====== Word clouds: monthly chi square ====== Keywords with month frequency at lest 6 and largest values of the square root of the chi square term. > for(j in 1:12){ + Ei <- TF[,1]*(S[j+1]/S[1]) + di <- ifelse(TF[,j+1]<6,-10,(TF[,j+1] - Ei)/sqrt(Ei)) # cHi 2 + or <- order(di,decreasing=TRUE) + ro <- invPerm(or) + names(di) <- N + cat("\n",j,":\n") + ff <- round(200000/sqrt(1+ro)) + for(i in 1:50){ + k <- or[i] + cat(i,k,N[k],TF[k,1],TF[k,j+1],P[k],di[k],"\n") + } + df <- data.frame(N=N[or],f=ff[or]) + wc <- wordcloud2(df,size=0.8,minRotation=0,maxRotation=0) + tmp <- paste("wcH",j,".html",sep="") + pdf <- paste("wcH",j,".pdf",sep="") + png <- paste("wcH",j,".png",sep="") + saveWidget(wc,tmp,selfcontained=FALSE) + webshot(tmp,pdf,delay=5,vwidth=800,vheight=600) + webshot(tmp,png,delay=5,vwidth=800,vheight=600) + } 1 : 1 12754 ncov 1132 40 11 20.36391 2 20946 cuba 82 7 5 13.92843 3 20916 automaton 67 6 9 13.22916 4 8696 wuhan 1828 33 12 12.09906 5 1174 plasmodium 125 7 11 11.07491 6 384 novel 9270 75 12 9.361519 7 90 virus 17975 104 12 7.274029 8 401 systematic 6525 50 12 7.209779 9 6075 intracranial 568 10 11 6.545722 10 740 season 326 7 11 6.260799 11 3843 occlusion 513 9 11 6.194651 12 1991 antibiotic 621 10 12 6.146107 13 324 review 14000 79 12 6.104149 14 332 china 7114 47 12 5.867134 15 110 influenza 5329 37 12 5.535963 16 11149 aneurysm 993 12 11 5.410744 17 1128 resistance 955 11 12 4.979904 18 2224 assist 1573 15 12 4.925156 19 31 cell 10223 56 12 4.90505 20 4758 preterm 360 6 11 4.878965 21 151 infectious 4837 32 12 4.849548 22 3915 postoperative 589 8 12 4.844944 23 3174 dimensional 364 6 12 4.84084 24 616 malaria 365 6 12 4.831397 25 4742 language 371 6 11 4.775464 26 1096 antimicrobial 734 9 12 4.74131 27 4454 scoping 767 9 12 4.574289 28 1386 b 1371 13 12 4.56092 29 627 reproduction 405 6 12 4.480013 30 2302 extend 406 6 11 4.471831 31 1279 resistant 659 8 12 4.434165 32 2531 rat 1093 11 12 4.431065 33 609 multiplex 535 7 12 4.402628 34 901 free 981 10 12 4.273615 35 2115 isolated 708 8 11 4.179212 36 614 vaccine 5422 32 12 4.154395 37 4219 closure 598 7 12 4.026101 38 414 ventilator 746 8 12 3.996766 39 1872 infant 1228 11 12 3.973807 40 166 characterization 2163 16 12 3.92065 41 1364 bronchitis 1079 10 12 3.914921 42 815 via 1785 14 12 3.912992 43 447 identification 2389 17 12 3.864117 44 187 human 8882 45 12 3.849044 45 2494 pressure 1271 11 12 3.841324 46 34284 thrombectomy 497 6 11 3.822846 47 4255 gastric 944 9 11 3.814256 48 170 genomic 788 8 12 3.808554 49 1086 fever 955 9 12 3.773136 50 1357 swine 653 7 11 3.737392 2 : 1 12754 ncov 1132 193 11 77.79413 2 384 novel 9270 355 12 44.66393 3 7642 2019 13357 403 12 40.48589 4 85375 2019‐ncov 27 12 7 31.91647 5 2899 coronavirus 31428 546 12 30.3805 6 8696 wuhan 1828 86 12 25.06445 7 332 china 