{"id":31625,"date":"2022-01-07T22:22:08","date_gmt":"2022-01-07T14:22:08","guid":{"rendered":"https:\/\/swarma.org\/?p=31625"},"modified":"2022-01-07T22:22:08","modified_gmt":"2022-01-07T14:22:08","slug":"pnas%e7%89%b9%e5%88%8a%ef%bc%9a%e8%ae%a9%e7%96%ab%e6%83%85%e8%bf%bd%e8%b8%aa%e9%a2%84%e6%b5%8b%e6%9b%b4%e6%9c%89%e6%95%88%e7%9a%84%e7%bb%bc%e5%90%88%e6%a8%a1%e5%9e%8b%e3%80%81%e6%95%b0%e6%8d%ae","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=31625","title":{"rendered":"PNAS\u7279\u520a\uff1a\u8ba9\u75ab\u60c5\u8ffd\u8e2a\u9884\u6d4b\u66f4\u6709\u6548\u7684\u7efc\u5408\u6a21\u578b\u3001\u6570\u636e\u6d41\u4e0e\u8d44\u6e90\u5e93"},"content":{"rendered":"
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Roni Rosenfeld\u3001Ryan J. Tibshirani <\/span>| \u4f5c\u8005<\/strong><\/span><\/strong><\/p>\n

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Epidemic tracking and forecasting: Lessons learned from a tumultuous year<\/span><\/p>\n

\u8bba\u6587\u5730\u5740\uff1a<\/span><\/section>\n
https:\/\/www.pnas.org\/content\/118\/51\/e2111456118<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n


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\u81ea2012\u5e74\u4ee5\u6765\uff0c\u8be5\u7bc7\u6587\u7ae0\u4e24\u4f4d\u4f5c\u8005Roni Rosenfelda\u548cRyan J. Tibshirani\u5171\u540c\u9886\u5bfc\u7684\u5361\u5185\u57fa\u6885\u9686\u5927\u5b66\u5fb7\u5c14\u83f2\u7814\u7a76\u7ec4\u7ec7<\/span>\uff08Carnegie Mellon Delphi Group\uff09<\/span>\u4e00\u76f4\u81f4\u529b\u4e8e\u201c\u6d41\u884c\u75c5\u9884\u6d4b\u201d\u548c\u201c\u6784\u5efa\u76f8\u5173\u8f85\u52a9\u4fe1\u53f7\u201d\u5de5\u4f5c\uff0c\u4ee5\u5efa\u7acb\u6b64\u7c7b\u6d41\u884c\u75c5\u9884\u6d4b\u6a21\u578b\u30022020\u5e74\u65b0\u51a0\u75ab\u60c5\u7206\u53d1\u540e\uff0c\u8be5\u7ec4\u7ec7\u4e5f\u4e00\u6837\u52aa\u529b\u5bfb\u627e\u529e\u6cd5\u4e3a\u7f8e\u56fd\u5e94\u5bf9\u75ab\u60c5\u505a\u51fa\u8d21\u732e\u3002\u8be5\u7ec4\u7ec7\u6700\u7ec8\u5c06\u91cd\u70b9\u51e0\u4e4e\u5b8c\u5168\u653e\u5728\u6570\u636e\u65b9\u9762\uff0c\u5bfb\u6c42\u591a\u4e2a\u65b9\u5411\u4ee5\u4fbf\u5efa\u7acb\u548c\u5411\u516c\u4f17\u63d0\u4f9b\u5404\u79cd\u5404\u79cd\u53cd\u6620\u7f8e\u56fd\u75ab\u60c5\u5b9e\u65f6\u52a8\u6001\u7684\u65b0\u6307\u6807\u3002\u8fd9\u7bc7\u7279\u520a\u4e2d\u4f5c\u8005\u901a\u8fc7\u4e09\u7bc7\u6587\u4e2d\u3001\u4e09\u79cd\u4e0d\u540c\u89d2\u5ea6\u4ecb\u7ecd\u4e86\u8fd9\u4e9b\u5de5\u4f5c\u3002\u6b64\u5916\uff0c\u8fd8\u6709\u4e00\u7bc7\u4ecb\u7ecd\u4e86\u8be5\u7ec4\u7ec7\u7684\u90e8\u5206\u56fd\u9645\u5de5\u4f5c\uff0c\u8fd9\u4e9b\u5de5\u4f5c\u4e0e\u5728\u7f8e\u56fd\u7684\u5728\u7ebf\u8c03\u67e5\u5de5\u4f5c\u76f8\u4f3c\u3002<\/span><\/section>\n
