|
 
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| ORIGINAL ARTICLE |
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| Year : 2021 | Volume
: 6
| Issue : 2 | Page : 38-44 |
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Association between climatic and nonclimatic parameters and transmission of SARS-CoV-2 infection in Nepal
Sarmila Tandukar1, Dinesh Bhandari2, Rajani Ghaju Shrestha3, Samendra P Sherchan4, Anil Aryal5
1 nterdisciplinary Center for River Basin Environment (ICRE), University of Yamanashi, Kofu, Yamanashi, 400-8510, Japan; Policy Research Institute, Sano Gaucharan, Kathmandu, Nepal
2 The University of Adelaide, School of Public Health, Adelaide, South Australia, Australia
3 Division of Sustainable Energy and Environmental Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan
4 Global Health Environmental Sciences, Tulane University, 1140 Canal Street, New Orleans, LA 70112, USA
5 Interdisciplinary Center for River Basin Environment (ICRE), University of Yamanashi, Kofu, Yamanashi, 400-8510, Japan
| Date of Submission | 14-Jan-2021 |
| Date of Decision | 22-Mar-2021 |
| Date of Acceptance | 19-Apr-2021 |
| Date of Web Publication | 06-Jul-2021 |
Correspondence Address:
Dr. Anil Aryal
nterdisciplinary Centre for River Basin Environment (ICRE), University of Yamanashi, 4-3-11 Takeda, Kofu, Yamanashi 400-8511
Japan
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ed.ed_2_21
Background: Preliminary evidence suggests that environmental factors may modify the transmission of SARS-CoV-2 infection. Although the role of non-pharmaceutical interventions (NPIs) on the reduction of SARS-CoV-2 transmission rate is well explored, the role of local climate across different geographical transects on the onset and transmission of SARS-CoV-two remains unclear.
Aims and Objectives: In this study, we explored the potential association among climatic factors, non-climatic factors and COVID-19 burden, via Pearson correlation analysis. We also investigated the association between COVID-19 cases and cumulative effect of NPIs or behavioral changes during lockdown as non-climatic factors in our analysis.
Setting and Design: The research was carried out in the COVID-19 impacted districts across Nepal.
Material and Methods: The meteorological/climatic factors consisting of temperature and rainfall as predictor variables and total laboratory confirmed COVID-19 cases reported between January and May 2020 were considered in the study.
Statistical Analysis Used: The statistical tests were carried out using R programming language.
Results: Of the total 375 confirmed positive cases until May 19, 2020, clusters of the cases were diagnosed from the Terai region, which was associated with comparatively higher temperature and open border to India. Upon time series and spatial analysis, the number of positive cases increased after the end of April, possibly due to expansion of diagnostic tests throughout the country. We found a positive correlation betweenCOVID-19, and temperature indices (mean and minimum) (P < 0.05).
Conclusions: In the absence of an effective vaccine, these findings can inform infection control division of Nepal on the implementation of effective NPIs based on the observed variability in meteorological factors, especially in prevention of possible second wave of infection during winter.
Keywords: Climatic and non-climatic parameters, COVID-2019, Lockdown, Nepal
How to cite this article:
Tandukar S, Bhandari D, Shrestha RG, Sherchan SP, Aryal A. Association between climatic and nonclimatic parameters and transmission of SARS-CoV-2 infection in Nepal. Environ Dis 2021;6:38-44 |
How to cite this URL:
Tandukar S, Bhandari D, Shrestha RG, Sherchan SP, Aryal A. Association between climatic and nonclimatic parameters and transmission of SARS-CoV-2 infection in Nepal. Environ Dis [serial online] 2021 [cited 2023 Mar 30];6:38-44. Available from: http://www.environmentmed.org/text.asp?2021/6/2/38/320786 |
| Introduction | |  |
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a member of the coronavirus family, has recently emerged from Wuhan, China, with a total of 4,789,205 confirmed cases and 318,789 deaths around the world (as of 20 May, 2021).[1],[2],[3],[4] It was reported that the outbreak occurred through zoonotic sources;[5],[6],[7],[8] however, there was no strong evidence to support this phenomenon.[4]
The SARS-CoV-2 is primarily transmitted through respiratory droplets and contact routes; however, recent evidence also suggests possible transmission through aerosols.[8],[9] To prevent and control the transmission of SARS-CoV-2, governments all over the world have implemented nonpharmaceutical interventions (NPIs) that include a varied combination of social distancing, restriction on public gatherings, promotion of frequent handwashing and use of alcohol-based hand hygiene, and strict measures of community containment through lockdown.[10],[11] In absence of an effective vaccine, pharmaceutical measures including social distancing must be effectively practiced slowing down the chain of virus transmission.[12],[13],[14] At the same time given the uncertainty of vaccine availability in the near horizon, it is important to regulate essential services and some business functions to prevent the indirect impact of long strict restriction measures. Hence, understanding the association between climate factors as well as the effectiveness of nonclimatic measures, lockdown in this case, in a modification of SARS-CoV-2 incidence can help the authority in making rational decisions.
