The first year of covid-19 in croatia - a mathematical model

Aviv-Sharon E, Asaph Aharoni E. Generalized logistic growth modeling of the COVID-19 pandemic in Asia. Infectious Disease Modelling 2020(5):502-509. https://doi.org/10.1016/j.idm.2020.07.003.10.1016/j.idm.2020.07.003Search in Google Scholar

Bertolaccini L, Spaggiari L. The hearth of mathematical and statistical modelling during the Coronavirus pandemic. Interact CardioVasc Thorac Surg 2020; Published online 2020 Apr 9. doi:10.1093/icvts/ivaa076.10.1093/icvts/ivaa076Search in Google Scholar

Bonita R. Basic epidemiology. 2nd edition..World Health Organization. 2006. https://apps.who.int/iris/bitstream/handle/10665/43541/9241547073_eng.pdf (Accessed: 18.04.2021.)Search in Google Scholar

Bulut C, Kato Y. Epidemiology of COVID-19 Turk J Med Sci (2020) 50: p563-57010.3906/sag-2004-172Search in Google Scholar

Chen TM, Rui T, Wang QP, Zhao ZY, Cui JA and Yin L. A mathematical model for simulating the phase-based transmissibility of a novel coronavirus Chen et al. Infectious Diseases of Poverty (2020) 9: p2410.1186/s40249-020-00640-3Search in Google Scholar

Ferguson NM, Laydon D, Nedjati-Gilani G et al. Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand. Imperial College London (16-03-2020), doi: https://doi.org/10.25561/77482.Search in Google Scholar

Hellewell J, Abbott S, Gimma A, Bosse NI, et al. Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group. Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. Lancet Glob Health 2020; 8: e488–9610.1016/S2214-109X(20)30074-7Search in Google Scholar

Hsieh, Ying-Hen. (2009). Richards Model: A Simple Procedure for Real-time Prediction of Outbreak Severity. Modeling and Dynamics of Infectious Diseases Series in Contemporary Applied Mathematics (CAM). 11. 10.1142/9789814261265_0009.10.1142/9789814261265_0009Search in Google Scholar

Kanji, JN, Zelyas N, MacDonald C et al. False negative rate of COVID-19 PCR testing: a discordant testing analysis. Virol J 18, p 13 (2021).https://doi.org/10.1186/s12985-021-01489-010.1186/s12985-021-01489-0779461933422083Search in Google Scholar

Kim S, Seo YB, Jung E. Prediction of COVID-19 transmission dynamics using a mathematical model considering behavior changes in Korea. Epidemiology and Health 2020; 42: e2020026.https://doi.org/10.4178/epih.e202002610.4178/epih.e2020026728544432375455Search in Google Scholar

Kretzschmar M, Wallinga J. (2009). Mathematical Models in Infectious Disease Epidemiology. Modern Infectious Disease Epidemiology: Concepts, Methods, Mathematical Models, and Public Health, 209–221.https://doi.org/10.1007/978-0-387-93835-6_1210.1007/978-0-387-93835-6_12Search in Google Scholar

Kucharski AJ, Russell TW, Diamond C, Liu Y et al. Centre for Mathematical Modelling of Infectious Diseases COVID-19 working group. Early dynamics of transmission and control of COVID-19: a mathematical modelling study Lancet Infect Dis 2020; 20: 553–5810.1016/S1473-3099(20)30144-4Search in Google Scholar

Lee, Se Yoon; Lei, Bowen; Mallick, Bani (2020). “Estimation of COVID-19 spread curves integrating global data and borrowing information”. PLOS ONE. 15 (7): e0236860. doi:10.1371/journal.pone.0236860.10.1371/journal.pone.0236860739034032726361Search in Google Scholar

Magner LN. (2009). A History of Infectious Diseases and the Microbial World. ABC-CLIO. (2009) PraegerSearch in Google Scholar

Mandal S, Bhatnagar T, Arinaminpathy N, Agarwal A et el. Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach. Indian J Med Res 151, February & March 2020, pp 190-199. doi:10.4103/ijmr.IJMR_504_2010.4103/ijmr.IJMR_504_20725875832362645Search in Google Scholar

Miquel P (2014). A Dictionary of Epidemiology (6th ed.). New York: Oxford University Press. ISBN 978-0-19-997673-7.Search in Google Scholar

Motulsky HJ, Christopoulos A. (2003). Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting. USA: Oxford University Press.Search in Google Scholar

Nogrady B. What the data say about asymptomatic COVID infections. Nature 587, 534-535 (2020) doi:https://doi.org/10.1038/d41586-020-03141-310.1038/d41586-020-03141-333214725Search in Google Scholar

Panovska-Griffiths J. Can mathematical modelling solve the current Covid-19 crisis? BMC Public Health (2020) 20 p551.https://doi.org/10.1186/s12889-020-08671-z10.1186/s12889-020-08671-z718140032331516Search in Google Scholar

Peirlinck M, Linka K, Costabal FS, Kuhl E. Outbreak dynamics of COVID-19 in China and the United States. Biomechanics and Modeling in Mechanobiology 2020 Dec;19(6) pp2179-2193, https://doi.org/10.1007/s10237-020-01332-5.10.1007/s10237-020-01332-5718526832342242Search in Google Scholar

Puc M, Wolski T. Forecasting of the selected features of Poaceae (R. Br.) Barnh., Artemisia L. and Ambrosia L. pollen season in Szczecin, north-western Poland, using Gumbel’s distribution. Annals of Agricultural and Environmental Medicine 2013, Vol 20, No 1, pp64-70Search in Google Scholar

Wang, X. S., Wu, J., & Yang, Y. (2012). Richards model revisited: validation by and application to infection dynamics. Journal of theoretical biology, 313, 12–19.https://doi.org/10.1016/j.jtbi.2012.07.02410.1016/j.jtbi.2012.07.02422889641Search in Google Scholar

Zhang S, Diao MY, Yu W, Pei L, Lin Z, Chen D. Estimation of the reproductive number of novel coronavirus (COVID-19) and the probable outbreak size on the Diamond Princess cruise ship: A data-driven analysis International Journal of Infectious Diseases 93 (2020) pp201–20410.1016/j.ijid.2020.02.033711059132097725Search in Google Scholar