Introduction:

Coronaviruses, a family of enveloped RNA viruses, have long captivated the interest of scientists and public health experts due to their ability to cause a wide range of diseases in animals and humans. The ongoing COVID-19 pandemic has underscored the significant impact coronaviruses can have on global health and economy. In this article, we delve into the rich history of coronaviruses, tracing their origins, evolution, and the impact they have had on human populations.

Influenza viruses belong to a different family called Orthomyxoviridae. While both influenza viruses and coronaviruses can cause respiratory illnesses, they are distinct types of viruses with different genetic structures, modes of transmission, and clinical presentations.

Influenza viruses are characterised by segmented RNA genomes, and they are further classified into types A, B, and C. Influenza A viruses are known to infect a wide range of animals, including birds and mammals, whereas influenza B and C viruses primarily infect humans.

Coronaviruses, on the other hand, belong to the family Coronaviridae and are characterised by their single-stranded RNA genomes. Coronaviruses are named for the crown-like spikes that protrude from their surface under electron microscopy. They can cause illnesses ranging from the common cold to more severe diseases such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and COVID-19.

While both influenza viruses and coronaviruses are respiratory viruses that can lead to similar symptoms, such as fever, cough, and respiratory distress, they are genetically distinct and require different approaches for prevention, diagnosis, and treatment.

Another project of mine is entitled FluNet, Global Influenza Programme - WHO. Which looks at the importance of monitoring the global uptrend of influenza. link

Origins and Discovery:

Coronaviruses were first identified in the mid-20th century. The term "coronavirus" originates from the Latin word "corona," meaning crown or halo, referring to the characteristic appearance of the virus under electron microscopy, with spike proteins protruding from its surface. The first coronavirus, infectious bronchitis virus (IBV), was discovered in chickens in the 1930s. However, it wasn't until the 1960s that human coronaviruses were identified.

Human Coronaviruses:

The first human coronaviruses, HCoV-229E and HCoV-OC43, were identified in the 1960s and were primarily associated with mild respiratory illnesses such as the common cold. These early discoveries led to further investigations into the diversity and pathogenicity of coronaviruses. Subsequently, other human coronaviruses, including HCoV-NL63 and HCoV-HKU1, were identified.

A table summarising the key information about known human coronaviruses, in chronological order of discovery. link

Emergence of Severe Acute Respiratory Syndrome (SARS):

The emergence of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) in 2002 marked a significant turning point in the understanding of coronaviruses. SARS-CoV originated in bats and likely crossed into humans through intermediate hosts such as palm civets in wet markets in China. The virus caused severe respiratory illness with high mortality rates, leading to a global outbreak that affected over 8,000 people in 29 countries. The SARS outbreak highlighted the potential of coronaviruses to cause severe and sometimes fatal diseases in humans.

Middle East Respiratory Syndrome (MERS):

In 2012, another novel coronavirus, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), emerged in the Arabian Peninsula. MERS-CoV is believed to have originated in bats and transmitted to humans through dromedary camels. MERS-CoV causes severe respiratory illness with a high fatality rate, particularly in individuals with underlying health conditions. While the spread of MERS-CoV has been limited compared to SARS-CoV, sporadic outbreaks continue to occur in the Middle East.

COVID-19 Pandemic:

The most recent and devastating coronavirus pandemic began in late 2019 when a novel coronavirus, SARS-CoV-2, emerged in Wuhan, China. The virus quickly spread globally, leading to the declaration of a pandemic by the World Health Organisation in March 2020. COVID-19, the disease caused by SARS-CoV-2, has resulted in millions of infections and deaths worldwide, overwhelmed healthcare systems, and caused profound social and economic disruptions. The rapid spread of SARS-CoV-2 has been facilitated by its being highly transmissible, including transmission from asymptomatic individuals, as well as factors such as global travel and urbanisation. While most individuals experience mild to moderate symptoms, COVID-19 can lead to severe respiratory illness, acute respiratory distress syndrome (ARDS), multi-organ failure, and death, particularly in older adults and those with underlying health conditions.

COVID-19 Origin:

The origins of COVID-19, caused by the SARS-CoV-2 virus, remain a subject of ongoing investigation and debate among scientists, public health experts, and policymakers. Several hypotheses have been proposed regarding the emergence of the virus, including zoonotic spillover from animals to humans, laboratory-related incidents, and other scenarios. Here are some key points regarding the latest developments on the origin of COVID-19:

• Zoonotic Spillover:
  • The leading hypothesis is that SARS-CoV-2 originated in bats and may have been transmitted to humans through an intermediate host, possibly in a wet market in Wuhan, China, where live animals were sold.
  • Research indicates that bats harbour a diverse array of coronaviruses, some of which are closely related to SARS-CoV-2. However, the exact intermediate host species and the circumstances of transmission to humans remain uncertain.
  • The hypothesis of recombination between viruses harboured by bats and pangolins, leading to the emergence of SARS-CoV-2, is compelling. The observed genomic concordance in specific regions, particularly in the ACE (Angiotensin Converting Enzyme 2) receptor binding domain crucial for viral entry into human cells, suggests a potential role for pangolins as intermediate hosts in the transmission of the virus to humans. However, further research is needed to confirm this hypothesis and elucidate the exact mechanisms and conditions under which such recombination events occurred. link
• Laboratory-related Incident:
  • Another hypothesis suggests the possibility of a laboratory-related incident, such as accidental release from a research laboratory studying coronaviruses. This theory has gained attention due to concerns about bio-safety practices and the proximity of the Wuhan Institute of Virology (WIV) to the initial COVID-19 outbreak.
  • However, investigations into this hypothesis have been hampered by limited access to relevant data, samples, and facilities in China, as well as geopolitical tensions and lack of international cooperation.
• Ongoing Investigations:
  • The World Health Organisation (WHO) conducted a joint study with China in early 2021 to investigate the origins of COVID-19. The study concluded that zoonotic spillover was the most likely scenario and that a laboratory-related incident was "extremely unlikely." However, the study was criticised for its limited access to data and the need for further investigation. link
  • The WHO has called for additional studies, transparency, and collaboration to better understand the origins of the virus. In May 2021, the WHO proposed a second phase of studies, including audits of relevant laboratories and markets in Wuhan.
  • BEIJING, July 22 (Reuters) - China rejected on Thursday a World Health Organisation (WHO) plan for a second phase of an investigation into the origin of the coronavirus, which includes the hypothesis it could have escaped from a Chinese laboratory, a top health official said. link
• International Calls for Investigation:
  • The origins of COVID-19 have sparked international debate and calls for independent, transparent, and comprehensive investigations. Some countries, including the United States, have called for further inquiry into the possibility of a laboratory-related incident and have urged China to cooperate fully with international investigations.
  • Efforts to investigate the origins of COVID-19 have been complicated by geopolitical tensions, lack of cooperation, and challenges in accessing relevant data and facilities in China.

