Home > Software engineering >  How can I scrape WHO influenza data without using Selenium?
How can I scrape WHO influenza data without using Selenium?

Time:11-20

I'm on a team that's working on a number of disease modeling efforts, and we're wanting to collect historical weekly influenza data from the WHO on a number of countries. The data are nominally available at WHO FluMart website screenshot

My goal is to collect the data shown in this table for a number of countries.

API oddities (AFAIK)

When a user fills out the form at the top and hits "Display report", a POST request is submitted to https://apps.who.int/flumart/Default?ReportNo=12 that includes the obvious data, as well as some not-obvious data. Here's an example from the above screenshot:

ScriptManager1=ScriptManager1|ctl_ReportViewer$ctl09$Reserved_AsyncLoadTarget
__EVENTTARGET=ctl_ReportViewer$ctl09$Reserved_AsyncLoadTarget
__EVENTARGUMENT
__LASTFOCUS
__VIEWSTATE=ZQnn KQPTBErZEeAiltLkqQacil1QjaeFOf1IqWbMAPxuMB5LD uuauz7e 3E5utXo5bc8LrfjT4OVbbQgQWlmgujiYRvA8Rhho2C9oB4ITAK7S/i/PPcxHq/qB0bn3 ew icHucMe8gi3c O Fem8Mmjt4fvHNRD87JqiVw6MVgrqSw3Gj9LAft07Sfs1Jh7XpTqCqa97nP8iTpZD33Vnk8Nnh16SPJ70eGekMUbayZ/nEmmOX p M07txepWFCPQs9nhYXAm38a/5tvdOHtRpGpY1d2t7rYgNwuv0Pwf/gZa3yX79sI5J9tm9OLZ1TVlU7TkiYxG5ZhSHtJvHV0ARa2RTWE4pvQULNFJ6opzI/OSDRpjKUXREHNr0dfFCW1nXrZOmKBa/rzns8EEFTesfbHD28QwpWfXxyjKHPa2kOi/cCri1ejiQ1 kBQG5juc3ZhhfYiBwXCb4U6eBhU6VMcsfwHGHM2EXYEaLo85oYYjAh3EBbGQJKjIi9lms40YfsSsjqb25u7Sdja6xsCH5MPMPihRa5gSm2OgqzIS1bsDpO3 JDUh33NnKG/3hYWq/3OECYZ6cvAQpN03lwmD1DRebHJwLq19fkuaa mNhir58isOKAck/qPNuzvmZvm/nOcFoS6xW18/v0TTylPA07P2M2UMLA0LL6tfOgGlF2fN07Ze/ejSgDX0Xv0Iy8pswrGerIun5pcKxazxEKrPYJ4GQrbWe6pvkZn039EzN/rp6PFgtOOFVqrYrHxHXEbToxeA Fpe4W9a4RhRsPgE64rHT2qls5/lvon5O0JLWbwh9X8EmFQgHHKTPWktB0G3cXWp WDiJ0lnh27o6toUj0lUT108GKVYBO5QT0aQe8efFEWidnrZGyAxBV22OXd8Duh0Kdv2KCRoHY3YgKe1OD 93izHXq7r5yEksr7g61EPT8kodlj/NPFpOlgyFP/Oyhd3LWlZeBHGkNtpWg1Si4WzwEe2ummEgDBS/p e6vLlG67aEoKiaAk4AaJx4J7n4UJj hOqScKYPPpafToPqV04lo 2n zBEEPzFQtiESz6SqIuX5W4kGNVt21cBwWRhuk7MrXPHcHmDoiI1bu9mmWTjuXnsaFQLDcZfhtkQnqX8b w7oBdMXEHMdI6CO6zdchoGRraCinGvQ36FGFRfEJA2cdbaLngwb9GwPr1purdsPoJln9mFeoDsHhwMuXse4T7h lst5BqYqNM9f2YUGacjh//L0LelwpWHSrxVgjQaY EnUhAPDkCWkYWkXPEBULz0Y8I8qmZtTm4GCcAs81Kzsu3VNrev1x/aV//II/PecVMF9FkgcrIOw6RG1KOZoSCYu5NxWctVBkNYohRDoXPJm2CXaVTXI 4Qghv4FrGpfyNPfHbDNADX mtDbrVVV6/SslbEh58oxO15MBjyIG ecbEUiNk6VRXR37eafaJlP3z5WT4 4wUJXc5LXDSrpQVL3kSplx er6WbL19UfW7c5 PtKU7IXstykbbi/v8wu0lqpBedBr44Y8qEP9snh1iYshKYU D73Azi/mPYU1vz098Qd8adEAjT8JPMhzlkwmyKB iSvBwbM92iaCt2dRJ4n2tiOdT6sxEGDgifjjuWFWGcq8lEHKGylCXzJFyrB7wLExJiCx55h1bSwfej CqjfeIE0zoO3tamkPjjC4h7R/fSqhcFvlJGFMYY5RfpkiWajZ8rLDB DU87N/TvppJcHTPi34Gwf1oBSI2vKQIBoU62A/TLPrFtwmslMo0YX417eMBlgicJ/sm /u46W PMCiz/XHWaOs vuiWqJa1p1NnDyLk2oHj4 JSSeG2KqZo5CQDOHfVt1Uqwc600aP5L c2M WD3S1FBRD13Yc0chPPFS5n6AjBr1irCWrIEyoEf0RuirTcEXs3hCn5Cf97qZvgkLlqILM5f7z nWRarLWVERkWBP7OzqiF6dOltCstZh9jEwJ8V8lwf19Qj21SON1k96nnXG0/W5stBlDrtP0lTRIOy 0tDcQg1oPL3FsW2zBEaxoTHGG0QxuOmE8gXlFqnK2GXXKW70q/YZaQJusl3bBhZV0YMcbZChZXaLkdP8DvIovCcC8NO4fg4T43eyOQpsuwaZ2f l277wMdV