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During a global health crisis, a country’s borders are a weak point through which carriers from countries with high morbidity rates can enter, endangering the health of the local community and undermining the authorities’ efforts to prevent the spread of the pathogen. Therefore, most countries have adopted some level of border closure policies as one of the first steps in handling pandemics. However, this step involves a significant economic loss, especially for countries that rely on tourism as a source of income. We developed a pioneering model to help decision-makers determine the optimal border closure policies during a health crisis that minimize the magnitude of the outbreak and maximize the revenue of the tourism industry. This approach is based on a hybrid mathematical model that consists of an epidemiological sub-model with tourism and a pandemic-focused economic sub-model, which relies on elements from the field of artificial intelligence to provide policymakers with a data-driven model for a border closure strategy for tourism during a global pandemic.
Rocznik
Tom
Strony
583--601
Opis fizyczny
Bibliogr. 87 poz., rys., tab.
Twórcy
autor
- Department of Mathematics, Ariel University, 3 Kiryat Hamada St., 40700 Ariel, Israel
- Department of Cancer Biology, University College London Cancer Institute, 72 Huntley St., WC1E 6DD London, UK
autor
- Department of Economics, Western Galilee College, Hamichlala Rd., 2412101 Acre, Israel
- Department of Mathematics, Ariel University, 3 Kiryat Hamada St., 40700 Ariel, Israel
Bibliografia
- [1] Acemoglu, D., Chernozhukov, V., Werning, I. and Whinston, M.D. (2020). Optimal targeted lockdowns in a multi-group sir model, Working Paper 27102, National Bureau of Economic Research, Cambridge.
- [2] Adiga, A., Dubhashi, D., Lewis, B., Marathe, M., Venkatramanan, S. and Vollikanti, A. (2020). Mathematical models for COVID 19 pandemic: A comparative analysis, Journal of the Indian Institute of Science 100(4): 793-807.
- [3] Aglar, O., Baxter, A., Keskinocak, P., Asplund, J. and Serban, N. (2020). Homebound by COVID 19: The benefits and consequences of non-pharmaceutical intervention strategies, BMC Public Health 21: 655.
- [4] Airey, D. (2015). Developments in understanding tourism policy, Tourism Review 70(4): 246-258.
- [5] Alagar, V.S. and Periyasamy, K. (2011). Specification of Software Systems, Springer, London.
- [6] Alalyani, A. and Saber, S. (2022). Stability analysis and numerical simulations of the fractional COVID-19 pandemic model, International Journal of Nonlinear Sciences and Numerical Simulation 24(3): 1-14.
- [7] Aldao, C., Blasco, D., Espallargas, M.P. and Rubio, S.P. (2021). Modelling the crisis management and impacts of 21st century disruptive events in tourism: The case of the COVID-19 pandemic, Tourism Review 76(4): 929-941.
- [8] Anderson, J.E. (2011). The gravity model, Annual Review of Economics 3(1): 133-160.
- [9] Andersson, C., Fuhrer, C. and Åkesson, J. (2015). Assimulo: A unified framework for ODE solvers, Mathematics and Computers in Simulation 116: 26-43.
- [10] Baggio, R. (2020). The science of complexity in the tourism domain: A perspective article, Tourism Review 75(1): 16-19.
- [11] Becken, S., Mahon, R., Rennie, H.G. and Shakeela, A. (2014). The tourism disaster vulnerability framework: An application to tourism in small island destinations, Natural Hazards 71(1): 955-972.
- [12] Bellman, R.E. (1957). Dynamic Programming, Princeton University Press, Princeton.
- [13] Bhuiyan, M.A., Crovella, T., Paiano, A. and Alves, H. (2021). A review of research on tourism industry, economic crisis and mitigation process of the loss: Analysis on pre, during and post pandemic situation, Sustainability 13(18): 10314.
- [14] Boyd, M., Baker, M.G. and Wilson, N. (2020). Border closure for island nations? Analysis of pandemic and bioweapon-related threats suggests some scenarios warrant drastic action, Australian and New Zealand Journal of Public Health 44(2): 89-91.
