Modelling and Assessing Public Health Policies to Counteract Italian Measles Outbreaks

Scire', Giovanni (2021): Modelling and Assessing Public Health Policies to Counteract Italian Measles Outbreaks. Published in: INTERNATIONAL JOURNAL OF SIMULATION AND PROCESS MODELLING , Vol. 16, No. 4 (3 November 2021): pp. 271-284.

Abstract

This study aims to understand, through explanatory research, the key factors that led to the 2017 measles outbreak in Italy, the causes of the low level of immunisation and the causes of possible cyclical phenomena of measles epidemics. This topic's comprehension has required a holistic approach, merging epidemiological aspects, socioeconomic aspects (including the evolution of mistrust in vaccinations, infodemy and fake news) and health law constraints. A specific SIR System Dynamics (SD) model was built to reproduce the relevant cause-and-effect relationships between social interactions, the public institutions behaviour and the measles outbreaks. SD results permit the assessment of the health policies to counteract the measles outbreaks. Findings, limits and further research recommendations are briefly reported in the conclusions.

• Item Type: MPRA Paper
• Language: English
• Keywords: System Dynamics; Infodemic; Measles; Communicable diseases; SIR Model; Vaccination; Public Health
• Subjects: I - Health, Education, and Welfare > I1 - Health; I - Health, Education, and Welfare > I1 - Health > I10 - General; I - Health, Education, and Welfare > I1 - Health > I12 - Health Behavior; I - Health, Education, and Welfare > I1 - Health > I14 - Health and Inequality; I - Health, Education, and Welfare > I1 - Health > I15 - Health and Economic Development; I - Health, Education, and Welfare > I1 - Health > I18 - Government Policy ; Regulation ; Public Health; N - Economic History > N3 - Labor and Consumers, Demography, Education, Health, Welfare, Income, Wealth, Religion, and Philanthropy > N34 - Europe: 1913-; Z - Other Special Topics > Z1 - Cultural Economics ; Economic Sociology ; Economic Anthropology > Z10 - General; Z - Other Special Topics > Z1 - Cultural Economics ; Economic Sociology ; Economic Anthropology > Z13 - Economic Sociology ; Economic Anthropology ; Social and Economic Stratification; Z - Other Special Topics > Z1 - Cultural Economics ; Economic Sociology ; Economic Anthropology > Z18 - Public Policy

References

Adamo, G., Baccolini, V., Marzuillo, C. & Sturabotti, G., 2016. Slow progress in measles and rubella elimination in Italy. Epidemiology Biostatistics and Public Health., Vol. 13, No. 3, pp. 1-2.

Adamo, G. et al., 2018. Regional reports for the subnational monitoring of measles elimination in Italy and the identification of local barriers to the attainment of the elimination goal. PLOS ONE, Vol. 13, e0205147.

Adamo G, Baccolini V, Massimi A, et al. 2019. Towards elimination of measles and rubella in Italy: Progress and challenges. PLoS One, Vol. 14, No. 12, e0226513. Published 2019 Dec 16. DOI:10.1371/journal.pone.0226513

Allen, L. J. S., Jones, M. A. & Martin, C. F., 1991. A discrete-time model with vaccination for a measles epidemic. Mathematical Biosciences, Vol. 105, No. 1, pp. 111-131.

Andersen, K.G., Rambaut, A., Lipkin, W.I. et al. 2020. The proximal origin of SARS-CoV-2. Nat Med, Vol. 26, pp. 450–452.

Arif, N., Al-Jefri, M., Bizzi, I. H., Perano, G. B., Goldman, M., Haq, I., … Ghezzi, P. (2018). Fake News or Weak Science? Visibility and Characterization of Antivaccine Webpages Returned by Google in Different Languages and Countries. Frontiers in immunology, Vol. 9, No. 1215. DOI:10.3389/fimmu.2018.01215

Bai S., 2020. Simulations of COVID-19 spread by spatial agent-based model and ordinary differential equations. International Journal of Simulation and Process Modelling. Vol. 15, No. 3, pp. 268–277.

Bechini, A., Levi, M., Boccalini, S. & et al., 2013. Progress in the elimination of measles and congenital rubella in Central Italy. Human Vaccines & Immunotherapeutics, 9(3), pp. 649-656.

Bianchi, C., 2016. Dynamic Performance Management. Zurich, Switzerland: Springer International Publishing.

Bianchi, C. et al., 2010. Applying System Dynamics to Foster Organizational Change, Accountability and Performance in the Public Sector. A Case-Based Italian Perspective. Systems Research and Behavioral Science, Vol. 27, pp. 395-420.

