Local risk perception enhances epidemic control
Autoři:
José L. Herrera-Diestra aff001; Lauren Ancel Meyers aff004
Působiště autorů:
ICTP South American Institute for Fundamental Research, São Paulo, Brazil
aff001; IFT-UNESP, São Paulo, Brazil
aff002; CeSiMo, Facultad de Ingeniería, Universidad de Los Andes, Mérida, Venezuela
aff003; Department of Integrative Biology, The University of Texas at Austin, Austin, Texas, United States of America
aff004
Vyšlo v časopise:
PLoS ONE 14(12)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225576
Souhrn
As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decision-making in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population.
Klíčová slova:
Decision making – Epidemiology – Infectious disease control – Infectious disease epidemiology – Scale-free networks – Social epidemiology – Vaccination and immunization – Vaccines
Zdroje
1. Serpell L, Green J. Parental decision-making in childhood vaccination. Vaccine 2006; 24: 4041–4046. doi: 10.1016/j.vaccine.2006.02.037 16530892
2. Koh D, Takahashi K, Lim M-K, Imai T, Chia S-E, Qian F et al. SARS risk perception and preventive measures, Singapore and Japan. Emerg. Infect. Dis., 11 (4) (2005), pp. 641–642. doi: 10.3201/eid1104.040765 15834989
3. Bhattacharyya S, Bauch CT (2015) Parental Decisions Unfold in Layers during a Vaccine Scare: Insights from Measles Vaccine Uptake Data. JSM Math Stat 2(1): 1007.
4. National Public Health Information Coalition: Promote the importance of immunizations with this communications toolkit. From: https://www.nphic.org/niam. Accessed 8 October 2018.
5. Larson HJ, de Figueiredo A, Xiahong Z, Schulz WS, Verger P, Johnston IG, Jones NS (2016). The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey. EBioMedicine, 12, 295–301. http://doi.org/10.1016/j.ebiom.2016.08.042 27658738
6. Fifty-Sixth World Health Assembly. Agenda item 14.14. Available from: http://www.who.int/immunization/sage/1_WHA56_19_Prevention_and_control_of_influenza_pandemics.pdf. Accessed 8 September 2017.
7. Available from: https://www.cdc.gov/flu/fluvaxview/coverage-1516estimates.htm. Accessed 8 October 2018.
8. Hotez PJ. Texas and its measles epidemics. PLoS Med. 2016;13(10):e1002153. doi: 10.1371/journal.pmed.1002153 27780206
9. Omer SB, Enger KS, Moulton LH, Halsey NA, Stokley S, Salmon DA. Geographic clustering of nonmedical exemptions to school immunization requirements and associations with geographic clustering of pertussis. Am J Epidemiol. 2008;168(12):1389–1396. doi: 10.1093/aje/kwn263 18922998
10. National Conference of State Legislatures. States with religious and philosophical exemptions from school immunization requirements. http://www.ncsl.org/research/health/school-immunization-exemption-state-laws.aspx. Published August 23, 2016. Accessed November 16, 2017.
11. Larson HJ, Jarrett C, Eckersberger E, Smith DM, Paterson P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007-2012. Vaccine. 2014;32(19):2150–2159. doi: 10.1016/j.vaccine.2014.01.081 24598724
12. Report of the SAGE Working Group on Vaccine Hesitancy. World Health Organization. http://www.who.int/immunization/sage/meetings/2014/october/1_Report_WORKING_GROUP_vaccine_hesitancy_final.pdf. Published October 1, 2014. Accessed November 16, 2017.
13. Chapman GB, Coups EJ. Predictors of influenza vaccine acceptance among healthy adults. Prev. Med., 29 (1999), pp. 249–262. doi: 10.1006/pmed.1999.0535 10547050
14. American Academy of Pediatrics. Vaccine safety: examine the evidence. https://www.aap.org/en-us/Documents/immunization_vaccine_studies.pdf. Updated April 2013. Accessed November, 2017.
