content/ch/02/data_scope_big_data_hubris.ipynb

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    "(sec:scope_bigdata)=\n",
    "\n",
    "# Big Data and New Opportunities\n",
    "\n",
    "The tremendous increase in openly available data has created new roles and opportunities in data science. For example, data journalists look for interesting stories in data much like how traditional beat reporters hunt for news stories. The data lifecycle for the data journalist begins with the search for existing data that might have an interesting story, rather than beginning with a research question and looking for how to collect new or use existing data to address the question.\n"
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    "Citizen science projects are another example. They engage many people (and instruments) in data collection. Collectively, these data are made available to researchers who organize the project, and often they are made available in repositories for the general public to further investigate.\n"
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    "The availability of administrative and organizational data creates other opportunities. Researchers can link data collected from scientific studies with, say, medical data that have been collected for health-care purposes; these administrative data have been collected for reasons that don't directly stem from the question of interest, but they can be useful in other settings. Such linkages can help data scientists expand the possibilities of their analyses and cross-check the quality of their data. In addition, found data can include digital traces, such as your web-browsing activity, your posts on social media, and your online network of friends and acquaintances, and they can be quite complex.\n"
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    "When we have large amounts of administrative data or expansive digital traces, it can be tempting to treat them as more definitive than data collected from traditional, smaller research studies. We might even consider these large datasets to be a replacement for scientific studies and essentially a census. This overreach is referred to as the [\"big data hubris\"](https://doi.org/10.1126/science.1248506). Data with a large scope does not mean that we can ignore foundational issues of how representative the data are, nor can we ignore issues with measurement, dependency, and reliability. (And it can be easy to discover meaningless or nonsensical relationships just by coincidence.) One well-known example is the Google Flu Trends tracking system.\n"
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    "## Example: Google Flu Trends\n",
    "\n",
    "[Digital epidemiology](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5754279/), a new subfield of epidemiology, leverages data generated outside the public health system to study patterns of disease and health dynamics in populations.\n",
    "The Google Flu Trends (GFT) tracking system was one of the earliest examples of digital epidemiology.\n",
    "In 2007, researchers found that counting the searches people made for flu-related\n",
    "terms could accurately estimate the number of flu cases.\n",
    "This apparent success made headlines, and many researchers became excited about the possibilities of big data.\n",
    "However, GFT did not live up to expectations and was abandoned in 2015.\n",
    "\n",
    "What went wrong? After all, GFT used millions of digital traces from online queries for terms related to influenza to predict flu activity. Despite initial success, in the 2011–2012 flu season, Google's data scientists found that GFT was not a substitute for the more traditional surveillance reports of three-week-old counts collected by the US Centers for Disease Control and Prevention (CDC) from laboratories across the country. In comparison, GFT overestimated the CDC numbers for 100 out of 108 weeks. Week after week, GFT came in too high for the cases of influenza, even though it was based on big data:\n"
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RSgxNsqwRJJEFixzuKawFlTVr1gifLIJBao8sYhTmwlI7NJ5p06ahfv364qSlAo1k7aIDAZTXWQuFoUsLs6DTPjB0yZ04hi65ens7dJWHLIYu+etP7RbJ6kMZFpwNPksO7eSU7mjAYPLXorYpnIMS3Jmgq7i4WFinTIPvlh+ztc/KX0unH+mHxkiFIF5LscSoTwxdaq9qL6qPoUvuZDN0ydWboausZYuhS/768+QWTaHLk8dZfmwMXd402yqPlaFLZUFtVMfQJVdvhq6y0DXWMEskvQ7sHI68XZlumQzeXnSLrJqslGJv0TONwlJ4U2Ho8qbZVnmsDF0qC8rQJVdQO1rzZkf68pat8iEk7JDP4UsYuhyWjG/QmQIMXTqbMC11l6FL7mywpUuu3mzpKmvpYuiSv/64Rc9TgKHL8+ZU2ogYuqRJLRpi6JKrt7dDV/uIAdiftQFjEmdirmECGLrkrz9u0fMUYOjyvDmVNiKGLmlSM3TJldrYmjdvL5YPhpq95wC6hdyC6vUCUPzXcbfMCG8vukVWrlRDCjB0aWgy9NYVhi65M8aWLrl6e7uly1wEeiWHHYUAcEdh6HKHqlynlhRg6NLSbOisLwxdcieMoUuu3gxdnXHyUA5ME1wzdMlfg9yiZynA0OVZ8yl1NAxdUuVmny65covWeHuRoes6LDtdNJmeni7S+bRt29ZqfynVEKXyMY1er9xAFlPKD6lGTkRqh/JMtmjRwin9KBk3Re2PjY01m0/Y3vHaapyhy5ZC/LlFBRi65C4OtnTJ1dvbocuvWRucOXYYqzdtxaCeYUJ8tnTJX4Outnj33XeDUuFs27atTCT6v/76S6TVoVyFCxYscLiZJ598UiTGjoyMtHrv0KFDMXr0aERERBivo5RAlBpo3bp1In9jvXr1xDW26rLWELXz1FNPiaTZzhRL6YWUuuwdr622GbpsKcSfM3RpZA0wdMmfCG+2dJkDLIYu+WvQ1RYJuii34aJFi0TSaaVQ7kRKbk0w9O677zrcjL0QYg66yKq0du1a3H///SJNz7fffiuAafv27Q6nGFI6ztDl8BS65wZK4Hn48GH3VO7ltbKlS+4CYOiSq7e3W7oYuuSvN3e0SNAVHh4u3BPIuqSUqKgo3HbbbThx4oQRur766iu89dZbOHLkCLp16yagjLYQqezevRuJiYki6TVZk8hSRomxyTpFEPXOO++IJNhUYmJiQFBGxRx0lR/nr7/+ivvuuw87duwQCatNyyOPPILHHnsMd955Jyj3Y79+/fC///1PWNmoXfo7tT158mSjpevrr7/GvHnzxHYhWdFMi6Uxlrd0WRuvK/PEli5X1PPyexm65C4Ahi65ejN0+QjBTU8qsqXL+hosuVaCrGlZ4qJKlSrhnqmlW13u/ndrvSLomjFjhgAhAgnynzpw4IAAlLFjx+KLL74Q0EVAExwcLEArNDQUlKbnl19+MYJUnz59BGwNGzYMn3/+OWbOnIk333xTQBdZqJ5//nnxb1euXBGQtHr1atx8881WoYv6cfDgQSxfvhw33nijuL98eemll1CrVi08/fTTIJiitEEEWrNmzcL+/ftFf3bt2oWHH35YjIksZ+SXtWTJEnTs2LFMddbGWB66rI3XlScRQ5cr6nn5vQxdchcAQ5dcvRm6KkJX3WatcfbYkTInGtWcFb2HjLh2+RqmVZtWCl1VK2Hqpaniz+7+d1vQtXTpUhgMBgwYMACDBg3C66+/LuArICBAABRB1+bNm8X/V6xYIapTAOX777/HpUuXEBYWhn379gkfLCq0NThmzBgBXQR1lStXFgBEZdq0aejcuTMef/xxq9BFVjACLypkhevZs2eFoWzatEn0a9WqVZg6dSr8/f1B49m5c6foKwEXARjV1bdvX2GpS0lJEda98sXaGGvUqCEsf1RfcXGx1fG6suYZulxRz8vvZeiSuwAYuuTqzdBVEbrMxe5Sc1b0Dl3utmhZqt8e6Nq7d6+wKBGwkG/XJ598AgIqBbqmT58uqpkyZYqxuv79+2PixInCEX/OnDnYsGGD8TPa9iPgoh+yChUWFpbZyiMH/Weffdbm9iJtEe7ZswfR0dGiL8ePH8ejjz4q2iFAnDRpknD4JysdWe3I+Z62LskiR9Ysgiva5iToImAiqxiBWqNGjSrIYm2MtH2pQFd2drbV8bqy5hm6XFHPy+9l6JK7ABi65OrtzdBlKeUPQ5f8NehqiwQqBFoEIbR9SFBFFh/yvyJndgW6PvjgA2zduhX0fyoEWrfeeiuysrKQl5cntifJKkUWLSq0Bfncc88J6CLgIch64oknKnTXHp8uuol8zB588EEBWgRwVMj6RJYtgj/aUqQtSwIqsmQRrBF0UagICkdB7dA1hw4dEv2lgwNKX5VOWRtjgwYNjND1ww8/WB2vK3PC0OWKel5+L0OX3AXA0CVXb4auSDRs0wl/HNxtFJ6hS/4adLVFBbqCgoKE1WjNmjViK48gxxS6CDQGDhyIZcuWibhatHX38ccfC+sWQdB//vMfsS1JW5QEZ+S3pfh0kcM6XTt79mwRt4uc7WkbkkDPHHSdPHlSOOLTtQRPZHEbMWKE8C9r1apVhSFTfz/66COMGjUK8fHxyM3NFX5btEW6ZcsWcb1yevGOO+4QIDh48GDh/2VarI3R1KeL+mRtvK7MCUOXK+p5+b0MXXIXAEOXXL0Zuhi65K849Vs0hS5yeB85cqSAFtqGo606Ah0lThcBGYHVqVOnhL8XQRVt7VH59NNP8cILL4BibLVr1058ThYogjB6Nr3yyiti+1IpSogKc9BFEQUIssh3igo5vJOFi0DQXCFrm+KcT9cSFN1+++2ibXK0N4UucvYnixz1jcbToUOHMlVaGmN5R3pr43Vllhi6XFHPy+9l6JK7ABi65OrN0MXQJX/FaaPF06dPw8/Pr0Jnrl27JiLI0wlBc4Vg6OzZs8ICVX5rr/z1dCqWfPiqVavmdGwuV9SyNEbTOm2N15n2GbqcUY3vEQowdMldCAxdcvVm6GLokr/iuEVPV4Chy9Nn2I3jY+hyo7hmqmbokqu3N0PXQ+OnYsWcl3FreH/8mLneKLzi05WYsgiG8bGqT4jeTy+qLghX6HEKMHR53JTKGxBDlzytqSWGLrl6ezN09R45DhuXzkWvEWORvmSOUXhL/67WzDB0qaUk16NVBRi6tDozOugXQ5fcSWLokqs3QxdDl/wVxy16ugIMXZ4+w24cH0OXG8Xl7UW54lpozVsTXrOlSxPLjzvhgQowdHngpMoaEkOXLKVL22FLl1y92dLFli75K45b9HQFGLo