In summary, the origins of COVID-19 remain a complex and contentious issue, and investigations into the virus's origins are ongoing. While the zoonotic spillover hypothesis remains the most widely accepted explanation, questions and uncertainties persist, highlighting the need for continued research, collaboration, and transparency to better understand the origins of the pandemic and prevent future outbreaks. Although zoonotic diseases have become increasingly prominent in modern health agendas, historical zoonoses have received scant attention, despite the potential importance of earlier transmission events in shaping past and present health landscapes. link

Long COVID:

"Long COVID," also known as post-acute sequelae of SARS-CoV-2 infection (PASC), refers to a range of persistent symptoms that continue for weeks or months after the acute phase of COVID-19 has resolved. While many individuals recover from COVID-19 within a few weeks, a significant proportion experience lingering symptoms that can significantly impact their quality of life and ability to function normally.

Symptoms of long COVID can vary widely among individuals and may affect multiple organ systems. Common symptoms include:

• Fatigue: Persistent feelings of exhaustion or lack of energy, even after minimal physical or mental exertion.
• Shortness of breath: Difficulty breathing or shortness of breath, particularly with exertion.
• Cognitive impairment: Difficulty concentrating, memory problems, "brain fog," or other cognitive deficits.
• Muscle and joint pain: Persistent muscle aches, joint pain, or weakness.
• Headaches: Recurring headaches or migraines.
• Chest pain: Persistent chest discomfort or tightness.
• Loss of smell or taste: Changes in the sense of smell or taste that persist beyond the acute phase of illness.
• Heart palpitations: Awareness of irregular or rapid heartbeat.
• Gastrointestinal symptoms: Digestive issues such as diarrhoea, nausea, or abdominal pain.
• Sleep disturbances: Insomnia, disrupted sleep patterns, or excessive daytime sleepiness.

The exact mechanisms underlying long COVID are not fully understood, but several factors may contribute to its development. These include persistent inflammation, immune dysregulation, organ damage, neurological effects, and psychological factors such as stress and anxiety.

Long COVID can affect individuals of all ages, including those who had mild or asymptomatic COVID-19 infections initially. Risk factors for developing long COVID may include the severity of the initial illness, pre-existing health conditions, age, and genetic predisposition.

Managing long COVID requires a multidisciplinary approach tailored to individual symptoms and needs. Treatment strategies may include medications to alleviate specific symptoms, such as pain relievers, anti-inflammatory drugs, or medications to manage cardiovascular or respiratory symptoms. Rehabilitation therapies, including physical therapy, occupational therapy, and cognitive-behavioural therapy, may also be beneficial in improving functional abilities and quality of life.

Supportive care, such as adequate rest, hydration, nutrition, and stress management, is essential for promoting recovery. Additionally, ongoing monitoring and follow-up with healthcare providers are crucial to identify and address any new or worsening symptoms and to adjust treatment plans as needed. Understanding the burden of long COVID. link

Research into long COVID is ongoing, with efforts focused on better understanding its underlying mechanisms, identifying risk factors, and developing effective interventions to support recovery and improve outcomes for affected individuals. As our understanding of long COVID continues to evolve, healthcare professionals and public health authorities are working to raise awareness, improve access to care, and provide support for individuals living with this complex and challenging condition. link

Excess Deaths:

Excess deaths, in the context of public health, refer to the difference between the number of deaths actually observed in a specific period and the number of deaths expected during that same period under normal circumstances. Here's a breakdown:

• What are "normal circumstances"?
  • This usually refers to historical data, like the average number of deaths observed in the same period during previous years (e.g., 2019 for pre-pandemic data).
• How is it measured?
  • You compare the actual number of deaths to the estimated "expected" number of deaths based on past trends.
  • The difference between these two figures represents the excess deaths.
  • This can be expressed as a number or a percentage.
• Why is it important?
  • Excess deaths provide a broader picture of mortality beyond deaths directly attributed to a specific cause, like a pandemic or natural disaster.
  • It can capture indirect deaths caused by disruptions to healthcare systems, changes in behaviour (e.g., less access to healthcare), or worsening chronic conditions due to stress or lack of resources.
  • This information helps public health officials understand the overall impact of an event and guide resource allocation and interventions.
• Examples:
  • During the COVID-19 pandemic, excess mortality figures were significantly higher than reported COVID-19 deaths, highlighting the indirect effects of the pandemic on healthcare systems and individuals' health.
  • Heatwaves or natural disasters can lead to excess deaths due to heatstroke, injuries, and disruptions to essential services.
• Additional points:
  • Calculating excess deaths can be complex due to data limitations and the need to account for seasonal variations and other factors.
  • Different organisations may use slightly different methods to estimate expected deaths, leading to variations in reported excess death figures.
  • It is crucial to interpret excess death data with caution and consider other relevant information for a comprehensive understanding.