7114 170 12 22.14692 8 413 pneumonia 5940 140 12 19.88425 9 393 outbreak 9294 145 12 14.13322 10 450 epidemic 6523 115 12 14.13084 11 14329 hubei 311 17 10 12.21624 12 26724 princess 93 8 9 10.90848 13 6438 outside 315 15 12 10.54681 14 1749 traveller 130 9 11 10.22056 15 8073 february 314 13 11 8.989617 16 17771 pangolin 84 6 10 8.497289 17 853 estimation 794 21 12 8.40421 18 16419 diamond 115 7 10 8.359238 19 1263 falciparum 94 6 11 7.958827 20 396 transmission 4864 64 12 7.841693 21 629 number 1016 23 12 7.808616 22 8753 simulate 101 6 12 7.628255 23 4902 january 327 11 11 7.211372 24 1179 infect 2901 42 12 7.049929 25 1726 mainland 200 8 10 6.897431 26 2473 province 809 18 12 6.813557 27 631 estimate 1293 24 12 6.759676 28 1174 plasmodium 125 6 11 6.703412 29 1196 epidemiological 1615 27 12 6.51899 30 134 control 7836 80 12 6.303863 31 11 infection 24401 195 12 6.280413 32 2802 incubation 183 7 11 6.267055 33 173 analysis 15135 131 12 6.08827 34 777 characteristic 4020 48 12 6.049614 35 13134 edition 155 6 10 5.847491 36 3246 tree 211 7 11 5.698566 37 678 structure 2497 33 12 5.659021 38 2940 coronaviruses 1794 26 12 5.552861 39 1975 wean 174 6 10 5.415982 40 3244 ct 2514 32 12 5.336761 41 7 feature 2310 30 12 5.289219 42 401 systematic 6525 63 12 5.126609 43 506 spread 3081 36 12 5.097655 44 1716 expert 853 15 12 5.091628 45 447 identification 2389 30 12 5.085426 46 3290 korea 1285 19 12 4.846525 47 627 reproduction 405 9 12 4.813161 48 6631 ship 207 6 11 4.801488 49 2988 traffic 278 7 11 4.677187 50 649 prediction 1869 24 12 4.669425 3 : 1 7642 2019 13357 517 12 27.5238 2 2899 coronavirus 31428 910 12 26.63049 3 332 china 7114 307 12 23.46998 4 384 novel 9270 360 12 23.04116 5 393 outbreak 9294 335 12 20.64528 6 450 epidemic 6523 236 12 17.39416 7 12754 ncov 1132 74 11 16.10286 8 13938 retrieval 171 25 10 15.79003 9 8696 wuhan 1828 95 12 15.29586 10 7254 rank 82 14 11 12.93766 11 60 march 778 49 12 12.74874 12 11068 uscap 17 6 4 12.66555 13 1494 competition 149 18 10 11.94413 14 14329 hubei 311 27 10 11.85085 15 20 disease 28569 569 12 11.6139 16 3553 entity 137 16 11 11.02907 17 3507 query 87 12 11 10.56733 18 3244 ct 2514 88 12 10.26689 19 12341 symbolic 25 6 6 10.26685 20 17343 verification 110 13 11 10.01448 21 506 spread 3081 99 12 9.928505 22 137 model 10504 242 12 9.92775 23 631 estimate 1293 55 12 9.805608 24 413 pneumonia 5940 156 12 9.706201 25 7 feature 2310 79 12 9.492687 26 12491 corona 1812 67 12 9.473242 27 8073 february 314 22 11 9.23082 28 649 prediction 1869 66 12 8.97287 29 1196 epidemiological 1615 59 12 8.782847 30 23933 embeddings 33 6 9 8.781716 31 8501 2020 4992 130 12 8.757236 32 6631 ship 207 16 11 8.43298 33 396 transmission 4864 125 12 8.428733 34 21172 semantic 85 9 9 7.780644 35 13134 edition 155 12 10 7.311289 36 