documentation and tests

.gitignore

@@ -40,6 +40,8 @@
 !input/people_test.csv
 !input/gi_trajectories.csv
 !tests/data/gi_trajectory.csv
+!tests/data/three_columns.csv
+!tests/data/empty.csv
 
 #####
 # Python

docs/natural-history-inputs.md

@@ -0,0 +1,28 @@
+# Infectiousness over time
+
+## Overview
+
+We provide a way for reading in a user-specified infectiousness over time distribution (generation interval)
+and appropriately scheduling infection attempts based on the distribution. The user provides an input file
+that contains samples from the cumulative distribution function (CDF) of the generation interval (GI) over
+time at a specified $\Delta t$, describing the fraction of an individual's infectiousness that has passed
+by a given time. The input data are assumed to have a format where the columns represent the times since
+the infection attempt (so starting at $t = 0$) and the entries in each row describe the value of the GI
+CDF. Each row represents a potential trajectory of the GI CDF.
+
+People are assigned a trajectory number (row number) when they are infected. This allows for each person
+to have a different GI CDF if each of the trajectories are different. However, that trajectory number will
+be used for also drawing the person's other natural history characteristics, such as their symptom onset
+and improvement times or viral load trajectory. This allows easily encoding correlation between natural
+history parameters (the user provides input CSVs where the first row in each CSV is from a joint sample
+of GI, symptom onset, symptom improvement, etc.) or allowing each of the parameters to be independent.
+
+## Assumptions
+1. There are no requirements on the number of trajectories fed to the model. Trajectory numbers are assigned
+to people uniformly and randomly. However, this means that an individual who is reinfected could have the exact
+same infectiousness trajectory as their last infection.
+2. There must be the same number of parameter sets for each parameter provided as an input CSV. For now, we are focusing
+only on GI, but we will soon expand our work to also include symptom onset and symptom improvement times.
+3. We have not yet crossed the barrier of how to separately treat individuals who are asymptomatic only. Are their
+GIs drawn from a separate CSV? Should their $R_i$ just be multiplied by a scalar? Part of the reason we are deferring
+this decision is because our previous isolation guidance work focused only on symptomatic individuals.