Data Visualisations.

Total COVID -19 Cases & Deaths Over Time, Distribution of Excess Deaths & Excess Deaths in Individuals Aged 65 and Older Over Time: 2020-2024, from the data - covid.csv A Markdown document with the R code for the 4 above plots from the data: covid.csv - link

Charts:

• Total COVID Cases Over Time:
  • This plot shows a cumulative increase in the number of confirmed COVID-19 cases reported globally since the beginning of the pandemic in early 2020.
  • The y-axis shows the number of cases in millions, while the x-axis shows the year.
  • As of February 25, 2024, there have been over 703 million confirmed cases reported worldwide.
• Total COVID Deaths Over Time:
  • This plot shows a cumulative increase in the number of confirmed COVID-19 deaths reported globally since the beginning of the pandemic in early 2020.
  • The y-axis shows the number of deaths in millions, while the x-axis shows the year.
  • As of February 25, 2024, there have been over 6.9 million confirmed deaths reported worldwide.
• Distribution of Excess Deaths:
  • This plot shows the distribution of excess deaths, which are deaths that are above the expected number for a specific time period, across different age groups.
  • The y-axis shows the frequency of deaths, while the x-axis shows the age group.
  • It is important to note that this plot does not necessarily show COVID-19 deaths specifically, but rather all excess deaths, which could be caused by a variety of factors.
• Excess Deaths in Individuals Aged 65 and Older Over Time:
  • This plot shows the number of excess deaths in individuals aged 65 and older over time.
  • The y-axis shows the number of deaths, while the x-axis shows the year.
  • The plot shows that the number of excess deaths in this age group has been increasing since the beginning of the pandemic.

It is important to note that these graphs are just a snapshot of the COVID-19 pandemic. The pandemic is a complex and constantly evolving situation, and the data is constantly changing. However, these graphs can help us to understand some of the general trends of the pandemic in the world.

Excess Deaths by Country & Age: 2020-2023, from the data - ED.csv

A Markdown document with the R code for the above plot from the data: ED.csv - link

A Markdown document with the R code for data examination for the data set: ED.csv - link

A document that explains the R code for data examination: ED.csv - link

The x-axis of the chart shows the year, from 2020 to 2023. The y-axis shows the number of excess deaths. The lines on the chart show the number of excess deaths in each country for each age group. The different colours represent different age groups:

• 0 to 44 years old (red).
• 45 to 64 years old (green).
• 65 and over (blue).

The chart shows that there have been excess deaths in all of the countries shown, for all age groups. However, the number of excess deaths varies by country and age group. For example, there have been more excess deaths in the United States than in any other country shown. There have also been more excess deaths among people aged 65 and over than in any other age group.

It is important to note that this chart does not show the total number of deaths in each country. It only shows the number of deaths that are above what would be expected based on historical data. This means that the chart may not give a complete picture of the mortality situation in each country. link

Cardiovascular Death Rate and Diabetes Prevalence Over 65 yrs Old: 2020-2024, from the data - covid.csv

A Markdown document with the R code for the above plot from the data: covid.csv - link

• The chart is a line chart that shows the trends of cardiovascular death rate and diabetes prevalence in the data from 2020 to 2024:
  • The x-axis represents the date, and the y-axis represents the rate or prevalence.
  • The two lines in the chart represent the two variables: the blue line represents cardiovascular death rate per 100,000 population, and the orange line represents diabetes prevalence in percentage of the population.

• The tibble output summarises the data for each variable:
  • The first column, "variable", shows the name of the variable.
  • The second column, "mean_value", shows the mean value of the variable.
  • The third column, "median_value", shows the median value of the variable.
  • The fourth column, "sd_value", shows the standard deviation of the variable.
  • The fifth column, "min_value", shows the minimum value of the variable.
  • The sixth column, "max_value", shows the maximum value of the variable.

As you can see from the chart, both cardiovascular death rate and diabetes prevalence have been increasing over time. However, the rate of increase has been slowing down in recent years (this trend is easier to see in the below chart). The tibble output confirms this trend, as the mean and median values for both variables are higher in 2024 than in 2020. However, the standard deviation is also higher in 2024, which suggests that there is more variability in the data.

Cardiovascular Death Rate and Diabetes Prevalence Over 65 yrs Old: 2020-2024, from the data - covid.csv

A Markdown document with the R code for the above plot from the data: covid.csv - link

• For the "Cardiovascular Death Rate" plot:
  • The y-axis scale ranges from 0 to 750, as specified in the coord_cartesian() function.
  • This scale represents the cardiovascular death rate per 100,000 population. For example, if the y-axis value is 250, it means there were 250 cardiovascular deaths per 100,000 population at that specific time point.
• For the "Diabetes Prevalence" plot:
  • Since I haven't explicitly set the y-axis limits for this plot, it will use the same scale as the "Cardiovascular Death Rate" plot.
  • However, based on the dataset (covid.csv), the maximum diabetes prevalence is around 30.53, so the y-axis scale will adjust accordingly.
  • This scale represents the percentage of individuals aged 65 and older who have diabetes. For example, if the y-axis value is 10, it means that 10% of the population aged 65 and older have diabetes at that specific time point.

