Siettos K

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Full Terms & Conditions of access and use can be found athttps://www.tandfonline.com/action/journalInformation?journalCode=kvir20VirulenceISSN: 2150-5594 (Print) 2150-5608 (Online) Journal homepage: https://www.tandfonline.com/loi/kvir20Editorial: Mathematical modeling of infectiousdisease dynamicsConstantinos I SiettosTo cite this article: Constantinos I Siettos (2016) Editorial: Mathematical modeling of infectiousdisease dynamics, Virulence, 7:2, 119-120, DOI: 10.1080/21505594.2016.1150402To link to this article:  https://doi.org/10.1080/21505594.2016.1150402© 2016 Taylor and Francis Group, LLCPublished online: 23 Mar 2016.Submit your article to this journal Article views: 819View related articles View Crossmark dataCiting articles: 1 View citing articles Editorial: Mathematical modeling of infectiousdisease dynamicsConstantinos I Siettos*School of Applied Mathematics and Physical Sciences; National Technical University of Athens; Athens, GreeceIntroduction to the Special IssueEpidemics have left their own stamp onmankind’s history. Civilizations have beenwiped off the face of the earth through theages. The list is long: biblical pharaonicplagues which hit Ancient Egypt in themiddle of the Bronze Age around1715 B.C.; the “loimosB in Athens from430 to 425 B.C., which set the end of thePericlean golden era; the Black DeathBubonic Plague (1348–1353), which isestimated to have killed over 25 millionpeople in Europe alone; and the pandemicinfluenza of 1918–1919 which sweptthrough the globe, leaving a death-toll ofaround 40 million people.1In recent decades, emerging and re-emerging epidemics such as AIDS, mea-sles (one of the leading causes of deathamong children), malaria, and tuberculo-sis have killed millions of people eachyear. Today, the UNAIDS reports around37 million people living with HIV.WHO reported around 440,000 deathsfrom malaria in 2015; related deaths frommeasles were about 314 every day, while1.5 million died from tuberculosis.Recently, the Ebola epidemic devastatedpopulations in the countries of WestAfrica, leaving more than 11,300 dead.2In this battle, mathematical modelsand systems theory enhance our arsenal incombating epidemics. Recent advances indifferent fields ranging from computa-tional science and engineering to biologyand epidemiology have advanced ourknowledge, such as the molecular struc-ture of a variety of viruses and the impactof network transmission properties on theevolution of epidemics. State-of-the-artcomputational techniques allow the effi-cient extraction of important informationfrom very large databases (VLDB) includ-ing publicly available biological, transpor-tation, sociological, demographical, andepidemiological data, which in turn helpus construct more realistic models thatshed light into the mechanisms that gov-ern the outbreaks.1 Thus, public authori-ties take a closer look at the use ofmathematical models to make decisionsand to design efficient interventions.Brief review of ContributionsThis special issue of Virulence containseight high-quality contributions fromresearchers worldwide that discuss a vari-ety of topics ranging from agent-basedmodeling of infectious diseases to immu-nology, to hospital-acquired infections tosocial diseases such as obesity. All papersunderwent a very careful peer review pro-cess and were revised to accommodatecomments made by the reviewers. Specialattention was given to the selection of theappropriate reviewers. The choice wasbased on strict academic criteria includingexpertise in the field and international rep-utation. In what follows, I briefly reviewthe published contributions in alphabeti-cal order with respect to the name of thefirst author.Aquino and Nunes3 investigated influ-enza A phylogeny dynamics’s rapidmutationof the pathogen in a host population with aweak immune response. Their model is adetailed stochastic individualistic one withinthe allowing for reinfection by the samestrain. Each viral strain was characterized byseveral epitopes and infected persons devel-oped only one type of antibody. Protectionfrom the infection was analogous to thenumber of matching antibodies for the anti-genic sites of the strain. The results underpinthe importance of developing multiscalemodels that can approximate the dynamicsranging from the response of the immunesystem to phenotypic heterogeneity (host-pathogen interactions) to the contact trans-mission network (host-host interactions) forunderstanding the epidemic evolution offast-mutating viruses such as influenza A.Demongot et al.4 developed a socialnetwork model based on preferentialattachment to investigate the dynamics ofobesity. Under this framework obesity isregarded as a contagious “social” diseasewhere individuals tend to create sociallinks and behave similarly to other indi-viduals with analogous attributes and hab-its while they cut links with individualswith different habits. Thus, obesity propa-gates as a disease from individual to indi-vidual through the social network. Theirmodel is based on demographic data con-sidering age and sex distributions, mortal-ity, fertility, composition of families, aswell as socio-economic factors.Doan et al.5 investigated the efficacy ofinfection control interventions against Aci-netobacter baumannii, a common factor ofhospital-related infections. In particular,the authors propose a compartmentalmodel to evaluate “what if” scenarios ofdifferent interventions for reducing A.baumannii prevalence in intensive careunits (ICUs). The model describes the*Correspondence to Constantinos I Siettos; Email ksiet@mail.ntua.grSubmitted: 01/31/2016; Accepted: 01/31/2016http://dx.doi.org/10.1080/21505594.2016.1150402www.tandfonline.com 119VirulenceVirulence 7:2, 119--120; February/March 2016; © 2016 Taylor & Francis Group, LLCEDITORIALprobabilities of infection and colonizationwith or without antibiotic exposure fromfree-living bacteria in the environment.Sensitivity analysis to changes in themodel parameters is also provided. Theirstudy addresses important insights fordesigning effective infection control inter-ventions, emphasis of the importance ofeffective antimicrobial stewardship pro-grams to reduce the duration of antibiotictherapy, as well