15. Jain A, Marshall J, Buikema A, Bancroft T, Kelly JP, Newschaffer CJ. Autism occurrence by MMR vaccine status among US children with older siblings with and without autism. JAMA. 2015;313(15):1534–1540. doi: 10.1001/jama.2015.3077 25898051
16. Taylor LE, Swerdfeger AL, Eslick GD. Vaccines are not associated with autism: an evidence-based meta-analysis of case-control and cohort studies. Vaccine. 2014;32(29):3623–3629. doi: 10.1016/j.vaccine.2014.04.085 24814559
17. Lau JT, Yang X, Pang E, Tsui HY, Wong E, Wing YK, SARS-related perceptions in Hong Kong. Emerg. Infect. Dis. 11 (3).
18. Philipson T, Private vaccination and public health: an empirical examination for U.S. measles. J. Hum. Resour. 31 (3), 1996. doi: 10.2307/146268
19. Ahituv A, Hotz VJ, Philipson T, The responsiveness of the demand for condoms to the local prevalence of AIDS. J. Hum. Resour. 31 (4), 1996. doi: 10.2307/146150
20. Funk S, Salathé M, Jansen VAA. Modeling the influence of human behaviour on the spread of infectious diseases: a review. J. R. Soc. Interface, 7 (50) (2010), pp. 1247–1256. doi: 10.1098/rsif.2010.0142 20504800
21. Durham DP, Casman EA. Incorporating individual health-protective decisions into disease transmission models: a mathematical framework. J. R. Soc. Interface, 9 (68) (2012), pp. 562–570. doi: 10.1098/rsif.2011.0325 21775324
22. De Zwart O, Veldhuijzen I, Elam G. Perceived threat, risk perception, and efficacy beliefs related to SARS and other (emerging) infectious diseases: results of an international survey. Int. J. Behav. Med., 16 (2009), pp. 30–40. doi: 10.1007/s12529-008-9008-2 19125335
23. Perra N, Balcan D, Gonçalves B, Vespignani A (2011) Towards a Characterization of Behavior-Disease Models. PLoS ONE 6(8): e23084. doi: 10.1371/journal.pone.0023084 21826228
24. Thomas EA, Goldstone SE (2011) Should I or shouldn’t I: Decision making, knowledge and behavioral effects of quadrivalent HPV vaccination in men who have sex with men. Vaccine 29: 570–6. doi: 10.1016/j.vaccine.2010.09.101 20950728
25. Hackett AJ (2008) Risk, its perception and the media: The MMR controversy. Community Pract 81: 22–5. 18655642
26. Brown KF, Kroll JS, Hudson MJ, Ramsay M, Green J, Long SJ et al. (2010) Factors underlying parental decisions about combination childhood vaccinations including MMR: A systematic review. Vaccine 28: 4235–48. doi: 10.1016/j.vaccine.2010.04.052 20438879
27. Samba E, Nkrumah F, Leke R (2004) Getting polio eradication back on track in Nigeria. N Engl J Med 350: 645–6. doi: 10.1056/NEJMp038210 14960740
28. Myers LB, Goodwin R (2011) Determinants of adults’ intention to vaccinate against pandemic swine flu. BMC Public Health 11: 15. doi: 10.1186/1471-2458-11-15 21211000
29. Fabry P, Gagneur A, Pasquier JC (2011) Determinants of A (H1N1) vaccination: Cross-sectional study in a population of pregnant women in Quebec. Vaccine 29: 1824–9. doi: 10.1016/j.vaccine.2010.12.109 21219988
30. Liu R, Wu J, Zhu H (2007) Media/psychological impact on multiple outbreaks of emerging infectious diseases. Comput Math Method M 8: 153–64. doi: 10.1080/17486700701425870
31. Cui J, Sun Y, Zhu H (2008) The impact of media on the control of infectious diseases. J Dyn Differ Equ 20: 31–53.J. doi: 10.1007/s10884-007-9075-0
32. Li Y, Cui J (2009) The effect of constant and pulse vaccination on SIS epidemic models incorporating media coverage. Commun Nonlinear Sci 14: 2353–65. doi: 10.1016/j.cnsns.2008.06.024
33. Tchuenche J, Dube N, Bhunu C (2011) The impact of media coverage on the transmission dynamics of human influenza. BMC Public Health 11: S5. 21356134
34. Cornforth DM, Reluga TC, Shim E, Bauch CT, Galvani AP, Meyers LA (2011) Erratic Flu Vaccination Emerges from Short-Sighted Behavior in Contact Networks. PLoS Comput Biol 7(1): e1001062. https://doi.org/10.1371/journal.pcbi.1001062 21298083
35. Fu F, Christakis NA and Fowler JH. (2017) Dueling biological and social contagions. Scientific Reports volume 7, Article number: 43634