Cardiovascular Death Rate and Diabetes Prevalence All Ages: 2020-2024, from the data - covid.csv

A Markdown document with the R code for the above plot from the data: covid.csv - link

Chart:

• The cardiovascular death rate plot shows a downward trend from 2020 to 2023. Then, there is a slight upward trend in 2024.
• The diabetes prevalence plot shows an upward trend from 2020 to 2024.

Tibble Output:

• Cardiovascular Death Rate: Has a higher average (mean) and median value compared to diabetes prevalence, indicating a generally higher death rate.
• Cardiovascular Death Rate: Shows a much larger standard deviation compared to diabetes prevalence. This suggests greater variability in cardiovascular death rates across the observations.
• Cardiovascular Death Rate: Exhibits a notably wider range (difference between minimum and maximum) compared to diabetes prevalence. The presence of extreme values in the cardiovascular data is influencing this result.

COVID-19 Transmission:

Coronaviruses, like many respiratory viruses, can be transmitted through various routes, including respiratory droplets, aerosols, and contact with contaminated surfaces. However, not all coronaviruses are primarily airborne. The mode of transmission can vary depending on the specific virus strain, its characteristics, and the environmental conditions: link

• Respiratory Droplets:
  • Many coronaviruses, including the common human coronaviruses (e.g., HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU1) and the viruses responsible for severe acute respiratory syndrome (SARS-CoV), Middle East respiratory syndrome (MERS-CoV), and COVID-19 (SARS-CoV-2), are primarily spread through respiratory droplets produced when an infected person coughs, sneezes, talks, or breathes. These droplets can travel through the air over short distances and can be inhaled by individuals nearby, leading to infection.
• Aerosol Transmission:
  • Some coronaviruses, particularly COVID-19 (SARS-CoV-2), have been shown to spread through aerosols, which are smaller respiratory particles that can remain suspended in the air for longer periods and travel farther distances. Aerosol transmission may occur in enclosed or poorly ventilated spaces, especially in settings where there is prolonged exposure to respiratory emissions from infected individuals. Aerosol transmission has been implicated in certain outbreaks, particularly in indoor environments such as healthcare facilities, workplaces, and crowded gatherings.
• Contact Transmission:
  • Coronaviruses can also be transmitted through direct contact with respiratory secretions from infected individuals or indirect contact with contaminated surfaces or objects. When a person touches a surface or object contaminated with the virus and then touches their mouth, nose, or eyes, they can become infected. Proper hand hygiene and surface disinfection are essential for reducing the risk of contact transmission.

While respiratory droplets and aerosols are important modes of transmission for many coronaviruses, including COVID-19 (SARS-CoV-2), the relative contribution of each route to overall transmission may vary depending on factors such as viral load, infectiousness, environmental conditions, and human behaviour. Public health measures, such as wearing masks, maintaining physical distance, improving ventilation, and practising hand hygiene, are critical for reducing the spread of coronaviruses and mitigating the risk of infection. link

COVID-19: Airborne Transmission.

The understanding that COVID-19 can be transmitted through airborne particles evolved over time as scientists conducted research, gathered evidence, and analysed epidemiological data. Here are key milestones in the recognition of airborne transmission of COVID-19:

• Early Recognition of Respiratory Transmission:
  • In the early stages of the COVID-19 pandemic, it became evident that the virus primarily spread through respiratory droplets produced when an infected person coughed, sneezed, talked, or breathed. Public health guidance emphasised measures such as wearing masks, physical distancing, and hand hygiene to reduce the risk of droplet transmission.
• Emerging Evidence of Airborne Transmission:
  • Throughout 2020, researchers began to accumulate evidence suggesting that SARS-CoV-2 could also be transmitted through aerosols, smaller respiratory particles that can remain suspended in the air for longer periods. Studies documented instances of COVID-19 transmission in indoor settings, including poorly ventilated spaces, where aerosols could play a role.
• Scientific Studies and Investigations:
  • Researchers conducted laboratory experiments and field studies to investigate the behaviour of SARS-CoV-2 in aerosols and to assess the risk of airborne transmission in different environments. These studies provided evidence of virus-containing aerosols in indoor air and highlighted the potential for airborne transmission, particularly in enclosed spaces with poor ventilation.
• Expert Consensus and Updated Guidance:
  • As scientific understanding of COVID-19 transmission evolved, leading public health organisations, including the World Health Organisation (WHO), the Centres for Disease Control and Prevention (CDC), and other health authorities, revised their guidance to acknowledge the role of airborne transmission. Recommendations for ventilation, air filtration, and other measures to reduce indoor airborne transmission were emphasised.
• Recognition by Health Authorities:
  • In July 2021, Chinese scientists published a study in the journal Environment International. Providing evidence supporting the airborne transmission of SARS-CoV-2. The study, titled "Airborne transmission of COVID-19 in indoor environments," highlighted the potential for airborne transmission of the virus, particularly in enclosed and poorly ventilated spaces. This acknowledgement of airborne transmission by Chinese scientists was an important development in our understanding of how COVID-19 spreads and informed public health measures to mitigate transmission risks.
  • In July 2021, the WHO issued a scientific brief acknowledging the possibility of airborne transmission of SARS-CoV-2 in certain circumstances, particularly in crowded and inadequately ventilated indoor settings. The CDC also updated its guidance to highlight the importance of indoor air quality and ventilation in mitigating the risk of COVID-19 transmission.

It's worth noting that while there may have been earlier indications and scientific discussions regarding the potential for airborne transmission of COVID-19, the formal acknowledgement and recognition of airborne transmission by health authorities, including those in China, occurred as scientific evidence accumulated and understanding of the virus evolved.