8fYbdOD6GLjeKy5YuueKypauMAt0GjET2hqUYOu4FLJ9dmkuQCvt0aWJZcid0rABDl44n73p3naFL7gywpUuu3t5s6bIUeZ6hS/4a5BY9SwGGLs+aT6mjYeiSKjdvL8qVW7TmrT5dDF3XYbFxk16hAEOXV0yzewbJ0OUeXS3VypYuuXp7M3T51m+Ci4UF2J77E0KD2xmFtxS/S62Z4ZARainJ9WhVAYYurc6MDvrF0CV3khi65OrtzdDl4+MjxKZULabl7eXrMWZYxUj1as0MQ5daSnI9WlWAoUurM6ODfjF0yZ0khi65ejN0MXTJX3HcoqcrwNDl6TPsxvExdLlRXDNVM3TJ1dtbocuaNYstXfLXILfoWQowdHnWfEodDUOXVLnZkV6u3KI1b3SkZ+i6DguNm/QaBRi6vGaq1R8oQ5f6mlqrkS1dcvX2Vugaa5iFeUkTEdg5HHm7MsuIzpYu+WuQW/QsBRi6PGs+pY6GoUuq3Gzpkiu311q6rMXiYui6DouQm/QoBRi6PGo65Q6GoUuu3mzpkqu3t1q6GLrkrzNu0XsUYOjynrlWfaQMXapLarVChi65ensrdCmBURNTFsEwPraM6Nl7DqBbyC2oXi8AxX8dV31COGSE6pJyhRpTQHfQde3aNVy6dAm+vr52Sdm6dWscPnzYrmv5IscUYOhyTC9Xr2boclVBx+/3Rkd6S9HoFfUsxfByXN2KdzB0qaEi16FlBXQFXTNmzMCnn36Ky5cv49Zbb8XLL7+MG2+80aq+DF3uW34MXe7T1lzNDF1y9fZWS5cSjX71pq0Y1DOsgugMXfLXIbfoOQroBrr27NmDuLg4bN68GTVq1MArr7yCypUrY8qUKQxd12k9MnTJFZ6hS67e3gpdtqDK1ueuzBJbulxRj+/VgwK6ga5vv/0WBoMB6enpqFSpEt5++20cP34c06ZNY+i6TiuNoUuu8AxdcvVm6CqbAoi3F+WvP27R8xTQDXTRF87jjz8O8unq0qUL1q9fj/fffx8tW7Zk6LpO65KhS67wDF1y9fZG6FJCQtRp2gpnfjfvC8uWLvnrkFv0HAV0A10k+Zo1azBp0iShflRUlPDpql69unE2evXqVWFmfvnlF2zdutVzZoxHwgqwAqyAmxRITfsKk+OG44ZWIfhha7rZVgICAsS/FxQUuKkX2qm2cePG2ukM98QjFNANdGVkZIitxEWLFgmfrqlTp6JmzZqYNWuWcSLICla+tG3blk8vummpsqXLTcJaqJYtXXL19kZLl2F2KpLiR5mNRq+oz5Yu+euQW/QcBXQDXS+88AKaNGmCMWPGCPUpDMR///tfHDhwQDjUWyp8etF9i5Why33amquZoUuu3t4IXdYCoyrq123WGmePHYGl042uzBI70ruiHt+rBwV0A11paWlYtWoV3n33XVSrVg2LFy/Gjh07sGDBAqs6M3S5bxkydLlPW4Yuudpaas3b4nTZA1224ni5MnMMXa6ox/fqQQHdQFdxcTGSkpJA24xUOnTogAkTJoh4XdYKQ5f7liFDl/u0ZeiSqy1DV6kC5aErPSEdJ/acQK9ZvRAQUurLxdCljbXJvdCnArqBLkVe8tuibRby67KnMHTZo5Jz1zB0Oaebs3fx9qKzyjl/n7dZuoJuj0D+7iyMSZyJuYYJSI1IRX5WPmIyYhAUEcTQ5fxS4jtZAaGA7qDL0Xlj6HJUMfuvZ+iyXys1rmToUkNFx+rwNuiyx4plzzWOqfzv1by96KxyfJ9eFGDo0stMabCfDF1yJ4WhS67e1BpDV0XNGbrkr0Nu0XMUsAu6rl69igsXLlQYde3atTWvBFu63DdFDF3u09ZczQxdcvVm6BqAgtwCHEw7iOCYYPgH+YsJYOiSvw65Rc9RwCp0kan3rbfeAp0c/PvvvyuMeufOnfDz89O0Ggxd7psehi73acvQJVdbS615u6UrySdJSBOeGI4IQwRDlzaWJfdCxwpYha5PP/0UM2bMwLPPPott27ahYcOGIlYWhWsYOXIkhg8frvmhM3S5b4oYutynLUOXXG0ZukoVqFKjDq4Wn8Ovx/9A84CGMAdd9oSVcHb22KfLWeX4Pr0oYBW6pkyZghtvvBFjx44VCaYpEvGTTz6JvXv3YvDgwfjhhx/KpOHR4qAZutw3Kwxd7tOWoUuutgxdpQqUjzavQJfp6UWGLm2sTe6FPhWwCl3Tp0/H+fPnRY5Dynu4ceNGEYyUfLzatWsn/n7TTTdpeuQMXe6bHoYu92nL0CVXW4Yu89BlTheGLm2sTe6FPhWwCl2bNm0S+Q63bNmCvLw89OzZUwQkvXz5ssiBSD5dVapU0fTIZUNXyo4UxATHwN+31OnUkwtDl9zZZUd6uXpTa97m02VPXkWGLvnrkFv0HAWsQhfBVWFhIRo1aiRGPHv2bMyZMwcNGjTA008/jSFDhmheCZnQlZmXie6LuyM8MByZsZma18bVDjJ0uaqgY/czdDmmlxpXezt0FeUVYc/iPQgMDzQGR2XoUmNlcR3eqoBdISNMxaEHP1m3rCWZ1pKYMqFr3YF1GLhyICLbRmLd0HVaksEtfWHocousFitl6JKrt7dZutZ8uQ2D778LdZq2wpnfDyMvMw+Luy8WohN0xWbGij8zdMlfh9yi5yhgFbo+/PBDZGdnQ3GoNx32vffei3Xr1qGMPhVSAAAgAElEQVRu3bqaVkMmdBkyDUjKSkJieCIMEQZN66JG5xi61FDR/joYuuzXSq0rvcnS9fby9RgzLBIN23TCHwd3M3SptYi4HlbARAGr0PXGG29g4cKFqFWrlnCm79evn/FWghmO01Uqh2KCz7iQgaQapdDVvWV38RltNXpqYeiSO7MMXXL19jZLlyXo8gv0EzG6QmJDxAQ8NH4qVsx5GaH9R2D7+iWqTgqHjFBVTq5MgwrYhK4bbrhBJJeeOnUqIiMj8eKLLwrrFkPXv7NpNMPXBoqqFMFvqR+SdpUGFSxJLNHgtKvTJYYudXS0txaGLnuVUu86b7Z0WVKxPJyppzbA0KWmmlyXFhWwCV3kRB8TE4OjR4/imWeeEad5Zs6ciWHDhrGl658ZNfV9oH/yi/NDakgq9pzYg5y4HIQElL4helph6JI7owxdcvX2dksXQ5f89cYter4CdkMXSXHlyhW888474hQjFd5eLF0gtL2Ym5qLlV+tRMC2APh39cfa0WuRlZ+FjJgMRASVps/wtMLQJXdGGbrk6u1t0GVu27C4qBg7UnaAthiV7UW2dMlfh9yi5yhgFbpyc3Ph6+srAqGaFvr3JUuW4JVXXhFbj1ouMh3pe7zdAz9//zMWJS4COdUzdGl5Zeivbwxd8ufMm7YXy59KzDRkIispyyh6Ykmi+DNDl/x1yC16jgIOh4zQ29BlQldCegJyC3KR3DtZQFfawTSsjV6LqHZRepPNrv6ypcsumVS7iKFLNSntroihi6HL7sXCF7ICdihgFboo3c8XX3yB9PR07N+/v0J1aWlpqFOnjh3NXL9LZEDXd2tysWXZd2gUeiNGPFN6wtMbwkcwdMld1wxdcvWm1hi6soyBUekEI1u65K9BbtGzFLAKXTt27MDw4cNF5PmQkBBUqlSpzOgHDBiAatWqaVoRGdD1xgMLcH51ATKRidiIWBTkFuDC2gseH7OLoUvu0mfokqu3t0OXJbWz9xxAt5BbUL1eAIr/Oq7qpPDpRVXl5Mo0qIDdCa812He7uiQbuiJQ+jZYklHC0GXXDPFF9irA0GWvUupd502WrkZtO+PkoRwkpiyCYXxp9HlLxZ4cjc7MAkOXM6rxPXpSwCp0UbLrkpISEZtLr0UGdKVMXYZ1L7+LPOTh0VqP4erfV/Dfg//Fs98+K8JFkI+XJxa2dMmdVYYuuXp7m6VLga55y9Lw1EMDjGIrzvThif8Gembokr8WuUXPUMDm9mJcXBy+/PJLNGzYUJcjlgFdylFrEijl1RV4YnQkfP19damXI51m6HJELdevZehyXUNHa/BGS5cCXakRqcjPyjdKFpMRY0x6zdDl6Eri61mBUgWsQtfmzZvx/PPP49SpU2jfvj3q169fRre5c+eiZs2amtZSBnQpR61JCHtM85oWzIHOMXQ5IJYKlzJ0qSCig1UwdDF0Obhk+HJWwKoCVqHrm2++QU5OjsUKyApGcby0XGRA14iwZ/H7tzuRi1x0HTEc6UvmaFkS1frG0KWalHZVxNBll0yqXsTQlY/ckFxEhkciKiEK/kH+pW/rPj7i/+R+omZhny411eS6tKgAx+lSYVaeDngBdU9URSpS0aPjg2j1a31ELopEu6iyQWVVaEpTVTB0yZ0Ohi65elNr3gRd5kDKJ+kfuCqXQ9a3fgAuFp7A9tyfEBqs3nOOoUv+GucW5SpgE7oo9U9GRobIvdihQwd069YN9GVLP0FBQXJ760RrMixdcTXH4cYLNyA1IBUdq92Fzr+2AjmdKnFtnOi2Lm5h6JI7TQxdcvVm6AIsQZclp3tXZ4ihy1UF+X6tK2AzOOrgwYPx448/inGMGzcO48ePR2ZmJhISEkTuxSpVqmh6jDKg654mUajsexq5Ubm4Y+NdCPupC0OXpleFPjvH0CV/3tjS5YOQ3BAkhyQjOCbYuL3I0CV/LXKLnqGAVeiiaPTTp08XeRbXr18v9vEJuuhBFBYWBnK0b9GihaaVkAFd4gF0Yw4oRFejrUE4+G6O8eGkaXFc7BxbulwU0MHbGbocFEyFy70ZupJ8koSCmRGZiMiMKPMiydClwuLiKrxSAavQNWHCBDRv3hwTJ05ESkqKEbpOnz6NLl264PPPPwdBjZaLNOi6KQcIBZAOlGxX17lUq/oydMmdGYYuuXp7+/aiAl0lMSXwWezD0CV/+XGLHqiAVehKTk7Gtm3bsGrVqjLQtWzZMiQmJmLfvn18ehFAd5/uQAiQGZUJpAIlR0uhi5Jfn754GsGNg+HvW3rqx5MKQ5fc2WTokqu3N0HX28vXY8ywSDRs0wl/HNwthFagi+Jz5WXmiRhd9EOFLV3y1yK36BkKWIWuY8eOoW/fvsLaRTkW6adGjRrYsmULxowZI/y6tF5kWLqUh5PBYEDAwgCMvzoBTdo1Qu7kXKTsSMGsXrOQEKp9rRydS4YuRxVz7XqGLtf0c+Zub9leNAdd1vRqHzEA+7M2YEziTMw1THBGWrP3sCO9alJyRRpVwObpxfz8fMyfPx+7du0SvlwEMQ8++CAGDRqEypUra3RY/3ZLNnQFzQ9CbEEsAsMDkWfI8+j8iwxdcpc/Q5dcvb3d0mVNbSUgdK8RY1WNS8jQJX+Nc4tyFbAJXXK7o35r7oau3wpOYmSTB4FQH2T2zkDAWwEYfXY0AkICcHzWcYYu9afUa2tk6JI/9d5u6UrNTUXayjREFEUgMjrSuL3I0CV/LXKLnqGAVeii2Fy0xWipUMwurVu73A1dilkehn9UMgADBsYjbU0yDJkGhi7P+D3RxCgYuuRPg7dAl2F2KpLiRxl9usiHa3H3xUAwkNopFbGppdb72MxYMQkMXfLXIrfoGQpYha433ngDCxcuNDvSBg0a4KuvvkLt2rU1rYQs6Kr6VG3UqeOHv2Ycg2Jyp7fEUWmjEBMcg9SoVE3r5EzneHvRGdWcv4ehy3ntnL3TW6CrPEQxdDm7Yvg+VsC6Ag5tL169ehXHjx8HwZifnx9eeuklzevrbuiaM/sTrImfh6sBfrj3uSjxthjYORx5uzKRmZeJ7ou7IzwwHJmxmZrXytEOMnQ5qphr1zN0uaafM3czdAHJ3ZMRXxiPqIgohMSGsKXLmYXE97AC/yjgEHQpqv3yyy/o1asXdu/ejTp16mhaTHdD14TYN+C/+DxO+p/FrW/fI45dP1FjrEgLFLYnDD3X9mTo0vQK0U/nGLrkz5W3QpeitCUXCWU7UnnBVGtm2JFeLSW5Hq0q4BR0/f333wgJCRHpgJo2barVsYl+uRu6Huw1Ee03+eFM48sIW9oHg++/C49UeQItrtyI4OXBGHhwIEOXpleIfjrH0CV/rrwZuvKK8oR7BFnsE8MTYYhQHFcBR0NM2DtzDF32KsXX6VUBq9B16tQpkdjatNDfV69ejezsbHz99deaH7e7oStyUALOrN2DZnd2wdJtb4io/bGIRRCCELY6DC+feRkhASFI7p2sea0c7SBvLzqqmGvXM3S5pp8zd3sLdHUbMBLZG5Zi6LgXsHz2NCGV4h7hX+SP6N+jMbrvaOP2IkOXM6uJ72EFAKcc6cnKFRcXhx49emheQ3dDV3kHVIIuf/ijsKRQ89q42kGGLlcVdOx+hi7H9FLjam+BrvIR5jMNmchKyhJ5F+mHAj9TSSxJFP9n6FJjdXEd3qiAVegiU+/58+fL6OLr6wt/f/2ktHE3dAXdHoH83VlITFkEw/hY1G3WGmePHcG8ZWl46qEBHr2mGLrkTi9Dl1y9qTWGLoYu+auOW/RkBZzy6bregly7dg1nz54V4SpsxQlzN3TdGtQdjfOB/qNjMPGdWLflJLvemptrn6FL7qwwdMnVm6Gr1NJVGF2IyUcmI6B2ACIMEWzpkr8MuUUPUqACdM2dOxe5ubl2DXH27NmoWbOmXdeqcVFhYaFItP3NN9+gfv36GDt2rEhHZK24G7r63TAMd5xqi5qDA/DsJ3GgnGTVsn5DFKIQnhhufEipMX6t1cHQJXdGGLrk6u3N0EVjtxbyhjJxtGjSCJV9a+PKhbOqTQw70qsmJVekUQUqQFdaWhooEr09ZfTo0aDtRlll8ODBuPPOO0WibVsWLqVPsqCreEAlxC0egdHj38LFpT8gAhEMXbIWhpe0w9Alf6K9ZXvRr1kbnDl2GKs3bcWgnmFCaFtxBsl/lUpJSYlqE8PQpZqUXJFGFagAXeQw2aVLF/Tr1w//93//h6pVq6Jhw4bXvfvff/89Jk2ahC+//NJu4KJOuxu6bq5+K1rc2Bh5EXmIiYlB9gencGLpVmHpahvZFkPXDb3u2rmrA2zpcpey5utl6JKrtzdZuswBlCl0JeUnCfHJeq8Uhi7565Fb1L8CFaDrkUceQd++fUFWJYo836hRIwET17ssXrwY+/fvFxBF/7/vvvvE6cnq1atb7Zq7oUs8eMjNIQIilk2j453x9LBo3NCqI347vON6y+bW9hm63CpvhcoZuuTq7c3QtSJqBQ6mHcSKoSvQK6YX6vWpJ8RXTi/Snxm65K9HblH/ClSArvfeew/Lli0rtdpkZ4uTij179jQ70nvuuQdVqlSRosLrr7+Od999FxMmTECrVq2waNEidOjQAVOmTDG2T/5o5UtKSoqInO+uUrduXSN0PdftOTQ8FoKJj0SjYesQ/LzrG3c1q4l6CQJo/u3d6tVEp3XcCUrDdeXKFZsvGjoeoua6Tqe3a9SoYQQMzXVQpQ6J5xgA2t478+sZLA1biounL+LS25cQFh6G79t/Lz6feGaisUXTe1TqBi5duoRKlSpJ+16x1W+tZ1yx1X/+XHsKVICu06dPC6BZsWIFKDiqtbJz506Rg1FGmT59Os6dOwf6P5UDBw4gNjYW27ZtM37pkz9a+fLMM89gz549buniwlVfICX2GbS+9TYc/s8PGDZsGHrWHYLuoZ1QvV4A/vr9Z+SfzsevZ35Fi7otEOgX6JZ+XK9KL1y4ILafZYH39RqnVtol4Lp8+bKAAC5yFKBnDp2S9vRSq1YtMUTKNvLH3j+wNnotWtzdAn0W9hH//sOHP+B0/mncNvw2+AWWPvNN71FLn+LiYvE8p+eKForMg2JaGC/3wf0KWA0Z8dFHH4k3ay1sLxIIHj582Ahdv/32G/r37w/y9bL2C+rO7UUKELhl2HK0Qzthho+Lj0NCaIJ4K56MyfCFL0oySpCUlVQhjYb7p9b9LfD2ovs1Nm2Btxfl6k2teYMjffaeA+gWcgsqV6+FK8XnjCIXFxXD19/yQSklJqGp872rM8SO9K4qyPdrXQGr0EVWL3rT00J+RYKse++9Fxs2bMBNN92EOXPm4M8//8Srr75qVWN3QhdFo2+y9JxI+ZMam4pFiYsQERQhoCsBCSIyfcnyEiQdZOjS+i+CHvrH0CV/lrwBupyNLl8+ir0as8PQpYaKXIeWFbArOCpt5R05cqTCOHr16iXVDLx27VokJf1ziiY8HPHx8QLArBV3QhflKyvesA9Nwlpg+x3fYG38WgFd9DDqeyhYwFjJrBIknWbo0vIvgV76xtAlf6a8Dbq+W7AG+Vn54uR1QEiAELz74u4I2BqA0c1GIzgmGP5BpRlJGLrkr0duUf8KWIUueuAMHToUZGWi/fvyMbk2bdoExZlSlhQUjZ72/e3da3cndCkPndYzbsPhCz8gIybDCF1nD/2Et5atxB9Ndovtxfiu8R6X9Jq3F2Wt+tJ2GLrk6k2teRt0vd5kvICumIwYBEUECcF9knwQmxqLoLygMv/O0CV/PXKL+lfAKnSRMz35dc2fP18TW4zOyO1O6FICCnZP6Y9rdc8IqAoJCBFR6fdnbcDQcS8gbuJ94k0xPDAcmbGZzgxBs/cwdMmdGoYuuXozdDF0yV9x3KKnK2AVup599lm0aNEC48aN060O7oQu8t0KQQiSDcllzO7k67Vx6Vz0GjEWk18azNCl29WjrY4zdMmfD2+wdI01zMK8pIm4Nbw/Pop9CUV5RQiJDTFuI5KlKyQ3BLOCZ5X5d7Z0yV+P3KL+FbAKXRSz66uvvhLhI/Ra3AVdSu4xxWE+LifO6ANB0KVEpW86qikeD3ycLV16XUAa6jdDl/zJ8AboMn1JTF8yp4LIBF1UShLLpvshn9bsDUuFRX/57GmqTA470qsiI1eiYQWsQtfJkydFWIaoqCiEhoZW8N8KDg7WfGBMd0GXcuLHAIOYXtNIzfTm+FnSbMQiFv5d/ZHQJ4GhS8O/BHrpGkOX/JnyZuhad2Ad9pzYA0Nm6TOuPHTZgjVnZouhyxnV+B49KWAVuigN0MKFCy2OR2ZwVGdFdRd0GWanIil+FPo2eAjPjH0CEQbKBVRaCMjeGBYvoKvp3U3x4SMfwt/XH+uGrnN2GJq8j3265E4LQ5dcvak1b4Ou525+QIhMORYjUiOQlZ8l/k7bi8khlt0ozFnInJkthi5nVON79KSAVehSImBbGpAeImO7C7qsveU5G/dGTwuH+srQJXfGGLrk6u1t0DWobwI6fuYvIs4n5CWUgS5zpxfZ0iV/PXKL+lfArjhdeh6mu6BL8WcgZ/nyb3kMXXpeMdrtO0OX/LnxBkuXcto6IeZ1+C8+j8DwQMRmxpaBrqh1UcLaFb02Gu2i2omJYOiSvx65Rf0rUAG6Dh48iMTERBHpnVLspKenWxwlJZi2N17W9ZLKXdBFJ3cuHzqK5wfPQL/h9xgfRDROBbrqNm2N078ful5Dd3u7bOlyu8RlGmDokqu3t1i6lFOIs1NWocNfN4hTi3R6UdlenNVrlojR5fuDL9r1aGeM3/XQ+KlYMedlcerxx8z1qkwOby+qIiNXomEFKkDXiRMnRMLrxx9/HD/++CNycnIsdj8uLq5CwFStjdVd0EUxuuofuyz8tpQ3Q9OxUzgJ+oyi0psGGtSaPq70h6HLFfUcv5ehy3HNXL3DGyxdlkI/KNClBH0ur6U7LPoMXa6uWL5f6wrw9qKTM0RQFYpQ9EZvNHukGW5/5XYRGFUpDF1OCsu3WVSAoUv+4mDoyjJm2mDokr/+uEXPU4Chy4k5VWJ03VS1HaY+NUnkViRrl2nEeYKuKESJ4KmRiyKFud7TClu65M4oQ5dcvak1b4Ku5Bc+QkiVpuJZRimATC1dtL1I6YGUz0gbtnTJX4/cov4VYOhyYg5NHzYfb5xpNuI8QReVkpKyAQWdaE6ztzB0yZ0ahi65ensLdFWpUQdXi89h0cPLkP/RIfSa1QuhCaFloAupQFZSVmkoiX/C4ygvn2o+53h7Uf4a5xblKsDQ5YTeSoyuwM7hSF1tMAtdvvUDcLHwBFZv2opBPcOcaEX7tzB0yZ0jhi65ensLdCkviIvCF5VJdp2am4q8ojzEhsQiLzmvAnSRPmq/XDJ0yV/j3KJcBaxC1/bt21GrVi107NixQq9SUlLwxBNPQOuxutzhSK8clR5+5zPo8Z/bQKmAKDq/6faiqXPqnd1b4PTF0whuHCyCpHpKYeiSO5MMXXL19jboylmUUyHvoqJ4XmYe6Ie2HelHKQxd8tckt6hvBWxGpG/UqBFiYmLKjPLq1au4/fbb8fHHH6NNmzaaVsAd0KXE6FJSABkMhgppfgi6ah0qFCcYi9sX47Uhr2FR5CLx1ugphaFL7kwydMnV29ugyxlXiCq+tXH14t/YnvsTQoNL43e5UtjS5Yp6fK8eFDALXadOncKePXuwatUq+Pv7o2fPnsaxEHBt3boVaWlpIo5X1apVNT1Od0CXYsWyBV2VDx3HaIwGAgDDaAMSwxNhiCjNY+YJhaFL7iwydMnVm6HLtt6Wwk3YvtP8FQxdzirH9+lFAbPQlZeXJ4Kjfv3112bH0aVLF4wcORJ9+vTR/DjdAV2+9ZvAp7AIM0csxIlKPyOpZVIFoDIHZgxdml8umu4gQ5f86fH004trvtyGwfffhTpNW2HD4+8JgclZ3rSQb1fayjREFEUgMjqyzPYiQ5f8Nckt6lsBq9uLqampwlGy/PainobsDugy9WMwZBqQlFURukxTZIQ+0sDsNXrS0Vxf2dIldwYZuuTq7Q2WLtOT2GMORQqBE0sSywhNzzg6uRiRGVHm9CJdxNAlf01yi/pWwCp0Xbt2TYQ8qFy5sm5HqTZ0Ze85gG4ht6By9Vq4UnwO6w6sQ3J2svDVMvXXYujS7ZLRbMcZuuRPjadbuhToCmnWE1G/h6FxcGOMzh1dAbpWLliJoSuGom1kWwxdN9T4ubUctM7MFm8vOqMa36MnBaxCF/lvffHFFyL/4v79+yuMi/y66tSpo+nxqg1dykOq643/xYwn/lcmWKCpEAxdml4WuuwcQ5f8afN06BprmIV5SRNxR9fBeL33WGPeRVOlydKVkpaC+MJ4REVElQn0rHbSa4Yu+WucW5SrgFXo2rFjB4YPH44hQ4YgJCQElSpVKtO7AQMGoFq1anJ77GBrakOX8pCKahKLkONBFcztSveUh9HExs+j7omqWDF0BaLjotmR3sH548v/VYChS/5q8HTosgeaLLlQ0GzYc78js8bQ5YhafK0eFbAKXdOnT8f58+fx8ssv63Fsos9qQ1d5mIpeG412URWPSisBVBU4y4zIFIDGpxd1u5Sue8cZuuRPgTdDF/mqUiEXE3N+q/SZ8hJ6a3h//Ji53uUJYuhyWUKuQOMKWIWuadOmiV+4F198UePDsNw9taGrfcQA7M/agMf7TsWwLvcKU7t/UMWAp8o2ZN8GD+GOU21x5KEjuPmJmxm6dLuSrn/HGbrkz4GnQ5fiCF8+7yIp7ZNUmsqMTl2L7cWieESFl91eVDv/IkOX/DXOLcpVwOb2YlxcHL788ks0bNhQbs9Uak1t6LqnSRR+K9iDZ96biTGPDrTYS7UfRirJoWo1fHpRVTltVsbQZVMi1S/wFuh6ffB8nF9dUMZdwhS6Fi9ejNjUWOHDGpv5b4BntZ9zDF2qL2GuUGMKWIWuzZs34/nnnwcFS23fvj3q169fpvtz585FzZo1NTakst1RG7qervI/1L1aC/VT2mPc+AcYumrUQPXq1TW9Bjylcwxd8mfS06GLYg5eLCzA0tGr8PP8H81C19rotZgwYwJDl/zlxy16oAJWoeubb75BTk6OxWGTFczX11fTsqgNXUk+pX4O5WPZlBdBCTpYt2lrnP79EIqLiuHrr22tHJ1ItnQ5qphr1zN0uaafM3d7OnQpMQePZhwtk1sxMy8T3Rd3F+nN1g1dh+yd2ShaV4R2t7Urc3qRNFUz/yJbupxZpXyPnhSwCl16GoilvqoJXQROfer1QQACsLxkuU15lIeRki7IFqjZrFBjFzB0yZ0Qhi65elNr3gJd5fMumkJXZmymVeEZuuSvS25RvwrYhK4rV64gIyMDR48eRYcOHdCtWzfQly39BAX9m21eqxKoCV2mKTPO/H5YDJkeTvTQCW4cDH/fsg71DF1aXRXq9IvSo+SfzkdMcAyC/N3/u8DQpc68OVKLJ0OXNX8shi5HVglfywrYr4DN4KiDBw/Gjz/+KGocN24cxo8fj8zMTCQkJGDnzp2oUqWK/a1dhyvVhC5zD6mWKS2RV5SHnLgchASElBmhAl2v+r0qthYp0rMnbTF6u6UrIjUCWflZyIjJQERQhNtXN0OX2yWu0IC3QNem5z7A6fzTCI4JFqexy0NXUV4R9izeA79Avwrbi771A3Cx8AS25/6E0OCK4XMcmTXeXnRELb5WjwpYhS6KRk+xupYsWYL169cLiw5BFz2IwsLCQI72LVq00PS41YSuCbFvIHvxGpy9zQ8/7EsX41ZO+JQkllTQQU2zuxZF9nboqjejHoqKi1A4qbCCldMd88XQ5Q5VrdfpydBlGmPrWQxCflY+YjJiRELr8tCVl5mHxd0XVzi9SOqpmX+RoUv+GucW5SpgFbomTJiA5s2bY+LEiUhJSTFC1+nTp9GlSxd8/vnnIvioloua0PV0wAsiuvyP95/Gxxtn2oSuus1a4+yxI1i9aSsG9QzTskxO9c3bocsacDslqI2bGLrcoar3QpdpNPluS28QQih+p2S9p+1z2jZXcspaOkTE0CV/XXKL+lXAKnQlJydj27ZtWLVqVRnoWrZsGRITE7Fv3z6vOr34vwZTUeuvKtjbtwirP51lE7pMH0bt7/TDlTNXcF+H+/S7Wsr13Juhi76UaGs50C8QeQl5UuaUoUuKzGUa8WRLlyl0Tb5psBh3hMHyNnmmodSh3n+0PyZsnCD8WJN7J7OlS/6y5BZ1rIBV6Dp27Bj69u0rrF2UY5F+atSogS1btmDMmDHCr0vrRU1LV78bhqHqqQvouWwUnnpogN3QNaP3PFxIP4n03unY/sV2rUtmd/+8GbpMt1+SkYyspCzhD2PtS8tuYS1cyNDlqoKO3+/J0OWshUrJxUg+rOTLqmb+Rd5edHyN8h36UsDm6cX8/HzMnz8fu3btEr5cBDEPPvggBg0ahMqVK2t+tGpCV/mHlC1rh3K9Eu2Z8i/SSVBPKd4MXesOrMPAlQMx+MfB6LStE6783xWLyc/Vmm+GLrWUtL8ehq6KWiWkJyBlR4r4gHxZHxo/FSvmvAw18i8ydNm/NvlKfSpgFbouXbqEqlWrGoPf6XGIakJXlRp1cLX4nPGUjq1j1UG3RyB/dxb+F/4qamZdxLqodchZaznYrN709WboUt72x787HvWPlWZqoITmbOnS2yq23l9Phi4lGv3Xn+9Epe/OmT2ZqKhDgVKD1pX6dyUGJopTuwp0Kae6lUDQrqwAhi5X1ON79aCAVegiC9eRI0fw5ptvlhkLWb0effRR5Obman6MakEXnd4Z1X0UDuAAjpccF+O2BV2mZveNN881PqQ0L5qdHfR26KIkwAnJCSj2LUbOtBykP1N6otVdhS1d7lLWcr2eDF2m0egtnUxUlKFDIwaDQfw1NTm1Qpgcpa5fj/+B5gHO5+ll6JK/xrlFuQpYha5nnnlGBEAdO3ZsmUDJXEUAACAASURBVF6dO3cOnTp1AqUJatKkidweO9iaWtD1yRNr8eO7e/FN5W+x+cpG0QvaXiSLBwVFJYfS8oWhy8HJ0tHltL2YW5CL0CqhmDBvAgpCCkToCHcWhi53qmu+boauUl1Moasko0SsfUoPpBRn/cPKq87QJX+Nc4tyFbAKXfRmQ8707777bpleHTx4EP369fMq6Hq50au4evISVgZmYn+efX5ZptBVcOcWnNhzAl8kflEhiKrcKVevNW+2dJmqKCt0BEOXemvX3po8FbpMAz0f2vg1clNzRVDUkNiyAZ4VnWiNR2RGIDE80ewWercBI5G9YSl6jRiL9CVz7JW3wnUMXU5LxzfqRAGbwVEpGOobb7yBu+66C/Xq1QMBF8XsIl+vuXNLt8y0XNSydM16eim+mbkG29rk44+Du+0asvJgC2nWE1G/hyEvKE8EH5QRvdyuDrp4EUNXqYAh80PQLakbAgoCbCZCd0Vyhi5X1HPuXm+ALnueZ7ZeLAyzU5EUPwoN23Sy+/lobkYYupxbp3yXfhSwCl2UBHXatGlYunRpmRE1aNAAH3zwAW699VbNj1Qt6FKsVqH9R2D7+iV2jZuhyy6ZdHkRJT+fUW8GAkICsDx+ObqP6i7G4c6k5gxd8peKp0KXrROH5K9KzvLhgeHiJVGBLkvZF34rOIkWTRqhsm9tXLlw1umJYuhyWjq+UScK2AwZQeOgZNcHDhwARaK/8cYbcccdd4h4XXooakOXI+bz8tBVEFaAuE/ieHtRDwvHRh9N06LkTs5FjYE14Fvsi0mFk9yWX5OhS/7C8VToMnV9mP/I0yIFUGB4oEgBREU5nUvbiYYIAyjPqM9iH0S1i0L8O/FmJ0LJwehKBg6GLvlrnFuUq4BV6CIrV8uWLTF8+HC5vVKxNVehiywaCzotwLbz+7Dqj/eRmLIIhvGxdvVQrbc/uxq7Dhd58/ZidnI2Nk7YiK7xXZEdlQ06yRgfGS++oNxVGLrcpazlej0Vukx9sCgaPQX3NQ15Uh66KCXQnu574F/kj/ij8cL/q3xRQuSMSZyJuYYJTk0WQ5dTsvFNOlLAKnS9+uqrOH78OGbPnq25IdEXEEXIV44qW+qgq9B1YN0BrBy4Ej/7/o6lxe9h3rI0YzR6e0Tx5KTX3gxdytwX5RUhF7kifAhtw7jTX4+hy57fOHWv8VToMj1teOPGa9izeA96zeqF0IRQs5Yu+scnWz2JgJ8D0GtDL4T2K73OtKgRmZ6hS931y7