From e99b98290194f6a24350a425fec59a03c3e5725b Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 15:31:01 -0700
Subject: [PATCH 01/10] Create config_HIV.md

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+# FAVITES Configuration File for COVID in Wuhan, 2020
+
+## Contact Network:
+#### Through nodes (people) and edges (interactions), describe social interaction.
+#### Model: `Barabasi-Albert (BA)`
+- The Barabasi-Albert network's scale-free properties "recapitulate infectious disease spread" (Pekar et al., 2021). For example, scale-free networks can model "hubs", which illustrates the idea that somewhere with more infections would be more likely to accumulate even more infections.
+#### Number of nodes: `10000`
+- Picked an even, easy to work with number.
+#### m: `8`
+-  \# edges attached from new to existing nodes.
+- 2m is equal to the expected degree, which is set to an intermediate value of 16 contacts per day ([Mossong et. al](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0050074)).
+___
+
+
+## Transmission Network:
+#### Describe how the virus moves through the contact network, based on transition rates for individuals in our simulation. Transition rates are based on the reciprocal of expected time to arrival (make sure to keep track of your 'time' units!). 
+#### Model: `SAPHIRE`
+- This model was developed specifically to illustrate the dynamics of COVID-19 transmission, with states to model presymptomatic phases, testing, hospitalization, and more. See below for each state and its abbreviation ([Hao et. al](https://www.nature.com/articles/s41586-020-2554-8)).
+- S: Susceptible; A: Unascertained case; P: Presymptomatic Infectious Case; H: Hospitalized Case; I: Ascertained Infectious Case; R: Removed; E: Exposed
+
+#### Duration: `100 days`
+- This simulation is meant to look at the start of the COVID-19 pandemic, so we look at a span of 100 days, or around 3 months. 
+
+#### N_S: `9999`
+- The number of susceptible individuals at the start of the simulation
+- Whichever nodes that do not start as infected are susceptible.
+
+#### N_A: `0`
+- The number of unascertained cases at the start of the simulation
+- At the start of the simulation, there are no unascertained ([potentially including asymptomatic and mildly symptomatic individuals](https://www.nature.com/articles/s41586-020-2554-8)) cases yet.
+
+#### N_P: `0 `
+- The number of presymptomatic individuals at the start of the simulation
+- At the start of the simulation, no one is in the presymptomatic stage yet.
+
+#### N_H: `0 `
+- The number of hospitalized individuals at the start of the simulation
+- At the start of the simulation, no one has been hospitalized yet.
+
+#### N_I: `0`
+- The number of ascertained infectious cases at the start of the simulation
+- At the start of the simulation, one person is exposed but no one has tested positive yet.
+#### N_R: `0`
+- The number of removed individuals at the start of the simulation
+- At the start of the simulation, no individuals have died or recovered. 
+#### N_E: `1`
+- The number of exposed individuals at the start of the simulation
+- Start with one person exposed to witness how the virus spreads.
+#### R_S-E: `0`
+- Transition rate from susceptible to exposed
+- See table S7, no infections from outside the contact network. 
+#### R_S-E_E: `0`
+- The transition rate from susceptible to exposed induced by exposed neighbors
+- Exposed neighbors can't expose someone else if they are not infected (knowingly or unknowingly). 
+#### R_S-E_P: `4.83`
+- The transtion rate from susceptible to exposed induced by presymptomatic neighbors 
+- See table S7; The ratio of transmission of unascertained to ascertained cases is 0.55 and the transmission rate of ascertained cases is 0.385. There are an estimated 16 contacts per day. To get the transmission rate, we perform $`0.55*0.385*365/16`$.
+#### R_S-E_I: `8.78`
+- The transition rate from susceptible to exposed induced by ascertained infectious neighbors
+- See table S7; The transmission rate of ascertained cases is 0.385. There are an estimated 16 contacts per day. To get the transmission rate, we perform $0.385*365/16$.
+#### R_S-E_A: `4.83`
+- The transition rate from susceptible to exposed induced by unascertained neighbors
+- See table S7; We perform the same calculation as we did for `R_S-E_P`, as unascertained neighbors infect others because they are presymptomatic.
+#### R_E-P: `125.86`
+- The transition rate from exposed to presymptomatic
+- See table S7 for Timing of Covid Pandemic Pekar et al.; The latent period in days of the virus is estimated to be 2.9. In years (the unit of time for this simulation) the period is $2.9 / 365$, and its reciprocal is $`365/2.9=125.86`$.
+#### R_P-A: `134.89`
+- The transition rate from presymptomatic to unascertained
+- See table S7; The presymptomatic period in days is 2.3 and the rate of unascertainment is $`1 - 0.15`$. To find the transition rate we perform $`(1-0.15) * 365 / 2.3`$.
+#### R_P-I: `23.80`
+- The transition rate from presymptomatic to ascertained infectious 
+- See table S7; The presymptomatic period in days is 2.3 and the ascertainment rate is 0.15. To find the transition rate we perform $`0.15 * 365 / 2.3`$.
+#### R_A-R: `125.86`
+- The transition rate from unascertained to removed
+- See table S7; $`365/2.9`$ is the reciprocal of the expected time for arrival to move from an unascertained case to either being removed or recovered.
+#### R_I-H: `17.38`
+- The transition rate from ascertained infectious to hospitalized
+- See table S7. The expected arrival time of hospitalization is 21 days / 365 days (1 year), and since transition rates are in terms of the reciprocal of expected arival, we use $`365/21`$.
+#### R_I-R: `125.86`
+- The transition rate from ascertained infectious to removed
+- See table S7; $`365/2.9`$ is the reciprocal of the expected time for arrival to either be removed or recovered. 
+#### R_H-R: `12.17`
+- The transition rate from hospitalized to removed
+- See table S7; $`365/30`$ is the reciprocal of the expected time for someone in the hospital to either be removed or recovered.
+___
+
+## Sample Times:
+#### Describe when the individuals in the transmission network are sampled (sequenced). 
+#### Model: `State Entry (Initial)`
+- We want to sample each infected individual as they enter the infected state (to somewhat mirror the idea of sequencing once having tested positive).
+#### sampled_states: `I`
+- We want to sample individuals after they are ascertained and infectious, since it's not practical to expect to consistently sequence those whose states have not yet been ascertained. 
+
+___
+## Viral Phylogeny (Transmissions):
+#### Describe the topology and branch lengths (in time units) of the viral phylogeny thoughout the epidemic, constrained by the transmission network.
+#### Model: `Transmission Tree`
+- In a transmission tree, nodes are infected hosts; edges are transmission events. Coalescent events occur as late in time as possible.
+___
+## Viral Phylogeny (Seeds):
+#### Describe the topology and branch lengths (in time units) of the viral phylogeny prior to the epidemic (viral phylogeny of the seed individuals).
+#### Model: `Coalescent (Neutral)`
+- The [neutral coalescent model](https://en.wikipedia.org/wiki/Coalescent_theory) helps to model variation in DNA sequences due to genetic drift and/or mutation. 
+___
+## Mutation Rates:
+#### Describe how mutation rates (mutations/time) are sample along each branch of the viral time phylogeny.
+#### Model: `Constant`
+- Each branch's length is multiplied by a constant rate (to convert from time units to mutations).
+#### rate: `0.00092`
+- The constant mutation rate, inferred from BEAST results in the multiple zoonotic origins paper (Pekar et al.).
+___
+## Ancestral Sequence:
+#### Describe how the ancestral (root) sequence is generated/selected.
+#### Model: `Exact base frequencies`
+- We can looke at the COVID-19 genome and calculate base frequencies.
+#### k: `29903`
+- The value of the COVID-19 genome is around 30,000 bases.
+- Frequencies (see below) were calculated using the [NCBI reference sequence for Wuhan-Hu-1, complete genome](https://www.ncbi.nlm.nih.gov/nuccore/1798174254).
+#### p_A: `0.299`
+#### p_C: `0.184`
+#### p_G: `0.196`
+#### p_T: `0.321`
+- Frequency of A, C, G, T bases (respectively).
+
+___
+## Sequence Evolution:
+#### Describe how sequences evolve down the phylogeny.
+#### Model: `General Time-Reversible (GTR) + Gamma`
+- Adding on the Gamma parameter to the GTR model allows for variance in substitution probabilities for different sites.
+- NOTE: the following parameters were inferred by running `IQ-Tree` with the `GTR+I+G model`, which allows for a proportion of sites to be invariable.
+#### p_A: `0.299`
+#### p_C: `0.184`
+#### p_G: `0.196`
+#### p_T: `0.321`
+#### r_A-C: `0.52308`
+#### r_A-G: `2.65466`
+#### r_A-T: `0.42982`
+#### r_C-G: `0.38506`
+#### r_C-T: `7.58369`
+#### r_G-T: `1.00000`
+#### alpha: `1.039`
+- The shape parameter of Gamma model of rate heterogeneity.
+- We pull substitution probabilities from a Gamma distribution. An alpha of < 1 tells us that there is a lot of variance between substitution sites. 
+#### num_cats: `0`
+- The number of categories in discrete Gamma model (or 0 for continuous).
+#### prop_invariable: `0.521`
+- Proportion of invariable sites.