- [15] Burns, J., Movsisyan, A., Stratil, J.M., Coenen, M., Emmert-Fees, K.M., Geffert, K., Hoffmann, S., Horstick, O., Laxy, M., Pfadenhauer, L.M., von Philipsborn, P., Sell, K., Voss, S. and Rehfuess, E. (2020). Travel-related control measures to contain the COVID-19 pandemic: A rapid review, Cochrane Database of Systematic Reviews 10(9), DOI: 10.1002/14651858.CD013717.
- [16] Cevik, S. (2022). Going viral: A gravity model of infectious diseases and tourism flows, Open Economies Review 33(1): 141-156.
- [17] Chevalier, J.M., Sy, K.T.L., Girdwood, S.J., Khan, S., Albert, H., Toporowski, A., Hannay, E., Carmona, S. and Nichols, B.E. (2022). Optimal use of COVID-19 AG-RDT screening at border crossings to prevent community transmission: A modeling analysis, PLOS Global Public Health 2(5): e0000086.
- [18] Chinazzi, M., Davis, J.T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Piontti, A.P., Mu, K., Rossi, L. and Sun, K. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak, Science 368(6489): 395-400.
- [19] Chui-Hua, L., Gwo-Hshiung, T. and Ming-Huei, L. (2012). Improving tourism policy implementation-The use of hybrid MCDM models, Tourism Management 33(2): 413-426.
- [20] Chumachenko, D., Dobriak, V., Mazorchuk, M., Meniailov, I. and Bazilevych, K. (2018). On agent-based approach to influenza and acute respiratory virus infection simulation, 2018 14th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), Lviv-Slavske, Ukraine, pp. 192-195.
- [21] Cortés, J., El-Labany, S.K., Navarro-Quiles, A., Selim, M.M. and Slama, H. (2020). A comprehensive probabilistic analysis of approximate SIR-type epidemiological models via full randomized discrete-time Markov chain formulation with applications, Mathematical Methods in the Applied Sciences 43(14): 8204-8222.
- [22] Darabi, S.F. and Scoglio, C. (2011). Epidemic spread in human networks, 50th IEEE Conference on Decision and Control/European Control Conference, Orlando, USA, pp. 3008-3013.
- [23] Di Domenico, L., Pullano, G., Sabbatini, C.E., Bo Elle, P.Y. and Colizza, V. (2020). Impact of lockdown on COVID-19 epidemic in Ile-de-France and possible exit strategies, BMC Medicine 18: 240.
- [24] Dickens, B.L., Koo, J.R., Lim, J.T., Sun, H., Clapham, H.E., Wilder-Smith, A. and Cook, A.R. (2020). Strategies at points of entry to reduce importation risk of COVID-19 cases and reopen travel, Journal of Travel Medicine 27(8): taaa141.
- [25] Diseases, T.L.I. (2020). Air travel in the time of COVID-19, The Lancet Infectious Diseases 20(9): 993.
- [26] Dwyer, L. (2015). Computable general equilibrium modelling: An important tool for tourism policy analysis, Tourism and Hospitality Management 21(2): 111-126.
- [27] Getz, D. (1986). Models in tourism planning: Towards integration of theory and practice, Tourism Management 7(1): 21-32.
- [28] Goldenbogen, B., Adler, S.O., Bodeit, O., Wodke, J.A.H., Escalera-Fanjul, X., Korman, A., Krantz, M., Bonn, L., Morán-Torres, R., Haffner, J.E.L., Karnetzki, M., Maintz, I., Mallis, L., Prawitz, H., Segelitz, P.S., Seeger, M., Linding, R. and Klipp, E. (2022). Control of COVID-19 outbreaks under stochastic community dynamics, bimodality, or limited vaccination, Advanced Science 9(23): e2200088.
- [29] Gopalakrishnan, B.N., Peters, R. and Vanzetti, D. (2020). COVID-19 and tourism: Assessing the economic consequences, Report UNCTAD/DITC/INF/2020/3, United Nations Conference on Trade and Development, Geneva.
