Merge branch 'develop' into ahil-elastic

.github/workflows/pythonapp.yml

@@ -43,7 +43,7 @@ jobs:
         python -m pytest
     - name: Run App
       run: |
-        PARAMETERS=./defaults/cypress.cfg streamlit run st_app.py &
+        PARAMETERS=./defaults/cypress.cfg ASSETS=./defaults/assets streamlit run st_app.py &
     - name: Cypress
       uses: cypress-io/github-action@v1
       with:

Dockerfile

@@ -1,5 +1,7 @@
 FROM python:3.7.7-slim-buster
+ENV ASSETS=./defaults/assets/
 ENV PARAMETERS=./defaults/webapp.cfg
+ENV PORT=8000
 WORKDIR /app
 COPY README.md .
 COPY setup.cfg .
@@ -11,5 +13,4 @@ COPY src src
 COPY st_app.py st_app.py
 RUN pip install -q .
 
-CMD ["streamlit", "run", "st_app.py"]
-
+CMD STREAMLIT_SERVER_PORT=$PORT streamlit run st_app.py

Procfile

@@ -1 +1 @@
-web: PARAMETERS=defaults/webapp.cfg STREAMLIT_SERVER_PORT=$PORT streamlit run st_app.py
+web: PARAMETERS=./defaults/webapp.cfg ASSETS=./defaults/assets STREAMLIT_SERVER_PORT=$PORT streamlit run st_app.py

docs/contributing/app-dev.md

@@ -46,12 +46,14 @@ pip install streamlit
 ## Run the Streamlit Web App
 
 ```bash
+ASSETS=./defaults/assets \
 PARAMETERS=-./defaults/webapp.cfg streamlit run st_app.py
 ```
 
 ## Run the Command Line Interface
 
 ```bash
+ASSETS=./defaults/assets \
 PARAMETERS=./defaults/cli.cfg penn_chime
 ```
 
@@ -66,6 +68,7 @@ penn_chime --help
 If you want a different set of default parameters, you may use your own configuration file.
 
 ```bash
+ASSETS=./defaults/assets \
 PARAMETERS=./defaults/yours.cfg streamlit run st_app.py
 ```
 
@@ -77,7 +80,19 @@ Be sure to include `--mitigation-date` in the file if social distancing was impl
 If you need to run the application on a different port than the default (8000), you can set an environment variable.
 
 ```bash
-STREAMLIT_SERVER_PORT=1234 PARAMETERS=./defaults/webapp.cfg streamlit run st_app.py
+ASSETS=./defaults/assets \
+STREAMLIT_SERVER_PORT=1234 \
+PARAMETERS=./defaults/webapp.cfg streamlit run st_app.py
+```
+
+### Choosing a Different Language
+
+If you want to run the application in another language, do the following. You can select Japanese as the language other than English.
+
+```bash
+ASSETS=./defaults/assets \
+LANG=ja \
+PARAMETERS=./defaults/webapp.cfg streamlit run st_app.py
 ```
 
 ## Project Layout

docs/contributing/operations-support.md

@@ -1,3 +1,4 @@
 # Contributing: Operations Support
 
-*Coming soon*
+- This repo is associated with Penn Medicine's Chime1.0 model, which uses a Streamlit app that is built on an SIR model
+- Contributors are encouraged to consider supporting Penn Medicine's Chime2.0 model, which still uses a Streamlit app, but leverages an enhanced Bayesian SEIR model.

heroku.yml

@@ -1,5 +1,5 @@
 build:
   docker:
-    web: Dockerfile.dash
+    web: Dockerfile
   config: 
-    PORT: ${PORT}
+    PORT: ${PORT}

setup.py

@@ -1,9 +1,9 @@
 """Setup file for chime
 """
-__version__ = "1.1.3"  # update VERSION in constants.py
+__version__ = "1.1.4"  # update VERSION in constants.py
 __author__ = "Predictive Healthcare @ Penn Medicine"
 
