As Coronavirus outbreak (COVID-19) has continued to explore and grow, we are interested in the relationship between the growth rates of confirmed cases and the effectiveness of the “Stay-at-home” Order. To better understand worldwide community mobilities and the number of diagnoses, we selected the 5 most representatives regions around the world, including Wuhan, Lombardia, Los Angeles, Suffolk County, and New York City. At the same time, we collected the dates of issuing the “Stay-at-Home” order in each region. In addition, we also want to find the correlation or causation between different factors that might affect the growth of COVID-19.
Wuhan - China 1/23
Italy - Lombardia 3/8
CA -Los Angeles 3/19
Massachusetts - Suffolk county 3/24
New York City - NY 3/22
Confirmed and death Cases By regions / The “Stay-at-home” order start date - Singing
First of all, we researched a large number of datasets about COVID-19 by county in the US and in the world and used open refine and excel to organize and classify the data.
(Raw data from https://data.humdata.org/dataset/nyt-covid-19-data)
We Classified data according to different regions and highlighted the dates of releasing the “Stay-at-home” order. We also used Tableau to create a simple chart of how COVID cases increased in recent months
Meanwhile, we compared the data of Wuhan, which implemented lockdown on January 23. - Ruoyi
(Raw data from Wuhan case)
The discounting table created by Tableau shows the population trend. After the lockdown on January 23, there was a spike in diagnoses on February 10, but since then the Numbers have been controlled and the line has leveled off. By March 20, there were no new cases for four consecutive days.
Data from Citymapper mobility Index - Hope
(% of the city moving compared to usual)
(Raw data downloaded from https://citymapper.com/cmi)
We cleaned and selected the dates and regions that we are going to analyze.
At first, we downloaded the whole data from the Citymapper website. Citymapper integrates raw data from walking and cycling to driving through the free mobile app and the desktop website, so we think it can represent people's activity on some levels. The data has 41 cities in total, we chose New York City, Boston (Suffolk county), Los Angeles, Milan (Lombardia) as our target cities to analyze, we cleaned the data to our target cities and deleted some useless rows. After cleaning the data, we put it into Tableau first to see how different the data each city looks like.
Population demographic and aging in regions - Ruoyi
The elderly have the highest proportion of deaths from corona due to weakened immunity and multiple complications. We hypothesized from our data that infection rates in different regions were related to mortality rates and the proportion of the population aging.
In 2019, Wuhan has a total population of 9.064 million, with 1.2425 million aged 65 and above. It accounts for 14.06% of the total population.
New York's population will reach 19.45 million in 2019. In the same year, the proportion of New York state's population 65 and older rose to a record 16 16%. New York's population of 85 and older is exploding.
Los Angeles, the second-largest city in the United States, has a population of 24 million in 2019, including 5.3 million people over the age of 65, accounting for 22% of the total population.
In 2019, the population of Suffolk county ma is 803,907 and 18.2% are 65 and older.
Italia-Lombardia’s population reached 10.06 million in 2019, 22.6% are over 65 years old. With 5,476 COVID 19 deaths in Italy by March 23, the death rate is 9%. Is the highest proportion of deaths in the world. Lombardy, Italy's most populous region, has a higher mortality rate than other regions because of its aging population.
(https://www.statista.com/statistics/569240/population-distribution-by-age-group-in-italy-by-region/)
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