6630 cruise 179 13 11 7.27861 37 6276 iran 776 31 12 6.946179 38 3 clinical 14239 267 12 6.945157 39 258 search 675 28 11 6.837559 40 853 estimation 794 31 12 6.796155 41 2270 graph 308 17 12 6.789036 42 2473 province 809 31 12 6.674374 43 13527 doctor 739 29 12 6.605429 44 13571 warns 54 6 10 6.548082 45 777 characteristic 4020 95 12 6.480722 46 12481 cancel 73 7 9 6.440679 47 26724 princess 93 8 9 6.411379 48 1716 expert 853 31 12 6.332891 49 121 case 14195 257 12 6.239979 50 281 data 5075 111 12 6.151024 4 : 1 2280 19 147108 6499 12 27.64816 2 8683 covid 149210 6570 12 27.5256 3 16281 covid‐19 5103 448 10 22.73018 4 2899 coronavirus 31428 1450 12 14.7388 5 393 outbreak 9294 530 12 13.93869 6 7642 2019 13357 682 12 12.81866 7 450 epidemic 6523 380 12 12.23764 8 108 pandemic 42664 1778 12 11.97093 9 18077 sars‐cov‐2 811 85 11 11.79621 10 506 spread 3081 206 12 11.10606 11 817 italy 2754 188 11 10.91562 12 19089 seir 161 29 12 10.64854 13 6995 statement 893 84 12 10.56647 14 771 “ 2104 151 12 10.4478 15 3414 chloroquine 817 77 11 10.13624 16 332 china 7114 373 12 10.00825 17 137 model 10504 508 12 9.807816 18 773 ” 2023 141 12 9.719437 19 24496 sfmt 11 6 1 9.620763 20 631 estimate 1293 99 12 9.163909 21 25875 covid19 1143 89 10 8.863828 22 2270 graph 308 37 12 8.786843 23 23426 avril 13 6 3 8.751515 24 14329 hubei 311 37 10 8.714214 25 23869 travail 55 13 6 8.577426 26 12264 épidémie 162 24 10 8.384939 27 3244 ct 2514 153 12 8.333961 28 232 – 2085 132 12 8.220981 29 6140 forecast 284 33 11 8.06357 30 1400 distance 2073 130 11 8.043569 31 649 prediction 1869 118 12 7.741061 32 8696 wuhan 1828 116 12 7.733362 33 853 estimation 794 63 12 7.622989 34 629 number 1016 74 12 7.452049 35 70 time 9291 419 12 7.448167 36 12462 recommandations 78 14 6 7.380067 37 12453 chirurgie 66 12 7 6.895536 38 1110 cov 28352 1096 12 6.891419 39 4494 guidance 998 69 11 6.726798 40 942 mathematical 625 49 12 6.629917 41 4270 consideration 2173 123 12 6.628778 42 735 measure 2959 156 12 6.543281 43 7127 keep 423 37 11 6.506994 44 5319 n95 407 36 10 6.494497 45 12467 pratique 44 9 8 6.480891 46 8013 exit 167 20 9 6.443234 47 6276 iran 776 56 12 6.407406 48 60 march 778 56 12 6.386459 49 12491 corona 1812 105 12 6.375826 50 18214 suggestion 241 25 10 6.336316 5 : 1 16281 covid‐19 5103 1086 10 55.26666 2 8683 covid 149210 9109 12 26.44288 3 2280 19 147108 8949 12 25.8722 4 18077 sars‐cov‐2 811 156 11 19.32484 5 9857 fuzzy 196 57 9 15.90536 6 108 pandemic 42664 2676 12 15.74683 7 773 ” 2023 218 12 12.84163 8 771 “ 2104 223 12 12.71848 9 86854 disease–2019 8 7 2 10.89105 10 137 model 10504 708 12 10.04224 11 232 – 2085 190 12 9.507723 12 1110 cov 28352 1629 12 8.7163 13 12154 hpc 12 7 3 8.643999 14 871 2 31487 1787 12 8.60619 15 18615 imbalanced 20 9 6 8.388656 16 2270 graph 308 45 12 