References
- Araujo, M. B., & Naimi, B. (2020). Spread of SARS-CoV-2 Coronavirus likely to be constrained by climate. medRxiv. 2020.03.12.20034728. https://doi.org/10.1101/2020.03.12.20034728
- Bai, Y., & Jin, Z. (2005). Prediction of SARS epidemic by BP neural networks with online prediction strategy. Chaos, Solitons, and Fractals, 26(2), 559–569. https://doi.org/10.1016/j.chaos.2005.01.064
- Bandyopadhyay, S. K., & Dutta, S. (2020). Machine learning approach for confirmation of covid-19 cases: Positive, negative, death and release. medRxiv.
- Bărbulescu, A. (2018). Do the time series statistical properties influence the goodness of fit of GRNN models? Study on financial series. Applied Stochastic Models in Business and Industry, 34(5), 586–596. https://doi.org/10.1002/asmb.2315
- Bloom-Feshbach, K., Alonso, W. J., Charu, V., Tamerius, J., Simonsen, L., Miller, M. A., et al. (2013). Latitudinal variations in seasonal activity of influenza and respiratory syncytial virus (RSV): A global comparative review. PLoS One, 8(2), 3–4. https://doi.org/10.1371/journal.pone.0054445
- Bukhari, Q., & Jameel, Y. (2020). Will coronavirus pandemic diminish by summer? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3556998
- Burrascano, P. (1991). Learning vector quantization for the probabilistic neural network. IEEE Transactions Neural Networks, 2(4), 458–461. https://doi.org/10.1109/72.88165
- Cacoullos, T. (1966). Estimation of a multivariate density. Annals of the Institute of Statistical Mathematics, 18(2), 179–189. Tokyo. https://doi.org/10.1007/BF02869528
- Caramelo, F., Ferreira, N., & Oliveiros, B. (2020). Estimation of risk factors for COVID-19 mortality Preliminary results. Preprint at Med RXIV. https://doi.org/10.1101/2020.02.24.20027268
- Casanova, L. M., Jeon, S., Rutala, W. A., Weber, D. J., & Sobsey, M. D. (2010). Effects of air temperature and relative humidity on coronavirus survival on surfaces. Applied and Environmental Microbiology, 76(9), 2712–2717. https://doi.org/10.1128/AEM.02291-09
- Chan, K. H., Peiris, J. S. M., Lam, S. Y., Poon, L. L. M., Yuen, K. Y., & Seto, W. H. (2011). The effects of temperature and relative humidity on the viability of the SARS Coronavirus. Advances in Virology, 2011, 1–7. https://doi.org/10.1155/2011/734690
- Chen, Z. L., Zhang, Q., Lu, Y., Guo, Z. M., Zhang, X., Zhang, W. J., et al. (2020). Distribution of the COVID-19 epidemic and correlation with population emigration from Wuhan, China. Chinese Medical Journal. PMID: 32118644.
- Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., et al. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489), 395–400.
- Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73–80. https://doi.org/10.1080/2573234X.2018.1543535
- Doremalen, N. V., Bushmaker, T., & Munster, V. J. (2013). Stability of Middle East respiratory syndrome coronavirus (MERS-CoV) under different environmental conditions. Eurosurveillance, 18(38), pii=20590. https://doi.org/10.2807/1560-7917.ES2013.18.38.20590
- Eslami, H., & Jalili, M. (2020). The role of environmental factors to transmission of SARS-CoV-2 (COVID-19). AMB Express, 10(1). https://doi.org/10.1186/s13568-020-01028-0
- Ficetola, G. F., & Rubolini, D. (2020). Climate affects global patterns of COVID-19 early outbreak dynamics. MedRxiv. https://doi.10.1101/2020.03.23.20040501
- Fong, S. J., Li, G., Dey, N., Crespo, R. G., & Herrera-Viedma, E. (2020). Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Applied Soft Computing, 106282. https://doi.org/10.1016/j.asoc.2020.106282
- Gupta, S., Raghuwanshi, G. S., & Chanda, A. (2020). Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020. Science of the Total Environment, 728, 138860. 1 August 2020. https://doi.org/10.1016/j.scitotenv.2020.138860
- Gurney, K. (1997, August 5). An introduction to neural networks. UCL Press.
- Herrmann, H., & Bucksch, H. (2014). Clausius-Clapeyron equation. Dictionary Geotechnical Engineering/Wörterbuch GeoTechnik. https://doi.10.1007/978-3-642-41714-6_ 32107.