From 00f76b2a65f3da74a0d8a5c8e3def1f71dfdb142 Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 15:32:45 -0700
Subject: [PATCH 02/10] Update config_HIV.md

---
 example/config_HIV.md | 283 +++++++++++++++++++++++-------------------
 1 file changed, 158 insertions(+), 125 deletions(-)

diff --git a/example/config_HIV.md b/example/config_HIV.md
index 5bd7c55..ba29385 100644
--- a/example/config_HIV.md
+++ b/example/config_HIV.md
@@ -1,147 +1,180 @@
-# FAVITES Configuration File for COVID in Wuhan, 2020
+# FAVITES Configuration File for MSM Population in San Diego, 2017
 
 ## Contact Network:
-#### Through nodes (people) and edges (interactions), describe social interaction.
+#### Through nodes (people) and edges (interactions), describe social interactions.
 #### Model: `Barabasi-Albert (BA)`
-- The Barabasi-Albert network's scale-free properties "recapitulate infectious disease spread" (Pekar et al., 2021). For example, scale-free networks can model "hubs", which illustrates the idea that somewhere with more infections would be more likely to accumulate even more infections.
+- Uses preferential attachment, so nodes with more connections are likely to make connections (think about how someone with lots of friends might be more likely to make even more friends).
 #### Number of nodes: `10000`
 - Picked an even, easy to work with number.
-#### m: `8`
--  \# edges attached from new to existing nodes.
-- 2m is equal to the expected degree, which is set to an intermediate value of 16 contacts per day ([Mossong et. al](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0050074)).
+#### m (number of edges attached from new to existing nodes): `2`
+- There are estimates of between 3-4 sexual partners over 10 years: ~3 in [Wertheim et al. (2018)](https://scholar.google.com/scholar_lookup?title=Growth%20of%20HIV-1%20molecular%20transmission%20clusters%20in%20New%20York%20city&author=J.O.%20Wertheim&publication_year=2018&journal=J.%20Infect.%20Dis&volume=218&pages=1943-1953) and 3-4 in [Rosenberg et al. (2011)](https://scholar.google.com/scholar_lookup?title=Number%20of%20casual%20male%20sexual%20partners%20and%20associated%20factors%20among%20men%20who%20have%20sex%20with%20men%3A%20results%20from%20the%20National%20HIV%20Behavioral%20Surveillance%20system&author=E.S.%20Rosenberg&publication_year=2011&journal=BMC%20Public%20Health&volume=11&pages=189). Expected degree (number of partners) is equal to 2*m, so we set m to 2.
 ___
 
