Abstract
Strategies to prevent pandemics can be based on manifold policy responses, not limited to health system. This study shows main aspects of different policy responses based on lessons learned from COVID-19 to constrain the emergence of novel viral agents and the diffusion of other similar infectious diseases in society.
Keywords. COVID-19, Fatality rates, Health strategy, Air pollution, Sustainability, Crisis management, Policy response, Testing, Country monitoring, Pandemic response, Preventing transmission, Preparedness.
JEL. G2, G10, F21, F68, O53, K23.
References
Ackoff, R.L., & Rovin, S. (2003) Redesigning Society. Stanford University Press, Stanford
Anttiroiko, A.-V. (2021). Successful government responses to the pandemic: Contextualizing national and urban responses to the Covid-19 outbreak in East and West. International Journal of E-Planning Research, 10(2), 1-17.
Ardito, L., Coccia, M., Messeni, P.A. (2021). Technological exaptation and crisis management: Evidence from COVID-19 outbreaks. R&D Management, doi. 10.1111/radm.12455
Brooks, S.K., Webster, R.K., Smith, L.E., Woodland, L., Wessely, S., Greenberg, N., & Rubin, G.J. (2020). The psychological impact of quarantine and how to reduce it: rapid review of the evidence, Lancet, 14;395(10227), 912-920. doi. 10.1016/S0140-6736(20)30460-8
Bundy, J., Pfarrer, M.D., Short, C.E., & Coombs, W.T. (2017). Crises and crisis management: Integration, interpretation, and research development. Journal of Management, 43(6), 1661-1692. doi. 10.1177/0149206316680030
Cairney, P. (2016). The Politics of Evidence Based Policymaking. London: Palgrave.
Chang, S., Pierson, E., Koh, P.W. et al., (2020). Mobility network models of Covid-19 explain inequities and inform reopening, Nature, doi. 10.1038/s41586-020-2923-3
Coccia, M. (2019). A Theory of classification and evolution of technologies within a generalized Darwinism, Technology Analysis & Strategic Management, 31(5), 517-531. doi. 10.1080/09537325.2018.1523385
Coccia, M. (2015). Spatial relation between geo-climate zones and technological outputs to explain the evolution of technology. Int. J. Transitions and Innovation Systems, 4(1), 5-21. doi. 10.1504/IJTIS.2015.074642
Coccia, M., & Bellitto, M. (2018). Human progress and its socioeconomic effects in society, Journal of Economic and Social Thought, 5(2), 160-178. doi. 10.1453/jest.v5i2.1649
Coccia, M. (2017). Sources of disruptive technologies for industrial change. L’industria –Rivista di Economia e Politica Industriale, 38(1), 97-120. doi. 10.1430/87140
Coccia, M. (2018). The origins of the economics of Innovation, Journal of Economic and Social Thought, 5(1), 9-28. doi. 10.1453/jest.v5i1.1574
Coccia, M. (2019). Why do nations produce science advances and new technology? Technology in Society, 59, 101124, 1-9. doi. 10.1016/j.techsoc.2019.03.007
Coccia, M. (2019a). The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting, Technological Forecasting and Social Change, 141, 289-304. doi. 10.1016/j.techfore.2018.12.012
Coccia, M., & Watts, J. (2020). A theory of the evolution of technology: technological parasitism and the implications for innovation management, Journal of Engineering and Technology Management, 55, 101552. doi. 10.1016/j.jengtecman.2019.11.003
Coccia, M. (2020a). Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence. Technology in Society, 60, 1-11. doi. 10.1016/j.techsoc.2019.101198
Coccia, M. (2020). Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Science of the Total Environment, 729, 138474. doi. 10.1016/j.scitotenv.2020.138474
Coccia, M. (2020b). How (un)sustainable environments are related to the diffusion of COVID-19: The relation between coronavirus disease 2019, air pollution, wind resource and energy. Sustainability, 12, 9709. doi. 10.3390/su12229709
Coccia, M. (2020a). An index to quantify environmental risk of exposure to future epidemics of the COVID-19 and similar viral agents: Theory and practice. Environmental Research, 191, 110155. doi. 10.1016/j.envres.2020.110155
Coccia, M. (2021). Effects of the spread of COVID-19 on public health of polluted cities: results of the first wave for explaining the dejà vu in the second wave of COVID-19 pandemic and epidemics of future vital agents. Environmental Science and Pollution Research 28(15), 19147-19154. doi. 10.1007/s11356-020-11662-7
Coccia, M. (2021a). How do low wind speeds and high levels of air pollution support the spread of COVID-19? Atmospheric Pollution Research, 12(1), 437-445. doi. 10.1016/j.apr.2020.10.002
Coccia, M. (2021b). The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to Covid-19. Science of The Total Environment, 145801. doi. 10.1016/j.scitotenv.2021.145801
Coccia, M. (2021c). The effects of atmospheric stability with low wind speed and of air pollution on the accelerated transmission dynamics of COVID-19. International Journal of Environmental Studies, 78(1), 1-27. oi. 10.1080/00207233.2020.1802937
Coccia, M. (2021d). The impact of first and second wave of the COVID-19 pandemic: comparative analysis to support control measures to cope with negative effects of future infectious diseases in society. Environmental Research, 111099, doi. 10.1016/j.envres.2021.111099
Crow, D.A., Albright, E.A., Ely, T., Koebel, E., & Lawhon, L. (2018). Do disasters lead to learning? Financial policy change in local government. Review of Policy Research, 35(4), 564–589.
Daszak, P., Olival, K.J., & Li, H. (2020). A strategy to prevent future epidemics similar to the 2019-nCoV outbreak, Biosafety and Health, doi. 10.1016/j.bsheal.2020.01.003
Evans, S., & Bahrami, H. (2020). Super-flexibility in practice: Insights from a crisis. Global Journal of Flexible Systems Management, 21(3), 207-214
Fong, M.W., Gao, H., Wong, J.Y., et al. (2020). Nonpharmaceutical measures for pandemic influenza in nonhealthcare settings—social distancing measures. Emerg Infect Dis 2020. doi. 10.3201/eid2605.190995
Gigerenzer, G., & Todd, P.M. (1999). Ecological rationality: the normative study of heuristics. In Gigerenzer Gerd; Todd Peter M. The ABC Research Group (eds.). Ecological Rationality: Intelligence in the World. (pp.487-497), New York: Oxford University Press.
Groh, M. (2014). Strategic management in times of crisis. American Journal of Economics and Business Administration, 6(2), 49-57.
Janssen, M., & van der Voort, H. (2020). Agile and adaptive governance in crisis response: Lessons from the COVID-19 pandemic. International Journal of Information Management, 55, 102180.
Jenkins-Smith, H.C., Nohrstedt, D., Weible, C.M., & Ingold, K. (2018). The advocacy coalition framework: An overview of the research program. In C.M. Weible, & P.A. Sabatier (Eds.), Theories of the Policy Process, (pp. 135–171). Abingdon: Routledge.
Johns Hopkins Center for System Science and Engineering, (2021). Coronavirus COVID-19 Global Cases, accessed in 4 January 2021 [Retrieved from].
Kahneman, D., Slovic, P., & Tversky, A. (1982). Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.
Kapitsinis, N. (2020). The underlying factors of the COVID-19 spatially uneven spread. Initial evidence from regions in nine EU countries. Regional Science Policy and Practice, 12(6), 1027-1045.
Kluge, H.H.P., Nitzan, D., & Azzopardi-Muscat, N. (2020). COVID-19: reflecting on experience and anticipating the next steps. A perspective from the WHO Regional Office for Europe. Eurohealth 2020; 26(2).
Nicoll, A, & Coulombier, D. (2009). Europe’s initial experience with pandemic (H1N1) 2009 - mitigation and delaying policies and practices. Euro Surveill. 14(29), pii=19279.
Nussbaumer‐Streit, B, Mayr, V., Dobrescu, A.I. et al., (2020). Quarantine alone or in combination with other public health measures to control COVID‐19: a rapid review. Cochrane Database Syst Rev, 2020.
Renardy, M., Eisenberg, M., & Kirschner, D. (2020). Predicting the second wave of COVID-19 in Washtenaw County, MI, Journal of Theoretical Biology, 507, 110461. doi. 10.1016/j.jtbi.2020.110461
Prem, K., Liu, Y., Russell, T. W. et al., (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study, The Lancet Public Health, doi. 10.1016/S2468-2667(20)30073-6
Sagan, A., Thomas, S., McKee, M. et al., (2020). COVID-19 and health systems resilience: lessons going forwards, Eurohealth, 26(2).
Seligman, B., Ferranna, M., & Bloom, D.E. (2021). Social determinants of mortality from COVID-19: A simulation study using NHANES. PLoS Med, 18(1), e1003490. doi. 10.1371/journal.pmed.1003490
Seeger, M.W., Sellno, T.L., & Ulmer, R.R. (1998). Communication, organization and crisis. Communication Yearbook. 21, 231-275.
Shrivastava, P., Mitroff, I.I., Miller, D., & Miclani, A. (1988). Understanding industrial crises. Journal of Management Studies. 25(4), 285–303. doi. 10.1111/j.1467-6486.1988.tb00038.x
Walensky, R.P., & Del Rio, C. (2020). From mitigation to containment of the Covid-19 pandemic: Putting the SARS-CoV-2 Genie Back in the Bottle. JAMA, 323(19), 1889–1890.
Weible, C.M., Nohrstedt, D., Cairney, P. et al., (2020). COVID-19 and the policy sciences: initial reactions and perspectives. Policy Sci, 53, 225–241.
U.S. Department of Health & Human Services, (2021). Public health Emergency-Executive Summary. accessed March 2021. [Retrieved from].
