AMRbeta01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import folium\n",
    "from folium import plugins as fol_pi\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interact\n",
    "import IPython.display as IPdisp\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Transmission_score_formula(x, y, z):\n",
    "    return 2*(x+y+z)\n",
    "\n",
    "def Access_score_formula(x, y):\n",
    "    if x < 3:\n",
    "        return 2*y\n",
    "    elif x < 5:\n",
    "        return 1 + 2*y\n",
    "    else:\n",
    "        return 2 + 2*y\n",
    "\n",
    "def Herd_size_score(herd):\n",
    "    if herd < 1:\n",
    "        return 0\n",
    "    elif herd <10:\n",
    "        return 1\n",
    "    elif herd < 50:\n",
    "        return 2\n",
    "    elif herd < 100:\n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "\n",
    "def Disease_score_formula(c_herd, b_herd, g_herd, c_dis, b_dis, g_dis):\n",
    "    return (Herd_size_score(c_herd)+Herd_size_score(b_herd)+Herd_size_score(g_herd)+c_dis+b_dis+g_dis)/3\n",
    "\n",
    "def Use_score_formula(u, v, w, x, y, z):\n",
    "    return (u + v + w + x + y + z)\n",
    "\n",
    "def Overall_score_formula(tra, acc, dis, use):\n",
    "    return (tra + acc + dis + use)\n",
    "\n",
    "def base_AMR_metr(manip_amr_data):\n",
    "    manip_amr_data['Transmission score'] = manip_amr_data.apply(lambda row: Transmission_score_formula(row['Spreading dung on fields'], row['Not cleaning with disinfectant'], row['Visited by animal healthcare worker']), axis=1)        \n",
    "    manip_amr_data['Access score'] = manip_amr_data.apply(lambda row: Access_score_formula(row['Frequency of antibiotic purchase'], row['Use of trained animal healthcare professional']), axis=1)        \n",
    "    manip_amr_data['Disease score'] = manip_amr_data.apply(lambda row: Disease_score_formula(row['Cattle herd size'], row['Buffalo herd size'], row['Goat herd size'], row['Disease priority cattle'], row['Disease priority buffalo'], row['Disease priority goats']), axis=1)        \n",
    "    manip_amr_data['Use score'] = manip_amr_data.apply(lambda row: Use_score_formula(row['Mixing antibiotics in the same animal'], row['Not completing course'], row['Not measuring dose accurately'], row['Using for prevention'], row['Drink milk from treated animals'], row['Eat meat from treated animals']), axis=1)\n",
    "    # Now four component scores are combined for final AMR Metric\n",
    "    manip_amr_data['Overall score'] = manip_amr_data.apply(lambda row: Overall_score_formula(row['Transmission score'], row['Access score'], row['Disease score'], row['Use score']), axis=1)\n",
    "    return manip_amr_data\n",
    "\n",
    "#manip_amr_data_community['Overall score'] = manip_amr_data_community.apply(lambda row: Overall_score_formula(row['Transmission score'], row['Access score'], row['Disease score'], row['Use score']), axis=1)\n",
    "\n",
    "\n",
    "# NEXT scores are aggregated at community level, only necessary variables are kept\n",
    "#Group by works fine, almost instant\n",
    "# manip_amr_data_community = manip_amr_data.groupby(['Community']).mean()\n",
    "\n",
    "# amr_metric_community = manip_amr_data_community[['Latitude','Longitude','Transmission score','Access score','Disease score','Use score','Overall score']]\n",
    "\n",
    "#set ID as index for visual display purposes\n",
    "# manip_amr_data.set_index('ID', inplace=True)\n",
    "\n",
    "#amr_metric_community\n",
    "#manip_amr_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_Transmission_score_formula(dung_field, shed_clean, health_visit, add_dung, add_clean, add_visit):\n",
