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On the global trends and spread of the COVID-19 outbreak:preliminary assessment of the potential relationbetween location-specific temperature and UV index
Sachin S. Gunthe1 & Basudev Swain1& Satya S. Patra2 & Aneesh Amte3
Received: 20 March 2020 /Accepted: 1 April 2020# Springer-Verlag GmbH Germany, part of Springer Nature 2020
AbstractThe novel coronavirus, since its first outbreak in December, has, up till now, affected approximately 114,542 people across 115countries. Many international agencies are devoting efforts to enhance the understanding of the evolving COVID-19 outbreak onan international level, its influences, and preparedness. At present, COVID-19 appears to affect individuals through person-to-person means, like other commonly found cold or influenza viruses. It is widely known and acknowledged that viruses causinginfluenza peak during cold temperatures and gradually subside in the warmer temperature, owing to their seasonality. Thus,COVID-19, due to its regular flu-like symptoms, is also expected to show similar seasonality and subside as the global temper-atures rise in the northern hemisphere with the onset of spring. Despite these speculations, however, the systematic analysis in theglobal perspective of the relation between COVID-19 spread and meteorological parameters is unavailable. Here, by analyzingthe region- and city-specific affected global data and corresponding meteorological parameters, we show that there is an optimumrange of temperature and UV index strongly affecting the spread and survival of the virus, whereas precipitation, relativehumidity, cloud cover, etc. have no effect on the virus. Unavailability of pharmaceutical interventions would require greaterpreparedness and alert for the effective control of COVID-19. Under these conditions, the information provided here could bevery helpful for the global community struggling to fight this global crisis. It is, however, important to note that the informationpresented here clearly lacks any physiological evidences, which may merit further investigation. Thus, any attempt for manage-ment, implementation, and evaluation strategies responding to the crisis arising due to the COVID-19 outbreakmust not considerthe evaluation presented here as the foremost factor.
Keywords Coronavirus . COVID-19 . Temperature . UVindex . Non-physiological reaction
Introduction
As of 10th March 2020, 114,814 confirmed cases of novelcoronavirus (also called COVID-19) infection have been re-ported worldwide since its emergence in mid-December (Du
Toit 2020). Almost 70.3% are being reported from Chinaalone, followed by Italy (8.0%), South Korea (6.5%), Iran(6.2%), France (1.2%), Spain (1%), Germany (1%), theUSA (0.6%), and Japan (0.6%), constituting more than~95% of the total reported confirmed cases. In a report re-leased by theWorld Health Organization (WHO). a major goaland objective is outlined to rapidly disseminate the national(for China) and international planning on the preparedness inresponse to the increasing and ongoing outbreak of COVID-19. The virus traces its origin for the recent outbreak in China,when three samples for bronchoalveolar lavage were collectedand detailed microbiological analysis confirmed that the virushad typical features resembling the coronavirus family.COVID-19 has a transmission characteristic typical of anyother cold-causing influenza virus: human-to-human. As perthe report published by the WHO, the common symptoms
* Sachin S. [email protected]
1 EWRE Division, Department of Civil Engineering, Indian Instituteof Technology Madras, Chennai 600 036, India
2 Transportation Engineering Division, Department of CivilEngineering, Indian Institute of Technology Madras, Chennai 600036, India
3 Aasha Endoscopy Center, Vasant Prestige, 5 Bunglow, Shahupuri,Kolhapur 416001, India
Journal of Public Health: From Theory to Practicehttps://doi.org/10.1007/s10389-020-01279-y
include fever, dry cough, fatigue, sputum production, short-ness of breath, sore throat, headache, etc., which can be rec-ognized as normal flu symptoms.
