The flu is estimated to cause 400,000 respiratory deaths each year on average across the world. These deaths come from pneumonia and other respiratory symptoms caused by the flu. People also die from other complications of the flu – such as a stroke or heart attack – but global estimates have not been made of their death toll. The Spanish flu caused the largest influenza pandemic in history. Yet, data on the flu is limited. With better testing, countries could improve their response to flu epidemics. It could help to rapidly identify new strains, detect epidemics early, and design better-matched vaccines to target flu strains circulating in the population.

this data set contains the vaccine coverage around the world from 2018 to 2022.

this graph was created in OurDataWorld, R , Loocker and Tableau

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Introduction: Seasonal influenza, often perceived as a common illness, carries a significant global burden, claiming hundreds of thousands of lives annually. Despite advancements in healthcare and vaccination efforts, the flu remains a formidable threat, particularly affecting vulnerable populations such as infants and the elderly. This article delves into the intricacies of influenza-related mortality, examining regional disparities, contributing factors, and the implications for public health.

The Global Landscape of Influenza Mortality: Data from the Global Pandemic Mortality Project II sheds light on the magnitude of influenza-related deaths, drawing from surveillance metrics spanning from 2002 to 2011. These estimates, while informative, underscore the challenge of accurately gauging mortality rates, especially in low-income countries where testing and mortality records may be lacking.

Respiratory Symptoms and Beyond: The conventional understanding of influenza-related fatalities primarily revolves around respiratory complications. Pneumonia and other respiratory ailments serve as prominent causes of death, contributing to the staggering toll of 400,000 lives claimed annually. However, it is imperative to acknowledge that the impact of influenza extends beyond respiratory symptoms. Complications such as strokes and heart attacks, though not explicitly captured in mortality estimates, further amplify the disease's lethality, warranting comprehensive preventive measures.

Vulnerability Across Age Groups: Influenza's lethality is not uniform across age demographics. Infants and the elderly emerge as the most susceptible cohorts, bearing the brunt of severe complications and mortality. Among individuals aged over 65, the mortality rate stands at approximately 31 per 100,000 in Europe alone, reflecting the disproportionate impact on older populations. The interplay of age-related factors, including weakened immune responses and underlying health conditions, exacerbates the severity of influenza outcomes among these groups.

Regional Disparities and Determinants: A notable aspect of influenza mortality lies in its disparate distribution across regions. While Europe and North America exhibit relatively lower death rates, countries in South America, Africa, and South Asia grapple with higher mortality burdens. This regional divide underscores the complex interplay of socio-economic factors, healthcare accessibility, and vaccination coverage. Poverty, inadequate healthcare infrastructure, and suboptimal vaccination rates converge to heighten vulnerability to influenza-related complications, amplifying mortality rates in resource-constrained settings.

Implications for Public Health: The revelation of significant regional differentials in influenza mortality necessitates a tailored approach to public health interventions. Strengthening healthcare systems, particularly in low-income regions, is paramount to bolstering surveillance, enhancing diagnostic capabilities, and facilitating timely interventions. Furthermore, targeted vaccination campaigns, coupled with education initiatives, hold promise in mitigating influenza's toll, especially among vulnerable populations. Addressing socio-economic disparities and bolstering healthcare resilience emerge as pivotal strategies in fortifying global defenses against seasonal influenza.

Conclusion: Seasonal influenza, often underestimated in its impact, exacts a substantial toll on global health each year. The multifaceted nature of influenza-related mortality underscores the need for a nuanced understanding and comprehensive mitigation strategies. By addressing regional disparities, prioritizing vulnerable populations, and fortifying healthcare systems, the global community can strive towards mitigating the burden of seasonal influenza, safeguarding lives, and fostering resilient health systems for generations to come.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

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.

Motivation

Why did I create this dataset? This is my first time creating a notebook in Kaggle and I am interested in learning more about COVID-19 and how different countries are affected by it and why. It might be useful to compare different metrics between different countries. And I also wanted to participate in a challenge, and I've decided to join the COVID-19 datasets challenge. While looking through the projects, I noticed https://www.kaggle.com/koryto/countryinfo and it inspired me to start this project.