Nepal confirmed its first case of COVID-19 on January 13, 2020, an imported case from Wuhan, China.[15],[16] There were no further reported cases until March 23, 2020,[17] after 2 months, five cases were confirmed in Nepalese who returned from Europe and Middle East countries.[16],[18] Gradually, more cases were reported of imported cases with sudden confirmed cases of locally acquired infection.[18] Then, the number of locally transmitted cases rose along with the increase in the number of immigrants returning from India. This resulted in a daily maximum of 57 infected persons in Parsa district on May 12, 2020. Based on this evidence, the epidemiologists believe that Nepal has reached Stage II.[17] As of May 19, 2020, the districts with the highest number of COVID-19 cases are shown in [Table 1]. The total number of cases has reached 357 as of May 18, 2020, which is confirmed from 31 different districts of Nepal lying geographically in mid-hill and Terai.[19] | Table 1: Districts with highest number of COVID-19 cases and climate data acquisition centers (as of May 19, 2020)
Click here to view |
Initially, the growth rate of COVID-19 was slow in the South Asian countries including Nepal, India, Bangladesh, and more. The tropical and subtropical climate of these regions might be a cause for the slow emergence of the COVID-19.[20] Several climatic and nonclimatic variables play a significant role in the diffusion of COVID-19.[20],[21] The climatic variables include rainfall, temperature, humidity, and others. Beside the climatic variables, environmental and social variables also pose a significant distress across globe.[22] Furthermore, a study carried out in Indonesia has also portrayed that the weather plays an important role in determining the incidence of COVID-19.[23] As this study is limited to specific areas, and there is considerable variation in the climates of Nepal, it is important to understand the role of climatic and nonclimatic factors in COVID-19 emergence in Nepal. Thus, we aimed to investigate the association between climatic variables and COVID-19-reported cases in Nepal.
| Methods | | |
The overall methodological framework of the study conducted is shown in [Figure 1]. Data required for the research purpose were collected from various sources. The climatic data on rainfall and temperature (minimum, maximum, and average) were collected from the Department of Hydrology and Meteorology (DHM),[24] Kathmandu, Nepal. The daily COVID-19-infected, recovered, and death cases were extracted from the Ministry of Health and Population in Nepal.[25] The collected data were used for the analysis using an R programming language (data analysis) and ArcGIS 10.3 software (spatial distribution).[26],[27] The correlations among the climatic variables were analyzed using the R programming language and spatial distributions were analyzed in the ArcGIS environment.[26] We conducted Pearson's correlation analysis between the climatic variables and COVID-19 cases using corrplot[27] R package. The package has several methods to create correlation matrices to calculate the correlation absolute values based on Friendly.[28] | Figure 1: Overall methodological framework to perform dependence between the emergence of COVID-19 cases and climatic and nonclimatic variables. DHM: Department of Hydrology and Meteorology, MoHP: Ministry of Health and Population
Click here to view |
The climatic variables were collected from the DHM for the regions that have a high number of COVID-19-infected persons. Climate data were collected from the representative stations for respective topographical belts (Tarai) and major administrative divisions (headquarter of each province) within the country while the COVID-19 statistics were collected for all the high-infected districts [Table 1]. Since a total number of COVID-19 cases were more concentrated in the Tarai topographical belt, the climate data were collected from the districts located in the Tarai belt (South of Nepal). The 90-m digital elevation model from the Shuttle Radar Topography Mission was used to observe the topographical variation among the topographic zones in Nepal. Topographically, the country was divided as 0–500 m, 500–1,000 m, 1,000-2,000 m, 2,000–3,000 m, 3,000–4,000 m, and >4,000 m to visualize the elevation difference among the infected districts. The spatial variation of the COVID-19 cases was calculated for total, recovered, and active cases using manual breaks in the ArcGIS environment. The topographical variation was observed in a similar way dividing the topography into different elevations. The elevations were so divided that the clear difference in elevation can be viewed in low-lying areas of the country.
