since the outbreak. Natural recovery in the southern half of the range is unlikely over the short-term and assisted recovery will likely be required for recovery in the southern half of the range on ecologically-relevant time scales., MethodsThirty research groups from Canada, the United States, Mexico, including First Nations, shared 34 datasets containing field surveys of Pycnopodia (Table S1). The data included 48,810 surveys from 1967 to 2020 derived from trawls, remotely operated vehicles, SCUBA dives, and intertidal surveys. We compiled survey data into a standardized format that included at minimum the coordinates, date, depth, area surveyed, and occurrence of Pycnopodia for each survey. When datasets contained more than one survey at a site in the same day (e.g. multiple transects), we divided the total Pycnopodia count in all surveys by the total survey area and averaged the latitude, longitude, and depth as necessary. Using breaks in data coverage, political boundaries, and biogeographic breaks we assigned each survey to one of twelve regions: Aleutian Islands, west Gulf of Alaska (GOA), east Gulf of Alaska, southeast Alaska, British Columbia (excluding the Salish Sea), Salish Sea (including the Puget Sound), Washington outer coast (excluding the Puget Sound), Oregon, northern California, central California, southern California, and the Pacific coast of Baja California (Fig. S1; see Supplementary Material)., Usage notesDocumentation, data, and code accompanying Hamilton et al., 2021 Pycnopodia Rangewide Assessment paper. Data MasterPycno_ToShare: Dec_lat = latitude in decimal degrees. Numeric. Dec_lon = longitude in decimal degrees. Numeric. Depth = depth in meters. Numeric. Pres_abs = presence or absence of Pycnopodia on that survey. Binary. Presence = 1, absence = 0 Density_m2 = density in meters squared if available for that set of surveys. Numeric. NA = no density data available for that survey. Source = shorthand name of the group that shared the data with us and the type of data (e.g. trawl, dive). To get further info on who that dataset, group, and group contact, see Table S1. Character. Note: When datasets contained more than one survey at a site in the same day (e.g. multiple transects), we divided the total Pycnopodia count in all surveys by the total survey area and averaged the latitude, longitude, and depth as necessary in order to minimize the impacts of pseudoreplication on the dataset. Used in MaxentSWD_Final and Density-Inc_Models_Figs_Tables_ToShare. CrashEventsForRPlot: Crash Dates were determined trends in Pycnopodia occurrence (site-level presence or absence) to estimate ‘crash date’, defined as the date when the occurrence rate of Pycnopodia in a region decreased by 75% from pre-outbreak levels. Used in OutbreakTimelineFigs_ToShare.R EpidemicPhases: See manuscript methods for information on how the column ‘EpidemicPhases’ was created. “Start-End” specifies whether that date was the start or the end of that epidemic phase for that region. Used in OutbreakTimelineFigs_ToShare.R Incidence_2012-2019: Columns G-J were calculated by fitting a logistic regression model to the occurrence of Pycnopodia over time for each region. We fit a logistic regression model to the occurrence of Pycnopodia from 1/1/2012 to 12/31/2019 to model the shape of the population decline for each region (Fig. 1a). From these models, we 1) estimated regional Pycnopodia occurrence rates on 1/1/2012 and 12/31/2019, 2) calculated the predicted occurrence value corresponding to a 75% decline in starting versus ending occurrence in each region, and 3) solved the inverse logistic equations for the date at which this occurrence value was predicted. All other columns are identifying information derived ... Visit https://dataone.org/datasets/sha256%3Ad5473633780710452e0d852d4a25d7e05c82c63b26e6c94b9d9e5f493924d8c3 for complete metadata about this dataset.