Method

My approach is to scour the Internet and Kaggle looking for country data that can potentially have an impact on how the COVID-19 pandemic spreads. In the end, I ended up with the following for each country:

• Monthly temperature and precipitation from Worldbank
• Latitude and longitude
• Population, density, gender and age
• Airport traffic from Worldbank
• COVID-19 date of first case and number of cases and deaths as of March 26, 2020
• 2009 H1N1 flu pandemic cases and deaths obtained from Wikipedia
• Property affordability index and Health care index from Numbeo
• Number of hospital beds and ICU beds from Wikipedia
• Flu and pneumonia death rate from Worldlifeexpectancy.com (Age Adjusted Death Rate Estimates: 2017)
• School closures due to COVID-19
• Number of COVID-19 tests done
• Number of COVID-19 genetic strains
• US Social Distancing Policies from COVID19StatePolicy’s SocialDistancing repository on GitHub
• DHL Global Connectedness Index 2018 (People Breadth scores)
• Datasets have been merged by country name whenever possible. I needed to rename some countries by hand, e.g. US to United Sates, etc. but it's possible that I might have missed some. See the output file covid19_merged.csv for the merged result.

See covid19_data - data_sources.csv for data source details.

Notebook: https://www.kaggle.com/bitsnpieces/covid19-data

Caveats

Since I did not personally collect each datapoint, and because each datasource is different with different objectives, collected at different times, measured in different ways, any inferences from this dataset will need further investigation.

Other interesting sources of information

• IMF Policy Tracker
• nCov strain analysis across the globe!
• Google Mobility Report
• Oxford government response tracker
• Impact on aviation
• Impact on restaurants
• Harvard
• Tableau datahub
• Unemployment outlook from ILO

Acknowledgements

I want to acknowledge the authors of the datasets that made their data publicly available which has made this project possible. Banner image is by Brian.

I hope that the community finds this dataset useful. Feel free to recommend other datasets that you think will be useful / relevant! Thanks for looking.

https://fluprint.com/about/section_1.png" alt="Influenza vaccination responders">

The problem

Influenza virus has a devastating societal impact, causing up to 650,000 deaths every year worldwide. Specifically, vulnerable are children and elderly. It is estimated that 1 in 1000 children and elderly every year are hospitalized due to influenza infection. Vaccination can prevent influenza-like illnesses, and thus lower the risk of the virus outbreak. However, currently available vaccines do not always provide protection, even among otherwise-healthy people, leading to serious pandemics. Development of better vaccines depends on our understanding why current vaccines work in some individuals, while fail in others.

About dataset

The FluPRINT is the name of a unified database for a large-scale study exploring novel cellular and molecular underpinnings of successful immunity to influenza vaccines. It contains information on more than 3,000 parameters measured using mass cytometry, flow cytometry, phosphorylation-specific cytometry (phospho-flow), multiplex ELISA, clinical lab tests (hormones and complete blood count), serological profiling with hemagglutination inhibition assay, and virological tests. The dataset represents fully integrated and normalized immunology measurements from 747 individuals from eight clinical studies conducted between 2007 to 2015 at the Human Immune Monitoring Center of Stanford University. The dataset represents a unique source in terms of value and scale, which will broaden our understanding of influenza immunity.

Additional info: https://zenodo.org/record/3222451#.XOb7MaR7lPY

Citation

Tomic A, Tomic I, Dekker CL, Maecker HT and Davis MM. The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system. Sci Data, doi: 10.1038/s41597-019-0213-4, 2019. 

Context

In this dataset, we will take a look at vaccination, a key public health measure used to fight infectious diseases. Vaccines provide immunization for individuals, and enough immunization in a community can further reduce the spread of diseases through "herd immunity."

As of the update of this dataset, vaccines for the COVID-19 virus are still under development and not yet available. The dataset will instead revisit the public health response to a different recent major respiratory disease pandemic.Beginning in spring 2009, a pandemic caused by the H1N1 influenza virus, colloquially named "swine flu," swept across the world. Researchers estimate that in the first year, it was responsible for between 151,000 to 575,000 deaths globally.