 
-## Transmission Network:
-#### Describe how the virus moves through the contact network, based on transition rates for individuals in our simulation. Transition rates are based on the reciprocal of expected time to arrival (make sure to keep track of your 'time' units!). 
-#### Model: `SAPHIRE`
-- This model was developed specifically to illustrate the dynamics of COVID-19 transmission, with states to model presymptomatic phases, testing, hospitalization, and more. See below for each state and its abbreviation ([Hao et. al](https://www.nature.com/articles/s41586-020-2554-8)).
-- S: Susceptible; A: Unascertained case; P: Presymptomatic Infectious Case; H: Hospitalized Case; I: Ascertained Infectious Case; R: Removed; E: Exposed
-
-#### Duration: `100 days`
-- This simulation is meant to look at the start of the COVID-19 pandemic, so we look at a span of 100 days, or around 3 months. 
-
+## Transmission Network: 
+#### Describe how the virus moves through the contact network, based on transition rates for individuals in our simulation. Transition rates are based on the reciprocal of expected time to arrival (make sure to keep track of your 'time' units!).
+#### Model: `Granich et al. (2008)`
+- The model uses states based on HIV progression, including states for indidividuals on ART (antiretroviral therapy) and states for those who are not. Whether or not a node is on ART, there are 4 stages of progression: 1, 2, 3 and 4 [(Granich et al., 2008)](https://www.sciencedirect.com/science/article/pii/S0140673608616979?via%3Dihub)
+#### Duration: `10`
+#### N_NS: `0`
+- This configuration file only looks at susceptible nodes, so there are no non-susceptible nodes in the network.
 #### N_S: `9999`
-- The number of susceptible individuals at the start of the simulation
-- Whichever nodes that do not start as infected are susceptible.
+- Every node that is not starting as infected will fall into the susceptible category.
+#### N_I1: `1`
+- Chose starting with 1 person in I1 to emphasize progression of the pandemic but this is easily changeable. Initial counts may have little to no effect on overall counts if the duration of the epidemic is long enough.
+#### N_I2, N_I3, N_I4, N_A1, N_A2, N_A3, N_A4: `0`
+- Nodes at the start are either susceptible or infected in this configuration file. This can be easily changed to include numbers of people in any of the above states.
+#### R_S-I1: `2`
+- Transition rate from susceptible to infected stage 1.
+- Based on MSM populations [(Grey et al., 2016)](https://publichealth.jmir.org/2016/1/e14/), with San Diego County's estimation being 1,204,728. Between 2017 and 2021 in California, an average of 70% of new diagnoses came from male-to-male sexual contact (MMSC) [(California HIV Suveillance Report - 2021)](https://www.cdph.ca.gov/Programs/CID/DOA/CDPH%20Document%20Library/California_HIV_Surveillance_Report2021_ADA.pdf). In 2021, there were 379 new HIV diagnoses in San Diego [(California HIV Surveillance Report - 2021)](https://www.cdph.ca.gov/Programs/CID/DOA/CDPH%20Document%20Library/California_HIV_Surveillance_Report2021_ADA.pdf). So out of 379 diagnoses and an estimated 70% of those coming from MMSC, we can estimate there are about 265 diagnoses for MSM. Looking at the whole population: 265/1,204,728 = 0.00022. Scaling this to our chosen number of nodes: $`10,000 * 0.00022 = 2.2`$ diagnoses a year. In a Poisson distribution, the expected value (2) will be equal to the reciprocal of the time until next arrival.
+#### R_S-I1_I1: `0.1125`
+- The transition rate from susceptible to infected stage 1 induced by infected stage 1 neighbors. 
+- In the [FAVITES](https://academic.oup.com/bioinformatics/article/35/11/1852/5161084) paper, relative rates were scaled so that number of new cases over ten years was roughly accurate. This yielded a rate of infectiousness 0.1125. Expected time for arrival would be $`1/(0.1125 * n)`$, n being the number of nodes in state I1 that are neighbors to the current node.
+#### R_S_I1_I2: `0.0225`
+#### R_S-I1_I3: `0.0225`
+#### R_S-I1_I4: `0.0225`
+-The transition rate from susceptible to infected stage 1 induced by infected stage 2, 3, 4 (respectively) neighbors.
+- In the [FAVITES](https://academic.oup.com/bioinformatics/article/35/11/1852/5161084) paper, relative rates were scaled so that number of new cases over ten years was roughly accurate.
+- Non-acute stages of HIV were used for baseline. We kept I2, I3, and I4 as having the same rates of infectiousness, but these parameters can easily be reset. Given infectivity `a` and number of neighbor nodes in "inducing" state `n`, expected time for next infection arrival would be $`1/(a * n)`$.
 