iZKaGzip7K1cRzkuXpeO8ceQO0g60XjQ1XVk8T2SUAgmeuvIVGJM8bBeUn9Ah7hu/ZKebiE Kp0U5zUWISPRhQzWVLOisyS0zlC3hOFEkrIPzW6WZKM5iw28kwX04/pXfqxHqyjx1qbdB2urdAOZOHYl6VE5BDD5pCV7tvJ5ymIkHSsT/w8x742gLZoP9qIz38u8ZTfgjbey/3kHxQUS3c1EaO9eOl320As2r76WDxGMCz2fv5eGcAdxijVAxm5O4nzLYj9hM58rJDW6RkTdt5haG YwOnEoMzOCY6cno4V0JgKa5XoMiJygjPoKNfbJ1HO30zumi/wUOTyhDz9FYXsFWUVEN9PGIpre55WyVx//6f2rkCaiXWSgtOYicGsoE8Jl3MdsuyFWsOmBV45vpv3a8ajrgq23IMhaRGQPB3sLNcqIg4Ev1xKhy6Fr7q3h1RyjVs4mmOMj7jmBqgFzuyJmM3Bt0WgJHEgfDcFVkcG0g pklt3QE2YeGyUMfHf3s EmwOcYg6/PZod4xaLVBvlJlG1SYF9q5t0Pl3pdlRa/QIX7ChxRHTEuGyi25LwQKbHiHVeTQDTxq4FGyIF jTboyZlS9VP9RVc1bA94lc8gebbArX1bRDUU/Pi0YQHUlE4XweoN/V1IRPT1pTmn6oZFc5LdvV/127PKC VgSQRP0o5Iq g7gSLHV7yAEqa7Hc aqRZczeGhWcIB5RkXSiOhBMNaRGiVY9YDaJe4HnTjCTH/ArPlYYO/y3aO7bwExO0hennXvMFZ3Ks3I bflK0kju0ondc2N6hre0V7 3MdaxVDzXz70TlVZrY1caRjJcUmaJlt0tt3dLhfN3Q0mI8ME2gFiEH1gbLFrWRqa1J x0 RMkRL8X1VOmQlGkfIRq0NbX exgqM BbMrRj5t/UiWitn8DP1sSQYEJ0hB5sG15Vw3I48zynr5Qr bC7H6TxnzXWJod7Sei6QSrB OZ8dwrw5aDOhxNXQzBznE9kOak/cYGVDuZs xu2uBeMYTBryCjshX4h/2CHWcJ1yJrwS4V5LgX5xgMPg2qhVCSwMddng6wtjWlGXaTpeNdfBDzwXs3yWilwdsIbujzl0DPaJlDIPY8sG1HEARVCAW4HaBTJmgggwugTpJBx2Bjj1KlcGjDCwdwpWHMkzJ30pbFYaJByZK3fk0OG J0UoRoZ/aZxMbUPScUP6VvTuyHEMOY8C59XEeeVo5e/xSWphkcdcjcrNAfDyj6PR6JwrsOdvdbJqC6Vzm7De0YCDQYBNphbIa/HEN2nLeFkSZp5uuyszlfVHAurVNtONuMr5grfgTp8Q4lTGGVmZEndvcTdTYYevEmDMaXKfyPeCOhsiWYMKfrySgl6gkdHCnc9ODuv IJJiWdD2/wJh68eRBYdIOA5qchEo4Dk9HoWEpR7VlJOKYTcj4MwQAk3LzhJxKDRGslrDtEkK W8xeZjsDpJQTQtmESTj8B RmCGgN5FqTDM2poIkXL3WMOUf2KUd2DLNzVSwHA/JWCbglneQRvdVYSPk9nk3Fwmsba22rSVFAjqStTT8kTWatEXtPSGZKkn b4CbnY7 K0Zvd6BuLbOLSdnUiPNdR jfvIEl08hwBaegXf4tvdofUFmlg5 liEd5cF9XUMb1vJyP3oqoP5RsHxsvLIjwa9c Q/cJPRrsebX7XU/bIO5DtkjUmc5A60h uankOJBsuJpf3gDcYOeilMT6ZIsivJtswUEBnCBOzyyoxJAIXrmoQ/upPuk0/X8bPtH2F1SXVdvYMMIJP1HREMGykOX02OXHHAj82Hu7bEHS60i73 xouUXZbbgmFSaQo9e5RvGKb0l9Y7a40BqcCIjo8BXTOCY4wwLlPg6yGV9ZE7U0b661v/0E0w41xw0xKqXGp2Zw2B/Yuy96F9FnzRDdSfbEPd93aCh78EsYzTUGNmZoOEtMvIIJq9Gf0Nj99uDvkaGvZn7Fw9Uj h28z5ERsuNEZgnjmgsctDlUqtoTi/aQXdDUFSp7v40vKqOi6QWYq7bpVufPaTPSkqGZyGRDgqdm23abZAfpBfeMpnoHiYT6BH3vRTFJHITKjAuf295Zjcv0j9XMiW8Fx6GFPWSiCJgh6b AcfVy3fiWfoA55EpbMV33x8YuXujALWYMqz2xwxX3duMlqDiBaZ  f7BY6NV2jVApqUOZLacflmXbbWP3z92yB7XpF02iSl3on3 x4CbtOMIhKGLskoxYYy6Yj7sLsLq03wNXcqr89bQU/diDIIj/HrBz5NExqjuVN1xdmdRu8Qx1n9X3fBaT1T7T87RGXSRc3sqHLS8JO1uGzrM1D8yaPG4Tccj3Dh35Log9/miJ3whoElBojtkYCY0N0G7PQAlvOB5TsFGJ0BVCNFhC87YTlUe555HbO36kO2H0MSUXjgJwDKAQr5uRXMeGfSLrFaWdWWmFtScESHQA5yKeNDoBtCMTAFu06M N4PuNwNT7EZaiqjJN000Z7afJEjP5BcdJ7s1vo WMfXDmFOVU5crh1By2W8KBr82rOeUYTLiHjnbMcRvnEYR8nI3Fp9CICP1jjvShwS2zRH uV8GTdG7CyBmYwvpM1Rf81SjpTHjyeH3Vyo RaugLXsPWNsm1ef8VzTEE5cNbrXBlIWNkUlRolaE3uOK7pKIs/YZk50BcC9KJwcHS2qeeCwTQjF9YydIG6hKhBtFYKLa5rsxmNideG4KuzWzGBXFlGBgzhuDfDKKlyIHb0 7 v6Ii9yMIRMwNQjW70VffLXALP770RSS0nvfQKRUJrRhVytS3O N7eNSltVgqSEo22yaQx NDGNm369stOL4MO/cipbpGSTt9MffIKFoSFDJaEt/Snuv9EbYaYR2SbcRueUtBtPk7oEhsT1Xb WrNpqFbfRR1tiYtIs26bAMBIbxH1g7Y9jK5BG MPWegU1S/X2MCURqaBIVxv3a1taTc28ZmOiKo7WroUnuVg27E/sWlPZhnLc9ht8AYTSK5aR1IJf7T T8xi0UT80o c0Plpaia973lwEWEDkBhrkDgnKhyzrXs b/ ejgjsEN5Jq9MVlBtHAfXKK3jnKNubnsGcYFRmLobuQg/aBBX4Wm/40xByMRzKBwdVGRo/1H1/I5z9/snNdtvvrKIBBUgMiy6 