Window of Opportunity for Mitigation to Prevent Overflow of ICU capacity in Chicago by COVID-19

Maslov, Sergei; Goldenfeld, Nigel

doi:10.1101/2020.03.20.20040048

Cited by 12 publications

(15 citation statements)

References 10 publications

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“…Resolving (a) requires extracting information from the available epidemiological data. Recent impressive successes of many inter-disciplinary research teams in related tasks, see [32, 92], makes us hopeful that the hurdle can be overcome either based on the actual observational data or via synthetic data generated by a properly calibrated ABM. Bottleneck in implementing (b) is on the computational side and – we ought to rely on an efficient heuristics to resolve the inference problem (computing the partition function) discussed in Sections III B 2,VI C,VI D. An attractive “beyond sufficient statistics” approach consists in learning the GM of pandemics based on the pseudo-log-likelihood and/or interaction screening methodology developed recently.…”

Section: Path Forwardmentioning

confidence: 99%

“…The three T s in the non-pharmaceutical public health response – Testing, Treating , including preventing large gatherings, voluntary or enforced social distancing, curfews, road blocks, closure of businesses, and so on, and contact Tracing influence very significantly how we model the pandemic spreads; see [51], [92] and references therein for early illustrations of difficulties in modeling and uncertainty in the results of the COVID-19 spread. Recent work (https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30785-4/fulltext) illustrates the impact of both the introduction and lifting of non-pharmaceutical intervention policies on SARS-CoV-2 transmission…”

Section: Introduction: Setting the Stage For The Graphical Models Of Pandemicmentioning

confidence: 99%

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Graphical Models of Pandemic

Chertkov

Abrams

Sikaroudi

et al. 2021

Preprint

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Both COVID-19 and novel pandemics challenge those of us within the modeling community, specifically in establishing suitable relations between lifecycles, scales, and existing methods. Herein we demonstrate transitions between models in space/time, individual-to-community, county-to-city, along with models for the trace beginning with exposure, then to symptom manifest, then to community transmission. To that end, we leverage publicly available data to compose a chain of Graphical Models (GMs) for predicting infection rates across communities, space, and time. We'll anchor our GMs against the more expensive yet state-of-the-art Agent-Based Models (ABMs). Insight obtained from designing novel GMs calibrated to ABMs furnishes reduced, yet reliable surrogates for the end-to-end public health challenge of community contact tracing and transmission. Further, this novel research transcends and synergizes information integration and informatics, leading to an advance in the science of GMs. Cognizance into the data lifecycle using properly coarse-grained modeling will broaden the toolkit available to public health specialists, and hopefully empower governments and health agencies, here and abroad, in addressing the profound challenges in disease and vaccination campaigns confronting us by COVID and future pandemics. In this proof of principle study, focusing on the GM methodology development, we show, first, how static GM of the Ising model type (characterised by pair-wise interaction between nodes related to traffic and communications between nodes representing communities, or census tracts within a given city, and with local infection bias) emerge from a dynamic GM of the Independent Cascade type, introduced and studied in Computer and Networks sciences mainly in the context of the spread of social influences. Second, we formulate the problem of inference in epidemiology as inference problems in the Ising model setting. Specifically, we pose the challenge of computing Conditional A-posteriori Level of Infection (CALI), which provides a quantitative answer to the questions: What is the probability that a given node in the GM (given census tract within the city) becomes infected in the result of injection of the infection at another node, e.g. due to arrival of a super-spreader agent or occurrence of the super-spreader event in the area. To answer the question exactly is not feasible for any realistic size (larger than 30-50 nodes) model. We therefore adopt and develop approximate inference techniques, of the variational and variable elimination types, developed in the GM literature. To demonstrate utility of the methodology, which seems new for the public health application, we build a 123-node model of Seattle, as well as its 10-node and 20-node coarse-grained variants, and then conduct the proof of principles experimental studies. The experiments on the coarse-grained models have helped us to validate the approximate inference by juxtaposing it to the exact inference. The experiments also lead to discovery of interesting and most probably universal phenomena. In particular, we observe (a) a strong sensitivity of CALI to the location of the initial infection, and (b) strong alignment of the resulting infection probability (values of CALI) observed at different nodes in the regimes of moderate interaction between the nodes. We then speculate how these, and other observations drawn from the synthetic experiments, can be extended to a more realistic, data driven setting of actual operation importance. We conclude the manuscript with an extensive discussion of how the methodology should be developed further, both at the level of devising realistic GMs from observational data (and also enhancing it with microscopic ABM modeling and simulations) and also regarding utilisation of the GM inference methodology for more complex problems of the pandemic mitigation and control.