-#### N_A: `0`
-- The number of unascertained cases at the start of the simulation
-- At the start of the simulation, there are no unascertained ([potentially including asymptomatic and mildly symptomatic individuals](https://www.nature.com/articles/s41586-020-2554-8)) cases yet.
+#### R_S-I1_A1: `0.005625`
+- The transition rate from susceptible to infected stage 1 induced by neigbors on [ART](https://hivinfo.nih.gov/understanding-hiv/fact-sheets/hiv-treatment-basics), stage 1.
+- Those in the acute treated stage were found to have 1/20 the infectiousness of chronic untreated individuals [(Cohen et al., 2011)](https://scholar.google.com/scholar_lookup?title=Prevention%20of%20HIV-1%20infection%20with%20early%20antiretroviral%20therapy&author=M.S.%20Cohen&publication_year=2011&journal=N.%20Engl.%20J.%20Med.&volume=365&pages=493-505). Expected time for arrival would be $`1/(0.0.005625 * n)`$, n being the number of nodes in state A1 that are neighbors to the current node.
+#### R_S-I1_A2: `0`
+#### R_S-I1_A3: `0`
+#### R_S-I1_A4: `0`
+- The transition rate from susceptible to infected stage 1 induced by neighbors in ART stage 2, 3, 4 (respectively).
+- Individuals on ART stage 2 and beyond had extremely low infectivity.
+#### R_I1-D: `0`
+- The transition rate from infected stage 1 to death.
+- In this configuration, death is only a possible transition in either I4 or A4.
+#### R_I1-I2: `6`
+- The transition rate from infected stage 1 to infected stage 2.
+- Individuals spend an expected 2 months in the acute state [(Granich et. al)](https://www.sciencedirect.com/science/article/pii/S0140673608616979?via%3Dihub). Transition rates are in unit of reciprocal of next arrival so $`2`$ months = $`1/6`$ year; reciprocal of $`1/6`$ is $`6`$.
+#### R_I1-A1: `1`
+- The transition rate from infected stage 1 to ART stage 1.
+- Expected 1 year to start ART [(McCreesh et al., 2017)](https://scholar.google.com/scholar_lookup?title=Universal%20test%2C%20treat%2C%20and%20keep%3A%20improving%20ART%20retention%20is%20key%20in%20cost-effective%20HIV%20control%20in%20uganda&author=N.%20McCreesh&publication_year=2017&journal=BMC%20Infect.%20Dis.&volume=17&pages=322).
+#### R_A1-D: `0`
+- The transition rate from ART stage 1 to death.
+- In this configuration, death is only a possible transition in either I4 or A4. 
+#### R_A1-A2: `0.0883`
+- The transition rate from ART stage 1 to ART stage 2.
+- Length of stay in A1 is expected to be 11.33 years, find the reciprocal [(Zingoni et al., 2019)](https://pubmed.ncbi.nlm.nih.gov/31803702/).
+#### R_A1-I1: `0.481`
+- The transition rate from ART stage 1 to infected stage 1.
+- Expected time to stop ART is 25 months, find the reciprocal [(McCreesh et al., 2017)](https://scholar.google.com/scholar_lookup?title=Universal%20test%2C%20treat%2C%20and%20keep%3A%20improving%20ART%20retention%20is%20key%20in%20cost-effective%20HIV%20control%20in%20uganda&author=N.%20McCreesh&publication_year=2017&journal=BMC%20Infect.%20Dis.&volume=17&pages=322).
+#### R_I2-D: `0`
+- The transition rate from infected stage 2 to death.
+- In this configuration, death is only a possible transition in either I4 or A4. 
+#### R_I2-I3: `0.125`
+- The transition rate from infected stage 2 to infected stage 3.
+- Expected time in chronic state is 8 years [(Granich et al., 2008)](https://www.sciencedirect.com/science/article/pii/S0140673608616979?via%3Dihub). Find the reciprocal: $`1/8 = 0.125`$.
+#### R_I2-A2: `1`
+- The transition rate from infected stage 2 to ART stage 2.
+- Expected time to start ART is 1 year [(McCreesh et al., 2017)](https://scholar.google.com/scholar_lookup?title=Universal%20test%2C%20treat%2C%20and%20keep%3A%20improving%20ART%20retention%20is%20key%20in%20cost-effective%20HIV%20control%20in%20uganda&author=N.%20McCreesh&publication_year=2017&journal=BMC%20Infect.%20Dis.&volume=17&pages=322).
+#### R_A2-D: `0`
+- The transition rate from ART stage to to death.
+- In this configuration, death is only a possible transition in either I4 or A4. 
+#### R_A2-A3: `0.181`
+- The transition rate from ART stage 2 to ART stage 3. 
+- Length of stay in A2 is estimated to be 5.51 years [(Zingoni et al., 2019)](https://pubmed.ncbi.nlm.nih.gov/31803702/). Find the reciprocal: $`1/5.51 = 0.181`$
+#### R_A2-I2: `0.481`
+- The transition rate from ART stage 1 to infected stage 1.
+- Expected time to stop ART is 25 months, find the reciprocal [(McCreesh et al., 2017)](https://scholar.google.com/scholar_lookup?title=Universal%20test%2C%20treat%2C%20and%20keep%3A%20improving%20ART%20retention%20is%20key%20in%20cost-effective%20HIV%20control%20in%20uganda&author=N.%20McCreesh&publication_year=2017&journal=BMC%20Infect.%20Dis.&volume=17&pages=322).
+#### R_I3-D: `0`
+- The transition rate from infected stage 3 to death.
+- In this configuration, death is only a possible transition in either I4 or A4.
+#### R_I3-I4: `0.5`
+- The transition rate from infected stage 3 to infected stage 4.
+- Expected time in stage 3 is 2 years without ART [(Granich et al., 2008)](https://www.sciencedirect.com/science/article/pii/S0140673608616979?via%3Dihub). Find the reciprocal: $`1/2 = 0.5`$.
+#### R_A3-D: `0`
+- The transition rate from ART stage 3 to death.
+- In this configuration, death is only a possible transition in either I4 or A4. 
+#### R_A3-A4: `0.139`
+- The transition rate from ART stage 3 to ART stage 4. 
+- Length of stay in A3 is estimated to be 7.17 years [(Zingoni et al., 2019)](https://pubmed.ncbi.nlm.nih.gov/31803702/).
+#### R_I4-D: `1.82`
+- The transition rate from infected stage 4 to death.
+- Expected time in stage 4 is 5% of survival time without ART, with mean survival time without ART being 11 years [(Zingoni et al., 2019)](https://pubmed.ncbi.nlm.nih.gov/31803702/). $`0.05 * 11 = 0.55`$; $`1/0.55 = 1.8181`$.
+#### R_I4-A4: `0.481`
+- The transition rate from infected stage 4 to ART stage 4.
+- Expected time to stop ART is 25 months, find the reciprocal [(McCreesh et al., 2017)](https://scholar.google.com/scholar_lookup?title=Universal%20test%2C%20treat%2C%20and%20keep%3A%20improving%20ART%20retention%20is%20key%20in%20cost-effective%20HIV%20control%20in%20uganda&author=N.%20McCreesh&publication_year=2017&journal=BMC%20Infect.%20Dis.&volume=17&pages=322).
+#### R_A4-D: `0.146`
+- The transition rate from infected stage 4 to death.
+- Length of stay in A4 is expected to be 6.86 years [(Zingoni et al., 2019)](https://pubmed.ncbi.nlm.nih.gov/31803702/). Find the reciprocal: $`1/6.86 = 0.146`$.
+#### R_A4-I4: `0.481`
+- Expected time to stop ART is 25 months [(McCreesh et al., 2017)](https://scholar.google.com/scholar_lookup?title=Universal%20test%2C%20treat%2C%20and%20keep%3A%20improving%20ART%20retention%20is%20key%20in%20cost-effective%20HIV%20control%20in%20uganda&author=N.%20McCreesh&publication_year=2017&journal=BMC%20Infect.%20Dis.&volume=17&pages=322). Find the reciprocal: $`1/(25/12) = 0.481`$.
 
-#### N_P: `0 `
-- The number of presymptomatic individuals at the start of the simulation
-- At the start of the simulation, no one is in the presymptomatic stage yet.
+## Sample Times: 
+#### How are individuals in the transmission network sampled?
+#### Model: `End`
+- End was chosen to get a good idea of every node's state at the end of the simulation.
+#### sampled_states: `I1,I2,I3,I4,A1,A2,A3,A4`
+- Comma-separated list of states in which individuals are sampled.
+- For this configuration, all states were sampled to get as much information as possible.
+#### num_samples: `1`
+- With this sampling configuration, we choose to sample every single individual once at the end of the simulation.
 
-#### N_H: `0 `
-- The number of hospitalized individuals at the start of the simulation
-- At the start of the simulation, no one has been hospitalized yet.
+## Viral Phylogeny (Transmissions): 
+#### With a coalescent framework, describe topology and branch lengths (in unit of time) of the viral phylogeny throughout the epidemic under the constraints of our chosen transmission network.
+#### Model: `Transmission Tree`
+- In a transmission tree, nodes are infected hosts; edges are transmission events. Coalescent events occur as late in time as possible.
 