mHEPfTrVw8gkL6wsPQEEKFT3ncmer4juAY3YRoe6EM0QNGSyj4Rmc5yiXIx4OD35rDAS6srTBcesvLm47XWnqdoeWiCbc67XAW pNK5rBJVibPD8GIue1//O L96dkEHs0uBw3OqFc2wD978RJKVZHqVgPjCgkgwZmJxqeHiPkSeWJ29CWcXgSexX4FEmyuAQ8Oo3jfeqthZ4ZUIgm6sm7VggQNKgyAIWTYahCx/CMWAeacjLTC NTiXIka3FvgRMZK18IDODA7kbJQ5BVEVtjIDKdMMrpRt9qIUh4 BZskTQXH3 1I/HmPYmZ9waw/OM/5UD0zhh/6KcHactFaRZe67rXwDKZIyTpBSlo nr0g289K7a6pMTvUc0iVE/A1grlWjjPzTBVf0NL7zFfzTHBHC6Ac7AgMsf19c90rAXS0HlnJLkpn1hCLHpCe4MaqDCoYrUpoAn1F6hfgfBaA/T0DcJpFHjRS2WLfWGPCmzyutj961Gw4zFll3JqYWa1ep8BUW78GmSf1zoLeMhc1/XB/2uIE8xgRR wYfZW8U80iXs0Vt73nKqJLAk/Lnti cnbKGioi23PERevEuKPWmz3hr7vVJkNYylL6pvtKCKWALn8DJp8tJr0uQRFNnEbVvZMxaUissLDLCRZaynmOfWHwuewl6Ajr/zbwRU2NuQV0CXHzH1IuzUvTvAPDnufheBiyJN 14x7q0g3kH6/uoz4EdkR90rubDzydNrYAm1QfyiGl4SjjfmKDujz/g1ecsx1EGE2XA9SyaU2tbj4JN06By87X7F3Rex4UvpjdxGdXaVMHdV0K S7qGbrOdYy9VwG/exlkewukYzzyr167lYtj4BIcqz6eve3qgmMMiQ4mrihxrqOCYLvIdLfgUmJJ CmDUl0DLyVvMivcogeNL aZMNF1rlQw2boI0mbDR6zOAJBpAVZ 6uNqSbQstrc2k1xmGxVqaHCBAov7rxcI yq0zTQicCiFzemZzAv/Ha7Q5tRnxmCWXCxUqFplVK4ROABp8Mnd GIxY0sxPhxrt2H4z5sZZgGwK4aa0t4h5QkP2pf9tzleYzYxJc0zCtoLoeKUHmMKqXTCkv93n7DyPveIfzL5BL3rVquaFSKPsDYWtXLQreuPIGITLfxJF5DtfsOM MBc702c8Qof/7RLhOoXJ VajCOu8gMPEZKX36sPodC5k/ce3DLceAbjFIOnpekPxD8FPFA3sjabZ/i2UnrhO6ORDlU4pljI15xtutr4ajPlPHMKIcIZnrNwWsAGhEiQZ96kcojTJlIyLKQRziqFOFQ/1u JL7LWEuY4C9cQWIgrJbiUKgX60dr2dCab C2dfvZhXcjMYwqJE7F80VwRHCTo5C0YnB9Yv10nonYVEiEjNi0 wgc RlNGiHx3Hq4mX9pq/5S/Og7nexJxkeN4Fe7Eyz 4qUrK7vENghZvetfINPEXPVSC6MwQyzjrqCZC9j64Ju2S1sH1lf9KC6tlo/a aJhRPyhAqr7V0CKU0cmpVexuZj5KMRSJdASKiCCpryJPLZPX78yiF991TfAJszGeWYjPvGSCDM9OQWfHIZn/hfHXvzWZmdcsmuKru/cWcT0uCPg5zRvokE6eJLr/kjgwL7tC9v0f0BadbaVYVCeQioT60ivZ//DVRcd8O27XhS3Sq3fLKLz2U7bJYBzCzBcM SdRZc50oQupZpcZFA91bX TSkPf7jmEgwLiRuw9w1keXs1nKyRx6H8bYuYwaII20NwR/bw2oYOIv5p  Qp23om90PsE9nmtLO3sbCjoFiixspYvqC5m2BsUi2K/XyTQrbfhpsir3hMbvJXUWUo7kFRY4dbKnu2kY7uHUKQN1aFwiLOdNnXdWd/P52VKQydzEbcW0VP0egziME/CdeIcJJRQZaFepjk3uVCgmbsYvEvJF TZHJMIqby/q2sC2qkjKCiIV628deJrPOIKi3C3moPPB3FJ PWK90a8koY4nQd7uCHK7/cM Nw bwtcsjdVFD4fpwhka06qlJGz9f9sQ4b pJCRm4 HdfQYf4XCA5fy NSKmzDg3pQyCdAn8ivCCyh3Oh 1TsvKMVU6pvn5kmLeXjg3A6KZ0/ygTR/Ura7s7oRr W7YTBfP7oMdyS79QaL3Vu/NSjf//E/GfZifuDmNHQVFoEArn9pLYbWsvGEdp8eCeyV3Zm2KZEh7BPt aqV1ML416EBgeAsWj7O0gyPvVicL3j/7htogBh072ddg1DeUDlVYth0 pXb3E4EXqKdnUx7ntYHE9Ylaflb6Yp9SisMrQGVOw0S4jdBjaGUkxzWnU/kch6nprAxqyzQrxPNcySTcoiJoNcR0 HTsAbeDTlhs6CRHdB5IJ LLOyRIA7el14PYKVMldWUvU JVSupdmUfKobVMCeYPzGa7hADxbhehJKFKpRt/ZjGRCY4VJiJxI4PPzE6WyBiDKzeh4CpE16cbos0DVk33LWlrnsXGewdKcT/dWfaErGm nSWAqKSLj9fzYFcwBXAOxpGp5WazsjWFADWFShF0vXQD59xq1gR2PQA q9IkyU U0c cx fACOzC/dOq sIT2SW9ebFKylELPsu5x1GWgyRoDmCL8ysLueJnMynWA07pIgJXSIyFh1o/di8uSqKqUFSik9a rPAXpfx4Bskb6ld95KRouY6NfXlx/mtdYOWvdGvMPXgdyGC 725vF9Av3rmybs2 ZmTyrpRlDbTwy8o71hWJLQzRm54Q6Z0AYvYKqcNGG /0IESzbSjMOz1RoedglK4oje4JaYpbppWSAvOn80R52GFw2l1W/373tvjiuqdHWuBpCKGVI/58KMCr9eXd0UXnSJvKWuH/limDKR5vt/Rr1 OZ9lk4nl6rdot77tZUl9FMjSh5lmL3N40bs6xPZRlETpPlRfTS6HPTxSB/7FPmSMAS6F3UAVGQi4Zw XTd1F0tjB/TRUvKKJ fhyKvBVu2ZawPsNZcqxor0GBKf6hWC2M/54NBkcu8SL3FfDdSSHuiHDcHDicX0kKti98LW9wxR7zvyMdFjQVaY/6U16 XG4IEEVdpO27lFctIaUM4sthDooh ndwEYbOC9FSdbL 4TL6nim3v7yeSmAx 