Bivona, E. & Cosenz, F., 2017a. Designing Outcome-Based Performance Management Systems to assess policies impacting on caesarean section rate: an analysis of the Sicilian maternity pathway. In: E. Borgonovi, E. Anessi-Pessina & C. Bianchi, eds. Outcome-Based Performance Management in the Public Sector. S .l.: Springer.

Bivona, E. & Cosenz, F., 2017b. Framing Caesarean Section Reduction Policies Through a Dynamic Performance Management Approach: A Maternity Pathway Case-Based Analysis. In: H. Qudrat-Ullah & P. Tsasis, eds. Innovative Healthcare Systems for the 21st Century. S .l.: Springer.

Borgonovi E., Anessi-Pessina E. & Bianchi C., eds. Outcome-Based Performance Management in the Public Sector. s.l.:Springer.

Bovaird, T., 2014. Attributing outcomes to social policy interventions - 'Gold Standard' or 'fool's gold' in public policy and management?. Social Policy and Administration, Vol. 48, No. 1, pp. 1-23.

Box, G. E. P., & Draper, N. R. 1987. Wiley series in probability and mathematical statistics. Empirical model-building and response surfaces. John Wiley & Sons.

Brauer, F., 2017. Mathematical epidemiology: Past, present, and future. Infectious Disease Modelling, Vol. 2, pp. 113-27.

Cauchemez, S. et al., 2011. Role of social networks in shaping disease transmission during a community outbreak of 2009 H1N1 pandemic influenza. Proceedings of the National Academy of Sciences, Vol. 108, No. 7, pp. 2825-2830.

Chen, S. C., Chang, C. F., Jou, L. J. & Liao, C., 2006. Modelling vaccination programmes against measles in Taiwan. Epidemiology and Infection, Vol 135, No.5, pp. 775-786.

Darabi, N. & Hosseinichimeh, N., 2020, System dynamics modeling in health and medicine: a systematic literature review. System Dynamics Review, Vol. 36, pp. 29-73. https://doi.org/10.1002/sdr.1646

De Gooyert, V., 2016. Nothing so practical as a good theory; Five ways to use system dynamics for theoretical contributions. Proceedings of the 34th International Conference of the System Dynamics Society, Delft, the Netherlands.

De Vries, R. D., Duprex, W. P. & de Swart, R. L., 2015. Morbillivirus infections: An introduction. Viruses, Vol. 7, p. 699–706.

Fanta, G. B., Pretorius, L. & Erasmus, L., 2016. A System Dynamics Model of eHealth Acceptance: A Sociotechnical Perspective. Conference IAMOT 2016, Orlando.

Fast, S. M., González, M. C., Wilson, J. M. & Markuzon, N., 2015. Modelling the propagation of social response during a disease outbreak. Journal of the Royal Society Interface, Vol. 12, No. 104.

Fauci, A. S., 2001. Infectious Diseases: Considerations for the 21st Century. Clinical Infectious Diseases, Vol. 32, No. 5, pp. 675-685.

Filia, A. et al., 2013. Analysis of national measles surveillance data in Italy from October 2010 to December 2011 and priorities for reaching the 2015 measles elimination goal. Eurosurveillance, Vol. 18, No.20, pp. 1-7.

Filia A., Bella A., Del Manso M., Rota M. C., 2018. Morbillo & Rosolia News, December 2018 http://www.epicentro.iss.it/problemi/morbillo/ bollettino.asp

Forrester, J. W., 1961. Industrial dynamics. Waltham, MA: Pegasus Communications.

Forrester, J. W., 1969. Urban Dynamics. s.l.:Pegasus Communications.

Forrester, J. W., 1971. World Dynamics. s.l.Wright-Allen Press.

Gasparini, R., 2009. Quali modelli e quali evidenze per la valutazione farmacoeconomica della vaccinazione anti-HPV. Naples, Italy, s.n., pp. 187-192.

Gesser-Edelsburg, A., Diamant, A., Hijazi, R. & Mesch, G. S., 2018. Correcting misinformation by health organizations during measles outbreaks: A controlled experiment. PLoS ONE, Vol. 13, No. 12, pp. 1-23.

Ghazvini, A. & Shukur, Z., 2013. System Dynamics in E-health Policy Making and the “Glocal” Concept. Procedia Technology, Vol. 11.

