From 9fa0efb2779beb05f7504f11f8c014f2e67caa87 Mon Sep 17 00:00:00 2001
From: John Pesavento <54077093+jpes707@users.noreply.github.com>
Date: Tue, 1 Sep 2020 01:02:38 -0400
Subject: [PATCH] Finalized code for public release
---
README.md | 10 +-
config-files/default-config.cfg | 20 +-
docs/cbgs-to-topics-1.html | 2 +
docs/cbgs-to-topics-10.html | 2 +
docs/cbgs-to-topics-50.html | 2 +
docs/index.html | 3 +-
docs/sigspatial.ico | Bin 0 -> 903 bytes
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docs/topics-to-pois-10.html | 2 +
docs/topics-to-pois-50.html | 2 +
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simulation-naive.py | 594 +
simulation.py | 2 +-
311 files changed, 138965 insertions(+), 363877 deletions(-)
create mode 100644 docs/sigspatial.ico
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diff --git a/README.md b/README.md
index b926866..7e86bbf 100644
--- a/README.md
+++ b/README.md
@@ -10,7 +10,7 @@ Codebase for SIGSPATIAL ARIC 2020: Foot-Traffic Informed COVID-19 Simulation and
3. Download the SafeGraph Open Census Data [here](https://www.safegraph.com/open-census-data). Save it to `safegraph-data/safegraph_open_census_data`.
-4. Get free access to the remaining SafeGraph data by following [these steps](https://www.safegraph.com/covid-19-data-consortium).
+4. Get free access to the remaining SafeGraph data by following [these steps](https://www.safegraph.com/covid-19-data-consortium). A non-disclosure agreement must be signed as part of the process.
5. Download the SafeGraph Weekly Patterns (v2) data. Save it to `safegraph-data/safegraph_weekly_patterns_v2`.
@@ -20,8 +20,12 @@ Codebase for SIGSPATIAL ARIC 2020: Foot-Traffic Informed COVID-19 Simulation and
## Usage
-* `county-parser.py` is used to extract locality data. Run this at least once, and then run `simulation.py`. The results will be located in `results/`.
+* `county-parser.py` is used to extract locality data. Run this at least once, and then run `simulation.py`. To create custom simulation configurations, place new `.cfg` files in `config-files/`. Use `config-files/default-config.cfg` as a template.
-* To create custom simulation configurations, place new `.cfg` files in `config-files/`. Use `config-files/default-config.cfg` as a template.
+* `simulation-naive.py` is used to simulate community and household spread in a naive way by weighting POIs equally, ignoring the time of day, and using a constant dwell time. In most cases, the virus will only reach a very small number of agents because agents will not congregate at popular POIs, making this simulation version unrealistic.
* `mobility-stats.py` is used to evaluate mobility data assuming no virus has been introduced. It provides the total number of visitors to each POI within a specified timeframe.
+
+* `sigspatial-trials.txt`, `sigspatial-trial-runner.py`, and any files in a folder that is titled `sigspatial-trials/` were used to generate results for the conference publication. These files will likely not be applicable in other cases.
+
+* All results will be located in `results/`.