| Results and Discussion | | |
Temporal distribution of COVID-19
As shown in [Figure 2], the temporal distribution of the COVID-19-infected persons in both daily and cumulative numbers of total positive cases increased exponentially after April 20 in Nepal. Until May 19, 2020, Nepal has been among the nations that experienced the negligible impact of the deadly coronavirus pandemic with no reported death. The daily cases went up to 83 in a single day on May 12, 2020, reaching the total cumulative cases to be 217. The temporal distribution of the COVID-19 cases shows a slow increase rate during initial state. However, the numbers started increasing rapidly as the mobility of the residents increased. The total cumulative cases were 250 after 114 days of COVID-19 cases' emergence in the country. Likewise, with the 62 number of daily cases after 118 days, the total cumulative cased reached to 357 showing a rapid increase in the last 8 days (May 11 to May 18, 2020). The number of COVID-19 cases was observed to increase initially in the lowlands which are open boarder to India. The number of cases in the least developing countries has comparatively lower with a gradual rising rate of 0.56% of global cases and 0.23% of global deaths.[29] The sudden increase in cases in the context of Nepal can be attributed to the nationwide expansion of laboratory-testing facilities, which was initially reliant only on the National Public Health Laboratory, Kathmandu.[30] | Figure 2: Time series data of COVID-19 cases in Nepal (as of May 2019). The black line shows the total cumulative cases and green bar shows the daily number of cases
Click here to view |
Spatial distribution of COVID-19
The spatial distribution of the COVID-19 (as of May 19, 2020) total cases, recovered cases, and active cases shows the dominance of the cases in the southern region of Nepal [Figure 3]a and [Figure 3]b. Parsa district received the highest number of infected cases followed by Banke, Kapilvastu, Udaypur, Rupandehi, and Rautahat districts, respectively [Figure 3]a. All the districts receiving the highest number of COVID-19 patients belonged to the tropical climatic zones of Nepal. Province 2 and the southern district of Karnali Province received the highest number of the COVID-19 cases till May 19, 2020. The southern districts, a higher number of COVID-19 cases, received an extreme maximum temperature of 41.2°C (Dhangadhi), while the districts (topography >1,000-m absolute sea level [asl]), with the smaller number of the COVID-19 cases, received the extreme maximum temperature of 26.7°C (Taplejung district). The result showed the increasing trend of the COVID-19 cases with the temperature. The central and northern parts of other provinces were found to have a comparatively lesser number of COVID-19 cases, which was analogous to the climatic pattern. Like temperature, rainfall did not show homogeneity in the distribution, which might be the reason for poor correlation (P = 0.01) with the daily COVID-19 cases. Further, a high number of cases reported from these districts are probably due to the failure of proactive containment of imported cases from the neighboring high-risk districts of India. Even though the government tightened the lockdown, mobility of people from vulnerable areas (India) continued unrestricted resulting in the exponential increase of cases near the districts sharing borders with India.
The topographical variations among the different infected districts were observed [Figure 3]c. Most of the districts that have elevation <1,000-m asl were found to have a higher number of the COVID-19 cases compared to districts with higher elevations. These districts have relatively higher average temperature compared to the districts with greater ground elevation. The result of the topographical variations also portrays that the COVID-19 cases are highly susceptible to temperature rather than the amount of rainfall. Among the seven provinces in Nepal, Province 2 [Figure 2] is highly infected by the increasing number of the COVID-19 cases since province falls on the low topographic regions and has high air temperature. The districts of the Terai physiographic zones of Lumbini Province and Karnali Province are the most affected districts in Nepal. The districts with the highest number of COVID-19 cases fall in these regions. Highest number of recovered cases were observed in province 1 and lesser cases in Lumbini and Sudur Paschin Province. [Figure 3]d.