-#### N_I: `0`
-- The number of ascertained infectious cases at the start of the simulation
-- At the start of the simulation, one person is exposed but no one has tested positive yet.
-#### N_R: `0`
-- The number of removed individuals at the start of the simulation
-- At the start of the simulation, no individuals have died or recovered. 
-#### N_E: `1`
-- The number of exposed individuals at the start of the simulation
-- Start with one person exposed to witness how the virus spreads.
-#### R_S-E: `0`
-- Transition rate from susceptible to exposed
-- See table S7, no infections from outside the contact network. 
-#### R_S-E_E: `0`
-- The transition rate from susceptible to exposed induced by exposed neighbors
-- Exposed neighbors can't expose someone else if they are not infected (knowingly or unknowingly). 
-#### R_S-E_P: `4.83`
-- The transtion rate from susceptible to exposed induced by presymptomatic neighbors 
-- See table S7; The ratio of transmission of unascertained to ascertained cases is 0.55 and the transmission rate of ascertained cases is 0.385. There are an estimated 16 contacts per day. To get the transmission rate, we perform $`0.55*0.385*365/16`$.
-#### R_S-E_I: `8.78`
-- The transition rate from susceptible to exposed induced by ascertained infectious neighbors
-- See table S7; The transmission rate of ascertained cases is 0.385. There are an estimated 16 contacts per day. To get the transmission rate, we perform $0.385*365/16$.
-#### R_S-E_A: `4.83`
-- The transition rate from susceptible to exposed induced by unascertained neighbors
-- See table S7; We perform the same calculation as we did for `R_S-E_P`, as unascertained neighbors infect others because they are presymptomatic.
-#### R_E-P: `125.86`
-- The transition rate from exposed to presymptomatic
-- See table S7 for Timing of Covid Pandemic Pekar et al.; The latent period in days of the virus is estimated to be 2.9. In years (the unit of time for this simulation) the period is $2.9 / 365$, and its reciprocal is $`365/2.9=125.86`$.
-#### R_P-A: `134.89`
-- The transition rate from presymptomatic to unascertained
-- See table S7; The presymptomatic period in days is 2.3 and the rate of unascertainment is $`1 - 0.15`$. To find the transition rate we perform $`(1-0.15) * 365 / 2.3`$.
-#### R_P-I: `23.80`
-- The transition rate from presymptomatic to ascertained infectious 
-- See table S7; The presymptomatic period in days is 2.3 and the ascertainment rate is 0.15. To find the transition rate we perform $`0.15 * 365 / 2.3`$.
-#### R_A-R: `125.86`
-- The transition rate from unascertained to removed
-- See table S7; $`365/2.9`$ is the reciprocal of the expected time for arrival to move from an unascertained case to either being removed or recovered.
-#### R_I-H: `17.38`
-- The transition rate from ascertained infectious to hospitalized
-- See table S7. The expected arrival time of hospitalization is 21 days / 365 days (1 year), and since transition rates are in terms of the reciprocal of expected arival, we use $`365/21`$.
-#### R_I-R: `125.86`
-- The transition rate from ascertained infectious to removed
-- See table S7; $`365/2.9`$ is the reciprocal of the expected time for arrival to either be removed or recovered. 
-#### R_H-R: `12.17`
-- The transition rate from hospitalized to removed
-- See table S7; $`365/30`$ is the reciprocal of the expected time for someone in the hospital to either be removed or recovered.
-___
+## Viral Phylogeny (Seeds): 
+#### Describe the topology and branch lengths (in unit of time) of the viral phylogeny of the seed individuals.
+#### Model: `Non-Homogeneous Yule`
+- Real HIV trees have short internal branches close to the root and long leaves (cite or link to pic?). Since most phylogenetic models don't produce this shape easily, a rate function that is high early in the timeline, decreasing as time progresses, can be used in non-homogeneous Yule processes to replicate a pattern reminiscent of real HIV trees.
+#### rate_func (Rate function of the initial tree): $`e^-(t^2)+1`$
+- Chosen to emulate short branches close to the base of the tree.
+#### height: `36`
+- Scaled so that height matches the 1980 tMRCA estimate using SD [(Moshiri et al., 2019)](https://academic.oup.com/bioinformatics/article/35/11/1852/5161084).
 
-## Sample Times:
-#### Describe when the individuals in the transmission network are sampled (sequenced). 
-#### Model: `State Entry (Initial)`
-- We want to sample each infected individual as they enter the infected state (to somewhat mirror the idea of sequencing once having tested positive).
-#### sampled_states: `I`
-- We want to sample individuals after they are ascertained and infectious, since it's not practical to expect to consistently sequence those whose states have not yet been ascertained. 
+## Mutation Rates: 
+#### How are mutation rates sampled along each branch of the viral time phylogeny?
+#### Model: `Truncated Normal`
+- Other distributions deviate significantly from real distributions, so we use Truncated Normal.
+#### mu: `0.0008`
+- Location parameter of the Truncated Normal distribution.
+- With the scale parameter (see below), branch lengths were scaled from years to expected number of per-site mutations [(Moshiri et al., 2019)](https://academic.oup.com/bioinformatics/article/35/11/1852/5161084).
+#### sigma: `0.0005`
+- Scale parameter of the Truncated Normal distribution.
+- Chosen to scale branch lengths from years to expected number of per-site mutations [(Moshiri et al., 2019)](https://academic.oup.com/bioinformatics/article/35/11/1852/5161084).
+#### a: `0`
+#### b: `infinity`
+- Minimum, maximum (respectively) of the Truncated Normal distribution.
 
-___
-## Viral Phylogeny (Transmissions):
-#### Describe the topology and branch lengths (in time units) of the viral phylogeny thoughout the epidemic, constrained by the transmission network.
-#### Model: `Transmission Tree`
-- In a transmission tree, nodes are infected hosts; edges are transmission events. Coalescent events occur as late in time as possible.
-___
-## Viral Phylogeny (Seeds):
-#### Describe the topology and branch lengths (in time units) of the viral phylogeny prior to the epidemic (viral phylogeny of the seed individuals).
-#### Model: `Coalescent (Neutral)`
-- The [neutral coalescent model](https://en.wikipedia.org/wiki/Coalescent_theory) helps to model variation in DNA sequences due to genetic drift and/or mutation. 
-___
-## Mutation Rates:
-#### Describe how mutation rates (mutations/time) are sample along each branch of the viral time phylogeny.
-#### Model: `Constant`
-- Each branch's length is multiplied by a constant rate (to convert from time units to mutations).
-#### rate: `0.00092`
-- The constant mutation rate, inferred from BEAST results in the multiple zoonotic origins paper (Pekar et al.).
-___
-## Ancestral Sequence:
-#### Describe how the ancestral (root) sequence is generated/selected.
-#### Model: `Exact base frequencies`
-- We can looke at the COVID-19 genome and calculate base frequencies.
-#### k: `29903`
-- The value of the COVID-19 genome is around 30,000 bases.
-- Frequencies (see below) were calculated using the [NCBI reference sequence for Wuhan-Hu-1, complete genome](https://www.ncbi.nlm.nih.gov/nuccore/1798174254).
-#### p_A: `0.299`
-#### p_C: `0.184`
-#### p_G: `0.196`
-#### p_T: `0.321`
+## Ancestral Sequence: 
+#### Describe how ancestral (root) sequence is generated/selected.
+#### Model: `Exact Base Frequencies`
+- A simple option with the ability to define the frequency of each base.
+#### k (Length of ancestral sequence): `9200`
+- The length of the HIV genome is about 9200 bases.
+- Frequences (see below) were calculated using the [NCBI reference sequence for HIV-1, complete genome](https://www.ncbi.nlm.nih.gov/nuccore/NC_001802.1?report=genbank).
+#### p_A: `0.3564`
+#### p_C: `0.1788`
+#### p_G: `0.2424`
+#### p_T: `0.2224`
 - Frequency of A, C, G, T bases (respectively).
 
