scholarly journals Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic

2020 ◽  
Author(s):  
Guanlin Li ◽  
Shashwat Shivam ◽  
Michael E. Hochberg ◽  
Yorai Wardi ◽  
Joshua S Weitz
Keyword(s):  
Optimal Control ◽  
Feedback Control ◽  
Return To Work ◽  
Serological Test ◽  
Control Policy ◽  
Disease Status ◽  
Serological Tests ◽  
Control Policies ◽  
Control Approach ◽  
Dependent Interaction

Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, the indiscriminate nature of mitigation - applying to all individuals irrespective of disease status - has come with substantial socioeconomic costs. Here, we explore how to leverage the increasing reliability and scale of both molecular and serological tests to balance transmission risks with economic costs involved in responding to Covid-19 epidemics. First, we introduce an optimal control approach that identifies personalized interaction rates according to an individual's test status; such that infected individuals isolate, recovered individuals can elevate their interactions, and activity of susceptible individuals varies over time. Critically, the extent to which susceptible individuals can return to work depends strongly on isolation efficiency. As we show, optimal control policies can yield mitigation policies with similar infection rates to total shutdown but lower socioeconomic costs. However, optimal control policies can be fragile given mis-specification of parameters or mis-estimation of the current disease state. Hence, we leverage insights from the optimal control solutions and propose a feedback control approach based on monitoring of the epidemic state. We utilize genetic algorithms to identify a 'switching' policy such that susceptible individuals (both PCR and serological test negative) return to work after lockdowns insofar as recovered fraction is much higher than the circulating infected prevalence. This feedback control policy exhibits similar performance results to optimal control, but with greater robustness to uncertainty. Overall, our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.

Muscle Synergy–Driven Robust Motion Control

2018 ◽  
Vol 30 (4) ◽  
pp. 1104-1131 ◽  
Author(s):  
Kyuengbo Min ◽  
Masami Iwamoto ◽  
Shinji Kakei ◽  
Hideyuki Kimpara
Keyword(s):  
Feedback Control ◽  
Motion Control ◽  
Control Policy ◽  
Muscle Synergy ◽  
Linear Feedback ◽  
Joint Control ◽  
Control Policies ◽  
Control Muscle ◽  
Dynamic Conditions ◽  
Control Robustness

Humans are able to robustly maintain desired motion and posture under dynamically changing circumstances, including novel conditions. To accomplish this, the brain needs to optimize the synergistic control between muscles against external dynamic factors. However, previous related studies have usually simplified the control of multiple muscles using two opposing muscles, which are minimum actuators to simulate linear feedback control. As a result, they have been unable to analyze how muscle synergy contributes to motion control robustness in a biological system. To address this issue, we considered a new muscle synergy concept used to optimize the synergy between muscle units against external dynamic conditions, including novel conditions. We propose that two main muscle control policies synergistically control muscle units to maintain the desired motion against external dynamic conditions. Our assumption is based on biological evidence regarding the control of multiple muscles via the corticospinal tract. One of the policies is the group control policy (GCP), which is used to control muscle group units classified based on functional similarities in joint control. This policy is used to effectively resist external dynamic circumstances, such as disturbances. The individual control policy (ICP) assists the GCP in precisely controlling motion by controlling individual muscle units. To validate this hypothesis, we simulated the reinforcement of the synergistic actions of the two control policies during the reinforcement learning of feedback motion control. Using this learning paradigm, the two control policies were synergistically combined to result in robust feedback control under novel transient and sustained disturbances that did not involve learning. Further, by comparing our data to experimental data generated by human subjects under the same conditions as those of the simulation, we showed that the proposed synergy concept may be used to analyze muscle synergy–driven motion control robustness in humans.

scholarly journals Travel-blocking Optimal Control Policy on Borders of a Chain of Regions Subject to SIRS Discrete Epidemic Model

2018 ◽  
pp. 1-12
Author(s):  
Sara Bidah ◽  
Mostafa Rachik ◽  
Omar Zakary ◽  
Hamza Boutayeb ◽  
Ilias Elmouki
Keyword(s):  
Optimal Control ◽  
Control Policy ◽  
Discrete Version ◽  
Discrete Time System ◽  
Modeling Framework ◽  
Control Approach ◽  
Discrete Epidemic Model ◽  
Travel Restrictions ◽  
A Chain ◽  
Spread Of Infection

With thousands of people moving from one area to another day by day, in a chain of regions tightly more interconnected than other regions in a given large domain, an epidemic may spread rapidly around it from any point of borders. It might be sometimes urgent to impose travel restrictions to inhibit the spread of infection. As we aim to protect susceptible people of this chain to contact infected travelers coming from its neighbors, we follow the so-called travel-blocking vicinity optimal control approach with the introduction of the notion of patch for representing our targeted group of regions when the epidemic modeling framework is in the form of a Susceptible-Infected-Removed-Susceptible (SIRS) discrete-time system to study the case of the removed class return to susceptibility because of their short-lived immunity. A discrete version of the Pontryagin’s maximum principle is employed for the characterization of the travel-blocking optimal control. Finally, with the help of discrete progressive-regressive iterative schemes, we provide cellular simulations of an example of a domain composed with 100 regions and where the targeted chain includes 7 regions.

