Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources

Nikolaos Rachaniotis , Thomas K. Dasaklis , Costas Pappis

Journal of Systems Science and Systems Engineering ›› 2017, Vol. 26 ›› Issue (2) : 219 -239.

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Journal of Systems Science and Systems Engineering ›› 2017, Vol. 26 ›› Issue (2) : 219 -239. DOI: 10.1007/s11518-016-5327-z
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Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources

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Abstract

Infectious disease outbreaks occurred many times in the past and are more likely to happen in the future. In this paper the problem of allocating and scheduling limited multiple, identical or non-identical, resources employed in parallel, when there are several infected areas, is considered. A heuristic algorithm, based on Shih’s (1974) and Pappis and Rachaniotis’ (2010) algorithms, is proposed as the solution methodology. A numerical example implementing the proposed methodology in the context of a specific disease outbreak, namely influenza, is presented. The proposed methodology could be of significant value to those drafting contingency plans and healthcare policy agendas.

Keywords

Resource allocation / healthcare management / epidemics / heuristics

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Nikolaos Rachaniotis, Thomas K. Dasaklis, Costas Pappis. Controlling infectious disease outbreaks: A deterministic allocation-scheduling model with multiple discrete resources. Journal of Systems Science and Systems Engineering, 2017, 26(2): 219-239 DOI:10.1007/s11518-016-5327-z

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References

[1]

AlemanD. M., WibisonoT. G., SchwartzB.. A nonhomogeneous agent-based simulation approach to modeling the spread of disease in a pandemic outbreak. Interfaces, 2011, 41(3): 301-315

[2]

AlexanderM. E., MoghadasS. M., RöstG., WuJ.. A delay differential model for pandemic influenza with antiviral treatment. Bulletin of Mathematical Biology, 2008, 70(2): 382-397

[3]

BoëlleP. Y., AnsartS., CoriA., ValleronA. J.. Transmission parameters of the A/H1N1 (2009) influenza virus pandemic: a review. Influenza and other Respiratory Viruses, 2011, 5(5): 306-316

[4]

BrandeauM.. Allocating resources to control infectious diseases. Operations Research and Health Care, Vol., 2005, 70: 443-464

[5]

BretthauerK., ShettyB.. The nonlinear resource allocation problem. Operations Research, 1995, 43(4): 670-683

[6]

BurkeD. S., EpsteinJ. M., CummingsD. A. T., ParkerJ. I., ClineK. C., SingaR. M., ChakravartyS.. Individual-based computational modeling of smallpox epidemic control strategies. Academic Emergency Medicine, 2006, 13(11): 1142-1149

[7]

CarrS., RobertsS.Planning for infectious disease outbreaks: a geographic disease spread, clinic location, and resource allocation simulation, 20102171-2184

[8]

CarratF., LuongJ., LaoH., SalléA. V., LajaunieC., WackernagelH.. A ‘small-world-like’ model for comparing interventions aimed at preventing and controlling influenza pandemics. BMC Medicine, 2006, 4(1): 26

[9]

ChowellG., AmmonC. E., HengartnerN. W., HymanJ. M.. Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: assessing the effects of hypothetical interventions. Journal of Theoretical Biology, 2006, 241(2): 193-204

[10]

ChowellG., ViboudC., WangX., BertozziS. M., MillerM. A.. Adaptive vaccination strategies to mitigate pandemic influenza: Mexico as a case study. PLoS ONE, 2009, 4(12): e8164

[11]

Ciofi degli AttiM. L., MerlerS., RizzoC., AjelliM., MassariM., ManfrediP., FurlanelloC., TombaG. S., IannelliM.. Mitigation measures for pandemic influenza in Italy: an individual based model considering different scenarios. PLoS ONE, 2008, 3(3): 811-827

[12]

Coburn, B. J., Wagner, B. G. & Blower, S. (2009). Modeling influenza epidemics and pandemics: insights into the future of swine flu (H1N1). BMC Medicine, 7.

[13]

ColizzaV., BarratA., BarthelemyM., ValleronA. J., VespignaniA.. Modeling the worldwide spread of pandemic influenza: baseline case and containment interventions. PloS Medicine, 2007, 4(1): 0095-0110

[14]

CooperB. S., PitmanR. J., EdmundsW. J., GayN. J.. Delaying the international spread of pandemic influenza. PLoS Medicine, 2006, 3(6): 0845-0855

[15]

Cruz-Aponte, M., McKiernan, E. C. & Herrera-Valdez, M. A. (2011). Mitigating effects of vaccination on influenza outbreaks given constraints in stockpile size and daily administration capacity. BMC Infectious Diseases, 11.

[16]

DasT. K., SavachkinA., ZhuY.. A large-scale simulation model of pandemic influenza outbreaks for development of dynamic mitigation strategies. IIE Transactions (Institute of Industrial Engineers), 2008, 40(9): 893-905

[17]

Department of Health. (2005). Smallpox mass vaccination -an operational planning framework. Department of Health, Scottish Government. Retrieved June 11, 2013 from http://www.scotland.gov.uk/Publications/2005/09/20160232/02428.

