scholarly journals How disease risk awareness modulates transmission: coupling infectious disease models with behavioral dynamics

2021 ◽  
Author(s):  
Jaime Cascante-Vega ◽  
Samuel Torres-Florez ◽  
Juan Cordovez ◽  
Mauricio Santos-Vega
Keyword(s):  
Steady State ◽  
Disease Risk ◽  
Mechanistic Modeling ◽  
Transmission Dynamics ◽  
Epidemiological Models ◽  
Response Dynamics ◽  
Modeling Framework ◽  
Sis Model ◽  
Behavioral Dynamics ◽  
General Terms

Epidemiological models often assume that individuals do not change their behavior or that those aspects are implicitly incorporated in parameters in the models. Typically these assumption is included in the contact rate between infectious and susceptible individuals. For example models incorporate time variable contact rates to account for the effect of behavior or other interventions than in general terms reduce transmission. However, adaptive behaviors are expected to emerge and to play an important role in the transmission dynamics across populations. Here, we propose a theoretical framework to couple transmission dynamics with behavioral dynamics due to infection awareness. We first model the dynamics of social behavior by using a game theory framework. Then we coupled the model with an epidemiological model that captures the disease dynamics by assuming that individuals are more aware of that epidemiological state (i.e. fraction of infected individuals) and reduces their contacts. Our results from a mechanistic modeling framework show that as individuals increase their awareness the steady-state value of the final fraction of infected individuals in a susceptible-infected-susceptible (SIS) model decreases. We also extend our results to a spatial framework, incorporating a spatially-defined theoretical contact network (social network) and we made the awareness parameter dependent on a global or local contact structure. Our results show that even when individuals increase their awareness of the disease, the spatial structure itself defines the steady state solution of the system, in which more connected networks (networks with random or constant degree distributions) results in a population with no change in their behavior. Our work then shows that explicitly incorporating dynamics about the behavioral response dynamics might significantly change the predicted course of the epidemic and therefore highlights the importance of accounting for this source of variation in the epidemiological models.

scholarly journals How disease risk awareness modulates transmission: coupling infectious disease models with behavioural dynamics

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Jaime Cascante-Vega ◽  
Samuel Torres-Florez ◽  
Juan Cordovez ◽  
Mauricio Santos-Vega
Keyword(s):  
Steady State ◽  
Disease Risk ◽  
Mechanistic Model ◽  
Transmission Dynamics ◽  
Sis Model ◽  
Final Fraction ◽  
Adaptive Behaviours ◽  
Parameter Dependent ◽  
Behavioural Dynamics ◽  
Theory Framework

Epidemiological models often assume that individuals do not change their behaviour or that those aspects are implicitly incorporated in parameters in the models. Typically, these assumptions are included in the contact rate between infectious and susceptible individuals. However, adaptive behaviours are expected to emerge and play an important role in the transmission dynamics across populations. Here, we propose a theoretical framework to couple transmission dynamics with behavioural dynamics due to infection awareness. We modelled the dynamics of social behaviour using a game theory framework, which is then coupled with an epidemiological model that captures the disease dynamics by assuming that individuals are aware of the actual epidemiological state to reduce their contacts. Results from the mechanistic model show that as individuals increase their awareness, the steady-state value of the final fraction of infected individuals in a susceptible-infected-susceptible (SIS) model decreases. We also incorporate theoretical contact networks, having the awareness parameter dependent on global or local contacts. Results show that even when individuals increase their awareness of the disease, the spatial structure itself defines the steady state.

scholarly journals Linking mosquito surveillance to dengue fever through Bayesian mechanistic modeling

2020 ◽  
Vol 14 (11) ◽  
pp. e0008868
Author(s):  
Clinton B. Leach ◽  
Jennifer A. Hoeting ◽  
Kim M. Pepin ◽  
Alvaro E. Eiras ◽  
Mevin B. Hooten ◽  
...  
Keyword(s):  
Human Disease ◽  
Mosquito Control ◽  
Disease Risk ◽  
Case Reports ◽  
Mechanistic Modeling ◽  
Adult Mosquito ◽  
Dengue Case ◽  
Modeling Framework ◽  
Mosquito Abundance ◽  
Mosquito Mortality

