- Diagnosis of COVID-19 is complex and beyond means of human mind:
- Non respiratory symptoms of COVID-19
- Symptom clusters for COVID-19
- Order of occurrence matters
- Network model for diagnosis of COVID-19
- Free text, and guided, interview for diagnosis of COVID-19 at home
Trained ChatGPT A.I.►
For this assignment you can use any statistical software.
The data in this assignment has changed from Teach One documents
previously posted. In addition, you are asked to report specific
parts of your findings.
Question 1: Classify COVID-19 based on its
- Describe the order of occurrence of the variables
- Assume that age, and gender occur at birth. Assume that
vaccination information occurs before onset of symptoms.
Assume home tests occurs after onset of symptoms. Assume
that laboratory PCR test occurs after home test.
- Establish the order with which symptoms occur
- Count for each pair of symptoms, the number of times one
symptom occurs before another. Use column AK in the
database to identify if one symptom has occurred before
- Use the pairwise count of one symptom occurring before
another to establish a sequence of occurrence of symptoms.
- Create a table reporting for each variable what
other variables precede it.
Table 1: Portion of Table showing number of times row variable
occurs before column variable (number of pairs of symptoms occurring)
(Gray cells indicate factors that do not occur before column
variables for majority of patients)
Loss of Appetite
- Create a Causal Network for clusters of symptoms of COVID-19
- Create the structure of the network:
- Using logistic LASSO, regress the PCR test results on all variables
and pairwise or triple cluster of variables that precede it.
- List the variables that are direct predictors
of PCR test results. This list should
include the coefficients for the non-zero Logistic
regression variables, including coefficients for pairs or
triple of variables.
- Report the percent of variation explained by
the LASSO regression of PCR tests on independent
variables. Calculate and report the
McFadden Pseudo R-Square.
- Using LASSO, regress each variable that is a direct
predictor of PCR test results on all preceding
variables. In this regression, the statistically significant variables are parents in the
Markov blanket of the regression response variable.
- For each regression, report the independent
variables that are significant (non-zero) predictors of
the response variable (the response variables are the
direct predictors of PCR tests)
- For each regression, report the percent of
variation explained by the regression
the network using Netica.
- Provide an image of the structure of the
network, organized so that nodes that occur later are put
to the right of nodes that occur earlier. Please
note that if you do not have a license to Netica, you can
make the network and take a screen shot before you save
the network and need a license.
- Estimate the parameters of the network
- Using the LASSO regression, calculate the predicted value
for all combinations of the parents in the Markov blanket of
the regression's response variables. Enter this information
into Netica Tables.
- In Excel or in Netica provide tables predicting
probability of each node in the network. Provide the table for
predicting fever in a word document as well.
- What is the probability of COVID for a patient less than 30,
female, with runny nose, muscle aches, and with unknown fever status.
What is the same probability if we knew that the patient does not have
- Report the two probabilities
The following resources may be helpful:
This page is part of the course on Comparative Effectiveness by Farrokh Alemi, Ph.D.