Mathematical modeling of infectious disease dynamics

Siettos, Constantinos; Russo, Lucia

doi:10.4161/viru.24041

Cited by 364 publications

(320 citation statements)

References 127 publications

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“…Dynamic Bayesian networks constitute another modeling technique that can incorporate data autocorrelation effects [74,45,60,77,64]. Bayesian networks represent probabilistic dependencies between variables via a graph.…”

Section: Machine Learning-based and Case-based Methodsmentioning

confidence: 99%

Long-Term Forecasting of Influenza-Like Illnesses in Russia

Kondratyev

Цыбалова²

2013

Int. J. of Pure and Appl. Math.

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This paper compares the feasible methods for the long-term forecasting of the incidence rates of influenza-like illnesses (ILI) and acute respiratory infections (ARI), which is important for strategic management. A literature survey shows that the most appropriate techniques for long-term ILI & ARI morbidity projections are the following well-known statistical methods: simple averaging of observations, point-to-point linear estimates, Serfling-type regression models, autoregressive models such as autoregressive integrated moving average (ARIMA) models, and generalized exponential smoothing using the Holt-Winters approach. Using these methods and official data on the total number of ILI & ARI cases per week in 2000-2012 in Moscow, St. Petersburg, Novosibirsk, Yekaterinburg, Nizhny Novgorod and Yakutsk, we developed oneyear projections and evaluated their accuracy. Different methods yielded the best results, depending on the time series. Generally, it is preferable to use the Serfling model. The Serfling model forecasts almost matched the pointto-point linear estimates. In certain cases, ARIMA outperformed the Serfling model. Simple averaging can ensure a fairly good prediction when the ILI & ARI time series do not exhibit a trend. The results of exponential smoothing were poorer than those of other techniques.

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Section: Machine Learning-based and Case-based Methodsmentioning

confidence: 99%

Long-Term Forecasting of Influenza-Like Illnesses in Russia

Kondratyev

Цыбалова²

2013

Int. J. of Pure and Appl. Math.

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“…Such SI-models evolved from ecological population dynamics, e.g. food-chain [74] (predator, prey, and top-predator), first used for infectious diseases (reviewed in [75]) then eventually for viruses as early as 1996 by Nowak and Bangham [76]. In 1995, Gatenby modeled cancer as a population competing with normal cells [77].…”

Section: Mathematical Modeling Of Infection: Susceptible and Infectedmentioning

confidence: 99%

Fighting Cancer with Mathematics and Viruses

Santiago¹,

Heidbuechel²,

Kandell³

et al. 2017

Preprint

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After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex anti-tumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these fields of research for more efficient development and effective treatments.

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“…Three are the main categories encompassing mathematical modeling [1]. The statistical methods deal with real epidemics.…”

Section: Mathematical Modellingmentioning

confidence: 99%

“…History tells us the terrible impact of biblical plagues in Ancient Egypt, bubonic plague in Europe in the Middle Age, influenza at the beginning of the twentieth century or AIDS the more recently emerging pandemics [1]. Mathematical modeling is playing a very important role to assess and control the potential outbreaks [2].…”

Section: Introductionmentioning

confidence: 99%

The Susceptible-Infectious Model of Disease Expansion Analyzed Under the Scope of Connectivity and Neighbor Rules

Pont

Mora

Castillo

2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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This paper presents a model to approach the dynamics of infectious diseases expansion. Our model aims to establish a link between traditional simulation of the Susceptible-Infectious (SI) model of disease expansion based on ordinary differential equations (ODE), and a very simple approach based on both connectivity between people and elementary binary rules that define the result of these contacts.The SI deterministic compartmental model has been analysed and successfully modelled by our method, in the case of 4-connected neighbourhood.

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Mathematical modeling of infectious disease dynamics

Cited by 364 publications

References 127 publications

Long-Term Forecasting of Influenza-Like Illnesses in Russia

Long-Term Forecasting of Influenza-Like Illnesses in Russia

Fighting Cancer with Mathematics and Viruses

The Susceptible-Infectious Model of Disease Expansion Analyzed Under the Scope of Connectivity and Neighbor Rules

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