- [30] Gunn, H. (2001). Spatial and temporal transferability of relationships between travel demand, trip cost and travel time, Transportation Research E: Logistics and Transportation Review 37: 163-189.
- [31] Hall, C.M., Scott, D. and Gössling, S. (2020). Pandemics, transformations and tourism: Be careful what you wish for, Tourism Geographies 22(3): 577-598.
- [32] Hassani, H., Silva, E.S., Antonakakis, N., Filis, G. and Gupta, R. (2017). Forecasting accuracy evaluation of tourist arrivals, Annals of Tourism Research 63(3): 112-127.
- [33] Henseler, M., Maisonnave, H. and Maskaeva, A. (2022). Economic impacts of COVID-19 on the tourism sector in Tanzania, Annals of Tourism Research Empirical Insights 3(1): 100042.
- [34] Ivorra, B., Ferrandez, M.R., Vela-Perez, M. and Ramos, A.M. (2020). Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections: The case of China, Communications in Nonlinear Science and Numerical Simulation 88: 105303.
- [35] Jiehao, C., Jin, X., Daojiong, L., Zhi, Y., Lei, X., Zhenghai, Q., Yuehua, Z., Hua, Z., Ran, J., Pengcheng, L., Xiangshi, W., Yanling, G., Aimei, X., He, T., Hailing, C., Chuning, W., Jingjing, L., Jianshe, W. and Mei, Z. (2020). A case series of children with 2019 novel coronavirus infection: Clinical and epidemiological features, Clinical Infectious Diseases 76(6): 1547-1551.
- [36] Kermack, W.O. and McKendrick, A.G. (1927). A contribution to the mathematical theory of epidemics, Proceedings of the Royal Society 115: 700-721.
- [37] Khalid, U., Okafor, L.E. and Shafiullah, M. (2020). The effects of economic and financial crises on international tourist flows: A cross-country analysis, Journal of Travel Research 59(2): 315-334.
- [38] Kingsley, D. (2015). The Urbanization of the Human Population, Routledge, London.
- [39] Kuo, H., Chen, C., Tseng, W., Ju, L. and Huang, B. (2008). Assessing impacts of SARS and avian flu on international tourism demand to ASIA, Tourism Management 29(5): 917-928.
- [40] Lauer, S.A., Grantz, K.H., Bi, Q., Jones, F.K., Zheng, Q., Meredith, H.R., Azman, A.S., Reich, N.G. and Lessier, J. (2020). The incubation period of coronavirus disease 2019 (COVID-19) from publicly reported confirmed cases: Estimation and application, Annals of Internal Medicine 172(9): 577-582.
- [41] Law, R., Li, G., Fong, D.K.C. and Han, X. (2019). Tourism demand forecasting: A deep learning approach, Annals of Tourism Research 75: 410-423.
- [42] Lazebnik, T. and Alexi, A. (2022). Comparison of pandemic intervention policies in several building types using heterogeneous population model, Communications in Nonlinear Science and Numerical Simulation 107(4): 106176.
- [43] Lazebnik, T. and Blumrosen, G. (2022). Advanced multi-mutation with intervention policies pandemic model, IEEE Access 10: 22769-22781.
- [44] Lazebnik, T. and Bunimovich-Mendrazitsky, S. (2021). The signature features of COVID-19 pandemic in a hybrid mathematical model-Implications for optimal work-school lockdown policy, Advanced Theory and Simulations 4(5): 2000298.
- [45] Lazebnik, T., Shami, L. and Bunimovich-Mendrazitsky, S. (2021a). Pandemic management by a spatio-temporal mathematical model, International Journal of Nonlinear Sciences and Numerical Simulation 24(6): 2307-2324.
- [46] Lazebnik, T., Shami, L. and Bunimovich-Mendrazitsky, S. (2021b). Spatio-temporal influence of non-pharmaceutical interventions policies on pandemic dynamics and the economy: The case of COVID-19, Economic Research-Ekonomska Istraživanja 35(1): 1833-1861.
- [47] Li, X., Gong, J., Gao, B. and Yuan, P. (2021). Impacts of COVID-19 on tourists’ destination preferences: Evidence from China, Annals of Tourism Research 90: 103258.
