Directions
The purpose of this assignment is to hone your pandas and Python skills. The truth is, data wrangling and preparation consumes the majority of time spent by a data scientist. Before predictions can be made, a model must be trained on clean data - that is, void of missing values, properly formatted, et cetera.
The following paper is included in Brightspace. This study aimed at determining the relationship of temperature to COVID-19 infection in the state capital cities of Brazil.
David N. Prata, Waldecy Rodrigues, Paulo H. Bermejo. Temperature significantly changes COVID-19 transmission in (sub)tropical cities of Brazil. Science of The Total Environment. Volume 729. 2020. 138862. ISSN 0048-9697. https://doi.org/10.1016/j.scitotenv.2020.138862.
In this work, the authors considered a model similar to the following:
where
It is not required to read the paper by Prata et al. to complete this assignment. However, if you have the time, then give it a glance - I find it interesting. In fact, I find the model used by these authors to be rather suspect. Here is one example underscoring my reservations: The authors claim that temperature significantly changes COVID-19 transmission, but the resolution of the temperature data that was considered by the model is far too coarse-grained. Is the annual average temperature of each capital city really sufficient for elucidating such a trend? Would this characterization change if the model considered the variation in temperature between months for each capital city?
The raw data I supply you with does include monthly average temperatures for these cities; if you want to try fitting a few models, indulge! However, you are not required to train any models
for this assignment. Note that I was unable to reproduce any of the trends published by the authors, but I employed different models than that used in the study.
Your task for this assignment is to perform the data preparation necessary to train the above model; however, you will not actually train any models for this assignment. You must construct a Jupyter notebook that reads in data from the supplied Excel file, then performs the necessary data manipulations to obtain two Pandas DataFrames.
The first DataFrame must be called features, which is your feature matrix. The features DataFrame must contain the following feature columns in this exact order: days_cube, days_sq, days, temp, pop_dense_sq, pop_dense, pop. These features correspond to the features present in the equation above.
● days_cube and days_sq are the cubed and squared values of days, respectively.
● days is the number of days since the outbreak began in the respective capital city, which always begins with zero and adds 1 day for each row of recorded data for each capital city. There are 27 capital cities in Brazil. Each capital city has 151 days of recorded data.
● pop_dense_sq is the squared value of pop_dense
● pop is the city population
● Population density is defined as the population of a city divided by the area in kilometers squared of the city.
The second DataFrame contains target values and must be called response, which are the accumulated case counts column for the capital cities. Your task is to construct these two Pandas DataFrames using the data provided.
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