Since the beginning of the COVID-19 outbreak, extensiveefforts have been constantly devoted to improving under-standing about the virus. As a result, this virus, previouslyunknown to many, not only garnered tremendous researchattention, but also became a household name within a spanof merely 90 days. The outbreak has also brought many majorcities in China to a standstill and resulted in the isolation of alarge number of citizens in Italy. Such a curfew-like situationcan be difficult to impose and may not yield the desired resultsunder many circumstances (Fong et al. 2020), which is evi-dent from the steps being taken by countries to contain furtherspread of the virus. At the same time, another prevalent issueis considering the COVID-19 features that are similar to com-mon influenza viruses causing colds. The most common fea-ture of common influenza viruses is seasonality, which im-plies that, during colder temperatures, influenza caused byviruses increases, which then subside with the warming ofair temperature. This assumption, at this moment, is notbacked by any robust analysis and scientific investigations.Under the circumstances of unobtainability of any pharmaceu-tical relief to treat the COVID-19 virus, specific informationabout the non-physiological causes augmenting the survivaland spread of COVID-19 can be very valuable information forpolicy-makers dealing with management and effective controlmeasures. Since the onset of the COVID-19 outbreak, a lot ofattention has been paid to understanding cross-species trans-mission (Ji et al. 2020), efforts regarding the discovery, emer-gence, clinical diagnostic, and genomic characteristics ofCOVID-19 (Phan 2020), and initial public response (Patelet al. 2020). Further, there are requirements for details relatedto the necessity of maintaining regular physical activity whileexercising precautions (Chen et al. 2020) and, more interest-ingly, even developing an accurate model to forecast the novelCOVID-19 outbreak from small datasets (Fong et al. 2020).The relation of COVID-19 with meteorological parameters,which are very location-specific where outbreaks have beenreported, however, has not been investigated. In this study, weanalyzed the number of confirmed cases up till 7thMarch 2020 and investigated location-specific outbreaks withmeteorological parameters, including temperature, relativehumidity, UV index, and precipitation.
Materials and methods
To better understand the demographic distribution of COVID-19 on the global scale, we used data that were not derived forany in situ measurements or monitoring. Instead, we capturedthe country-/region-dependent spread from alerts generated bythe Program for Monitoring Emerging Diseases (ProMED),
which is a community-based online reporting system (Yadavet al. 2020). The very fact that reports in the ProMED are fullyscreened by a group of expert members for its validity makesthe reports reliable to be used in scientific publishing (Fisheret al. 2012). We used the key word “COVID-19” with theoption enabled for subject and post; for exclusions, see alsothe ProMED-mail database. Thereafter, we selectivelyscreened the number of infected cases listed for each countryfrom 2nd February 2020, where the accumulated infectedcases were already provided. As shown in Table 1, we con-sidered the cumulative dataset for 85 locations, including thehighly affected provinces in China, South Korea, Italy, Iran,France, Germany, the USA, Spain, and Japan. Further, themeteorological data for minimum, maximum, and averagetemperature (in °C); relative humidity (in %), UV index (anindex indicating the strength of sunburn-producing UV radia-tion), precipitation (in mm), and cloud cover were obtainedfrom the website https://www.worldweatheronline.com (lastaccessed 10th March 2020, 04:30 h IST), which is dedicatedto providing global weather content and has been used byprevious researchers (Ibekwe and Ukonu 2019). The pro-viders claim to cover around 3 million sites across the globe,and the data are used by many organizations. While it is dif-ficult to independently verify the quality and accuracy of thedata, random validation for a few cities with actual measure-ments in the past have given a strong agreement between thetwo datasets. To obtain the data for a specific city, we appliedthe search criteria by narrowing on the province/region andthen querying the most affected cities in the respectiveprovince/region obtained from various other data sources.Thus, we are of the strong opinion that the meteorologicaldata presented here are not only the true representation ofthe city/place with infected cases, but also give an accurateindication of the relation between the number of confirmedcases and the meteorological parameters.
Further, for the countries with more than 500 confirmedcases, we only chose cities that constituted more than 70%of the total confirmed cases for that respective country.Thus, we scrutinized a total of 107,351 confirmed cases across85 locations.
Once the data were obtained, we prepared scatter plotsbetween the number of confirmed cases and various meteoro-logical parameters. Further, by applying various statistical fits,the dependence of virus spread on individual meteorologicalparameters was investigated, as discussed below.