[18]

EpsteinJ. M., GoedeckeD. M., YuF., MorrisR. J., WagenerD. K., BobashevG. V.. Controlling pandemic flu: the value of international air travel restrictions. PLoS ONE, 2007, 2(5): e401

[19]

EubankS., GucluH., KumarV. S. A., MaratheM. V., SrinivasanA., ToroczkaiZ., WangN.. Modelling disease outbreaks in realistic urban social networks. Nature, 2004, 429(6988): 180-184

[20]

FergusonN., KeelingM., EdmundsW., GaniR., GrenfellB., AndersonR., LeachS.. Planning for smallpox outbreaks. Nature, 2003, 425(6959): 681-685

[21]

FergusonN. M., CummingsD. A. T., CauchemezS., FraserC., RileyS., MeeyaiA., IamsirithawornS., BurkeD. S.. Strategies for containing an emerging influenza pandemic in Southeast Asia. Nature, 2005, 437(7056): 209-214

[22]

FlahaultA., VerguE., CoudevilleL., GraisR. F.. Strategies for containing a global influenza pandemic. Vaccine, 2006, 24(44-46): 6751-6755

[23]

GlasserJ., TaneriD., FengZ., ChuangJ. H., TüllP., ThompsonW., McCauleyM. M., AlexanderJ.. Evaluation of targeted influenza vaccination strategies via population modeling. PLoS ONE, 2010, 5(9): 1-8

[24]

HallI. M., EganJ. R., BarrassI., GaniR., LeachS.. Comparison of smallpox outbreak control strategies using a spatial metapopulation model. Epidemiology and Infection, 2007, 135(7): 1133-1144

[25]

HansenE., DayT.. Optimal control of epidemics with limited resources. Journal of Mathematical Biology, 2011, 62(3): 423-451

[26]

HethcoteH.. The mathematics of infectious diseases. SIAM Review, 2000, 42(4): 599-653

[27]

HollingsworthT. D., KlinkenbergD., HeesterbeekH., AndersonR. M.. Mitigation strategies for pandemic influenza a: balancing conflicting policy objectives. PLoS Computational Biology, 2011, 7(2): e1001076

[28]

HupertN., CuomoJ., CallahanM., MushlinA., MorseS.Community-Based Mass Prophylaxis: A Planning Guide for Public Health Preparedness, 2004

[29]

IbarakiT., KatohN.Resource Allocation Problems: Algorithmic Approaches, 1988

[30]

KaplanE. H., CraftD. L., WeinL. M.. Emergency response to a smallpox attack: the case for mass vaccination. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(16): 10935-10940

[31]

KaplanE. H., CraftD. L., WeinL. M.. Analyzing bioterror response logistics: the case of smallpox. Mathematical Biosciences, 2003, 185(1): 33-72

[32]

KoyuncuM., ErolR.. Optimal resource allocation model to mitigate the impact of pandemic influenza: a case study for Turkey. Journal of Medical Systems, 2010, 34(1): 61-70

[33]

KrumkampR., KretzschmarM., RudgeJ. W., AhmadA., HanvoravongchaiP., WestenhoeferJ., SteinM., PutthasriW., CokerR.. Health service resource needs for pandemic influenza in developing countries: a linked transmission dynamics, interventions and resource demand model. Epidemiology and Infection, 2011, 139(1): 59-67

[34]

LeeB. Y., BrownS. T., KorchG. W., CooleyP. C., ZimmermanR. K., WheatonW. D., ZimmerS. M., GrefenstetteJ. J., BaileyR. R., AssiT. M., BurkeD. S.. A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic. Vaccine, 2010, 28(31): 4875-4879

[35]

LeeS., GolinskiM., ChowellG.. Modeling optimal age-specific vaccination strategies against pandemic influenza. Bulletin of Mathematical Biology, 2012, 74(4): 958-980

[36]

MatrajtL., HalloranM. E., LonginiI. M.. Optimal vaccine allocation for the early mitigation of pandemic influenza. PLoS Computational Biology, 2013, 9(3): e1002964

[37]

MatrajtL., LonginiI. M.. Optimizing vaccine allocation at different points in time during an epidemic. PLoS ONE, 2010, 5(11): e13767

[38]

MbahM. L. N., GilliganC. A.. Resource allocation for epidemic control in metapopulations. PLoS ONE, 2011, 6(9): e24577

[39]

MedlockJ., GalvaniA. P.. Optimizing influenza vaccine distribution. Science, 2009, 325(5948): 1705-1708

[40]

Meyers, L., Galvani, A. & Medlock, J. (2009). Optimizing allocation for a delayed influenza vaccination campaign. PLoS Currents (DEC).