Our ability to effectively prevent the transmission of the dengue virus through targeted control of its vector, Aedes aegypti, depends critically on our understanding of the link between mosquito abundance and human disease risk. Mosquito and clinical surveillance data are widely collected, but linking them requires a modeling framework that accounts for the complex non-linear mechanisms involved in transmission. Most critical are the bottleneck in transmission imposed by mosquito lifespan relative to the virus’ extrinsic incubation period, and the dynamics of human immunity. We developed a differential equation model of dengue transmission and embedded it in a Bayesian hierarchical framework that allowed us to estimate latent time series of mosquito demographic rates from mosquito trap counts and dengue case reports from the city of Vitória, Brazil. We used the fitted model to explore how the timing of a pulse of adult mosquito control influences its effect on the human disease burden in the following year. We found that control was generally more effective when implemented in periods of relatively low mosquito mortality (when mosquito abundance was also generally low). In particular, control implemented in early September (week 34 of the year) produced the largest reduction in predicted human case reports over the following year. This highlights the potential long-term utility of broad, off-peak-season mosquito control in addition to existing, locally targeted within-season efforts. Further, uncertainty in the effectiveness of control interventions was driven largely by posterior variation in the average mosquito mortality rate (closely tied to total mosquito abundance) with lower mosquito mortality generating systems more vulnerable to control. Broadly, these correlations suggest that mosquito control is most effective in situations in which transmission is already limited by mosquito abundance.

scholarly journals Reaction mechanism of the magnesium ion-dependent adenosine triphosphatase of frog muscle myosin and subfragment 1

1978 ◽  
Vol 171 (1) ◽  
pp. 165-175 ◽  
Author(s):  
M A Ferenczi ◽  
E Homsher ◽  
R M Simmons ◽  
D R Trentham
Keyword(s):  
Steady State ◽  
Rate Constants ◽  
Myosin Atpase ◽  
Forward Rate ◽  
Magnesium Ion ◽  
Skeletal Muscle Myosin ◽  
General Terms ◽  
Predominant Component ◽  
Main Components ◽  
Muscle Myosin

The Mg2+-dependent ATPase (adenosine 5′-triphosphatase) mechanism of myosin and subfragment 1 prepared from frog leg muscle was investigated by transient kinetic technique. The results show that in general terms the mechanism is similar to that of the rabbit skeletal-muscle myosin ATPase. During subfragment-1 ATPase activity at 0-5 degrees C pH 7.0 and I0.15, the predominant component of the steady-state intermediate is a subfragment-1-products complex (E.ADP.Pi). Binary subfragment-1-ATP (E.ATP) and subfragment-1-ADP (E.ADP) complexes are the other main components of the steady-state intermediate, the relative concentrations of the three components E.ATP, E.ADP.Pi and E.ADP being 5.5:92.5:2.0 respectively. The frog myosin ATPase mechanism is distinguished from that of the rabbit at 0-5 degrees C by the low steady-state concentrations of E.ATP and E.ADP relative to that of E.ADP.Pi and can be described by: E + ATP k' + 1 in equilibrium k' − 1 E.ATP k' + 2 in equilibrium k' − 2 E.ADP.Pi k' + 3 in equilibrium k' − 3 E.ADP + Pi k' + 4 in equilibrium k' − 4 E + ADP. In the above conditions successive forward rate constants have values: k' + 1, 1.1 × 10(5)M-1.S-1; k' + 2 greater than 5s-1; k' + 3, 0.011 s-1; k' + 4, 0.5 s-1; k'-1 is probably less than 0.006s-1. The observed second-order rate constants of the association of actin to subfragment 1 and of ATP-induced dissociation of the actin-subfragment-1 complex are 5.5 × 10(4) M-1.S-1 and 7.4 × 10(5) M-1.S-1 respectively at 2-5 degrees C and pH 7.0. The physiological implications of these results are discussed.

scholarly journals A mechanistic modeling framework for gas-phase adsorption kinetics and fixed-bed transport

AIChE Journal ◽  
2017 ◽  
Vol 63 (11) ◽  
pp. 5029-5043 ◽  
Author(s):  
Austin P. Ladshaw ◽  
Sotira Yiacoumi ◽  
Ronghong Lin ◽  
Yue Nan ◽  
Lawrence L. Tavlarides ◽  
...  
Keyword(s):  
Gas Phase ◽  
Adsorption Kinetics ◽  
Fixed Bed ◽  
Mechanistic Modeling ◽  
Modeling Framework ◽  
Gas Phase Adsorption

Mathematical Study of a Class of Epidemiological Models with Multiple Infectious Stages

2020 ◽  
Vol 21 (3-4) ◽  
pp. 259-274
Author(s):  
S. Bowong ◽  
A. Temgoua ◽  
Y. Malong ◽  
J. Mbang
Keyword(s):  
Theoretical Study ◽  
Reproduction Number ◽  
Communicable Disease ◽  
Transmission Dynamics ◽  
Asymptotically Stable ◽  
Epidemiological Models ◽  
Mathematical Study ◽  
Globally Asymptotically Stable ◽  
Disease Free

AbstractThis paper deals with the mathematical analysis of a general class of epidemiological models with multiple infectious stages for the transmission dynamics of a communicable disease. We provide a theoretical study of the model. We derive the basic reproduction number $\mathcal R_0$ that determines the extinction and the persistence of the infection. We show that the disease-free equilibrium is globally asymptotically stable whenever $\mathcal R_0 \leq 1$, while when $\mathcal R_0 \gt 1$, the disease-free equilibrium is unstable and there exists a unique endemic equilibrium point which is globally asymptotically stable. A case study for tuberculosis (TB) is considered to numerically support the analytical results.

scholarly journals A practical guide to mechanistic systems modeling in biology using a logic-based approach

2020 ◽  
Author(s):  
Anna Niarakis ◽  
Tomáš Helikar
Keyword(s):  
Computational Modeling ◽  
Computational Models ◽  
Regulatory System ◽  
Mechanistic Modeling ◽  
Lac Operon ◽  
Modeling Framework ◽  
Validation Criteria ◽  
Hands On ◽  
Modeling Software ◽  
Logical Modeling

Abstract Mechanistic computational models enable the study of regulatory mechanisms implicated in various biological processes. These models provide a means to analyze the dynamics of the systems they describe, and to study and interrogate their properties, and provide insights about the emerging behavior of the system in the presence of single or combined perturbations. Aimed at those who are new to computational modeling, we present here a practical hands-on protocol breaking down the process of mechanistic modeling of biological systems in a succession of precise steps. The protocol provides a framework that includes defining the model scope, choosing validation criteria, selecting the appropriate modeling approach, constructing a model and simulating the model. To ensure broad accessibility of the protocol, we use a logical modeling framework, which presents a lower mathematical barrier of entry, and two easy-to-use and popular modeling software tools: Cell Collective and GINsim. The complete modeling workflow is applied to a well-studied and familiar biological process—the lac operon regulatory system. The protocol can be completed by users with little to no prior computational modeling experience approximately within 3 h.

scholarly journals Using the Mutation-Selection Framework to Characterize Selection on Protein Sequences

Genes ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 409 ◽  
Author(s):  
Ashley Teufel ◽  
Andrew Ritchie ◽  
Claus Wilke ◽  
David Liberles
Keyword(s):  
Protein Sequences ◽  
Original Model ◽  
Mechanistic Modeling ◽  
Mathematical Framework ◽  
Conditional Probabilities ◽  
Modeling Framework ◽  
Mutational Pressure ◽  
Generative Process ◽  
Different Types ◽  
Selection Framework

When mutational pressure is weak, the generative process of protein evolution involves explicit probabilities of mutations of different types coupled to their conditional probabilities of fixation dependent on selection. Establishing this mechanistic modeling framework for the detection of selection has been a goal in the field of molecular evolution. Building on a mathematical framework proposed more than a decade ago, numerous methods have been introduced in an attempt to detect and measure selection on protein sequences. In this review, we discuss the structure of the original model, subsequent advances, and the series of assumptions that these models operate under.

scholarly journals Calibrating an SIR model for South Korea COVID-19 infections and predicting vaccination impact

2021 ◽  
Author(s):  
Brandon Pae
Keyword(s):  
South Korea ◽  
Vaccination Coverage ◽  
Herd Immunity ◽  
Sir Model ◽  
Transmission Dynamics ◽  
Effective Vaccine ◽  
Epidemiological Models ◽  
Policy Decisions

In the span of 1.5 years, COVID-19 has caused more than 4 million deaths worldwide. To prevent such a catastrophe from reoccurring, it is necessary to test and refine current epidemiological models that impact policy decisions. Thus, we developed a deterministic SIR model to examine the long-term transmission dynamics of COVID-19 in South Korea. Using this model, we analyzed how vaccines would affect the number of cases. We found that a 70% vaccination coverage with a 100% effective vaccine would effectively eliminate the number of cases and herd immunity would have been obtained approximately 85 days after February 15 had there not been a reintroduction of cases.

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