By:
- Anran Zheng, anranz@design.upenn.edu
- Johnathan Clementi, clementi@design.upenn.edu
- Lan Xiao, lanxiao@design.upenn.edu
COVID-19 has been spread worldwide since 2019, and there has been a new peek of the outbreak in the US at the beginning of 2021. So it is still significantly necessary for the government and institutions that face crowd gathering risk in Philadelphia to track the trend of reported cases, death, and hospitalization condition of COVID. In addition, as COVID vaccines are developed and used this year, the government and other stakeholders, such as universities, start to pay more attention to vaccine coverage, breakthrough infections, and hospitalization accessibility.
First and foremost, this COVID dashboard will help to track general infection trends by time curve and 14-day changes of reported cases, death, hospitalization, and vaccination. Also, age, racial and sexual composition will be demonstrated to assist government and stakeholders in identifying how to allocate related resources. On top of that, hot pot maps by zip code will be helpful in targeting breakout places and containing the spread of the epidemic promptly. What's more important, the dashboard will combine data from different sources and engineer effect metrics and graphics, including propotion of report cases who have been vaccinated that will contribute to estimate breakthrough infections, ratio of treated/death that will contribute to warn higher mortality due to poor hospitalization accessibility, and a multiple line plot of hospitalization & vaccine that help to explore whether neighborhoods are not active to take vaccine are more apt to not be hospitalized.
We choose four aspects of COVID-19 data, confirmed cases, hospitalization, death and vaccination mainly from NYC Open Data and New York State Department of Health. The data form that we will utilized in our project includes csv and geojson. We will also use the aggregated number by different groups of race and age categories provided by all these datasets except vaccination due to data unavailability. In terms of time, we consider the real-time, latest 7 or 28 days, and historical daily data. In terms of space, we choose MODZCTA (modified ZIP code tabulation area) level of NYC. All the datasets are open-accessed to us.
Daily historical case/death/hospitalization: -- NYC Open Data, updated every day from Feb 29th 2020 to present. The fields we may consider includes the date DATE_OF_INTEREST, daily new cases/death/hospitalization number CASE/DEATH/HOSPITALIZED_COUNT and the average of 7 days new cases/death/hospitalization number CASE/DEATH/HOSPITALIZED_7DAY_AVG.
Weekly breakthrough: -- NYC Open Data, updated every day. The fields we may consider includes date Week_of_diagnosisand the count or rate of cases/death/hospitalization under the vaccination or not vax/unvax_case/hosp/death_count/rate.
Weekly cases/death/hospitalization rate by age or race: -- NYC Open Data, updated weekly. The fields we may consider includes date week_ending, the age category label (e.g. age_0_4...age_75up) and the race category label (e.g. Asian_Pacific_Islander... White).
Cases/death/hospitalization/vaccine by MODZCTA: -- NYC Open Data, updated daily for vaccination, weekly for cases and monthly for death and hospitalization. The fields we may consider includes date, ZIP codemodzcta,modzcta_name, the count or rate of last 28 days hospitalization/death hospitalization/death_rate/count_28day,daterange, the positivity rate of past 7 days percentpositivity_7day, and the count that people were fully vaccinated COUNT_FULLY_CUMULATIVE etc.
the historical vaccine data of NYC: -- New York State Department of Health, updated every day from Dec 14th 2020 to present. The fields we may consider includes date, region and the full vaccinated doses Series Complete.
NYC base map by MODZCTA: -- NYC Open Data. The above data is csv format but this geographic file is geojson format.