[41]

MjeldeK.. Discrete resource allocation by a branch and bound method. Journal of the Operational Research Society, 1978, 29(10): 1021-1023

[42]

MyliusS. D., HagenaarsT. J., LugnérA. K., WallingaJ.. Optimal allocation of pandemic influenza vaccine depends on age, risk and timing. Vaccine, 2008, 26(29-30): 3742-3749

[43]

NishiuraH., ChowellG.ChowellG., HymanJ., BettencourtL. A., Castillo-ChavezC.. The effective reproduction number as a prelude to statistical estimation of time-dependent epidemic trends. Mathematical and Statistical Estimation Approaches in Epidemiology, 2009103-121

[44]

OmpadD. C., GaleaS., VlahovD.. Distribution of influenza vaccine to high-risk groups. Epidemiologic Reviews, 2006, 28(1): 54-70

[45]

PappisC. P., RachaniotisN. P.. Scheduling in a multi-processor environment with deteriorating job processing times and decreasing values: the case of forest fires. Journal of Heuristics, 2010, 16(4): 617-632

[46]

R Core Team. (2013). R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

[47]

RachaniotisN. P., DasaklisT. K., PappisC. P.. A deterministic resource scheduling model in epidemic control: a case study. European Journal of Operational Research, 2012, 216(1): 225-231

[48]

RileyS., FraserC., DonnellyC. A., GhaniA. C., Abu-RaddadL. J., HedleyA. J., LeungG. M., HoL. M., LamT. H., ThachT. Q., ChauP., ChanK. P., LoS. V., LeungP. Y., TsangT., HoW., LeeK. H., LauE. M. C., FergusonN. M., AndersonR. M.. Transmission dynamics of the etiological agent of SARS in Hong Kong: impact of public health interventions. Science, 2003, 300(5627): 1961-1966

[49]

SamsuzzohaM., SinghM., LucyD.. A numerical study on an influenza epidemic model with vaccination and diffusion. Applied Mathematics and Computation, 2012, 219(1): 122-141

[50]

SamsuzzohaM., SinghM., LucyD.. Uncertainty and sensitivity analysis of the basic reproduction number of a vaccinated epidemic model of influenza. Applied Mathematical Modelling, 2013, 37(3): 903-915

[51]

SanderB., NizamA. G., JrL. P., PostmaM. J., HalloranM. E., LonginiI. M.. Economic evaluation of influenza pandemic mitigation strategies in the United States using a stochastic microsimulation transmission model. Value in Health, 2009, 12(2): 226-233

[52]

ShihW.. A new application of incremental analysis in resource allocations. Operational Research Quarterly, 1974, 25(4): 587-597

[53]

ShihW.. Branch and bound procedure for a class of discrete resource allocation problems with several constraints. Operational Research Quarterly, 1977, 28(2): 439-451

[54]

Stein, M. L., Rudge, J. W., Coker, R., Weijden, C., Krumkamp, R., Hanvoravongchai, P., Chavez, I., Putthasri, W., Phommasack, B., Adisasmito, W., Touch, S., Sat, L. M., Hsu, Y. C., Kretzschmar, M. & Timen, A. (2012). Development of a resource modelling tool to support decision makers in pandemic influenza preparedness: the AsiaFluCap Simulator. BMC Public Health: 870.

[55]

TuiteA. R., FismanD. N., KwongJ. C., GreerA. L.. Optimal pandemic influenza vaccine allocation strategies for the Canadian population. PLoS ONE, 2010, 5(5): e10520

[56]

Uribe-SánchezA., SavachkinA., SantanaA., Prieto-SantaD., DasT. K.. A predictive decision-aid methodology for dynamic mitigation of influenza pandemics. OR Spectrum, 2011, 33(3): 751-786

[57]

Van Der WeijdenC. P., SteinM. L., JacobiA. J., KretzschmarM. E. E., ReintjesR. v., SteenbergenJ. E., TimenA.. Choosing pandemic parameters for pandemic preparedness planning: a comparison of pandemic scenarios prior to and following the influenza A(H1N1) 2009 pandemic. Health Policy, 2013, 109(1): 52-62

[58]

WallingaJ., Van BovenaM., LipsitchM.. Optimizing infectious disease interventions during an emerging epidemic. Proceedings of the National Academy of Sciences of the United States of America, 2010, 107(2): 923-928

[59]

WichmannO., StöckerP., PoggenseeG., AltmannD., WalterD., HellenbrandW., KrauseG., EckmannsT.. Pandemic influenza A(H1N1) 2009 breakthrough infections and estimates of vaccine effectiveness in Germany 2009-2010. Eurosurveillance, 2010, 15(18): 1-4

[60]

YaesoubiR., CohenT.. Dynamic health policies for controlling the spread of emerging infections: influenza as an example. PLoS ONE, 2011, 6(9): e24043

[61]

Yang, Y., Atkinson, P. M. & Ettema, D. (2011). Analysis of CDC social control measures using an agent-based simulation of an influenza epidemic in a city. BMC Infectious Diseases, 11.

[62]

YarmandH., IvyJ. S., DentonB., LloydA. L.. Optimal two-phase vaccine allocation to geographically different regions under uncertainty. European Journal of Operational Research, 2014, 233(1): 208-219

[63]

ZhouL., FanM.. Dynamics of an SIR epidemic model with limited medical resources revisited. Nonlinear Analysis: Real World Applications, 2012, 13(1): 312-324

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