    "    return max(0,min(2*(dung_field+add_dung),2)) + max(0,min(2*(shed_clean+add_clean),2)) + max(0,min(2*(health_visit+add_visit),2)) #to be applied at community level\n",
    "\n",
    "def user_Access_score_formula(freq_purch, trained_risk, add_freq, add_trained):\n",
    "    risk = max(0,min(trained_risk + add_trained,2))\n",
    "    freq = freq_purch + add_freq\n",
    "    if freq < 3:\n",
    "        return 0 + 2*risk\n",
    "    elif freq < 5:\n",
    "        return 1 + 2*risk\n",
    "    else:\n",
    "        return 2 + 2*risk\n",
    "\n",
    "def user_Disease_score_formula(c_herd, b_herd, g_herd, c_dis, b_dis, g_dis,\\\n",
    "                               add_c_herd, add_b_herd, add_g_herd,\\\n",
    "                              add_c_dis, add_b_dis, add_g_dis):\n",
    "    user_c_dis = min(2,max(0,c_dis + add_c_dis))\n",
    "    user_b_dis = min(2,max(0,b_dis + add_b_dis))\n",
    "    user_g_dis = min(2,max(0,g_dis + add_g_dis))    \n",
    "    return (Herd_size_score(c_herd+add_c_herd)+Herd_size_score(b_herd+add_b_herd)+Herd_size_score(g_herd+add_g_herd)+\\\n",
    "            user_c_dis+user_b_dis+user_g_dis)/3\n",
    "\n",
    "\n",
    "def user_Use_score_formula(mix_anti, not_comp, not_meas, use_prev, drink_mlk, eat_meat,\\\n",
    "                          add_mix_anti, add_not_comp, add_not_meas, add_use_prev, add_drink_mlk, add_eat_meat):\n",
    "    return max(0,min((mix_anti+add_mix_anti),1)) + max(0,min((not_comp+add_not_comp),1)) + max(0,min((not_meas+add_not_meas),1))\\\n",
    "+ max(0,min((use_prev+add_use_prev),1)) + max(0,min((drink_mlk+add_drink_mlk),1)) + max(0,min((eat_meat+add_eat_meat),1))\n",
    "\n",
    "#to be applied at community level\n",
    "\n",
    "\n",
    "def user_Overall_score_formula(tra, acc, dis, use):\n",
    "    return (tra + acc + dis + use)\n",
    "                                                                                    \n",
    "                                                                                    \n",
    "# Next we apply Access and Disease score formula at household level, then take the mean to allow Transmission and Use scored to be manipulated by user\n",
    "\n",
    "def user_AMR_comp(amr_input_data, add_dung, add_clean,\\\n",
    "                  add_visit, add_freq, add_trained,\\\n",
    "                 add_c_herd, add_b_herd, add_g_herd,\\\n",
    "                  add_c_dis, add_b_dis, add_g_dis,\\\n",
    "                 add_mix_anti, add_not_comp, add_not_meas,\\\n",
    "                  add_use_prev, add_drink_mlk, add_eat_meat):\n",
    "    \n",
    "    #first apply the household level user manipulation scores\n",
    "    amr_input_data['Access score'] = amr_input_data.apply(lambda row: user_Access_score_formula(row['Frequency of antibiotic purchase'], row['Use of trained animal healthcare professional'], add_freq, add_trained), axis=1)        \n",
    "    amr_input_data['Disease score'] = amr_input_data.apply(lambda row: user_Disease_score_formula(row['Cattle herd size'], row['Buffalo herd size'], row['Goat herd size'], row['Disease priority cattle'], row['Disease priority buffalo'], row['Disease priority goats'], add_c_herd, add_b_herd, add_g_herd, add_c_dis, add_b_dis, add_g_dis), axis=1)        \n",
    "    #next groupby to community\n",
    "    manip_amr_data_community = amr_input_data.groupby(['Community']).mean()\n",
    "    \n",
    "    #now apply two community level scores\n",
    "    manip_amr_data_community['Transmission score'] = manip_amr_data_community.apply(lambda row: user_Transmission_score_formula(row['Spreading dung on fields'], row['Not cleaning with disinfectant'], row['Visited by animal healthcare worker'], add_dung, add_clean, add_visit), axis=1)        \n",