diff --git a/config-files/default-config.cfg b/config-files/default-config.cfg
index a9b4fc9..ace7abf 100644
--- a/config-files/default-config.cfg
+++ b/config-files/default-config.cfg
@@ -1,5 +1,3 @@
-[Settings]
-
# preliminary parameters
LOCALITY = 'Fairfax' # can be a county, borough, or parish; independent cities (e.g. Baltimore City) are not currently compatible
STATE_ABBR = 'VA' # state abbreviation of where the locality is located
@@ -28,12 +26,12 @@ CLOSED_POI_TYPES = {} # closes the following POI types (from SafeGraph Core Pla
# virus parameters
# For COVID-19, a close contact is defined as ay individual who was within 6 feet of an infected person for at least 15 minutes starting from 2 days before illness onset (or, for asymptomatic patients, 2 days prior to positive specimen collection) until the time the patient is isolated. (https://www.cdc.gov/coronavirus/2019-ncov/php/contact-tracing/contact-tracing-plan/contact-tracing.html)
-percent_asymptomatic = 0.4 # (recommended: 0.4) https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
-secondary_attack_rate = 0.05 # (recommended: 0.05) chance of contracting the virus on close contact with someone, DO NOT DIVIDE BY SIMULATION_TICKS_PER_HOUR https://jamanetwork.com/journals/jama/fullarticle/2768396
-asymptomatic_relative_infectiousness = 0.75 # (recommended: 0.75) https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
-distribution_of_exposure = scipy.stats.gamma(4, 0, 0.75) # (recommended: 4, 0, 0.75) gamma distribution of the duration (days) between exposure and infectiousness, k=4 μ=3 => midpoint is 2.754 days https://www.nature.com/articles/s41591-020-0962-9
-distribution_of_preclinical = scipy.stats.gamma(4, 0, 0.525) # (recommended: 4, 0, 0.525) gamma distribution of the duration (days) between infectiousness and symptoms for symptomatic cases, k=4 μ=2.1 => midpoint is 1.928 days https://www.nature.com/articles/s41591-020-0962-9
-distribution_of_clinical = scipy.stats.gamma(4, 0, 0.725) # (recommended: 4, 0, 0.725) gamma distribution of the duration (days) between symptoms and non-infectiousness (recovery) for symptomatic cases, k=4 μ=2.9 => midpoint is 2.662 days https://www.nature.com/articles/s41591-020-0962-9
-distribution_of_subclinical = scipy.stats.gamma(4, 0, 1.25) # (recommended: 4, 0, 1.25) gamma distribution of the duration (days) between infectiousness and non-infectiousness (recovery) for asymptomatic cases, k=4 μ=5 => midpoint is 4.590 days https://www.nature.com/articles/s41591-020-0962-9
-total_chance_of_small_household_transmission = 0.204 # (recommended: 0.091) chance of an infected agent spreading the virus to a given household member over the agent's entire period of infection when the household size is six or less https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30471-0/fulltext
-total_chance_of_large_household_transmission = 0.091 # (recommended: 0.204) chance of an infected agent spreading the virus to a given household member over the agent's entire period of infection when the household size is more than six https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30471-0/fulltext
+PERCENT_ASYMPTOMATIC = 0.4 # (recommended: 0.4) https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
+SECONDARY_ATTACK_RATE = 0.05 # (recommended: 0.05) chance of contracting the virus on close contact with someone, DO NOT DIVIDE BY SIMULATION_TICKS_PER_HOUR https://jamanetwork.com/journals/jama/fullarticle/2768396
+ASYMPTOMATIC_RELATIVE_INFECTIOUSNESS = 0.75 # (recommended: 0.75) https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html
+DISTRIBUTION_OF_EXPOSURE = scipy.stats.gamma(4, 0, 0.75) # (recommended: 4, 0, 0.75) gamma distribution of the duration (days) between exposure and infectiousness, k=4 μ=3 => midpoint is 2.754 days https://www.nature.com/articles/s41591-020-0962-9
+DISTRIBUTION_OF_PRECLINICAL = scipy.stats.gamma(4, 0, 0.525) # (recommended: 4, 0, 0.525) gamma distribution of the duration (days) between infectiousness and symptoms for symptomatic cases, k=4 μ=2.1 => midpoint is 1.928 days https://www.nature.com/articles/s41591-020-0962-9
+DISTRIBUTION_OF_CLINICAL = scipy.stats.gamma(4, 0, 0.725) # (recommended: 4, 0, 0.725) gamma distribution of the duration (days) between symptoms and non-infectiousness (recovery) for symptomatic cases, k=4 μ=2.9 => midpoint is 2.662 days https://www.nature.com/articles/s41591-020-0962-9
+DISTRIBUTION_OF_SUBCLINICAL = scipy.stats.gamma(4, 0, 1.25) # (recommended: 4, 0, 1.25) gamma distribution of the duration (days) between infectiousness and non-infectiousness (recovery) for asymptomatic cases, k=4 μ=5 => midpoint is 4.590 days https://www.nature.com/articles/s41591-020-0962-9
+TOTAL_CHANCE_OF_SMALL_HOUSEHOLD_TRANSMISSION = 0.204 # (recommended: 0.091) chance of an infected agent spreading the virus to a given household member over the agent's entire period of infection when the household size is six or less https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30471-0/fulltext
+TOTAL_CHANCE_OF_LARGE_HOUSEHOLD_TRANSMISSION = 0.091 # (recommended: 0.204) chance of an infected agent spreading the virus to a given household member over the agent's entire period of infection when the household size is more than six https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30471-0/fulltext
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