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Section: Path Forwardmentioning

confidence: 99%

Section: Introduction: Setting the Stage For The Graphical Models Of Pandemicmentioning

confidence: 99%

Graphical Models of Pandemic

Chertkov

Abrams

Sikaroudi

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The mathematical core of this work is a stochastic model, once again for influenza [40]. Another recent work [41] attempts to find the required hospital capacity in Chicago in the presence and absence of a lockdown, using the model [34]. Yet another contribution [42] predicts the effects of imposing a lockdown in India, using an in-house lumped-parameter model.…”

Section: Mathematical Modeling Techniquesmentioning

confidence: 99%

Transmission Dynamics of COVID-19 and Impact on Public Health Policy

Shayak

Sharma

Rand

et al. 2020

Preprint

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In this work we construct a mathematical model for the transmission and spread of coronavirus disease 2019 or COVID-19. Our model features delay terms to account for (a) the time lapse or latency period between contracting the disease and displaying symptoms, and (b) the time lag in testing patients for the virus due to the limited numbers of testing facilities currently available. We find that the delay introduces a significant disparity between the actual and reported time-trajectories of cases in a particular region. Specifically, the reported case histories lag the actual histories by a few days. Hence, to minimize the spread of the disease, lockdowns and similarly drastic social isolation measures need to be imposed some time before the reported figures are approaching their peak values. We then account for the social reality that lockdowns can only be of a limited duration in view of practical considerations. We find that the most effective interval for imposing such a limited-time lockdown is one where the midpoint of the lockdown period coincides with the actual peak of the spread of the disease in the absence of the lockdown. We further show that the true effectivity of imposing a lockdown may be misrepresented and grossly underestimated by the reported case trajectories in the days following the action.

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“…Поэтому в модели значение показателя AvarageIllnessPeriod принималось равным 7 дням. Оценки периода нахождения больных в стационаре и реанимационном отделении представлены в работе [13], где данные периоды оказались одинаковыми и также равны 7 дням. Однако, учитывая официально представленную статистику по динамике выздоровления в нашей стране, видно, что данные значения являются весьма оптимистическими и далеко не совсем соответствуют реальной ситуации.…”

Section: оценка исходных параметров для моделированияunclassified

The mathematical modeling of the effective measures against the COVID-19 spread

Матвеев

2020

National Security and Strategic Planning

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The article is presented a modified simulation SEIR-model of the epidemic spread. The proposed model is created in the AnyLogic System. This model allows to estimate the restricting measures are aimed at reducing the growth of the epidemic. The calculation results are presented in four main scenarios: 1) Without restrictive measures; 2) With lite restrictive measures; 3) In the conditions of hard restrictive measures with recommended character; 4) The conditions of full quarantine and the announcement of an emergency situation. The results allow us to predict the approximate dates of the epidemic peak, the end of the epidemic, the number of deaths and the load on the health system in each of the scenarios for two cities (Moscow and St. Petersburg).

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Window of Opportunity for Mitigation to Prevent Overflow of ICU capacity in Chicago by COVID-19

Cited by 12 publications

References 10 publications

Graphical Models of Pandemic

Graphical Models of Pandemic

Transmission Dynamics of COVID-19 and Impact on Public Health Policy

The mathematical modeling of the effective measures against the COVID-19 spread

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