8.137478 17 50323 eua 18 8 3 7.848656 18 214 classifier 75 18 9 7.794002 19 281 data 5075 354 12 7.758413 20 817 italy 2754 214 11 7.654212 21 27731 algebra 15 7 4 7.564726 22 70 time 9291 586 12 7.504778 23 38197 dessler 12 6 2 7.30264 24 38198 0521173159 12 6 2 7.30264 25 2348 forecasting 590 65 12 7.206889 26 408 test 6690 433 12 6.995975 27 7493 assimilation 38 11 9 6.964915 28 66902 renin–angiotensin–aldosterone 22 8 7 6.915844 29 11095 logic 88 18 10 6.897074 30 25875 covid19 1143 103 10 6.880384 31 20916 automaton 67 15 9 6.753516 32 1111 sars 31530 1717 12 6.716431 33 393 outbreak 9294 567 12 6.581097 34 3229 interval 371 44 12 6.469287 35 15332 hydroxychloroquine 1955 152 12 6.458075 36 17353 registration 91 17 9 6.227656 37 26848 flatten 188 27 10 6.199162 38 5536 finite 67 14 11 6.185843 39 120 patient 40007 2117 12 6.134031 40 3294 aware 112 19 9 6.064613 41 6439 sentiment 162 24 10 6.022531 42 1935 italian 1521 120 11 5.904013 43 132 network 3081 213 12 5.885076 44 22159 252 17 6 3 5.874463 45 87165 chilblain‐like 17 6 6 5.874463 46 7642 2019 13357 763 12 5.804022 47 23094 agile 73 14 10 5.775049 48 1160 algorithm 714 66 12 5.726449 49 783 learn 5246 332 12 5.71144 50 26820 sortie 18 6 2 5.658226 6 : 1 16281 covid‐19 5103 1049 10 53.24919 2 8683 covid 149210 8579 12 20.93384 3 2280 19 147108 8430 12 20.44332 4 18077 sars‐cov‐2 811 161 11 20.29149 5 108 pandemic 42664 2645 12 15.53288 6 6720 petri 23 14 5 12.60025 7 771 “ 2104 213 12 11.85179 8 773 ” 2023 207 12 11.84977 9 27422 tutor 26 14 4 11.72501 10 1110 cov 28352 1668 12 10.17509 11 22389 sat 15 9 3 10.0184 12 871 2 31487 1816 12 9.764286 13 2086 rough 50 17 9 9.708532 14 1160 algorithm 714 87 12 9.475405 15 232 – 2085 182 12 8.825509 16 28432 solver 15 8 5 8.813063 17 19235 swarm 44 14 8 8.431795 18 1111 sars 31530 1765 12 8.364955 19 26397 boolean 10 6 4 8.17999 20 15214 lockdown 3634 269 11 7.917883 21 14870 math 27 10 5 7.870978 22 19196 gamification 24 9 7 7.526707 23 129 trace 999 96 11 7.408299 24 8885 mitteilungen 189 30 10 7.242027 25 4331 era 4082 284 12 7.064702 26 22357 mayo 64 15 9 7.039275 27 27670 theorem 22 8 6 6.957455 28 6974 mental 4050 278 12 6.760143 29 783 learn 5246 343 12 6.59196 30 3314 approximate 43 11 9 6.426236 31 120 patient 40007 2107 12 6.329447 32 27736 algebraic 16 6 5 6.145531 33 19698 ‐ 53 12 7 6.135316 34 5385 solve 162 24 11 6.076061 35 87165 chilblain‐like 17 6 6 5.910086 36 12237 multidisciplinary 417 44 11 5.684289 37 3309 india 2550 178 12 5.638031 38 3017 uk 1874 138 12 5.608359 39 687 priority 370 40 10 5.587174 40 9918 learner 86 15 8 5.564335 41 19775 arbeit 19 6 4 5.492094 42 90104 evidence‐based 19 6 7 5.492094 43 4112 decomposition 71 13 9 5.397269 44 479 text 243 29 12 5.345344 45 4370 neural 665 60 12 5.337596 46 10586 prof. 26 7 6 5.316365 