Modelling human response to information in voluntary vaccination behaviour using epidemic data

Bruno Buonomo; Rossella Della Marca; Manalebish Debalike Asfaw; Bruno Buonomo; Rossella Della Marca; Manalebish Debalike Asfaw

doi:10.3934/mbe.2025043

Mathematical Biosciences and Engineering

2025, Volume 22, Issue 5: 1185-1205. doi: 10.3934/mbe.2025043

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Modelling human response to information in voluntary vaccination behaviour using epidemic data

1.
Department of Mathematics and Applications, University of Naples Federico Ⅱ, via Cintia, Naples, I-80126, Italy
2.
Department of Mathematics, University of Addis Ababa, Addis Ababa, Ethiopia

Received: 13 December 2024 Revised: 19 March 2025 Accepted: 01 April 2025 Published: 10 April 2025

Here, we considered Holling functional responses, a core concept in population dynamics, and discussed their potential interpretation in the context of social epidemiology. Then, we assessed which Holling functional response best represents the vaccination behaviour of individuals when such a behaviour is influenced by information and rumours about the disease. In particular, we used the Holling functionals to represent the information-dependent vaccination rate in a socio-epidemiological model for meningococcal meningitis. As a field case test, we estimated the information-related parameters by using official data from a meningitis outbreak in Nigeria and numerically assessed the impact of the functionals on the solutions of the model. We observed significant inaccuracies on parameter estimates when either Holling type Ⅰ or Holling type Ⅲ functional were used. On the contrary, when the Holling type Ⅱ functional was employed, epidemiological data were well reproduced, and reasonable values of the information parameters were obtained. Given the socio-epidemiological interpretation of the Holling type Ⅱ functional, this means that the rate at which susceptible individuals come into contact with information may be assumed to be constant and that the time needed to handle the available information cannot be neglected.
- mathematical epidemiology,
- vaccination,
- information,
- human response,
- meningitis,
- Holling functionals
Citation: Bruno Buonomo, Rossella Della Marca, Manalebish Debalike Asfaw. Modelling human response to information in voluntary vaccination behaviour using epidemic data[J]. Mathematical Biosciences and Engineering, 2025, 22(5): 1185-1205. doi: 10.3934/mbe.2025043

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Abstract

Here, we considered Holling functional responses, a core concept in population dynamics, and discussed their potential interpretation in the context of social epidemiology. Then, we assessed which Holling functional response best represents the vaccination behaviour of individuals when such a behaviour is influenced by information and rumours about the disease. In particular, we used the Holling functionals to represent the information-dependent vaccination rate in a socio-epidemiological model for meningococcal meningitis. As a field case test, we estimated the information-related parameters by using official data from a meningitis outbreak in Nigeria and numerically assessed the impact of the functionals on the solutions of the model. We observed significant inaccuracies on parameter estimates when either Holling type Ⅰ or Holling type Ⅲ functional were used. On the contrary, when the Holling type Ⅱ functional was employed, epidemiological data were well reproduced, and reasonable values of the information parameters were obtained. Given the socio-epidemiological interpretation of the Holling type Ⅱ functional, this means that the rate at which susceptible individuals come into contact with information may be assumed to be constant and that the time needed to handle the available information cannot be neglected.

