Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

Mario Coccia; Mario Coccia

doi:10.3934/publichealth.2023012

AIMS Public Health

2023, Volume 10, Issue 1: 145-168. doi: 10.3934/publichealth.2023012

Previous Article Next Article

Review

Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency

Mario Coccia ^,

National Research Council of Italy, Department of Social Sciences, Turin Research Area of the National Research Council-Strada delle Cacce, 73-10135 - Torino (Italy)

Received: 14 November 2022 Revised: 09 February 2023 Accepted: 19 February 2023 Published: 02 March 2023

Scholars and experts argue that future pandemics and/or epidemics are inevitable events, and the problem is not whether they will occur, but when a new health emergency will emerge. In this uncertain scenario, one of the most important questions is an accurate prevention, preparedness and prediction for the next pandemic. The main goal of this study is twofold: first, the clarification of sources and factors that may trigger pandemic threats; second, the examination of prediction models of on-going pandemics, showing pros and cons. Results, based on in-depth systematic review, show the vital role of environmental factors in the spread of Coronavirus Disease 2019 (COVID-19), and many limitations of the epidemiologic models of prediction because of the complex interactions between the new viral agent SARS-CoV-2, environment and society that have generated variants and sub-variants with rapid transmission. The insights here are, whenever possible, to clarify these aspects associated with public health in order to provide lessons learned of health policy that may reduce risks of emergence and diffusion of new pandemics having negative societal impact.
- COVID-19 pandemic,
- infectious diseases,
- environmental factors,
- compartmental models,
- epidemiologic models,
- outlook,
- preparedness,
- surveillance,
- public health,
- health policy,
- policy responses,
- crisis management
Citation: Mario Coccia. Sources, diffusion and prediction in COVID-19 pandemic: lessons learned to face next health emergency[J]. AIMS Public Health, 2023, 10(1): 145-168. doi: 10.3934/publichealth.2023012

Related Papers:

Abstract

Scholars and experts argue that future pandemics and/or epidemics are inevitable events, and the problem is not whether they will occur, but when a new health emergency will emerge. In this uncertain scenario, one of the most important questions is an accurate prevention, preparedness and prediction for the next pandemic. The main goal of this study is twofold: first, the clarification of sources and factors that may trigger pandemic threats; second, the examination of prediction models of on-going pandemics, showing pros and cons. Results, based on in-depth systematic review, show the vital role of environmental factors in the spread of Coronavirus Disease 2019 (COVID-19), and many limitations of the epidemiologic models of prediction because of the complex interactions between the new viral agent SARS-CoV-2, environment and society that have generated variants and sub-variants with rapid transmission. The insights here are, whenever possible, to clarify these aspects associated with public health in order to provide lessons learned of health policy that may reduce risks of emergence and diffusion of new pandemics having negative societal impact.

Conflict of interests

I declare that I am the sole author of this manuscript, and I have no known competing financial interests or personal relationships that could influence the work reported in this paper. This study has no funders.