Assessment
Figure 1 shows the relation between the number of confirmedcases and temperature assessed for more than 10,000 casesglobally, constituting ~85 individual locations. As can be seenfrom Fig. 1a, the data points are clearly following the log
J Public Health (Berl.): From Theory to Practice
Table1
Listo
fcountriesandprovincesin
China
considered
forthisstudyandcorrespondingmaxim
um,m
inim
um,and
averagetemperature,relativehumidity
(RH),andUVindex.The
results
were
averaged
forJanuaryandFebruary
January
Region/Province/City
Num
berof
Cases
Temp
(max)
Temp
(average)
Temp
(min)
RH
Precipitatio
nRainy
Days
Cloud
Coverage
UV-
Index
Temp
(max)
Temp
(average)
Temp
(min)
RH
Hubei
66907
97
474
157.3
2163%
416
139
66Daegu
5381
74
068
137.1
1249
19
51
62Guangdong
1349
2319
1364
42.5
1446
521
1914
73Gyeonggi
141
53
–171
90.4
955
17
4–1
69Henan
1272
105
159
205
463
86
258
Seoul
120
53
055
82.1
748
17
40
56Zhejiang
1205
86
381
97.5
2370
315
128
71Barcelona
7214
1210
65262.9
732
416
1411
75Hunan
1018
98
582
154
1968
415
139
80Álava
122
1310
677
66.9
1753
315
128
75Anhui
990
76
382
148
2272
214
117
71Madrid
578
119
668
54.2
1144
418
139
63Jiangxi
935
1110
779
143.5
2166
415
139
77LaRioja
102
86
379
59.5
1551
313
106
74Sh
andong
756
64
057
27.3
441
311
83
52Florida
1323
1912
78209.2
2850
426
2011
76Jiangsu
631
86
379
113
2373
313
116
70Texas
1316
1512
78116
2046
416
1512
77Chongquing
576
1312
964
36.1
1660
315
1310
68California
105
1310
553
42.1
164
1713
737
Sichuan
538
1311
762
1015
543
1613
960
Wahington
136
96
265
77.2
1244
210
73
64Heilongijang
480
–12
–15
–20
938.9
263
2–8
–11
–17
92New
York
106
64
161
99.9
1648
17
51
62Beijin
g413
42
–245
1.6
129
37
51
45Île-de-France
104
98
579
43.1
1557
311
97
74Massachusetts
283
1–4
7371.6
1250
23
1–4
72Sh
anghai
337
119
571
116.9
2065
314
127
66Auvergne-Rhône-A
lpes
102
97
378
40.3
1045
312
105
71Hebei
318
52
–352
7.7
138
310
61
44Hauts-de-France
173
87
483
26.6
1753
310
84
77Fu
jian
296
1815
1079
14.4
1059
418
1610
80Grand
Est
250
86
381
23.3
1244
310
84
75Guangxi
252
2018
1471
3415
665
2119
1575
Bourgogne-Franche-Com
té129
85
285
7812
513
107
480
Shaanxi
245
86
157
7.6
546
215
115
39North
Rhine-W
estphalia
484
86
383
35.6
1757
39
84
78Yunnan
174
118
270
71.1
1228
213
105
65
J Public Health (Berl.): From Theory to Practice
Tab
le1
(contin
ued)
Baden-W
uerttemberg
204
75
279
27.2
1345
39
73
74Hainan
168
2222
1878
37.8
2656
523
2219
80Bavaria
256
64
076
41.6
1046
38
62
71Guizhou
146
119
582
96.2
2071
213
117
83Mazandaran
633
1210
567
7114
434
1411
668
Tianjin
136
52
–246
2.3
233
38
51
51Gilan
524
1210
777
291
2156
413
106
72Sh
anxi
133
10
–467
24.7
139
28
50
43Qom
712
97
359
35.7
1135
213
105
47Liaoning
122
–5–7
–12
803.9
132
20
–2–7
66Esfahan
601
96
249
7.4
521
313
104
37HongKong
9520
1916
7421.6
1547
520
1916
78Markazi
389
75
251
25.6
1035
210
84
45Jilin
93–9
–13
–18
909.5
352
2–6
–8–13
78Tehran
1945
75
153
2511
323
107
248
Gansu
912
–1–5
665.6
430
27
4–2
39Milan(Lom
bardia)
506
97
569
55.5
635
314
117
58Xinjiang
76–5
–8–12
7624
535
1–1
–3–7
68Piacenza
(EmiliaRom