VpTwGr0EVbikOHDhVxurp3766ZCPRffPEFyMF/06ZNwhJnrbgKXZRvjN4CM//5j/zcHCmm0FWQWyB8gDylMHTJnUmGLrl6U2veAF13XmwBenmgk4uKBau8pYu0WJ+yHtVOVkPoY6FmLV2KM31g53Dk7SrN0+hoYehyVDG+Xm8KWIWur7/+GvPmzcPevXtRq1YtdO7cucz46PRizZo1pY755MmTiI6Oxl9//YW1a9e6HbroYbThk60Y/+wIXKhXHcV/FTg0XsXPwYDSbSd3Olo71DEVLvZW6CJ49vX3LfPFQ0fu00aluTUqPUOXCovWwSo8FbqU7BqWgpmag67y0pU/0ahGZHqGLgcXKF+uOwWsQhcFP83JsZy2Ji4uDr6+vlIHTW326dMHb731FpYsWeJ26KLBmca0MT1enZSVJJziI9tGWtRAMeMzdEldJm5rTDm1SA00DmuM0VtHg+Y4+NemuKu4C0OX25S/PhV7KnTZcnuwB7rIuZ5OOGbEZBi31W3Va2sWGbpsKcSf610Bu04vamWQa9asAW0tUrDW0NBQLF++vAx05eXlVehqz5498dNPP7k0hL6jErDpw3m4f/gYfLbo38jzVV+pKuq9/Pxli/Xf2P4/OHkoBy/dMB0Nb6yH4ZuHu+10m0uDdOLms2fPonr16sK3zlsKBbh9s8ebwqH4P+P+g54ze4qYdaEIRW/0Nv6bO/SgXKhk7apTp447quc6zShAFnWKT2jLd1RP4v124hRuahaAytVrofhcEb6b8x0uFl1Ex5EdRf5FKoNXDcb6Q+vxyQOfGF8q9y7Zi9P5p43X3bf0Pnzz6zf4avhXIrQEldoNm+Fi0Qls2fUDQju2dVgWynZCv0/0XNFCqVKliha6wX3wIAVsQteVK1eQkZEhwkZ06NAB3bp1A20r0Q+lCJJVKDL+4MGDkZaWhsaNG5uFrscff7xCdzIzM5Gdne10N7c8vwVfrtiKNfU+wtVh1RB/3xMYHTwav575FZ2XdEbzOs2RE2PZGtipx0D8lrsFj06ejhnPPOF0P7R449WrV1GpUiWP+kKypfMPf/6AiBURCDkTghUDVmDD4d149vFh8IUvgu6+D1vXLrZVhdOfkz/htWvXNONb6fRAdHQjPf887Yt30dpNYs3e0DoEB7Z/hbk3lAa5Hvvnv+neBqwdgG+PfYu0gWkIaxomPl87YC2OfXsMA9MGomlYU4z9aixWHFiBOffNwUO3PCSuadetB/48nIs33l2GUQPvd3imaX1ToeeKFgrFpOTCCqipgFXooi9VAp0ff/xRtDlu3DjhwE4gk5CQgJ07d0p5INEvYmxsLO6880488MADoi+Uhoj8zShAq7WYYa460lMATNpSSn4kGUUtikSexaPxR0Xuse6Lu4s3vMxYy06japzoUXPC1azL23y66CRr1uYsvHbuNRHTiOZdmV/S1dVo3Lbmhn26bCmk/ueeuL1Y3l0iySdJCGfqb0ohIugZFxsSa4wrmBqRKuJ5UVYNiudlbgvS1XRAvL2o/hrmGrWlgM00QNOnTxe+U+vXrxcWDYIuehCFhYVh8+bNaNGihdtHpCTYNtfQK6+8ggcffNBiH1yFLuWBRHkolRITHCMeRo5A1z2jRmLKo8PQsllLtAls43bNZDTgbdBlPMkakSl8tygml+KzR3q7Go3b1pwxdNlSSP3PPRG6lGj0SnYNWtdUIgwRVgWkwyKmpxzNQVf5uh2dEYYuRxXj6/WmgFXomjBhApo3b46JEyeKfIsKdFFk+i5duuDzzz8HQc31KOZ8usz1w1Xo+m5NLqKnDkTeg3nCqkWOo1QWRS7CqLRRdlu64pqMRZPjN6BkVgkMCe4LoClzLrwNutbFrhPxjNJ7p2PSrEkiOrdyCox0n4zJYpsxd0Wu+IzWS5C/elvwDF0yV3dpW54IXWpZ39cdWIeBKweWeQZaOnRk78wxdNmrFF+nVwWsQldycjK2bduGVatWlYGuZcuWITExEfv27ZN+elERmqBrxYoVNv3KXIUu8RBZHwm0A9ZGrxVBMIuKi5BXlCcATEmTYWkBKA+h/q0G4/YjHVAypgSGuQxdevyFoe3FpPeTkN4qHRmTMvDLvr8x+P67UL1eY1Tr6oun00eJYSlWUVtrw1ENGLocVcz16z0RutpHDMD+rA0YOu4FLJ89zWmR6FlY3tqfvecAuoXc4rTVl6HL6engG3WigFXoIuf1vn37CmsXnVCjH/Kf2rJlC8aMGSP8urReXIUuxVx+U99w/Pzpv75b9hypJm0U6OrVciBuKQzE6aGn8cE7H2hdNrv6522WLhLFNDaRadLgw+0z8PQHY0X+xeSEZBT5F9kEcrtENrmIoctRxVy/3hOhyzQa/X+bdBTW27aRbdEuqp1VwWh7kU4vBscEixh15qBL/I74+Ih6LMUAs9YIQ5fra5Zr0LYCNk8v5ufnY/78+di1a5cwtRPEkA/VoEGDdHGKyhXooiCYr/ZIxqFTe1AyohXSl8wxzqaj0OV/d2sU3XfY5naktpdL2d55G3QpXzLBjYOROzoXfs3a4Myxw6Bcc6vOz8EftY6WEYgtXXpazeb76unQZS4FkKVZS09Ix46UHWXSBZm7VoE6R/LUKvUwdOn/d4ZHYF0Bm9CldwFdgS7FcTob2ei/7MkyORfpC5h8GijXHvnvWCvizY9ce2LB0KXjBbVq5Cqcb3AewZOCjVuLFOvoSvE5dJgcjh9qfFNmdBQ0d93QdaqNmC1dqklpd0WeCF2mlqhlt3wgTmdbSmJtKpTyPDRNjG1OSMVnTHHUt1tsAAxdjqjF1+pRAbPQdfnyZSxYsABZWVkiSF3v3r3x8MMP6zIekxrQRXkXX819B6HB1s3vlhaAAl3+Uf7o2rQr0p9J1+NaqdBnb7N0mR6tV47GK18sfec+hM9PrRAa0RZjsW+x6oDN0CX/18aToYvivpE1n34o76KtkpeZB/qhcBH0Y6m4kg6IocvWLPDnelfALHSRAz3FwAoPDwcFByRn+kmTJuGxxx7T3XhdgS46Ht2pZScUoQiFJYVOj52gKyggCLEFsShqW4RZB2Y5XZeWbvQm6DJN/0PxjJRTi6s3bcWgnmF4bOVYfJa5GqPnj0ZeUB5SY1MZurS0WJ3si6dBl9HR/R8LrZOy2LzNWb8uhi6b0vIFOlfALHTRycCYmBg8+eSTYniUfuell15Cbm6u7obrCnQpb2x1mrbCmd8POz32us1ao8HVKwxdTit4/W8k6CJH4vM+l/Ho0heRtysTpuuCtpofWvwQJr82WXSWTjBSuAgKpKtWYUuXWkraX4+nQZc9IR0oMGr+6XxQPEJnQ54469fF0GX/2uQr9alABeiiB/ttt92GlStXonPnzmJUlH+sa9euIgK9n19pbi69FFega6xhFuYlTURg53DxJetsoQdQrT8L8fD5h3Gt9zVMXzvd2ao0dZ83WbpIeLIS3BV6B64WnxN56154fS4M42PFnChO9gnJCahVXAtvJbwlthhLEktUmzOGLtWktLsiT4aup/+IRkBIgIgwb1rMJbKmz2lrkSLSUzYGa9uLdK2zfl0MXXYvTb5QpwpUgC4l8OmOHTtQv359MSxKB9SuXTt8/fXXInyEnoor0BXf4nmc+e3/8NdAP6St+TfRtaPjNz2i/dRDAxy9XbPXext0KVYCisuVmZFZxsdPgS4UANGtxqGgwd5SGLOSIsrRiWXoclQx16/3NOhSYGjI/RPQfpMfGgc3xujc0XZBl72O9FSZs35dDF2ur1muQdsKWISue++9t0ym9y+++AJ33303ateubRzRjBkzrOY91MLQXYEuxXG6JCXQaNFwZkxKMEIKLTDXMMGZKjR5jzdBF20tzn99BTb+tBJtR/QrEz5EWAGK8tDDEI2fs75Drw5jK3yuxgQydKmhomN1eDp0kdUqNrPUWquUejPqiQDQhZMKRa5ZpTgCXXSPM35dDF2OrU++Wn8KVICuCxcu4NVXX7VrJM8995xXQBf557iyTaS8XfaNHod5Tz4tzPOeULwJuox5F5GJgHF3mY3kbRosdXPyQrF1o2Zh6FJTTfvq8jToMn0BfDPhSZFLkdYpxR2kDBvkx0XpzaiUf+bZe3pRUdYZvy6GLvvWJV+lXwU4TpeVuWvi0wS+sb7iNJoa0GVAafofOv3mCcVboWvIsgllYrYpc0lbKtOGxWE0RosvsricOFWnmaFLVTntqszToMuSq4MS7Dm+azxSdqTAr7ofiiYX2dTI9L7k3mVdMJzx62Losik5X6BzBRi6LEygOFodegtlMTb71ufIvCsWEIYuR1TT1rUUy2jQ6ME42vgXfLdgL5oHNKzQQeU4vrvmmaFL/prwFuiKXReLxXsWC0sX/Z+Stdvjj2gpFRDNlDN+XQxd8tc4tyhXAYYuC3qLB8aUSFWiyCsPnymVnkfru1tV8KGQO+XqteYtli7adrnBpyluW9TaJoCTHwtZuoKDgxGVGqXqFiNDl3pr196aPA26FD+rjMQMkXORostTYFTlxCKlrkrKSjILXfTicTDtoHC+V/I0WoMu0thRvy6GLntXJl+nVwUYuizM3MwnU3H4gx04cOcBlMSU2PXWZ2kR2BMbR48LyBugS/kyeqDho/jk5PuoUlgDl5PPW5wuisl29tgRKEFT1ZxXhi411bSvLk+GrqykLAFdEYYII3RR6qq0g2mgbcby24Xk07W4+2Lhk6o439uCLkf9uhi67FuXfJV+FWDosjB3jwdPQrO9NZEZkakadNVt2hqnfz+k39VSrufeAF2Kz8oTqU/iUNBPyO6QiwuzLWcncGd4EIYu+b86ngRda77chsH33wUKeTK/42si5lb02mhhtTK1dJHKlFOWfkyLOegS1qwkH4sWYEf9uhi65K9xblGuAgxddkBXYHwgUqNSXZoZYWYPABamLES3dt1wW8fbXKpPCzd7E3TRCVYqsye9i1Ov/W5RfuVLZsDAeCx4cTJvL2phobrQB0+CrvIWdzq56OvvK34shYkwlY6up9Ap/kH+ZXI1WoMuR/26GLpcWKx8qy4UYOiyME305Xlw36coiihCfGQ8DBGlX7rOFoKudve2w9Cvh8K/qz/is+OdrUoz93kDdNH2ycCUgaBI81TWTv8Suc9ttTgHdGgifc5cJCABVepWQd+DfRESYDuZsD2TypYue1RS9xpPgi7D7FQkxY9Cwzad8MfB3WWEsgZOthS1da8jfl0MXbbU5s/1rgBDl4UZFNtEN+YAEQA5l6oBXQF3BmD0t6OFxSvxuP7DRngDdOUW5KLTgk4IygtCQEEA6vcNwmdjl1v8vVfe7JUTjFvmbcFXT32lynOCoUsVGR2qxJOgS7HC9hpRMXivLXCyJlrI/BDsObEHOXE5Zl8wgm6PQP7uLCSmLLIZZJqhy6HlyRfrUAGGLmvQdSgH85almY3J5OhcCwfr1keQkJsA/0B/JOYydDmq4fW6XvlCovZtAbjiNzOsZgyqNfLB3iF7sev1Xap0naFLFRkdqsRToavb0hvKxJKjCPT0glHej8tULNpepBOPfoF+ZbYXyR+M7qUQE+asuqZBg3/MXG9Vf4Yuh5YnX6xDBRi6LEzalMov4Pi1Y7hj2UBVoEtYzmrmAFEQsXBc9RHTwlrzdEvX0ieW4kz+GUy9IxGFVU8JyWf1moWE0NKtRktF2U75JxauS4F1Tdtg6JK/6j0JukwtTj7x+UJMRwI1W3KktzUr/zrwB6D4r+MMXbYE4889WgGGLgvTq+RddOShZG2lCOi6lKNK3C+trEhPhq7iomK83ORlVC2uiiL/IqwYugIX/6yFX5buLJOPztxc+NYPwMXCE2Do0spKdb4fngRdvvWb4GJhgbDenxyWIw26qKEqvrVx9eLf2J77U5lE8eVnhi1dzq9VvlMfCjB0SYKubgNGInvfUvgO9UUv9MKEHhMQfl+4PlaJhV56InQpJ7q6DIzGsVu+Qe+M3mj2fy3w5qDX0cn3v9j9zmc250wJG9Hmkba4dO0ict7JsQlqNisFwJYue1RS9xpPgS7Fn4vCRRT/VSBE+uyLz1D71toiEKo9xdLpRXvuVaxsYxJnYq5hgsVbGLrsUZOv0bMCDF0WZq+GTw0EIABHS46qMr/KQy8oNgixqbEoaluE5IeSkRGTYdWPQpXG3VSJJ0LXoj5LkPvNPiw5/x6KDKW555p9+B/8fuQ7uxyB6XoC7NwNqzD5nxxS4UfDVZljhi43LWQr1XoCdP1WcBItW96Eq8XnyviouuI87+hM2OvXxdDlqLJ8vd4UYOgyM2PuiCCvQFfA6ACMnj8a5+qfw5vj37R44kcPC8kToeuFaomoerkSkusmo2hiESpdrgKft3zFF5atrRFlzpS5Vk4wMnTpYTWb76MnQJcCPKahIpRI8sGNg4UDvL+vv1snyV6/LoYut04DV64BBRi6JEHXWMMszEuaiDpjA/D0F6OR2TwT2aHZWBG7AvTgC/IPsrgcKPcfXePuB6Oj69HToIu2T964+U2cv/Y39k48ha/rLoXPr5VR8sFVVK5eC1eKz9klkQLtg9oOQd2LtfBw2sPo0bGHXfdau8ibLF30JX1jowZW/X9cFtSOCjwBusqn4qEAp2mj0sTz50LcBYdSnFHqICqUPsjRYo9fF0OXo6ry9XpTgKHLzIy98cAC/LU6D9sCfsI3x9epMqem1rNNWR+I2E9KsbbFqKTn0OI2pKdBF80HnTz0hz8W7FqE6A0DgTwAqTAbUNLSwlDe6quE+SHssRAR483aUXx7F5i3QFf2ngO4K/QOYV205QNkr3bOXucJ0KU40Cv5QDMNmSB4cibFmSsHjOzx62Locnal8n16UYChy8xMKXkXj7QpxNKDyarMpSl0fbxxJrov7m4XdCWkJyBlR4rN+FCqdNLBSjwNuujLvlvILcKq9UL6M0jKSgKyAaQD5gJKWpNLCRtRUlLioKqWL/cW6GofMQD7szYIIUL7j8D29UtU09DRijwBusqvRVPoCp4UXCGxtTWNXIEue/y6GLocXaF8vd4UYOgyM2Ojb5mMJgdqOPUmaMv6QUmvkz55CjMWz0Dv9N4oCChAr1m9LMZ+UhIu2wrKeT0WnqdB1+zXViL+uaHCqvXUggGl0JUJ8WNPNG3TOaCwETUKL2L2m0tx9+23ISjC8vaxvXPnDdClvJwomphLWWOvXmpcp3foUvSs07QVzvx+2CgJPVdS0lJEijPy78o/nY+10WttpqwiYKMSYSibDJv+jQKkUnDUgtwCHEw7iOCYYJGnUSn2+HUxdKmxarkOLSvA0GVmdsgRese3H6J4SDG6tu3qkM+DPdaPxIxE8YWuJFEuySixmGYoNTcVo9JGaTKgqidBF31RLOi0AAUoQG7/vxE9pTNW7l2F7He+BXJhtxO9Mv/kR9P0UAmiECW+fKJSo1x+DngDdClWLrJwZW9YKjSz11pIX+p//PkX+nYPRfOAhi7rTRXoHboUX9Jbw/vDNBq86cscQRf5jbriwmB6EjI1IhX5WfmIXhuNdlHtysyDLb8uhi5Vli1XomEFGLrMTI7wPaibpVreRaUJxcwfvihcPOTsgS7llBHF0qFTRloqngRd2cnZ2DhhI3KRi6aJ91iNJWTPHFDYiIINWxCLWASGByI2M9ae26xe4w3Q5desDc4cOwzyPxoWPVgEmbXn1KhpWAQ1/cD0Dl2W8i3mFeVh3YF1wjJFL3X0d0u5E+1ZuAp0ZZRkiFRBdCiFnO3LW8Rs+XUxdNmjNl+jZwUYuszMntrJrstDV/eU/vjl2l6EFIRgx+kdiI6MNutXobyN0v30cCTz/+I9ixHoF4jYENe/xF1duJ4EXXSia+ljy7D76vcYsmyCy6mf6Mtuz9JPMKzxoxgyuR9CE0Jdldvjg6MSOLVo0sho3XIkUbJi0aGbaQv/9O+HXNbbEyxdyslF0xyyiiUqLidO5F90JF6Xcnqxa3xX+Pr7GjVWDvy8+fGbOLf/nLDuhsSGVNhWV+YpsHM48nZVfIlk6FJl2XIlGlaAocvM5JCzaFHNIiT/L9muXHv2zq/iSFrZtzaOHv0FPxf/KBzqLVmxolZEIe1gmrF6yvs3YWNpNOfCSYXXPYSEJ0EXaaqm87s7Yr15uqWrvGbCSrNhLnr1H4v0JXOs/popFjLlIrKOTUx8FUFBgZgxeZzT2416t3QpJxdNrYUpLVOEJSr+aLzwuXIEuhRgi8mIKQNUBF07Du7A5NcmiymwlD7NFKzNWTAZuuz9NuHr9KoAQ5cF6KJ/pu0/V/wczC0K5c2zUZvOWLnxLavQpbw9KvVQomyydFEhq1dUO9f9hFxZuJ4EXf86+f6bJsUVbZT61LS6eDp0KVthyolFAWGbIlG5YTWMu+8pRLaNNBt6w/TUabP2XZC/Owv0YkMhJ6jQn3sMibUJbubmW+/QVf5FgnwXV0StEENNyEsQTvTKSeqSRNsnba1BF7lMbLhjA1pdalXBl8tUW8WCOXTcC1g+e1oZ2Rm6XHnq8L16UIChq9ws0RsgvQkWVyvGa1NeUx26TN/0PnnpE2xdthUt+7XE+DfGV1gvCnTRdiIFT6WHGhVXfC/UXJSeBF2GSQuR8vokVG3TEn8c3K2KTPSFR6mk0lPThV+X6UkuZxrwdOgqH8RTwFTCLcK3UikEBvQ7dE+/B9G33wDheyfymm5YCnIWn/b8JAy+/65S2KpeC/UD2+DkoRyntxz1DF2WrK2UzJ1+aD2aRqbPHZ1rc1nSNjw9I2nr0HQ9K6FtyBqfEJqAHSk7RBvmgqgaZqciKX6U2Tlh6LI5BXyBzhVg6DIzgSJA5pP+KGpchKPxR61Gi3dm/us2a42zx47g9cHzcX51gVmHU6q3ZUpL4eCq9MGRbQBn+uXoPZ4EXUr8oe+jT+HTFbMdlcLs9RQ2YuD57mh3sR36fdwPtw+53aV6PR26qtSoUyHdkjjtVu9v+D3VAKevnUJCs1fwzrNzcLGwAGQtPnFwF5T7lOCfpPuV8+fw8YaNGNQzzKVtYz1Dl6WTi6aLsKi4SIR6oGwX5DfqbCH/0xkbZ2BSr0niJHZyUDJO5582bmGWr1c5xajMmfI5Q5ezM8D36UUBhq5yM6W8HVZ6pgqu1b4Ce0zujk62cix+7H9exA3fVRLQRXGcKLaNX6Cf0em6xnM1RCwvQ4IBheGFVrciHe2DGtd7InSVpATCMF6dQwpkuWlf0w8RuREIjA9EbLJr9XoydClbhNXrld3eVfwgffpVRUmXy8a4acr6JesWBVI1jUNF9wzp31sAFxXlJaf8F7w9vwN6hi7Fn4tOcz4TMVBEoaeYgOQ8r3YxvGeAz+M+KIkpgSHVAEvbkEq7inWyfNBhhi61Z4br05oCDF0WoMudQRmVL5LgsCise3mWeAjmZeZh5cCVojd0qohM9xNumYCAggC0jWyLgskFwok+vmu8QxGk3bng9AxdFP+MAkLSIQZK0fNo1TjUvVITd296wPhl7ap2BNfVrv6Gdn+2Q3RiNKKGuuaD58nQZe1Um3hJObEBGAqRlqnX1bFiajYunWucInP+QcqHtsIUWJtnvUAXbdmdKizEw1F9RL5KRU8FYkUMulzLVnV71zptL5IFq3zg07cGvIVzG86henR1TF4xGco2JMXpMgd5lqxwDF32zgRfp1cFGLrKzZySAuhA+z+x/AfrJ6acnXRL57pUegAAIABJREFUvhbpCek4sO4Ahq4bCp8mPjCEGFDZpzLe3P8min2LxTYAFTXy+Dnbd9P79Axd5X1QTB2OaUuXgEzxpXNWK+EYXnkuEARVfAM9GbosWT5Ie/LhuvXOUJyL+UVMBZ3cPVt02Rhegv7t1+N/mD2hSEE6x7zzFj5bOcfhVE5Urx6gyzRcBvWZDm9cPH9WbMGSleuxoO4iwTVZ0cl53pWipBAqH4NLsWzRicg83zycvngawY2DLZ6wVp6B5Q+aMHS5Mjt8rx4UYOiyAF2/dzyPd/fMcNscWgpPQM6nFP+GHFz7LOiDsOZh+Oqpryz2g3wpyMF+UeQi1X3PbA1ez9Blmkh8//YzGDMs0rhFpYTqIE1diYcmHIbXjlItyK4nQ5cS8sE0npTp+iPwGpE+RKx15eSusk1fPto63Ue/R+JATFEx6k68CRNnjnQoabnSttahSznxSf0l6zwdGlCK6VYtOb+TFmR1opc3sprTaWhH17cl6KI2qQ0EAPVm1BNdsPX7Y+4ZyNBl66nLn+tdAYaucjP4qP84tDh9A0qGNYDho9JtDHcUcvalaNtvBKfgav4FcRqocXBj8X/TQg8y5SRQ5KLICl0xhQfZFjA9Q5dySIFOgi56bTPWp8xFzfAOIlWKWpqKt/mFkQhoF4C44jiMjh/tkj+NJ0OX8gV8NOOoWO/V/apXSJ1ELxjJ2clie90WLJDVmOoh607zR9vigRf/K0JHXLlw1qFfZ61Dl3KIwDQKP8H+xq++Rq8e95r1T3QltRi5QdAP+aCayydKUe4HrhwI/yJ/ROVFIS48zmJgYMXXzhS0GbocWp58sQ4VYOgqN2nKsfW5763BmEcHum1KFT8TAwzGNs7deg59P+tbYftQOVmnBDM07ZSyTXY9EmLrGbpMT4I+2PExtN/XHNvv+g5D5wxByo4UYQ1QI0abT3cf9C7ujdDsUFAU797JvZ1eU54KXaZJmQ99ukXkwCRr76TCSU5rRafyUp5MAR4AEvsmGk8wWtqGtNSQlqHLND7ZlX9iktkjmGneRTpp6GohXzGaL/JDVZ5HjUoa4amkp0TVNI+m0euV9hRLpak/HkOXq7PB92tdAYaucjOkvDk6+nB2dKKVbYGn605BnTPVxO2ZEZloPrQjTn5bgoUzphp9VOaHzMeJPSfEg4t+KJEsPeDoz/Tmr2wVpEalOtoNl67XK3SRzxZZushni0rLtJaIyIwQ+s9aPgudFnQS/67GydXKHWugxe0BiE2NFVYuOiThbPFU6KLfhYtLf0BY1XvwwMKBoC0sctZWtCI/x/I5/GxpqPxe0HXvf/c+9n95EO9fXohXli12KMWTlqHLVkodRSPlpU2JEq82dJmeVOy+p7sxj2Pu0FyR+JpOTJpLg6UcKFKC4VJ/GbpsrWz+XO8K6A666IunUqVKqFq1ql3at27dGocPH7brWrpIzVQw1hpV3u5vCuqMQU/di52f70TlTpWx77NC/PH/7V0JXM/nH38jKrJyljvX5M41uctV7tz5M2I2GVFGsQ01zDUU2zBXaGZzhZmMjYxNcxRzRDa5JmeRI/f/9Xny/Pbr/l19+/Xr87xevaTf832O9/P8nu/7+ZwXUoJz0k3w9P7twriebpPk8k1qFzKGJcNYstGwnWGLKQumwLqXteIJsfMq6VJPIk44Wy63FF6i13vF4+eg3VqlRcluY5Vo+zYSO8TCPcwdvhN90XZw2+weyfRzUyVdJPVtcaIcHOCQ6kJx6OwhRHlE4VXSKxHvSV46MgOIvg9rXdaKl7x8+VPdOTvnIPl4MkIQglrvdtcqMr0xk66MJEVpsSFM5pZIsU2VpCutE4k2G5JUi0SkKNivVC9Kz8geB3ugya9NYG1ujcTJiUINSedWZnlHM3IoYtKlzWpw3byIQJ4hXTExMZg5cyZOnz4tcHZ1dRVpeiwtLbPEXRvSRalb/uq8D8kFnmL2q89zdD3JMLiqfVW8bPFIFXH7XbtxWO+1WETSfvn0kehf2juQCzbZqcgDTBKwItZFkHQlCWHuYYja9p8RbY4O/k3jeZV0qUsHzZ5aYVXMV2JGZKBNkf9J0kVSsDifOL1hFC/GAjvRvGkPHJm/Q6/2TJV0UTypQQlusIc9ZE4/QRYqzQUok08rYPqh6WL/0wtfSnoprh2FU5ElzDMMJ9eehFVtK0wcOFH1d7+f/FD0z6LYiI24PjYRSaXjoak63phJV3bOBwQAER8iokSSPA+kxInTx2Yxo/ZI0kW2p85HnEFOKBRkNcwjLNVep+8cpTCjEC1kkydL2ksuky69jgh+OA8gkGdI16lTp3D16lV07doVjx8/xnvvvYcBAwagT58+BiNddPO6/b8U4pJZwlZDrqlQMV79UkW6ymxuiNunTwrX9vuJ90VqE3XRO72IqNDtUd7o6fZPUq84+zhMj5quqAdjXiVd6uqV6GPXsP3RKhR6VAQv5j1VpUXJLAm5tusv1chpg0Bq2w7VN0XSpW6XdCfmWippVsDkANxcexO2K2zhaeUp9jwVUjtKAlYqsBQWWC8QnnhkP0el78m+2Pd4n1hLShjfsnBLBNRYgM4DWwC+BQHrVxrHuzNW0qWeTuz16+xzJqrvN0OTLk32cgvvFoh/GI9Eh0QR8kMW6VAkA9cy6dIETa6TlxHIM6QrLciLFy9GUlISPvnkE4ORLl/P+bBZ+xiXq1zBmuGrxeFA6TFyqtALZ3S7QXB+6ow7lR8i9MJKIeW6FHcJO/f/IcIYZJYw+UjQESH1IiI2tcFUHKt+DNPXTFc0hldeJV1k00VeVuTt6d7NBwVOXEarQQMQumGu+Dt5X1Fy5bS3dV32gcwzR+7867vOFpIHXe26TIV0kS1P135NMHT/G0eVAKBK43aIO34gFcTq5Jg84ehyIeNDkeRXxJ7qYA3fNr5CghLuHi7wpYCcshDBoHUmg3Gr2vZ45HFZfKQpqTZW0qW+r7TNFUohHcjRQBdHEZJoEfZkU5rW0zqz78fq2atx9eOrSLRJRJBPUKrcsWkD1zLp0uWU4WfyEgJ5knTRza53794YM2YMOnXqZDDSpYp588ahxxCG1Nlthm4Ow/H2eRskIlHYnKhLRGR+sj+iz4ko05mV/t95YPOF77ONi5PdWLT9PK+SLjlPKWWRHqQk3STpCKlC3B3csw1LoAleKkmOhRW+sJ2hMhDXJRWLqZAuUic+r5eAVx2epkC4DNiy7lC6TAAyXpqMy0UvfJlkmS4b5GDiMMQBXQp3Ec1k931tPLoboux+QtHEUni06I4my2e0wVH1kaAaMocrrQM5+VC4m4w8FCXI8yvNx+Nrj4WzChFn6TUp5yEl+ky6NNqWXCkPI5AnSdfy5cvx22+/ISQkBGZmZir4J0yYkG4pdu7cicjISI2WqJX7UMT8tgsyisNd77saPadPpR8P/okRHv1QvnZjdHbrivGefVGhTCnRpGMHd1yN/g3vTZ6NeZM+yLCbuSu/x4KFE/HS8jGsClTDh14T4T9yIA5fP4yeW3ui8luVETUsZ2y9Xr58KZwapF2GPjgo+ezqrT+jesHS2DtrH879EwlnOKN0vdIYFDEoR4ZRtnxlYaP3ldsK3A6/jjaz2sDRS/vkwnTZePXqFQoVKpQj41Si0WNnLsK1bfMUlTr90D8nesN5R0PEOMTAerY1Pm+bYk9J+5f28Y4+O9CqQkoexYyKY4gjkq4mIcwrDPXL1E9X5fZft/GL9y+IS7iC+SPnolBkMdwKvaLRdJ8/f66x045GDRqoUvkadUScP7+5X4vve2Zl15Bd+Gf3P+j4ZUfUHlRbVCNMqWSFqabDvH74Orb23IoKrSqgz47MTT2o3i8bfsFcm7mo3qS6WFMq9F2c9P4glK7piPNHfgGdKXSe0LliDKVkyZLGMAwegwkhkOdI165duzBnzhyEhYWhVKkUciLLX3/9lW5pyObr7NmzGi1ZhbrNcft5FOAJtK3cFvuGZB4JXqMG9ahU5POUMBJEAEn1Entkb6rWrt68i/a9PHD5RIT4uyMchSFyGMIweuoXKO1wH0uOLhEi/VsTbuWImpTUu+bm5ihS5M1Y9Zivko9avlUSnZJbwwlOiEMcajjURaNOddBpQdZSU13HWNOpk1gn7/fnYdyA3sKoWZfy7NkzYddVvHhxXR43imeGTAjED1/OQvHyNZBU+SLgBnz4yBtl55cSpOtfv39V37uOoR1x8MpB7B28V6gDMysfLv4QFSdWhGU7S/ju9c2w2udFUogcOd8gBPhh6QG4d2iZLSYJCQmwsbExqovFZ1+tw0zfkTC3sUXSratZziG0YyiuHLyCwXsH67zv1DugUB6n1p0SQWcbDG0gfv9x5I+o/2599FjVI8uxkEqz7MKyos6zj5+Jf+kcq16pnAhc++TBPTx69EhcpOlcMYaiqZe8MYyVx5A3EMhTpCsiIgL+/v5Yu3YtatX6z2spK6i18V7sZNkDrZObChH462GvFQ/BIOdBgTnJg66mtQNifWPEgfTDjnCV+oXsOWb6e+Nl8kNhA9b/A1+0O1hBiPnJQ6toYRt0MeuIu9Z3sMxrmU62G5ps37yoXiQP1b6dW8MHPrCBDdaX3YWLN//UZLo615HxiDJKV6NNo3lZvUgYfLd4BtRteGzrFca0M97wjPKE/Xb7dN87TQ2+A9sEAofIgh6Yvnl6hpDKWFXhPf7Gkb3rUad5D5F9ILtijDZdtrWaiLAyWSX5lvOSMf7IjlAXlXZG+KjH/SL7LvohO7rMQkOot0Gqe/IQJvW9LOpmFHWqlheSxey80rNbN/6cETBWBPIM6Tp27BhGjhyJVatWoW7duio8LSwsssRWG9LVpUAXIf0IdwuH7TBbgxhSa7PwRLYoUWzCkwRhzE23+xOfXkfS9YuCeL3j2gc3rl1RGRyTcfaOHzYIey+ZE+166URUuJNi/B/tGI0ei3ugT7M+OSLpyiuka0mzpShY2QJ9p3aB7+qvsHHJTLRsMhBD6nSD7bvV0tkSabNmmtSVRM+8hB2S793Q5JEM6+RV0lXPpZcgONOD1yBw/HAxNxl8WNoXkdNKucByIrG7tM2iiwd9JyhVE4UhyKx8vehrrDi4AtV6VMOWEVsyrEYev0Q6pI0dVcrOVpLqGBvpkntJOtxUsisj5ivnp/Pm0uLBtMFWtXg0xdP6QJyIuyaLzAJC+2OCZx8mXdoAynXzHAJ5hnS9//77OHAgtXcToX3mzJks1Vuaki46zHZ2XilUdCGeIRg2bJjK2FOpVZVBCylG1OX7l4UbPEWZl0EQ5TikdIskB7LQoUuHmWV9a4x6dxyeNr+Dk3X/wuOJD4WHozRANuRc8gLp2rAsHLGjU2z6XpoD6+ruF6o+9Vx1hsQks7ak3ds3AasRv/wamg1opnVKoLxIukjCFb1kM+xghxjEIB7xqsTihBU5LpDkg37SGniT9yKRruy8SNWD3R7wTH9GpF2TrBJlp61rbKRLjl0anpNzgXoojayM2Q21z+mCR0XbLAH0jJS8Uaw16WWq7hRAqmeWdBlqpbgdY0Qgz5AuXcHTlHRJF2zrQVVwv9ZljYMn6jqujJ6TLvLyM/UAjjS+I8eOi48CPhqTpTdj+Jn96LKqAxyPNoT7IXc0HNYwXfJgQ4zb2EkXxTJyr+eBnnf/iwK/osg63H32L/ZHR2WJoSHwUW9D3uZrDq+FwWsGoWT9kvA+5a1VN3mFdBHuTdul5JgkNZh7SXc43nNEnE0cQhJTe+iqA2Azx0ZIei+Nv6RVvDmyFaIwCBTeRT0GVGbgkrSrdfOmqgDEJEUuVfltdOjSDRuCPkv1mDGRLvXYXCSls4hKFt6wpN4j8iXDacgJ0GUrp0kYXfae3n+qsb0YhbrZ47snVR5S9fAXF48eYNKl1anAlfMaAky63qyYvG1VH/8OrByewsfJxyAhA7TZEGlJ1yLXRWIcuhSS3I3p3A9e8IJVpeL46Ep6z05d2lV/xthJV9WmLkg8Hg0bTxuRW9EizkKE5rBHVfRa1FMjGxR9MZLPy/1V1KcU/IK8Uah4IXz64FOtms8rpEsVeuXN7Own14ZbSDthKB/z8B5mukxFjWIpajEKNfB97+/F7/vX7EfE5QidbBCJeBHpGr49RX1JFxaSnslCxIRe9pS6pteaXnAfNwl71n+ZDn+yOTv44w+qvKfGRLokrjKm2Ub3jaCo/HSpolRhlExdPXZWRtHjtdpwGlRWz7so0wJl9xiNS72ueliVezevM+nKDkD+PE8jwKRLvhiaOOPmiUi8N/1zfBmQsQdUTq+0DMwp+9EleKH6GEm68t4FVxSqUgwjo4eIgIhZ2cZoO7/cJF2kij1586R4uVLwy7RFvqAKVrbEqxFPYPHwLeCLZ8KA3gIpScPVg2hqO3dt68sXS4GORWBboyRGDRyltfo6L5Aukd6qajXh5CFVuFJt2NC2IUJbhmJLw//srsjAm3L3UUnYnSDUiZQmRtd9mlkMKklAqB9KHeQR5qFaQhrzqh92YabfWCH9Ug9IbEyky8yyuMBVpgajnIokzcosLyVF7Y8MjhQOBnHvxoko/Lriqr7fKQUZFZKsqY9BHxMGaUxfu213HA//gQ3ptT1guH6eQYBJ15ulqlSuEUbGu8OsiiU+ifPLlQWUtimy8+wMiLMb5NiARfgqcIJ4iWw/9A1c1roYVG2am6RLvlzpRaKey40wkYl06XeflbMQdO0TVDGrhaef3ocHPFCkgjXmXnsTATc7EA34+VsVayKpzEXAHTpFvM8LpEuSXXLyoEjpxzcdR8ffO4rQJdJAXtoEkeqLJB5Eukji5RXtpTfaWQX+JBUcERGbiTbo8kuXlDyACBLEhTzviBg7uziL+FcySHHUmfNwrPN2roeMkN/l4hVq4MG1WDFmIpL0QxIu9SLVipJ0hY8Nx5HSR7J1SNAUfHWPSOqDiszrqGkbtBaUR5NCT9AeIMn8gB6ugvTq6+Wr6Ri4HiOQGwgw6XqDetUCVeEJz1SJYZVekLSkK7sI2+rjC4kOQXBksDC+V1dJyhukJB+apj/RZO65RboojU/V4KpiiHRzJ3JKhf4+a88CrF6wAq+OPkXP3uPhdNYWB0schK2/LUI9Not63323C/17Zx73SZO561JHvDiXTwC8IFRfUb2itHJwMDbSRRKiLoNG4va/1zA9YDpu303AjEljVdKYEV06C0kIld+++g37PszZuHfy+0MStWiv6EyXSNbz2ucFu0N2cF3kqlI1qxN2uqw8uB4rLi3d+nmks/fSZQ/o+oxMbp2VAwiRLVLV0r+UOJxI7ZXzV1BlY0pcOG3Ok6zGqYtKMW170rZL3d5UnXhJaZ6uePFzjICxIsCkCxC3rBmdx8Id7qkMPJVeNEkmNDUIVh+fep46mWKDPifJQ8T6lehcpR9OuRxKl3BWnznmFumSBEsSL2l47bvqcyHVolJlRzscWBAiPLsoGXiVNVW0Vufpg01GzwrVm31VvJzyKCVIp5aJ1Y2JdNF3Zvjw4YKUpC1SykV2RkQCyJbLfLa58MTNyaKpFyN9z1pMaQGvZSmSNbLxkrZQtF+2PTqC7UeXpRsqeQ6e3r89J6eQYduSCJLX8ovkFG9kKmmN5OnvQfZBwrCdPiO1Y3h8uCr8jCaenZpMThruE2a6qhTJAJ8knK9sXyEwPlDVbafBH2LfhqUo+3Zj3Dyf4jjEhREwJQSYdJE34OIQET9Ivixyc4F1zYtGQQd99/gKuw11dRu96D0rDkHbly0R6xCLbz2+1do7LDM8cpN00Zhkbr41vdbAf+ASPLhwFh7lPRDyQQi+77ENb5+yR9jYMEQ0i0Cv2b10dkow5H4QLv9Vd8Ir3At28XZCIqGpAbKxkC4hkejpJiRapO5q2d4NB37cJGDqPeR99LhZF8WKWgqVIRmvU9w7V3/XHCe98jsgQ61ktW4uLi5wOuKE8X+MVwUNlUm0i1ibI9Txd7g4u2Dah4MR+HUols+ZJlRfSocaoTnIgLJS5UlEltSKZJdIe4dsNelHBB41s4fVZ1ao0b6GCOmQ2WXMEHta5l00tzbXKfCqdy1vHKt2DMnuycJ5gi6b52IvoX79+gJritsVMM7TEEPlNhgBo0GASdcbaRB5Mqknm86tFaIo3HT4ZBebKO34srrlDxk1FZW+eSEMyIN8grBt/LYMjc+1nXNuky75knVAM8QEHEVjs+bo+aJLqpe8d2Nv7Ku1D0tnLzXInLXFKG19IbX4oRfc4t3QMq4l+i/tr7FBvzGQLnXCRV50v+3aJDz9yF6Q9m3PCj1xpU1KXkP/BH8RtDPGLAYO9Ry0CgOhC87aEAy63BDpnew5WVxUqBBx+araV3hx44XK0UIa0nsHBgn7SCpbfk6fnFuX8WryjHowVwooe33j34LIEtHxifNBTHKMyF5BanaS4NEc1ENuyKj+Mmm4Jn1qWkdf70hymqCLE8UklOOjhNd+85Zj+Sw/6BtMWNN5cD1GQEkEmHQBIvjovYij6DvdL9c8F/Vd9OxUK53K9sedUhcR7RFtMGP63CZdInBmTBhmj/oSzy7cxeSGs2Bx8rkgXeYDzUUaJ01TyeiLvzbPF25tDbMCzzCw93iETJij8aPGQLqk1IWMnScF9RHSVfIepXWg4hDjAI+NHrBqZoWP/vxI47lldImg8BEktVIP/ZBVg55hnlh7ci00CbUi9wW1R5JST8cUiUr9UfVxp+AdnFt0TpBIde/FFj2H4sjO9am8G3WeoIYPyj6lcTkRHQoV4R7iriLrMr4ZNZnWnk094j/NxxCFJIIUH+xJwhPhHdl8fHOtA/3KcaSVThLpouCoJSpUFQ4NuSFZNARG3AYjkBkCTLoA2JSwh2/im/g+rzPO3WbsW0gGiKRxZhRcUqQP8WstPOc627thz7Ddek8pN0kXzefWnXvYHr4X4euWwLyELcKGhuDstrOY0WeGyluOcCFyllFYCb0B0LEBmYtRxlvStJncJl3qwTlJ6jL1yCRBcqhYm1sL9S2RL8oBapFsgUX+i1Ll2NN0nlRPG6mVbFcbgi0JWq9avVJJldVV1kTEiHQVL1xczKmQQzHUbdpSpOWSEeG1mZO2ddXDb6hL19IGPZVzofbVzQs0dSzQdlzSK1ImbicVpy7R6anfrTO2Yv3W9Yj2jBbnliRdk+YuE5JFlnZpuzpc39gRyPeki+y51o4PFJ6Ltq1sUfvb2uK2aKhboZIbQN5q1W/u6v2XdKqNhC4xKH+vMq4HXxaqCG0ieaedS26RLnoZ1WvaKpURt/qN2HGZo4jhlRMqFUOsp7rK6NCaSLy4/ETEPMqu5DbpUieL0VvCELkqEvMfzcexsseEVFHGgCLpBX1/pPQou3ll9LkupIvItYxFp8n3l9RxVE+9LnkBU4BVImMhTiF4VvQZTnx+Qkh0KKzEg7YWGNAnJS5cTnvYSbzJ1vTU9z9lajdF8yYVIxX1MDPSMUcTGzdt1kjmeU0bAV+bNmTdOTZzhOE/mT1ETY9CyYIlVcFRRYiV6xeNwuxDl7nxM4xARgjke9JF6pICJy5jcHFPFG4JBLQIMJj6TektRy8qKvSyy0glQwTz8qII2MfZC8+msMQwVQRvXQKx5hbpkgmUSbpVpGhxPHuchOR78Sq4dXlhK71WtO8oGO+kIv4o9KwA7ofcx1DXoVkGr8xt0iXDFvgMmwebtY8FZBXaVEDTH5qKcZP9FhVKLK1rkUnf10St0VhVqGtfGT1HpK3R6EZCRVrWtiz67e2HDV9vQHJQiscgEY05/2wR0exJChN79pQqer0hx0FtWZQsh6cJ8ZjutxwF5t2ARVMLuO50zXCPkFSLSlqJLn0X6G+GlPTK+GAk4dLUCSQzbGRU/Y0eG+Hv748+1f5LeK3y2rSwwqVL/+QYzoZeN26PEcgKgXxFumRcoc8+9kOfTq1EMMQWjrUFPqQu8T7wPraf3260EhJDbOV+5v9D/We1kNywMGqFlAdFdqd8d8ZOumjt5i0Pxf4DB3DmwA6Q+/wPO/egfc26CK4aLA5/kkRQtHG64ZPEQj10hiGwM2Qb0mO2r+Ug1H9SC0ecjiDGI0ZIakhakRFpzk3SpUrV8iZsAYUmIMwpNAEF5ySVF4UAkDGidCVe6rZWhLcu+1LfdSIVYznfcsLQvnzL8jg86TCORx3H1H+mYsD6AaJ5KYXJqTAScn/QxSK4SqAgtFGtorC903aDeR/rg5MMbkoeqrquNfVPBG7Hrh2Y/mI62jVvh3Vd1wlJV4GnBUQ+yQYDu+L2hSiWdumzWPysUSGQL0jX+s07UcG2VCqVVIteQ/H3udMiIa80UtXGJsSoVlGLwYz3WYCiwXfwp9kJhFwNxaqYrxAYESike5MdJ+Pk2pMiSnRGKXKkFIICrFJRUtKlyudnAdgk2+A99ymYuWacOLRl7j7y6JqcOFkLNHK3KgWurfS0DN5p44Sfm4QLO7RGDxvh909+zzBRcW6SLom//K6ktSuSKid6CVN0cl0TLavbJ+UW6SLCPvr70ZiwcgL6r+gPn0SfdDkhhY1k59ZiA+UE8ZIOCx7en2JUyQ6IWBaBOcPnoHmt5kKVm9tFhtdIm1JJl3FJNSidKzv67EBceBx2j9otLlD/uhbEmP/1AiUlP3TkqKJJ6nWZCz/DCGSHQL4gXa+uFMarZ0+QjGTcNk8SMWBkoTAKs7yWYsJST6P0dMtuAXX5nHIy0u2RjIFdJ1RTkS63aDfhjq4eJVq9fWkzJqUPOUG6SNLT+/veQoVCXmhUSMrS2qkZXuIhzPzM0fTPRnALd1ONU6ZEoX/VE/7qgo2Sz0jPtJIjq+FexX/gGO0I9zB3UD7CjKQHuUm6SNVVIgEY5ReIgLkfpIOJJF9EtPQhXNSoVA2TTRUZ5htSLabp2kobKZI2Rg6KhO1SW/GojOguLyZfHNiGZXOmivPEkIb1tN+HO/ZGHOJw4cYVFE8ujDZr2uB0wdMKCsqhAAAgAElEQVS5IvlLixtddCj/Iv1rCLsucYF7k7CcDOlX1V+FB1ceiMCrZAYhYttF7OT0QJpuYK5n1AjkC9I15OIQsQgPCj1E7wNDcerqJRERvINrNwwr0w6R0w6jWo9qGNpkaKrD1ahXTo/Bqd/SJ/ecgaA6s8QNeuimobj601VU+rwSdlbbKaRf0jhaPUUR/Z1UdzlBuqQhM01PGgXLQ9d6UBXcr3UZE1dPhNUVK8WTVusBeYaPSpVdwbct0bZ8czgfSDHQNjbSJfdL37cGov6D2qnS5siJGcKei9oiwkOG7ZqGiTD0msj25AVjW69t6L29N6pYV0GcTxykhIdIMa2T+nfpj+hzBpHE9Orjg8bbSuC52UuYbSuI4DPBgpTIMeTUnDVtV2JAkmUi2fqoF6lPSeAo2Ouzgs9w448buLT7klBfk8Rd3Qwkp50XNMWA6zECuiKQL0iXfzl/3Iy6hfKtK6LFqHfEl5zcnckWheyByHbg9YzXCHwZqFMiYl3Bz83nSF30dP1pOMMZDys/RN3tdTF37lwRZ6mqT1VsO7ANw1yHIcAjxThfSiDIs5MIl7uDuyBdmy9uxqzDs0SsI0NIJaTLPvVJbToUcEqxu7MBrCYVx8PnSVj/cj0e7niot1QlN/GXfUup47Cyo1D1VjlVShqSIpBEhSQJMtXK0K1DcSnhEpZ0W5Klwb2h5yUlclPMP4b50yJ4veI1AkYqnzDc0PPKqj0i/0T8Tl05hfH7x4vk2FKtJz3uJDk2pCSGbBd9a45D/YcOSOj+BInvxavCcmTmlawkLtSXvkFR045XPZdjycb/eS+q15PqbUoPtP2Hbw1CbrPCjYhe6LbdeTZuo9J7gvvTHIF8QbrujLwjPPoohs3WnVuRNDRJlZuMbm2UI863i6+4TeaG4a7my2W4mnS4t67lgj4POsEGNuj+Q3c0PdsUeFoA7tuGwDGmOuJbxWPpoaWi04wiWxPp8v7VG6F/hWoUkFKT0UsJA5E7Ui9Reh+yu2vezRlddjnjn4/+wdovUmJDmUIRqtPmTdHjaSc09OuiUt0t6b0E98LuobZXbQxYOkBIgGRYAPVYTGQPQ7GySB0npZKGxsXMsjiKJ5uhR9OeKHatKAb+MdAgBNvQ48yJ9ibtmoQvjn2RyqOZkjVLVXY0ovHRLn+c2PcnEJbikEMR+nUtVZu6ALfj4HHLA0cXHMXY9mOFup3ioCVOTtS1WYM+R5dUOjfpMmAIdb6M+0USs4ajGgqzAUtLy1RjpvOqZp36ImAqFbLxyimPxnpDXHHm+M9ADPD69WuDYseNMQL5gnRdHHIx1UpP/H4irM5ZqfLeSZVW2mjOpr49yCV7xf9SgsHGVI5B8ohkIA6w2WoDnwc+4u9kU0GHK8XzQjww59gc2JS2Eao9Il3OG51FTCxDkFUKrEkvmIHHBmJKvyk4XOSyMKItWtEG4+54iYCb5DLv1N3JpJZmbMAiVYoZGW/swykfwnaOLQqWK4ip/04VXqbBkcFi3uoJ0WVEb5LKUHBJQxeVF51dWTz1uiWal7ZNhu7LGNvrsq4Lwi+Fp4parz5OdcksggDXHmNFsF66wJFKniTCmhQiMkE7wkQO2AIdC8O8aSH4u/obtQeuJvPSpI4MQUEaiLcqvQWv814iSOoW/y14feM1xuwZI5qh82reFwtR/tRrJLy4hbvlCuHWv6c16UKjOkTsmrZzw62WJwA7oNZRN8Ts0z+ItEadc6V8g0C+IF0r9q4QOb6oUHgEcgWPDoiGrW2KgaxMFkuSAk0PSVPZIaQ6OnPiDzR5WREW9haIa3ITsSsvoFP9NoAZcK0Y4DdtFD5fOgUvG7zEiLkjRDBDStRs42iDEsElDPYiJmIRsTxCGJOT+tf/RoqUq8Ggpui6rSPMapphxqkZpgJ9qnlIFRX9kX7vN70RIqdEovl7zYUqL+KXCIQFhWF1q9V48PSBigSov/QT/BMMHtRXetG1/qg/DhXflErNZpILkWZS9b+qj9N3TqcKOiqrEKkK7h2MOPs4vGptjlMzI1EoOUUCI6P1a6ISPH3qNDa13ITYZ39j2/MtsPzUGglmNw1ykckraySlXRQjrdOCTlh+cjnut7gvhk85PKU3LGklpLfyRmxEchM7fLdqqV7qRpI2By78Gnt/WIOXdg+BNzm2DR1UNq+sBY8zZxHIF6QrNjZWxG4iaYBUk2lyGOYs9MbVemDNQOAihL2OWzMPuKxqgeRSiUAI4HTNCW4v3PCk/HO4vd9R2MS5LnLFnY530GNLD4O+iOdXno/HVx+juFcVfLRsOArWtMCrwcko+7osjg46isq1KhsXcAYcjbrEy9LFFk/a3RRqw41uG4XtIam0rMZZYWLJiXBPdse22dtQNbiq2NtUDCFtVJ+ONGB2ghNq9KiJH6vvxPSB04XaN78Uqe5OK92bsHYCnkx+Ii5wL8q8wCcXPkHl5i1VMaWWLf5IrA0VMm0YVWiUSPot7fPU8ZvbbC6SjyUjrmIcvnPbhqcVU8hGTpBoY103kvQRoSrTrAzKNy6P8/fPI7hFsAjkTFJ19RA2FJ7k3D9XMfX7ybj77F8xJbqorAyaqxX5ov090sdfxP2TxXyktcCfbPhC3FPs+rgwAoZEIN+QLgla2gSrhgQzr7ZFBx691KlUiaoiXhLScL7ijQbotKIZqryqhAM4gCibSxjuMRYTpg/DV9ELMDdybqp8b/piQGMhmxn3dcNFNO5GPu0RZfOrwWzG9B1fTj9P3nADerjiZblH4sZdxawWwuuGidt95TaV0X5TewwfORztfmyHHgd74LPrnwl7r8v3Lxs8kwIZL0esX4kJhSeiyHMzbHp/Ew4tPmRwaVpOY6pP+5R78cyjM3Cp6pKqmdW/rUZs51gkWyTDfbM72nVohyWLN8Nv/Lt4bmGGF0+SRIBeSilEZfKcyShSqAimXJuSLobZtOZz8Tw6EUETgkR7ZL9FL/z8JnUnnGTuRbLpqvdBPTx6/gj7F+0X+1vGB5QLQaTpvfF+IpyELJqQL1JTBgYECim6LBR/zmNET2zYvQCPKj/CqcBT+Wqf6/Md4We1QyDfkS4ZiC9tolvtYDO92mkDw6rnoBtx7AMc3vgnlj9ag/uJV8TkKQVKq/n18Ou1fZnau6ijRMSAbo2a5MQjic/hwHVwKdgBzXs2Q7W51dDs7WamB3omMyLbEseuLrjX6wyQDDRf1xXdn7WG9+kP8dr8NVaPWY2kDUmqOGVkC0eXCSLL+uQ7lMMhVQ8ZNTtN6IKqCTbwgAfi7eJh+51tvrAxUl8WIl1lypRBgQIF0q1W6LRQ2PWzQ8cGHcVFgWLcHS11HrvufickLwE1hyPq7UvYcz0MzTbWEVKx4v8rjgnfTlC1JWPQFU5+gVoB7wC29wXhyimnCGP/EqmTLnkmkbf0xmUbxfmxe+l/NlZ0phBOS39Zjy8WrcI/uyJSka+xH4zEmQsXcTchAXMne+PoXxcweuw4FdmirBa1nNpjVfA8ISEb+c1IlBlfBuaFzOF1yAvnt5/XKCeqsWPK4zMuBPId6SL4ZSA+41qK3B0NGcoTLmSMTYebeoBIdQNtMqxeOG8Wnl+/hqYd38Gh1gfTpSWh2z2FjyCbCFlc1rqIm2pmqXkozhOpXm4nJ6FmnQaqlz3FAvKK9spQLZO7iOV871K1hTfRGSjxMeWne6+9q5BMkt2bR5iHUPeSx5e+8ZLEd+ON1DOp2mss+CcQ5nZl0bJpXVytdhVH5x7ViDTnPDLK9ZAV6VIfBe1fSoNEZUmRb1DzWWW4wQ3xiMcyLEOVZnXR5WobHO9zAj8v2CNwJKxruXYQJKBK43aIO577keaVQzbjntRJl5S2kwONT5CPcKQhW1KK39VldBfYhduhTJ8ymP/WfCFt9yjnlU7yJXshb0cqL5MfglIrOXfvjxXzpqXyNB3YdCDqHK+DSl0roU6nOoJET3+d4mjEhREwFAL5knQZCjxTakfeKtVtV8hQmG6SaaVTpALb6xoKu9d22DpoG05uiBb2RvTiOV7sOPr91E+oSH4q9RNu/34br/1fC69EKjLgaVrsiEQ8TniCVebb8PetP1G8Qg08uBYrXkwZ2cGYEvaZzcU+yF6oVVpe80Dk+p2qTAoUq2ia36cY815vyPQ7ZGhMnqa6pt+hMfju8UW58HJ4EvQE5wtexHevQvFF6AYsuT0lReXsbNqxuTJaB01JFz0b5hkm9muh7qVxzv8IzF8VxharcFwofCkl1IEXZQMHCkVaoVVUB7SPb4QwhOEv81hcirukV6gJU/k+qJMuuviRc03tlbVhd9gOpTuVxpifUzwZPxj2ASqsq4Box2iEuYcJafv93vfFWfHg4WMsK7Yb54/8iop1m+LxwwfC1o4KZQ744ZsF6bAmyX5gcCDax7THgu8WiHbIq1LfhN6msi48D8MhwKTLcFjmq5aGN/OD/bFiiLaMRlyTAnC7WhsWl4sgvkY8lg1ZhlXtV+FWj1vC0/HiOxcR2jVU4EMkjoiXepEeSQ8KPcLCl/MF4Tpz7Pd8/xJSV/lWt6iL9yYG4tfNISryRflD18+bhb0DfsTNkzf1itAvJZtdo7uizk8N8OOzMDxpbCukL1KNk682+JvJakO61PE5+cs5bFv9CwK+HQtSF7/v9xki4/YgsVWscE4JiE8hsCcLnUaDhd0QMO6Ny1x+BFltzuqkS/6ZLnMUQNUnzkd1qVi4bSGS+iQJtfdu/92I8YzB3BJzVS1JCZXcu2THdev2vUxxJtJFkjVyEslPjiL5fLvlyvSZdOUK7Hm/U6mGikEMyHXbvYY7HC86Cvf58yPPi6TN8rC8X/E+vhv5nZj0zZs34XnRU0jPRn86GgWsLeE5dDLe2VsW4QjHuRI3cGD/Aa28kPI+mhnPgF4EZINILwHCi3IvXrx8DR/NDMKe9V+Kh8i2bv2qjWhWokq6WznZelGIFFLrUiy1hCcJmdp8yRhpDaya49TESJC9y6HIY/l+HXQlXZntSVrPIz9exPnxkSjRpAzG/Zw+j6Wp7mdN5pUR6aLn0iZYJzK1tvZaxDjEYODSgap9TdIpKiSh6ru6Lx6veAz3pu5o7N0Yh+8dZkKlySJwnRxFgElXjsJr2o3//vXvePGOFUZP/BRny+2EAxzEIVhwgyVKo7aYfPHnReFYtxkelXyBe5bXUS2iiEg1RCW01/e4dewFHlyPhT3sUevd7unsLEwbQe1mp57wmlS8nsM9kXQ9JfAveRruXrtY/E4vHjL8do52VoWToL+rB1VN27O0nyHV18vdD+H6bkqQz/xeDE268jue2c0/M9KV0XMBKwOAGshU7T1/3nw89n8sHiUVJKkiyT715tqbwgaSVPEUWT8yOFKQuubjm8PJx7QCL2eHN3+uPAJMupTH3GR6VE94PXXnbMz9fQZe/vsKr9Y9zXCOFrCAm5UbEm0SReDV6EfRSLydKNSJIWtC0KdTK5PBJicmok66ZPsyLyL9/60KNbFw0Cxc++Ks+NiiiYWQMJJ0xTzZHEUvF8Ui/0UZhiIgVSYFuo2+F40bSU+QfC8+J6aQ59pk0qXskmlDurIbGTkGjWsyDhX+qYADzgdwxOkIhlwYghobasC2oa1w0CE7PMpxSoViDzLpyg5V/lxfBJh06YtgPn5enXSpw0Bu8CfOXlD9acfuvarfY878BYe69eHUtAnKlimJ3/44inkfj8v39luabKOMSBc9R/YqE8Z8IIy1KxSpBr92/ihy65W4yXseSLEVmus7F4nLEnE7+DZWfrAyXXcN/9cIfb5zR2LBRNQMdcGHg3pqMiSTr8OkS9klNiTpopGP+HYEfr39K4LcgjBuzTgMXjxYeEHKgKtSHUl1yftXH0cUZZHi3vIqAky68urKGcG4MyNdRjA0kxxCZqSLJkvG2m269cflExEiGXCpym+jeu16sLa2Ri+3TngWeg0JP93CtWrXsOLvFanw8V28AHFrDsEx2hExde/gu9OsVpQAMelS9qtkaNJF0i4qpFqnWHZn/ncG1d+pjskbJys7Me6NEXiDAJMu3go6I8CkS2fodHowK9IlG1TP4ajeCal2fYr4IK5aHA5XPo+GJdsg+Zd/UfG2FY6UPYLEPoloFtkC67auzrchOjJaFCZdOm1VnR8yNOlKOxBy5KFwNizR0nmJ+EE9EWDSpSeA+flxJl3Krr4mpItGJNW7pLqNi7uMv8+fxoPb/6JAxftILpUMRAMOiQ4i0jyVK6WuY7X3Cvg2mYSF3ecpOykj741Jl7ILlNOkS9nZcG+MQHoEmHTxrtAZASZdOkOn04Oakq6sGpfxio4cO44W5+3RvHtDlHUuiVPFT6UkhLd31mlspvoQky5lV5ZJl7J4c2/KI8CkS3nMTaZHJl3KLqUhSJeyI877vTHpUnYNmXQpizf3pjwCTLqUx9xkemTSpexSMulSFm/qjUmXspgz6VIWb+5NeQSYdCmPucn0yKRL2aVk0qUs3ky6lMebSZfymHOPyiLApEtZvE2qNyZdyi4nky5l8WbSpTzeTLqUx5x7VBYBJl3K4m1SvTHpUnY5mXQpizeTLuXxZtKlPObco7IIMOlSFm+T6o1Jl7LLyaRLWbyZdCmPN5Mu5THnHpVFgEmXsnibVG9MupRdTiZdyuLNpEt5vJl0KY8596gsAky6lMXbpHpj0qXscjLpUhZvJl3K482kS3nMuUdlEWDSpSzeJtUbky5ll5NJl7J4M+lSHm8mXcpjzj0qiwCTLmXxNqnemHQpu5xMupTFm0mX8ngz6VIec+5RWQSYdCmLt0n1xqRL2eVk0qUs3ky6lMebSZfymHOPyiLApEtZvE2qNyZdyi4nky5l8WbSpTzeTLqUx5x7VBYBJl3K4m1SvTHpUnY5mXQpizeTLuXxZtKlPObco7IIMOlSFm+T6o1Jl7LLyaRLWbyZdCmPN5Mu5THnHpVFIF+QLmUh5d4YAUaAEWAETAGB2NhYU5gGz8GIEDB50mVEWJvcUAIDA9GhQwe0bt3a5OZmjBM6dOgQfvnlF0yfPt0Yh2eSY+revTu2bNkCc3Nzk5yfsU0qKCgINWvWRLdu3YxtaDweRsAgCDDpMgiM+bMRJl3KrjuTLmXxpt6YdCmLOZMuZfHm3pRHgEmX8pibTI9MupRdSiZdyuLNpEt5vJl0KY8596gsAky6lMXbpHpj0qXscjLpUhZvJl3K482kS3nMuUdlEWDSpSzeJtUbky5ll5NJl7J4M+lSHm8mXcpjzj0qiwCTLmXx5t4YAUaAEWAEGAFGIJ8iwKQrny48T5sRYAQYAUaAEWAElEWASZeyeHNvjAAjwAgwAowAI5BPEWDSlU8XnqfNCDACjAAjwAgwAsoiwKRLWbzzXG9JSUmwsrJCgQIFtBr7/fv3Ubx4cRQsWFCr57hyCgLPnz8X2BUqVEgFycuXL5GcnIxixYqlg+nJkycwMzND4cKFGUIdECBsX79+LTDUpFBdSlljbW2tSXWuoyECWe1jPlM0BJGrGTUCTLqMenlyb3Br1qzB8uXLVQPo3bs3/P39xf/Ji2748OGpBhcREYHy5cvjypUrGDVqFG7cuCE+nzdvHjp37px7E8mDPROGFOl/9uzZ6Nevn5jBV199BfLsIsLVrFkz1e/0kpo0aZJYEyrvvvsuJkyYoDVJzoMwGWzIz549Q9++feHg4ID58+eLdvv374/o6GhVH4MGDcJnn30m/k97ffz48eL3smXLYtmyZahWrZrBxmOqDWV1bmS1j/lMMdUdkT/nxaQrf657trP++eefxUuocuXKuH37Njp16oSQkBA4Ojrixx9/xK+//ipIgSwyTcr777+P+vXrY9y4cTh79ix69eqFY8eOsUQgW8RTKpDEZciQIbh58yY+/PBDQbpOnTqFDz74ANu3b0fp0qUxceJEVKpUSZCr1atX48CBA1i1ahUeP34MIsdEDjg1k4aAA4LA7tixA02aNFGRrvbt22Pp0qWwt7cXDZEEjKSOJGl0cnLC119/jZYtW2LDhg0iTRD9cMkagazOjaz2MZ8pvLNMCQEmXaa0mjk0FyICzs7OCAgIEBKY0NBQXL16FVOmTEnVI91WGzRogN9//x1lypQRn3l6eqJPnz7o2bNnDo3OtJolCeOFCxfEC54ILpGu4OBgocqaOnWqmOzx48fh4+OD3377DR4eHkLq6OrqKj4jqcu1a9cwc+ZM0wImh2Zz8uRJTJ48WexTuhxISRfl/zt69ChsbGxS9Ux7m74HdCmhQiSMLhlEfCtUqJBDozSNZjM7N2h2me3jTz75hM8U01h+nsUbBJh08VbIFAEiVn///Td27dolXvT0r6WlJZYsWYLDhw+jYcOGqF27NlxcXIQki1729HtsbKyqTXpBkdqRJDVcskbg4sWLgkCRRGDOnDlC8kKk6+OPPxZSx6FDh4oGbt26hVatWiEmJkaQYVID16lTR3y2e/dubNq0SUjAuGSNAF0SSBJLKvC4uDixp4l0PX36FPXq1YOXl5ewkSOMiViRXSNJG8PDw4UUTBaSin3xxRdo3LgxQ54FApmdG/RImzZtMtzHJLXlM4W3lSkhwKTLlFbTwHOhF39YWBju3r0r7LlI7WVhYYETJ07gzz//FPZFdPPft2+fsHMhaQzdWNVtYeiFRgbhpBLjkjkCL168EASLJFj0kicpoiRdY8eORdu2bTFgwADRAOFMnxHOJA3bs2ePyqaIJC70cmN1V/a77fPPPxekimziaJ9L0kWS3RUrVqBo0aJCzUuqR5Lw0gWCpDW0/xcuXKjqwN3dXah6aY24ZI5AZucGXcpIspjRPp41axafKbypTAoBJl0mtZw5Mxkyih88eLCQVhGpSluIFJDEi+yJ2rVrl0rSNW3aNGF/RHYZXDJHgGyEoqKiVHZyM2bMEJJEMugm4lq9enWhAqMSHx8vJAPnz58XeJPUhSQzVEhKtnXrVpZ0ZbPZ6NJAdoebN28WF4mffvpJXCRIslKyZMlUT5NEsUePHmJ9iNTu3LkzlZMJSbqIhBEB5qI5AvLcGDNmjNjPGe1jUpPzmaI5plzT+BFg0mX8a2QUIyS1C6kbFy9enG48ZIhMqkW6ldLL/+DBgyhXrpyoR15f9MM2XVkvY1pvOVmb1F9EuIj4Su+5I0eOCOkM2XSRypEkYN27dxePfPnll4KUsU1X1njTniWP0LSlVKlSIHzVy8OHD9GoUSOhVkxISICfn59wJKEi7RjZpkv7Y0qeG6SazWwfkx0jnynaY8tPGC8CTLqMd21ybWQUgygyMlKEJiCD7suXLwvVIkkGiByQmoAOwiJFigijbzowiYy988478PX1FYSLSAEZKVP9jAySc21yeaRjdfUi2XqR6pFs6uzs7MQ6kFcdYbxx40ahGiPPUvJeJJJGUjJSUXLRHAF19SLZzD169AhVq1YV/5J93LfffitILqnKyZ6OPHfJ1og+I/UjPc8lawSyOjey2sd8pvDOMiUEmHSZ0moaaC5k00Jxi86cOQO6+ZMnItm0eHt7CxJGEpf169cLmy6SwpCURcbtohfW6NGjhQE+FZK4SCmMgYaXL5oh0tW0aVOxDlTWrVunsiMig+1FixYJ5wUKovrpp58KexgiCCNGjBD2dxyUVrttok66SG1LGJLEkLwTu3TpIuKfSWcF8nIk1Rh9RqpIUg2TowOXrBHI6tzIah/zmcI7y5QQYNJlSqtp4LmQ6oQCR2YUdTurz2gYFMmeSBm//A23KByR3nBYatISkSq6ZGQW5Z8ipHNEek2Q/K9OdudGVhHp+UzRDmuubZwIMOkyznXhUTECjAAjwAgwAoyAiSHApMvEFpSnwwgwAowAI8AIMALGiQCTLuNcFx4VI8AIMAKMACPACJgYAky6TGxBeTqMACPACDACjAAjYJwIMOkyznXhUTECjAAjwAgwAoyAiSHApMvEFpSnwwgwAowAI8AIMALGiQCTLuNcFx4VI8AIMAKMACPACJgYAky6TGxBeTqMACPACDACjAAjYJwIMOkyznXhUTECjAAjwAgwAoyAiSHApMvEFpSnwwgwAowAI8AIMALGiQCTLuNcFx4VI8AIMAKMACPACJgYAky6TGxBeTqMACPACDACjAAjYJwIMOkyznXhUTECjAAjwAgwAoyAiSHApMvEFpSnwwgwAowAI8AIMALGiQCTLuNcFx4VI8AIMAKMACPACJgYAky6TGxBeTqMACPACDACjAAjYJwIMOkyznXhUTECjAAjwAgwAoyAiSHApMvEFpSnkz8QePnyJa5duwZbW1tYWFioJn3//n08fPgQFSpUSAVEfHy8qGdjY6MXQOPGjUOfPn3g7OysVzv8MCPACDAC+REBJl35cdV5znkegeTkZNSvXx9z5sxB3759VfMZPXo09u3bh6NHj6oIFhG0Jk2aYMKECRg6dKhec2/fvj28vb3Ru3dvvdrhhxkBRoARyI8IMOnKj6vOczYJBEaMGCGI1cKFC8V8njx5ggYNGojfFyxYgJ49e4rfz5w5A3d3d+zcuRMODg56zZ1Jl17w8cOMACOQzxFg0pXPNwBPP+8isHr1aixevBgnTpxAwYIFERERgfHjx2PAgAEgdSJ9RiVtva1bt2LNmjW4evUqHB0dMW3aNFSrVk0FRFafq5Ou58+fY+LEiaJvIn4FChTIu2DyyBkBRoARUAABJl0KgMxdMAI5gcDZs2fRq1cv7Nq1C2+//TY+++wzJCQkoH///hg2bBhOnToFS0tLjBw5EiVKlMD8+fOxZ88ejB07Fr6+vqhXr54gZH///Tf27t0rbL6y+1ySLurXz89PEL5NmzahVKlSOTFFbpMRYAQYAZNCgEmXSS0nTyY/ISBttUjaNHjwYLRo0QKTJ09Gly5dBKFasWIFWrZsibp16wrCRSpGsv8iqRb9n0psbCy6du2KVatWoW3bttl+TqSLSNu5c+eEunLLli3pjGJQWyIAAALBSURBVPbz0xrwXBkBRoAR0AYBJl3aoMV1GQEjQ4AkVs+ePcOYMWOE1Ovw4cMoW7asIEYlS5YURGvgwIE4ePAg7OzshESsatWqqFKlipjJ69evhVpyxowZol5Wn3t4eIBIF0m1oqOjsXLlSrRr187IEOHhMAKMACNgvAgw6TLeteGRMQLZIrB582bMnDlT2FbR72FhYeIZsstatGgRhg8fjo0bN+Lnn38GScbIkJ6IWFqyRNK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stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\" width=\"131\" height=\"67\" x=\"0\" y=\"0\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url(#legendda70ee)\"><g class=\"groups\"><g class=\"traces\" transform=\"translate(0,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">GFT</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(0, 0, 0); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"125.4375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">CDC reports</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(0, 128, 0); stroke-opacity: 1; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"125.4375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">Model 3-wk old</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(128, 0, 128); stroke-opacity: 1; stroke-dasharray: 3px, 3px; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"125.4375\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g></g></g><rect class=\"scrollbar\" rx=\"20\" ry=\"3\" width=\"0\" height=\"0\" style=\"fill: rgb(128, 139, 164); fill-opacity: 1;\" x=\"0\" y=\"0\"/></g><g class=\"g-gtitle\"/><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"229.5\" y=\"239.70625\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Week</text></g><g class=\"g-ytitle\" transform=\"translate(4.9248046875,0)\"><text class=\"ytitle\" transform=\"rotate(-90,10.075000000000003,100.5)\" x=\"10.075000000000003\" y=\"100.5\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Percent influenza</text></g></g></svg>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "gft_df = pd.read_csv('data/gft.csv', \n",
    "                     usecols=['gflu','dlscflu09','degflu09'])\n",
    "\n",
    "gft_df = gft_df.iloc[302:519]\n",
    "\n",
    "gft_df['week'] = np.arange(303,520)\n",
    "\n",
    "fig = px.line(gft_df, x='week', y='gflu', width=550, height=250)\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "        x=gft_df['week'], y=gft_df['gflu'],\n",
    "        mode=\"lines\", name=\"GFT\",\n",
    "        line=go.scatter.Line(color='black', width=2)))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "        x=gft_df['week'], y=gft_df['dlscflu09'],\n",
    "        mode=\"lines\", name=\"CDC reports\",\n",
    "        line=go.scatter.Line(color='green', width=2, dash='dash')))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "        x=gft_df['week'], y=gft_df['degflu09'],\n",
    "        mode=\"lines\", name=\"Model 3-wk old\",\n",
    "        line=go.scatter.Line(color='purple', width=2, dash='dot')))\n",
    "                 \n",
    "fig.update_yaxes(title_text='Percent influenza')\n",
    "fig.update_xaxes(title_text='Week')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From weeks 412 to 519 in this plot, GFT (solid line) overestimated the actual CDC reports (dashed line) 100 times. Also plotted here are predictions from a model based on three-week-old CDC data and seasonal trends (dotted line), which follows the actuals more closely than GFT.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data scientists found that a simple model built from past CDC reports that used three-week-old CDC data and seasonal trends did a better job of predicting flu prevalence than GFT. GFT overlooked considerable information that can be extracted by basic statistical methods. This does not mean that big data captured from online activity is useless. In fact, [researchers have shown](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/#:~:text=The%20paper%20demonstrated%20that%20search,into%20potentially%20life%2Dsaving%20insights.) that the combination of GFT data with CDC data can substantially improve both GFT predictions and the CDC-based model. It is often the case that combining different approaches leads to improvements over individual methods.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The GFT example shows us that even when we have tremendous amounts of information, the connections between the data and the question being asked are paramount. Understanding this framework can help us avoid answering the wrong question, applying inappropriate methods to the data, and overstating our findings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{note}\n",
    "\n",
    "In the age of big data, we are tempted to collect more and more data to answer a question precisely. After all, a census gives us perfect information, so shouldn't big data be nearly perfect? Unfortunately, this is often not the case, especially with administrative data and digital traces. The inaccessibility of a small fraction of the people you want to study (see the 2016 election upset in {numref}`Chapter %s <ch:theory_datadesign>`) or the measurement process itself (as in this GFT example) can lead to poor predictions. It is important to consider the scope of the data as it relates to the question under investigation.\n",
    "\n",
    ":::\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A key factor to keep in mind is the scope of the data. Scope includes considering the population we want to study, how to access information about that population, and what we are actually measuring. Thinking through these points can help us see potential gaps in our approach. We investigate this in the next section.\n"
   ]
  }
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