Exploring Factors that Make Countries Vulnerable to COVID-19 Risk
Authors: Samantha Chung, Akshat Srivastav, Erica Zhou, Jim Liu, Megha Velakacharla, Shubh Kathuria
The COVID — 19 pandemic has led to a wide array of impact on various countries and their respective healthcare systems. Depending on a country’s economic situation and structure of their healthcare system, they may be either more or less vulnerable to the impact of the pandemic respectively. Here, utilizing a dataset from UNICEF, we aim to explore factors that impact a country’s “COVID 19 Risk Class” — where a high risk means that this country is more vulnerable to the negative impacts of COVID 19.
The three graphs above depict COVID 19 Risk Class by HIV Priority, Malarial Priority, and Child Poverty Risk. If a “yes” is depicted, this most likely means that the country (or in this case, the survey that the country was assessed at), would be a target for UNICEF to resolve issues regarding HIV, Malaria, or Child Poverty. This also implies that these countries are more vulnerable or may have relatively weaker infrastructure to deal with diseases such as HIV and Malaria; or issues such as Child Poverty may impose serious effects on the country’s development and economic well being. The graph shows that for the COVID 19 Risk Class labeled as “high” , there seems to be relatively fewer occurrences of a “yes” for HIV and Malaria, but a larger occurrence for a “yes” for Child Poverty Risk. This means that for countries with high COVID 19 Risk Class, they are more likely to be at risk for Child Poverty as well. Child Poverty could often be linked to poor nutrition, as families do not have the financial resources available for healthier foods, this can then lead to poorer nutrition, and a decreased immune system as a result, making children more vulnerable to the effects of COVID 19. Also, the Child Poverty rates being a high priority means that the country may not have a sustainable economy, leading to poor infrastructure to deal with the consequence of COVID 19. Factors such as Malaria or HIV are due to more “external” or “natural” factors such as the environment, and therefore have less of an impact on COVID 19 Risk Class. We now move on to looking at more societal issues such as gender-based violence and their impact on the country’s vulnerability to COVID-19.
The graph above compares the reported incidents of gender-based violence against women or adolescent girls based on the COVID-19 Risk Class of a country, according to administrative and representative survey data collected by UNICEF country offices. A ‘True’ indicates that an increase in gender-based violence reports was observed during COVID-19. Evidently, there is not much correlation between gender-based violence reports and COVID-19 Risk Class, so a higher risk class likely does not impact the amount of gender-based violence reported within a country.
However, we cannot assume that the pandemic has not largely impacted those who have experienced gender-based violence. Survivors who do not report their experiences are not accounted for. In some cases, survivors would be less likely to seek help, or leave home due to health concerns regarding the pandemic. Consequently, it is difficult to estimate the scope of the impact of COVID-19 on the amount of gender-based violence occurring, and the impact on survivors. From the data collected above, we can only infer that the amounts of gender-based violence reports were impacted equally by the pandemic, regardless of each country’s vulnerability to COVID-19.
An essential component of poverty that might exacerbate the vulnerability of the country to COVID-19 is food insecurity. The graphs above show the degree of food insecurity during the pandemic and how it is related to internet instability within households. As seen in the first graph, around 25% of the total surveyed population reported having difficulty providing enough food for their families. Based on the second graph, among the population under food insecurity, around 25% are students and around 37.7% are employees (either full-time or part-time) that rely heavily on stable internet connection within households.
Since people under food insecurity tend to have less financial support, we hypothesized that the same population would be less likely to have stable internet connection within households, making it even more difficult to earn income in an economy already strained due to the pandemic. To verify our hypothesis, we created two pie charts. On the left hand side, it shows the percentage of unstable internet connection among populations with food insecurity. On the right hand side, it shows the percentage of unstable internet connection among populations who could afford enough food. We were surprised to find out that there was no strong correlation between food insecurity and internet stability. Moreover, based on the survey alone, it appears that a lower percentage of the populations under food insecurity reported having unstable internet connection within households.
In short, we concluded that financial support was not the only determinant of internet stability, which could also be largely impacted by policies, locations, and occupation organizations. As a result, to support students and working class during the pandemic, it’s essential for the government and respective organizations to provide stable internet connection, in addition to sufficient food supplies.
A recession induced by COVID-19 also brought attention to differing impacts of COVID-19 on various income-classes. The graph below shows the proportion of each of the COVID-19 risk class and is separated by different world bank income groups. The graph showed us two pieces of information. First, the Medium COVID-19 risk class has the highest amount of population, followed by high, and then low, and very-high COVID-19 risk class has the least amount of people. Second, this graph also showed us a relationship between the world bank income group and the Inform COVID-19 risk class. We can see an inverse relationship between the covid-19 risk class and the income group. The higher the income, the lower on the COVID-19 risk class. For our next exploration, we thought that perhaps we can break this observation down by region.
In parsing the composition of income groups by region, we created the bar chart above. The bar chart depicts the percentage of respondents that fall into one of the four risk categories by region. In this case, the risk categories are Low, Medium, High, and Very High. For each region, we get the percentage breakdown of these risk categories. It appears that in most regions barring Oceania, roughly more than 50% of respondents fall into the Medium risk category. The Sub-Saharan African region appears to have the largest percentage of respondents of nearly 80% who fall into the High risk category. This region also has the highest percentage of individuals who fall into High risk with about 15% individuals within this category.
The two graphs below represent a heat map of a) COVID-19 RISK to women and children and b)the economic wellbeing of these countries (based on World Bank Income Group). The sub-Saharan African region in particular represents how such a comparison can be interesting. It indicates a direct correlation between the two case studies.
In conclusion, our article has explored the following factors and their respective impact on COVID 19 Risk Class: HIV and Malaria prevalence, availability of nutritional foods, income, and gender based violence, as well as food security. From the following, it appears that factors such as gender based violence do not have a strong correlation with the COVID 19 Risk Class, whereas factors such as income of a country do. This is because the income of a country is most strongly correlated with the availability of various infrastructures to combat the impacts of COVID 19. Henceforth, we derived at the conclusion that it is most beneficial for organizations such as UNICEF or other humanitarian organizations, that when deciding how to help countries at most risk, to focus on benefitting the country’s healthcare infrastructure.
WORKS CITED
“Tracking the Situation of Children during COVID-19.” UNICEF DATA, 20 Dec. 2021, data.unicef.org/resources/rapid-situation-tracking-covid-19-socioeconomic-impacts-data-viz/.
“INFORM Covid-19.” Inform Covid-19, drmkc.jrc.ec.europa.eu/inform-index/inform-covid-19.
GITHUB
https://github.com/datares/The-Study-Sandwich-git-hub-profile