47 14198 forage 21 6 6 5.130534 48 12206 pharmacy 452 44 12 5.107127 49 9594 leitlinien 35 8 4 5.045332 50 15332 hydroxychloroquine 1955 136 12 4.887362 7 : 1 16281 covid‐19 5103 755 10 32.58833 2 18077 sars‐cov‐2 811 107 11 10.90971 3 871 2 31487 1902 12 10.04554 4 8683 covid 149210 7978 12 9.636441 5 2280 19 147108 7850 12 9.382567 6 1110 cov 28352 1693 12 9.000299 7 771 “ 2104 186 12 8.458198 8 773 ” 2023 180 12 8.411568 9 11832 asm 17 8 3 7.952566 10 42380 reprieve 12 6 1 7.14652 11 1111 sars 31530 1781 12 6.875406 12 10948 microbiologist 27 9 4 6.767037 13 30944 covid‐19‐related 28 8 8 5.741134 14 1585 mortality 4550 303 12 5.723661 15 14361 meta‐analysis 89 15 9 5.190183 16 232 – 2085 152 12 5.189264 17 2004 social 5153 328 12 5.127489 18 481 dynamic 3155 214 12 5.082881 19 8773 halo 41 9 8 5.012433 20 18876 anorexia 34 8 6 4.984548 21 4331 era 4082 263 12 4.790208 22 18877 nervosa 36 8 6 4.771078 23 1234 radiography 108 16 8 4.7502 24 401 systematic 6525 394 12 4.56458 25 1334 cohort 3167 208 12 4.539898 26 120 patient 40007 2119 12 4.531052 27 56 care 14837 833 12 4.525993 28 18327 gravitational 24 6 7 4.516683 29 23589 brève 24 6 2 4.516683 30 1230 ‘ 810 67 12 4.509302 31 4293 spore 39 8 7 4.478654 32 2214 death 2380 162 12 4.467755 33 108 pandemic 42664 2248 12 4.419895 34 14683 co‐infection 25 6 7 4.381608 35 1587 retrospective 2816 186 12 4.371478 36 2699 orthopaedic 538 48 11 4.363486 37 723 meta 3717 236 12 4.310255 38 2649 ace2 1726 122 12 4.300841 39 2785 york 1134 86 12 4.278281 40 606 screen 2942 192 12 4.272777 41 18391 radiomics 50 9 9 4.260089 42 16997 semiconductor 26 6 4 4.253552 43 15332 hydroxychloroquine 1955 135 12 4.248328 44 40120 bet 42 8 6 4.214318 45 2953 black 538 47 11 4.166708 46 1971 protocol 2947 191 12 4.164894 47 34958 d614g 80 12 8 4.163921 48 20259 lethality 71 11 9 4.1123 49 1995 diabetes 2348 156 12 4.077627 50 23212 statewide 62 10 9 4.07143 8 : 1 16281 covid‐19 5103 766 10 35.4332 2 18077 sars‐cov‐2 811 123 11 14.33476 3 7052 chromatography 154 36 9 11.05227 4 871 2 31487 1701 12 7.595243 5 23094 agile 73 17 10 7.573591 6 14361 meta‐analysis 89 19 9 7.49951 7 3393 liquid 351 45 11 7.357382 8 232 – 2085 164 12 7.260238 9 3395 chromatographic 44 12 5 7.126687 10 1110 cov 28352 1519 12 6.853139 11 771 “ 2104 160 12 6.72824 12 773 ” 2023 155 12 6.719073 13 7868 stationary 44 11 8 6.415604 14 311 survey 3809 255 12 6.404069 15 515 impact 12012 687 12 6.330215 16 66820 19–related 40 10 8 6.117039 17 30654 thiamine 17 6 3 5.989803 18 13637 assistant 75 14 8 5.789023 19 1334 cohort 3167 210 12 5.67016 20 34460 cross‐sectional 38 9 6 5.57957 21 12299 nieuws 20 6 9 5.380106 22 1293 healthcare 4363 269 12 5.205256 23 12101 resect 35 8 5 5.124004 24 15214 lockdown 3634 228 11 5.05931 25 8171 chiral 69 12 7 5.052941 26 