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Data-driven optimization and parameter estimation for an epidemic model

99. Kirkcaldy RD, Weston E, Segurado AC, Hughes Comput Biol Med. 2021;134:104369.
G. Epidemiology of gonorrhoea: A global per- http://dx.doi.org/10.1016/j.compbiomed.2021.
spective. Sex Health. 2019;16(5):401-411. 104369
http://dx.doi.org/10.1071/SH19061 111. Gogolewski K, Miasojedow B, Sadkowska-Todys
100. Elder H, Platt L, Leach D, et al. Factors associ- M, et al. Data-driven case fatality rate estimation
ated with delays in presentation and treatment of for the primary lineage of SARS-CoV-2 in Poland.
gonorrhea, Massachusetts 2015-2019. Sex Transm Methods. 2022;203:584-593.
Dis. 2023;50(10):1097. http://dx.doi.org/10.1016/j.ymeth.2022.01.006
http://dx.doi.org/10.1097/OLQ.0000000000001917 112. Rosi´nska M, Czarkowski MP, Sadkowska-Todys
101. Ru˜ao T, Silva SCM. Strategic science commu- M. Infectious diseases in Poland in 2021. Epi-
nication: The “flatten the curve” metaphor in demiol Rev/Przegl Epidemiol. 2023;77(4):411.
COVID-19 public risk messaging. In: Balonas S, http://dx.doi.org/10.32394/pe.77.36
Ru˜ao T, Carrillo-Dur´an M-V, eds. Strategic Com- 113. Generalna Dyrekcja Dr´og Krajowych i Au-
munication in Context: Theoretical Debates and tostrad. Ruch Drogowy 2020/2021. Warszawa:
Applied Research. UMinho Editora/Centro de Es- Heller Consult; 2022. Available from:
tudos de Comunica¸c˜ao e Sociedade; 2021:175-211. https://www.gov.pl/web/gddkia/generalny-
http://dx.doi.org/10.21814/uminho.ed.46.9 pomiar-ruchu-20202021
102. Amidon TR, Nielsen AC, Pflugfelder EH, 114. McMahon T, Havlin S, Gallos LK. Effect of
Richards DP, Stephens SH. Visual risk literacy cities and distance on COVID-19 spreading in the
in “flatten the curve” COVID-19 visualizations. J United States. Phys Rev E. 2023;107(3):034302.
Bus Tech Commun. 2021;35(1):101-109. http://dx.doi.org/10.1103/PhysRevE.107.034302
http://dx.doi.org/10.1177/1050651920963439 115. G l´owny Urzad Statystyczny (National Statistics
103. Pana TA, Bhattacharya S, Gamble DT, et al. Office of Poland). Ludno´s´c w rejonach statysty-
Country-level determinants of the severity of the cznych i obwodach spisowych wed lug p lci, eko-
first global wave of the COVID-19 pandemic: An nomicznych i 10-letnich grup wieku; 2021.
ecological study. BMJ Open. 2021;11(2):e042034. 116. Morris MD. Factorial sampling plans for prelimi-
http://dx.doi.org/10.1136/bmjopen-2020-042034 nary computational experiments. Technometrics.
104. Baird CE, Lake D, Panagiotou OA, Gozalo P. 1991;33(2):161-174.
County-level mandates were generally effective http://dx.doi.org/10.1080/00401706.1991.10484804
at slowing COVID-19 transmission: Study ex- 117. Qian G, Mahdi A. Sensitivity analysis meth-
amines county-level public health mandates and ods in the biomedical sciences. Math Biosci.
COVID-19 transmission. Health Aff (Millwood). 2020;323:108306.
2024;43(3):433-442. http://dx.doi.org/10.1016/j.mbs.2020.108306
http://dx.doi.org/10.1377/hlthaff.2023.00431 118. Herman JD, Kollat JB, Reed PM, Wagener T.
105. Sleator RD, Smith N. COVID-19: did the masks Technical note: Method of Morris effectively re-
work? Future Microbiol. 2024;19(11):997-1002. duces the computational demands of global sensi-
http://dx.doi.org/10.1080/17460913.2024.2343558 tivity analysis for distributed watershed models.
106. Wielechowski M, Czech K, Grzeda L. Decline Hydrol Earth Syst Sci. 2013;17(7):2893-2903.
in mobility: public transport in Poland in the http://dx.doi.org/10.5194/hess-17-2893-2013
time of the COVID-19 pandemic. Economies. 119. Sobol IM. Global sensitivity indices for non-
2020;8(4):78. linear mathematical models and their Monte
http://dx.doi.org/10.3390/economies8040078 Carlo estimates. Math Comput Simul. 2001;55(1-
107. Dur´on C, Kravitz H, Brio M. 3):271-280.
Kolmogorov–Smirnov-based edge centrality mea- http://dx.doi.org/10.1016/S0378-4754(00)00270-
sure for metric graphs. Dynamics. 2025;5(2):16. 6
http://dx.doi.org/10.3390/dynamics5020016 120. Helton JC, Davis FJ. Latin hypercube sampling
108. Oke M, Ogunmiloro O, Akinwumi C, Raji R. and the propagation of uncertainty in analy-
Mathematical modeling and stability analysis of ses of complex systems. Reliab Eng Syst Saf.
a SIRV epidemic model with non-linear force of 2003;81(1):23-69.
infection and treatment. Commun Math Appl. http://dx.doi.org/10.1016/S0951-8320(03)00058-
2019;10(4):717-731. 9
http://dx.doi.org/10.26713/cma.v10i4.1172 121. Fung IC-H, Antia R, Handel A. How to min-
109. Zareba A, Widawski K, Ko lodziejczyk K, imize the attack rate during multiple influenza
Krzemi´nska A, Marek A, Rozenkiewicz A. City outbreaks in a heterogeneous population. PLoS
profile: Pozna´n – one of the “normals” in the cen- One. 2012;7(6):e36573.
tre of Europe. Cities. 2021;111:103095. http://dx.doi.org/10.1371/journal.pone.0036573
http://dx.doi.org/10.1016/j.cities.2020.103095 122. Rocha Filho T, Moret M, Chow C, et al. A data-
110. Li KK, Jarvis SA, Minhas F. Elementary ef- driven model for COVID-19 pandemic–evolution
fects analysis of factors controlling COVID-19 in- of the attack rate and prognosis for Brazil. Chaos
fections in computational simulation reveals the Solitons Fractals. 2021;152:111359.
importance of social distancing and mask usage. http://dx.doi.org/10.1016/j.chaos.2021.111359
767