-from setuptools import setup, find_namespace_packages
+from setuptools import setup, find_packages
 
 
 setup(
@@ -19,7 +19,7 @@
         "Documentation": "https://codeforphilly.github.io/chime/",
     },
     package_dir={'': 'src'},
-    packages=find_namespace_packages(where='src', exclude=('tests')),
+    packages=find_packages(where='src', exclude=('tests')),
     install_requires=[
         "altair",
         "black",

src/penn_chime/cli.py

@@ -6,17 +6,21 @@
 from .model.parameters import Parameters
 from .model.sir import Sir
 
+
 def run(argv):
+    """Eun cli."""
     p = Parameters.create(os.environ, argv[1:])
     m = Sir(p)
 
     for df, name in (
         (m.sim_sir_w_date_df, "sim_sir_w_date"),
         (m.admits_df, "projected_admits"),
         (m.census_df, "projected_census"),
+        (m.ppe_df, 'ppe_data')
     ):
         df.to_csv(f"{p.current_date}_{name}.csv")
 
+
 def main():
     """Main."""
     run(sys.argv)

src/penn_chime/constants.py

@@ -8,7 +8,7 @@
 re-run their reports
 """
 CHANGE_DATE = date(year=2020, month=4, day=8)
-VERSION = 'v1.1.3'
+VERSION = 'v1.1.4'
 
 DATE_FORMAT = "%b, %d"  # see https://strftime.org
 DOCS_URL = "https://code-for-philly.gitbook.io/chime"
@@ -17,3 +17,5 @@
 
 FLOAT_INPUT_MIN = 0.0001
 FLOAT_INPUT_STEP = 0.1
+
+PREFIT_ADDITIONAL_DAYS = 300

src/penn_chime/locales/en.yml

@@ -3,6 +3,10 @@ en:
   app-new-admissions-text: "Projected number of **daily** COVID-19 admissions."
   app-admitted-patients-title: "Admitted Patients (Census)"
   app-admitted-patients-text: "Projected **census** of COVID-19 patients, accounting for arrivals and discharges."
+  app-PPE-title: "Personal Protective Equipment (PPE) Calculator"
+  app-PPE-screenshot: "Show a screenshot of the tool"
+  app-PPE-documentation: |+
+    Refer to our <a href="{link_to_docs}">user documentation for instructions on how to use the tool</a>.
   app-SIR-title: "Susceptible, Infected, and Recovered"
   app-SIR-text: "The number of susceptible, infected, and recovered individuals in the hospital catchment region at any given moment"
   charts-date: "Date"
@@ -81,6 +85,8 @@ en:
   presentation-copyright: "© 2020, The Trustees of the University of Pennsylvania"
   presentation-download: |+
     <a download="{filename}" href="data:file/csv;base64,{csv}">Download {filename}</a>
+  presentation-excel-download: |+
+    Download the PPE Calculator here: <a download="{filename}" href="data:file/xlsx;base64,{excel}">{filename}</a>.
   admits_hospitalized: "Hospitalized Admissions"
   admits_icu: "ICU Admissions"
   admits_ventilated: "Ventilated Admissions"

src/penn_chime/locales/ja.yml

@@ -3,6 +3,10 @@ ja:
   app-new-admissions-text: "**日毎の**COVID-19関連新規入院患者数予測"
   app-admitted-patients-title: "累計入院患者数"
   app-admitted-patients-text: "入院と退院を考慮したCOVID-19患者の予測**累計患者数**"
+  app-PPE-title: "個人用防護具 (PPE) 計算機"
+  app-PPE-screenshot: "ツールのスクリーンショットを表示"
+  app-PPE-documentation: |+
+    ツールの使い方については<a href="{link_to_docs}">ユーザドキュメントの説明</a>を確認してください。
   app-SIR-title: "感受性保持者、感染者と回復者"
   app-SIR-text: "与えられた時間範囲での、病院の担当範囲領域における感受性保持者、感染者と回復者の人数"
   charts-date: "日付"
@@ -79,6 +83,8 @@ ja:
   presentation-copyright: "© 2020, The Trustees of the University of Pennsylvania"
   presentation-download: |+
     <a download="{filename}" href="data:file/csv;base64,{csv}">{filename} をダウンロード</a>
+  presentation-excel-download: |+
+    個人用防護具計算機のダウンロードはこちら: <a download="{filename}" href="data:file/xlsx;base64,{excel}">{filename}</a>.
   admits_hospitalized: "新規入院患者数"
   admits_icu: "新規集中治療患者数"
   admits_ventilated: "新規人工呼吸患者数"

src/penn_chime/model/parameters.py

@@ -137,6 +137,7 @@ def validate(string):
     "ventilated": ValDisposition,
     "hospitalized": ValDisposition,
     "icu": ValDisposition,
+    "use_log_scale": OptionalValue
 }
 