Overall, the recognition of airborne transmission of COVID-19 was a gradual process, informed by scientific research, epidemiological investigations, and expert consensus. As our understanding of the virus continues to evolve, ongoing research and surveillance efforts are essential for refining public health strategies and minimising the spread of COVID-19.

The Effects of Temperature and Humidity on Transmission:

The role of temperature and humidity in the transmission of COVID-19 has been a subject of scientific investigation since the early stages of the pandemic. While these environmental factors can influence the stability of the virus and the dynamics of transmission, their precise impact is complex and multifaceted:

• Temperature:
  • Laboratory studies have shown that SARS-CoV-2, the virus that causes COVID-19, can survive for varying lengths of time on different surfaces and in different environmental conditions. Generally, higher temperatures have been associated with shorter survival times of the virus on surfaces.
  • In outdoor environments, higher temperatures may reduce the viability of respiratory droplets and aerosols containing the virus, potentially decreasing the risk of transmission. However, it's important to note that outdoor transmission can still occur, particularly in crowded or close-contact settings.
  • Some studies have suggested a possible seasonal variation in COVID-19 transmission, with higher transmission rates observed during colder months in certain regions. However, the extent to which temperature directly influences transmission dynamics remains uncertain, as other factors such as human behaviour, indoor crowding, and public health interventions also play significant roles.
• Humidity:
  • Humidity levels can also affect the stability and transmission of respiratory viruses like SARS-CoV-2. Low humidity levels may lead to drier conditions, which can help respiratory droplets and aerosols remain suspended in the air for longer periods, potentially increasing the risk of airborne transmission.
  • On the other hand, higher humidity levels can cause respiratory droplets to settle more quickly, reducing the risk of airborne transmission. Additionally, some studies suggest that higher humidity levels may also affect the viability of the virus on surfaces, potentially reducing its persistence.
  • Like temperature, the relationship between humidity and COVID-19 transmission is complex and may vary depending on other factors such as ventilation, population density, and public health measures.

Overall, while temperature and humidity can influence the transmission of COVID-19 to some extent, they are just two of many factors that contribute to the dynamics of the pandemic. Public health interventions such as mask-wearing, physical distancing, vaccination, testing, contact tracing, and ventilation are crucial for controlling the spread of the virus regardless of environmental conditions. Additionally, ongoing research is needed to better understand the interplay between environmental factors and COVID-19 transmission and to inform effective strategies for mitigating the impact of the pandemic.

Environmental Factors Influencing COVID-19 Incidence and Severity:

Emerging evidence supports a link between environmental factors—including air pollution and chemical exposures, climate, and the built environment—and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and coronavirus disease 2019 (COVID-19) susceptibility and severity. Climate, air pollution, and the built environment have long been recognised to influence viral respiratory infections, and studies have established similar associations with COVID-19 outcomes. More limited evidence links chemical exposures to COVID-19. Environmental factors were found to influence COVID-19 through four major interlinking mechanisms: increased risk of preexisting conditions associated with disease severity; immune system impairment; viral survival and transport; and behaviours that increase viral exposure. Both data and methodologic issues complicate the investigation of these relationships, including reliance on coarse COVID-19 surveillance data; gaps in mechanistic studies; and the predominance of ecological designs. We evaluate the strength of evidence for environment–COVID-19 relationships and discuss environmental actions that might simultaneously address the COVID-19 pandemic, environmental determinants of health, and health disparities. link

Carbon dioxide has a vital role in determining the lifespan of airborne viruses like SARS-CoV-2, the virus that caused COVID-19, a new study has revealed. The Nature Communications study also highlights the importance of keeping tabs on CO₂ levels to reduce virus survival and minimise the risk of infection. link

A previous project of mine entitled Global CO₂ Emissions. link

Viral Load: link

• The Quantification of Infection: Viral load provides a more tangible metric than simply testing positive for COVID-19. It helps track how much virus is actively replicating within a person's system.
• Disease Severity – It's Not Always Clear-Cut: While higher viral load generally correlates with worse symptoms, it's not a perfect predictor. Underlying health conditions and individual immune responses significantly impact how someone experiences COVID-19.
• Transmission – Timing Matters: People with COVID-19 tend to be most infectious in the earliest stages of the disease, often before severe symptoms appear. This is linked to high viral loads during the initial period of infection.
• Asymptomatic Spread: People can have significant viral loads and be highly contagious even if they never show symptoms. This complicates efforts to contain the virus.
• Treatment Guidance: Monitoring viral load with repeated testing can inform how well antiviral medications are working. If the viral load isn't decreasing, doctors may need to adjust treatment or consider alternative options.
• Vaccine Breakthroughs: While vaccines dramatically reduce the chance of severe COVID-19, breakthrough infections do happen. Studies are examining if viral load plays a part in how likely someone is to transmit the virus even if they are vaccinated.
• Viral load is dynamic: It changes throughout the course of the infection, usually peaking early and then declining. Individual variability exists: Age, health factors, and vaccination status can influence viral load trajectories.
• Public health implications: Understanding viral load patterns has helped shape recommendations on isolation duration, masking guidelines, and targeted testing strategies to reduce the spread of COVID-19.

Waste Water Testing: Potential for Early Detection of Viruses.