scholarly journals Seropositivity to canine tick-borne pathogens in a population of sick dogs in Italy

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jairo Alfonso Mendoza-Roldan ◽  
Giovanni Benelli ◽  
Marcos Antonio Bezerra-Santos ◽  
Viet-Linh Nguyen ◽  
Giuseppe Conte ◽  
...  
Keyword(s):  
Borrelia Burgdorferi ◽  
Serological Test ◽  
Control Strategies ◽  
Central Italy ◽  
Rickettsia Conorii ◽  
High Seroprevalence ◽  
The South ◽  
Serum Samples ◽  
Serological Tests ◽  
Distribution Maps

Abstract Background Canine vector-borne diseases (CVBDs) associated to ticks are among the most important health issues affecting dogs. In Italy, Ehrlichia canis, Anaplasma spp., Rickettsia conorii and Borrelia burgdorferi (s.l.) have been studied in both healthy canine populations and those clinically ill with suspected CVBDs. However, little information is currently available on the overall prevalence and distribution of these pathogens in the country. The aim of this study was to assess the prevalence and distribution of tick-borne pathogens (TBPs) in clinically suspect dogs from three Italian macro areas during a 15-year period (2006–2020). Methods A large dataset (n = 21,992) of serological test results for selected TBPs in three macro areas in Italy was analysed using a Chi-square test to evaluate the associations between the categorical factors (i.e. macro area, region, year, sex and age) and a standard logistic regression model (significance set at P = 0.05). Serological data were presented as annual and cumulative prevalence, and distribution maps of cumulative positive cases for TBPs were generated. Results Of the tested serum samples, 86.9% originated from northern (43.9%) and central (43%) Italy. The majority of the tests was requested for the diagnosis of E. canis (47%; n = 10,334), followed by Rickettsia spp. (35.1%; n = 7725), B. burgdorferi (s.l.) (11.6%; n = 2560) and Anaplasma spp. (6.2%; n = 1373). The highest serological exposure was recorded for B. burgdorferi (s.l.) (83.5%), followed by Rickettsia spp. (64.9%), Anaplasma spp. (39.8%) and E. canis (28.7%). The highest number of cumulative cases of Borrelia burgdorferi (s.l.) was recorded in samples from Tuscany, central Italy. Rickettsia spp. was more prevalent in the south and on the islands, particularly in dogs on Sicily older than 6 years, whereas Anaplasma spp. was more prevalent in the north and E. canis more prevalent in the south and on the islands. Conclusions The results of this study highlight the high seroprevalence and wide distribution of the four TBPs in dogs with clinically suspected CVBDs from the studied regions of Italy. The very high seroprevalence of B. burgdorferi (s.l.) exemplifies a limitation of this study, given the use of clinically suspect dogs and the possibility of cross-reactions when using serological tests. The present research provides updated and illustrative information on the seroprevalence and distribution of four key TBPs, and advocates for integrative control strategies for their prevention. Grapic abstract

scholarly journals Optimal Pressure Boundary Control of Steady Multiscale Fluid-Structure Interaction Shell Model Derived from Koiter Equations

Fluids ◽  
2021 ◽  
Vol 6 (4) ◽  
pp. 149
Author(s):  
Andrea Chierici ◽  
Leonardo Chirco ◽  
Sandro Manservisi
Keyword(s):  
Optimal Control ◽  
Solid Wall ◽  
Fluid Structure Interaction ◽  
Computational Cost ◽  
Robin Boundary Condition ◽  
Design Parameters ◽  
Fluid Structure ◽  
Control Approach ◽  
Structure Interaction ◽  
Fluid Boundary

Fluid-structure interaction (FSI) problems are of great interest, due to their applicability in science and engineering. However, the coupling between large fluid domains and small moving solid walls presents numerous numerical difficulties and, in some configurations, where the thickness of the solid wall can be neglected, one can consider membrane models, which are derived from the Koiter shell equations with a reduction of the computational cost of the algorithm. With this assumption, the FSI simulation is reduced to the fluid equations on a moving mesh together with a Robin boundary condition that is imposed on the moving solid surface. In this manuscript, we are interested in the study of inverse FSI problems that aim to achieve an objective by changing some design parameters, such as forces, boundary conditions, or geometrical domain shapes. We study the inverse FSI membrane model by using an optimal control approach that is based on Lagrange multipliers and adjoint variables. In particular, we propose a pressure boundary optimal control with the purpose to control the solid deformation by changing the pressure on a fluid boundary. We report the results of some numerical tests for two-dimensional domains to demonstrate the feasibility and robustness of our method.

Sign in / Sign up

Export Citation Format

Share Document