References

[1]	Chowdhury T, Chowdhury H, Bontempi E, et al. (2022) Are mega-events super spreaders of infectious diseases similar to COVID-19? A look into Tokyo 2020 Olympics and Paralympics to improve preparedness of next international events. Environ Sci Pollut R 30: 10099-10109. https://doi.org/10.1007/s11356-022-22660-2
[2]	Coccia M (2020) Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Sci Total Environ 729: 138474. https://doi.org/10.1016/j.scitotenv.2020.138474
[3]	Coccia M (2020) Two mechanisms for accelerated diffusion of COVID-19 outbreaks in regions with high intensity of population and polluting industrialization: the air pollution-to-human and human-to-human transmission dynamics. MedRxiv . https://medrxiv.org/cgi/content/short/2020.04.06.20055657v1
[4]	Coccia M (2020) Effects of Air Pollution on COVID-19 and Public Health, Research Article-Environmental Economics-Environmental Policy. ResearchSquare . https://doi.org/10.21203/rs.3.rs-41354/v1
[5]	Coccia M (2021) Recurring waves of Covid-19 pandemic with different effects in public health. J Econ Bib 8: 28-45. http://dx.doi.org/10.1453/jeb.v8i1.2184
[6]	Coccia M (2021) High health expenditures and low exposure of population to air pollution as critical factors that can reduce fatality rate in COVID-19 pandemic crisis: a global analysis. Environ Res 199: 111339. https://doi.org/10.1016/j.envres.2021.111339
[7]	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. Environ Sci Pollut R 28: 19147-19154. https://doi.org/10.1007/s11356-020-11662-7
[8]	Coccia M (2022) COVID-19 pandemic over 2020 (with lockdowns) and 2021 (with vaccinations): similar effects for seasonality and environmental factors. Environ Res 208: 112711. https://doi.org/10.1016/j.envres.2022.112711
[9]	Bontempi E, Coccia M (2021) International trade as critical parameter of COVID-19 spread that outclasses demographic, economic, environmental, and pollution factors. Environ Res 201: 111514. https://doi.org/10.1016/j.envres.2021.111514
[10]	Bontempi E, Coccia M, Vergalli S, et al. (2021) Can commercial trade represent the main indicator of the COVID-19 diffusion due to human-to-human interactions? A comparative analysis between Italy, France, and Spain. Environ Res 201: 111529. https://doi.org/10.1016/j.envres.2021.111529
[11]	Akan AP, Coccia M (2022) Changes of Air Pollution between Countries Because of Lockdowns to Face COVID-19 Pandemic. Appl Sci 12: 12806. https://doi.org/10.3390/app122412806
[12]	Coccia M (2022) Meta-analysis to explain unknown causes of the origins of SARS-COV-2. Environ Res 111: 113062. https://doi.org/10.1016/j.envres.2022.113062
[13]	Johns Hopkins Center for System Science and EngineeringCoronavirus COVID-19 Global Cases (2022).
[14]	Núñez-Delgado A, Bontempi E, Coccia M, et al. (2021) SARS-CoV-2 and other pathogenic microorganisms in the environment. Environ Res 201: 111606. https://doi.org/10.1016/j.envres.2021.111606
[15]	El-Sadr Wafaa M, Ashwin Vasan, Ayman El-Mohande (2023) Facing the New Covid-19 Reality. N Engl J Med 388: 385-387.
[16]	Coccia M (2023) Effects of strict containment policies on COVID-19 pandemic crisis: lessons to cope with next pandemic impacts. Environ Sci Pollut R 30: 2020-2028. https://doi.org/10.1007/s11356-022-22024-w
[17]	Coccia M (2021) 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. Sci Total Environ 775: 145801. https://doi.org/10.1016/j.scitotenv.2021.145801
[18]	Coccia M (2022) Improving preparedness for next pandemics: Max level of COVID-19 vaccinations without social impositions to design effective health policy and avoid flawed democracies. Environ Res 213: 113566. https://doi.rg/10.1016/j.envres.2022.113566
[19]	Coccia M (2022) COVID-19 Vaccination is not a Sufficient Public Policy to face Crisis Management of next Pandemic Threats. Publc Organ Rev . https://doi.org/10.1007/s11115-022-00661-6
[20]	Farazmand A (2001) Handbook of crisis and emergency management.CRC Press.
[21]	Farazmand A Crisis and Emergency Management, Theory and Practice (2014).
[22]	Coccia M (2022) Preparedness of countries to face covid-19 pandemic crisis: Strategic positioning and underlying structural factors to support strategies of prevention of pandemic threats. Environ Res 203: 1678. https://doi.org/10.1016/j.envres.2021.111678
[23]	Dai H, Cao W, Tong X, et al. (2022) Global prediction model for COVID-19 pandemic with the characteristics of the multiple peaks and local fluctuations. BMC Med Res Methodol 22: 137. https://doi.org/10.1186/s12874-022-01604-x