-___
-## Sequence Evolution:
+## Sequence Evolution: 
 #### Describe how sequences evolve down the phylogeny.
 #### Model: `General Time-Reversible (GTR) + Gamma`
-- Adding on the Gamma parameter to the GTR model allows for variance in substitution probabilities for different sites.
-- NOTE: the following parameters were inferred by running `IQ-Tree` with the `GTR+I+G model`, which allows for a proportion of sites to be invariable.
-#### p_A: `0.299`
-#### p_C: `0.184`
-#### p_G: `0.196`
-#### p_T: `0.321`
-#### r_A-C: `0.52308`
-#### r_A-G: `2.65466`
-#### r_A-T: `0.42982`
-#### r_C-G: `0.38506`
-#### r_C-T: `7.58369`
-#### r_G-T: `1.00000`
-#### alpha: `1.039`
+- General Time-Reversible "doesn't care" whether a sequence is an ancestor or descendent. The Gamma parameter allows the number of substitutions per site to change, as sampled from the Gamma distribution.
+#### p_A (Frequency of A): `0.3564`
+#### p_C (Frequency of C): `0.1788`
+#### p_G (Frequency of G): `0.2424`
+#### p_T (Frequency of T): `0.2224`
+#### r_A-C (Transition rate between A and C): `1.812`
+#### r_A-G (Transition rate between A and G): `9.934`
+#### r_A-T (Transition rate between A and T): `0.718`
+#### r_C-G (Transition rate between C and G): `0.971`
+#### r_C-T (Transition rate between C and T): `9.934`
+#### r_G-T (Transition rate between G and T): `1.000`
+- These frequencies and transition rates were pulled from the FAVITES paper, which used parameters inferred by IQ-TREE [(Moshiri et al., 2019)](https://academic.oup.com/bioinformatics/article/35/11/1852/5161084).
+#### alpha: `0.405`
 - The shape parameter of Gamma model of rate heterogeneity.
 - We pull substitution probabilities from a Gamma distribution. An alpha of < 1 tells us that there is a lot of variance between substitution sites. 
 #### num_cats: `0`
-- The number of categories in discrete Gamma model (or 0 for continuous).
-#### prop_invariable: `0.521`
-- Proportion of invariable sites.
+- Number of categories in discrete Gamma model (or 0 for continuous).
+#### prop_invariable (Proportion of invariable sites): `0`
+- Proportion of invariable sites. 

From bfa45b59e1015a07dc2a8110ddc93e9cbf7dc810 Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 15:33:18 -0700
Subject: [PATCH 03/10] Create config_COVID.md