Gori, D., Costantino, C., Odone, A., Ricci, B., Ialonardi, M., Signorelli, C., Vitale, F., & Fantini, M. P., 2020. The Impact of Mandatory Vaccination Law in Italy on MMR Coverage Rates in Two of the Largest Italian Regions (Emilia-Romagna and Sicily): An Effective Strategy to Contrast Vaccine Hesitancy. Vaccines, Vol. 8, No.1, 57. https://doi.org/10.3390/vaccines8010057

Guest, G., Bunce, A., Johnson, L. 2006. How many interviews are enough? An experiment with data saturation and variability. Field Methods, Vol. 18, No. 1, 24

Holmes KK, Bertozzi S, Bloom BR, et al., 2017. Major Infectious Diseases: Key Messages from Disease Control Priorities, Third Edition. In: Holmes KK, Bertozzi S, Bloom BR, et al., editors. Major Infectious Diseases. 3rd edition. Washington (DC): The International Bank for Reconstruction and Development / The World Bank; Nov 3. Chapter 1. Available from: https://www.ncbi.nlm.nih.gov/books/NBK525197/ DOI: 10.1596/978-1-4648-0524-0/ch1

Homer, J. B., & Hirsch, G. B., 2006. System dynamics modeling for public health: background and opportunities. American journal of public health, Vol. 96, No. 3, 452–458. DOI:10.2105/AJPH.2005.062059

Huppert, A. & Katriel, G., 2013. Mathematical modelling and prediction in infectious disease epidemiology. Clinical Microbiology and Infection, Vol. 19, No. 11, pp. 999-1005.

Hviid A, Vinsløv Hansen J, Frisch M, Melbye M. 2019. Measles, mumps, rubella vaccination and autism: a nationwide cohort study. Ann Int Med. Vol. 170, pp. 513–20. DOI: 10.7326/M18-2101

Italian Ministry of Healthcare, 2017b. Piano Nazionale Prevenzione Vaccinale - PNPV 2017-2019. [Online] Available at: http://www.salute.gov.it/imgs/C_17_pubblicazioni_2571_allegato.pdf [Accessed 20 10 2018].

Jansen, V. A. A. et al., 2003. Measles Outbreaks in a Population with Declining Vaccine Uptake. Science, Vol. 301, No. 5634, p. 804.

Kermack, W. & McKendrick, A., 1991. Contributions to the mathematical theory of epidemics—I. Bulletin of Mathematical Biology, Vol. 53, No. 1-2, pp. 33-55.

Klepac, P., Funk, S. & Hollingsworth, T. D., 2015. Six challenges in the eradication of infectious diseases. Epidemics, Vol. 10, p. 97–101.

Lambert P-H and Podda A (2018) Education in Vaccinology: An Important Tool for Strengthening Global Health. Front. Immunol. Vol. 9, No. 1134. DOI: 10.3389/fimmu.2018.01134

Lasco, G. & Curato, N., 2019. Medical populism. Social Science & Medicine, Vol. 221, pp. 1-8.

Levin, O. S. et al., 2011. The future of immunisation policy, implementation, and financing. The Lancet, Vol. 378, no. 9789, pp. 439-448.

Li M., Yu W., Tian W., Ge Y., Liu Y., Ding T., Zhang L., 2019. System dynamics modeling of public health services provided by China CDC to control infectious and endemic diseases in China. Infection and Drug Resistance. Vol. 12, pp. 613-625. 10.2147/IDR.S185177.

Luna-Reyes, L. F., & Andersen, D. L., 2003. Collecting and analyzing qualitative data for system dynamics: methods and models. System Dynamics Review, Vol. 19, pp. 271-296.

Mackey, T. K. & Liang, B. A., 2013. Pharmaceutical digital marketing and governance: illicit actors and challenges to global patient safety and public health. Globalization and Health, Vol. 9, p. 45.

Manfredi, P. & D'Onofrio, A., 2013. Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases. S .l.: Springer.

Massei M. & Di Matteo R., 2019. Innovative social networks modelling for population simulation. International Journal of Simulation and Process Modelling. Vol. 14, No. 3, pp. 294-307

McLaughlin, D. & Olson, J., 2012. Healthcare Operations Management. Second Edition ed. Arlington, VA: Health Administration Press.

Morecroft, J., 1998. Resource System Management and Dynamic Complexity. Paper Presented at the 4th International Conference on Competence-based Management.

Osservatorio Nazionale sulla Salute nelle Regioni Italiane, 2015. First Report on Vaccination Prevention. Rome: Istituto di Sanità Pubblica - Sezione Igiene, Università Cattolica del Sacro Cuore.