References

[1]	J. Bedford, J. Farrar, C. Ihekweazu, G. Kang, M. Koopmans, J. Nkengasong, A new twenty-first century science for effective epidemic response, Nature, 575 (2019), 130–136. https://doi.org/10.1038/s41586-019-1717-y doi: 10.1038/s41586-019-1717-y
[2]	D. Weston, A. Ip, R. Amlôt, Examining the application of behaviour change theories in the context of infectious disease outbreaks and emergency response: A review of reviews, BMC Public Health, 20 (2020), 1483. https://doi.org/10.1186/s12889-020-09519-2 doi: 10.1186/s12889-020-09519-2
[3]	S. Funk, M. Salathé, V. A. Jansen, Modelling the influence of human behaviour on the spread of infectious diseases: a review, J. R. Soc. Interface, 7 (2010), 1247–1256. http://doi.org/10.1098/rsif.2010.0142 doi: 10.1098/rsif.2010.0142
[4]	P. Manfredi, A. d'Onofrio, Modeling the Interplay Between Human Behavior and the Spread of Infectious Diseases, Springer, New York, 2013. https://doi.org/10.1007/978-1-4614-5474-8
[5]	Z. Wang, C. T. Bauch, S. Bhattacharyya, A. d'Onofrio, P. Manfredi, M. Perc, et al., Statistical physics of vaccination, Phys. Rep., 664 (2016), 1–113. https://doi.org/10.1016/j.physrep.2016.10.006 doi: 10.1016/j.physrep.2016.10.006
[6]	V. Capasso, Mathematical Structures of Epidemic Systems, Springer, Berlin, 1993. https://doi.org/10.1007/978-3-540-70514-7
[7]	V. Capasso, G. Serio, A generalization of the Kermack–McKendrick deterministic epidemic model, Math. Biosci., 42 (1978), 43–61. https://doi.org/10.1016/0025-5564(78)90006-8 doi: 10.1016/0025-5564(78)90006-8
[8]	S. Funk, E. Gilad, C. Watkins, V. A. A. Jansen, The spread of awareness and its impact on epidemic outbreaks, PNAS, 106 (2009), 6872–6877. https://doi.org/10.1073/pnas.0810762106 doi: 10.1073/pnas.0810762106
[9]	M. Riera-Montes, I. Velicko, The Chlamydia surveillance system in Sweden delivers relevant and accurate data: results from the system evaluation, 1997–2008, Euro Surveill., 16 (2011), 19907. https://doi.org/10.2807/ese.16.27.19907-en doi: 10.2807/ese.16.27.19907-en
[10]	A. d'Onofrio, P. Manfredi, Bistable endemic states in a susceptible–infectious–susceptible model with behavior–dependent vaccination, in Mathematical and Statistical Modeling for Emerging and Re–emerging Infectious Diseases (eds. G. Chowell and J. M. Hyman), Springer, Cham, 2016,341–354. https://doi.org/10.1007/978-3-319-40413-4_21
[11]	B. Buonomo, A. d'Onofrio, D. Lacitignola, Global stability of an SIR epidemic model with information dependent vaccination, Math. Biosci., 216 (2008), 9–16. https://doi.org/10.1016/j.mbs.2008.07.011 doi: 10.1016/j.mbs.2008.07.011
[12]	A. d'Onofrio, P. Manfredi, Information–related changes in contact patterns may trigger oscillations in the endemic prevalence of infectious diseases, J. Theor. Biol., 256 (2009), 473–478. https://doi.org/10.1016/j.jtbi.2008.10.005 doi: 10.1016/j.jtbi.2008.10.005
[13]	A. d'Onofrio, P. Manfredi, E. Salinelli, Vaccinating behaviour, information, and the dynamics of SIR vaccine preventable diseases, Theor. Popul. Biol., 71 (2007), 301–317. https://doi.org/10.1016/j.tpb.2007.01.001 doi: 10.1016/j.tpb.2007.01.001
[14]	B. Buonomo, R. Della Marca, A. d'Onofrio, M. Groppi, A behavioural modelling approach to assess the impact of COVID–19 vaccine hesitancy, J. Theor. Biol., 534 (2022), 110973. https://doi.org/10.1016/j.jtbi.2021.110973 doi: 10.1016/j.jtbi.2021.110973
[15]	C. Zuo, Y. Ling, F. Zhu, X. Ma, G. Xiang, Exploring epidemic voluntary vaccinating behavior based on information-driven decisions and benefit-cost analysis, Appl. Math. Comput., 447 (2023), 127905. https://doi.org/10.1016/j.amc.2023.127905 doi: 10.1016/j.amc.2023.127905
[16]	B. Buonomo, R. Della Marca, Effects of information–induced behavioural changes during the COVID–19 lockdowns: the case of Italy, R. Soc. Open Sci., 7 (2020), 201635. https://doi.org/10.1098/rsos.201635 doi: 10.1098/rsos.201635
[17]	B. Buonomo, R. Della Marca, A behavioural vaccination model with application to meningitis spread in Nigeria, Appl. Math. Model., 125 (2024), 334–350. https://doi.org/10.1016/j.apm.2023.09.031 doi: 10.1016/j.apm.2023.09.031
[18]	O. Andersson, P. Campos-Mercade, A. N. Meier, E. Wengström, Anticipation of COVID–19 vaccines reduces willingness to socially distance, J. Health Econ., 80 (2021), 102530. https://doi.org/10.1016/j.jhealeco.2021.102530 doi: 10.1016/j.jhealeco.2021.102530
[19]	B. Buonomo, R. Della Marca, S. S. Sharbayta, A behavioral change model to assess vaccination-induced relaxation of social distancing during an epidemic, J. Biol. Syst., 30 (2022), 1–25. https://doi.org/10.1142/S0218339022500085 doi: 10.1142/S0218339022500085
[20]	A. Kumar, P. K. Srivastava, Y. Dong, Y. Takeuchi, Optimal control of infectious disease: Information–induced vaccination and limited treatment, Phys. A, 542 (2020), 123196. https://doi.org/10.1016/j.physa.2019.123196 doi: 10.1016/j.physa.2019.123196