State of emergency (lockdown)
The government declared the first state of emergency (lockdown system) to be effective from March 24, 2020. Till then, a total number of cases were found to be only two with a maximum of one in a single day. However, as the lockdown advanced, both the daily maximum and total cumulative number of cases rose exponentially. During the first and second stages of lockdown, a total number of COVID-19 cases were found to be <20. On the progression of lockdown, the number of cases increased to 52 at the start of the third stage of lockdown. The total number of cases increased to 357 as the country advanced to the fourth stage of lockdown. During this period, the daily maximum cases were found during the third and fourth stages of lockdown [Figure 4]. The possible reason for the increase in the number of cases was found to have been driven by various factors such as the shift toward the advancement of testing kit from rapid diagnostic test to polymerase chain reaction and haphazard and uncontrolled mobility of people within the country and from India. Similar trends were reported in Italy and Spain.[31] Few researchers reported that climatic factors are considered as one of the driving factors in predicting the cases imposed by viruses.[32] While adaptation of NPIs such as social distancing, washing hand, personal hygiene, imposing lockdown, enhancing and improving isolation, and quarantine facilities helps in minimizing the virus spread. | Figure 4: Trend in the total number cases and daily maximum during lockdown in Nepal
Click here to view |
Relation between COVID-19 and climatic variables
The daily time series between different climatic variables (minimum, maximum, and average temperature and rainfall) and COVID-19 cases (daily and cumulative) were observed [Figure 5]. Overall, there is no clear picture of the relation between the rainfall and COVID-19 cases [Figure 5]. Despite some rainfall occurring after April 15, 2020 (daily maximum of 23.7 mm), it did not affect the association between the temporal variation of rainfall and COVID-19 cases. The minimum and average temperature showed a positive correlation with COVID-19 cases, while no significant association was observed with maximum temperature beyond April 15, 2020. Air temperature is found to have an impact on the seasonal variation[33] and is negatively associated with viruses.[34] A low correlation (0.26) is observed between temporal rainfall distribution and the cumulative number of COVID-19 cases. No or zero correlation exists between the daily number of COVID-19 cases and the daily rainfall distribution. This implies that the climatic variable (rainfall) and the number of the COVID-19 cases have had no relation. In contrast, a medium (0.41)-to-low (0.22) correlation is between maximum temperature and the cumulative COVID-19 cases. A similar relation exists between average temperature and the number of COVID-19 cases. A medium correlation (0.53) and a low correlation (0.28) subsist with cumulative cases and the average temperature, respectively. However, a high (0.62)-to-medium (0.34) level of correlation happens between cumulative and daily numbers of COVID-19 cases [Figure 6]. This finding implicates that COVID-19 cases are likely to increase during the winter season. Change in mixing pattern of people during winter, i.e., more indoor interaction among people, is likely to increase proximity among people, thereby facilitating increased airborne or droplet transmission. Indeed, such events of higher transmission attributed to proximity have been previously reported for other gastroenteric and respiratory viruses.[35],[36] Similarly, viruses have been reported to survive for a longer period (once released from the host into the environment) in laboratory experiments. This mechanism of higher survival and transmission of respiratory viruses in low temperatures has been reported in both epidemiological and laboratory studies.[37] Our analysis was consistent with the study conducted by Bashir et al[38] A study conducted by Dalziel et al[12] already reported that temperature change might influence disease transmission. It was reported that the number of COVID-19 cases were high in temperate regions. In contrast, the research done in warm climate regions such as Brazil, Indonesia, India, and the Philippines reported no relation with temperature.[39] Further studies need to be conducted within a large number of samples to draw a clear picture related to SARS and factors affecting the transmission of COVID-19. | Figure 5: Daily and cumulative COVID-19 cases in relation with climatic variables
Click here to view |
 | Figure 6: Correlation between COVID-19 cases' climatic variables in Nepal
Click here to view |
| Conclusions | | |
The interdependencies between the climatic and non-climatic variables with COVID-19 cases in Nepal was analyzed. The climatic variables showed mix dependency tendency. However, the nonclimatic variables (state of emergency) are observed to have a significant impact on the onset of the COVID-19 cases.[40]
Among the climatic factors included in our analysis, we found average and minimum temperature to be positively correlated (medium to high) with the reported number of COVID-19 cases. The spatial and time series data showed that the reported cases of COVID-19 increased after April, which was related to a possible expansion of testing kits and molecular techniques throughout the country. Rainfall shows a medium to no level of correlation with cumulative cases and daily cases, respectively, which implies that the number of COVID-19 cases has no relation with rainfall. The temperature shows a medium to a high level of correlation with cumulative cases and the daily cases. Thus, our findings reveal that there exists a high-moderate correlation between climatic variables with cumulative cases and a medium-poor correlation with the daily COVID-19 cases. Even though a moderate level of correlation exists between the climatic variables and the number of COVID-19 cases, the result of the research can be used as a basis for reducing the transmission of the case number. Furthermore, the research provides an insight in adopting the wise way in decreasing the total number of COVID-19 cases. The nonclimatic variables (state of emergency) showed an increase in the number of the daily cases which concludes that effective measure needs to be implemented for further transmission. These findings provide an evidence base to the infection control planning division to implement effective interventions such as increased compliance to social distancing, hand hygiene, and other NPIs to control the transmission of SARS-CoV-2 during the high-risk winter season. Consequently, based on the observed meteorological evidence of decreasing temperature, the infection control division of Nepal can implement intermittent restriction measures in high-risk regions instead of long-sustained nationwide lockdown. Given the higher detection of positive cases from southern Terai districts boarding India, strict restriction of mobility of people across the border should be implemented to control imported cases of COVID-19 from India. The result of the research can provide the policymakers and the governing bodies in drafting the policies and laws regarding the emergence of an epidemic in future. Further, the study also helps in formulating and executing the strategic plans in combating the pandemic for smooth functioning. The researchers, practitioners of heath science, and scientists can use the output of the research as a base for further study on different aspects such as environment, migration, and economics.