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\u5fb7\u5c14\u83f2\u7814\u7a76\u7ec4\u7ec7\u5b98\u7f51\u9875\u9762\u3002\u5fb7\u5c14\u83f2\u7814\u7a76\u7ec4\u7ec7\u662f\u5361\u5185\u57fa\u00b7\u6885\u9686\u5927\u5b66\u7684\u4e00\u4e2a\u7814\u7a76\u5c0f\u7ec4\uff0c\u81f4\u529b\u4e8e\u6d41\u884c\u75c5\u8ddf\u8e2a\u548c\u9884\u6d4b\u7684\u7406\u8bba\u548c\u5b9e\u8df5\u7814\u7a76\uff0c\u957f\u671f\u613f\u666f\u662f\u4f7f\u8fd9\u4e9b\u6280\u672f\u80fd\u50cf\u5f53\u4e0b\u7684\u5929\u6c14\u9884\u62a5\u4e00\u6837\u6709\u7528\u4e14\u88ab\u666e\u904d\u63a5\u53d7\u3002\u7ec4\u7ec7\u5b98\u7f51\uff1ahttps:\/\/delphi.cmu.edu\/<\/span>
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\u5b9e\u65f6 COVID-19 \u6307\u6807\u5f00\u653e\u8d44\u6e90\u5e93<\/span><\/section>\n
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An open repository of real-time COVID-19 indicators<\/span><\/h1>\n
\u8bba\u6587\u5730\u5740\uff1a<\/span><\/section>\n
https:\/\/www.pnas.org\/content\/118\/51\/e2111452118<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

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Reinhart \u7b49\u4eba\u63cf\u8ff0\u4e86\u5fb7\u5c14\u83f2\u7814\u7a76\u7ec4\u7ec7\u5f53\u4e0b\u5efa\u7acb\u548c\u7ef4\u62a4COVIDcast\u7cfb\u7edf\u7684\u76f8\u5173\u5de5\u4f5c\uff0c\u8fd9\u662f\u4e00\u4e2a\u5f00\u653e\u7684\u3001\u5b9e\u65f6\u7684\u7f8e\u56fd\u65b0\u51a0\u75c5\u6bd2\u75be\u75c5\u6307\u6807\u5e93\u3002\u8fd9\u4e9b\u6307\u6807\u6765\u57fa\u4e8e\u4e00\u7cfb\u5217\u4e0d\u540c\u7684\u6570\u636e\u6e90\uff1a\u533b\u7597\u6d4b\u8bd5\u8bbe\u5907\u3001\u533b\u7597\u4fdd\u9669\u7d22\u8d54\u3001\u4e92\u8054\u7f51\u641c\u7d22\u8d8b\u52bf\u3001\u57fa\u4e8e\u5e94\u7528\u7a0b\u5e8f\u7684\u79fb\u52a8\u6570\u636e\u548c\u5728\u7ebf\u8c03\u67e5\u7b49\u7b49\u3002\u8bb8\u591a\u6307\u6807\u88ab\u8bc1\u660e\u4e0e\u5927\u6d41\u884c\u7684\u5173\u952e\u6570\u5b57<\/span>\uff08\u62a5\u544a\u7684\u75c5\u4f8b\u3001\u4f4f\u9662\u3001\u6b7b\u4ea1\uff09<\/span>\u6709\u7740\u6709\u610f\u4e49\u7684\u7edf\u8ba1\u5173\u7cfb\uff0c\u800c\u5176\u4ed6\u6307\u6807\u5219\u552f\u4e00\u5730\u53cd\u6620\u4e86\u67d0\u4e9b\u53ef\u80fd\u63a8\u52a8\u6216\u5f71\u54cd\u75ab\u60c5\u4f20\u64ad\u7684\u6d3b\u52a8\u3002\u8be5\u6587\u7ae0\u901a\u8fc7\u4e00\u7cfb\u5217\u5b9e\u4f8b\u8bf4\u660e\uff0cCOVIDcast\u77e5\u8bc6\u5e93\u4e2d\u7684\u6307\u6807\u53ef\u4ee5\u63d0\u9ad8\u4f20\u7edf\u516c\u5171\u536b\u751f\u62a5\u544a\u6570\u636e\u7684\u53ca\u65f6\u6027\u3001\u53ef\u9760\u6027\u548c\u5f71\u54cd\u8303\u56f4\u3002<\/span><\/section>\n

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\u8bba\u6587\u9898\u76ee\uff1a<\/span><\/section>\n
Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?<\/span><\/section>\n
\u8bba\u6587\u5730\u5740\uff1a<\/span><\/section>\n
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McDonald\u7b49\u4eba\u8be6\u7ec6\u5206\u6790\u4e86 COVIDcast \u8d44\u6e90\u5e93\u4e2d\u7684\u4e00\u7ec4\u6838\u5fc3\u6307\u6807\u662f\u5426\u53ef\u7528\u4e8e\u63d0\u9ad8 COVID-19 \u77ed\u671f\u9884\u6d4b\u548c\u70ed\u70b9\u68c0\u6d4b\u6a21\u578b\u7684\u51c6\u786e\u6027\u3002\u8fd9\u8bf4\u660e\u6307\u6807\u7684\u5b9a\u91cf\u6548\u7528\u4e0e\u76f8\u5173\u4e0b\u6e38\u5efa\u6a21\u4efb\u52a1\u4e2d\u89c2\u5bdf\u5230\u7684\u6536\u76ca\u76f4\u63a5\u76f8\u5173\u3002\u8be5\u8bba\u6587\u53d1\u73b0\uff0cCOVID-19\u9884\u6d4b\u6a21\u578b\u5728\u8003\u8651\u548c\u75ab\u60c5\u76f8\u5173\u7684\u533b\u7597\u7d22\u8d54\u3001\u81ea\u6211\u62a5\u544a\u75c7\u72b6\u548c\u4e0e\u75ab\u60c5\u6709\u5173\u7684\u8c37\u6b4c\u641c\u7d22\u53ef\u4ee5\u63d0\u9ad8\u9884\u6d4b\u51c6\u786e\u6027\u3002<\/span><\/section>\n