Rate of deaths by age/gender (per 100,000 population) for people killed in crashes involving a driver with BAC =>0.08%, 2012, 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File. Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The impacts of high ambient temperatures on mortality in humans and domestic animals are well understood. However, much less is known about how hot weather affects mortality in wild animals. High ambient temperatures have been associated with African wild dog Lycaon pictus pup mortality, suggesting that high temperatures might also be linked to high adult mortality. We analysed mortality patterns in African wild dogs radio-collared in Kenya (0°N), Botswana (20°S), and Zimbabwe (20°S), to examine whether ambient temperature was associated with adult mortality. We found that high ambient temperatures were associated with increased adult wild dog mortality at the Kenya site, and there was some evidence for temperature associations with mortality at the Botswana and Zimbabwe sites. At the Kenya study site, which had the highest human impact, high ambient temperatures were associated with increased risks of wild dogs being killed by people, and by domestic dog diseases. In contrast, temperature was not associated with the risk of snare-related mortality at the Zimbabwe site, which had the second-highest human impact. Causes of death varied markedly between sites. Pack size was positively associated with survival at all three sites. These findings suggest that while climate change may not lead to new causes of mortality, rising temperatures may exacerbate existing anthropogenic threats to this endangered species, with implications for conservation. This evidence suggests that temperature-related mortality, including interactions between temperature and other anthropogenic threats, should be investigated in a greater number of species to understand and mitigate the likely impacts of climate change. Methods Study sites We analysed adult African wild dog mortality at three sites: the Ewaso ecosystem, Kenya; the Okavango Delta, Botswana; and Savé Valley Conservancy, Zimbabwe. All three study sites fall within semi-arid savanna ecosystems.

Field Data Collection At the Kenya study site 130 African wild dogs (56 female, 74 male) from 41 packs were monitored using either Vectronics GPS collars (GPS Plus, Vectronic Aerospace GmbH), Televilt GPS collars (GPS-Posrec, Televilt, Lindesberg, Sweden), Berlin, Germany), or VHF radio-collars (Telonics, Mesa AZ, USA). All three collar types included a mortality sensor programmed to emit a characteristic radio signal if stationary for ≥4h. At the Zimbabwe study site, 59 African wild dogs (22 female, 37 male) from 34 packs were monitored using either radio collars or GPS collars (African Wildlife Tracking, Rietondale, Pretoria, South Africa). Using radio-collars (Sirtrack, Havelock West, New Zealand) 31 African wild dogs (10 female, 21 male) from 16 packs were monitored at the Botswana site. Collars were fitted using the procedures outlined in McNutt (1996), Woodroffe (2011) and Jackson et al. (2017). At all three sites, packs were located every 1-2 weeks where possible. Any collared animal found dead was carefully examined with the aim of establishing a cause of death. At the Kenya site necropsies were carried out on all dead individuals located. At the Botswana site cause of death was only recorded in cases where the death was directly observed, or during disease outbreaks, and therefore the majority of causes of death were unconfirmed. Most deaths at the Botswana site are likely to be due to natural causes given the low human activity in this area. For all three sites, the date of first detection of a mortality signal from the collar was used to estimate the date of death when not observed directly, and where this was not possible an estimated date of mortality was made based on the date midway between the last sighting, or the last detection of the radio-collar without a mortality signal, and the discovery of the carcass or collar. If any study animal was not observed in its resident pack for over 30 days, no mortality signal was detected, and no carcass was found, it was considered lost from the study and censored from the day of the last observation (Kenya: n=51, Zimbabwe: n=34, Botswana: n=8). If a carcass or collar was discovered more than 30 days after the last sighting (n=2), the animal was considered lost from the study due to the inaccuracy of the date of death and was censored from the date of the last sighting. Group and individual characteristics were recorded at each site. At all three sites dispersal status of the individual was recorded. Individuals were defined as dispersing if they left their pack for multiple days and did not return, otherwise they were defined as resident (Woodroffe et al. 2019b). Group size – either the pack size for resident individuals or the dispersal group size for dispersing individuals – was recorded for each individual, and was defined as the number of adults (>12 months in age) in the group. African wild dog pup-rearing involves the pups being left at a den site for the first three months of life while the majority of the rest of the pack hunt daily, bringing food back to provision the pups. This pup rearing period is referred to as denning. For each pack, denning periods were identified using either direct observations or GPS-collar data. At the Kenya site a number of other individual and pack characteristics were also monitored. Individuals’ alpha status was inferred based on consistent close association with a specific individual of the opposite sex, coordinated scent marking, and reproductive activity; all animals not identified as alpha were considered subdominant. African wild dog age was known for many individuals, otherwise it was estimated from tooth wear when the individual was collared (Woodroffe et al. 2019b). Age range at collaring ranged from 1 to 7 years old (mean: 2.43 ±1.27). The age of the majority of individuals at the Zimbabwe and Botswana sites was not known. Weather data is from weather stations within the field site at Mpala research station at the Kenya site (detailed in Caylor K., Gitonga, J. and Martins 2016), 30km outside the study site at Maun airport for the Botswana site and the Middle Sabi Research Station, 12km from the study area boundary at the Zimbabwe research site.