    "    manip_amr_data_community['Use score'] = manip_amr_data_community.apply(lambda row: user_Use_score_formula(row['Mixing antibiotics in the same animal'], row['Not completing course'], row['Not measuring dose accurately'], row['Using for prevention'], row['Drink milk from treated animals'], row['Eat meat from treated animals'], add_mix_anti, add_not_comp, add_not_meas, add_use_prev, add_drink_mlk, add_eat_meat), axis=1)                                                                                     \n",
    "    \n",
    "                                                                                    \n",
    "    # All four components scored, we can find the overall                                                                                \n",
    "    manip_amr_data_community['Overall score'] = manip_amr_data_community.apply(lambda row: user_Overall_score_formula(row['Transmission score'], row['Access score'], row['Disease score'], row['Use score']), axis=1)\n",
    "    \n",
    "    return manip_amr_data_community\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create colour rules for each rag score\n",
    "def colour_rag_component(val):\n",
    "    colour = 'red'\n",
    "    if type(val) is not str and val<=4:\n",
    "        colour = 'orange'\n",
    "    if type(val) is not str and val<2:\n",
    "        colour = 'green'\n",
    "    return 'color: %s' % colour\n",
    "\n",
    "def colour_rag_overall(val):\n",
    "    colour = 'red'\n",
    "    if type(val) is not str and val<=18:\n",
    "        colour = 'orange'\n",
    "    if type(val) is not str and val<8:\n",
    "        colour = 'green'\n",
    "    return 'color: %s' % colour\n",
    "\n",
    "# Allow zoom for table view\n",
    "def magnify():\n",
    "    return [dict(selector=\"th\", #TableHeaders\n",
    "                 props=[(\"font-size\", \"11pt\")]),\n",
    "            dict(selector=\"td\", #TableData\n",
    "                 props=[('padding', \"0em 3em\"),\n",
    "                        ('font-size', '10pt')]),\n",
    "            dict(selector=\"th:hover\",\n",
    "                 props=[(\"font-size\", \"14pt\")]),\n",
    "            dict(selector=\"tr:hover td:hover\",\n",
    "                 props=[('max-width', '200px'),\n",
    "                        ('font-size', '14pt')])]\n",
    "\n",
    "# Adapt for map\n",
    "\n",
    "def colour_rag_overall_marker(val):\n",
    "    colour = 'red'\n",
    "    if type(val) is not str and val<=18:\n",
    "        colour = 'orange'\n",
    "    if type(val) is not str and val<8:\n",
    "        colour = 'green'\n",
    "    return colour\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_map(user_AMR_data, lat, lon, tiles=\"OpenStreetMap\", zoom=13):\n",
    "    m = folium.Map(location=(lat, lon), tiles=tiles, zoom_start=zoom)    \n",
    "\n",
    "# amr_metric_community = manip_amr_data_community[['Latitude','Longitude','Transmission score','Access score','Disease score','Use score','Overall score']]\n",
    "    \n",
    "    for index, row in user_AMR_data.iterrows():\n",
    "        #print(index, row['village'], row['Latitude'], row['Longitude'])\n",
    "        html=\"\"\"\n",
    "        <h1>Community: {com}</h1>\n",
    "        <p style=\"{col_ov};\"><strong>Overall score {ov:.2f}</strong></p>\n",
    "        <p style=\"{col_tr};\">Transmission score: {tr:.2f}</p>\n",
    "        <p style=\"{col_ac};\">Access score: {ac:.2f}</p>\n",
    "        <p style=\"{col_di};\">Disease score: {di:.2f}</p>\n",
    "        <p style=\"{col_us};\">Use score: {us:.2f}</p>\n",
