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Table 1 Characteristics of the 30 study counties

From: Characterizing the effect of temperature fluctuation on the incidence of malaria: an epidemiological study in south-west China using the varying coefficient distributed lag non-linear model

County Cases Annualized average incidences (/100,000) Mean temperature (°C) DTR (°C) Rain (mm) Relative humidity (%)
Ruili 3,442 348.204 21.2 (17.7, 24.6) 10.8 (7.7,14) 26.53 (0, 43.03) 73.0 (67, 80)
Tengchong 9,255 246.049 20.4 (16, 24.5) 10.7 (7.8, 13.8) 17.80 (1, 24.7) 65.0 (54, 77)
Gongshan 300 136.897 6.7 (1.4,12.3) 10.8 (8, 13.8) 12.29 (2, 17.63) 69.6 (61, 80)
Fugong 455 80.657 12.3 (7.1, 17.7) 12.4 (9, 15.9) 16.59 (2, 27.85) 67.6 (58, 78)
Mengla 1,203 79.980 22.0 (18.9, 25.1) 11.3 (8.2,14.1) 28.02 (0, 41.83) 80.6 (77, 85)
Cangyuan 859 65.931 19.8 (16, 23.4) 12.3 (8.5, 16.3) 23.63 (0, 37.93) 72.4 (65, 81)
Menglian 735 55.274 20 (16.6, 23.1) 12.2 (8.4, 16.1) 32.56 (0, 51.6) 75.3 (70, 82)
Jinping 966 47.375 16.5 (12.7, 20.7) 7.3 (4.6, 9.6) 28.95 (2.25, 40.35) 84.8 (81, 91)
Longyang 1,976 37.041 16.6 (12.1, 20.7) 11.2 (7.9, 14.6) 17.94 (0.08, 27.23) 73.1 (66, 81)
Congjiang 688 34.928 19 (12.3, 25.7) 8.7 (5.1, 11.9) 22.03 (0.7, 33.8) 78.6 (73, 84)
Jiangcheng 142 22.283 19.1 (15.7, 22.4) 9.9 (6.7, 13) 41.89 (0.38, 69.28) 79.2 (76, 84)
Menghai 420 21.036 22.8 (19.8, 25.6) 11.8 (8.6, 14.5) 23.17 (0, 38.63) 77.4 (72, 84)
Weixi 174 18.725 7.0 (1.6, 12.8) 12.5 (7.9, 16.7) 11.76 (0, 17.93) 65.9 (58, 74)
Shuangjiang 113 10.580 18.3 (14.6, 21.6) 11.3 (7.8, 14.9) 21.22 (0.08, 32.63) 67.6 (59, 77)
Simao 119 8.132 19.3 (16.2, 22.3) 10.2 (7.2, 13.1) 27.18 (0, 43.8) 75.9 (71, 83)
Mojiang 173 7.565 24.1 (20.1, 28.1) 11.6 (8.4, 14.3) 15.40 (0, 21.4) 66.6 (59, 75)
Jingdong 166 7.382 19 (14.6, 23.1) 12.4 (8.3, 16.6) 22.21 (0.38, 31.23) 74.7 (70, 82)
Dechang 86 7.335 17.6 (13.1, 22.2) 11.4 (8.5, 14.4) 18.39 (0, 28.1) 59.3 (50, 71)
Gejiu 156 5.535 19.5 (16.1, 23.2) 8.7 (6.7, 10.6) 15.90 (0, 21.18) 68.3 (63, 75)
Dushan 102 4.984 15.6 (9.7, 22) 7.3 (4.6, 9.5) 23.94 (1.5, 32.68) 79.4 (73, 88)
Changshun 53 3.598 16.4 (10.3, 22.6) 8.1 (5.1, 10.5) 22.30 (1.48, 29.1) 77.5 (72, 84)
Liping 75 2.522 16.3 (9.2, 23.8) 7.8 (4.3, 10.9) 23.68 (1.9, 33.33) 81.3 (74, 90)
Wenshan 64 2.325 16.5 (12.9, 20.6) 9.6 (6.8, 12.1) 17.64 (0.5, 26.03) 78.3 (74, 85)
Wangmo 29 1.609 20.0 (14.8, 25.6) 9.1 (5.9, 11.8) 22.43 (0.5, 26.43) 73.2 (67, 80)
Guangnan 74 1.575 17.5 (13.1, 22.4) 10.0 (6.5, 13) 16.98 (0.5, 23.8) 76.8 (72, 84)
Weishan 28 1.482 15.5 (11.5, 19.5) 10.6 (8.2, 13.1) 20.37 (0, 33.28) 66.2 (55, 78)
Nanhua 19 1.318 16.5 (12.3, 20.5) 10.4 (7.6, 13.1) 15.66 (0, 24.08) 68.2 (59, 80)
Weng’an 32 1.263 15.9 (9.1, 22.8) 7.3 (3.8, 10.1) 19.65 (2.6, 27.85) 77.1 (71, 85)
Eshan 11 1.156 16.4(12.3, 20.3) 10.8 (7.8, 13.8) 16.23 (0, 23.63) 72.6 (67, 81)
Huili 29 1.089 15.6 (10.7, 20.3) 11.9 (8.2, 15.9) 21.53 (0, 27.95) 68.0 (60, 77)
  1. : weekly mean, and the two values in the parenthesis are 25% and 75% percentiles, respectively.
  2. : weekly total, and the two values in the parenthesis are 25% and 75% percentiles, respectively.