COVID19 Cases Trend Analysis
- nmariousasilo
- Aug 11, 2023
- 4 min read
The COVID-19 pandemic, caused by the novel coronavirus (SARS-CoV-2), has had a profound impact on global health and well-being since its emergence in late 2019. As the pandemic swept across the globe, it raised questions about the potential links between the number of COVID-19 cases and the overall happiness and well-being of different nations. In this time series analysis project, we investigate the relationship between COVID-19 cases and world happiness during the initial months of the pandemic.
CONTENT OF THE PROJECT
DATA SOURCE
Our study utilizes two primary datasets: the COVID-19 cases dataset, which contains daily records of the number of COVID-19 cases reported per country from January 22, 2020, to April 30, 2020, and the world happiness dataset, which provides measures of happiness and well-being for various countries during the same period.
OBJECTIVE
Our analysis aimed to answer two main questions: Does the overall happiness of a nation affect the number of COVID-19 cases, and does a country's happiness influence its ability to cope with the pandemic? Correlation between COVID-19 Cases and Happiness: Through statistical analysis and data visualization, we examined whether there is a correlation between the number of COVID-19 cases and the happiness scores of different countries. We used correlation coefficients and regression analysis to identify any potential links. Impact of Happiness on Coping with the Pandemic: We also investigated whether countries with higher happiness scores demonstrated any differences in their COVID-19 response strategies and outcomes compared to countries with lower happiness scores. This analysis considered factors such as public health measures, economic policies, and societal resilience.
For complete data analysis process, visit the notebook on Kaggle.
EXPLORATORY DATA ANALYSIS
Visualization always helps us better understand the data. Let's have an example and let's see the COVID19 cases in China, Italy, and Spain.
The graph illustrates the acceleration of COVID cases in China during early January, followed by a stabilization phase in mid-February. This indicates that the virus originated in China and then began spreading to Italy and Spain around mid-March. Subsequently, both countries experienced a significant surge in cases, surpassing China's case count by the end of March.
We need to find a good measure represented as a number, describing the spread of the virus in a country.
By computing the graph's first derivative and plotting it, we will be able to visualize how the rate of COVID cases changes over time.
Base on the graph, the maximum rate of change in China is 15136. Now, let's compute for the maximum infection rate for all other countries.
Country/Region | Max Infection Rate |
Afghanistan | 232.0 |
Albania | 34.0 |
Algeria | 199.0 |
Andorra | 43.0 |
Angola | 5.0 |
For the sake of simplicity, the table above displays only the initial 5 entries from the complete dataset.
CORRELATION BETWEEN COVID CASES AND WORLD HAPPINESS
In this graph, it becomes evident that there exists a positive correlation between the infection rate and both GDP per capita and healthy life expectancy.
GDP vs MAXIMUM INFECTION RATE
In this graph, it becomes evident that there exists a positive correlation between the infection rate and GDP per capita. Countries with higher GDP per capita generally exhibit a higher infection rate compared to countries with lower GDP per capita.
SOCIAL SUPPORT vs MAXIMUM INFECTION RATE
In this graph, there is no clear correlation between social support and maximum infection rate.
HEALTHY LIFE EXPECTANCY vs MAXIMUM INFECTION RATE
The data depicted in this graph indicates that countries with greater healthy life expectancy experience higher maximum infection rates.
FREEDOM TO MAKE LIFE CHOICES vs MAXIMUM INFECTION RATE
In this graph, there is no clear correlation between freedom to make life choices and maximum infection rate.
CONCLUSION
The findings from the analysis and visualizations in this study shed light on the relationship between COVID-19 infection rates and two key socioeconomic indicators: GDP per capita and healthy life expectancy. From the graph depicting the infection rate and GDP per capita, it becomes evident that there is a positive correlation between these two factors. Countries with higher GDP per capita tend to exhibit higher infection rates, while those with lower GDP per capita generally experience lower infection rates. This suggests that wealthier nations may face unique challenges in managing the spread of the virus. Furthermore, the data displayed in the graph on healthy life expectancy and maximum infection rates reveals a noteworthy trend. Countries with greater healthy life expectancy also tend to experience higher maximum infection rates. This observation implies that populations with better overall health and longer life expectancies are not immune to the impact of the pandemic. It suggests that factors beyond the healthcare system alone may play a significant role in determining a country's susceptibility to COVID-19. Combining these two insights, we can infer that the impact of the COVID-19 pandemic is influenced by a complex interplay of socioeconomic and health-related factors. While wealthier countries may have the resources to manage the pandemic, they may also face challenges due to various factors like population density, international travel, and healthcare infrastructure. Similarly, countries with better health indicators are not immune to the virus and may still experience significant infection rates due to other contributing factors. It is essential to recognize that this analysis is based on a specific time frame and dataset, and the pandemic's dynamics may continue to evolve. Therefore, policymakers and global health authorities should continue to assess and adapt their strategies to combat the pandemic effectively. This study provides a foundation for further exploration and emphasizes the significance of considering multiple variables when addressing the complex and multifaceted impacts of COVID-19 on different countries.
Our time series analysis on the relationship between COVID-19 cases and world happiness during the early months of the pandemic provides valuable insights into how the pandemic affected global well-being. The findings may offer guidance to policymakers and public health authorities in understanding the socio-economic and psychological dimensions of COVID-19 and its impact on societies. However, it is essential to recognize the limitations of this study, such as the relatively short time span considered and the dynamic nature of the pandemic.