- Hopkins, J. (2020). Track reported cases of COVID-19 Coronavirus resource center (WWW Document, online).
- Hsieh, Y. H., Lee, J. Y., & Chang, H. L. (2004). SARS epidemiology modeling. Emerging Infectious Diseases, 10(6), 1165–1167. https://doi.org/10.3201/eid1006.031023
- Huang, C.-J., Chen, Y.-H., Ma, Y., & Kuo, P.-H. (2020). Multiple-input deep convolutional neural network model for covid-19 forecasting in china. medRxiv.
- Ji, Y., Ma, Z., Peppelenbosch, M. P., & Pan, Q. (2020). Potential association between covid-19 mortality and health-care resource availability. The Lancet Global Health, 8(4), e480. PMID: 32109372. https://doi.org/10.1016/S2214-109X(20)30068-1
- Lai, D. (2005). Monitoring the SARS epidemic in china: A time series analysis. Journal of Data Science, 3(3), 279–293.
- Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., et al. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. New England Journal of Medicine, 382(13), 1199–1207. https://doi.org/10.1056/NEJMoa2001316
- Lin, K., Fong, D. Y. T., Zhu, B., & Karlberg, J. (2006). Environmental factors on the SARS epidemic: Air temperature, passage of time and multiplicative effect of hospital infection. Epidemiology and Infection, 134(2), 223–230. https://doi.org/10.1017/S0950268805005054
- Lofgren, E., Fefferman, N. H., Naumov, Y. N., Gorski, J., & Naumova, E. N. (2007). Influenza seasonality: Underlying causes and modeling theories. Journal of Virology, 81(11), 5429–5436. https://doi.org/10.1128/JVI.01680-06
- Lowen, A. C., Mubareka, S., Tumpey, T. M., Garcia-Sastre, A., & Palese, P. (2006). The guinea pig as a transmission model for human influenza viruses. Proceedings of the National academy of sciences of the United States of America, 103: 9988–9992.
- Lowen, A. C., Steel, J., Mubareka, S., & Palese, P. (2008). High temperature (30 degrees C) blocks aerosol but not contact transmission of influenza virus. Journal of Virology, 82(11), 5650–5652. https://doi.org/10.1128/JVI.00325-08
- Majumder, H., & Maity, K. (2018). Application of GRNN and multivariate hybrid approach to predict and optimize WEDM responses for Ni-Ti shape memory alloy. Applied Soft Computing, 70, 665–679. https://doi.org/10.1016/j.asoc.2018.06.026
- Munster, V. J., Koopmans, M., Doremalen, N. V., Riel, D. V., & Wit, E. D. (2020). A novel coronavirus emerging in China — Key Questions for Impact Assessment. New England Journal of Medicine, 382(8), 692. https://doi.org/10.1056/NEJMp2000929
- Oliveiros, B., Caramelo, L., Ferreira, N. C., & Caramelo, F. (2020). Role of temperature and humidity in the modulation of the doubling time of COVID-19 cases. medRxiv. https://doi.org/10.1101/2020.03.05.20031872
- Pal, R., Sekh, A. A., Kar, S., & Prasad, D. K. (2020). Neural network based country wise risk prediction of covid-19. arXiv Preprint arXiv: 2004.00959.
- Parzen, E. (1962). On estimation of a probability density function and mode. Annals of Mathematical Statistics, 33(3), 1065–1076. https://doi.org/10.1214/aoms/1177704472
- Pica, N., & Bouvier, N. M. (2012). (Environmental factors affecting the transmission of respiratory viruses. Current Opinion in Virology, 2(1), 90–95. https://doi.org/10.1016/j.coviro.2011.12.003
- Polwiang, S. (2020). The time series seasonal patterns of dengue fever and associated weather variables in Bangkok (2003-2017). BMC Infectious Diseases, 20(1), 208. https://doi.org/10.1186/s12879-020-4902-6
- Riou, J., & Althaus, C. L. (2020). Pattern of early human-to-human transmission of Wuhan 2019-nCoV. https://doi.org/10.1101/2020.01.23.917351
- Sajadi, M. M., Habibzadeh, P., Vintzileos, A., Shokouhi, S., Miralles-Wilhelm, F., & Amoroso, A. (2020). Temperature, humidity and latitude analysis to predict potential spread and seasonality for COVID-19 (March 5, 2020). SSRN. https://doi.org/http://dx.doi.10.2139/ssrn.3550308
- Schoeman, D., & Fielding, B. C. (2019). Coronavirus envelope protein: Current knowledge. Virology Journal, 16(1). https://doi.org/10.1186/s12985-019-1182-0
- Shaman, J., & Kohn, M. (2009). Absolute humidity modulates influenza survival, transmission, and seasonality. Proceedings of the National academy of sciences of the United States of America, 106: 3243–3248.