7CfdUucIekprQMyBa6sSKXlzZpdLQHKH7WCtami8DpcXWRwx4naG4JroHIYRXkS z6E556mCp2R4uOGguQCEN5x2aE4OzwsqQ5PQl7m3/3TJBd9hGC9mc0fPipxZ4L3aTuPoh8s ghSrpIUYo9RjUff/m5ekGvq4aMSuqGn5ScSFyw3d1C7fmiHYZE E2n1QReG1gf1JqZY2cmj0gZJS0My6nzpL hKD2keFxWdeI3RebQi1ExxNvnPuwnTJR/Ar07JmMndmnhjtNSRa/5kRyjbmzgXf5GPqMJN1qbIhRbPaIHG5oCT1mmQgOmhiw3MnggLnWm1vwxGUisSeyH7UqIpCG4jtGEJbqiO7NhzpM/LORXCAe6u c435U1YkfI5vNuJmA1phfbV84IBR3yu2Vng/hASaNK5ru07ggEWHxCLWHb2nW4Glw287tKO8U1oCal2TgxwG0y/uQBlLgtKbiXT8ntHJKmBx1zBHlNR5D7TxLNH3bOWQ4rR2N/Qgex/ZMyQ m1whcYbx3Qdi6e0i1bB3AMmSrDMWm hMdf0nAltwMSF6j0a6SEqvphfO55TtuxOO7mX5cxCnd9FY5HzEjs9T6l45yBcWqzseou9ooOgDuwE/7eAXONnnzAmSMF0Sx oipWIOzgTb4hbKtCosD/jbqiDKUV6WSs/VUv2ejI FLXNvaApVvpVc8g2OSO9AafW cJjHz4iX7sHrSugSk1TXp66LKHN6/4J7wLU/kJOsWhd6jKUHoWd66PN7XfgA2xnIGdmkG0quwbIvRBCBlG417N1mj5n2NRtOiJL9snmpT89xAfu4uysMMs9H6zajTzf4iOmgGkBJK09b4bmj/rrGqT2yCtKCRehc5R9MsMicYl0A9eMAX50 2StSO0S4qBK8pV4xMTazEjk06el3fS92kC7iaiyEAlEz/Ippisxd9ga48IQXiGg/ e1ifVEBsMnmq VHJTJXYvDzw8D4eFi3AkTwUNb4aiETUSfV9mwKuK7QW7Z4 Eg3biw5rbJH3unAwweFdZdooZaqSZAEtMUWqZN3d7PCmd Y4LmDciVaHmBuFGBoN18wtwTxfsoPF XRgFtrEp8oFRmZeliMJcLa3CY9UkgHvtxmyBYInisgSAo6S7N/VXU7baYX9hrtY/CGphLuM93WWGPezfwl0aXbgjJkQy6I3pKANJ1G5qzvzYKmzoF sDpSHhNIx9sjqN8eIlfJ 4RtMk0155KQflrn5iKMkjSBDzC8iCRF0eKwGzwyAFFC7wTsRFm55a84FxJIJRnoi1TLPbowT 7AsEwjU6BWHieyTCkO/BhwewPzVOjMoh87KfR3HAAtLJJUqDSlhTorrsYdA3vSIRf9 T7QnmkRCFUAPC5lG5HcOZwWQ/GmXNbq2QodRBB IF5WyGRiKh8SBwOXMkJQS h8u0TWkZ09/U7grBsjPX9qVzp7RPz3dg3aO4ZIum2CPv3D9IBUeHS0/aCOCBfRflS37a6xl7CasHUpQ9Z6PFDzm2/9hlsZhgepuQCFNQnu4izndHv7cvq7E64RcDAI5MH9I69ymOd2dCoxjvF6P/icItPic O5BwUpcePF1CelpC6Y3efu3dTLG0 mvX1MvQQN EzusB26HSU3I9lMkrrbT7bRJpLv8fz4RjCVsBHgBgygNS5QpwU2lRdMb2nXqZbDUK19gHxohlFVbG9sQMC60wfrvFdTeGSUyy1gPVebiAwFom0L fO rZsMkxE1j9gV4zOaWNAhOsMch2vMqLCGVFZDukd9MtzG8sXQMKAGsbqZxVu/p/ddnHXd3s6aWF 1KSwIPOqLtcNC4wX2rvSklJj970vpz4LhIszyXOe9bTxUDrcwOpXMvMk56RhHrIqzo1HulmZs2GMSE0bI9lTPN0dkCnzkYT/ftF8hV/79HJLqjRhx38GqYRDCguRsl0mqKQuF0N6YpQmn1TPRqkQQ2cOuHZANMVr8VG2fCNe64K2zGvb47/xl1Yj6Uvl9XK56wMXgCAAo8Dmblt2XqEl9ZLu vtgRW1tb4Mt6/si4uz5yxGPcMN5B JOB0hp/Skmks8TQSHKd5jqoUfzI9nWbe/SWZA0JXq5afdzvdqQcwsM4p5aCI8rIfaTIiGI JQEEf1DANRjZd4jfSzJY2tGX15ZcgH/cvgIPQVUAQE2JLyeKdaLVMPnxB7 GN6P3dN73ooQiQ3QyeVeKzwV1/8xSE3D6Ho/KEw9x6BjFbQRu6s3kfVHuhbqUgp et0/6jrLr8TlNJUcjjyKECfiRtfWGVdCrMqjN8zc8Nce60IBlMD9zjkbEeFmgVRULXrR0XLxifYg6ms0Q1B4A2hDBZSeLrgXTKfHloXNFvM67zm/vB0OHsFA2L6w3UAz80cgB3zUzoCJIr2giONutEjZ8n09gN8Wrga2gNmDaamlRlxyNFXhxfgjXjp9YFtO4jifSAvbJylVVipYrnflhlNNCiFnMBMiZdgtXae5oTforXmPgD4jz3Xx1fSYDn4u3NoIfTDZeTKC0lk9tpDPkf1kpzju30VAnm0XwOpA06CeSOU9JYDQCEkTQAhZMM M7rmjdrt0kZsJ5hPY2qsNS0689KWwc xjwoJOw/GukXvDlvutFwGdVOSojM27moXYIu0NRy9YBwPAhQyInVaZdY809f8oEjv3tdWQw7KqF7w5aN4QiLFkMNyQPYk3/WJjyQ2wwautM84msJx3Vtf6OqOMHEPpCfT91yj4a9yPMtF23RftDnqrDhSANrycLy2U5bNAJfW9ku0Ud0/ 1PSmLyFnxNTHV0lVEGzI93IC6kyXjcvHHwIIv0gnkfLH3K6hWnyCaFD5uzWbRMHKnqgNtr4gv3pHHSqwFHsf4OQ3exVxKeGlHXaGbaV6syGJjC7go1rff tLslQlmVJayCbbkpOfLtcKl3GXx1l0xKg9uhq90uh PZEKUx9HFSgEfxT4vOSyZErSBY9zGyJaX ZFcN5D7j3qvkrDA3TJDRasOyaVV5QmqIcrvPSLIq1bm9NNNlOEyEpuC0WEUpsSsqUQpMtF efp/qcX8Ja9OxOtAtbB7ErEtoPiXjcKkj90G7/TJ1RJ5x