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\u7b2c\u4e09\u7bc7\uff1a\u5982\u4f55\u91cf\u5316\u75ab\u60c5\u5f71\u54cd\uff1f<\/span><\/strong><\/span><\/section>\n

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\u7f8e\u56fd COVID-19 \u8d8b\u52bf\u548c\u5f71\u54cd\u8c03\u67e5\uff1a\u6301\u7eed\u5b9e\u65f6\u6d4b\u91cf COVID-19 \u75c7\u72b6\u3001\u98ce\u9669\u3001\u4fdd\u62a4\u884c\u4e3a\u3001\u6d4b\u8bd5\u548c\u75ab\u82d7\u63a5\u79cd<\/span> <\/span><\/span><\/section>\n
\u8bba\u6587\u9898\u76ee\uff1aThe US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination  <\/span><\/section>\n
\u8bba\u6587\u5730\u5740\uff1a<\/span>https:\/\/www.pnas.org\/content\/118\/51\/e2111454118<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n

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Salomon\u7b49\u4eba\u5173\u6ce8\u7f8e\u56fdCTIS<\/span> (COVID-19 Trends and Impact Survey)<\/span>\uff0c\u4e00\u4e2a\u5fb7\u5c14\u83f2\u7814\u7a76\u7ec4\u7ec7\u4e0eFacebook\u5408\u4f5c\u8fd0\u8425\u7684\u5728\u7ebf\u8c03\u67e5\u9879\u76ee\u3002CTIS\u662f\u9488\u5bf9\u5927\u6d41\u884c\u53ca\u5176\u5bf9\u4eba\u4eec\u5f71\u54cd\u7684\u4e00\u4e2a\u975e\u5e38\u5b9e\u7528\u4e14\u4e30\u5bcc\u7684\u6570\u636e\u6765\u6e90\uff0c\u90e8\u5206\u53cd\u6620\u5728COVIDcast \u8d44\u6e90\u5e93\u7684\u6307\u6807\u4e2d\u3002\u6839\u636e\u6570\u636e\u4f7f\u7528\u534f\u8bae\uff0c\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u83b7\u5f97\u4e2a\u4eba\u3001\u533f\u540d\u8c03\u67e5\u56de\u7b54\u7684\u5b8c\u6574\u6570\u636e\u96c6\u3002\u6539\u6587\u7ae0\u63d0\u51fa\u4e86\u63cf\u8ff0\u6027\u5206\u6790\uff0c\u53cd\u6620\u4e86CTIS\u4f5c\u4e3a\u516c\u5171\u536b\u751f\u62a5\u544a\u7684\u91cd\u8981\u8865\u5145\u7684\u72ec\u7279\u4ef7\u503c\uff0c\u7279\u522b\u662f\u4f5c\u4e3a\u8861\u91cf\u6709\u5173\u884c\u4e3a\u3001\u6001\u5ea6\u3001\u7ecf\u6d4e\u5f71\u54cd\u548c\u5176\u4ed6\u4f20\u7edf\u516c\u5171\u536b\u751f\u6d41\u672a\u6db5\u76d6\u7684\u4e3b\u9898\u7684\u5173\u952e\u4fe1\u606f\u7684\u91cd\u8981\u5de5\u5177\u3002<\/span><\/section>\n

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\u7b2c\u56db\u7bc7\uff1a\u5982\u4f55\u5f00\u5c55\u56fd\u9645\u5408\u4f5c\uff1f<\/span><\/strong><\/span><\/section>\n

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\u901a\u8fc7\u4ece Facebook \u7528\u6237\u7fa4\u4e2d\u62bd\u6837\u7684\u5728\u7ebf\u8c03\u67e5\u5bf9 COVID-19 \u5927\u6d41\u884c\u7684\u5f71\u54cd\u8fdb\u884c\u5168\u7403\u76d1\u6d4b<\/span><\/section>\n
\u8bba\u6587\u9898\u76ee\uff1aGlobal monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base  <\/span><\/section>\n
