The effect of weekend curfews on epidemics: a Monte Carlo simulation
The effect of weekend curfews on epidemics: a Monte Carlo simulation
The ongoing COVID-19 pandemic is being responded with various methods, applying vaccines, experimental treatment options, total lockdowns or partial curfews. Weekend curfews are among the methods for reducing the number of infected persons, and this method is practically applied in some countries such as Turkey. In this study, the effect of weekend curfews on reducing the spread of a contagious disease, such as COVID-19, is modeled using a Monte Carlo algorithm with a hybrid lattice model. In the simulation setup, a fictional country with three towns and 26,610 citizens were used as a model. Results indicate that applying a weekend curfew reduces the ratio of ill cases from 0.23 to 0.15. The results also show that applying personal precautions such as social distancing is important for reducing the number of cases and deaths. If the probability of disease spread can be reduced to 0.1, in that case, the death ratio can be minimized down to 0.
___
- Andronico A, Kiem CT, Paireau J, Succo T, Bosetti P et al. (2021). Evaluating the impact of curfews and other measures on SARS-CoV-2 transmission in French Guiana. Nature Communications 12: 1634. doi:10.1038/s41467-021-21944-4
- Berker AN, Wortis M (1976). Blume-Emery-Griffiths-Potts model in two dimensions: Phase diagram and critical properties from a position-space renormalization group. Physical Review B 14: 4946-4963. doi: 10.1103/PhysRevB.14.4946
- Bestehorn M, Riascos AP, Michelitsch TM, Collet BA (2021). A Markovian random walk model of epidemic spreading. Continuum Mechanics and Thermodynamics 1-15. doi: 10.1007/s00161-021-00970-z
- Cooper I, Mondal A, Antonopoulos CG (2020). A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons & Fractals 139: 110057. doi: 10.1016/j.chaos.2020.110057
- Demirbilek Y, Pehlivantürk, G, Özgüler ZÖ, Meşe EA (2020). COVID-19 outbreak control, example of ministry of health of Turkey. Turkish Journal of Medical Sciences 50: 489-494. doi:10.3906/sag-2004-187
- Draief M, Ganesh A (2011). A random walk model for infection on graphs: Spread of epidemics & rumours with mobile agents. Discrete Event Dynamic Systems: Theory and Applications. 21: 41-61. doi: 10.1007/s10626-010-0092-5
- Eskier D, Akalp E, Dalan Ö, Karakülah G, Oktay Y (2021). Current mutatome of SARS-CoV-2 in Turkey reveals mutations of interest. Turkish Journal of Biology 45: 104-113. doi: 10.3906/ biy-2008-56
- Farsalinos K, Poulas K, Kouretas D, Vantarakis A, Leotsinidis M et al. (2021). Improved strategies to counter the COVID-19 pandemic: Lockdowns vs. primary and community healthcare. Toxicology Reports 8: 1-9. doi: 10.1016/j.toxrep.2020.12.001
- Filipe JAN, Gibson GJ (1998). Studying and approximating spatio-temporal models for epidemic spread and control. Philosophical Transactions of the Royal Society B: Biological Sciences 353 (1378): 2153-2162. doi: 10.1098/rstb.1998.0354
- Filipe JAN, Gibson GJ (2001). Comparing approximations to spatiotemporal models for epidemics with local spread. Bulletin of Mathematical Biology 63 (4): 603-624. doi: 10.1006/ bulm.2001.0234
- Hoston W, Berker AN (1991). Multicritical phase diagrams of the blume-emery-griffiths model with repulsive biquadratic coupling. Physical Review Letters 67 (8): 1027-1030. doi: 10.1103/PhysRevLett.67.1027
- Huber M, Langen H (2020). Timing matters: the impact of response measures on COVID-19-related hospitalization and death rates in Germany and Switzerland. Swiss Journal of Economics and Statistics: 156 (1): 10. doi: 10.1186/s41937-020-00054-w
- Huppert A, Katriel G (2013). Mathematical modelling and prediction in infectious disease epidemiology. Clinical Microbiology and Infection 19 (11): 999-1005. doi: 10.1111/1469-0691.12308
- Kermack WO, McKendrick AG (1927). A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London A 115 (772): 700-721. doi: 10.1098/ rspa.1927.0118
- Liccardo A, Fierro A (2013). A lattice model for ınfluenza spreading. PLoS ONE 10 (10): e0141065. doi: 10.1371/journal. pone.0141065
- Maltezos S, Georgakopoulou A (2021). Novel approach for Monte Carlo simulation of the new COVID-19 spread dynamics. Infection, Genetics and Evolution 92: 104896. doi: 10.1016/j. meegid.2021.104896
- Nakkazi E (2020). Obstacles to COVID-19 control in east Africa. The Lancet Infectious Diseases 20 (6): P660. doi:10.1016/S1473- 3099(20)30382-0
- Odagaki T (2020). Analysis of the outbreak of COVID-19 in Japan by SIQR model. Infectious Disease Modelling 5: 691-698. doi:10.1016/j.idm.2020.08.013
- Odagaki T (2021). Exact properties of SIQR model for COVID-19. Physica A: Statistical Mechanics and its Applications 564: 125564. doi:10.1016/j.physa.2020.125564
- Shu P, Wang W, Tang M, Zhao P, Zhang YC (2016). Recovery rate affects the effective epidemic threshold with synchronous updating. Chaos 26 (6): 063108. doi: 10.1063/1.4953661
- de Sousa LE, Neto PHDO, Filho DADS (2020). Kinetic Monte Carlo model for the COVID-19 epidemic: Impact of mobility restriction on a COVID-19 outbreak. Physical Review E 102 (3): 032133. doi: 10.1103/PhysRevE.102.032133
- Tarighi P, Eftekhari S, Chizari M, Sabernavaei M, Jafari D et al. (2021). A review of potential suggested drugs for coronavirus disease (COVID-19) treatment. European Journal of Pharmacology 895: 173890. doi: 10.1016/j.ejphar.2021.173890
- Triambak S, Mahapatra DP (2021). A random walk Monte Carlo simulation study of COVID-19-like infection spread. Physica A: Statistical Mechanics and its Applications 574: 126014. doi: 10.1016/j.physa.2021.126014
- Ugurel OM, Ata O, Turgut-Balik D (2020). An updated analysis of variations in SARS-CoV-2 genome. Turkish Journal of Biology 44: 157-167. doi: 10.3906/biy-2005-111
- Vyklyuk Y, Manylich M, Škoda M, Radovanović MM, Petrović MD (2021). Modeling and analysis of different scenarios for the spread of COVID-19 by using the modified multi-agent systems – Evidence from the selected countries. Results in Physics 20: 103662. doi: 10.1016/j.rinp.2020.103662
- Xie G (2020). A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time. Scientific Reports 10: 13120. doi: 10.1038/s41598-020-70091-1