Acknowledgment
The authors would like to acknowledge the Ministry of Health and Population, Kathmandu, for providing the necessary data on COVID-19 cases and the Department of Hydrology and Meteorology (DHM), Kathmandu, for providing climatic data.
Declaration of ethical approval and patient consent
Ethics committee approval was not applicable as this is a meta-analysis article. Patient informed consent was waived since the data were collected anonymously.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Wuhan Municipal Health Commission. Report of Clustering Pneumonia of Unknown Etiology in Wuhan City. The Wuhan Center for Disease Control and Prevention (CDC): Wuhan Municipal Health Commission, Wuhan City Health Committee; 2019.  |
| 2. | Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020;382:727-33. |
| 3. | Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med 2020;382:1199-207. |
| 4. | World Health Organization. WHO Announces COVID-19 Outbreak a Pandemic 2020. WHO Regional Office for Europe, UN City, Marmorvej 51 DK-2100 Cophenhagen, Denmark: World Health Organization; 2020. |
| 5. | Lau SK, Li KS, Tsang AK, Lam CS, Ahmed S, Chen H, et al. Genetic characterization of betacoronavirus lineage C viruses in bats reveals marked sequence divergence in the spike protein of pipistrellus bat coronavirus HKU5 in Japanese pipistrelle: Implications for the origin of the novel Middle East respiratory syndrome coronavirus. J Virol 2013;87:8638-50. |
| 6. | Ye ZW, Yuan S, Yuen KS, Fung SY, Chan CP, Jin DY. Zoonotic origins of human coronaviruses. Int J Biol Sci 2020;16:1686-97. |
| 7. | Su S, Wong G, Shi W, Liu J, Lai AC, Zhou J, et al. Epidemiology, genetic recombination, and pathogenesis of coronaviruses. Trends Microbiol 2016;24:490-502. |
| 8. | Li X, Song Y, Wong G, Cui J. Bat origin of a new human coronavirus: There and back again. Sci China Life Sci 2020;63:461-2. |
| 9. | Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet 2020;395:514-23. |
| 10. | Blocken B, Malizia F, van Druenen T, and Marchal T. Towards aerodynamically equivalent COVID19 1.5 m social distancing for walking and running. Urban Physics, 11. 2020. |
| 11. | Ong SW, Tan YK, Chia PY, Lee TH, Ng OT, Wong MS, et al. Air, surface environmental, and personal protective equipment contamination by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from a symptomatic patient. JAMA 2020;323:1610-2. |
| 12. | Dalziel BD, Kissler S, Gog JR, Viboud C, Bjørnstad ON, Metcalf CJ, et al. Urbanization and humidity shape the intensity of influenza epidemics in U.S. cities. Science 2018;362:75-9. |
| 13. | Wu X, Lu Y, Zhou S, Chen L, Xu B. Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environ Int 2016;86:14-23. |
| 14. | Roberts KE, Hadfield JD, Sharma MD, Longdon B. Changes in temperature alter the potential outcomes of virus host shifts. PLoS Pathog 2018;14:e1007185. |
| 15. | Bastola A, Sah R, Rodriguez-Morales AJ, Lal BK, Jha R, Ojha HC, et al. The first 2019 novel coronavirus case in Nepal. Lancet Infect Dis 2020;20:279-80. |
| 16. | Shrestha R, Shrestha S, Khanal P, Kc B. Nepal's first case of COVID-19 and public health response. J Travel Med 2020;27:746-61. |
| 17. | Health Emergency Operation Center. Ministry of Health and Population, Government of Nepal. Resource Materials on Novel Coronavirus (2019-nCoV). Kathmandu: Health Emergency Operation Center; 2020. |