\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/www.pnas.org\/content\/118\/51\/e2111455118<\/span><\/section>\n<\/section>\n<\/section>\n<\/blockquote>\n
 <\/span>
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Astley\u7b49\u4eba\u5173\u6ce8\u56fd\u9645\u7248\u7684CTIS\uff0c\u8fd9\u662f\u4e00\u4e2a\u7531\u9a6c\u91cc\u5170\u5927\u5b66\u548cFacebook\u5408\u4f5c\u7684\u5728\u7ebf\u8c03\u67e5\u3002\u8fd9\u4e00\u56fd\u9645\u8c03\u67e5\u8986\u76d6\u4e86100\u591a\u4e2a\u56fd\u5bb6\u548c\u5730\u533a\uff0c\u5e76\u4e0e\u7f8e\u56fd\u56fd\u5185CTIS\u7684\u534f\u8c03\uff0c\u56e0\u6b64\u4e24\u8005\u5177\u6709\u76f8\u4f3c\u7684\u7ed3\u6784\u548c\u66f4\u65b0\u529f\u80fd\u3002\u6839\u636e\u6570\u636e\u4f7f\u7528\u534f\u8bae\uff0c\u4e2a\u4eba\u7684\u3001\u533f\u540d\u7684\u56fd\u9645\u8c03\u67e5\u56de\u7b54\u7684\u5b8c\u6574\u6570\u636e\u4f1a\u518d\u6b21\u63d0\u4f9b\u7ed9\u4e2a\u4eba\u7814\u7a76\u4eba\u5458\u3002\u8be5\u7814\u7a76\u7684\u5206\u6790\u53cd\u6620\u4e86\u56fd\u9645\u8c03\u67e5\u7684\u4e00\u4e9b\u57fa\u672c\u548c\u91cd\u8981\u7279\u5f81\uff0c\u53cd\u6620\u4e86CTIS\u5728\u56fd\u5916\u7684\u4ef7\u503c\uff0c\u5c24\u5176\u662f\u5728\u4e00\u4e9b\u516c\u5171\u536b\u751f\u5efa\u8bbe\u8f83\u6709\u9650\u7684\u5730\u533a\u3002<\/span><\/section>\n

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\u201cCOVID-19\u75c5\u4f8b\u201d\u6216\u201cCOVID-19\u4f4f\u9662\u60c5\u51b5\u201d\u7b49\u6807\u7b7e\u9690\u85cf\u4e86\u5927\u91cf\u7684\u590d\u6742\u6027\u548c\u6f5c\u5728\u7684\u6a21\u7cca\u6027\uff0c\u7279\u522b\u662f\u5f53\u5e94\u7528\u4e8e\u5177\u6709\u826f\u597d\u5730\u7406\u548c\u65f6\u95f4\u5206\u8fa8\u7387\u7684\u6570\u636e\u65f6\u3002\u5fc5\u987b\u505a\u5230\u4e0d\u542b\u7cca\u3001\u5c3d\u6700\u5927\u53ef\u80fd\u4fdd\u6301\u6570\u636e\u5b9a\u4e49\u7684\u4e00\u81f4\u6027\uff0c\u5e76\u4e14\u5fc5\u987b\u8bb0\u5f55\u548c\u660e\u786e\u4f20\u8fbe\u7684\u672f\u8bed\u7684\u4e0d\u540c\u4e4b\u5904<\/strong>\u3002\u4e0b\u9762\u5c06\u901a\u8fc7\u51e0\u4e2a\u5c0f\u4f8b\u5b50\u6765\u8be6\u7ec6\u8bf4\u660e\u8fd9\u4e00\u70b9\u3002<\/span><\/section>\n
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\u4e3e\u4f8b1\uff1a<\/span><\/strong>\u75c5\u4f8b\u53ef\u4ec5\u7ecf\u5b9e\u9a8c\u5ba4\u786e\u8bca\u6216\u5b9a\u4e49\u4e3a\u7591\u4f3c<\/span>\uff08\u201c\u7591\u4f3c\u201d\u7684\u5b9a\u4e49\u56e0\u7ba1\u8f96\u533a\u548c\u65f6\u95f4\u800c\u5f02\uff09<\/span>\uff1b\u5b83\u4eec\u53ef\u4ee5\u6309\u8f96\u533a\u7f51\u7ad9\u4e0a\u62a5\u544a\u7684\u65e5\u671f\u3001\u5411\u516c\u5171\u536b\u751f\u4e3b\u7ba1\u90e8\u95e8\u62a5\u544a\u7684\u65e5\u671f\u3001\u68c0\u6d4b\u65e5\u671f\u3001\u6807\u672c\u91c7\u96c6\u65e5\u671f\u6216\u5076\u5c14\u6309\u75c7\u72b6\u51fa\u73b0\u65e5\u671f<\/span>\uff08\u4fe1\u606f\u6700\u5145\u5206\uff0c\u4f46\u5f80\u5f80\u65e0\u6cd5\u83b7\u5f97\u6216\u4e0d\u9002\u7528\uff09<\/span>\u5217\u51fa\u3002\u56e0\u6b64\uff0c\u968f\u4fbf\u6d4f\u89c8\u4e00\u4e0b\u5f53\u5730\u548c\u5dde\u536b\u751f\u90e8\u95e8\u7684\u7f51\u7ad9\u5c31\u4f1a\u53d1\u73b0\uff0c\u62a5\u544a\u7684\u5185\u5bb9\u5b58\u5728\u5f88\u5927\u7684\u5f02\u8d28\u6027\u3002<\/span><\/section>\n
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\u4e3e\u4f8b2\uff1a<\/span><\/strong>\u6b7b\u4ea1\u62a5\u544a\u901a\u5e38\u6309\u5176\u53d1\u751f\u7684\u53bf\u5206\u5217\uff0c\u4f4f\u9662\u60a3\u8005\u7684\u6b7b\u4ea1\u62a5\u544a\u53ef\u80fd\u4e0e\u5176\u5c45\u4f4f\u7684\u53bf\u4e0d\u540c\u3002<\/span><\/section>\n
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1 M. Biggerstaff et al.; Influenza Forecasting Contest Working Group, Results from the centers for disease control and prevention\u2019s predict the 2013\u20132014 Influenza Season Challenge. BMC Infect. Dis. 16, 357 (2016).<\/span><\/section>\n
2 M. Biggerstaff et al., Results from the second year of a collaborative effort to forecast influenza seasons in the United States. Epidemics 24, 26\u201333 (2018).<\/span><\/section>\n
3 N. G. Reich et al., A collaborative multiyear, multimodel assessment of seasonal influenza forecasting in the United States. Proc. Natl. Acad. Sci. U.S.A. 116,3146\u20133154 (2019).<\/span><\/section>\n
4 M. A. Johansson et al., An open challenge to advance probabilistic forecasting for dengue epidemics. Proc. Natl. Acad. Sci. U.S.A. 116, 24268\u201324274 (2019).<\/span><\/section>\n
5 S. Y. Del Valle et al., Summary results of the 2014-2015 DARPA Chikungunya challenge. BMC Infect. Dis. 18, 245 (2018).<\/span><\/section>\n
6 M. Ajelli et al., The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation. Epidemics 22, 3\u201312 (2018).<\/span><\/section>\n
7 C. Viboud et al.; RAPIDD Ebola Forecasting Challenge group, The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt. Epidemics 22, 13\u201321 (2018).<\/span><\/section>\n
8 Centers for Disease Control and Prevention, FluSight: Flu forecasting (2020). https:\/\/www.cdc.gov\/flu\/weekly\/flusight\/. Accessed 18 October 2021.<\/span><\/section>\n
9 J. Ginsberg et al., Detecting influenza epidemics using search engine query data. Nature 457, 1012\u20131014 (2009).<\/span><\/section>\n
10 J. S. Brownstein, C. C. Freifeld, L. C. Madoff, Digital disease detection\u2014Harnessing the Web for public health surveillance. N. Engl. J. Med. 360, 2153\u20132157 (2009).<\/span><\/section>\n
11 M. Salath\u00e9 et al., Digital epidemiology. PLoS Comput. Biol. 8, e1002616 (2012).<\/span><\/section>\n
12 T. A. Kass-Hout, H. Alhinnawi, Social media in public health. Br. Med. Bull. 108, 5\u201324 (2013).<\/span><\/section>\n
13 M. Santillana et al., Combining search, social media, and traditional data sources to improve influenza surveillance. PLoS Comput. Biol. 11, e1004513 (2015).<\/span><\/section>\n
14 M. J. Paul, M. Dredze, Social monitoring for public health. Synth. Lect. Inf. Concepts Retr. Serv. 9, 1\u2013183 (2017).<\/span><\/section>\n