agna)
602
97
382
5810
403
1310
569
InnerMongolia
75–6
–9–14
7910
138
21
–3–8
61Lodi(Lom
bardia)
928
97
478
38.3
635
313
106
67Ningxia
730
2–5
553
229
27
3–1
30Brescia(Lom
bardia)
739
97
472
25.8
534
312
106
63Taipei
3919
1713
7557
2260
420
1914
71Bergamo(Lom
bardia)
1245
97
368
35.9
530
312
105
60Qinghai
18–1
–5–11
603
728
25
–1–8
38Cremona(Lom
bardia)
916
97
481
26.9
637
313
76
68Aichi
670
1110
759
6820
463
119
656
Switzerland
268
84
–177
38.6
1136
29
61
77Hokkaidō
4189
119
654
95.3
1451
213
107
48UK
209
98
580
67.2
1662
310
85
70To
kyo
1180
119
554
106.7
1749
213
107
47Netherlands
188
87
485
80.2
2164
29
75
77Busan
979
74
61108.2
1348
310
84
58Belgium
169
86
383
36.8
1754
39
74
78Gyeongbuk
1081
85
271
95.8
945
29
73
67Sw
eden
161
54
079
31.7
1855
14
30
75Chungnam
987
40
7261.7
1053
19
61
70Norway
156
21
–585
47.3
651
12
1–3
74Singapore
138
2928
2679
146.5
2756
730
2826
77Malaysia
9333
3023
74192.7
3051
634
3023
72Bahrain
8519
1815
6316.6
718
520
1917
64Austria
794
30
7718.2
744
39
74
64Australia
6326
2421
77196.8
2455
725
2320
75Kuw
ait
6118
1614
510
013
519
1715
49Thailand
5035
3228
509.8
1424
735
3227
51
J Public Health (Berl.): From Theory to Practice
Tab
le1
(contin
ued)
February
Average
Region/Province/City
Precipitatio
nRainy
Days
Cloud
Coverage
UV-
Index
Temp(m
ax)
Temp(average)
Temp(m
in)
RH
Precipitatio
nRainy
Days
Cloud
Coverage
UV-
Index
Hubei
77.5
1351
312.5
106.5
70117.4
1725.815
3.5
Daegu
52.9
937
38
4.5
0.5
6595
10.5
432
Guangdong
227
1660
522
1913.5
68.5
134.75
1553
5
Gyeonggi
106.2
741
36
3.5
–170
98.3
848
2
Henan
31.9
751
49
5.5
1.5
58.5
25.95
648.5
3.5
Seoul
70.4
937
36
3.5
055.5
76.25
842.5
2
Zhejiang
103.9
1651
311.5
95.5
76100.7
19.5
60.5
3
Barcelona
2.2
622
415
1310.5
70132.55
6.5
274
Hunan
231.1
1867
312
10.5
781
192.55
18.5
67.5
3.5
Álava
47.7
1237
314
117
7657.3
14.5
453
Anhui
46.5
1245
310.5
8.5
576.5
97.25
1758.5
2.5
Madrid
0.8
319
214.5
117.5
65.5
27.5
731.5
3
Jiangxi
182
1658
313
11.5
878
162.75
18.5
623.5
LaRioja
9.6
933
310.5
84.5
76.5
34.55
1242
3
Shandong
25.6
635
48.5
61.5
54.5
26.45
538
3.5
Florida
275.6
2856
524.5
19.5
11.5
77242.4
2853
4.5
Jiangsu
36.7
946
210.5
8.5
4.5
74.5
74.85
1659.5
2.5
Texas
40.4
1651
516
1512
77.5
78.2
1848.5
4.5
Chongquing
76.5
2263
414
12.5
9.5
6656.3
1961.5
3.5
California
1.3
114
515
11.5
645
2.65
1.55
154.5
Sichuan
9.2
1349
514.5
128
619.6
1451.5
4
Wahington
118.9
1654
19.5
6.5
2.5
64.5
98.05
1449
1.5
Heilongijang
26.6
464
1–10
–13
–18.5
92.5
17.75
363.5
1.5
New
York
122.8
1759
16.5
4.5
161.5
111.35
16.5
53.5
1
Beijin
g28.6
229
25.5
3.5
–0.5
4515.1
1.5
292.5
Île-de-France
157.6
2572
210
8.5
676.5
100.35
2064.5
2.5
Massachusetts
87.4
1254
13
1–4
72.5
79.5
1252
1.5
Shanghai
52.8
1045
312.5
10.5
668.5
84.85
1555
3
Auvergne-Rhône-A
lpes
55.2
1848
310.5
8.5
474.5
47.75
1446.5
3
Hebei
13.9
327
47.5
4–1
4810.8
232.5
3.5
Hauts-de-France
116.5
2571
29
7.5
480
71.55
2162
2.5
Fujian
6310
545
1815.5
1079.5
38.7
1056.5
4.5
Grand
Est
188.7
2263
39
73.5
78106
1753.5
3
J Public Health (Berl.): From Theory to Practice
Tab
le1
(contin