[24]	Krechetov M, Esmaieeli Sikaroudi AM, Efrat A, et al. (2022) Prediction and prevention of pandemics via graphical model inference and convex programming. Sci Rep 12: 7599. https://doi.org/10.1038/s41598-022-11705-8
[25]	Kuvvetli Y, Deveci M, Paksoy T, et al. (2021) A predictive analytics model for COVID-19 pandemic using artificial neural networks. Decis Anal J 1: 100007. https://doi.org/10.1016/j.dajour.2021.100007
[26]	Liu Z, Magal P, Webb G (2021) Predicting the number of reported and unreported cases for the COVID-19 epidemics in China, South Korea, Italy, France, Germany and United Kingdom. J Theor Biol 509: 110501. https://doi.org/10.1016/j.jtbi.2020.110501
[27]	Šušteršič T, Blagojević A, Cvetković D, et al. (2021) Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union. Front Public Health 9: 727274.
[28]	Coccia M (2021) Pandemic Prevention: Lessons from COVID-19. Encyclopedia 1: 433-444.
[29]	Khandia R, Singhal S, Alqahtani T, et al. (2022) Emergence of SARS-CoV-2 Omicron (B.1.1.529) variant, salient features, high global health concerns and strategies to counter it amid ongoing COVID-19 pandemic. Environ Res 209: 112816. https://doi.org/10.1016/j.envres.2022.112816
[30]	Groh M (2014) Strategic Management in Times of Crisis. Am J Econ Sociol Bus Adm 6: 49-57. https://doi.org/10.3844/ajebasp.2014.49.57
[31]	Alsobh A (2022) Prediction of COVID-19 Disease by ARIMA Model and Tuning Hyperparameter Through GridSearchCV. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2: 543-551.
[32]	Keshavamurthy R, Dixon S, Pazdernik KT, et al. (2022) Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches. One Health 15: 100439.
[33]	Magazzino C, Mele M, Coccia M (2022) A machine learning algorithm to analyze the effects of vaccination on COVID-19 mortality. Epidemiol Infect 150: e168. https://doi.org/10.1017/S0950268822001418
[34]	Coccia M (2022) Optimal levels of vaccination to reduce COVID-19 infected individuals and deaths: A global analysis. Environ Res 204: 112314. https://doi.org/10.1016/j.envres.2021.112314
[35]	Rosenfeld R, Tibshirani RJ (2021) Epidemic tracking and forecasting: Lessons learned from a tumultuous year. PNAS 118: e2111456118. https://doi.org/10.1073/pnas.2111456118
[36]	Collins GS, Ma J, Dhiman P (2021) There are no shortcuts in the development and validation of a COVID-19 prediction model. Transbound Emerg Dis 68: 210-211.
[37]	PubMedSearch (2023). Available from: https://pubmed.ncbi.nlm.nih.gov/.
[38]	ScopusStrart exploring (2023). Available from: https://www.scopus.com/search/form.uri?display=basic#basic.
[39]	Web of ScienceDocuments (2023). Available from: https://www.webofscience.com/wos/woscc/basic-search.
[40]	Herby J, Jonung L, Hanke SH (2022) A literature review and meta-analysis of the effects of lockdowns on COVID-19 mortality. Stud Appl Econ .
[41]	Petticrew M, Roberts H (2005) Systematic reviews in the social sciences: A practical guide. Malden, MA: Blackwell Publishing.
[42]	Shakeel SM, Kumar NS, Madalli PP, et al. (2021) Covid-19 prediction models: A systematic literature review. Osong Public Health and Research Perspectives 12: 215-229.
[43]	Clarke J (2011) What is a systematic review?. Evid-Based Nu 14: 64.
[44]	Daszak P, Olival KJ, Li H (2020) A strategy to prevent future epidemics similar to the 2019-nCoV outbreak. Biosafety and Health 2: 6-8. http://dx.doi.org/10.1016/j.bsheal.2020.01.003
[45]	Anderson RM, May RM (1991) Infectious Diseases of Humans: Dynamics and Control.Oxford Univisity Press.
[46]	Dobson AP, Carper ER (1996) Infectious diseases and human population history. Bioscience 46: 115-126.
[47]	Wolfe N, Dunavan C, Diamond J (2007) Origins of major human infectious diseases. Nature 447: 279-283. https://doi.org/10.1038/nature05775
[48]	Ménard AD, Trant JF (2020) A review and critique of academic lab safety research. Nat Chem 12: 17-25. https://doi.org/10.1038/s41557-019-0375-x
[49]	Hellman MA, Savage EP, Keefe TJ (1986) Epidemiology of accidents in academic chemistry laboratories. J Chem Educ 63: A267.
[50]	Van Noorden R (2013) Safety survey reveals lab risks. Nature 493: 9-10.
[51]	Ayi HR, Hon CY (2018) Safety culture and safety compliance in academic laboratories: A Canadian perspective. J Chem. Health Saf 25: 6-12.
[52]	Simmons HE, Matos B, Simpson SA (2017) Analysis of injury data to improve safety and training. J Chem Health Saf 24: 21-28.
[53]	Kou Y, Peng X, Dingwell CE, et al. (2021) Learning experience reports improve. Acad Res Saf J Che Educ 98: 150-157.
[54]	Li Na, Hu LF, Jin AJ, et al. (2019) Biosafety laboratory risk assessment. J Bios Biosecur 1: 90-92. https://doi.org/10.1016/j.jobb.2019.01.011
