Interrelationship between daily COVID-19 cases and average temperature as well as relative humidity in Germany

Ganegoda N.C., Wijaya K.P., Amadi M., Erandi K.K.W.H., Aldila D.

Department of Mathematics, University of Sri Jayewardenepura, Nugegoda, 10250, Sri Lanka; Mathematical Institute, University of Koblenz, Koblenz, 56070, Germany; Department of Mathematics and Physics, Lappeenranta University of Technology, Lappeenranta, 53851, Finland; Department of Mathematics, University of Colombo, Colombo, 00300, Sri Lanka; Department of Mathematics, Universitas Indonesia, Depok, 16424, Indonesia


Abstract

COVID-19 pandemic continues to obstruct social lives and the world economy other than questioning the healthcare capacity of many countries. Weather components recently came to notice as the northern hemisphere was hit by escalated incidence in winter. This study investigated the association between COVID-19 cases and two components, average temperature and relative humidity, in the 16 states of Germany. Three main approaches were carried out in this study, namely temporal correlation, spatial auto-correlation, and clustering-integrated panel regression. It is claimed that the daily COVID-19 cases correlate negatively with the average temperature and positively with the average relative humidity. To extract the spatial auto-correlation, both global Moran’s I and global Geary’s C were used whereby no significant difference in the results was observed. It is evident that randomness overwhelms the spatial pattern in all the states for most of the observations, except in recent observations where either local clusters or dispersion occurred. This is further supported by Moran’s scatter plot, where states’ dynamics to and fro cold and hot spots are identified, rendering a traveling-related early warning system. A random-effects model was used in the sense of case-weather regression including incidence clustering. Our task is to perceive which ranges of the incidence that are well predicted by the existing weather components rather than seeing which ranges of the weather components predicting the incidence. The proposed clustering-integrated model associated with optimal barriers articulates the data well whereby weather components outperform lag incidence cases in the prediction. Practical implications based on marginal effects follow posterior to model diagnostics. © 2021, The Author(s).


Journal

Scientific Reports

Publisher: Nature Research

Volume 11, Issue 1, Art No 11302, Page – , Page Count


Journal Link: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106977820&doi=10.1038%2fs41598-021-90873-5&partnerID=40&md5=f7501da8186b72b57bd7c0c8a4a35f10