    "        \"\"\".format(com=index,ov=row['Overall score'],col_ov=colour_rag_overall(row['Overall score']),\\\n",
    "                   tr=row['Transmission score'],col_tr=colour_rag_component(row['Transmission score']),\\\n",
    "                   ac=row['Access score'],col_ac=colour_rag_component(row['Access score']),\\\n",
    "                   di=row['Disease score'],col_di=colour_rag_component(row['Disease score']),\\\n",
    "                   us=row['Use score'],col_us=colour_rag_component(row['Use score']))\n",
    "\n",
    "        iframe = folium.IFrame(html=html, width=300, height=300)\n",
    "        popup = folium.Popup(iframe, parse_html=True)#, max_width=2650)\n",
    "\n",
    "        folium.Marker([row['Latitude'],row['Longitude']],popup=popup, icon = folium.Icon(color=colour_rag_overall_marker(row['Overall score']),icon='fire',prefix='fa',icon_color='aquamarine')).add_to(m)\n",
    "        # Note - icon colors restricted - see help(folium.Icon), icon_colour accepts HTML - see https://www.rapidtables.com/web/color/html-color-codes.html\n",
    "        # Note - for icons see https://fontawesome.com/icons?d=gallery&m=free (not all work)\n",
    "    \n",
    "\n",
    "    m.add_child(folium.LatLngPopup())#, folium.LayerControl().add_to(m)\n",
    "    \n",
    "    fol_pi.Fullscreen(\n",
    "    position='topright',\n",
    "    title='Full screen',\n",
    "    title_cancel='Exit full screen',\n",
    "    force_separate_button=True).add_to(m)\n",
    "    \n",
    "    return m\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "VBox(children=(HBox(children=(HTML(value=\"<h1 style='font-size:40px;background-color:LightBlue;text-align:cent…"
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     "metadata": {},
     "output_type": "display_data"
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       "Output()"
      ]
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     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "VBox(children=(HBox(children=(HTML(value=\"<h1 style='font-size:40px;background-color:LightBlue;text-align:cent…"
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     "output_type": "display_data"
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       "Output()"
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     "output_type": "display_data"
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   ],
   "source": [
    "sample_locations = ['Please select region to display map and data:','Gujarat, India', 'Pune, India', 'Kajiado, Kenya']\n",
    "\n",
    "def AMR_tool(input_data='Gujarat, India', add_dung_widg = 0, add_clean_widg = 0, add_visit_widg = 0,\\\n",
    "             add_freq_widg=0, add_trained_widg=0,\\\n",
    "            add_c_herd_widg=0, add_b_herd_widg=0, add_g_herd_widg=0,\\\n",
    "             add_c_dis_widg=0, add_b_dis_widg=0, add_g_dis_widg=0,\\\n",
    "             add_mix_anti_widg=0, add_not_comp_widg=0, add_not_meas_widg=0,\\\n",
    "             add_use_prev_widg=0, add_drink_mlk_widg=0, add_eat_meat_widg=0):\n",
    "    \n",
    "    if input_data == 'Gujarat, India':\n",
    "        amr_input_data = pd.read_csv(\"data/gujarat_csv_data.csv\") #load csv\n",
    "        base_AMR_data = base_AMR_metr(amr_input_data) #calculate base AMR metric on all households\n",
    "        \n",
    "        lat_mean = (base_AMR_data['Latitude'].min() + base_AMR_data['Latitude'].max())/2\n",
    "        lon_mean = (base_AMR_data['Longitude'].min() + base_AMR_data['Longitude'].max())/2\n",
    "    else:\n",
    "        return\n",
    "        \n",
    "    # Generate user manipulated data\n",