The testing of influent waste, which is wastewater entering treatment facilities, has shown potential as an early warning system for viruses like COVID-19. This is because infected individuals shed the virus in their faeces, and this viral RNA can be detected in wastewater. By monitoring wastewater, public health officials can potentially identify the presence of the virus in a community even before clinical cases are reported. link

Several studies have demonstrated the feasibility of using wastewater surveillance as an early warning system for COVID-19 outbreaks. By analysing wastewater samples, researchers can track trends in virus prevalence and potentially detect increases in viral RNA concentrations before clinical cases are reported. This information can then be used to implement targeted public health interventions, such as increased testing and contact tracing, to control the spread of the virus. link

Reports of small amounts of the virus being detected in historical wastewater samples before the first clinical cases are intriguing but should be interpreted with caution. While such findings suggest that wastewater surveillance has the potential to detect early signals of viral outbreaks, further research is needed to validate these findings and understand the implications for public health surveillance. link

Overall, wastewater testing holds promise as a complementary tool for early detection of viral outbreaks, including COVID-19, and can provide valuable information for public health decision-making and response efforts. link

Vaccination:

Vaccines play a crucial role in preventive medicine by providing protection against infectious diseases, reducing the burden of illness, and saving lives. Here are several key reasons why vaccines are essential in public health:

• Disease Prevention:
  • Vaccines are designed to stimulate the body's immune system to recognise and fight specific pathogens, such as viruses or bacteria, without causing the disease itself. By receiving vaccines, individuals develop immunity against infectious diseases, reducing their risk of infection and the subsequent spread of the disease within communities.
• Eradication and Control of Diseases:
  • Vaccines have played a significant role in the eradication or control of many infectious diseases worldwide. For example, vaccines have led to the elimination of diseases such as smallpox and nearly eradicated polio. Through comprehensive vaccination programs, diseases like measles, mumps, rubella, pertussis, and hepatitis have been substantially controlled in many regions.
• Protection of Vulnerable Populations:
  • Vaccines provide protection not only to vaccinated individuals but also to vulnerable populations, such as infants, elderly individuals, pregnant women, and those with weakened immune systems. By achieving high vaccination coverage rates within communities, a concept known as herd immunity or community immunity, the spread of infectious diseases can be effectively limited, protecting those who may not be able to receive vaccines themselves.
• Reduction of Healthcare Costs:
  • Vaccines help to reduce the economic burden associated with infectious diseases by preventing illness, hospitalisations, and complications. By averting medical expenses and productivity losses associated with disease outbreaks, vaccination programs contribute to overall cost savings for individuals, healthcare systems, and society as a whole.
• Prevention of Outbreaks and Pandemics: -Vaccines are crucial tools for preventing outbreaks and controlling the spread of infectious diseases, including pandemics like COVID-19. By immunising populations against emerging infectious agents, vaccines can help to contain outbreaks and prevent them from escalating into larger-scale public health emergencies.
• Safe and Effective:
  • Vaccines undergo rigorous testing and evaluation for safety, efficacy, and quality before being approved for use. Continuous monitoring and surveillance systems are in place to assess vaccine safety and effectiveness post-license. Vaccines are one of the safest and most effective public health interventions available, with a long history of success in preventing disease.

In conclusion, vaccines are indispensable tools in preventive medicine, offering protection against a wide range of infectious diseases and contributing to improved public health outcomes, reduced healthcare costs, and the prevention of outbreaks and pandemics. Vaccination programs are critical components of comprehensive public health strategies aimed at promoting health and well-being for individuals and communities worldwide.

COVID-19 Vaccination:

The development and deployment of COVID-19 vaccines represent a remarkable feat of scientific collaboration, innovation, and global cooperation. Scientists and researchers from around the world worked tirelessly to accelerate the research and development process, leading to the rapid production and distribution of multiple vaccines to combat the pandemic. However, despite these achievements, significant challenges remain, including disparities in vaccine availability and access due to various factors such as resource limitations, geopolitical considerations, and vaccine distribution mechanisms:

• Global Scientific Collaboration:
  • The COVID-19 pandemic prompted an unprecedented level of collaboration among scientists, researchers, pharmaceutical companies, governments, and international organisations. Information sharing, data sharing, and cooperation across borders facilitated the rapid progress in vaccine development.
  • Scientists and research institutions around the world mobilised their expertise and resources to develop COVID-19 vaccines using various platforms, including mRNA, viral vector, protein sub-unit, and inactivated virus technologies.
  • International partnerships, such as the Coalition for Epidemic Preparedness Innovations (CEPI), the World Health Organisation (WHO), and initiatives like the Access to COVID-19 Tools (ACT) Accelerator, facilitated coordination and funding for vaccine research, development, and distribution efforts.
• Speed of Research and Deployment:
  • The development timeline for COVID-19 vaccines was unprecedentedly fast, with multiple vaccines authorised for emergency use within a year of the identification of the SARS-CoV-2 virus. This rapid progress was made possible by advances in vaccine technology, increased funding, streamlined regulatory processes, and global collaboration.
  • Vaccine manufacturers leveraged existing platforms and infrastructure, such as mRNA technology and viral vector platforms, to accelerate vaccine development. Clinical trials were conducted with unprecedented speed, enrolling tens of thousands of participants to evaluate vaccine safety and efficacy.
  • Emergency use authorisations and expedited regulatory approvals allowed for the rapid deployment of vaccines to populations at high risk of COVID-19, including front line healthcare workers, elderly individuals, and individuals with underlying health conditions.
  • Chinese virologist Zhang Yongzhen, shared the genomic sequence of SARS-CoV-2 with the world, speeding up the development of vaccines. Sleeps on the street after his lab shuts. link
  • Zhang's decision to sleep outside his lab, as depicted in social media posts, highlights the extent of his dedication to his research despite the adverse circumstances. His commitment to his work is evident in his willingness to endure discomfort, even sleeping outside in the rain. The dispute between Zhang and the SPHCC raises questions about the treatment of scientists and the importance of providing adequate support and resources for research endeavours, especially during a global health crisis. The lack of clear communication regarding alternative arrangements for Zhang's research team adds further complexity to the situation, emphasising the need for transparency and cooperation between researchers and institutions.
• Disparities in Vaccine Availability:
  • Despite the rapid development and production of COVID-19 vaccines, there have been significant disparities in vaccine availability and distribution globally. High-income countries secured large quantities of vaccine doses through advance purchase agreements with manufacturers, leading to limited supplies for low- and middle-income countries.
  • Limited vaccine manufacturing capacity, supply chain constraints, intellectual property barriers, and vaccine nationalism have hindered equitable access to vaccines, exacerbating global health inequalities.
  • Initiatives such as COVAX, a global vaccine-sharing mechanism led by WHO, Gavi, the Vaccine Alliance, and CEPI, aim to facilitate equitable access to COVID-19 vaccines for all countries, regardless of income level. However, challenges remain in scaling up vaccine production, overcoming logistical barriers, and ensuring fair allocation and distribution of doses.

Coronavirus (COVID-19) Vaccinations: link

• 70.6% of the world population has received at least one dose of a COVID-19 vaccine.
• 13.57 billion doses have been administered globally, and 8,645 are now administered each day.
• 32.7% of people in low-income countries have received at least one dose.

In conclusion, the rapid development and deployment of COVID-19 vaccines demonstrate the power of global scientific collaboration and innovation in addressing public health emergencies. However, efforts to achieve equitable access to vaccines for all populations must be intensified, with a focus on overcoming barriers to vaccine distribution, addressing supply shortages, and promoting cooperation among countries and stakeholders to ensure that vaccines reach those most in need.

Data Visualisations:

Excess Deaths & Smoothed COVID-19 Vaccination Rate (over 65 yrs old) Over Time: 2020-2024, from the data - covid.csv

COVID-19 Vaccination Rates vs Excess Deaths (over 65 yrs old) Over Time: 2020-2024, from the data - covid.csv

A Markdown document with the R code for the above 2 plots from the data: covid.csv - link

Print out from the above R code: [1] "Correlation between vaccination rate and excess deaths:-0.24"

A correlation value of -0.24 indicates a negative correlation between the vaccination rate and excess deaths:

• Correlation:
  • Correlation is a statistical measure that describes the extent to which two variables change together. It ranges from -1 to 1.
• Negative Correlation:
  • A negative correlation indicates that as one variable increases, the other tends to decrease, and vice versa. In this case, a correlation of -0.24 suggests that there is a weak negative relationship between the vaccination rate and excess deaths.
• Interpretation:
  • While the correlation is weak, it suggests that there may be some association between higher vaccination rates and lower excess deaths. However, it is essential to remember that correlation does not imply causation.
• Strength of Correlation:
  • The strength of the correlation can be interpreted based on the absolute value of the correlation coefficient.

Here's a comparison of the two plots and some additional insights:

• Similarities:
  • Both plots suggest a possible association between higher vaccination rates and lower excess deaths.
  • The line plot shows a general downward trend in excess deaths as the smoothed vaccination rate increases.
  • Similarly, the scatter plot shows a weak negative correlation between the two variables.
• Both plots highlight the limitations of drawing causal conclusions:
  • Neither plot can definitively establish that vaccination causes lower death rates.
  • Other factors may be influencing the observed relationships.
• Differences:
  • The line plot provides a clearer visual representation of the trend over time.
  • By smoothing the vaccination rate data, the line plot makes it easier to see how excess deaths have changed in relation to vaccination rates over time.
  • The scatter plot shows more individual country variation.
  • While the line plot shows a general trend, the scatter plot allows you to see how individual countries deviate from that trend.
  • This highlights the importance of considering other factors that might be affecting excess deaths in each country.
• Additional insights:
  • It's important to consider the time frame of the data.
  • Both plots only show data up to a certain point in time.
• Other factors besides vaccination rates could be influencing excess deaths:
  • Demographics (e.g., age structure, population density).
  • Socioeconomic factors (e.g., poverty, inequality).
  • Healthcare access and quality.
  • Public health measures (e.g., masking, lock downs).
  • Non-COVID-19 related deaths.
• Overall:
  • These two plots provide valuable insights into the possible relationship between vaccination rates and excess deaths.
  • However, it's crucial to remember that correlation does not equal causation, and other factors likely play a role.

Excess Mortality and COVID-19 Vaccination Rate Over Time: 2020-2024, from the data covid.csv

A Markdown document with the R code for the above plot from the data: covid.csv - link

Code explanation:

• Load Libraries:
  • We load dplyr for data manipulation, ggplot2 for visualisation, and zoo for creating rolling averages.
• Pre-processing:
  • Filter: Remove rows with missing values in either 'excess_mortality_cumulative_per_million' or 'total_vaccinations_per_hundred'.
  • Convert 'date' to Date: For accurate time-based analysis.
• Rolling Averages:
  • Compute 7-day rolling averages for excess_mortality_cumulative_per_million and total_vaccinations_per_hundred to smooth out noise and highlight broader trends.
• Correlation:
  • Calculate the correlation coefficient between the rolling averages. We use use = "complete.obs" to handle any remaining missing values.
• Visualisation:
  • Create a line plot with time ('date') on the x-axis.
  • Plot both smoothed excess mortality and smoothed vaccination rate with distinct colours.
  • Add informative labels and a clean theme.
  • Include an annotation on the plot displaying the calculated correlation value.
• Chart Correlation:
  • The correlation coefficient of -0.03 indicates a very weak negative association between the two variables.
  • This means that there might be a very slight tendency for excess deaths to decrease as vaccination rates increase, but the relationship is very weak and there are many exceptions.
• Simple Linear Regression Model:
  • Output from the code.