[55]	Jia P, Yang SJ (2020) China needs a national intelligent syndromic surveillance system. Nature Med 26: 990. https://doi.org/10.1038/s41591-020-0921-5
[56]	Yuan D, Gao W, Liang S, et al. (2020) Biosafety threats of the rapidly established labs for SARS-CoV-2 tests in China. Environ Int 143: 105964. https://doi.org/10.1016/j.envint.2020.105964
[57]	Coccia M (2020) An index to quantify environmental risk of exposure to future epidemics of the COVID-19 and similar viral agents: Theory and Practice. Environ Res 191: 110155. https://doi.org/10.1016/j.envres.2020.110155
[58]	Coccia M (2020) 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.
[59]	Coccia M (2021) How do low wind speeds and high levels of air pollution support the spread of COVID-19?. Atmos Pollut Res 12: 437-445. https://doi.org/10.1016/j.apr.2020.10.002
[60]	Coccia M (2021) 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: 1-2. https://doi.org/10.1080/00207233.2020.1802937
[61]	Coccia M, Bellitto M (2018) A critique of human progress: a new definition and inconsistencies in society. Quaderni IRCrES-CNR 4: 51-67. http://dx.doi.org/10.23760/2499-6661.2018.017
[62]	Jones AM, Harrison RM (2004) The effects of meteorological factors on atmospheric bio aerosol concentrations-a review. Sci Total Environ 326: 151e180.
[63]	Wei M, Liu H, Chen J, et al. (2020) Effects of aerosol pollution on PM2.5-associated bacteria in typical inland and coastal cities of northern China during the winter heating season. Environ pollut 262: 114188. https://doi.org/10.1016/j.envpol.2020.114188
[64]	Zhong J, Zhang X, Dong Y, et al. (2018) Feedback effects of boundary-layer meteorological factors on explosive growth of PM2.5 during winter heavy pollution episodes in Beijing from 2013 to 2016. Atmos Chem Phys 18: 247-258.
[65]	Rosario DKA, Mutz YS, Bernardes PC, et al. (2020) Relationship between COVID-19 and weather: Case study in a tropical country. Int J Hyg Envir Heal 229: 113587.
[66]	Benati I, Coccia M (2022) Effective Contact Tracing System Minimizes COVID-19 Related Infections and Deaths: Policy Lessons to Reduce the Impact of Future Pandemic Diseases. J Public Admin Gov 12: 19-33. https://doi.org/10.5296/jpag.v12i3.19834
[67]	Bradshaw WJ, Alley EC, Huggins JH, et al. (2021) Bidirectional contact tracing could dramatically improve COVID-19 control. Nat Commun 12: 232.
[68]	Yalaman A, Basbug G, Elgin C, et al. (2021) Cross-country evidence on the association between contact tracing and COVID-19 case fatality rates. Sci Rep-UK 11: 2145. https://doi.org/10.1038/s41598-020-78760-x
[69]	Coccia M, Benati I (2018) Rewards in public administration: A proposed classification. J Soc Adm Sci 5: 68-80. http://dx.doi.org/10.1453/jsas.v5i2.1648
[70]	Zhan C, Chen J (2021) An investigation of testing capacity for evaluating and modeling the spread of coronavirus disease. Inform Sciences 561: 211-229.
[71]	Coccia M (2012) Political economy of R&D to support the modern competitiveness of nations and determinants of economic optimization and inertia. Technovation 3: 370-379. https://doi.org/10.1016/j.technovation.2012.03.005
[72]	Coccia M (2014) Driving forces of technological change: The relation between population growth and technological innovation-Analysis of the optimal interaction across countries. Technol Forecast Soc 8: 52-65. https://doi.org/10.1016/j.techfore.2013.06.001
[73]	Coccia M (2018) Classification of innovation considering technological interaction. J Econ Bib 5: 76-93. http://dx.doi.org/10.1453/jeb.v5i2.1650
[74]	Coccia M (2018) General properties of the evolution of research fields: a scientometric study of human microbiome, evolutionary robotics and astrobiology. Scientometrics 117: 1265-1283. https://doi.org/10.1007/s11192-018-2902-8
[75]	Coccia M (2018) Types of government and innovative performance of countries. J Soc Adm Sci 5: 15-33. http://dx.doi.org/10.1453/jsas.v5i1.1573
[76]	Coccia M (2019) Comparative Institutional Changes. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer Nature. https://doi.org/10.1007/978-3-319-31816-5_1277-1
[77]	Coccia M (2019) Intrinsic and extrinsic incentives to support motivation and performance of public organizations. J Econ Biblio 6: 20-29. http://dx.doi.org/10.1453/jeb.v6i1.1795.
[78]	Coccia M (2019) Metabolism of Public Organizations. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer Nature. https://doi.org/10.1007/978-3-319-31816-5_3711-1
[79]	Coccia M (2020) Fishbone diagram for technological analysis and foresight. Int J Foresight and Innovation Policy 14: 225-247.
[80]	Coccia M (2020) The evolution of scientific disciplines in applied sciences: dynamics and empirical properties of experimental physics. Scientometrics 124: 451-487. https://doi.org/10.1007/s11192-020-03464-y