Data Processing The average mean temperature was taken on a 90 day rolling average at the Kenya and Zimbabwe sites, and a 30 day rolling average at the Botswana site. Rainfall was summed over a 30 day rolling time period at the Kenya and Botswana sites and a 90 day rolling period at the Zimbabwe site.

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Video on Risk factors of Lung Cancer - ![https://youtu.be/0vVRp5eNDlA?feature=shared]

Dataset: 1. GENDER: Gender of the individual (M: Male, F: Female) 2. AGE: Age of the individual 3. SMOKING: Smoking status (2: Yes, 1: No) 4. YELLOW_FINGERS: Presence of yellow fingers (2: Yes, 1: No) 5. ANXIETY: Anxiety level (2: High, 1: Low) 6. PEER_PRESSURE: Peer pressure level (2: High, 1: Low) 7. CHRONIC DISEASE: Presence of chronic disease (2: Yes, 1: No) 8. FATIGUE: Fatigue level (2: High, 1: Low) 9. ALLERGY: Allergy status (2: Yes, 1: No) 10. WHEEZING: Wheezing condition (2: Yes, 1: No) 11. ALCOHOL CONSUMING: Alcohol consumption status (2: Yes, 1: No) 12. COUGHING: Presence of coughing (2: Yes, 1: No) 13. SHORTNESS OF BREATH: Shortness of breath condition (2: Yes, 1: No) 14. SWALLOWING DIFFICULTY: Difficulty in swallowing (2: Yes, 1: No) 15. CHEST PAIN: Presence of chest pain (2: Yes, 1: No) 16. LUNG_CANCER: Lung cancer diagnosis (2: Yes, 1: No)

• Data has 309 rows and 16 columns with floating variables, integer, object which ranges from 0 - 308

• Lung cancer is the uncontrollable growth of abnormal cells in one or both of the lungs. Cigarette smoking causes most lung cancers when smoke gets in the lungs. Lung cancer kills 1.8 million people each year, more than any other cancer. It has an 80-90% death rate, and is the leading cause of cancer death in men, and the second leading cause of cancer death in women.

• The global cancer burden is estimated to have risen to 18.1 million new cases and 9.6 million deaths in 2018. One in 5 men and one in 6 women worldwide develop cancer during their lifetime, and one in 8 men and one in 11 women die from the disease. Worldwide, the total number of people who are alive within 5 years of a cancer diagnosis, called the 5-year prevalence, is estimated to be 43.8 million.

Shigellaa Gram-negative, non-motile bacillus, is the primary causative agent of the infectious disease shigellosis, which kills 1.1 million people worldwideevery year. The children under the age of five are primarily the victims of this disease. This study has been conducted to assess the prevalence of shigellosis through selective plating, biochemical test and conventional PCR assays, where the samples were collected from suspected diarrheoal patients. Invasive plasmid antigen H (ipaH) and O-antigenic rfc gene were used to identify Shigella spp. and S. flexneri respectively. For validation of these identification, PCR product of ipaH gene of a sample (Shigella flexneri MZS 191) has been sequenced and submitted to NCBI database (GenBank accession no- MW774908.1). Further this strain has been used as positive control. Out of 204, around 14.2% (n = 29)(P> 0.01) pediatric diarrheoal cases were screened as shigellosis. Another interesting finding was that most of shigellosis affected children were 7 months to 1 year (P> 0.01).The significance of this study lies in the analyses of the occurrenceand the molecular identification of Shigellaspp. and S. flexneri that can be utilized in improving the accurate identification and the treatment of the most severe and alarming shigellosis.