---
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diff --git a/example/config_COVID.md b/example/config_COVID.md
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+# FAVITES Configuration File for COVID in Wuhan, 2020
+
+## Contact Network:
+#### Through nodes (people) and edges (interactions), describe social interaction.
+#### Model: `Barabasi-Albert (BA)`
+- The Barabasi-Albert network's scale-free properties "recapitulate infectious disease spread" (Pekar et al., 2021). For example, scale-free networks can model "hubs", which illustrates the idea that somewhere with more infections would be more likely to accumulate even more infections.
+#### Number of nodes: `10000`
+- Picked an even, easy to work with number.
+#### m: `8`
+-  \# edges attached from new to existing nodes.
+- 2m is equal to the expected degree, which is set to an intermediate value of 16 contacts per day ([Mossong et. al](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0050074)).
+___
+
+
+## Transmission Network:
+#### Describe how the virus moves through the contact network, based on transition rates for individuals in our simulation. Transition rates are based on the reciprocal of expected time to arrival (make sure to keep track of your 'time' units!). 
+#### Model: `SAPHIRE`
+- This model was developed specifically to illustrate the dynamics of COVID-19 transmission, with states to model presymptomatic phases, testing, hospitalization, and more. See below for each state and its abbreviation ([Hao et. al](https://www.nature.com/articles/s41586-020-2554-8)).
+- S: Susceptible; A: Unascertained case; P: Presymptomatic Infectious Case; H: Hospitalized Case; I: Ascertained Infectious Case; R: Removed; E: Exposed
+
+#### Duration: `100 days`
+- This simulation is meant to look at the start of the COVID-19 pandemic, so we look at a span of 100 days, or around 3 months. 
+
+#### N_S: `9999`
+- The number of susceptible individuals at the start of the simulation
+- Whichever nodes that do not start as infected are susceptible.
+
+#### N_A: `0`
+- The number of unascertained cases at the start of the simulation
+- At the start of the simulation, there are no unascertained ([potentially including asymptomatic and mildly symptomatic individuals](https://www.nature.com/articles/s41586-020-2554-8)) cases yet.
+
+#### N_P: `0 `
+- The number of presymptomatic individuals at the start of the simulation
+- At the start of the simulation, no one is in the presymptomatic stage yet.
+
+#### N_H: `0 `
+- The number of hospitalized individuals at the start of the simulation
+- At the start of the simulation, no one has been hospitalized yet.
+
+#### N_I: `0`
+- The number of ascertained infectious cases at the start of the simulation
+- At the start of the simulation, one person is exposed but no one has tested positive yet.
+#### N_R: `0`
+- The number of removed individuals at the start of the simulation
+- At the start of the simulation, no individuals have died or recovered. 
+#### N_E: `1`
+- The number of exposed individuals at the start of the simulation
+- Start with one person exposed to witness how the virus spreads.
+#### R_S-E: `0`
+- Transition rate from susceptible to exposed
+- See table S7, no infections from outside the contact network. 
+#### R_S-E_E: `0`
+- The transition rate from susceptible to exposed induced by exposed neighbors
+- Exposed neighbors can't expose someone else if they are not infected (knowingly or unknowingly). 
+#### R_S-E_P: `4.83`
+- The transtion rate from susceptible to exposed induced by presymptomatic neighbors 
+- See table S7; The ratio of transmission of unascertained to ascertained cases is 0.55 and the transmission rate of ascertained cases is 0.385. There are an estimated 16 contacts per day. To get the transmission rate, we perform $`0.55*0.385*365/16`$.
+#### R_S-E_I: `8.78`
+- The transition rate from susceptible to exposed induced by ascertained infectious neighbors
+- See table S7; The transmission rate of ascertained cases is 0.385. There are an estimated 16 contacts per day. To get the transmission rate, we perform $0.385*365/16$.
+#### R_S-E_A: `4.83`
+- The transition rate from susceptible to exposed induced by unascertained neighbors
+- See table S7; We perform the same calculation as we did for `R_S-E_P`, as unascertained neighbors infect others because they are presymptomatic.
+#### R_E-P: `125.86`
+- The transition rate from exposed to presymptomatic
+- See table S7 for Timing of Covid Pandemic Pekar et al.; The latent period in days of the virus is estimated to be 2.9. In years (the unit of time for this simulation) the period is $2.9 / 365$, and its reciprocal is $`365/2.9=125.86`$.
+#### R_P-A: `134.89`
+- The transition rate from presymptomatic to unascertained
+- See table S7; The presymptomatic period in days is 2.3 and the rate of unascertainment is $`1 - 0.15`$. To find the transition rate we perform $`(1-0.15) * 365 / 2.3`$.
+#### R_P-I: `23.80`
+- The transition rate from presymptomatic to ascertained infectious 
+- See table S7; The presymptomatic period in days is 2.3 and the ascertainment rate is 0.15. To find the transition rate we perform $`0.15 * 365 / 2.3`$.
+#### R_A-R: `125.86`
+- The transition rate from unascertained to removed
+- See table S7; $`365/2.9`$ is the reciprocal of the expected time for arrival to move from an unascertained case to either being removed or recovered.
+#### R_I-H: `17.38`
+- The transition rate from ascertained infectious to hospitalized
+- See table S7. The expected arrival time of hospitalization is 21 days / 365 days (1 year), and since transition rates are in terms of the reciprocal of expected arival, we use $`365/21`$.
+#### R_I-R: `125.86`
+- The transition rate from ascertained infectious to removed
+- See table S7; $`365/2.9`$ is the reciprocal of the expected time for arrival to either be removed or recovered. 
+#### R_H-R: `12.17`
+- The transition rate from hospitalized to removed
+- See table S7; $`365/30`$ is the reciprocal of the expected time for someone in the hospital to either be removed or recovered.
+___
+
+## Sample Times:
+#### Describe when the individuals in the transmission network are sampled (sequenced). 
+#### Model: `State Entry (Initial)`
+- We want to sample each infected individual as they enter the infected state (to somewhat mirror the idea of sequencing once having tested positive).
+#### sampled_states: `I`
+- We want to sample individuals after they are ascertained and infectious, since it's not practical to expect to consistently sequence those whose states have not yet been ascertained. 
+
+___
+## Viral Phylogeny (Transmissions):
+#### Describe the topology and branch lengths (in time units) of the viral phylogeny thoughout the epidemic, constrained by the transmission network.
+#### Model: `Transmission Tree`
+- In a transmission tree, nodes are infected hosts; edges are transmission events. Coalescent events occur as late in time as possible.
+___
+## Viral Phylogeny (Seeds):
+#### Describe the topology and branch lengths (in time units) of the viral phylogeny prior to the epidemic (viral phylogeny of the seed individuals).
+#### Model: `Coalescent (Neutral)`
+- The [neutral coalescent model](https://en.wikipedia.org/wiki/Coalescent_theory) helps to model variation in DNA sequences due to genetic drift and/or mutation. 
+___
+## Mutation Rates:
+#### Describe how mutation rates (mutations/time) are sample along each branch of the viral time phylogeny.
+#### Model: `Constant`
+- Each branch's length is multiplied by a constant rate (to convert from time units to mutations).
+#### rate: `0.00092`
+- The constant mutation rate, inferred from BEAST results in the multiple zoonotic origins paper (Pekar et al.).
+___
+## Ancestral Sequence:
+#### Describe how the ancestral (root) sequence is generated/selected.
+#### Model: `Exact base frequencies`
+- We can looke at the COVID-19 genome and calculate base frequencies.
+#### k: `29903`
+- The value of the COVID-19 genome is around 30,000 bases.
+- Frequencies (see below) were calculated using the [NCBI reference sequence for Wuhan-Hu-1, complete genome](https://www.ncbi.nlm.nih.gov/nuccore/1798174254).
+#### p_A: `0.299`
+#### p_C: `0.184`
+#### p_G: `0.196`
+#### p_T: `0.321`
+- Frequency of A, C, G, T bases (respectively).
+
+___
+## Sequence Evolution:
+#### Describe how sequences evolve down the phylogeny.
+#### Model: `General Time-Reversible (GTR) + Gamma`
+- Adding on the Gamma parameter to the GTR model allows for variance in substitution probabilities for different sites.
+- NOTE: the following parameters were inferred by running `IQ-Tree` with the `GTR+I+G model`, which allows for a proportion of sites to be invariable.
+#### p_A: `0.299`
+#### p_C: `0.184`
+#### p_G: `0.196`
+#### p_T: `0.321`
+#### r_A-C: `0.52308`
+#### r_A-G: `2.65466`
+#### r_A-T: `0.42982`
+#### r_C-G: `0.38506`
+#### r_C-T: `7.58369`
+#### r_G-T: `1.00000`
+#### alpha: `1.039`
+- The shape parameter of Gamma model of rate heterogeneity.
+- We pull substitution probabilities from a Gamma distribution. An alpha of < 1 tells us that there is a lot of variance between substitution sites. 
+#### num_cats: `0`
+- The number of categories in discrete Gamma model (or 0 for continuous).
+#### prop_invariable: `0.521`
+- Proportion of invariable sites.

From f316d6df42899f2b2aea9a34fbe830c769a3f793 Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 15:34:50 -0700
Subject: [PATCH 04/10] Create README.md

---
 example/README.md | 2 ++
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diff --git a/example/README.md b/example/README.md
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+++ b/example/README.md
@@ -0,0 +1,2 @@
+# Configuration file bank for FAVITES-LITE
+### Each file is an example configuration file (JSON) for its namesake, with explanations for each parameter. These configuration files are meant to give users a better understanding of what each parameter means and how it can/should be chosen. 

