Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?

Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccine supplies are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-CoV-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies, using a generalized multi-site mean-field model, which can naturally assess the impact of manual and digital approaches alike. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of more complex pathogen dynamics. We use this technique to simulate a newly-defined epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.

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Modelling digital and manual contact tracing for COVID-19 Are low uptakes and missed contacts deal-breakers?

10.1101/2021.04.29.21256307 ◽

2021 ◽

Author(s):

Andrei Rusu ◽

Katayoun Farrahi ◽

Rémi Emonet

Keyword(s):

Public Health ◽

Mean Field ◽

Epidemiological Model ◽

Health Interventions ◽

Contact Tracing ◽

Public Health Interventions ◽

Fair Proportion ◽

Usage Patterns ◽

The Right ◽

The Impact

ABSTRACTComprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccines are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-Cov-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies using a generalized multi-site mean-field model, which can naturally assess the impact of both manual and digital approaches. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of several complex pathogens. We use this technique to simulate a new epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.

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COLLECTIVE BEHAVIOR OF BIOPHYSICAL SYSTEMS WITH THERMODYNAMIC FEEDBACK LOOPS: A CASE STUDY FOR A NONLINEAR MARKOV MODEL — THE TAKATSUJI SYSTEM

Modern Physics Letters B ◽

10.1142/s0217984911025845 ◽

2011 ◽

Vol 25 (08) ◽

pp. 551-568 ◽

Cited By ~ 2

Author(s):

T. D. FRANK

Keyword(s):

Collective Behavior ◽

Feedback Loop ◽

Field Model ◽

Mean Field ◽

Planck Equation ◽

Feedback Loops ◽

Mean Field Model ◽

The Impact ◽

Nonlinear Markov Process

We study order–disorder transitions and the emergence of collective behavior using a particular mean field model: the dynamic Takatsuji system. This model satisfies linear non-equilibrium thermodynamics and can be described in terms of a nonlinear Markov process defined by a nonlinear Fokker–Planck equation, that is, an evolution equation that is nonlinear with respect to its probability density. We discuss quantitatively the impact of a feedback loop that involves a macroscopic, thermodynamic variable. We demonstrate by means of semi-analytical methods and numerical simulations that the feedback loop increases the magnitude of order, increases the gap between the free energy of the ordered and disordered states, and increases the maximal rate of entropy production that can be observed during the order–disorder transition.

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Path Integral Method in the Mean-field Model for the Magnetic Vector Potential

Geomagnetism and Aeronomy ◽

10.1134/s0016793220070300 ◽

2020 ◽

Vol 60 (7) ◽

pp. 989-992

Author(s):

E. V. Yushkov ◽

C. R. Kamaletdinov ◽

D. D. Sokoloff

Keyword(s):

Path Integral ◽

Vector Potential ◽

Integral Method ◽

Field Model ◽

Mean Field ◽

Magnetic Vector Potential ◽

Magnetic Vector ◽

Mean Field Model ◽

The Mean ◽

Path Integral Method

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KDP-type crystal spontaneous polarization behavior better explained by bound charge semiconductor model than by mean field model

Ferroelectrics ◽

10.1080/00150193.2020.1791663 ◽

2020 ◽

Vol 569 (1) ◽

pp. 70-81

Author(s):

V. Hugo Schmidt

Keyword(s):

Spontaneous Polarization ◽

Field Model ◽

Mean Field ◽

Mean Field Model ◽

Polarization Behavior ◽

Type Crystal

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Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

Scientific Reports ◽

10.1038/s41598-021-90347-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jie Zhu ◽

Blanca Gallego

Keyword(s):

Public Health ◽

Disease Transmission ◽

Clustering Algorithm ◽

Health Interventions ◽

Transmission Model ◽

Model Parameters ◽

Public Health Interventions ◽

Forecasting Accuracy ◽

Time Lagged ◽

The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

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Quark mean-field model for single and double and hypernuclei

Progress of Theoretical and Experimental Physics ◽

10.1093/ptep/ptt119 ◽

2014 ◽

Vol 2014 (1) ◽

pp. 13D02-0 ◽

Cited By ~ 11

Author(s):

J. N. Hu ◽

A. Li ◽

H. Shen ◽

H. Toki

Keyword(s):

Field Model ◽

Mean Field ◽

Mean Field Model

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EVOLUTION OF SHELL STRUCTURE IN NUCLEI

International Journal of Modern Physics E ◽

10.1142/s0218301311019581 ◽

2011 ◽

Vol 20 (08) ◽

pp. 1663-1675 ◽

Cited By ~ 4

Author(s):

A. BHAGWAT ◽

Y. K. GAMBHIR

Keyword(s):

Shell Structure ◽

Crucial Role ◽

Field Model ◽

Mean Field ◽

Mass Region ◽

The Other ◽

Mean Field Model ◽

Relativistic Mean Field ◽

Shell Closures ◽

Low Mass

Systematic investigations of the pairing and two-neutron separation energies which play a crucial role in the evolution of shell structure in nuclei, are carried out within the framework of relativistic mean-field model. The shell closures are found to be robust, as expected, up to the lead region. New shell closures appear in low mass region. In the superheavy region, on the other hand, it is found that the shell closures are not as robust, and they depend on the particular combinations of neutron and proton numbers. Effect of deformation on the shell structure is found to be marginal.

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