- [48] Lindstrom, M.J. and Bates, D.M. (1988). Newton-Raphson and em algorithms for linear mixed-effects models for repeated-measures data, Journal of the American Statistical Association 83(404): 1014-1022.
- [49] Linka, K., Peirlinck, M., Sahli Costabal, F. and Kuhl, E. (2020). Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions, Computer Methods in Biomechanics and Biomedical Engineering 23(11): 710-717.
- [50] Liu, A., Vici, L., Ramos, V., Giannoni, S. and Blake, A. (2021). Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team, Annals of Tourism Research 88: 103182.
- [51] Liu, L., Miller, H.J. and Scheff, J. (2020). The impacts of COVID-19 pandemic on public transit demand in the united states, PLOS ONE 15(11): 1-22.
- [52] Macal, C. M. (2010). To agent-based simulation from system dynamics, Proceedings of the 2010 Winter Simulation Conference, Baltimore, USA, pp. 371-382.
- [53] Masud, S., Torraca, V. and Meijer, A.H. (2017). Modeling infectious diseases in the context of a developing immune system, in K.C. Sadler (Ed.), Zebrafish at the Interface of Development and Disease Research, Academic Press, Cambridge, pp. 277-329.
- [54] Nagayuki, Y., Ishii, S. and Doya, K. (2000). Multi-agent reinforcement learning: An approach based on the other agent’s internal model, Proceedings of the 4th International Conference on MultiAgent Systems, Boston, USA, pp. 215-221.
- [55] Nesteruk, L. (2020). Statistics-based predictions of coronavirus epidemic spreading in mainland China, Innovative Biosystems and Bioengineering 8: 13-18.
- [56] Ntounis, N., Parker, C., Skinner, H., Steadman, C. and Warnby, G. (2021). Tourism and hospitality industry resilience during the COVID-19 pandemic: Evidence from England, Current Issues in Tourism 1: 46-59.
- [57] Pappas, N. (2021). COVID19: Holiday intentions during a pandemic, Tourism Management 84: 104287.
- [58] Peer, S., Koopmans, C. and Verhoef, E.T. (2012). Prediction of travel time variability for cost-benefit analysis, Transportation Research A: Policy and Practice 46(1): 79-90.
- [59] Pham, T.D., Dwyer, L., Su, J. and Ngo, T. (2021). COVID-19 impacts of inbound tourism on Australian economy, Annals of Tourism Research 88: 103179.
- [60] Pindyck, R.S. (2020). COVID-19 and the welfare effects of reducing contagion, Working Paper 27121, National Bureau of Economic Research, Cambridge.
- [61] Polyzos, S., Samitas, A. and Spyridou, A.E. (2020). Tourism demand and the COVID-19 pandemic: An LSTM approach, Tourism Recreation Research 46: 175-187.
- [62] Privault, N. (2018). Understanding Markov Chains, Springer, Singapore.
- [63] Ram, V. and Schaposnik, L.P. (2021). A modified age-structured SIR model for COVID-19 type viruses, Scientific Reports 11: 15194.
- [64] Roberty, N.C. and de Araujo, L.S.F. (2021). SIR model parameters estimation with COVID-19 data, Journal of Advances in Mathematics and Computer Science 36(3): 97-117.
- [65] Ronald, L. (2011). The outlook for population growth, Science 333(6042): 569-573.
- [66] Rosselló, J., Becken, S. and Santana-Gallego, M. (2020). The effects of natural disasters on international tourism: A global analysis, Tourism Management 79: 104080.
- [67] Selbst, A.D. and Barocas, S. (2018). The intuitive appeal of explainable machines, Fordham Law Review 87(3): 1085-1140.
- [68] Shami, L. and Lazebnik, T. (2022). Financing and managing epidemiological-economic crises: Are we ready to another outbreak?, Journal of Policy Modeling 45(1): 74-89.
- [69] She, J., Liu, L. and Liu, W. (2020). COVID-19 epidemic: Disease characteristics in children, Journal of Medical Virology 92(7): 747-754.