ued)
Guangxi
63.3
1967
520.5
18.5
14.5
7348.65
1766.5
5
Bourgogne-Franche-Com
té127.6
2063
39
63
82.5
102.8
1657
3
Shaanxi
166
305
11.5
8.5
348
11.8
5.5
383.5
North
Rhine-W
estphalia
155.3
2372
28.5
73.5
80.5
95.45
2064.5
2.5
Yunnan
39.7
1427
412
93.5
67.5
55.4
1327.5
3
Baden-W
uerttemberg
179.5
1864
28
62.5
76.5
103.35
15.5
54.5
2.5
Hainan
40.8
2359
622.5
2218.5
7939.3
24.5
57.5
5.5
Bavaria
218.4
1962
27
51
73.5
130
14.5
542.5
Guizhou
113.6
2576
412
106
82.5
104.9
22.5
73.5
3
Mazandaran
262.7
1241
413
10.5
5.5
67.5
166.85
1342
4
Tianjin
23.6
330
26.5
3.5
–0.5
48.5
12.95
2.5
31.5
2.5
Gilan
243.9
1147
412.5
106.5
74.5
267.45
1651.5
4
Shanxi
12.2
425
34.5
2.5
–255
18.45
2.5
322.5
Qom
40.6
624
411
8.5
453
38.15
8.5
29.5
3
Liaoning
33.5
139
1–2.5
–4.5
–9.5
7318.7
135.5
1.5
Esfahan
5.2
313
511
83
436.3
417
4
HongKong
45.9
1652
620
1916
7633.75
15.5
49.5
5.5
Markazi
149.2
1028
48.5
6.5
348
87.4
1031.5
3
Jilin
8.5
248
1–7.5
–10.5
–15.5
849
2.5
501.5
Tehran
164.3
1132
48.5
61.5
50.5
94.65
1132
3.5
Gansu
6.6
421
34.5
1.5
–3.5
52.5
6.1
425.5
2.5
Milan(Lom
bardia)
8.4
827
211.5
96
63.5
31.95
731
2.5
Xinjiang
17.5
533
2–3
–5.5
–9.5
7220.75
534
1.5
Piacenza(EmiliaRom
agna)
2.6
328
211
8.5
475.5
30.3
6.5
342.5
InnerMongolia
00
222
–2.5
–6–11
705
0.5
302
Lodi(Lom
bardia)
2.9
728
211
8.5
572.5
20.6
6.5
31.5
2.5
Ningxia
00
142
3.5
2.5
–342.5
1.5
121.5
2
Brescia(Lom
bardia)
3.8
528
210.5
8.5
567.5
14.8
531
2.5
Taipei
49.8
2153
519.5
1813.5
7353.4
21.5
56.5
4.5
Bergamo(Lom
bardia)
9.2
1024
210.5
8.5
464
22.55
7.5
272.5
Qinghai
33
252
2–3
–9.5
493
526.5
2
Cremona(Lom
bardia)
1.6
329
211
75
74.5
14.25
4.5
332.5
Aichi
65.6
1336
211
9.5
6.5
57.5
66.8
16.5
412.5
Switzerland
134.9
1956
38.5
50
7786.75
1546
2.5
Hokkaidō
31.4
1440
412
9.5
6.5
5163.35
1445.5
3
UK
125.8
2167
29.5
85
7596.5
18.5
64.5
2.5
Tokyo
41.4
1640
312
9.5
650.5
74.05
16.5
44.5
2.5
J Public Health (Berl.): From Theory to Practice
Tab
le1
(contin
ued)
Netherlands
173
2576
28.5
74.5
81126.6
2370
2
Busan
89.6
835
39.5
7.5
459.5
98.9
10.5
41.5
3
Belgium
158.4
2473
28.5
6.5
3.5
80.5
97.6
20.5
63.5
2.5
Gyeongbuk
59.8
1142
28.5
62.5
6977.8
1043.5
2
Sweden
43.8
1663
14.5
3.5
077
37.75
1759
1
Chungnam
29.1
1044
48
50.5
7145.4
1048.5
2.5
Norway
83.8
756
12
1–4
79.5
65.55
6.5
53.5
1
Singapore
162.7
2250
729.5
2826
78154.6
24.5
537
Malaysia
78.4
2342
833.5
3023
73135.55
26.5
46.5
7
Bahrain
00
115
19.5
18.5
1663.5
8.3
3.5
14.5
5
Austria
52.2
1552
26.5
52
70.5
35.2
1148
2.5
Australia
568
2661
625.5
23.5
20.5
76382.4
2558
6.5
Kuw
ait
108
165
18.5
16.5
14.5
505
414.5
5
Thailand
1312
218
3532
27.5
50.5
11.4
1322.5
7.5
J Public Health (Berl.): From Theory to Practice
