added info about asymptomatic priors

experiments/calibration/abc-1.ipynb

@@ -1734,7 +1734,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

experiments/calibration/abc-2.ipynb

@@ -8,9 +8,19 @@
     "\n",
     "Multi-paramter calibration of the RAMP model using Approximate Bayesian Computation\n",
     "\n",
-    "Based on [abc-1.ipynb](abc-1.ipynb). See that file for a more detailed technical description. This file calibrates on the location-specific hazard multipliers (e.g. to see if the algorithm can work out how hazardous the different location types are). These are:\n",
-    " - XXXX \n",
-    " - XXXX "
+    "Based on [abc-1.ipynb](abc-1.ipynb). See that file for a more detailed technical description. This file calibrates on the location-specific hazard multipliers (e.g. to see if the algorithm can work out how hazardous the different location types are). The full list of parameters:\n",
+    "\n",
+    "## Asymptomatic multiplier (`asymptomatic`)\n",
+    "\n",
+    "This is tricky because we don't know the hazard associated with asymptomatic transmission. James Salter used a prior of: N(0.444, 0.155) which gives the middle 95% as [0.138, 0.75] ([0.138 estimated here](https://www.medrxiv.org/content/10.1101/2020.06.04.20121434v2), [0.58 estimated here](https://jammi.utpjournals.press/doi/abs/10.3138/jammi-2020-0030), [0.75 estimated here (Table 1)](https://www.cdc.gov/coronavirus/2019-ncov/hcp/planning-scenarios.html). \n",
+    "\n",
+    "## Presymptomatic and symptomatic multipliers\n",
+    "\n",
+    "Are fixed at 1 \n",
+    "\n",
+    "## Location hazards:\n",
+    "\n",
+    " - XXXX Outline the hazards and priors\n"
    ]
   },
   {
@@ -1227,7 +1237,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,