 
@@ -161,16 +162,17 @@ def validate(string):
     "relative_contact_rate": "Social distancing reduction rate: 0.0 - 1.0",
     "ventilated_days": "Average days on ventilator",
     "ventilated_rate": "Ventilated Rate: 0.0 - 1.0",
+    "use_log_scale": "Flag to use logarithmic scale on charts instead of linear scale."
 }
 
 
 ARGS = (
     (
         "parameters",
         str,
-        None,
-        None,
-        False,
+        None, # Min value
+        None, # Max value
+        False, # Whether it is required or optional.
     ),
     (
         "current_hospitalized",
@@ -298,15 +300,24 @@ def validate(string):
         1.0,
         True,
     ),
+    (
+        "use_log_scale",
+        bool,
+        None,
+        None,
+        False
+    )
 )
 
 
 def to_cli(name):
     return "--" + name.replace('_', '-')
 
-
 class Parameters:
-    """Parameters."""
+    """
+    Object containing all of the parameters that can be adjusted by the user, either from the command line or using
+    the side bar of the web app.
+    """
 
     @classmethod
     def parser(cls):
@@ -315,11 +326,20 @@ def parser(cls):
 
         for name, cast, min_value, max_value, required in ARGS:
             arg = to_cli(name)
-            parser.add_argument(
-                arg,
-                type=validator(arg, cast, min_value, max_value, required),
-                help=HELP.get(name),
-            )
+            if cast == bool:
+                # This argument is a command-line flag and does not need validation.
+                parser.add_argument(
+                    arg,
+                    action='store_true',
+                    help=HELP.get(name),
+                )
+            else:
+                # Use a custom validator for any arguments that take in values.
+                parser.add_argument(
+                    arg,
+                    type=validator(arg, cast, min_value, max_value, required),
+                    help=HELP.get(name),
+                )
         return parser
 
     @classmethod
@@ -396,6 +416,7 @@ def __init__(self, **kwargs):
         self.relative_contact_rate = None
         self.recovered = None
         self.ventilated = None
+        self.use_log_scale = False
 
         passed_and_default_parameters = {}
         for key, value in kwargs.items():

src/penn_chime/model/ppe.py

@@ -0,0 +1,14 @@
+"""Personal Protective Equipment."""
+
+import os
+from typing import Dict
+
+
+class PPE:
+
+    def __init__(self, env: Dict[str, str]):
+        """__init__."""
+        self.assets = assets = env['ASSETS']
+        self.filename = filename = "PPE_Calculator_for_COVID-19.xlsx"
+        self.src = os.path.join(assets, filename)
+        self.screenshot = os.path.join(assets, 'PPE_Screenshot.jpg')

src/penn_chime/model/sir.py

@@ -14,6 +14,7 @@
 import numpy as np
 import pandas as pd
 
+from ..constants import PREFIT_ADDITIONAL_DAYS
 from .parameters import Parameters
 
 
@@ -68,31 +69,42 @@ def __init__(self, p: Parameters):
 
             if p.mitigation_date is None:
                 self.i_day = 0 # seed to the full length
-                raw = self.run_projection(p, [(self.beta, p.n_days)])
+                raw = self.run_projection(p, [
+                    (self.beta, p.n_days + PREFIT_ADDITIONAL_DAYS)])
                 self.i_day = i_day = int(get_argmin_ds(raw["census_hospitalized"], p.current_hospitalized))
 