[21]	C. S. Holling, The components of predation as revealed by a study of small-mammal predation of the European pine sawfly, Can. Entomol., 91 (1959), 293–320. http://doi.org/10.4039/Ent91293-5 doi: 10.4039/Ent91293-5
[22]	C. S. Holling, Some characteristics of simple types of predation and parasitism, Can. Entomol., 91 (1959), 385–398. http://doi.org/10.4039/Ent91385-7 doi: 10.4039/Ent91385-7
[23]	J. P. DeLong, Predator Ecology: Evolutionary Ecology of the Functional Response, Oxford University Press, Oxford, 2021, https://doi.org/10.1093/oso/9780192895509.001.0001
[24]	F. Agusto, M. Leite, Optimal control and cost–effective analysis of the 2017 meningitis outbreak in Nigeria, Infect. Dis. Model., 4 (2019), 161–187. https://doi.org/10.1016/j.idm.2019.05.003 doi: 10.1016/j.idm.2019.05.003
[25]	G. Djatcha Yaleu, S. Bowong, E. H. Danga, J. Kurths, Mathematical analysis of the dynamical transmission of Neisseria meningitidis serogroup A, Int. J. Comput. Math., 94 (2017), 2409–2434. https://doi.org/10.1080/00207160.2017.1283411 doi: 10.1080/00207160.2017.1283411
[26]	Centers for Disease Control and Prevention, Meningococcal Disease, Available from: https://www.cdc.gov/meningococcal/index.html, 2023, (Accessed on September 2024).
[27]	World Health Organization, Meningitis, Available from: https://www.who.int/news-room/fact-sheets/detail/meningitis, 2023, (Accessed on October 2024).
[28]	N. MacDonald, Biological Delay Systems: Linear Stability Theory, Cambridge University Press, Cambridge, 1989.
[29]	Nigeria Centre for Disease Control and Prevention, An update of meningitis outbreak in Nigeria, Available from: https://www.ncdc.gov.ng/ncdc.gov.ng/diseases/sitreps/?cat=6&name = An 2022, (Accessed on November 2024).
[30]	C. Nnadi, J. Oladejo, S. Yennan, A. Ogunleye, C. Agbai, L. Bakare, et al., Large outbreak of Neisseria meningitidis Serogroup C – Nigeria, December 2016–June 2017, Morb. Mortal. Wkly Rep., 66 (2017), 1352–1356. https://doi.org/10.15585/mmwr.mm6649a3 doi: 10.15585/mmwr.mm6649a3
[31]	World Health Organization Africa, End of meningitis outbreak in Nigeria, December 2016–June 2017, Available from: https://www.afro.who.int/sites/default/files/2017-09/Dashboard 2017, (Accessed on November 2024).
[32]	R. Falcone, A. Sapienza, Information seeking behavior at the time of COVID-19, in Proceedings of the 22nd Workshop "From Objects to Agents", 2021,241–258.
[33]	M. H. Hayden, M. Dalaba, T. Awine, P. Akweongo, G. Nyaaba, D. Anaseba, et al., Knowledge, attitudes, and practices related to meningitis in northern Ghana, Am. J. Trop. Med. Hyg., 89 (2013), 265–270. https://doi.org/10.4269/ajtmh.12-0515 doi: 10.4269/ajtmh.12-0515
[34]	MATLAB, Matlab release 2023a. The MathWorks, Inc., Natick, MA, 2023.
[35]	C. Azzari, F. Nieddu, M. Moriondo, G. Indolfi, C. Canessa, S. Ricci, et al., Underestimation of invasive meningococcal disease in Italy, Emerg. Infect. Dis., 22 (2016), 469–475. https://doi.org/10.3201/eid2203.150928 doi: 10.3201/eid2203.150928
[36]	World Health Organization, Meningitis outbreak response in sub–Saharan Africa, Available from: https://www.who.int/publications/i/item/WHO-HSE-PED-CED-14.5, 2014, (Accessed on July 2024).
[37]	I. Ballalai, R. Dawson, M. Horn, V. Smith, R. Bekkat-Berkani, L. Soumahoro, et al., Understanding barriers to vaccination against invasive meningococcal disease: A survey of the knowledge gap and potential solutions, Expert Rev. Vaccines, 22 (2023), 457–467. https://doi.org/10.1080/14760584.2023.2211163 doi: 10.1080/14760584.2023.2211163
[38]	B. Murele, R. Vaz, A. Gasasira, P. Mkanda, T. Erbeto, J. Okeibunor, Vaccine perception among acceptors and non-acceptors in Sokoto State, Nigeria, Vaccine, 32 (2014), 3323–3327. https://doi.org/10.1016/j.vaccine.2014.03.050 doi: 10.1016/j.vaccine.2014.03.050
[39]	D. R. Timmermans, L. Henneman, R. A. Hirasing, G. Van der Wal, Attitudes and risk perception of parents of different ethnic backgrounds regarding meningococcal C vaccination, Vaccine, 23 (2005), 3329–3335. https://doi.org/10.1016/j.vaccine.2005.01.075 doi: 10.1016/j.vaccine.2005.01.075
[40]	G. I. Valderrama-Bahamóndez, H. Fröhlich, MCMC techniques for parameter estimation of ODE based models in systems biology, Front. Appl. Math. Stat., 5 (2019). https://doi.org/10.3389/fams.2019.00055
[41]	P. Van den Driessche, J. Watmough, Reproduction numbers and sub–threshold endemic equilibria for compartmental models of disease transmission, Math. Biosci., 180 (2002), 29–48. https://doi.org/10.1016/S0025-5564(02)00108-6 doi: 10.1016/S0025-5564(02)00108-6
[42]	J. C. Kamgang, G. Sallet, Computation of threshold conditions for epidemiological models and global stability of the disease-free equilibrium (DFE), Math. Biosci., 213 (2008), 1–12. https://doi.org/10.1016/j.mbs.2008.02.005 doi: 10.1016/j.mbs.2008.02.005

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