A complete categorization of multiscale models of infectious disease systems

Garira, Winston

doi:10.1080/17513758.2017.1367849

Cited by 51 publications

(114 citation statements)

References 117 publications

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“… The mathematical technology or technical frontier : While the importance of multiscale modelling in disease dynamics is increasingly recognized, there is little underpinning literature on the mathematical methods and generic descriptions of multiscale models of infectious disease dynamics. Efforts to develop multiscale models of infectious disease dynamics have recently been underpinned by a categorization framework that helps to identify the different categories of multiscale models that can be developed . Given this foundation, the frontiers of multiscale modelling of infectious disease dynamics lie more on the technical side—to find methods for developing multiscale models that belong to the different categories.…”

Section: Introductionmentioning

confidence: 99%

“…Efforts to develop multiscale models of infectious disease dynamics have recently been underpinned by a categorization framework that helps to identify the different categories of multiscale models that can be developed. 27 Given this foundation, the frontiers of multiscale modelling of infectious disease dynamics lie more on the technical side-to find methods for developing multiscale models that belong to the different categories. To advance this frontier, we develop a new method for developing multiscale models of acute viral infections using IAV as a paradigm in which a new set of metrics to measure both individual-level infectiousness and community-level infectiousness are presented.…”

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confidence: 99%

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Development and application of multiscale models of acute viral infections in intervention research

Garira

Mathebula

2020

Math Methods in App Sciences

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The objective of this study is to advance two frontiers in multiscale modelling of acute viral infections, which are (a) the mathematical technology or technical frontier, where we present a new method for development of multiscale models of acute viral infections using influenza A virus (IAV) as a paradigm in which a new set of metrics to measure both individual level and community level infectiousness are introduced, and (b) the scientific applications frontier, where we demonstrate the implementation of multiscale modelling in evaluating the comparative effectiveness of IAV health interventions from efficacy data. The multiscale model is developed by integrating the within‐host scale and the between‐host scale. Using the example of IAV as a paradigm, we demonstrate the utility and process by which multiscale modelling can be used to evaluate the comparative effectiveness of health interventions that operate at different scale domains. The multiscale modelling is general enough to be applicable to other acute viral infections.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Development and application of multiscale models of acute viral infections in intervention research

Garira

Mathebula

2020

Math Methods in App Sciences

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“…Furthermore, the mechanisms and processes (transmission, replication, pathogen shedding, infection) for infectious disease systems can be modeled for each hierarchical level. These can then be coupled via bridges between the microscale and the macroscale, leading to similar categories of multiscale models which include individual-based multiscale models (IMSMs), nested multiscale models (NMSMs), embedded multiscale models (EMSMs) and hybrid multiscale models (HMSMs) (Garira 2017(Garira , 2018Feng et al 2012;Murillo et al 2013;Gandolfi et al 2014;Yu and Bagheri 2016). Agent(individual)-based models (ABMs or IBMs) are particularly well suited to characterize biological phenomena in a multiscale, multiclass manner.…”

Section: Introductionmentioning

confidence: 99%

Coupling the Macroscale to the Microscale in a Spatiotemporal Context to Examine Effects of Spatial Diffusion on Disease Transmission

et al. 2020

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There are many challenges to coupling the macroscale to the microscale in temporal or spatial contexts. In order to examine effects of an individual movement and spatial control measures on a disease outbreak, we developed a multiscale model and extended the semi-stochastic simulation method by linking individual movements to pathogen's diffusion, linking the slow dynamics for disease transmission at the population level to the fast dynamics for pathogen shedding/excretion at the individual level. Numerical simulations indicate that during a disease outbreak individuals with the same infection status show the property of clustering and, in particular, individuals' rapid movements lead to an increase in the average reproduction number R 0 , the final size and the peak value of the outbreak. It is interesting that a high level of aggregation the individuals' movement results in low new infections and a small final size of the infected population. Further, we obtained that either high diffusion rate of the pathogen or frequent environmental clearance lead to a decline in the total number of infected individuals, indicating the need for control measures such as improving air circulation or environmental hygiene. We found that the level of spatial heterogeneity when implementing control greatly affects the control efficacy, and in particular, an uniform isolation strategy leads to low a final size and small peak, compared with local measures, indicating that a large-scale isolation strategy with frequent clearance of the environment is beneficial for disease control.

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“…The advantage of this approach is the possibility to integrate available data and knowledge across multiple scales of description. In this context, collected data was used to calibrate several multi-scale models of the foot-and-mouth epidemic [31]. Multi-scale models provide a rich description of the infection dynamics at the individual and environment levels than multi-agent models [32].…”

Section: Introductionmentioning

confidence: 99%

A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions

Bouchnita¹,

Jebrane²

2020

Preprint

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that emerged in Wuhan, China in December 2019. It has caused a global outbreak which represents a major threat to global health. Public health resorted to non-pharmaceutical interventions such as social distancing and lockdown to slow down the spread of the pandemic. However, the effect of each of these measures remains hard to quantify. We design a multi-scale model that simulates the transmission dynamics of COVID-19. We describe the motion of individual agents using a social force model. Each agent can be either susceptible, infected, quarantined, immunized or deceased. The model considers both mechanisms of direct and indirect transmission. We parameterize the model to reproduce the early dynamics of disease spread in Italy. We show that panic situations increase the risk of infection transmission in crowds despite social distancing measures. Next, we suggest that pre-symptomatic transmission could be a significant driver of the infection spread. Then, we show that the restricted movement of the individuals flattens the epidemic curve. Finally, model predictions suggest that measures stricter than social distancing and lockdown were used to control the epidemic in Wuhan, China. : medRxiv preprint human-to-human transmission. The rest of the interventions like the disinfection of public areas aim to reduce the chances of indirect transmission. While all of these measures slow down the propagation of the epidemic [7], the impact of each of these NPIs on the epidemic dynamics of COVID-19 remains hard to quantify.Mathematical modelling can be used to gain a deeper understanding of the transmission dynamics of infectious diseases. Compartmental models provide a theoretical framework to study disease transmission in a population of individuals [8]. These compartmental models can be implemented using a wide range of modelling methods. The simplest form of compartmental models uses ordinary differential equations to describe population dynamics during an epidemic. Susceptible-infected-recovered (SIR) models are one of the most commonly used compartmental models [9]. They describe the evolution of the number of infected individuals in a closed population. A recently published SIR model was used to quantify the effect of quarantine on the spread of COVID-19 [10]. Another class of widely used models consider four population classes: susceptible-exposed-infectious-recovered (SEIR) models [11]. These models are most suitable to describe the spread of diseases with a long incubation period, which is the case of the COVID-19 outbreak. In this context, an SEIR was recently developed and used to conduct a data-driven analysis of COVID-19 spread [12]. Other recent studies used SEIR models to estimate the basic reproduction number of this pandemic [13,14]. A more complex compartmental study was used to evaluate the capacity of the healthcare system in responding to . The impact of the control measures that were adopted to mitigate the COVID-19 o...

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A complete categorization of multiscale models of infectious disease systems

Cited by 51 publications

References 117 publications

Development and application of multiscale models of acute viral infections in intervention research

Development and application of multiscale models of acute viral infections in intervention research

Coupling the Macroscale to the Microscale in a Spatiotemporal Context to Examine Effects of Spatial Diffusion on Disease Transmission

A hybrid multi-scale model of COVID-19 transmission dynamics to assess the potential of non-pharmaceutical interventions

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