A vaccine for the H1N1 flu virus became publicly available in October 2009. In late 2009 and early 2010, the United States conducted the National 2009 H1N1 Flu Survey. This phone survey asked respondents whether they had received the H1N1 and seasonal flu vaccines, in conjunction with questions about themselves. These additional questions covered their social, economic, and demographic background, opinions on risks of illness and vaccine effectiveness, and behaviors towards mitigating transmission. A better understanding of how these characteristics are associated with personal vaccination patterns can provide guidance for future public health efforts.

Content

The goal is to predict how likely individuals are to receive their H1N1 and seasonal flu vaccines. Specifically, you'll be predicting two probabilities: one for h1n1_vaccine and one for seasonal_vaccine as well as any sophisticates EDAs.

Each row in the dataset represents one person who responded to the National 2009 H1N1 Flu Survey and there are two target variables:

• h1n1_vaccine: Whether respondent received H1N1 flu vaccine.
• seasonal_vaccine: Whether respondent received seasonal flu vaccine.

Both are binary variables: 0 = No; 1 = Yes. Some respondents didn't get either vaccine, others got only one, and some got both. This is formulated as a multilabel (and not multiclass) problem.

You are provided a dataset with 36 columns. The first column respondent_id is a unique and random identifier. The remaining 35 features are described below(For all binary variables: 0 = No; 1 = Yes) :

• age_group - Age group of respondent.
• education - Self-reported education level.
• race - Race of respondent.
• sex - Gender of respondent.
• income_poverty - Household annual income of respondent with respect to 2008 Census poverty thresholds.
• marital_status - Marital status of respondent.
• rent_or_own - Housing situation of respondent.
• employment_status - Employment status of respondent.
• h1n1_concern - Level of concern about the H1N1 flu. 0 = Not at all concerned; 1 = Not very concerned; 2 = Somewhat concerned; 3 = Very concerned.
• h1n1_knowledge - Level of knowledge about H1N1 flu. 0 = No knowledge; 1 = A little knowledge; 2 = A lot of knowledge.
• behavioral_wash_hands - Has frequently washed hands or used hand sanitizer. (binary)
• behavioral_large_gatherings - Has reduced time at large gatherings. (binary)
• behavioral_antiviral_meds - Has taken antiviral medications. (binary)
• behavioral_avoidance - Has avoided close contact with others with flu-like symptoms. (binary)
• behavioral_face_mask - Has bought a face mask. (binary)
• behavioral_outside_home - Has reduced contact with people outside of own household. (binary)
• behavioral_touch_face - Has avoided touching eyes, nose, or mouth. (binary)
• doctor_recc_h1n1 - H1N1 flu vaccine was recommended by doctor. (binary)
• doctor_recc_seasonal - Seasonal flu vaccine was recommended by doctor. (binary)
• chronic_med_condition - Has any of the following chronic medical conditions: asthma or an other lung condition, diabetes, a heart condition, a kidney condition, sickle cell anemia or other anemia, a neurological or neuromuscular condition, a liver condition, or a weakened immune system caused by a chronic illness or by medicines taken for a chronic illness. (binary)
• child_under_6_months - Has regular close contact with a child under the age of six months. (binary)
• health_worker - Is a healthcare worker. (binary)
• health_insurance - Has health insurance. (binary)
• opinion_h1n1_vacc_effective - Respondent's opinion about H1N1 vaccine effectiveness. 1 = Not at all effective; 2 = Not very effective; 3 = Don't know; 4 = Somewhat effective; 5 = Very effective.
• opinion_h1n1_risk - Respondent's opinion about risk of getting sick with H1N1 flu without vaccine. 1 = Very Low; 2 = Somewhat low; 3 = Don't know; 4 = Somewhat high; 5 = Very high.
• opinion_h1n1_sick_from_vacc - Respondent's worry of getting sick from taking H1N1 vaccine. 1 = Not at all worried; 2 = Not very worried; 3 = Don't know; 4 = Somewhat worried; 5 = Very wo...

Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.

Context

Influenza is a viral disease that infects millions of people worldwide on a yearly basis. Influenza has a strong seasonal bias infecting the northern hemisphere in winter and the southern hemisphere in summer. The reason for this is not well understood.

Content

This dataset is a collection of weekly reports that are submitted to the WHO FluNet by 167 countries. The number of reports is only a small subset of the actual infections and are not directly comparable across years or between countries.

Acknowledgements

WHO FluNet https://www.who.int/influenza/gisrs_laboratory/flunet/en/

Inspiration

In the time of large pandemic outbreaks understanding the dynamics of viral infection can be a useful tool in managing viral diseases. The data was not very accessible so I hope this format helps people dive a little into the field and maybe find some useful information in it.

Influenza viruses pose a serious threat to human health, infecting hundreds of millions of people worldwide each year, resulting in a significant increase in global morbidity and mortality. Influenza activity has declined at the onset of the COVID-19 pandemic, but the genetic diversity of B/Victoria lineage viruses has increased significantly during this period. Therefore, the prevention and treatment of the influenza B Victoria strain virus should continue to attract research attention. In this study, we found that Atractyloside A (AA), one of the effective components in Atractylodes lancea (Thunb.) DC shows potential antiviral properties. This study shows that AA not only possesses anti-influenza B virus infection effects in vivo and in vitro but also can regulate macrophage polarization to the M2 type, which can effectively attenuate the damage caused by influenza B virus infection. Therefore, Atractyloside A may be an effective natural drug against B/Victoria influenza infection.

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

Meta-regression analysis of the effect of the factors on vaccine acceptance in pregnant women.

Collection of socio-economic and meteorological indicators as well as travel patterns and cases of H1N1 during the swine flu pandemic in Sweden in 2009. Comprise the supplementary information for the paper titled "Socioeconomic and environmental patterns behind H1N1 spreading in Sweden" by András Bóta, Martin Holmberg, Lauren Gardner and Martin Rosvall, Sci Rep 11, 22512 (2021). https://doi.org/10.1038/s41598-021-01857-4 Identifying the critical socio-economic, travel and climate factors related to influenza spreading is critical to the prediction and mitigation of epidemics. In the paper we study the 2009 A(H1N1) outbreak in the municipalities of Sweden, following it for six years between 2009 and 2015. Our goal is to discover the relationship between the above indicators and the timing of the epidemic onset of the disease. We also identify the municipalities playing a key role in the outbreak as well as the most critical travel routes of the country.

Publication available at: https://doi.org/10.1038/s41598-021-01857-4

Municipality codes for the municipalities of Sweden can be found here: https://www.scb.se/en/finding-statistics/regional-statistics/regional-divisions/counties-and-municipalities/counties-and-municipalities-in-numerical-order/

Data available according to Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license

Model inputs 1. giim_kommun_graph.csv Set of frequent travel routes between the municipalities of Sweden. The graph was constructed from "Trafikanalys, 2016. Resvanor. (accessed 26.8.19). Available from: http://www.trafa.se/RVU-Sverige/." using the methodology described in the paper. Date of construction: 2018-12-01 Format: csv Structure: edge list in (kommun1;kommun2) format with rows indicating a directed link between two municipalities. Municipalities are denoted according to their official municipal code

1. giim_casecounts.xlsx Number of new H1N1 cases in the municipalities of Sweden between 2009 and 2015. Our data set consists of all laboratory-verified cases of A(H1N1)pdm09 between May 2009 and December 2015, extracted from the SmiNet register of notifiable diseases, held by the Public Health Agency of Sweden. Due to confidentiality reasons, cases are anonymized, and addresses are aggregated at the DeSo level together with the date of diagnosis, age, and gender. We obtained ethical approval for the data acquisition. Date of construction: 2018-12-01 Format: xlsx Structure: Each tab represents a single flu season from the 2009/2010 season to the 2014/2015 season. Each tab is a matrix with rows indicating municipalities according to their official municipal code, and columns indicating epidemic weeks. Values of the matrices indicate the number of new laboratory-verified cases of A(H1N1)pdm09