IStycvD6G7AZsymb6tHzizYMPthIvnb8Hdgg9Axl67/BtdqI2L7zCHtFa7Jbs btRoXvgwrYsZxb0EP
__VIEWSTATEGENERATOR=15FCE702
__EVENTVALIDATION=IcvINGO3WYGphvwIjY3WhJYPRleUDkcXbiphMGqZnFR/ymGlL3m7HcI4bT5GRDnZ2yp9xxbEBsmrqbNEGE65jR8RToCjkEijpsp/uFTSMukUC6FJKgkvdBZoXwsuRXmLbj6sld2KscfxhqUCT1bC40WPiSnh9MynPCU2SxVPfGAqQohvs2pqdEe0a8cHtemhwpKr3TTltJhNipL6O WFTcEb Qwz9ECCx3zcGrYZXqVJB0SQeyOO6VYlttKyPjRXch2qFMDGUI5KqjK/yQxoCv7zY3lOltlbEIZiwaGavXEjPn6YDVQmOhx0k5F hZcRptue7quy8s EVgDOCKuRCSA62klKCEK7/wKSDYbmPwTY28FOfjFkU hy6Do5/8AdaSe5iEuyiKHBlV0UFyIl9J4hbF7idM8Mk4RgYcr3BFoJ2yMJCicab64fN7kMzngLSercRbQMZI763Ck5CUKqTRgr4d2DoMORKUvmqy EoQgGdvacHJ2w4HB5M3ujpPhBTsN2GqDgIaTG7Ee 9fNtNyIvvR/CQkoVN5BNukMZITEL2wf/3oczthMtg1KCX8UL0VFWsaFzLp02CqghtwCvbr38vgxEbsWwo A33kOm6RFhR1PtnwEmQXFLdexNiPyWdo0PtrFnzzkaHX/JAoqBfVNGInvcg2UAuGxBtSN7ZyMfyLNIGiWD0Fo8DETOlzjlvh7RH/ sFEqaJF6OIfzI7QW8c697H70R40qAjGpsjDigMYvlAU6WpMeUFEJbZBxKU8pRjuQOI7ZDGTkqmyiBGvvDdxQtDZRdtgTjENidHR/IDyQdbaUXIaIHchGu7Ec3i4iwK6qBgjsnW3colFzPT4n21k43Fn0/Dr4A8s8n3Lqyx4esGKGE7CAqMew uTg8GDadD2aTSFEV9XNNRkdt3/abbprK4z6 9IUacl Cs2hzeRr2Yw L2o4q83c0PqQf9 ckQQBNGrXvwAhl/cDpvWCqnCFifgsxbGC4OHrD7SXrrOh5qvuCJv6FQO2lCPEdP4cGgR4 65ssKkayHeH/wzEJWvmWYy5u0PQL2in9BjFp BIuTaD9lJ5icS54axC7mdIQfnnSnoo6bXX147JqUf/o1NylbAQH5O83CTZVZ6SUk bw9U43FZRMZ  KZOMbgFoKNfFR9sx0Wh4fJ6ZsGz23QE3cRhb82xbdOCHaKCgNEa2IU J orG2YemmMOFWnL5eqdXt7Py69h9/ZatFnFl8yyM06rKznZ/yVrgoSVGwkt5nFgpbczR42ezz Qf2b0HB0Y4qmMWcI7pjLQiJ47 ShQfYZiqLnjdgdNykH WsrDP7kMT6Przl/JEUOLHu9uvS3gCGyPEhlImuKWBK65m0xOv0/5 ow0w15s3J7AXS3oJAWyCdOrf/fP6Qq3PS9Y /6o/cw4hiRX9yM9PU6hZZlC0ILIq9d4oQW8RF/QFC808 VYyUWMS7j2qBcXXQAi3iLocmIMvwWtyDGjqtBg7 YC7pF1CgARjMNNNkKsCFvD95cQ2KD49Ey/v7mzWs4BFlO7xo/9FkJllmjSMDPht /Oj51d2X03m3K7Tc70WhTYQy2KhXNhhnU4GIqpqRSUyCOjqSNdcMq0WEsZO5TQ9uUViFH48QsT0ATVApUuuxG1dyIHKAUYQn9Img3vAXywe H9cFl c0V897nKTW7tAV/cvqZQzlaxmUuwJ83YqTzvzBLr5R31gt xNguWdxZjkSB5kpT4Uvgj9zUlUVEjWepsAFxBWtUI9bDYcLEFUhZNjT8NW5F  QSnHheo2Kng0mO7oinRx5GhO5Nh9KXm8AgZXIBvF3xZqaWtEU9TCCnxBdxRVSEpEKVNlsLVPuHxvnxZnuB8poqO/LeyHOMw7yM2kA8W6F3s5bDliQ5roR9HLHI00Qw2FOmIxLpXAc8YTSVlJc7yOAY1hu7XEEVmdR2rahUwuo1oyhLw0W30SDqi9dWtBwdk6n/EpaPxzJHO0eD9HCkb934uI3DGvCV/65ZNVKn/dTjenaBZjD5dk5TbU64HdbgGvsfJRAk7DNGZHK o UXycQVLx2E7tSY2a5yiz/R2PLtDzlXgiTfotsAIoW46ZUPkT dRa0OYS /AXOdRyMHWa7wwjTTK g0SOnlEO2fMcv7wsqN /CfLc/o67hlP4tKOx8FNb QjwU13NMVHV2JZd/pM6NDB9ZqdFnoLPaoYqR6IyvQPLhWiAhvo6A840nAjfwedNiGM8nsc lvWf2mHyvUc0hqA75Cp4l6L3lACU39kGBSD2Q yYP2Idiysj0PIru2fkCXVvUUzIPTm3h2vyLs9st36ozYpStQkUD 21 ZCOH0hHht0hQIt7/UutVrvd4SgPKambz8/LeeanjV9sIyRJfkmtXjemDlOfK2QlMApCYkM2i9M4A1r5Gc3zX5BjXhe2CN85S65TsYsaxjBYgcPBvArRmmuaBZi8F7FIyo2/pUoXMzFDdpAVN/lGDQ1zDTqY2fNkfur0j0cgg84h9ReKVPDEvPqMynB2KS930ggdLn1wHTkX8Ik76DmQo8a q7F3 FuyCFanvnHzPgtoAkbTT2ZesASQN8WBIwCKKpOcpTMzYOzr/03cIZRak9rSgyrheO668tWfG4k6u1Xj/sYit7t62O5hEsNCMMtyETcZWXNGkR2qmLnl0fFz401WQCJN3a2JKRscFxYFSEx64oRSxn /goEU59qnToxLxjUgyTkxqaLMkx4DgEPEsI44qTRm6I4F1gdGMgsSp284VYvyR3clJQnx/RIoykY5o3ktJ/fyU7ZRtUJog7ywG1kjmMx56wUfUMqYxXI30Hgf7saSHBcOdvf7h9lWCtyihTiSsRWSyYXeXUfCbe98c5CeFdrGnJGBOd2agx2O12QzTloFWf6O vMGaglOglxHthwHUomAHlgNkoGCFy6Fh76lkhEC01CVXhygvpywjEtr/Q bA0VwmbEoBY/tn6aaTbNBtbyO01tmbTtuU32U4BHHiJMg2IIQBSOyQq1ZI3/fBpc