-                self.raw = self.run_projection(p, self.gen_policy(p))
+                self.raw = self.run_projection(p, self.get_policies(p))
 
                 logger.info('Set i_day = %s', i_day)
             else:
-                projections = {}
                 best_i_day = -1
                 best_i_day_loss = float('inf')
-                for i_day in range(p.n_days):
-                    self.i_day = i_day
-                    raw = self.run_projection(p, self.gen_policy(p))
+                for self.i_day in range(p.n_days + PREFIT_ADDITIONAL_DAYS):
+                    mitigation_day = -(p.current_date - p.mitigation_date).days
+                    if mitigation_day < -self.i_day:
+                        mitigation_day = -self.i_day
+
+                    total_days = self.i_day + p.n_days + PREFIT_ADDITIONAL_DAYS
+                    pre_mitigation_days = self.i_day + mitigation_day
+                    post_mitigation_days = total_days - pre_mitigation_days
+
+                    raw = self.run_projection(p, [
+                            (self.beta,   pre_mitigation_days),
+                            (self.beta_t, post_mitigation_days),
+                        ]
+                    )
 
                     # Don't fit against results that put the peak before the present day
-                    if raw["census_hospitalized"].argmax() < i_day:
+                    if raw["census_hospitalized"].argmax() < self.i_day:
                         continue
 
-                    loss = get_loss(raw["census_hospitalized"][i_day], p.current_hospitalized)
+                    loss = get_loss(raw["census_hospitalized"][self.i_day], p.current_hospitalized)
                     if loss < best_i_day_loss:
                         best_i_day_loss = loss
-                        best_i_day = i_day
-                        self.raw = raw
+                        best_i_day = self.i_day
 
                 self.i_day = best_i_day
+                self.raw = self.run_projection(p, self.get_policies(p))
 
             logger.info(
                 'Estimated date_first_hospitalized: %s; current_date: %s; i_day: %s',
@@ -127,7 +139,7 @@ def __init__(self, p: Parameters):
             intrinsic_growth_rate = get_growth_rate(p.doubling_time)
             self.beta = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, 0.0)
             self.beta_t = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, p.relative_contact_rate)
-            self.raw = self.run_projection(p, self.gen_policy(p))
+            self.raw = self.run_projection(p, self.get_policies(p))
 
             self.population = p.population
         else:
@@ -162,6 +174,15 @@ def __init__(self, p: Parameters):
             'census_icu': self.raw['census_icu'],
             'census_ventilated': self.raw['census_ventilated'],
         })
+        self.ppe_df = pd.DataFrame(data={
+            'day': self.raw['day'],
+            'date': self.raw['date'],
+            'census_hospitalized': self.raw['census_hospitalized'],
+            'census_icu': self.raw['census_icu'],
+            'census_ventilated': self.raw['census_ventilated'],
+            'admits_hospitalized': self.raw['admits_hospitalized'],
+        })
+        self.ppe_df = self.ppe_df[self.ppe_df['day']>=0]
 
         logger.info('len(np.arange(-i_day, n_days+1)): %s', len(np.arange(-self.i_day, p.n_days+1)))
         logger.info('len(raw_df): %s', len(self.raw_df))
@@ -195,9 +216,9 @@ def get_argmin_doubling_time(self, p: Parameters, dts):
             intrinsic_growth_rate = get_growth_rate(i_dt)
             self.beta = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, 0.0)
             self.beta_t = get_beta(intrinsic_growth_rate, self.gamma, self.susceptible, p.relative_contact_rate)
-    
-            raw = self.run_projection(p, self.gen_policy(p))
-    
+
+            raw = self.run_projection(p, self.get_policies(p))
+
             # Skip values the would put the fit past peak
             peak_admits_day = raw["admits_hospitalized"].argmax()
             if peak_admits_day < 0:
@@ -210,7 +231,7 @@ def get_argmin_doubling_time(self, p: Parameters, dts):
         min_loss = pd.Series(losses).argmin()
         return min_loss
 
-    def gen_policy(self, p: Parameters) -> Sequence[Tuple[float, int]]:
+    def get_policies(self, p: Parameters) -> Sequence[Tuple[float, int]]:
         if p.mitigation_date is not None:
             mitigation_day = -(p.current_date - p.mitigation_date).days
         else: