Reduce the Excessive Demand for TB Appointment Under the 'Max HealthSystem Functionality' Scenario Switch (#1400)

* edit ResourceFile_Improved_Healthsystem_And_Healthcare_Seeking

TB: rate_testing_general_pop removed as this refers to general population screening
NTP2019 update treatment_coverage to 99.99 for all years

Schisto:
delay until HSIs repeated was 0.999, should be integer value of 1 day (default is 5 days)

HSI_Tb_ScreeningAndRefer added condition if tested within last 2 weeks, do nothing

* replace 1 (delays in days) with 1.00001

The parameter type is defined as REAL, so this is the only way I've found to make Excel store it as a float (and not an int of 1 that would get coerned into a bool!).

Where this is used (line 944 in schisto.py) there is a coecision to int at the last minute so this should presevre the intended behaviour

* Update src/tlo/methods/tb.py

---------

Co-authored-by: Tim Hallett <[email protected]>

Author: tdm32

resources/ResourceFile_HIV.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e2345032931c1360046dc7394681cc39669687888f7f8f3e42469d8add067438
-size 160376
+oid sha256:58978c108515c3762addd18824129b2654f241d94bcc778ab17b27d0d8250593
+size 160402

resources/ResourceFile_Improved_Healthsystem_And_Healthcare_Seeking.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:da900375dda86f999e744bfb6d6dec7347d5b13f176046ba182740421a43d256
-size 48274
+oid sha256:1b462c20ca6cbf0ca1f98936416e015fa248289e5bf4f66838e1b9920874f651
+size 48142

resources/ResourceFile_TB.xlsx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3fc295cc70b8e86c75e1725a92ceb95bc86a26d3fbe1f680db379726bcab3ab3
+oid sha256:3cb13e128d4bcb3b694def108c3bd61b16508b48e389c3e5cdf8155717aab9e9
 size 55662

src/tlo/methods/tb.py

@@ -1467,7 +1467,7 @@ def apply(self, population):
             )
 
         # -------- 5) schedule screening for asymptomatic and symptomatic people --------
-        # sample from all new active cases (active_idx) and determine whether they will seek a test
+        # sample from all NEW active cases (active_idx) and determine whether they will seek a test
         year = min(2019, max(2011, now.year))
 
         active_testing_rates = p["rate_testing_active_tb"]
@@ -1610,8 +1610,19 @@ def apply(self, person_id, squeeze_factor):
         p = self.module.parameters
         person = df.loc[person_id]
 
-        # If the person is dead or already diagnosed, do nothing do not occupy any resources
-        if not person["is_alive"] or person["tb_diagnosed"]:
+        if not person["is_alive"]:
+            return self.sim.modules["HealthSystem"].get_blank_appt_footprint()
+
+        # If the person is already diagnosed, do nothing do not occupy any resources
+        if person["tb_diagnosed"]:
+            return self.sim.modules["HealthSystem"].get_blank_appt_footprint()
+
+        # If the person is already on treatment and not failing, do nothing do not occupy any resources
+        if person["tb_on_treatment"] and not person["tb_treatment_failure"]:
+            return self.sim.modules["HealthSystem"].get_blank_appt_footprint()
+
+        # if person has tested within last 14 days, do nothing
+        if person["tb_date_tested"] >= (self.sim.date - DateOffset(days=7)):
             return self.sim.modules["HealthSystem"].get_blank_appt_footprint()
 
         logger.debug(
@@ -1620,10 +1631,6 @@ def apply(self, person_id, squeeze_factor):
 
         smear_status = person["tb_smear"]
 
-        # If the person is already on treatment and not failing, do nothing do not occupy any resources
-        if person["tb_on_treatment"] and not person["tb_treatment_failure"]:
-            return self.sim.modules["HealthSystem"].get_blank_appt_footprint()
-
         # ------------------------- screening ------------------------- #
 
         # check if patient has: cough, fever, night sweat, weight loss