doi: 10.1038/s41598-021-90873-5

Issn: 20452322

Type: All Open Access, Gold, Green


References

Belser, J.A., Eckert, A.M., Tumpey, T.M., Maines, T.R., Complexities in ferret influenza virus pathogenesis and transmission models (2016) Microbiol. Mol. Biol. Rev., 80, pp. 733-744. , PID: 27412880; Storch, G.A., Diagnostic virology (2000) Clin. Infect. Dis., 31, pp. 739-751. , COI: 1:STN:280:DC%2BD3M%2FlvFCrug%3D%3D, PID: 11017824; Steinmeyer, S.H., Wilke, C.O., Pepin, K.M., Methods of modelling viral disease dynamics across the within- and between-host scales: The impact of virus dose on host population immunity (2010) Philos. Trans. R. Soc. B Biol. Sci., 365, pp. 1931-1941; Grassly, N.C., Fraser, C., Mathematical models of infectious disease transmission (2008) Nat. Rev. Microbiol., 6, pp. 477-487. , COI: 1:CAS:528:DC%2BD1cXlvFSisLk%3D, PID: 18533288; (2020) Coronavirus Disease (COVID-19): How is It Transmitted?, , https://www.who.int/news-room/q-a-detail/coronavirus-disease-covid-19-how-is-it-transmitted, Accessed 19 December 2020; Azuma, K., Environmental factors involved in SARS-CoV-2 transmission: Effect and role of indoor environmental quality in the strategy for COVID-19 infection control (2020) Environ. Health Prev. Med., 25, pp. 1-16. , COI: 1:CAS:528:DC%2BB3cXitlWgtLzF; Wijaya, K.P., An epidemic model integrating direct and fomite transmission as well as household structure applied to COVID-19 (2021) J. Math. Ind., 11, pp. 1-26. , COI: 1:CAS:528:DC%2BB3MXht1arsr4%3D, PID: 33425640; (2020) Clin. Med., pp. 1-4; Morawska, L., Milton, D., It is time to address airborne transmission of Coronavirus Disease 2019 (COVID-19) (2020) Clin. Infect. Dis., 71, pp. 2311-2313. , COI: 1:CAS:528:DC%2BB3cXis1SgtLjE, PID: 32628269; Bouffanais, R., Lim, S., Cities – try to predict superspreading hotspots for COVID-19 (2020) Nature, 583, pp. 352-355. , PID: 32651472, COI: 1:CAS:528:DC%2BB3cXhtlOjurzM; Wong, F., Collins, J.J., Evidence that coronavirus superspreading is fat-tailed (2020) Proc. Natl. Acad. Sci., 117, pp. 29416-29418. , COI: 1:CAS:528:DC%2BB3cXisVKgu7fN, PID: 33139561; Kain, P.M., Childs, M.L., Becker, A.D., Mordecai, E.A., Chopping the tail: How preventing superspreading can help to maintain COVID-19 control (2020) Epidemics, 34, p. 100430. , PID: 33360871; Wang, L., Inference of person-to-person transmission of COVID-19 reveals hidden super-spreading events during the early outbreak phase (2020) Nat. Commun., 11, p. 5006. , COI: 1:CAS:528:DC%2BB3cXitVWqs7bM, PID: 33024095; Badr, H.S., Association between mobility patterns and COVID-19 transmission in the USA: A mathematical modelling study (2020) Lancet Infect. Dis., 20, pp. 1247-1254. , COI: 1:CAS:528:DC%2BB3cXhtlegtLbK, PID: 32621869; Ebrahim, S.H., Memish, Z.A., COVID-19—The role of mass gatherings (2020) Travel Med. Infect. Dis., 34, p. 101617. , PID: 32165283; (2020) WHO Mass Gathering COVID-19 Risk Assessment tool—Generic Events, , https://www.who.int/publications/i/item/10665-333185, Accessed 25 October 2020; Assche, J.V., Politi, E., Dessel, P.V., Phalet, K., To punish or to assist? Divergent reactions to ingroup and outgroup members disobeying social distancing (2020) Br. J. Soc. Psychol., 59, pp. 594-606. , PID: 32602596; Belosi, F., Conte, M., Gianelle, V., Santachiara, G., Contini, D., On the concentration of SARS-CoV-2 in outdoor air and the interaction with pre-existing atmospheric particles (2021) Environ. Res., 193, p. 110603. , COI: 1:CAS:528:DC%2BB3cXis1ajsLzL, PID: 33307081; Tung, N.T., Particulate matter and SARS-CoV-2: A possible model of COVID-19 