36. Andrews MA, Bauch CT. The impacts of simultaneous disease intervention decisions on epidemic outcomes. J Theor Biol. 2016 Apr 21;395:1–10. doi: 10.1016/j.jtbi.2016.01.027 26829313
37. Fine P, Eames K, Heymann DL. “Herd Immunity”: A Rough Guide, Clinical Infectious Diseases, Volume 52, Issue 7, 1 April 2011, Pages 911–916, https://doi.org/10.1093/cid/cir007
38. John TJ and Samuel R. Herd Immunity and Herd Effect: New Insights and Definitions. European Journal of Epidemiology, Vol. 16, No. 7 (2000), pp. 601–606. doi: 10.1023/A:1007626510002
39. Yamin D, Jones FK, DeVincenzo JP, Gertler S, Kobiler O, Townsend JP, and Galvani AP (2016). Vaccination strategies against respiratory syncytial virus. Proceedings of the National Academy of Sciences of the United States of America, 113(46), 13239–13244. doi: 10.1073/pnas.1522597113 27799521
40. Chao DL, and Dimitrov DT (2016). Seasonality and the effectiveness of mass vaccination. Mathematical biosciences and engineering: MBE, 13(2), 249–59. doi: 10.3934/mbe.2015001 27105983
41. Ferrari MJ, Bansal S, Meyers LA, Bjornstad ON; Network frailty and the geometry of herd immunity; Proc R Soc B Biol Sci 2006, 273: 2743–2748. doi: 10.1098/rspb.2006.3636
42. Pastor-Satorras R, Vespignani A. (2002) Immunization of complex networks. Phys. Rev. E 65, 036104. doi: 10.1103/PhysRevE.65.036104
43. Pastor-Satorras R, Castellano C, Van Mieghem P, and Vespignani A. (2015) Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925. doi: 10.1103/RevModPhys.87.925
44. Goltsev AV, Dorogovtsev SN, Oliveira JG, and Mendes JFF. (2012) Localization and Spreading of Diseases in Complex Networks. Phys. Rev. Lett. 109, 128702. doi: 10.1103/PhysRevLett.109.128702 23006000
45. Meyers LA. (2007) Contact network epidemiology: Bond percolation applied to infectious disease prediction and control. Bulletin of the American Mathematical Society 44: 63–86. doi: 10.1090/S0273-0979-06-01148-7
46. Ndeffo Mbah ML, Liu J, Bauch CT, Tekel YI, Medlock J, Meyers LA, et al. (2012) The Impact of Imitation on Vaccination Behavior in Social Contact Networks. PLoS Comput Biol 8(4): e1002469. https://doi.org/10.1371/journal.pcbi.1002469 22511859
47. Herrera JL, Srinivasan R, Brownstein JS, Galvani AP, Meyers LA (2016) Disease Surveillance on Complex Social Networks. PLoS Comput Biol 12(7): e1004928. https://doi.org/10.1371/journal.pcbi.1004928 27415615
48. Bagnoli F Lió P, Sguanci L (2007) Risk perception in epidemic modeling. Phys. Rev. E 76, 061904. doi: 10.1103/PhysRevE.76.061904
49. Massaro E, Bagnoli F (2014) Epidemic spreading and risk perception in multiplex networks: A self-organized percolation method. Phys. Rev. E 90, 052817. doi: 10.1103/PhysRevE.90.052817
50. Rizzo A, Frasca M, and Porfiri M (2014) Effect of individual behavior on epidemic spreading in activity-driven networks. Phys. Rev. E 90, 042801. doi: 10.1103/PhysRevE.90.042801
51. Moinet A, Pastor-Satorras R, and Barrat A (2018) Effect of risk perception on epidemic spreading in temporal networks. Phys. Rev. E 97, 012313. doi: 10.1103/PhysRevE.97.012313 29448478
52. Meyers LA, Pourbohloul B, Newman MEJ., Skowronski DM, Brunham RC. (2005). Network theory and SARS: Predicting outbreak diversity. Journal of Theoretical Biology 232: 71–81. doi: 10.1016/j.jtbi.2004.07.026 15498594
53. Abbey H; An examination of the Reed-Frost theory of epidemics. Hum Biol 1952, 24:201–233. 12990130
54. Bansal S, Grenfell BT, Meyers LA. (2007). When individual behavior matters: homogeneous and network models in epidemiology. Journal of the Royal Society Interface 4: 879–891. doi: 10.1098/rsif.2007.1100
55. Newman MEJ, Strogatz SH, and Watts DJ (2001) Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64. doi: 10.1103/PhysRevE.64.026118
56. Barabási AL, Albert R (1999). Emergence of scaling in random networks. Science. 286 (5439): 509–512. doi: 10.1126/science.286.5439.509 10521342
57. Huai Y, Xiang N, Zhou L, Feng L, Peng Z, Chapman RS, et al. Incubation period for human cases of avian influenza A (H5N1) infection, China, Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 14, No. 11, November 2008.
58. De Serres G, Rouleau I, Hamelin M-E, Quach C, Skowronski D, Flamand L, et al. Contagious period for pandemic (H1N1) 2009, Emerging Infectious Diseases • www.cdc.gov/eid • Vol. 16, No. 5, May 2010.
59. Chowell G, Hengartner NW, Castillo-Chavez C, Fenimore PW, Hyman JM. The basic reproductive number of Ebola and the effects of public health measures: the cases of Congo and Uganda. J Theor Biol. 2004 Jul 7;229(1):119–26. doi: 10.1016/j.jtbi.2004.03.006 15178190
60. Althaus CL. Estimating the Reproduction Number of Ebola Virus (EBOV) During the 2014 Outbreak in West Africa. PLoS Currents. 2014;6. doi: 10.1371/currents.outbreaks.91afb5e0f279e7f29e7056095255b288 25642364
61. Kretzschmar M, Teunis PFM, Pebody RG (2010) Incidence and Reproduction Numbers of Pertussis: Estimates from Serological and Social Contact Data in Five European Countries. PLoS Med 7(6): e1000291. doi: 10.1371/journal.pmed.1000291 20585374
62. Wallinga J, Teunis P. Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures, American Journal of Epidemiology, Volume 160, Issue 6, 15 September 2004, Pages 509–516, https://doi.org/10.1093/aje/kwh255
63. Keeling MJ, Rohani P. Modeling Infectious Diseases in Humans and Animals (2008) Princeton University Press: 21.
64. Du Z, Zhang W, Zhang D, Yu S, and Hao Y. (2017). Estimating the basic reproduction rate of HFMD using the time series SIR model in Guangdong, China. PLoS ONE, 12(7), e0179623. http://doi.org/10.1371/journal.pone.0179623 28692654
65. Bacaer N, Abdurahman X, Ye J, Auger P. On the basic reproduction number R0 in sexual activity models for HIV/AIDS epidemics: example from Yunnan, China. Math Biosci Eng. 2007 Oct;4(4):595–607. doi: 10.3934/mbe.2007.4.595 17924713
66. Nsubuga RN, White RG, Mayanja BN, Shafer LA (2014) Estimation of the HIV Basic Reproduction Number in Rural South West Uganda: 1991–2008. PLoS ONE 9(1): e83778. https://doi.org/10.1371/journal.pone.0083778 24404138
67. Halloran ME. (2005). Secondary Attack Rate. In Encyclopedia of Biostatistics (eds P. Armitage and T. Colton).
68. Newman MEJ (2001), Ego-centered networks and the ripple effect. Social Networks 25, 83–95. doi: 10.1016/S0378-8733(02)00039-4
69. Cohen R, Havlin S, and ben-Avraham D. (2003) Efficient Immunization Strategies for Computer Networks and Populations. Phys. Rev. Lett. 91, 247901. doi: 10.1103/PhysRevLett.91.247901 14683159
70. Kitsak M, Gallos LK, Havlin S, Lijeros F, Muchnik L, Stanley HE, Makse HA. (2010) Identification of influential spreaders in complex networks, Nature 6, 888–893.
71. Christley RM, Pinchbeck GL, Bowers RG, Clancy D, French NP, Bennett R and Turner J. Infection in Social Networks: Using Network Analysis to Identify High-Risk Individuals, Am J Epidemiol 2005;162:1024–1031. doi: 10.1093/aje/kwi308 16177140
72. Taylor E, Atkins KE, Medlock J, Li M, Chapman GB, Galvani AP. Cross-Cultural Household Influence on Vaccination Decisions. Medical Decision Making Vol 36, Issue 7, pp. 844—853. First published date: June-17-2015.
Článek vyšel v časopise
PLOS One
2019 Číslo 12
- S diagnostikou Parkinsonovy nemoci může nově pomoci AI nástroj pro hodnocení mrkacího reflexu
- Je libo čepici místo mozkového implantátu?
- Pomůže v budoucnu s triáží na pohotovostech umělá inteligence?
- AI může chirurgům poskytnout cenná data i zpětnou vazbu v reálném čase
- Nová metoda odlišení nádorové tkáně může zpřesnit resekci glioblastomů
Nejčtenější v tomto čísle
- Methylsulfonylmethane increases osteogenesis and regulates the mineralization of the matrix by transglutaminase 2 in SHED cells
- Oregano powder reduces Streptococcus and increases SCFA concentration in a mixed bacterial culture assay
- The characteristic of patulous eustachian tube patients diagnosed by the JOS diagnostic criteria
- Parametric CAD modeling for open source scientific hardware: Comparing OpenSCAD and FreeCAD Python scripts
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy