Selma Gomez Orr

Examining the Possible Link Between Childhood Vaccinations and Reduced Severity of COVID-19 Symptoms

Since the start of the COVID-19 Pandemic, one of the most striking features is that children and youth seem to be largely protected from the most severe outcomes of COVID-19, with those infected often showing only mild or no symptoms. The below figure shows data provided by the CDC on US fatalities due to COVID-19 grouped according to age. As you can see the data show a clear picture with older individuals representing the largest proportion of fatal victims and young people relatively unaffected (CDC).

Age Distribution of COVID-19 Deaths in U.S.

Data Source for Figure: CDC statistics as of late September.

This contrasts strongly with the last pandemic, the Spanish Flu of 1918, and with regular influenza viruses. Normally, an influenza follows a U-shaped curve where the very youngest and the very oldest are the most vulnerable. In the case of the Spanish Flu, the curve took on a “W-Shape” where some of the young/healthy individuals in the middle were also greatly impacted (Taubenberger). COVID-19 on the other hand seems almost like a “Half-U-Shape” with only those on the higher end of the age spectrum most severely impacted.

Many researchers and individuals have puzzled over this disparity. Understanding the reason why might help us control the spread of the disease. One of the first guesses back in March, came from a group of Turkish researchers who proposed the idea that childhood vaccines including the Measles, Mumps, Rubella, or MMR, vaccine or the pertussis vaccine might explain this mystery (Okyay, et al). This idea seemed to receive little attention initially, but then in June reports from CNN and others began publishing stories about how some researchers were looking at the polio vaccine and the MMR vaccines as possible aids in the fight against COVID-19 (Fox). Our story has evolved to the point now in September with the announcement of a large-scale trial with 30,000 health care workers from 9 countries who will receive either a placebo or an MMR vaccine--Measles, Mumps, and Rubella (Reinberg).

Many scientists remain skeptical about the possible value of childhood vaccines in the fight against COVID-19 and point to specific biological differences between ACE2 receptor concentrations and lymphocyte levels in children versus adults to explain the mystery (Cristiani, et al). Proponents of the childhood vaccine hypothesis draw upon the idea of “trained immunity” from “live virus” vaccinations, such as the MMR vaccine and the polio vaccine, which provide a wider level of immunity (Hackett). They also highlight the similarity of surface proteins of the different viruses and how antibodies from one virus help to fight the other virus.

In any case, the answer is far from clear at this point but the year-long clinical trial starting now in September with the MMR vaccine and sponsored by the Bill and Melinda Gates Foundation should help to provide some important answers in the future. The benefit of being able to use a safe and widely produced vaccine, such as the MMR vaccine, to help reduce the severity of COVID-19 effects until better remedies are found could be groundbreaking.

research questions

Do the available data of worldwide vaccinations rates and the morbidity patterns of COVID-19 support the idea that childhood vaccinations reduce the severity of COVID-19 in children and youth?

Do the data point to a particular vaccine or vaccines among the several administered worldwide as a more likely candidate in reducing the severity of COVID-19?

Vaccinations Under study

The World Health organization provides worldwide, multi-year data for ten different vaccinations administered globally. These include Diptheria, Tetanus, and Pertussis (DTaP); Polio; Measles, Mumps, and Rubella Dose 1 (MMR); Tuberculosis; Tetanus Prevention at Birth (PAB); Hepatitis B; Haemophilus Influenzae (HIB); Pneumococcal Conjugate (PCV); Rotavirus, Measles Dose 2 (MCV).

Preliminary Evaluation Framework

Key criteria are geographic coverage and age coverage to select most interesting candidates. This methodology yields three of the candidates most often discussed in the literature. Graphs below provide different visualizations of these characteristics.

Measles, Mumps, and Rubella First Dose (MMR)

This is an example of a vaccination candidate from the HI Global Coverage and HI Age Cohort Coverage from the Evaluation Framework.

Tuberculosis (BCG)

This is an example of a vaccination candidate from the LOW Global Coverage and HI Age Cohort Coverage from the Evaluation Framework.

Hepatitis B

This is an example of a vaccination candidate from the HI Global Coverage and LOW Age Cohort Coverage from the Evaluation Framework.

Rotavirus

This is an example of a vaccination candidate from the LOW Global Coverage and LOW Age Cohort Coverage from the Evaluation Framework.

Diptheria, Tetanus, and Pertussis (DTaP)

This is an example of a vaccination candidate from the HI Global Coverage and HI Age Cohort Coverage from the Evaluation Framework.

Tetanus Prevention at Birth (PAB)

This is an example of a vaccination candidate from the LOW Global Coverage and HI Age Cohort Coverage from the Evaluation Framework.

Haemophilus Influenze (HIB)

This is an example of a vaccination candidate from the HI Global Coverage and LOW Age Cohort Coverage from the Evaluation Framework.