BackgroundPulmonary Tuberculosis (PTB) is a significant global health issue due to its high incidence, drug resistance, contagious nature, and impact on people with compromised immune systems. As mentioned by the World Health Organization (WHO), TB is responsible for more global fatalities than any other infectious illness. On the other side, WHO also claims that noncommunicable diseases (NCDs) kill 41 million people yearly worldwide. In this regard, several studies suggest that PTB and NCDs are linked in various ways and that people with PTB are more likely to acquire NCDs. At the same time, NCDs can increase susceptibility to active TB infection. Furthermore, because of potential drug interactions and therapeutic challenges, treating individuals with both PTB and NCDs can be difficult. This study focuses on seven NCDs (lung cancer (LC), diabetes mellitus (DM), Parkinson’s disease (PD), silicosis (SI), chronic kidney disease (CKD), cardiovascular disease (CVD), and rheumatoid arthritis (RA)) and rigorously presents the genetic relationship with PTB regarding shared genes and outlines possible treatment plans.ObjectivesBlueThis study aims to identify the drug components that can regulate abnormal gene expression in NCDs. The study will reveal hub genes, potential biomarkers, and drug components associated with hub genes through statistical measures. This will contribute to targeted therapeutic interventions.MethodsNumerous investigations, including protein-protein interaction (PPI), gene regulatory network (GRN), enrichment analysis, physical interaction, and protein-chemical interaction, have been carried out to demonstrate the genetic correlation between PTB and NCDs. During the study, nine shared genes such as TNF, IL10, NLRP3, IL18, IFNG, HMGB1, CXCL8, IL17A, and NFKB1 were discovered between TB and the above-mentioned NCDs, and five hub genes (NFKB1, TNF, CXCL8, NLRP3, and IL10) were selected based on degree values.Results and conclusionIn this study, we found that all of the hub genes are linked with the 10 drug components, and it was observed that aspirin CTD 00005447 was mostly associated with all the other hub genes. This bio-informatics study may help researchers better understand the cause of PTB and its relationship with NCDs, and eventually, this can lead to exploring effective treatment plans.

Contained here are files to support the submission of the article "CASE STUDY: Monsters, Microbiology and Mathematics: the epidemiology of a zombie apocalypse" currently under review at the Journal of Biological Education.

Simulations were generated using SimZombie, an agent-based model of monster outbreaks based on the SIR epidemiology system. Source code is available at http://code.google.com/p/simzombie/

Simulations are provided as animated GIF files, which will display in any browser, and can be embedded in PowerPoint presentations if required. In all scenarios, white smilies represent dead bodies and yellow smilies represent uninfected individuals, while zombies, vampires and werewolves all have their own icons.

zombies.gif - A 'standard' zombie outbreak. Note how even though the zombies are easy to kill, because the infection rate is high, the infection still spreads consistently outwards. At the end of the simulation, there are a large number of zombies and only few dead bodies.

vampires.gif - A 'standard' vampire outbreak. Vampires are 'fussier' than zombies, as they are aware of depleting their food source. As such, the infection rate is much lower - meaning the kill rate is higher. The simulation ends with many more dead bodies and much fewer vampires, but the outcome is equally as apocalyptic.

werewolves.gif - A 'standard' werewolf outbreak. Only active during the full moon, a werewolf outbreak is much slower than the other two monsters present. Able to move through the population undetected, however, and being particularly vicious monsters, means that when they are active, they have a large uninfected population around them and are successful in attacking or converting a number of individuals.

hiding.gif - In an attempt to protect themselves from the zombie apocalypse, a number of individuals have hidden themselves inside a large building. Unfortunately, a small area of the wall has been damaged. Once the zombies are able to get in, there is nowhere for the individuals to escape to, and because they are confined to a smaller space (i.e. their density is higher) the zombie disease spreads rapidly.

quarantine.gif - The only successful defense scenario shown here. Due to the ratio of zombies to susceptibles, reversing the hiding scenario means the break in the wall becomes a highly defensible position, ensuring that as the zombies slip out in small numbers, they are able to be taken care of more easily.

Also included are two workmats, which we have used successfully in a variety of public engagement activities described in the paper. The original workmat (designed for A0 size) is aimed at an older audience and covers some in-depth questions and requires some numerical knowledge, but develops parameters for direct input into SimZombie. The revised workmat (designed for A1 size) is better suited for a younger audience, but is more of a general prompt to think about the overarching questions relating to epidemiology than thinking of parameters directly for SimZombie.

For more information regarding Monsters, Microbiology and Mathematics, including our public engagement activities, please see the paper these materials support ( http://www.tandfonline.com/doi/full/10.1080/00219266.2013.849283 ) and the Manchester Metropolitan University's Public Engagement website ( http://www.sci-eng.mmu.ac.uk/engage/ ).