Explanation of the Linear Regression Call and Output:

• Call:
  • Code snippet: lm(formula = excess_mortality_rolling ~ vaccination_rate_rolling, data = data_filtered)
  • lm(): The standard function in R to fit linear regression models.
  • formula = ...: This defines the model structure. excess_mortality_rolling ~ vaccination_rate_rolling means it is regressing smoothed excess mortality (dependent variable) on the smoothed vaccination rate (independent variable).
  • data = data_filtered: Specifies the dataset used for the analysis.
• Output (summary(model)):
  • Residuals: This summarises the distribution of residuals (the differences between observed and model-predicted excess mortality values). An ideal model would have small, randomly distributed residuals.
  • Coefficients: The heart of the regression output.
  • (Intercept): The estimated baseline excess mortality when the vaccination rate is zero.
  • vaccination_rate_rolling: The estimated slope of the line. In this case, a coefficient of -0.6710 suggests that for every one-unit increase in the smoothed vaccination rate, excess mortality is estimated to decrease by about 0.67 units, all else held equal.
  • Std. Error: Variability around coefficient estimates.
  • t-value: Indicates how many standard errors away the coefficient is from zero. It helps assess the effect's strength.
  • Pr(>|t|): The p-value. A small p-value (typically < 0.05) suggests a statistically significant relationship between the predictor and the outcome.
• Goodness-of-Fit:
  • Residual standard error: Variability around the regression line (smaller is better).
  • Multiple R-squared: This explains the proportion of variation in excess mortality explained by the vaccination rate in the model. A very low value (0.0008317) indicates the model explains virtually none of the variation.
  • F-statistic and its p-value: Tests the overall model fit, comparing it to a model with no predictors.
• Interpretation of Results:
  • Tentative Negative Relationship: The negative coefficient for 'vaccination_rate_rolling' suggests a potential decreasing trend in excess mortality as vaccination rates increase. However, the relationship appears very weak in terms of proportion of variance explained.
  • Statistical Significance: With a p-value slightly above 0.05, the linear relationship's strength is on the cusp of statistical significance.
• Essential Caveats:
  • Causation vs. Correlation: Regression doesn't imply causation. Many other factors influence excess mortality, including per capita GDP.

Comparison of Vaccination and Booster Rates and Their Impact on Excess Mortality During the COVID-19 Pandemic in European Countries: PMC - link

No Evidence Excess Deaths Linked to Vaccines, Contrary to Claims Online: FactCheck.org - link

Slow vs. Fast Mutating Viruses:

The rate at which viruses mutate significantly impacts how we approach vaccination strategies. Let's dive deeper, using COVID-19 as an example, to understand the key differences and their effect on vaccine development:

• Mutation Rate:
  • Slow Mutating Viruses: These viruses, like influenza B, accumulate mutations slowly, leading to gradual changes in their surface proteins (vaccine targets).
  • Fast Mutating Viruses: Viruses like influenza A and HIV evolve rapidly, quickly accumulating mutations that can potentially render existing vaccines ineffective within months.
• Impact on Vaccines:
  • Slow Mutating Viruses: Due to gradual change, a single vaccine can sometimes offer long-term protection, like the current influenza B vaccine. However, occasional updates might be needed for emerging strains.
  • Fast Mutating Viruses: Rapid mutation necessitates more frequent vaccine updates to keep pace with the evolving virus. For example, the influenza A vaccine requires annual reformulation. In extreme cases, like HIV, developing an effective vaccine becomes extremely challenging.
• COVID-19 as a Case Study:
  • Mutation Rate: SARS-CoV-2, the virus causing COVID-19, falls somewhere between slow and fast mutating viruses. It exhibits more mutation than influenza B but less than influenza A.
  • Vaccine Development: This intermediate mutation rate led to an urgent need for multiple vaccines during the pandemic. Different variants, like Delta and Omicron, necessitated reformulations with updated targets to maintain efficacy.
  • Current Approach: While initial COVID-19 vaccines offered significant protection, booster shots and variant-specific adaptations (like bivalent vaccines) have become crucial to address evolving strains.
  • Type of Mutations: Not all mutations in COVID-19 significantly impact vaccine efficacy. Understanding how mutations affect binding to antibodies and immune response is key.
  • Immune Response Complexity: Vaccines can stimulate broader immune responses beyond surface proteins. This can offer protection against slightly mutated variants.
  • Combination Vaccines: Multi-strain vaccines targeting dominant variants are being explored to offer wider protection against circulating strains.
• Single vs. Multiple Vaccines:
  • Single Vaccine: This would be ideal, but the evolving nature of COVID-19 necessitates multiple vaccines targeting dominant variants like Delta and Omicron.
  • Multiple Vaccines: Currently, different vaccines and booster shots address the evolving virus.
• Universal Vaccine: Researchers are actively developing universal vaccines targeting conserved regions in coronaviruses, aiming for broad protection against various strains, including future ones.

The optimal vaccine strategy for COVID-19, and other viruses, depends on their specific mutation rate and ongoing evolution. Understanding this dynamic relationship is crucial for developing effective vaccination strategies. While COVID-19 presented challenges due to its mutation rate, ongoing research on variant-specific vaccines and universal approaches promise better control in the future.

Immune Exhaustion:

There is a theoretical concern about potential immune exhaustion or weakening of the immune response with repeated vaccinations over a short period. However, current evidence suggests that the benefits of vaccination, including protection against severe illness and death, generally outweigh the potential risks of immune exhaustion: link

• Immune exhaustion:
  • A theoretical concern: This concept suggests that repeatedly triggering the immune system with vaccinations could lead to its "overwork" and weakening, making it less effective in fighting off future infections.
• Lack of convincing evidence:
  • While theoretically possible, current research hasn't found conclusive evidence linking COVID-19 vaccination to immune exhaustion.