Pneumococcal Conjugate (PCV)

This is an example of a vaccination candidate from the LOW Global Coverage and LOW Age Cohort Coverage from the Evaluation Framework.

Polio

This is an example of a vaccination candidate from the HI Global Coverage and HI Age Cohort Coverage from the Evaluation Framework.

Measles Second Dose (MCV)

This is an example of a vaccination candidate from the HI Global Coverage and LOW Age Cohort Coverage from the Evaluation Framework.

Country Average Case Fatality RAte (CFR) versus country Average vaccination coverage

While the graphs are directionally consistent, no single candidate received an R-squared value greater than 0.05. Again a limitation of this analysis includes the lack of age disaggregation for the data, but the highest values included DTaP, MCV (second measles dose), Tuberculosis, and PAB. In the next tier were MMR and Polio. Case Fatality Rate (CFR) is defined as the "proportion of individuals who die from a specified disease among all individuals diagnosed with the disease over a certain period of time" (Britannica).

Multiple Candidates

As one would expect, combining all of the vaccinations together increased the R-squared value to 0.3175. However, in terms of looking at various combinations, combining DTaP with Polio alone resulted in an R-squared value of 0.1993.

Small sample of cfr by age distribution

Limited country data exists which reports on CFR by age distribution. A small country sample provides some insight for possible future analysis. Sample size is too small for any meaningful conclusions.

Conclusion and future work

While the available data do not establish a clear link between childhood vaccinations and reduced COVID-19 severity nor establish a clear vaccination candidate for best protection, certain vaccinations do appear to be more promising than others. The availability of data disaggregated by age and questions about the accuracy of reported data make it challenging to answer the research questions in a meaningful way, but some analysis does provide the glimmer of possibly interesting results. In terms of future work, a variation that would more effectively deal with the data issue would be to conduct this analysis based solely on U.S. vaccination and COVID-19 data on a state by state basis.

video summary By Project Phase

Phase 1

Phase 2

Phase 3

References

Britannica. "Case Fatality Rate." https://www.britannica.com/science/case-fatality-rate

CDC. "Weekly Updates by Select Demographic and Geographic Characteristics." National Center for Health Statistics, 25 November 2020. https://www.cdc.gov/nchs/nvss/vsrr/covid_weekly/index.htm.

Cristiani, Luca, Enrica Mancino, Luigi Matera, Raffaella Nenna, Alessandra Pierangeli, Carolina Scagnorali, and Fabio Midulla. "Will Children Reveal Their Secret? The Coronavirus Dilemma." European Respiratory Journal, vol. 55, 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113798/

Fox, Maggie. "Could an Everyday Childhood Vaccine Help Against Coronavirus?" CNN, 19 June 2020. https://www.cnn.com/2020/06/19/health/mmr-vaccine-immunity-coronavirus/index.html

Hackett, Don Ward. "MMR Vaccination May Prevent COVID-19." Precision Vaccinations, 22 September 2020. https://www.precisionvaccinations.com/mmr-vaccination-may-prevent-covid-19

Okyay, Ramazan Azim, Ahmet Riza Sahin, Rene A. Aguinada, and Ali Muhittin Tasdogan. "Why are Children Less Affected by Covid-19? Could there be an Overlooked Bacterial Co-Infection?" Eurasian Journal of Medicine and Oncology, vol. 4, no. 1, 2020, pp. 104-105. https://www.ejmo.org/10.14744/ejmo.2020.40743/pdf/

Reinberg, Steven. "Trial Tests MMR Vaccine to Help Prevent Covid-19." WebMD 4 September 2020. https://www.webmd.com/lung/news/20200908/could-the-mmr-vaccine-help-prevent-covid-19-new-trial-may-tell#1.

Taubenberger, Jefferey K. and David M. Morens. "1918 Influenza: The Mother of All Pandemics." Emerging Infectious Diseases, vol. 12, no. 1, 2006. https://wwwnc.cdc.gov/eid/article/12/1/05-0979_article

data sources

World Health Organization Vaccination Data: https://apps.who.int/gho/data/node.main.A824?lang=en

Statista - COVID-19 Morbidity by Age: https://www.statista.com/statistics/1105512/coronavirus-covid-19-deaths-by-gender-germany/

World Bank Population data: https://data.worldbank.org/indicator/SP.POP.0014.TO.ZS

John Hopkins University Coronavirus Resource Center, Case Fatality Rates by Country: https://coronavirus.jhu.edu/data/mortality

Our World in Data, Case Fatality Rate by Age for four countries: https://ourworldindata.org/mortality-risk-covid#case-fatality-rate-of-covid-19-by-age

Statista - COVID-19 Death Rate by Age for Italy: https://www.statista.com/statistics/1106372/coronavirus-death-rate-by-age-group-italy/

Repository

https://github.com/Pythonsgo/DataScienceCapstone.git

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