[81]	Coccia M (2021) Comparative Critical Decisions in Management. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer. https://doi.org/10.1007/978-3-319-31816-5_3969-1
[82]	Coccia M (2022) Critical innovation strategies for achieving competitive strategic entrepreneurship in ever-increasing turbulent markets. Strategic Entrepreneurship-Perspectives on Dynamics, Theories, and Practices.Springer. https://doi.org/10.1007/978-3-030-86032-5_12
[83]	Coccia M (2022) Technological trajectories in quantum computing to design a quantum ecosystem for industrial change. Technology Analysis & Strategic Management . https://doi.org/10.1080/09537325.2022.2110056
[84]	Coccia M, Benati I (2017) What is the relation between public manager compensation and government effectiveness? An explorative analysis with public management implications. Quaderni Ircres-CNR 2: 1-36. http://dx.doi.org/10.23760/2499-6661.2017.001
[85]	Coccia M, Mosleh M, Roshani S (2022) Evolution of quantum computing: Theoretical and innovation management implications for emerging quantum industry. IEEE Transactions on Engineering Management . https://doi.org/10.1109/TEM.2022.3175633
[86]	Coccia M, Roshani S, Mosleh M (2021) Scientific Developments and New Technological Trajectories in Sensor Research. Sensors 21: 7803. https://doi.org/10.3390/s21237803
[87]	Coccia M (2019) Revolutions and Evolutions. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springe. https://doi.org/10.1007/978-3-319-31816-5_3708-1
[88]	Coccia M, Roshani S, Mosleh M (2022) Evolution of Sensor Research for Clarifying the Dynamics and Properties of Future Directions. Sensors 22: 9419. https://doi.org/10.3390/s22239419
[89]	Mosleh M, Roshani S, Coccia M (2022) Scientific laws of research funding to support citations and diffusion of knowledge in life science. Scientometrics 127: 1931-1951. https://doi.org/10.1007/s11192-022-04300-1
[90]	Núñez-Delgado Avelino, Zhien Zhang, Elza Bontempi, et al. (2023) Editorial on the Topic “New Research on Detection and Removal of Emerging Pollutants”. Materials 16: 725. https://doi.org/10.3390/ma16020725
[91]	Pagliaro M, Coccia M (2021) How self-determination of scholars outclasses shrinking public research lab budgets, supporting scientific production: a case study and R&D management implications. Heliyon 7: e05998. https://doi.org/10.1016/j.heliyon.2021.e05998
[92]	Pronti A, Coccia M (2021) Agroecological and conventional agricultural systems: comparative analysis of coffee farms in Brazil for sustainable development. Int J Sustainable Development 2: 223-248. https://doi.org/10.1504/IJSD.2020.115223
[93]	Roshani S, Bagheri R, Mosleh M, et al. (2021) What is the relationship between research funding and citation-based performance? A comparative analysis between critical disciplines. Scientometrics 126: 7859-7874. https://doi.org/10.1007/s11192-021-04077-9
[94]	Roshani S, Coccia M, Mosleh M (2022) Sensor Technology for Opening New Pathways in Diagnosis and Therapeutics of Breast, Lung, Colorectal and Prostate Cancer. HighTech Innov 3: 356-375. http://dx.doi.org/10.28991/HIJ-2022-03-03-010
[95]	Sagan A, Thomas S, McKee M, et al. (2020) COVID-19 and health systems resilience: lessons going forwards. Eurohealth 26.
[96]	Benati I, Coccia M (2022) Global analysis of timely COVID-19 vaccinations: Improving governance to reinforce response policies for pandemic crises. Int J Health Gov . https://doi.org/10.1108/IJHG-07-2021-0072
[97]	Ajelli M (2018) The RAPIDD Ebola forecasting challenge: Model description and synthetic data generation. Epidemics 22: 3-12.
[98]	Johansson MA, Apfeldorf KM, Dobson S, et al. (2019) An open challenge to advance probabilistic forecasting for dengue epidemics. Proc Natl Acad Sci USA 116: 24268-24274.
[99]	Reinhart A, Brooks L, Jahja M, et al. (2021) An open repository of real-time COVID-19 indicators. Proc Natl Acad Sci USA 118: e2111452118.
[100]	McDonald DJ (2021) Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?. Proc Natl Acad Sci USA 118: e2111453118.
[101]	KhudaBukhsh WR, Bastian CD, Wascher M, et al. (2023) Projecting COVID-19 cases and hospital burden in Ohio. J Theor Biol 561: 111404.
[102]	Hurford A, Martignoni MM, Loredo-Osti JC, et al. (2023) Pandemic modelling for regions implementing an elimination strategy. J Theor Biol 561: 111378.
[103]	Khairulbahri M (2023) The SEIR model incorporating asymptomatic cases, behavioral measures, and lockdowns: Lesson learned from the COVID-19 flow in Sweden. Biomed Signal Proces 81: 104416.