2. giim_kommun_indicators.csv Socioeconomic and meteorological indicators are assigned to the municipalities of Sweden according to the methodology described in the paper. Indicators included are: a, mean temperature in degree Celsius, b, absolute humidity in grams per cubic metre, c, population size as the number of people living in each municipality, d, population density as the number of people per sq. km of land area, e, median income per household in thousand SEK, f, fraction of people on social aid (as a percentage), g, average number of children younger than 18 years per household. Meteorological data was obtained from the European Climate Assessment Dataset "Klein Tank A, Wijngaard J, Können G, Böhm R, Demarée G, Gocheva A, et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2002;22(12):1441–1453." Data from the dataset was converted to the municipality level according to the methodology described in the paper. Variables are mean temperature and relative humidity converted to absolute humidity for all municipalities of Sweden. Socioeconomic data was collected from Statistics Sweden between 2018 Ocotber and 2019 February. Available from: https://www.scb.se/en/. Variables are: The average household income as an economic indicator. The average number of children younger than 18 years per household to indicate family size. The fraction of people receiving social aid to represent poverty in a municipality. Population size and population density as the number of people per sq. km of land area. Date of construction: 2018-02-01 Format: csv Structure: Each row corresponds to a municipality denoted according to their official municipal code. Columns indicate socioeconomic and meteorological indicators as marked by the header row.

Model outputs 1. giim_export_risk.csv Exportation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate exportation risk values (should not be interpreted as probabilities).

1. giim_import_risk.csv Importation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate importation risk values (should not be interpreted as probabilities).

2. giim_transmission_prob.csv Transmission probabilities between all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Edge list with multiple edge weights. Rows indicate a directed link between the two municipalities (kommun1;kommun2) in the beginning of the row. The rest of the values in each row denote the corresponding transmission probabilities for each epidemic week computed according to the methodology described in the paper.

The bold values indicate the best results over all combinations of models and datasets. The first value in each cell indicates how many peaks the models predicted correctly. The second value indicates how many peaks were predicted in the ±2 weeks range. The green colour indicates the best results by either using the PV or the PC+PV dataset, by keeping both the country and the model fixed (no colour means no changes).

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

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• 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

Work has already begun towards developing a COVID-19 vaccine. From measles to the common flu, vaccines have lowered the risk of illness and death, and have saved countless lives around the world. Unfortunately in some countries, the 'anti-vaxxer' movement has led to lower rates of vaccination and new outbreaks of old diseases.

Although it may be many months before we see COVID-19 vaccines available on a global scale, it is important to monitor public sentiment towards vaccinations now and especially in the future when COVID-19 vaccines are offered to the public. The anti-vaccination sentiment could pose a serious threat to the global efforts to get COVID-19 under control in the long term.

The objective of this challenge is to develop a machine learning model to assess if a Twitter post related to vaccinations is positive, neutral, or negative. This solution could help governments and other public health actors monitor public sentiment towards COVID-19 vaccinations and help improve public health policy, vaccine communication strategies, and vaccination programs across the world.

Work has already begun towards developing a COVID-19 vaccine. From measles to the common flu, vaccines have lowered the risk of illness and death, and have saved countless lives around the world. Unfortunately in some countries, the 'anti-vaxxer' movement has led to lower rates of vaccination and new outbreaks of old diseases.

Although it may be many months before we see COVID-19 vaccines available on a global scale, it is important to monitor public sentiment towards vaccinations now and especially in the future when COVID-19 vaccines are offered to the public. The anti-vaccination sentiment could pose a serious threat to the global efforts to get COVID-19 under control in the long term.

The objective of this challenge is to develop a machine learning model to assess if a Twitter post related to vaccinations is positive, neutral, or negative. This solution could help governments and other public health actors monitor public sentiment towards COVID-19 vaccinations and help improve public health policy, vaccine communication strategies, and vaccination programs across the world.

Demographic characteristics of influenza-like illness (ILI) and severe acute respiratory illness (SARI) cases in Ghana from 2011 to 2019.