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16 S. J. Carlson et al., Flutracking weekly online community survey of influenza-like illness annual report 2011 and 2012. Commun. Dis. Intell. Q. Rep. 37, E398\u2013E406 (2013).<\/span><\/section>\n
17 C. Viboud et al., Demonstrating the use of high-volume electronic medical claims data to monitor local and regional influenza activity in the US. PLoS One 9, e102429 (2014).<\/span><\/section>\n
18 M. S. Smolinski et al., Flu near you: Crowdsourced symptom reporting spanning 2 influenza seasons. Am. J. Public Health 105, 2124\u20132130 (2015).<\/span><\/section>\n
19 M. Santillana et al., Cloud-based electronic health records for real-time, region-specific influenza surveillance. Sci. Rep. 6, 25732 (2016).<\/span><\/section>\n
20 V. Charu et al., Human mobility and the spatial transmission of influenza in the United States. PLoS Comput. Biol. 13, e1005382 (2017).<\/span><\/section>\n
21 C. E. Koppeschaar et al., Influenzanet: Citizens among 10 countries collaborating to monitor influenza in europe. JMIR Public Health Surveill. 3, e66 (2017).<\/span><\/section>\n
22 C. Y. Yang, R. J. Chen, W. L. Chou, Y. J. Lee, Y. S. Lo, An integrated influenza surveillance framework based on national influenza-like illness incidence and multiple hospital electronic medical records for early prediction of influenza epidemics: Design and evaluation. J. Med. Internet Res. 21, e12341 (2019).<\/span><\/section>\n
23 S. I. Leuba, R. Yaesoubi, M. Antillon, T. Cohen, C. Zimmer, Tracking and predicting U.S. influenza activity with a real-time surveillance network. PLoS Comput. Biol. 16, e1008180 (2020).<\/span><\/section>\n
24 J. M. Radin, N. E. Wineinger, E. J. Topol, S. R. Steinhubl, Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: A population-based study. Lancet Digit. Health 2, e85\u2013e93 (2020).<\/span><\/section>\n
25 S. F. Ackley et al., Assessing the utility of a smart thermometer and mobile application as a surveillance tool for influenza and influenza-like illness. Health Informatics J. 26, 2148\u20132158 (2020).<\/span><\/section>\n
26 A. Reinhart et al., An open repository of real-time COVID-19 indicators. Proc. Natl. Acad. Sci. U.S.A. 118, e2111452118 (2021).<\/span><\/section>\n
27 D. J. McDonald et al., Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction? Proc. Natl. Acad. Sci. U.S.A. 118, e2111453118 (2021).<\/span><\/section>\n
28 J. A. Salomon et al., The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination. Proc. Natl. Acad. Sci. U.S.A. 118, e2111454118 (2021).<\/span><\/section>\n
29 C. M. Astley et al., Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base. Proc. Natl. Acad. Sci. U.S.A. 118, e2111455118 (2021).<\/span><\/section>\n
30 Reich Lab, The COVID-19 Forecast Hub (2020). https:\/\/covid19forecasthub.org. Accessed 18 October 2021.<\/span><\/section>\n
