Optimal time-profiles of public health intervention to shape voluntary vaccination for childhood diseases

被引:17
作者
Buonomo, Bruno [1 ]
Manfredi, Piero [2 ]
d'Onofrio, Alberto [3 ]
机构
[1] Univ Naples Federico II, Dept Math & Applicat, Via Cintia, I-80126 Naples, Italy
[2] Univ Pisa, Dept Econ & Management, Via Ridolfi 1, I-56124 Pisa, Italy
[3] Int Prevent Res Inst, 95 Cours Lafayette, F-69006 Lyon, France
关键词
Vaccination; Human behavior; Public health system; Communication; Optimal control; Simulated annealing; Forward-backward sweep method; INFECTIOUS-DISEASES; TRANSMISSION DYNAMICS; BACKWARD BIFURCATION; CONTROL STRATEGY; SIR MODEL; OPTIMIZATION; EPIDEMIC; BEHAVIOR; PREVENTION; IMPACT;
D O I
10.1007/s00285-018-1303-1
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
In order to seek the optimal time-profiles of public health systems (PHS) Intervention to favor vaccine propensity, we apply optimal control (OC) to a SIR model with voluntary vaccination and PHS intervention. We focus on short-term horizons, and on both continuous control strategies resulting from the forward-backward sweep deterministic algorithm, and piecewise-constant strategies (which are closer to the PHS way of working) investigated by the simulated annealing (SA) stochastic algorithm. For childhood diseases, where disease costs are much larger than vaccination costs, the OC solution sets at its maximum for most of the policy horizon, meaning that the PHS cannot further improve perceptions about the net benefit of immunization. Thus, the subsequent dynamics of vaccine uptake stems entirely from the declining perceived risk of infection (due to declining prevalence) which is communicated by direct contacts among parents, and unavoidably yields a future decline in vaccine uptake. We find that for relatively low communication costs, the piecewise control is close to the continuous control. For large communication costs the SA algorithm converges towards a non-monotone OC that can have oscillations.
引用
收藏
页码:1089 / 1113
页数:25
相关论文
共 77 条
[1]   Comparing large-scale computational approaches to epidemic modeling: Agent-based versus structured metapopulation models [J].
Ajelli, Marco ;
Goncalves, Bruno ;
Balcan, Duygu ;
Colizza, Vittoria ;
Hu, Hao ;
Ramasco, Jose J. ;
Merler, Stefano ;
Vespignani, Alessandro .
BMC INFECTIOUS DISEASES, 2010, 10
[2]  
ANDERSON R M, 1991
[3]  
Andersson H., 2012, Stochastic epidemic models and their statistical analysis
[4]  
Ania S., 2011, An Introduction to Optimal Control Problems in Life Sciences
[5]  
[Anonymous], 1998, EVOLUTIONARY GAMES P
[6]  
Asano E, 2008, MATH BIOSCI ENG, V5, P219
[7]  
Banga JR, 1996, NONCON OPTIM ITS APP, V7, P563
[8]   Imitation dynamics predict vaccinating behaviour [J].
Bauch, CT .
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2005, 272 (1573) :1669-1675
[9]   Perspectives on optimal control of varicella and herpes zoster by mass routine varicella vaccination [J].
Betta, Monica ;
Laurino, Marco ;
Pugliese, Andrea ;
Guzzetta, Giorgio ;
Landi, Alberto ;
Manfredi, Piero .
PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, 2016, 283 (1826)
[10]   Backward Bifurcation and Optimal Control in Transmission Dynamics of West Nile Virus [J].
Blayneh, Kbenesh W. ;
Gumel, Abba B. ;
Lenhart, Suzanne ;
Clayton, Tim .
BULLETIN OF MATHEMATICAL BIOLOGY, 2010, 72 (04) :1006-1028