    "    user_AMR_data = user_AMR_comp(amr_input_data=amr_input_data,\\\n",
    "                                  add_dung=add_dung_widg, add_clean=add_clean_widg, add_visit=add_visit_widg,\\\n",
    "                                  add_freq=add_freq_widg, add_trained=add_trained_widg,\\\n",
    "                                  add_c_herd = add_c_herd_widg, add_b_herd = add_b_herd_widg, add_g_herd = add_g_herd_widg,\\\n",
    "                                  add_c_dis = add_c_dis_widg, add_b_dis = add_b_dis_widg, add_g_dis = add_g_dis_widg,\\\n",
    "                                  add_mix_anti = add_mix_anti_widg, add_not_comp = add_not_comp_widg, add_not_meas = add_not_meas_widg,\\\n",
    "                                  add_use_prev = add_use_prev_widg, add_drink_mlk = add_drink_mlk_widg, add_eat_meat = add_eat_meat_widg)\n",
    "        \n",
    "    # Show user manipulated data on map\n",
    "    m = create_map(user_AMR_data=user_AMR_data, lat=lat_mean, lon=lon_mean, tiles=\"OpenStreetMap\", zoom=13)\n",
    "    return IPdisp.display(m)\n",
    "\n",
    "# Build widgets\n",
    "\n",
    "title = widgets.HTML(value=\"<h1 style='font-size:40px;background-color:LightBlue;text-align:center;border:2px solid Black;'>\\\n",
    "    AMR Data Exploration Tool <a style='color:Orange;'>(beta v0.1)</a></h1> \\\n",
    "    <p style='font-size:20px;text-align:center;'>Optional text here - e.g. Communities from region selected are shown on map, data tables provided below</p>\",\\\n",
    "                     layout=widgets.Layout(width='100%', height='200%'))\n",
    "\n",
    "\n",
    "input_data = widgets.Dropdown(options=sample_locations)\n",
    "add_dung_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Spreading dung on fields:',readout_format='+.0%',continuous_update=False)\n",
    "add_clean_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Not cleaning shed with disinfectant:',readout_format='+.0%',continuous_update=False)\n",
    "add_visit_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Visited by animal healthcare worker:',readout_format='+.0%',continuous_update=False)\n",
    "\n",
    "add_trained_widg = widgets.FloatSlider(value=0,min=-2,max=2,step=1,description='Use of trained animal healthcare professional:',readout_format='+d',continuous_update=False)\n",
    "add_freq_widg = widgets.BoundedIntText(value=+0,min=-100,max=100,step=1,description='+/- Frequency of antibiotic purchase:',continuous_update=False)\n",
    "\n",
    "add_c_herd_widg = widgets.BoundedIntText(value=0,min=-100,max=100,step=10,description='+/- Cattle herd size:',continuous_update=False,style = {'description_width': 'initial'})\n",
    "add_b_herd_widg = widgets.BoundedIntText(value=0,min=-100,max=100,step=10,description='+/- Buffalo herd size:',continuous_update=False,style = {'description_width': 'initial'})\n",
    "add_g_herd_widg = widgets.BoundedIntText(value=0,min=-100,max=100,step=10,description='+/- Goat herd size:',continuous_update=False,style = {'description_width': 'initial'})\n",
    "\n",
    "add_c_dis_widg = widgets.FloatSlider(value=0,min=-2,max=2,step=1,description='Cattle disease risk household level:',readout_format='+d',continuous_update=False)\n",
    "add_b_dis_widg = widgets.FloatSlider(value=0,min=-2,max=2,step=1,description='Buffalo disease risk household level:',readout_format='+d',continuous_update=False)\n",
    "add_g_dis_widg = widgets.FloatSlider(value=0,min=-2,max=2,step=1,description='Goat disease risk household level:',readout_format='+d',continuous_update=False)\n",
    "\n",
    "add_mix_anti_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Mixing antibiotics in the same animal:',readout_format='+.0%',continuous_update=False)\n",
    "add_not_comp_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Not completing course:',readout_format='+.0%',continuous_update=False)\n",
    "add_not_meas_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Not measuring dose accurately:',readout_format='+.0%',continuous_update=False)\n",
    "add_use_prev_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Using for prevention:',readout_format='+.0%',continuous_update=False)\n",
    "\n",
    "add_drink_mlk_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Drink milk from treated animals:',readout_format='+.0%',continuous_update=False)\n",
    "add_eat_meat_widg = widgets.FloatSlider(value=0,min=-1,max=1,step=0.05,description='Eat meat from treated animals:',readout_format='+.0%',continuous_update=False)\n",
    "\n",
    "\n",
    "\n",
    "# Layout widgets\n",
    "\n",
    "title_box = widgets.HBox([title])\n",
    "\n",
    "in_box = widgets.HBox([input_data])\n",
    "\n",
    "tran_box = widgets.HBox([add_dung_widg, add_clean_widg, add_visit_widg])\n",
    "\n",
    "access_box = widgets.HBox([add_trained_widg,add_freq_widg])\n",
    "\n",
    "herd_size_box = widgets.HBox([add_c_herd_widg,add_b_herd_widg,add_g_herd_widg])\n",
    "herd_dis_box = widgets.HBox([add_c_dis_widg,add_b_dis_widg,add_g_dis_widg])\n",
    "\n",
    "disease_box = widgets.VBox([herd_size_box,herd_dis_box])\n",
    "\n",
    "use_anti_box = widgets.HBox([add_mix_anti_widg,add_not_comp_widg,add_not_meas_widg])\n",
    "use_risk_box = widgets.HBox([add_use_prev_widg,add_drink_mlk_widg,add_eat_meat_widg])\n",
    "\n",
    "use_box = widgets.VBox([use_anti_box,use_risk_box])\n",
    "\n",
    "accordion = widgets.Accordion(children=[tran_box, access_box, disease_box, use_box], selected_index=None)\n",
    "accordion.set_title(0, 'Transmission component data interaction (+/- proportion of \"Yes\" in communities by variable)')\n",
    "accordion.set_title(1, 'Access component data interaction (+/- \"Use of trained professional risk level\" and \"Antibiotic purchase frequency\" per household)')\n",
    "accordion.set_title(2, 'Disease component data interaction (+/- \"Herd size\" and \"Disease risk level\" by animal per household)')\n",
    "accordion.set_title(3, 'Use component data interaction (+/- proportion of \"Yes\" in communities by variable)')\n",
    "#ui = widgets.VBox([title_box, in_box,tran_box,access_box,herd_size_box,herd_dis_box,use_anti_box,use_risk_box])\n",
    "\n",
    "ui = widgets.VBox([title_box, in_box,accordion])\n",
    "\n",
    "out = widgets.interactive_output(AMR_tool, {'input_data': input_data,\\\n",
    "                                            'add_dung_widg': add_dung_widg,'add_clean_widg': add_clean_widg,'add_visit_widg': add_visit_widg,\\\n",
    "                                            'add_freq_widg': add_freq_widg, 'add_trained_widg': add_trained_widg,\\\n",
    "                                           'add_c_herd_widg':add_c_herd_widg,'add_b_herd_widg':add_b_herd_widg,'add_g_herd_widg':add_g_herd_widg,\\\n",
    "                                           'add_c_dis_widg':add_c_dis_widg,'add_b_dis_widg':add_b_dis_widg,'add_g_dis_widg':add_g_dis_widg,\\\n",
    "                                           'add_mix_anti_widg':add_mix_anti_widg,'add_not_comp_widg':add_not_comp_widg,'add_not_meas_widg':add_not_meas_widg,\\\n",
    "                                           'add_use_prev_widg':add_use_prev_widg,'add_drink_mlk_widg':add_drink_mlk_widg,'add_eat_meat_widg':add_eat_meat_widg})\n",
    "\n",
    "display(ui, out)"
   ]
  }
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