Okyere-Siabouh, S. & Adetunded, I. A., 2013. Mathematical Model for the Study of Measles in Cape Coast Metropolis. International Journal of Modern Biology and Medicine, Vol. 4, No. 2, pp. 110-133.

Orso, D., Federici, N., Copetti, R., Vetrugno, L., & Bove, T., 2020. Infodemic and the spread of fake news in the COVID-19-era. European journal of emergency medicine: official journal of the European Society for Emergency Medicine, 10.1097/MEJ.0000000000000713. Advance online publication. https://doi.org/10.1097/MEJ.0000000000000713

Ozawa, S., Paina, L., & Qiu, M. (2016). Exploring pathways for building trust in vaccination and strengthening health system resilience. BMC health services research, Vol. 16, Suppl 7, p. 639. https://doi.org/10.1186/s12913-016-1867-7

Pruyt E, Auping WL, Kwakkel JH. 2015. Ebola in West Africa: Model‐Based Exploration of Social Psychological Effects and Interventions. Systems Research and Behavioral Science. Vol. 32, No. 1, pp. 2–14.

Reperant LA, Osterhaus ADME. AIDS, Avian flu, SARS, MERS, Ebola, Zika… what next? Vaccine. 2017, Vol. 35, No. 35 Pt A, pp. 4470-4474.

Ropeik D. 2013. How society should respond to the risk of vaccine rejection. Human vaccines & immunotherapeutics, Vol. 9, No. 8, pp. 1815–1818. DOI:10.4161/hv.25250

Salmon D.A., Teret S.P., MacIntyre C.R., Salisbury D., Burgess M.A., Halsey N.A., 2006. Compulsory vaccination and conscientious or philosophical exemptions: past, present and future. The Lancet, Vol. 367, pp. 436-442

Scirè G., 2018 Modelling Strategies for the reduction of fat dormice in northern Italian hazel groves. Int. J. Agricultural Resources, Governance and Ecology, Vol. 14, No. 4, pp. 352 – 267

Soares, P., Rocha, J. V., Moniz, M., Gama, A., Laires, P. A., Pedro, A. R., Dias, S., Leite, A., & Nunes, C. 2021. Factors Associated with COVID-19 Vaccine Hesitancy. Vaccines, Vol. 9, No. 3, 300. https://doi.org/10.3390/vaccines9030300

Senge, P., 1990. The Fifth Discipline. New York, NY, USA: Doubleday.

Sharareh N, S. Sabounchi N, Sayama H, MacDonald R., 2016. The Ebola Crisis and the Corresponding Public Behavior: A System Dynamics Approach. PLOS Currents Outbreaks. Nov 3

Smith, R. D., 2006. Responding to global infectious disease outbreaks: Lessons from SARS on the role of risk perception, communication and management. Social Science & Medicine., Vol. 63, No. 12, p. 3113–3123.

Sterman, J., 1994. Learning In and About Complex Systems. Boston, MA.: Sloan School of Management. Massachusetts Institute of Technology.

Sterman, J. D., 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. New York: Irwin Professional McGraw–Hill.

Sterman, J. D., 2002. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review, Vol. 18, p. 501–531.

Subyan A., Abdul J., Nur F., Che R., Ahmad N., 2018. Dengue outbreak: a system dynamics approach. Journal of Technology Management & Business, Vol. 5, No. 1, pp. 43-50.

Tagliabue A., Leite L. C. C., Leroy O. Y., Rappuoli R. (2019). Editorial: A Global Perspective on Vaccines: Priorities, Challenges and Online Information, Frontiers in Immunology, Vol. 10, p. 2556, https://www.frontiersin.org/article/10.3389/fimmu.2019.02556, DOI=10.3389/fimmu.2019.02556

The Editors of The Lancet (2010), Retraction—Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children, The Lancet, Vol. 375, Issue 9713, Page 445, ISSN 0140-6736, https://doi.org/10.1016/S0140-6736(10)60175-4.

Thompson, K. M. & Odahowski, C. L., 2016. The Costs and Valuation of Health Impacts of Measles and Rubella Risk Management Policies. Risk Analysis, Vol. 36, p. 1357–1382.

van Dijck J., 2020, Governing digital societies: Private platforms, public values, Computer Law & Security Review, Vol.36. https://doi.org/10.1016/j.clsr.2019.105377

Vynnycky, E. & White, R. G., 2010. An Introduction to Infectious Disease Modelling. Oxford: Oxford University Press.

Wakefield, A. J. et al., 1998. Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. The Lancet, Vol. 351, No. 9103, pp. 637-641. RETRACTED ARTICLE.