- [70] Sun, S., Li, J., Guo, J.-E. and Wang, S. (2021). Tourism demand forecasting: An ensemble deep learning approach, Tourism Economics 28(8): 2021-2049.
- [71] Tan, M. (1993). Multi-agent reinforcement learning: Independent vs. cooperative agents, In Proceedings of the 10th International Conference on Machine Learning, Amherst, USA, pp. 330-337.
- [72] Teixeira, J.P. and Fernandes, P.O. (2012). Tourism time series forecast-different ANN architectures with time index input, Procedia Technology 5: 445-454.
- [73] Tinbergen, J. (1962). Shaping the World Economy; Suggestions for an international Economic Policy, The Twentieth Century Fund, New York.
- [74] Tuite, A.R., Fisman, D.N. and Greer, A.L. (2020). Mathematical modelling of COVID-19 transmission and mitigation strategies in the population of Ontario, Canada, Canadian Medical Association Journal 192: E497-E505.
- [75] UNWTO (2022). UNWTO World Tourism Barometer and Statistical Annex, January 2022 20(1), DOI: 10.18111/wtobarometereng.2022.20.1.1, (English version).
- [76] UNWTO (2021). UNWTO World Tourism Barometer and Statistical Annex, May 2021 19(3), DOI: 10.18111/wtobarometereng.2021.19.1.3, (English version).
- [77] Van Truong, N. and Shimizu, T. (2017). The effect of transportation on tourism promotion: Literature review on application of the computable general equilibrium (CGE) model, Transportation Research Procedia 25: 3096-3115.
- [78] Viguerie, A., Lorenzo, G., Auricchio, F., Baroli, D., Hughes, T.J.R., Patton, A., Reali, A., Yankeelov, T.E. and Veneziani, A. (2020). Simulating the spread of COVID-19 via a spatially-resolved susceptible-exposed-infected-recovered-deceased (SEIRD) model with heterogeneous diffusion, Applied Mathematics Letters 111: 106617.
- [79] Virlogeux, V., Li, M., Tsang, T.K., Feng, L., Fang, V.J., Jiang, H., Wu, P., Zheng, J., Lau, E.H.Y., Cao, Y., Qin, Y., Liao, Q., Yu, H. and Cowling, B.J. (2015). Estimating the distribution of the incubation periods of human avian influenza A(H7N9) virus infections, American Journal of Epidemiology 182(8): 723-729.
- [80] Watkin, C.J.C.H. and Dayan, P. (1989). Learning With Delayed Rewards, PhD thesis, King’s College, Cambridge.
- [81] Watkin, C.J.C.H. and Dayan, P. (1992). Technical note: Q-learning, Machine Learning 8: 279-292.
- [82] White, J.W., Rassweiler, A., Samhouri, J.F., Stier, A.C. and White, C. (2014). Ecologists should not use statistical significance tests to interpret simulation model results, Oikos 123(4): 385-388.
- [83] Wilder-Smith, A. (2006). The severe acute respiratory syndrome: Impact on travel and tourism, Travel Medicine and Infectious Disease 4(2): 53-60.
- [84] Wiratsudakul, A., Suparit, P. and Modchang, C. (2018). Dynamics of Zika virus outbreaks: An overview of mathematical modeling approaches, PeerJ 6: e4526.
- [85] Wut, T.M., Xu, J.B. and Wong, S. (2021). Crisis management research (1985-2020) in the hospitality and tourism industry: A review and research agenda, Tourism Management 85: 104307.
- [86] Zhao, S., Stone, L., Gao, D., Musa, S.S., Chong, M.K.C., He, D. and Wang, M.H. (2020). Imitation dynamics in the mitigation of the novel coronavirus disease (COVID-19) outbreak in Wuhan, China, from 2019 to 2020, Annals of Transnational Medicine 8(7): 448.
- [87] Zhu, Z., Weber, E., Strohsal, T. and Serhan, D. (2021). Sustainable border control policy in the COVID-19 pandemic: A math modeling study, Travel Medicine and Infectious Disease 41: 102044.
Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9fefa024-35cf-4ae5-b4fe-81f33a863c9a