mHTJJOxY3Vv2n5ZajZLZeNLHSZDUpzNYcV/qvwlI3Zfc/mJcQvPaeXrPKesKw4DTe7zp5ieInBdZ1WZnAtIdR4dri3NAe11fqJ8DEVzGchzRYncui5S2wG9T3kp Y2va9b bx/gduRtiBgz7TUWJE4/varBBLNlp6GSKje660NT1wEqv2J/5klPGO NiTHIEabScYUX4wwB8bVBs7uglZwske0E THIJrPVm1VKaFo9dCo1aFZmGpQBb xOai0C8T 2Ylah6FYLLwx2plm2LypUXjXaS 1tM24/a83ggN7uPRxY5ot5zSmn1ZCO/sL7xEMv1leahpdtwuEaDe0mThCFeffjytIFU2qEIR1/Pk/jDewfbTk6AYFxYq3jyqO48jpa4AefQq5QpKGkHTNyJiLZYkqCSzh1imndDX3v0sHM7UCsBXp1UrQm RjG51nVFns8Mx5 jxyvWnlmZSkAQ2qhnPqHCxkFO6UaG6e/C4CaaRfjp7744mmhs5DlPI7ZDcrKr1B81WaoC6RuV/EwBipLcoi9oMssn4f MxC xIsoqTzbT09N3d/2KqJW87B4RO4LmERBpCY062pqVf8U3v8ufj77wifoPgbgC6fYJRDkU0u0B0UqrIzegBZHfAwGROmcO0hREb8RON1DAC2peWV9sstTYqXqJsNrfDKd2lcicE3wx3dJ j/5I5jSuwpwa5qgmNPsktnC7pNm OReHVDbUtHG2d NbOP119sXC1gPgzdTnUf0ZrYP5Vg7mqN4cVVrEtkmS9cgO6agO49h4S4Y3F3/JhHcOXrMPjSO0KpbIymFkTBCa9qImztGhmsTIdcBxoEEAf2Qp76pF2JaFHTHNPYv3yUOM5a2dy6GwFeuvHAG15Qtv7ubdpiyccB3dV7Y/mcV/T3H5M9cwk0a0b3LCNgoPJil4gH7ePjHGgt/50IUnEn6HRTdxaUkTgk30hRb/y4JBiF3905lSRF/rIK7dXPBxTXvd3VV KYMEjP/VZR7AQmxqY3R6FY5iUUx1x8sNM28CABl8VdeDoJZCFoxFcRa3rwY BbAybScDR2vOUPpktNYA1ohvQ/Zr92Y94YWL/PhBHLiIywjUcjrnNEae1tqAx5zWUle8S54sE2cB/KKbRRZJXL pemn/UtZHtL19Ywi6jk 3FoewSmj632pGA3nqdyHHx3qNP9QUReZUjjMyVshQRzPkjvzcw5rfJoExze5AZmLTj9jMw/TeCZ7TfKUjIqHqMCHzO10mRTVSdJDofUmWEwOrOI1Czgiom5Id5WMF5AQE3bsyV4lCC6L6U7ohnteaj KdVXcqUU LKGtK/emoWLj B4SuOESOvMKwUsM1Btn8M3rCvDFmNCW hLnt NyVMSeyD1VpNV6KpnKIfD 76qY8Wb2OXG6JgdOc7EwGwGOvRDMmOmURHh1UsfjR1kpingOK9uVgYzCE8hMEzh/obAl2hOp45p9HocF9lQzrPXJbR2U5Eu7lozu3iEzwdpdctVLRzWjx9v6lcSIsLv8SbWhFJzFPvTTcuzS7 OzM8f3u9Bz1v7bc4g3zjynxRVTaN/8dFBavUc/hwthsUsCzIcz VHHrJSVrarjvhME6CjUmSqssGjD/Ka4aQrRBLoWaAWnHVhMq4D0sWgbl7QhAVV97iM6CcDpej/GKzHYpysm8SOWVV7GMgcQUsRKeLSB08ZflxzZoqwqzchs L8efK2fxC9eW3GKR81iHgG a8YAOufjiAyp5iTHWH frP8HcNSmi mIhgI1YCcmx68/vZBVQax6OsOXFJ5BL6bWf6M AkexxUHX7yJN47fkLNQE/LSJ5ODpWVfKcHxK1 XgBLCIJMc/jw9szim9BsipEEX7yY2BdS2SkQzemEW aF6YsP5NgMeoJpLPK8xlrlvz khwGfok ovrIEE9/PJl5bqdnHu4o7K/9PnvEUaufFlYXn2eZrIrYj6SHGkXS6sqBjVj/B8QUKMcjW/aQScUZs2iwMyu4Df50Q8vHyZXDWJR9epAtzK75GJIol4SrPJC5W cQsVZsUE xhfFEDfL9JC674F9El/i3aRrl1LqWgqueeTj/06W0aKuD oc6DEZIE/NvNWKbIpq89Y0JUKrSsVDlGeIlq10U7NlbO7lurfXuxyae92zIM3OWqA2RyX6 P hbk6h9C4y15DTwk6VOZRNgP6J1DmxsU2/L7yIEXEvi/7f2DeCwWZzdjlTkbpcbesZ4VjeAyMViPBa95tUvwzggvxPb74ckEK7/5g08V3k71SEmhqrs5vH2AjBQFgwBRA9o 5efhgA3L0FXN/WaZg59hNOUNJwVKyWvBiWO/81bfQbMKNe8CmoVk7g/8d5bgLWhaR iyL9aOfkWHc79NESmrJsYtfj5SI1DJkvOjTjolLRY5yryr0Rrkv3tu6YGVDgL7DfhZyWpoTkA0HkHkgMAnOt9b47/1o7mRp4e4dzYVC0yNSwLmJOeIinF2kWOl5wm7E8LXsjMvchK4z9nkqukcNaai1E47tFI8pSCQXLCVEJpWscc4Ip8VajUxCKuUkQWKI5MS oZtWczOb9 7rXcbrHf9bNoAzkjq7KSi F/wqKu8CoI3l2P2rWzyk4HyVaRF7pt3OJ8hyvv h6Czt1Ba32ExLf15w2tdp9tCw/oU4Q6WVEB2pWQxsSHAzJzsCPLLC5nr6fNhh6 k69tiwAEwYVMrbg1SpcwteOkUZ8pv7r9ZUT32yoW59rf9TcYbILS1S3JYalUeaww9WREuhmeRLDeEdOWeM2R0b01mBl5w0vFxrgQgbSt6DbLrPwaOShNQiyZfXFKNaFevFSqyoMxXqRCOTd7JgS1vEmGDQLARL1tz7XPadF2rOCxjCIaWdylF7JnnTQwdsaUltJlb2SxYbp80L1nF4ydFviPIK0Wm6L1xQDgrq8pAW1g2a7dMmzP2Xf7LcjI8KqD2A45lhofBiKt3e/pRRmmBpLOKE7PBMS66wwDSeh0DgV/bFHemUZzW7F86jceMQaIX Xnu7T40RDTQNWJSY97K84X2Lg VICm9Iq9a/uBnBHtgKZKCurOKmrI/M4jQEYhRu /SUYYCErLdBCSWwzZe/6YrtepvOh8YICtZZ5uIuJpSMnCDPuY8IxLF2xr6R5gQiSzgnr3Q23RSvTjcdPQxFhlg6qQ7d3f4/BJu waWdRUhUhYh4Lr5ngVzB V7H/hoJVE1LimqZcBTgCTez36qm26G2p7fjG5CLHfZ53oHGbU7x smPB6EO8guS4zXvVKnviKFvpjISY0FeEMo09fFj5DRB85X7DNi6zcRDq7egnEDgtNQg2ZMtUFfAq5rG6PZ7pgaJqlIvykzWldnWs4ulLnH8CTC3H2B/dcdVGx3HGvBbIRvOjVobZbpFchUAcmGBH9AWpiOg9X0NbhDQt6Ok GN/VZMB0xk/GxT5uBpoaRglC6XaRQ8aMFsjtKP0obrznHheEiGBMyOMBkl1L54kdHyveEDbHDSfuOOq2AuLK//HZgxjr1mFxTFUilrtqIFqmzoP0 1GBYmHPfblcnRa4pbJqR7Z6PnZbYKjStYh2NtWFbvxVm9T5ReU36caQRTm9tJjOpjdXr9dq e3U9Iv1WG1rQL8EsEX4f7l5dT ynRayF08Dr/ONLUpWdZKZ 33xNEWYK dvY05kq9r74nQ3 15ogaD6fVPHYhx21hAkUMUjvhy/61d1HelFXKT0Oiiewu0W95K/87E0 4vY9ZhBMeEHSNpKOTx/ZWyqxaM7iDbAxP7 71 xOcfJ7W 8tg0sjYNrXZOuU72EnRWohYRva6SDYr/qCabadL3aLwxfVwm9Z/SlOkn4AbifAxoRwsbrKg/CpON3pz2cw7s/Byn9xra4FHPa3 VkDGkNufoUM7L5nvJ95fqW 1PrplvdbqvZQPENbUVFZ2lMvBknWdPpKQ16zg32eXcwpzqlf1PIz89XErmr/GfFqHU1Mnpf815gfOCIyQqlzm777UQdLIRZ34dL9VJIMmgyp9AS0MnDLh0eK7FhpyBQUJITIKEaFgEXLjiYEQc3jXBZlHtmHDcFTTehbaRRV9m4vjAM8hJJM2coUMB50cJ4PD/Bpxt MBEHFLdKvOBHn24vGUmdjVOVsUVO9Wmq0pF7ACTvKTaiaeInoQKbyHWnKbvyh8KWmtKq FsmrY5a3j51wSwC/31ndXN39F2hjO5u9YKh cDGcar2oLi4wmVV4NXQnbYu4CikokYEQH 2IP8p3dHjspHwYXLzTkr5tn4xwMSINgbIAWBLuoI8CEiDRkM8nDcKHRMawSRNGRnIHzwYAO8YKa6SBJobmSSCu9LpdSVNeGy3FZA2SNwW0H W2Hs/j54iinu2dIP5q08iOy3QzbogdRoVb2ILmm60hqh9Ab989JgX/75IDKKJkrINbQ/tKE6qMxFzex9utyF1CHKS1OeMDH0W5rD5rA2NIb0tGe7R1LsSmM3a50 8ppTtycQyoo8VgmgVrCz3tBTNgLFk7bF50uIIbonILcfDISjRdjFQcgiKLgAw4BZQlMWYk3KjTTPcoGjkDkEBCPFrE8JbPK K4vAgiVeiCieAgWSVsUg66LQDroK1CrdhDvV3NNf5BpSwFX0tpqihVmNGHCe6c2ThdGYc9ORqKlNWpxjCTWRNbmrJKVJZM3kSRJpSDwNduMZFLnFckXOlh8F/ ixZcF nbRkWwYu AxGeRcnx8 17v4JDBXwIw0wixp9VTfBJL5pwZ89El 3XkJcjHc0jEOYlaRghJEYV4cO/oE8QyIdOWtqJwfVycggW/BPxeWqdMvG8twXheG/rRwDiRSjy4/3GiADqbDh4Lky55fYwlQjAY5rTwbKAq7RX/kcRaOFafDfYtL7xGvL8KGipJdeoBik3KvNyGS9Tn 7LUG74YQqN8kTpwFnPCpe2Y6 GREoS GUHm H/obAnBJaWnb4/N6Ui7/Kdac068xfr/oRXrdGfM53BGhx3SWpnYJpkzPsgZj/YhvWMn8oEjLZ/4l5/Xh dDc/mkHjRu8Dee/VQe0tiE4JgEE5UK8r2oiBu6nept6d 18M0NggkWnRI45fjtsq2cxJheSF4vdEn1RgAXdr0ewBmbTivefCDdiztdCidT4Rv6/o5HpuVbODMXsnIEbQZ/3QN4LIRFL6JyRlh5zogTpEMvsPZ09OHhbfR/qSK tBu3xfYKHKS7RLAGvGKqsG 5YKTamNElEmqcHKFyTj8g4BZnzWECZeEXeLplm54OYfyv6/cZAoAR2VStgZYlQ3jfvkXxSTgpJjrG8DlJE6zj00pRUaHuejzu3BtAGwehEq421T7EPB9CXMtOjyOSbIAM2Gzld/T2KArVXN8vleuzFQJLr
ddlFilterBy=1
lstSearchBy=Mexico
ctl_list_YearFrom=2017
ctl_list_WeekFrom=1
ctl_list_YearTo=2021
ctl_list_WeekTo=53
ctl_ReportViewer$ctl03$ctl00
ctl_ReportViewer$ctl03$ctl01
ctl_ReportViewer$ctl10=ltr
ctl_ReportViewer$ctl11=standards
ctl_ReportViewer$AsyncWait$HiddenCancelField=False
ctl_ReportViewer$ctl04$ctl03$ddValue=1
ctl_ReportViewer$ctl04$ctl05$ddValue=1
ctl_ReportViewer$ToggleParam$store
ctl_ReportViewer$ToggleParam$collapse=false
ctl_ReportViewer$ctl05$ctl00$CurrentPage
ctl_ReportViewer$ctl05$ctl03$ctl00
ctl_ReportViewer$ctl08$ClientClickedId
ctl_ReportViewer$ctl07$store
ctl_ReportViewer$ctl07$collapse=false
ctl_ReportViewer$ctl09$VisibilityState$ctl00=None
ctl_ReportViewer$ctl09$ScrollPosition
ctl_ReportViewer$ctl09$ReportControl$ctl02
ctl_ReportViewer$ctl09$ReportControl$ctl03
ctl_ReportViewer$ctl09$ReportControl$ctl04=100
__ASYNCPOST=true

I figured that I could whip out requests and issue a simple query like this:

import requests

with requests.Session() as s:
  data = {'lstSearchBy': 'Mexico',
          'ctl_list_YearFrom': '2017',
          'ctl_list_WeekFrom': '1',
          'ctl_list_YearTo': '2021',
          'ctl_list_WeekTo': '53'}
  r = s.post('https://apps.who.int/flumart/Default?ReportNo=12', data=data)
  print(r.text)

Unfortunately, rather than returning the table in the screenshot, this query simply returns the form at the top of the screenshot. At this point, I start to think that some of those other POST parameters must actually be required (__EVENTTARGET, __VIEWSTATE, __VIEWSTATEGENERATOR, etc.).

Looking at the original form that's returned when I visit https://apps.who.int/flumart/Default?ReportNo=12, I see this:

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 4.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<html xmlns="http://www.w3.org/1999/xhtml">
<head id="Head1"><title>
    WHO FLUMART OUTPUTS
</title></head>
<body>
    <form method="post" action="./Default?ReportNo=12" id="form1">
<div class="aspNetHidden">
<input type="hidden" name="__EVENTTARGET" id="__EVENTTARGET" value="" />
<input type="hidden" name="__EVENTARGUMENT" id="__EVENTARGUMENT" value="" />
<input type="hidden" name="__LASTFOCUS" id="__LASTFOCUS" value="" />
<input type="hidden" name="__VIEWSTATE" id="__VIEWSTATE" value="u38eXuRq1rlncPtmBq04xgVPASJukKZ8QVDUb
...

(this is truncated for brevity)

Here, there are a number of hidden inputs with names that correspond to the POST request that Firefox issues (__EVENTTARGET, __EVENTARGUMENT, __LASTFOCUS, __EVENTVALIDATION, etc.). This made me think that what I could do is just grab those values and then tack them onto the POST data like this:

import requests
from bs4 import BeautifulSoup

with requests.Session() as s:
  r = s.get('https://apps.who.int/flumart/Default?ReportNo=12')
  soup = BeautifulSoup(r.text, 'lxml')
  data = {'lstSearchBy': 'Mexico',
          'ctl_list_YearFrom': '2017',
          'ctl_list_WeekFrom': '1',
          'ctl_list_YearTo': '2021',
          'ctl_list_WeekTo': '53'}
  hidden_inputs = soup.find_all('input', {'type': 'hidden'})
  for i in hidden_inputs:
    data.update({i['id']: i['value']})
  r = s.post('https://apps.who.int/flumart/Default?ReportNo=12', data=data)
  print(r.text)

But this unfortunately doesn't work either; I just get the same submission form.

I'm a bit stuck at this point. I'm clearly missing something. Does anyone have any insights in how I could grab the table that's produced in the screenshot? Any help is appreciated!

CodePudding user response:

There are a couple complexities that occur when scraping this site:

  1. In order to get the actual data from a request to the site's endpoint, the entire headers of the original browser's request must be sent, containing in particular the user agent and cookies.
  2. The data payload of the POST request should be encoded as it is displayed in the browser's developer network settings.
import requests
from bs4 import BeautifulSoup as soup

def parse_headers(s):
    return dict(i.split(': ') for i in filter(None, s.split('\n')))

def get_page_data(headers, data):
    return requests.post('https://apps.who.int/flumart/Default?ReportNo=12', headers=headers, data=data).text

def parse_table(page):
    _, _, h, *body = [list(filter(None, [i.get_text(strip=True) for i in b.select('td')])) 
        for b in page.select('table table table table tr:nth-of-type(5) table tr')]
    return [dict(zip([*filter(None, h)], i)) for i in body]

def data_format(filter_by, year_from, week_from, year_to, week_to):
    return f'ScriptManager1=ScriptManager1|ctl_ReportViewer$ctl09$Reserved_AsyncLoadTarget&ddlFilterBy=1&lstSearchBy={filter_by}&ctl_list_YearFrom={year_from}&ctl_list_WeekFrom={week_from}&ctl_list_YearTo={year_to}&ctl_list_WeekTo={week_to}&ctl_ReportViewer$ctl03$ctl00=&ctl_ReportViewer$ctl03$ctl01=&ctl_ReportViewer$ctl10=ltr&ctl_ReportViewer$ctl11=standards&ctl_ReportViewer$AsyncWait$HiddenCancelField=False&ctl_ReportViewer$ctl04$ctl03$ddValue=2&ctl_ReportViewer$ctl04$ctl05$ddValue=1&ctl_ReportViewer$ToggleParam$store=&ctl_ReportViewer$ToggleParam$collapse=false&ctl_ReportViewer$ctl05$ctl00$CurrentPage=&ctl_ReportViewer$ctl05$ctl03$ctl00=&ctl_ReportViewer$ctl08$ClientClickedId=&ctl_ReportViewer$ctl07$store=&ctl_ReportViewer$ctl07$collapse=false&ctl_ReportViewer$ctl09$VisibilityState$ctl00=None&ctl_ReportViewer$ctl09$ScrollPosition=&ctl_ReportViewer$ctl09$ReportControl$ctl02=&ctl_ReportViewer$ctl09$ReportControl$ctl03=&ctl_ReportViewer$ctl09$ReportControl$ctl04=100&__EVENTTARGET=ctl_ReportViewer$ctl09$Reserved_AsyncLoadTarget&__EVENTARGUMENT=&__LASTFOCUS=&__VIEWSTATE=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&__VIEWSTATEGENERATOR=15FCE702&__EVENTVALIDATION=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&__ASYNCPOST=true&'

headers = """Host: apps.who.int
authority: apps.who.int
sec-ch-ua: "Chromium";v="94", "Google Chrome";v="94", ";Not A Brand";v="99"
sec-ch-ua-mobile: ?0
user-agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.81 Safari/537.36
content-type: application/x-www-form-urlencoded; charset=UTF-8
cache-control: no-cache
x-requested-with: XMLHttpRequest
x-microsoftajax: Delta=true
sec-ch-ua-platform: "macOS"
accept: */*
origin: https://apps.who.int
sec-fetch-site: same-origin
sec-fetch-mode: cors
sec-fetch-dest: empty
referer: https://apps.who.int/flumart/Default?ReportNo=12
accept-language: en-US,en;q=0.9
cookie: ASP.NET_SessionId=bnx31vt1hsfsldihuszbk452; BIGipServerpool_apps.who.int_http=rd2o00000000000000000000ffff9ee80c4eo80; TS01ac0ef4=015dd60f3e1f352f15a41d299fd9ecd7a082e0b85eb62504deed1220d15a2f9bb946cec46693ae3a5e8d2733baff23250470302f28f8d768bf0028a5807d15b3cf9569ac54cf66bc629d7233dd96a586a1ceb17a95
Content-Length: 24277"""

data = data_format('United States of America', 2020, 2, 2020, 9)
print(parse_table(soup(get_page_data(parse_headers(headers), data), 'html.parser')))

Due to the character limits on Stack Overflow posts, the output from the script can be found here.

  • Related