3564 mir 223 26 11 5.046386 27 2358 virtual 1902 132 11 5.03018 28 313 among 6847 394 12 4.916118 29 14922 additive 112 16 9 4.887427 30 32019 anmco 30 7 4 4.866929 31 12140 multisystem 538 48 9 4.843548 32 1064 disparity 760 62 12 4.763274 33 8150 vitamin 893 70 12 4.713418 34 13945 column 66 11 8 4.664197 35 12580 operationalizing 32 7 5 4.637427 36 2284 transition 727 59 12 4.604851 37 19007 inadequate 68 11 9 4.543674 38 14814 pm 78 12 8 4.536446 39 654 effect 9268 509 12 4.528389 40 16387 segmentation 170 20 10 4.470971 41 120 patient 40007 1986 12 4.42819 42 324 review 14000 739 12 4.374407 43 696 report 6902 386 12 4.301935 44 298 study 19798 1018 12 4.295111 45 8885 mitteilungen 189 21 10 4.290378 46 2126 bangladesh 428 38 12 4.277728 47 4677 driver 230 24 11 4.249112 48 1423 large 2034 132 12 4.243707 49 3235 masked 36 7 6 4.230869 50 18789 blast 28 6 5 4.226492 9 : 1 871 2 31487 2033 12 12.27493 2 1110 cov 28352 1843 12 11.98019 3 61358 caractéristiques 28 12 3 9.048015 4 23347 étude 87 23 9 9.045439 5 1111 sars 31530 1903 12 8.912841 6 773 ” 2023 186 12 8.661523 7 771 “ 2104 191 12 8.592727 8 13401 facteurs 28 11 1 8.196185 9 62861 hospitalisés 36 12 4 7.683793 10 60547 wnut 15 7 3 7.287509 11 2251 fertility 168 29 9 7.209422 12 1414 sectional 2086 175 12 7.13811 13 30209 associés 20 8 3 7.071026 14 1585 mortality 4550 329 12 7.019877 15 9651 chez 204 32 11 6.930015 16 12404 une 385 49 9 6.903251 17 515 impact 12012 759 12 6.900105 18 772 cross 3197 240 12 6.588405 19 313 among 6847 456 12 6.48201 20 298 study 19798 1175 12 6.424709 21 142 stress 2060 166 12 6.416303 22 314 cancer 7517 491 12 6.291568 23 61926 cohorte 37 10 5 6.06073 24 62991 infectés 16 6 4 5.873772 25 10335 hydrocortisone 22 7 4 5.686366 26 62742 multicentrique 17 6 3 5.644588 27 44820 unwell 18 6 3 5.433262 28 464 l 825 75 12 5.397437 29 1392 behavioral 617 60 12 5.37706 30 1904 seroprevalence 473 49 12 5.330393 31 70105 ehpad 19 6 3 5.237452 32 23424 résultats 25 7 4 5.201172 33 12140 multisystem 538 53 9 5.153635 34 8683 covid 149210 7781 12 5.099042 35 628 individual 1335 106 12 4.970628 36 24121 atteints 56 11 6 4.965475 37 343 perception 1389 109 12 4.914429 38 819 attitude 1069 88 11 4.878156 39 5636 convolutional 165 22 11 4.870144 40 347 gender 672 61 12 4.855497 41 2280 19 147108 7653 12 4.846938 42 12692 risque 75 13 8 4.84488 43 2025 longitudinal 760 67 12 4.838579 44 1393 anxiety 1460 112 12 4.735103 45 24068 dépistage 43 9 6 4.731634 46 13756 equity 431 43 10 4.7302 47 120 patient 40007 2177 12 4.684637 48 229 factor 6097 381 12 4.670598 49 2004 social 5153 327 12 4.607177 50 18006 tooth 53 10 9 4.576348 10 : 1 16281 covid‐19 5103 476 10 16.21721 2 871 2 31487 1874 12 12.06689 3 1110 cov 28352 1696 12 11.69055 4 65358 tct 28 14 1 11.33787 5 1111 sars 31530 1764 12 9.089504 6 298 study 19798 1144 12 8.419931 7 8683 covid 149210 7390 12 8.094467 8 1585 mortality 4550 320 12 8.022408 9 1334 cohort 3167 237 12 7.887001 10 60547 wnut 15 7 3 7.690417 11 2280 19 147108 7252 12 7.621042 12 8501 2020 4992 335 12 7.330732 13 1414 sectional 2086 164 12 7.215203 14 771 “ 2104 165 12 7.203637 15 311 survey 3809 266 12 7.196235 16 313 among 6847 430 12 6.907152 17 773 ” 2023 157 12 6.891059 18 34432 sars‐cov2 28 9 7 6.887388 19 6089 ethiopia 215 31 12 6.844507 20 27133 election 103 19 8 6.662828 21 730 qualitative 758 73 12 6.642858 22 1816 observational 1660 132 12 6.608895 23 4220 school 1337 111 11 6.534562 24 1433 wave 860 79 12 6.461664 25 18077 sars‐cov‐2 811 75 11 6.357769 26 772 cross 3197 220 12 6.321159 27 17417 digitalisierung 26 8 8 6.306961 28 281 data 5075 324 12 6.295909 29 783 learn 5246 333 12 6.276437 30 1411 student 2693 190 12 6.226532 31 36084 esthetic 16 6 1 6.215649 32 696 report 6902 420 12 6.172088 33 2721 machine 988 84 12 5.913197 34 31765 katrina 35 9 4 5.90905 35 232 – 2085 151 12 5.880653 36 18295 generalise 23 7 4 5.856404 37 6974 mental 4050 261 12 5.80476 38 15214 lockdown 3634 238 11 5.796155 39 515 impact 12012 675 12 5.737846 40 23589 brève 24 7 2 5.689758 41 27641 voting 38 9 6 5.567676 42 5242 online 2156 152 12 5.559797 43 5468 adolescent 1230 96 12 5.446184 44 215 quality 2439 167 12 5.441196 45 9002 über 64 12 9 5.366382 46 4653 interview 394 40 12 5.276976 47 21923 tweet 134 19 10 5.272923 48 8343 zambia 41 9 7 5.260637 49 736 across 978 79 12 5.258206 50 18448 purchase 49 10 8 5.242275 11 : 1 1110 cov 28352 1620 12 19.51507 2 871 2 31487 1730 12 18.49359 3 33452 acknowledgement 21 16 3 17.70898 4 1111 sars 31530 1673 12 16.72808 5 33451 referee 29 17 2 15.77806 6 8683 covid 149210 6403 12 15.54086 7 2280 19 147108 6297 12 15.21194 8 33700 relato 85 26 3 13.26617 9 298 study 19798 1038 12 12.7832 10 61963 sangue 27 13 1 12.32993 11 63843 experiência 53 18 1 11.78196 12 66644 falciforme 20 10 1 11.05241 13 33708 convalescente 21 10 2 10.74502 14 1433 wave 860 89 12 10.62833 15 15010 com 253 39 5 10.05073 16 64134 hematologia 16 8 1 9.885573 17 69323 hemoterapia 16 8 1 9.885573 18 60264 infecção 60 16 2 9.530154 19 65266 doação 14 7 1 9.247107 20 1904 seroprevalence 473 53 12 8.872646 21 1414 sectional 2086 149 12 8.764741 22 2025 longitudinal 760 72 12 8.708083 23 64135 acadêmica 12 6 1 8.561158 24 64136 liga 12 6 1 8.561158 25 67921 hemocentro 12 6 1 8.561158 26 29995 revisão 78 16 2 7.975311 27 39307 panbio 15 6 4 7.511699 28 1334 cohort 3167 191 12 7.471359 29 62822 doença 45 11 1 7.460474 30 15214 lockdown 3634 213 11 7.459378 31 313 among 6847 358 12 7.45428 32 515 impact 12012 575 12 7.298557 33 2663 deep 1428 102 12 7.251804 34 783 learn 5246 281 12 7.017536 35 772 cross 3197 185 