transmission (2021) Sci. Total Environ., 750, p. 141532. , COI: 1:CAS:528:DC%2BB3cXhs1ygsbzM, PID: 32858292; Lei, H., Xu, X., Xiao, S., Wu, X., Shu, Y., Household transmission of COVID-19—A systematic review and meta-analysis (2020) J. Infect., 81, pp. 979-997. , COI: 1:CAS:528:DC%2BB3cXhslSltLzF, PID: 32858069; Ooi, E.E., Low, J.G., Asymptomatic SARS-CoV-2 infection (2020) Lancet Infect. Dis., 20, pp. 996-998. , COI: 1:CAS:528:DC%2BB3cXhtFKis7%2FO, PID: 32539989; Lin, D., Co-infections of SARS-CoV-2 with multiple common respiratory pathogens in infected patients (2020) Sci. China Life Sci., 63, pp. 1-4; Richardson, S., Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area (2020) JAMA, 323, pp. 2052-2059. , COI: 1:CAS:528:DC%2BB3cXhtVGrs7bN, PID: 7177629; Kim, D., Quinn, J., Pinsky, B., Shah, N.H., Brown, I., Rates of co-infection between SARS-CoV-2 and other respiratory pathogens (2020) JAMA, 323, pp. 2085-2086. , COI: 1:CAS:528:DC%2BB3cXhtVGrs7nO, PID: 32293646; Boncristiani, H.F., Criado, M.F., Arruda, E., Respiratory viruses (2009) Encycl. Microbiol., 2009, pp. 500-518; Dasaraju, P.V., Liu, C., Infections of the respiratory system (1996) Medical Microbiology 4Th Edn (Ed Baron, S.) (University of Texas Medical Branch at Galveston; Azekawa, S., Namkoong, H., Mitamura, K., Kawaoka, Y., Saito, F., Co-infection with SARS-CoV-2 and influenza A virus (2020) IDCases, 20, pp. e00775-e773. , PID: 32368495; Mossad, S.B., COVID-19 and flu: Dual threat, dual opportunity (2020) Clevel. Clin. J. Med., 87, pp. 651-655; Dowell, S.F., Ho, M.S., Seasonality of infectious diseases and severe acute respiratory syndrome—What we don’t know can hurt us (2004) Lancet Infect. Dis., 4, pp. 704-708. , PID: 15522683; Shi, P., Impact of temperature on the dynamics of the COVID-19 outbreak in China (2020) Sci. Total Environ., 728, pp. 138890-138897. , COI: 1:CAS:528:DC%2BB3cXotVCqt7g%3D, PID: 32339844; Kronbichler, A., Asymptomatic patients as a source of COVID-19 infections: A systematic review and meta-analysis (2020) Int. J. Infect. Dis., 98, pp. 180-186. , COI: 1:CAS:528:DC%2BB3cXhsVKksbbP, PID: 32562846; Ozaras, R., Influenza and COVID-19 coinfection: Report of six cases and review of the literature (2020) J. Med. Virol., 92, pp. 2657-2665. , COI: 1:CAS:528:DC%2BB3cXht1Kgu7%2FE, PID: 32497283; Singh, B., Kaur, P., Reid, R.J., Shamoon, F., Bikkina, M., COVID-19 and influenza co-infection: Report of three cases (2020) Cureus J. Med. Sci., 12, pp. e9852-e9856; Pormohammad, A., Comparison of influenza type A and B with COVID-19: A global systematic review and meta-analysis on clinical, laboratory and radiographic findings (2020) Rev. Med. Virol., p. e2179; Cai, Q.C., Influence of meteorological factors and air pollution on the outbreak of severe acute respiratory syndrome (2007) Public Health, 121, pp. 258-265. , PID: 17307207; Chan, K.H., The effects of temperature and relative humidity on the viability of the SARS coronavirus (2011) Adv. Virol., 2011, p. 734690. , COI: 1:STN:280:DC%2BC383htVGgtQ%3D%3D, PID: 22312351; Casanova, L.M., Jeon, S., Rutala, W.A., Weber, D.J., Sobsey, M.D., Effects of air temperature and relative humidity on coronavirus survival on surfaces (2010) Appl. Environ. Microbiol., 76, pp. 2712-2717. , COI: 1:CAS:528:DC%2BC3cXmtVWht7s%3D, PID: 20228108; Sun, Z., Thilakavathy, K., Kumar, S.S., He, G., Liu, S.V., Potential factors influencing repeated SARS outbreaks in China (2020) Int. J. Environ. Res. Public Health, 17, p. 1633. , COI: 1:CAS:528:DC%2BB3cXhslyrs7rL; Gardner, E.G., A case-crossover analysis of the impact of weather on