- Specht, D. F. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568–576. https://doi.org/10.1109/72.97934
- Tamerius, J., Nelson, M. I., Zhou, S. Z., Viboud, C., Miller, M. A., & Alonso, W. J. (2011). Global influenza seasonality: Reconciling patterns across temperate and tropical regions. Environmental Health Perspectives, 119(4), 439–445. https://doi.org/10.1289/ehp.1002383
- Tan, J., Mu, L., Huang, J., Yu, S., Chen, B., & Yin, J. (2005). An initial investigation of the association between the SARS outbreak and weather: With the view of the environmental temperature and its variation. Journal of Epidemiology and Community Health (1979-), 59(3), 186–192. https://doi.org/10.1136/jech.2004.020180
- Tou, J. T., & Gonzalez, R. C. (1974). Pattern recognition principles. Addison-Wesley.
- United Nations website. (2020). https://www.un.org/en/
- Van Doremalen, N., Bushmaker, T., Morris, D. H., Holbrook, M. G., Gamble, A., Williamson, B. N., et al. (2020). Aerosol and surface stability of SARS-CoV-2 as compared with SARS-CoV-1. New England Journal of Medicine, 382(16), 1564–1567. https://doi.org/10.1056/NEJMc2004973
- Wang, J., Tang, K., Feng, K., Lin, X., Lv, W., Chen, K., et al. (2020). High temperature and high humidity reduce the transmission of COVID-19. SSRN. https://doi.org/http://dx.doi.10.2139/ssrn.3551767
- Wang, W., & Ruan, S. (2004). Simulating the SARS outbreak in Beijing with limited data. Journal of Theoretical Biology, 227(3), 369–379. https://doi.org/10.1016/j.jtbi.2003.11.014
- Wei, W., Jiang, J., Liang, H., Gao, L., Liang, B., Huang, J., et al. (2016). Application of a combined model with autoregressive integrated moving average (ARIMA) and generalized regression neural network (GRNN) in forecasting hepatitis incidence in Heng County, China. PLoS One, 11(6), e0156768. https://doi.org/10.1371/journal.pone.0156768
- World Health Organization. (2003). Consensus document on the epidemiology of severe acute respiratory syndrome (SARS). https://who.int/csr/sars/en/WHOconsensus.pdf
- World Health Organization. (2020). Novel coronavirus (2019-nCoV) situation reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports
- Wu, J. T., Leung, K., & Leung, G. M. (2020). Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: A modelling study. The Lancet, 395(10225), 689–697. https://doi.org/10.1016/S0140-6736(20)30260-9
- Wu, W., Guo, J., An, S., Guan, P., Ren, Y., Xia, L., et al. (2015). Comparison of two hybrid models for forecasting the incidence of hemorrhagic fever with renal syndrome in Jiangsu Province, China. PLoS One, 10, 1–13. https://doi.10.1371/journal. pone.0135492.
- Zhang, R., Liu, H., Li, F., Zhang, B., Liu, Q., Li, X., et al. (2020). Transmission and epidemiological characteristics of Novel Coronavirus (2019-nCoV)-Infected Pneumonia(NCIP): Preliminaryevidence obtained in comparison with 2003-SARS. MedRxiv. https://doi.org/10.1101/2020.01.30.20019836
- Zhang, X., Liu, Y., Yang, M., Zhang, T., Young, A. A., & Li, X. (2013). Comparative study of four time series methods in forecasting typhoid fever incidence in China. PLoS One, 8. https://doi.org/10.1371/journal.pone.0063116
- Zhao, J., Zhao, J., Legge, K., & Perlman, S. (2011). Age-related increases in PGD(2) expression impair respiratory DC migration, resulting in diminished T cell responses upon respiratory virus infection in mice. Journal of Clinical Investigation, 121(12), 4921–4930. https://doi.org/10.1172/JCI59777
- Zietz, M., & Tatonetti, N. (2020). Testing the association between blood type and COVID-19 infection, intubation, and death. medRxiv Preprint. https://doi.org/10.1101/2020.04.08.20058073