12 6.772056 36 12254 caso 141 20 10 6.725975 37 60465 literatura 53 11 4 6.667795 38 1585 mortality 4550 244 12 6.557616 39 239 base 10781 507 12 6.449675 40 9607 um 138 19 6 6.393937 41 1971 protocol 2947 169 12 6.351656 42 8893 der 1380 93 11 6.33118 43 61637 associada 20 6 1 6.295118 44 311 survey 3809 207 12 6.235813 45 819 attitude 1069 76 11 6.216281 46 408 test 6690 330 12 6.08134 47 9749 biobanking 28 7 5 6.041259 48 6089 ethiopia 215 24 12 5.948961 49 8885 mitteilungen 189 22 10 5.926767 50 8587 shorter 22 6 5 5.921991 12 : 1 56933 machining 9 9 1 42.37316 2 85711 engelures 6 6 1 34.59754 3 40072 weld 13 8 2 31.24151 4 43496 milling 13 8 3 31.24151 5 7266 manufacturing 211 23 11 21.45258 6 8935 chapter 423 28 8 17.87604 7 18909 steel 38 7 7 15.68467 8 10844 cut 203 16 11 14.93678 9 14922 additive 112 9 9 11.32602 10 445 process 1165 31 12 10.48772 11 23347 étude 87 7 9 9.995827 12 4514 laser 261 12 11 9.405535 13 16060 printing 135 8 11 8.955042 14 9089 deutschen 113 7 9 8.598491 15 6218 deposition 105 6 9 7.589766 16 8885 mitteilungen 189 8 10 7.291677 17 9651 chez 204 8 11 6.944498 18 12264 épidémie 162 7 10 6.910103 19 8501 2020 4992 59 12 6.876009 20 8905 für 490 13 11 6.776909 21 1492 energy 791 17 12 6.59874 22 2153 metal 277 9 12 6.503474 23 2721 machine 988 19 12 6.366001 24 4300 force 615 14 11 6.266417 25 14433 neurosurgery 314 9 10 5.961204 26 852 bayesian 267 8 12 5.798547 27 8893 der 1380 22 11 5.789434 28 12336 neurosurgical 330 9 11 5.752846 29 475 micro 217 7 10 5.707492 30 2103 3d 511 11 11 5.314866 31 1110 cov 28352 203 12 5.251235 32 3315 tobacco 250 7 10 5.170439 33 871 2 31487 220 12 5.09825 34 2482 material 552 11 12 4.990739 35 311 survey 3809 39 12 4.622084 36 1111 sars 31530 214 12 4.598068 37 515 impact 12012 94 12 4.453418 38 464 l 825 13 12 4.401141 39 1064 disparity 760 12 12 4.236688 40 239 base 10781 84 12 4.168911 41 408 test 6690 57 12 4.130059 42 783 learn 5246 47 12 4.108646 43 6591 article 603 10 12 4.050655 44 3678 turn 272 6 10 4.002269 45 6974 mental 4050 38 12 3.99262 46 16062 smart 359 7 11 3.909422 47 430 direct 1039 14 12 3.894457 48 4386 physical 1621 19 12 3.862372 49 2461 zealand 289 6 12 3.812324 50 15214 lockdown 3634 34 11 3.75922 > January, February {{notes:imfm:corona:pics:wch1.png?400}} {{notes:imfm:corona:pics:wch2.png?400}} March, April {{notes:imfm:corona:pics:wch3.png?400}} {{notes:imfm:corona:pics:wch4.png?400}} May, June {{notes:imfm:corona:pics:wch5.png?400}} {{notes:imfm:corona:pics:wch6.png?400}} July, August {{notes:imfm:corona:pics:wch7.png?400}} {{notes:imfm:corona:pics:wch8.png?400}} September, October {{notes:imfm:corona:pics:wch9.png?400}} {{notes:imfm:corona:pics:wch10.png?400}} November, December {{notes:imfm:corona:pics:wch11.png?400}} {{notes:imfm:corona:pics:wch12.png?400}}