primary cases of Middle East respiratory syndrome (2019) BMC Infect. Dis., 19, pp. 1-10; Altamimi, A., Ahmed, A.E., Climate factors and incidence of middle east respiratory syndrome coronavirus (2020) J. Infect. Public Health, 13, pp. 704-708. , PID: 31813836; Cai, J., Indirect virus transmission in cluster of COVID-19 cases (2020) Emerg. Infect. Dis., 26, pp. 1343-1345. , COI: 1:CAS:528:DC%2BB3cXhtlarurrN, PID: 32163030; Yeo, C., Kaushal, S., Yeo, D., Enteric involvement of coronaviruses: Is faecal-oral transmission of SARS-CoV-2 possible? (2020) Lancet Gastroenterol. Hepatol., 5, pp. 335-337. , PID: 32087098; Chin, A.W.H., Stability of SARS-CoV-2 in different environmental conditions (2020) Lancet Microbe, 1. , COI: 1:CAS:528:DC%2BB3cXit1Kms7%2FJ, PID: 32835322; Ahlawat, A., Wiedensohler, A., Mishra, S.K., An overview on the role of relative humidity in airborne transmission of SARS-CoV-2 in indoor environments (2020) Aerosol Air Qual. Res., 20, pp. 1856-1861. , COI: 1:CAS:528:DC%2BB3cXitFeqtbnE; Islam, A.R.T., Effect of meteorological factors on COVID-19 cases in Bangladesh (2020) Environ. Dev. Sustain., pp. 1-24; Lasisi, T.T., Eluwole, K.K., Is the weather-induced COVID-19 spread hypothesis a myth or reality? Evidence from the Russian federation. Environ. Sci. Pollut (2020) Res., pp. 1-5; Sarkodie, S.A., Owusu, P.A., Impact of meteorological factors on COVID-19 pandemic: Evidence from top 20 countries with confirmed cases (2020) Environ. Res., 191, p. 110101. , COI: 1:CAS:528:DC%2BB3cXhsl2itLzK, PID: 32835681; Sil, A., Kumar, V.N., Does weather affect the growth rate of COVID-19, a study to comprehend transmission dynamics on human health (2020) J. Saf. Sci. Resil., 1, pp. 3-11; Xie, J., Zhu, Y., Association between ambient temperature and COVID-19 infection in 122 cities from China (2020) Sci. Total Environ., 724, p. 138201. , COI: 1:CAS:528:DC%2BB3cXmvFKiu7c%3D, PID: 32408450; Pan, J., Warmer weather unlikely to reduce the COVID-19 transmission: an ecological study in 202 locations in 8 countries (2020) Sci. Total Environ., 753, p. 142272. , PID: 33207446, COI: 1:CAS:528:DC%2BB3cXhvVKgtLzK; Ward, M.P., Xiao, S., Zhang, Z., The role of climate during the COVID-19 epidemic in New South Wales, Australia (2020) Transbound. Emerg. Dis., 67, pp. 2313-2317. , COI: 1:CAS:528:DC%2BB3cXis1aisrnM, PID: 32438520; Tosepu, R., Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia (2020) Sci. Total Environ., 725; Qi, H., COVID-19 transmission in Mainland China is associated with temperature and humidity: A time-series analysis (2020) Sci. Total Environ., 728, p. 138778. , COI: 1:CAS:528:DC%2BB3cXotVCqsb0%3D, PID: 32335405; Guo, C., Meteorological factors and COVID-19 incidence in 190 countries: An observational study (2020) Sci. Total Environ., 757, p. 143783. , PID: 33257056, COI: 1:CAS:528:DC%2BB3cXisVClt7fP; Jahangiri, M., Jahangiri, M., Najafgholipour, M., The sensitivity and specificity analyses of ambient temperature and population size on the transmission rate of the novel coronavirus (COVID-19) in different provinces of Iran (2020) Sci. Total Environ., 728, p. 138872. , COI: 1:CAS:528:DC%2BB3cXotVCqt7w%3D, PID: 32335407; Sharma, P., Singh, A.K., Agrawal, B., Sharma, A., Correlation between weather and COVID-19 pandemic in India: An empirical investigation (2020) J. Public Affairs, 20, pp. e2222-e2225; Rosario, D.K.A., Mutz, Y.S., Bernardes, P.C., Conte-Junior, C.A., Relationship between COVID-19 and weather: Case study in a tropical country (2020) Int. J. Hyg. Environ. Health, 229, p. 113587. , COI: 1:CAS:528:DC%2BB3cXht1KgtrfJ, PID: 32917371; Mofijur, M., Relationship between weather variables and new daily COVID-19 cases in Dhaka, Bangladesh (2020) Sustainability, 12, p. 8319. , COI: 1:CAS:528:DC%2BB3MXhslGqsr8%3D; Bukhari, Q., Massaro, J., D’Agostino, R., Khan, S., Effects of weather on coronavirus pandemic (2020) Int. J. Environ. Res. Public Health, 17, p. 5399. , COI: 1:CAS:528:DC%2BB3cXitVGhtLrO; Rashed, E.A., Kodera, S., Gomez-Tames, J., Hirata, A., Influence of absolute humidity, temperature and population density on COVID-19 spread and decay durations: Multi-prefecture study in Japan (2020) Int. J. Environ. Res. Public Health, 17, p. 5354. , COI: 1:CAS:528:DC%2BB3cXitVGhtr7K, PID: 7432865; Mecenas, P., Baston, R., Vallinoto, A., Normando, D., Effects of temperature and humidity on the spread of COVID-19: A systematic review (2020) PLoS One, 15, pp. e0238339-e238321. , COI: 1:CAS:528:DC%2BB3cXhvFahs7jK, PID: 32946453; Malki, Z., Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches (2020) Chaos Solitons Fractals, 138, p. 110137. , PID: 32834583; Bruttoinlandsprodukt (VGR) Ergebnisse der Volkswirtschaftlichen Gesamtrechnungen der Länder (2020) Statistische Ämter Des Bundes Und Der Länder, , https://www.statistikportal.de/en/node/649, Accessed 04 January 2021; statista. Arbeitslosenquote in Deutschland nach Bundesländern. https://de.statista.com/statistik/daten/studie/36651/umfrage/arbeitslosenquote-in-deutschland-nach-bundeslaendern/ (2020). Accessed 04 January 2021; (2020) Coronavirus Disease 2019 (COVID-19): Daily Situation Report of the Robert Koch Institute, , https://www.rki.de/DE/Content/InfAZ/N/Neuartiges_Coronavirus/Situationsberichte/Gesamt.html, Accessed 31 December 2020; Adams, A., Chen, X., Li, W., Zhang, C., The disguised pandemic: The importance of data normalization in COVID-19 web mapping (2020) Public Health, 183, pp. 36-37. , COI: 1:STN:280:DC%2BB38vnslOksA%3D%3D, PID: 32416476; Wijaya, K., Learning from panel data of dengue incidence and meteorological factors in Jakarta (2020) Indonesia. Stoch. Environ. Res. Risk Assess., pp. 1-20; CDC-OpenData. Index of /climate_environment/CDC/. https://opendata.dwd.de/climate_environment/CDC/ (2020). Accessed 31 December 2020; (2020) Xinhuanews. Germany’s Bavaria Declares Emergency Situation Effective on Tuesday, , http://www.xinhuanet.com/english/2020-03/16/c_138884534.htm, Accessed 09 March 2021; Mladek, J., (2020) Bavaria Imposes Curfew, , https://www.nordkurier.de/politik-und-wirtschaft/bayern-verhaengt-ausgangssperre-2038792303.html, Nordkurier, Accessed 04 January 2021; Connor, R., (2020) German States Move Closer to Near-Total Lockdowns, , https://www.dw.com/en/german-states-move-closer-to-near-total-lockdowns/a-52863482, Accessed 09 March 2021; (2020) First Major German City Introduces Mandatory Masking, , https://www.welt.de/politik/deutschland/article206911189/Coronavirus-Erste-deutsche-Grossstadt-fuehrt-Maskenpflicht-ein.html, Accessed 04 January 2021; Riekhoff, L., Sommer, A., (streiflichter). Coronavirus in the Coesfeld district: 59 new infections with the coronavirus. https://www.streiflichter.com/lokales/coesfeld/coronavirus-kreis-coesfeld-aktuelle-fallzahlen-region-13643612.html (2020). Accessed 04 January 2021; (2020) . Coronavirus: Over 600 people test positive at German slaughterhouse., , https://www.dw.com/en/coronavirus-over-600-people-test-positive-at-german-slaughterhouse/a-53846038, Accessed 04 January; Coronavirus, B.B.C., (2020) Thousands Protest in Germany against Restrictions, , https://www.bbc.com/news/world-europe-53622797, Accessed 04 January 