normal fit, with very narrow distribution. The point ofWuhan,where the maximum confirmed cases are reported, also lieswithin the peak (not shown in the figure). As evident from thefigure, it clearly appears that the temperature range of 5–15 °Ccomprises almost 90% of the total confirmed cases. With verysmall σ value of the distribution, it is very apparent that thetemperature band for community infection is, indeed, verynarrow. Previous studies have clearly demonstrated that lowertemperatures are directly related to influenza virus activity(Deyle et al. 2016; Ianevski et al. 2019; Roussel et al. 2016).Thus, it is clear that increasing temperature may have someclear indication for the reduction in the number of infectedcases. Further, to understand if the diurnal variations in tem-perature, in addition to daily maximum temperature, has anyimpact on the infection among the community, we calculatedthe difference between the daily maximum andminimum tem-peratures and plotted it against the cumulative number of con-firmed cases for respective temperature difference values (Fig.1b). It is evident from the figure that the pronounced diurnal
variations with a difference of ~7 °C between the maximumand minimum temperatures have shown a consistent decreasein community infection. However, further investigations arerequired on if such a trend is also evident for warmer andrelatively colder regions.
Further, interestingly, we also found a very strong relationbetween UV index and number of confirmed cases. As shownin Fig. 2, it is evident that the number cumulative cases werefound to be highest for a UV index of 2.5 and gradually de-creased from a UV index of 3.5. For the areas where the UVindex was higher than 5, the number of confirmed infectedcases decreased further. It has been documented that highertemperature or prolonged exposure to UVC radiation lowersthe virus infectivity (Ianevski et al. 2019). This is mainlyresulting from the fact that increased temperatures andprolonged exposure to UV radiation are unable to kill theretinal pigment epithelium (RPE) layer of the cells. Warmerwinters in Europe and the USA have been associated withreduced influenza virus activities (Ballester et al. 2016). As
Fig. 1 Scatter plot of temperature and total number of confirmed cases uptill 7thMarch 2020. Temperature data have been obtained from the onlinedataset as mentioned in the ‘Materials and methods’ section and wereaveraged over January and February 2020. a Scatter plot betweenaverage daily temperature and number of confirmed cases. The line
shows the log normal fit to the data (N = 85; R2 = 0.35). b Scatter plotof the difference between the maximum and minimum daily temperaturesand accumulated number of confirmed cases. The orange line shows thelog normal fit to the data (N = 14; R2 = 0.74). All the fits are statisticallysignificant
Fig. 2 Scatter plot betweencumulative confirmed cases andUV index (refer to the ‘Materialsand methods’ section for moredetails). The daily UV index forthe months of January andFebruary was obtained from thesource mentioned in the‘Materials and methods’ section.The daily UV index data for themonths of January and Februarywere further averaged. The lineindicates the spline fit to the data(N = 14; R2 = 0.63)
J Public Health (Berl.): From Theory to Practice
shown in Fig. 3, we did not observe any strong dependencebetween the relative humidity and precipitation and commu-nity infection.