31 S. Simon, Inconsistent reporting practices hampered our ability to analyze COVID-19 data. Here are three common problems we identified (COVID Tracking Project (2021). https:\/\/covidtracking.com\/analysis-updates\/three-covid-19-data-problems. Accessed 18 October 2021.<\/span><\/section>\n
32 J. E. Wennberg, M. M. Cooper, The Dartmouth Atlas of Health Care in the United States (American Hospital Publishing, Chicago, IL, 1998).<\/span><\/section>\n
33 National Center for Health Statistics, Provisional death counts for coronavirus disease 2019 (COVID-19) (2021). https:\/\/www.cdc.gov\/nchs\/nvss\/vsrr\/ COVID19\/index.htm. Accessed 18 October 2021.<\/span><\/section>\n
34 N. Fillmore et al., The COVID-19 hospitalization metric in the pre- and post-vaccination eras as a measure of pandemic severity: A retrospective, nationwide cohort study. Research Square [Preprint] (2021). https:\/\/www.researchsquare.com\/article\/rs-898254\/v1 (Accessed 18 October 2021).<\/span><\/section>\n
35 S. Arvisais-Anhalt et al., What the coronavirus disease 2019 (COVID-19) pandemic has reinforced: The need for accurate data. Clin. Infect. Dis. 72, 920\u2013923 (2021).<\/span><\/section>\n
36 E. Dong, H. Du, L. Gardner, An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect. Dis. 20, 533\u2013534 (2020).<\/span><\/section>\n
37 National Center for Health Statistics, Pneumonia and influenza mortality surveillance from the national center for health statistics mortality surveillance system (2021). https:\/\/gis.cdc.gov\/grasp\/fluview\/mortality.html. Accessed 18 October 2021.<\/span><\/section>\n
38 A. Bilinski, E. Emanuel, J. A. Salomon, A. Venkataramani, Better late than never: Trends in COVID-19 infection rates, risk perceptions, and behavioral responses in the USA. J. Gen. Intern. Med. 36, 1825\u20131828 (2021).<\/span><\/section>\n
39 Centers for Disease Control and Prevention, CDC Stands Up New Disease Forecasting Center: Media statement for immediate release: Wednesday, August 18, 2021 (2021). https:\/\/stacks.cdc.gov\/view\/cdc\/108945. Accessed 18 October 2021.<\/span><\/section>\n
40 E. J. Williamson et al., OpenSAFELY: Factors associated with COVID-19 death in 17 million patients. Nature 584, 430\u2013436 (2020).<\/span><\/section>\n
41 HL7 Community, Welcome to FHIR (2021). https:\/\/www.hl7.org\/fhir\/. Accessed 18 October 2021.<\/span><\/section>\n
42 N. G. Reich, R. J. Tibshirani, E. L. Ray, R. Rosenfeld, On the predictability of COVID-19 (2021). https:\/\/forecasters.org\/blog\/2021\/09\/28\/on-the-predictability-of[1]covid-19\/. Accessed 18 October 2021.<\/span><\/section>\n
43 H. Jalal, K. Lee, D. S. Burke, Prominent spatiotemporal waves of COVID-19 incidence in the United States: Implications for causality, forecasting, and control. medRxiv [Preprint] (2021). https:\/\/doi.org\/10.1101\/2021.06.29.21259726 (Accessed 18 October 2021).<\/span><\/section>\n


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