Learning from Large-Scale Wearable Device Data for Predicting Epidemics Trend of COVID-19

<div class="floats-full-page floats-full-page-other"><h2>Table 2</h2><div class="floats-full-page-caption"><div>Correlation coefficient between the physiological anomaly rate and the officially reported number of newly confirmed COVID-19 cases.</div></div><div class="floats-full-page-content"><table class="table-group" id="tab2"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr /></td></tr><tr class="thead"><td class="align_left">City</td><td class="align_center"><span style="width: 7.13289ptpx;"><svg height="9.39034pt" id="M24" style="vertical-align:-3.42943pt" version="1.1" viewbox="-0.0498162 -5.96091 7.13289 9.39034" width="7.13289pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M516 277C516 378 460 448 376 448C335 448 288 434 240 401C158 345 127 277 107 159C53 -161 38 -221 23 -247L28 -261C43 -260 95 -250 114 -226C122 -216 129 -199 146 -75C152 -29 157 0 164 31C182 9 206 -12 237 -12C267 -12 325 8 376 46C451 102 516 179 516 277ZM432 259C432 134 366 40 259 40C236 40 204 48 171 76L195 203C219 327 253 400 326 400C398 400 432 336 432 259Z"></path></g></svg></span></td></tr><tr><td class="thead-hr" colspan="2"><hr /></td></tr><tr><td class="align_left">Beijing</td><td class="align_center">0.31</td></tr><tr><td class="align_left">Shijiazhuang</td><td class="align_center">0.58</td></tr><tr><td class="align_left">Tianjin</td><td class="align_center">0.53</td></tr><tr><td class="align_left">Jinan</td><td class="align_center">0.68</td></tr><tr><td class="align_left">Taiyuan</td><td class="align_center">0.55</td></tr><tr><td class="align_left">Shanghai</td><td class="align_center">0.51</td></tr><tr><td class="align_left">Hangzhou</td><td class="align_center">0.74</td></tr><tr><td class="align_left">Wuhan</td><td class="align_center">0.58</td></tr><tr><td class="align_left">Nanning</td><td class="align_center">0.52</td></tr><tr><td class="align_left">Xiaogan</td><td class="align_center">0.70</td></tr><tr><td class="align_left">Nanjing</td><td class="align_center">0.83</td></tr><tr><td class="align_left">Chengdu</td><td class="align_center">0.75</td></tr><tr><td class="align_left">Hefei</td><td class="align_center">0.84</td></tr><tr><td class="align_left">Huanggang</td><td class="align_center">0.87</td></tr><tr><td class="align_left">Guangzhou</td><td class="align_center">0.80</td></tr><tr><td class="align_left">Foshan</td><td class="align_center">0.81</td></tr><tr><td class="align_left">Fuzhou</td><td class="align_center">0.73</td></tr><tr><td class="align_left">Shenzhen</td><td class="align_center">0.82</td></tr><tr><td class="align_left">Dongguan</td><td class="align_center">0.75</td></tr><tr><td class="align_left"><b>Average</b></td><td class="align_center"><b>0.68</b></td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr" /></td></tr></table></td></tr></table></div></div>

Table 2 | Learning from Large-Scale Wearable Device Data for Predicting Epidemics Trend of COVID-19

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Table 2: Learning from Large-Scale Wearable Device Data for Predicting Epidemics Trend of COVID-19

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Table 2

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