Concluding remarks
While the world is putting every possible effort into tacklingthe global epidemic situation resulting from the novelCOVID-19 outbreak, very little information is available aboutthe novel virus. Under this situation, in this study, we present-ed the possible dependence between the infection within acommunity and the temperature and UV index. By analyzingthe data up to 7th March 2020 by considering more than 80locations comprising of a number of confirmed cases andlocation-specific meteorological data, we showed that thespread of the virus is strongly dependent on the temperatureand UV index. There is a very narrow temperature band of 3–
12 °C where 90% of the total confirmed cases are agglomer-ated. As can be seen from Fig. 4, major outbreaks across theglobe are located in the temperature areas lying in the rangeconsistent with our analysis. Most importantly, increase of theUV index has also shown a decrease in the number of cases.This is evident from the fact that the global UV index mapaveraged over 5 days for representation purposes clearlyshows a relatively lower UV index for the most affected areas(Fig. 5). This, in our opinion, is a very striking feature of ourfindings and we believe that artificial UV radiation could beone of the effective ways for sterilizing built-up environmentsfor reducing spread amongst the community. While we be-lieve that we are not presenting any physiological evidencesfor the decrease in community infections of COVID-19 due toincrease in temperature and UV index, we strongly believethat this information could be very useful, particularly whenpharmaceutical interventions are not possible at this momentto deal with the global crisis. It is important, however, to note
Fig. 3 Scatter plot between meteorological parameters and number ofconfirmed cases up till 7th March 2020. a Scatter plot between relativehumidity (%) and number of confirmed cases, with the orange and redlines indicating squared and linear fit, respectively (N = 85; R2 = 0.0005
for linear fit and R2 = 0.007 for squared fit). b Same as a but forprecipitation (N = 85; R2 = 0.0002 for linear fit and R2 = 0.01 forsquared fit
Fig. 4 Global map of the averagetemperature for January andFebruary 2020, with the countriesaffected by more than 1000 casesas of 9thMarch 2020 indicated bythe black circles. Importantly,countries, regions and locationsseverely affected are in a verynarrow band of latitude
J Public Health (Berl.): From Theory to Practice
that the information provided here is very preliminary and isnot based on any physiological studies involving either con-firmed cases and/or viruses. We advise that preparedness andother measures should take this study only for informationpurposes and propose further detailed physiological studies.
Acknowledgements The authors duly acknowledge the Program forMonitoring Emerging Diseases (ProMED), the https://www.worldweatheronline.com service, and the UV global map used in thisstudy was produced with the Giovanni online data system, developedand maintained by the NASA GES DISC. SSG acknowledges Nabha S.Gunthe and Nupura S. Gunthe for the encouragement and stimulatingdiscussions during the preparation of this manuscript. SSG is thankfulto selected faculty colleagues at Dept. of Civil Engineering at IIT Madrasfor helpful suggestions.
Compliance with ethical standards Not applicable.
Conflict of interest The authors declare that they do not have any con-flict of interest.
Role of funding source Not applicable.
Ethical approval Not applicable.
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Publisher’s note Springer Nature remains neutral with regard to jurisdic-tional claims in published maps and institutional affiliations.
Fig. 5 Similar to Fig. 4 butrepresenting the UV index andlocations indicated by the dots.Again, a very narrow band of UVindex is clearly evident for thelocations that are severelyaffected
J Public Health (Berl.): From Theory to Practice