[104]	Sasanami M, Fujimoto M, Kayano T, et al. (2023) Projecting the COVID-19 immune landscape in Japan in the presence of waning immunity and booster vaccination. J Theor Biol 559: 111384.
[105]	Li Z, Zhao J, Zhou Y, et al. (2023) Adaptive behaviors and vaccination on curbing COVID-19 transmission: Modeling simulations in eight countries. J Theor Biol 559: 111379.
[106]	Vasconcelos GL, Pessoa NL, Silva NB, et al. (2022) Multiple waves of COVID-19: a pathway model approach. Nonlinear Dyn . https://doi.org/10.1007/s11071-022-08179-8
[107]	Cherednik I (2022) Modeling the Waves of Covid-19. Acta Biotheor 70: 8. https://doi.org/10.1007/s10441-021-09428-w
[108]	Leonov A, Nagornov O, Tyuflin S (2023) Modeling of Mechanisms of Wave Formation for COVID-19. Epidemic Math 11: 167. https://doi.org/10.3390/math11010167
[109]	Cao Z, Qiu Z, Tang F, et al. (2022) Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework. Transbound Emerg Dis 69: e1584-e1594. https://doi.org/10.1111/tbed.14492
[110]	Namiki M, Yano R (2022) A numerical method to calculate multiple epidemic waves in COVID-19 with a realistic total number of people involved. J Stat Mech 2022: 033403.
[111]	Safaie Nasser, Kaveie Maryam, Mardanian Siroos, et al. (2022) Investigation of Factors Affecting COVID-19 and Sixth Wave Management Using a System Dynamics Approach. J Healthc Eng . https://doi.org/10.1155/2022/4079685
[112]	Wieland T (2020) A phenomenological approach to assessing the effectiveness of COVID-19 related nonpharmaceutical interventions in Germany. Safety Sci 131: 104924.
[113]	Douglas WA (2022) Covid-19 Lockdown Cost/Benefits: A Critical Assessment of the Literature. Int J Econ Bus 29: 1-32.
[114]	Biggs AT, Littlejohn LF (2021) Revisiting the initial COVID-19 pandemic projections. The Lancet Microbe 2: E91-E92.
[115]	Korolev I (2021) Identification and estimation of the SEIRD epidemic model for COVID-19. J Econometrics 220: 63-85.
[116]	Chen C, So M, Liu FC (2022) Assessing government policies' impact on the COVID-19 pandemic and elderly deaths in East Asia. Epidemiol Infect 150: e161. https://doi.org/10.1017/S0950268822001388
[117]	Mohammadi Z, Cojocaru MG, Thommes EW (2022) Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world. BMC Public Health 22: 1594.
[118]	Stangier U, Kananian S, Schüller J (2022) Perceived vulnerability to disease, knowledge about COVID-19, and changes in preventive behavior during lockdown in a German convenience sample. Curr Psychol 41: 7362-7370.
[119]	Goolsbee A, Syverson C (2021) Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020. J Public Econ 193: 104311.
[120]	Amaro JE (2023) Systematic description of COVID-19 pandemic using exact SIR solutions and Gumbel distributions. Nonlinear Dyn 111: 1947-1969. https://doi.org/10.1007
[121]	Noteboom CB, Zeng D, Godasu R, et al. Applications of deep learning augmented systems for Covid-19 predictions- A literature review (2021).
[122]	Jordan E, Shin DE, Leekha S, et al. (2021) Optimization in the Context of COVID-19 Prediction and Control: A Literature Review. IEEE Access 9: 130072-130093. https://doi.org/10.1109/ACCESS.2021.3113812
[123]	Hudda MT, Archer L, van Smeden M, et al. (2023) Minimal reporting improvement after peer review in reports of COVID-19 prediction models: systematic review. J Clin Epidemiol 154: 75-84. https://doi.org/10.1016/j.jclinepi.2022.12.005
[124]	Zhang W, Liu S, Osgood N, et al. (2023) Using simulation modelling and systems science to help contain COVID-19: A systematic review. Syst Res Behav Sci 40: 207-234. https://doi.org/10.1002/sres.2897
[125]	Bhatia D, Allin S, Di Ruggiero E (2023) Mobilization of science advice by the Canadian federal government to support the COVID-19 pandemic response. Hum Soc Sci Commun 10: 19. https://doi.org/10.1057/s41599-023-01501-8
[126]	Bundy J, Pfarrer MD, Short CE, et al. (2017) Crises and Crisis Management: Integration, Interpretation, and Research Development. J Manage 43: 1661-1692. https://doi.org/10.1177/0149206316680030
[127]	Seeger MW, Sellno TL, Ulmer RR (1998) Communication, organization and crisis. Communication Yearbook 21: 231-275. https://doi.org/10.1080/23808985.1998.11678952
[128]	Mahmoudi J, Xiong C (2022) How social distancing, mobility, and preventive policies affect COVID-19 outcomes: Big data-driven evidence from the District of Columbia-Maryland-Virginia (DMV) megaregion. PloS One 17: e0263820. https://doi.org/10.1371/journal.pone.0263820
[129]	Cao S, Gan Y, Wang C, et al. (2020) Post-lockdown SARS-CoV-2 nucleic acid screening in nearly ten million residents of Wuhan, China. Nat Commun 11: 5917. https://doi.org/10.1038/s41467-020-19802-w