From 770aaab4d77f6ebd4d3cf142a508104dfb7f1966 Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 15:44:37 -0700
Subject: [PATCH 05/10] Add files via upload

---
 example/config_COVID.json | 1 +
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diff --git a/example/config_COVID.json b/example/config_COVID.json
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--- /dev/null
+++ b/example/config_COVID.json
@@ -0,0 +1 @@
+{"Contact Network":{"model":"Barabasi-Albert (BA)","param":{"n":10000,"m":8}},"Transmission Network":{"model":"SAPHIRE","param":{"duration":0.274,"N_S":9999,"N_A":0,"N_P":0,"N_H":0,"N_I":0,"N_R":0,"N_E":1,"R_S-E":0,"R_S-E_E":0,"R_S-E_P":4.83,"R_S-E_I":8.78,"R_S-E_A":4.83,"R_E-P":125.86,"R_P-A":134.89,"R_P-I":23.8,"R_A-R":125.86,"R_I-H":17.38,"R_I-R":125.86,"R_H-R":12.17}},"Sample Times":{"model":"State Entry (Initial)","param":{"sampled_states":"I"}},"Viral Phylogeny (Transmissions)":{"model":"Transmission Tree","param":{}},"Viral Phylogeny (Seeds)":{"model":"Coalescent (Neutral)","param":{"height":0.0833}},"Mutation Rates":{"model":"Constant","param":{"rate":0.00092}},"Ancestral Sequence":{"model":"Exact Base Frequencies","param":{"k":29903,"p_A":0.299,"p_C":0.184,"p_G":0.196,"p_T":0.321}},"Sequence Evolution":{"model":"General Time-Reversible (GTR) + Gamma","param":{"p_A":0.299,"p_C":0.184,"p_G":0.196,"p_T":0.321,"r_A-C":0.52308,"r_A-G":2.65466,"r_A-T":0.42982,"r_C-G":0.38506,"r_C-T":7.58369,"r_G-T":1,"alpha":1.039,"num_cats":0,"prop_invariable":0.521}}}
\ No newline at end of file

From 4334cd70110e1adc1e1ab054464bcebe05824b0f Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 15:59:24 -0700
Subject: [PATCH 06/10] Add files via upload

---
 example/config_HIV.json | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 example/config_HIV.json

diff --git a/example/config_HIV.json b/example/config_HIV.json
new file mode 100644
index 0000000..39e2be1
--- /dev/null
+++ b/example/config_HIV.json
@@ -0,0 +1 @@
+{"Contact Network":{"model":"Barabasi-Albert (BA)","param":{"n":10000,"m":2}},"Transmission Network":{"model":"Granich et al. (2008)","param":{"duration":10,"N_NS":9999,"N_S":0,"N_I1":1,"N_I2":0,"N_I3":0,"N_I4":0,"N_A1":0,"N_A2":0,"N_A3":0,"N_A4":0,"N_D":0,"R_NS-D":0,"R_NS-S":0,"R_S-D":0,"R_S-I1":46,"R_S-I1_I1":0.1125,"R_S-I1_I2":0.0225,"R_S-I1_I3":0.0225,"R_S-I1_I4":0.0225,"R_S-I1_A1":0.005625,"R_S-I1_A2":0,"R_S-I1_A3":0,"R_S-I1_A4":0,"R_I1-D":0,"R_I1-I2":6,"R_I1-A1":1,"R_A1-D":0,"R_A1-A2":0.0883,"R_A1-I1":0.481,"R_I2-D":0,"R_I2-I3":0.125,"R_I2-A2":1,"R_A2-D":0,"R_A2-A3":0.181,"R_A2-I2":0.481,"R_I3-D":0,"R_I3-I4":0.5,"R_I3-A3":1,"R_A3-D":0,"R_A3-A4":0.139,"R_A3-I3":0.481,"R_I4-D":1.82,"R_I4-A4":1,"R_A4-D":0.146,"R_A4-I4":0.481}},"Sample Times":{"model":"End","param":{"sampled_states":"I1,I2,I3,I4,A1,A2,A3,A4","num_samples":1}},"Viral Phylogeny (Transmissions)":{"model":"Transmission Tree","param":{}},"Viral Phylogeny (Seeds)":{"model":"Non-Homogeneous Yule","param":{"rate_func":"e^(-t^2) + 1","height":36}},"Mutation Rates":{"model":"Truncated Normal","param":{"mu":0.0008,"sigma":0.0005,"a":1e-29,"b":1e+33}},"Ancestral Sequence":{"model":"Exact Base Frequencies","param":{"k":9200,"p_A":0.3564,"p_C":0.1788,"p_G":0.2424,"p_T":0.2224}},"Sequence Evolution":{"model":"General Time-Reversible (GTR) + Gamma","param":{"p_A":0.3564,"p_C":0.1788,"p_G":0.2424,"p_T":0.2224,"r_A-C":1.812,"r_A-T":0.718,"r_C-G":0.971,"r_C-T":9.934,"r_G-T":1,"alpha":0.405,"num_cats":0,"prop_invariable":0,"r_A-G":9.934}}}
\ No newline at end of file

From 1d8e16effec317f6483a108a49d1faafc475831e Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 16:03:42 -0700
Subject: [PATCH 07/10] Update config_COVID.md

---
 example/config_COVID.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/example/config_COVID.md b/example/config_COVID.md
index 5bd7c55..5069fe4 100644
--- a/example/config_COVID.md
+++ b/example/config_COVID.md
@@ -1,4 +1,4 @@
-# FAVITES Configuration File for COVID in Wuhan, 2020
+# FAVITES Configuration File for COVID in Wuhan, late 2019 through early 2020
 
 ## Contact Network:
 #### Through nodes (people) and edges (interactions), describe social interaction.

From 7f280f8b2ed621f55ba825e00f41510ee1592073 Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 16:04:10 -0700
Subject: [PATCH 08/10] Update config_HIV.md

---
 example/config_HIV.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/example/config_HIV.md b/example/config_HIV.md
index ba29385..788ec59 100644
--- a/example/config_HIV.md
+++ b/example/config_HIV.md
@@ -1,4 +1,4 @@
-# FAVITES Configuration File for MSM Population in San Diego, 2017
+# FAVITES Configuration File for MSM Population in San Diego, 2016 through 2024
 
 ## Contact Network:
 #### Through nodes (people) and edges (interactions), describe social interactions.

From 955f603eda110369146259bb7d945c20835321c9 Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 16:04:59 -0700
Subject: [PATCH 09/10] Rename config_HIV.md to README_HIV.md

---
 example/{config_HIV.md => README_HIV.md} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename example/{config_HIV.md => README_HIV.md} (100%)

diff --git a/example/config_HIV.md b/example/README_HIV.md
similarity index 100%
rename from example/config_HIV.md
rename to example/README_HIV.md

From c714b560abda209fbc6ed28d4d1836332d769456 Mon Sep 17 00:00:00 2001
From: kstad21 <120453937+kstad21@users.noreply.github.com>
Date: Wed, 5 Jun 2024 16:05:17 -0700
Subject: [PATCH 10/10] Rename config_COVID.md to README_COVID.md

---
 example/{config_COVID.md => README_COVID.md} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename example/{config_COVID.md => README_COVID.md} (100%)

diff --git a/example/config_COVID.md b/example/README_COVID.md
similarity index 100%
rename from example/config_COVID.md
rename to example/README_COVID.md