| 18. | Paudel S, Dangal G, Chalise A, Bhandari TR, Dangal O. The coronavirus pandemic: What Does the evidence show? J Nepal Health Res Counc 2020;18:1-9. |
| 19. | National Disaster Risk Reduction and Management Authority. Ministry of Home Affairs, Government of Nepal. COVID-19 Dashboard; Kathmandu: 2020. |
| 20. | Haque SE, Rahman M. Association between temperature, humidity, and COVID-19 outbreaks in Bangladesh. Environ Sci Policy 2020;114:253-5. |
| 21. | Bashir MFB, Komal B, Benghoul M, Bashir MA, Tan D. Nexus between the COVID-19 dynamics and environmental pollution indicators in South America. Risk Manag Healthc Policy 2021;14:67-74. |
| 22. | Bashir MF, Ma B, Shahzad L. A brief review of socio-economic and environmental impact of Covid-19. Air Qual Atmos Health 2020;13:1403-9. |
| 23. | Tosepu R, Gunawan J, Effendy DS, Ahmad OA, Lestari H, Bahar H, et al. Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Sci Total Environ 2020;725:138436. |
| 24. | Department of Hydrology and Meteorology. Meteorological Forecasting Division; 2020. |
| 25. | |
| 26. | Environmental Systems Research Institute (ESRI). ArcGIS Release 10.3. Redlands, CA: Environmental Systems Research Institute, Inc.,; 2015. |
| 27. | |
| 28. | Friendly M. Corrgrams: Exploratory displays for correlation matrices. Am Stat 2002;56:316-24. |
| 29. | United Nations. UN/DESA Policy Brief #66: COVID-19 and the Least Developed Countries; 2020. [Last accessed on 2020 May 20]. |
| 30. | |
| 31. | Tobías A. Evaluation of the lockdowns for the SARS-CoV-2 epidemic in Italy and Spain after one month follow up. Sci Total Environ 2020;725:138539. |
| 32. | Yuan J, Yun H, Lan W, Wang W, Sullivan SG, Jia S, et al. A climatologic investigation of the SARS-CoV outbreak in Beijing, China. Am J Infect Control 2006;34:234-6. |
| 33. | du Prel JB, Puppe W, Gröndahl B, Knuf M, Weigl JA, Schaaff F, et al. Are meteorological parameters associated with acute respiratory tract infections? Clin Infect Dis 2009;49:861-8. |
| 34. | Cui B, Zhang D, Pan H, Zhang F, Farrar J, Law F, et al. Viral aetiology of acute respiratory infections among children and associated meteorological factors in southern China. BMC Infect Dis 2015;15:124. |
| 35. | Harris JP, Lopman BA, Cooper BS, O'Brien SJ. Does spatial proximity drive norovirus transmission during outbreaks in hospitals? BMJ Open 2013;3(7). |
| 36. | Bhandari D, Bi P, Dhimal M, Sherchand JB, Hanson-Easey S. Non-linear effect of temperature variation on childhood rotavirus infection: A time series study from Kathmandu, Nepal. Sci Total Environ 2020;748:141376. |
| 37. | Lowen AC, Mubareka S, Steel J, Palese P. Influenza virus transmission is dependent on relative humidity and temperature. PLoS Pathog 2007;3:1470-6. |
| 38. | Bashir MF, Ma B, Bilal, Komal B, Bashir MA, Tan D, et al. Correlation between climate indicators and COVID-19 pandemic in New York, USA. Sci Total Environ 2020;728:138835. |
| 39. | Ficetola GF, Rubolini D. Containment measures limit environmental effects on COVID-19 early outbreak dynamics. Sci Total Environ 2021;761:144432. |
| 40. | Bilal MFB, Shahzad A, Tan D, Komal B, and Bashir MA. Nexus between temperature and COVID-19 pandemic: A meta- analysis. The 3rd International Electronic Conference on Environmental Research and Public Health 2020:1-5. |
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
[Table 1]
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