2021; Das, P., Choudhuri, T., Decoding the global outbreak of COVID-19: The nature is behind the scene (2020) Virus Dis., 31, pp. 1-7. , COI: 1:CAS:528:DC%2BB3cXisVKksrbE; Riddell, S., Goldie, S., Hill, A., Eagles, D., Drew, T.W., The effect of temperature on persistence of SARS-CoV-2 on common surfaces (2020) Virol. J., 17, pp. 1-7. , COI: 1:CAS:528:DC%2BB3cXitVagsb3I; Moran, P.A.P., Notes on continuous stochastic phenomena (1950) Biometrika, 37, pp. 17-23. , COI: 1:STN:280:DyaG3c%2FivFyktQ%3D%3D, PID: 15420245; Geary, R.C., The contiguity ratio and statistical mapping (1954) Inc. Stat., 5, pp. 115-146; Cliff, A., Ord, J., Spatial Autocorrelation (1973) Monographs in Spatial and Environmental Systems Analysis (Pion; Anselin, L., The Moran scatterplot as an ESDA tool to assess local instability in spatial association (1996) Spat. Anal. Perspect. GIS, 4, pp. 111-116; Sokal, R.R., Oden, N.L., Thomson, B.A., Local spatial autocorrelation in a biological model (1998) Geogr. Anal., 30, pp. 331-354; Ocampo, S., Rodríguez, N., An introductory review of a structural VAR-X estimation and applications (2012) Revista Colombiana de Estadística, 35, pp. 479-508; Lütkepohl, H., (2005) New Introduction to Multiple Time Series Analysis, , Springer; Prata, D.N., Rodrigues, W., Bermejo, P.H., Temperature significantly changes COVID-19 transmission in (sub) tropical cities of Brazil (2020) Sci. Total Environ., 729, p. 138862. , COI: 1:CAS:528:DC%2BB3cXosVymt7w%3D, PID: 32361443; Yuan, J., Non-linear correlation between daily new cases of COVID-19 and meteorological factors in 127 countries (2020) Environ. Res., 193, p. 110521. , PID: 33279492, COI: 1:CAS:528:DC%2BB3cXisFahs7zN; Bashir, M.F., Correlation between climate indicators and COVID-19 pandemic in New York, USA (2020) Sci. Total Environ., 728, p. 138835. , COI: 1:CAS:528:DC%2BB3cXotVCqsLs%3D, PID: 32334162; Menebo, M.M., Temperature and precipitation associate with Covid-19 new daily cases: A correlation study between weather and Covid-19 pandemic in Oslo, Norway (2020) Sci. Total Environ., 737, p. 139659. , COI: 1:CAS:528:DC%2BB3cXhtVGgtrrK, PID: 32492607; Lolli, S., Chen, Y.C., Wang, S.H., Vivone, G., Impact of meteorological conditions and air pollution on COVID-19 pandemic transmission in Italy (2020) Sci. Rep., 10, pp. 1-15. , COI: 1:CAS:528:DC%2BB3cXhvF2ksLfE; Raftery, A.E., Bayesian model selection in social research (1995) Sociol. Methodol., 25, pp. 111-163; Akaike, H., Information theory and an extension of the maximum likelihood principle. in Selected Papers of Hirotugu Akaike. Springer Series in Statistics (Perspectives in Statistics) (eds. Parzen, E. et al.) (Springer, 1998); Mansfield, E.R., Helms, B.P., Detecting multicollinearity (1982) Am. Stat., 36, pp. 158-160; Johnston, J., (1972) Econometric Methods 2Nd Edn, , McGraw Hill Higher Education; Farrar, D.E., Glauber, R.R., Multicollinearity in regression analysis: The problem revisited (1967) Rev. Econ. Stat., 49, pp. 92-107; Willis, C.E., Perlack, R.D., Multicollinearity: Effects, symptoms, and remedies (1978) J. Northeast. Agric. Econ. Council, 7, pp. 55-61; Hausman, J.A., Specification tests in econometrics (1978) Econometrica, 46, pp. 1251-1271; Davidson, R., MacKinnon, J.G., (1993) Estimation and Inference in Econometrics. OUP Catalogue, , Oxford University Press; Baltagi, B.H., Li, Q., A lagrange multiplier test for the error components model with incomplete panels (1990) Econom. Rev., 9, pp. 103-107; Wooldridge, J.M., (2002) Econometric Analysis of Cross Section and Panel Data, , MIT Press

Indexed by Scopus

Leave a Comment