as environmental cleaning.Kattis et al.6 proposes a systematic datamining-assisted framework to extractdominant collective statistics fromdetailed epidemic data. The paradigm isan individualistic Susceptible-Infected-Susceptible (SIS) model evolving on adap-tively changing networks. The cornerstoneof the proposed methodology is the equa-tion-free multiscale modeling approachthat can be exploited to extract “ondemand” useful macroscopic informationfrom detailed individualistic simulations/data. This allows a reduction of the high-dimensional phase-space of epidemicdynamics to a low-dimensional space thatit is spanned by a few macroscopic observ-ables. Bridging the equation-free approachwith Diffusion Maps (DMAPS), a state-of-the-art learning manifold technique,allows for the determination of the macro-scopic variables which shape the evolutionof the emergent epidemic dynamics.Kiskowski and Chowell7 developed adetailed agent-based model to approxi-mate the dynamics of the Ebola virus epi-demic that has ravaged West Africapopulations in the past two years. Particu-lar emphasis was given on the impact ofthe spatial constrained contact socialstructures in the disease transmissiondynamics. The proposed model incorpo-rates a network of households and com-munities of different sizes. Simulationresults showed good agreement with theepidemic growth observed in Guinea,Liberia, and Sierra Leone. Their analysisunderscores the importance of the spatialcontact structure in the spread and controlof the epidemic.Reppas et al.8 studied the interplaybetween the dynamics of vascularizedtumor growth and the associated immunesystem response in favor of the design ofeffective immunomodulatory therapies.Their analysis is based on a mean-fieldmodel that approximates the dynamics ofvascularized tumor growth as well as thecorresponding effector cell recruitmentdynamics. The authors performed a sys-tematic analysis of a model behavior withthe aid of bifurcation theory. By doing so,they determine the parameter regimes thatallow an immune-mediated tumor controlin terms of an external modification ofeffector cell dynamics.Sardar et al.9 developed a two strainmodel to approximate the dynamics ofdengue infection. Since 2002, dengue isan endemic disease in southeast Asia rav-aging the population with two seasonalpeaks per year. The authors exploit themodel to estimate the reproduction num-bers in two provinces of Sri Lanka duringthe period 2013–14. Seasonality wasincorporated in terms of periodicity in themosquito biting rate. Using univariatesensitivity analysis, the authors alsoassessed the sensitivity of the reproductionnumber with respect to various modelparameters such as the transmission prob-ability and the mosquito mortality rate.Scott et al.10 provide a review on math-ematical modeling approaches to hepatitisC virus (HCV) epidemic. The virus wasfirst isolated in 1989 and since then HCVtransmission dynamics has been the topicof many modeling studies. The reviewfocuses on the transmission between peo-ple who inject drugs (PWID), detailingassumptions, the way through which thevirus is transmitted, as well as the limita-tions of the modeling approaches. Thereview ranges from mean field Susceptible-Infected-Recovered (SIR) models withtreatment policies to individualistic net-worked models. Future challenges pin-pointing the need for developing strainmodels are also given.Concluding RemarksModern epidemiological research inte-grates advances from interdisciplinaryfields ranging from molecular biology andepidemiology to sociology and appliedmathematics, facilitating the design andimplementation of better public healthstrategies to combat epidemics. However,it should be stressed that the usefulness ofmathematical models should not be over-estimated. Despite huge technologicalprogress and concentrated wealth, break-downs and cuttings in public health infra-structure continue to cause rapidimpoverishment in developing countries,and remain the major reason of epidemicspread.AcknowledgmentsI gratefully acknowledge the authorsfor their generous contributions, the refer-ees for taking the time and effort toreview the papers in detail, the Editor-in-Chief of Virulence Professor EleftheriosMylonakis for inviting me as a guest editorof this special issue, and also the editorialteam of Taylor and Francis: Adam Weiss,Zachary Ayres, and David Penyak, fortheir help and patience throughout theprocess.References1. Siettos CI, Russo L. Mathematical modeling of infec-tious disease dynamics: a review. Virulence 2013; 4(4):295-306.2. CDC - National Center for Health Statistics - Home-page. http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/case-counts.html3. Aquino T, Nunes A. Host immunity and pathogendiversity: a computational study. Virulence 2016; 7(2):121-128.4. Demongeot J, Hansen O, Taramasco C. Discretedynamics of contagious social diseases: Example of obe-sity. Virulence 2016; 7(2):129-140.5. Doan TN, Kong DCM, Marshall C, Kirkpatrick CMJ,McBryde ES. Modeling the impact of interventionsagainst Acinetobacter baumannii transmission in inten-sive care units. Virulence 2016; 7(2):141-152.6. Kattis AA, Holiday A, Stoica A-A, Kevrekidis IG. Model-ing epidemics on adaptively evolving networks: a data-mining perspective. Virulence 2016; 7(2):153-162.7. Kiskowski M, Chowell G. Modeling household andcommunity transmission of Ebola virus disease: epi-demic growth, spatial dynamics and insights for epi-demic control. Virulence 2016; 7(2):163-173.8. Reppas AI, Alfonso JCL, Hatzikirou H. In silico tumorcontrol induced via alternating immunostimulatingand immunosuppressive phases. Virulence 2016; 7(2):174-86.9. Sardar T, Sasmal S, Chattopadhyay J. Estimating den-gue type reproduction numbers for two provinces of SriLanka during the period 2013–14. Virulence 2016; 7(2):187-200.10. Scott N, Hellard M, McBryde ES. Modeling hepatitisC virus transmission in developed settings: assump-tions, limitations and future challenges. Virulence2016; 7(2):201-208.120 Volume 7 Issue 2Virulence

Editorial: Mathematical modeling of infectious disease dynamics

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Editorial: Mathematical modeling of infectious disease dynamics

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