[130]	Tsiotas D, Magafas L (2020) The Effect of Anti-COVID-19 Policies on the Evolution of the Disease: A Complex Network Analysis of the Successful Case of Greece. Physics 2: 325-339. https://doi.org/10.3390/physics2020017
[131]	Warren GW, Lofstedt R, Wardman JK (2021) COVID-19: the winter lockdown strategy in five European nations. J Risk Res 24: 267-293. https://doi.org/10.1080/13669877.2021.1891802
[132]	Crow DA, Albright EA, Ely T, et al. (2018) Do disasters lead to learning? Financial policy change in local government. Rev Policy Res 35: 564-589. https://doi.org/10.1111/ropr.12297
[133]	Kapitsinis N (2020) The underlying factors of the COVID-19 spatially uneven spread. Initial evidence from regions in nine EU countries. Reg Sci Policy Pract 12: 1027-1045. https://doi.org/10.1111/rsp3.12340
[134]	Williams GA, Ulla Díez SM, Figueras J, et al. (2020) Translating evidence into policy during the covid-19 pandemic: bridging science and policy (and politics). Eurohealth 26: 29-33.
[135]	Newby JM, O'Moore K, Tang S, et al. (2020) Acute mental health responses during the COVID-19 pandemic in Australia. PloS One 15: e0236562. https://doi.org/10.1371/journal.pone.0236562
[136]	Sirois FM, Owens J (2021) Factors Associated With Psychological Distress in Health-Care Workers During an Infectious Disease Outbreak: A Rapid Systematic Review of the Evidence. Front Psychiatry 11: 589545. https://doi.org/10.3389/fpsyt.2020.589545
[137]	Whittaker C, Kamaura LT, Takecian PL, et al. (2021) Three-quarters attack rate of SARS-CoV-2 in the Brazilian Amazon during a largely unmitigated epidemic. Science 371: 288-292. https://doi.org/10.1126/science.abe9728
[138]	Christie A, Brooks JT, Hicks LA, et al. (2021) Guidance for implementing COVID-19 prevention strategies in the context of varying community transmission levels and vaccination coverage. MMWR Morb Mortal Wkly Rep 70: 1044-1047.
[139]	Bleser WK, Shen H, Crook HL, et al. Health policy brief: pandemic-driven health policies to address social needs and health equity (2022). Available from: https://www.healthaffairs.org/do/10.1377/hpb20220210.360906/. opens in new tab.
[140]	Overton D, Ramkeesoon SA, Kirkpatrick K, et al. (2021) Lessons from the COVID-19 crisis on executing communications and engagement at the community level during a health crisis. Washington, DC: National Academies of Sciences, Engineering, and Medicine.
[141]	Ackoff RL, Rovin S (2003) Redesigning Society. Stanford: Stanford University Press.
[142]	Gigerenzer G, Todd PM (1999) Ecological rationality: the normative study of heuristics, Ecological Rationality: Intelligence in the World. New York: Oxford University Press: 487-497.
[143]	Janssen M, van der Voort H (2020) Agile and adaptive governance in crisis response: Lessons from the COVID-19 pandemic. Int J Inform Manage 55: 102180.
[144]	Kahneman D, Slovic P, Tversky A (1982) Judgment Under Uncertainty: Heuristics and Biases.Cambridge University Press.
[145]	Weible CM, Nohrstedt D, Cairney P, et al. (2020) COVID-19 and the policy sciences: initial reactions and perspectives. Policy Sci 53: 225-241.
[146]	Liu XX, Fong SJ, Dey N, et al. (2021) A new SEAIRD pandemic prediction model with clinical and epidemiological data analysis on COVID-19 outbreak. Appl Intell 51: 4162-4198. https://doi.org/10.1007/s10489-020-01938-3
[147]	Chen B, Zhao Y, Jin Z, et al. (2023) Twice evasions of Omicron variants explain the temporal patterns in six Asian and Oceanic countries. Bmc Infect Dis 23: 25. https://doi.org/10.1186/s12879-023-07984-9
[148]	Milanesi S, Rosset F, Colaneri M, et al. (2023) Early detection of variants of concern via funnel plots of regional reproduction numbers. Scientific reports 13: 1052. https://doi.org/10.1038/s41598-022-27116-8
[149]	Cameron EE, Nuzz JR, Bell JA (2019) GHS Index Building. Global Health Security Index. Collective Action and Accountability. Washington, USA: Johns' Hopkins University.
[150]	Stribling J, Clifton A, McGill G, et al. (2020) Examining the UK Covid-19 mortality paradox: pandemic preparedness, healthcare expenditure, and the nursing workforce. J Adv Nurs 76: 3218-3227. https://doi.org/10.1111/jan.14562
[151]	Coccia M (2019) Comparative World-Systems Theories. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer. https://doi.org/10.1007/978-3-319-31816-5_3705-1
[152]	Coccia M (2018) World-System Theory: A sociopolitical approach to explain World economic development in a capitalistic economy. J Econ Pol Econ 5: 459-465. http://dx.doi.org/10.1453/jepe.v5i4.1787
[153]	Coccia M (2017) The source and nature of general purpose technologies for supporting next K-waves: Global leadership and the case study of the U.S. Navy's Mobile User Objective System. Technol Forecast Soc 11: 331-339. https://doi.org/10.1016/j.techfore.2016.05.019
[154]	Coccia M (2015) General sources of general purpose technologies in complex societies: Theory of global leadership-driven innovation, warfare and human development. Technol Soc 42: 199-226. http://doi.org/10.1016/j.techsoc.2015.05.008
[155]	Coccia M (2019) The Role of Superpowers in Conflict Development and Resolutions. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer. https://doi.org/10.1007/978-3-319-31816-5_3709-1
[156]	Coccia M (2020) Destructive Technologies for Industrial and Corporate Change. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer. https://doi.org/10.1007/978-3-319-31816-5_3972-1
[157]	Coccia M (2019) Theories of Development. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer. https://doi.org/10.1007/978-3-319-31816-5_939-1
[158]	Coccia M (2014) Converging scientific fields and new technological paradigms as main drivers of the division of scientific labour in drug discovery process: the effects on strategic management of the R&D corporate change. Technol Anal Strateg 26: 733-749. https://doi.org/10.1080/09537325.2014.882501
[159]	Coccia M (2016) Radical innovations as drivers of breakthroughs: characteristics and properties of the management of technology leading to superior organizational performance in the discovery process of R&D labs. Technol Anal Strateg 28: 381-395. https://doi.org/10.1080/09537325.2015.1095287
[160]	Coccia M (2020) Asymmetry of the technological cycle of disruptive innovations. Technol Anal Strateg 32: 1462-1477. https://doi.org/10.1080/09537325.2020.1785415
[161]	Coccia M (2021) Evolution and structure of research fields driven by crises and environmental threats: the COVID-19 research. Scientometrics 12: 9405-9429. https://doi.org/10.1007/s11192-021-04172-x
[162]	Coccia M (2019) Metabolism of public research organizations: how do laboratories consume state subsidies?. Publc Organ Rev 19: 473-491. https://doi.org/10.1007/s11115-018-0421-y
[163]	Coccia M (2021) Effects of human progress driven by technological change on physical and mental health. Studi Di Sociologia 2: 113-132. https://doi.org/10.26350/000309_000116
[164]	Coccia M (2018) Motivation and theory of self-determination: Some management implications in organizations. J Econ Bib 5: 223-230. http://dx.doi.org/10.1453/jeb.v5i4.1792
[165]	Coccia M (2017) New directions in measurement of economic growth, development and under development. J Econ Pol Econ 4: 382-395. http://dx.doi.org/10.1453/jepe.v4i4.1533
[166]	Coccia M (2010) Foresight of technological determinants and primary energy resources of future economic long waves. Int J Foresight Innov Policy 6: 225-232. https://doi.org/10.1504/IJFIP.2010.037468
[167]	Coccia M (2009) A new approach for measuring and analyzing patterns of regional economic growth: empirical analysis in Italy. A New Approach for Measuring and Analysing Patterns of Regional Economic Growth . https://doi.org/10.3280/SCRE2009-002004
[168]	Coccia M, Benati I (2018) Comparative Evaluation Systems. Global Encyclopedia of Public Administration, Public Policy, and Governance.Springer. https://doi.org/10.1007/978-3-319-31816-5_1210-1
[169]	Coccia M (2021) How a Good Governance of Institutions Can Reduce Poverty and Inequality in Society?. Legal-Economic Institutions, Entrepreneurship, and Management: Perspectives on the Dynamics of Institutional Change from Emerging Markets.Springer. https://doi.org/10.1007/978-3-030-60978-8_4
[170]	Coccia M (2018) Competition between basic and applied research in the organizational behaviour of public research labs. J Econ Lib 5: 118-133. http://dx.doi.org/10.1453/jel.v5i2.1652
[171]	Coccia M (2018) Economic inequality can generate unhappiness that leads to violent crime in society. Int J Happiness and Development 4: 1-24. https://doi.org/10.1504/IJHD.2018.090488
[172]	Coccia M (2022) Probability of discoveries between research fields to explain scientific and technological change. Technol Soc 68: 101874. https://doi.org/10.1016/j.techsoc.2022.101874
[173]	Coccia M (2021) Technological Innovation. The Blackwell Encyclopedia of Sociology.John Wiley & Son. https://doi.org/10.1002/9781405165518.wbeost011.pub2
[174]	Coccia M, Benati I (2018) Comparative Studies. Global Encyclopedia of Public Administration, Public Policy, and Governanc.Springer. https://doi.org/10.1007/978-3-319-31816-5_1197-1

Reader Comments

your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)