scholarly journals COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

2020 ◽  
Author(s):  
Arthur Charpentier ◽  
Romuald Elie ◽  
Mathieu Laurière ◽  
Viet Chi Tran
Keyword(s):  
Social Interactions ◽  
Optimal Strategies ◽  
Economic Cost ◽  
Limited Capacity ◽  
Epidemic Dynamics ◽  
Optimal Intervention ◽  
Intervention Measures ◽  
Capacity Level ◽  
Parametric Specification ◽  
Short Transition

AbstractWe consider here an extended SIR model, including several features of the recent COVID-19 outbreak: in particular the infected and recovered individuals can either be detected (+) or undetected (−) and we also integrate an intensive care unit capacity. Our model enables a tractable quantitative analysis of the optimal policy for the control of the epidemic dynamics using both lockdown and detection intervention levers. With parametric specification based on literature on COVID-19, we investigate sensitivity of various quantities on optimal strategies, taking into account the subtle tradeoff between the sanitary and the economic cost of the pandemic, together with the limited capacity level of ICU. We identify the optimal lockdown policy as an intervention structured in 4 successive phases: First a quick and strong lockdown intervention to stop the exponential growth of the contagion; second a short transition phase to reduce the prevalence of the virus; third a long period with full ICU capacity and stable virus prevalence; finally a return to normal social interactions with disappearance of the virus. We also provide optimal intervention measures with increasing ICU capacity, as well as optimization over the effort on detection of infectious and immune individuals.

scholarly journals COVID-19 pandemic control: balancing detection policy and lockdown intervention under ICU sustainability

2020 ◽  
Vol 15 ◽  
pp. 57 ◽  
Author(s):  
Arthur Charpentier ◽  
Romuald Elie ◽  
Mathieu Laurière ◽  
Viet Chi Tran
Keyword(s):  
Social Interactions ◽  
Optimal Strategies ◽  
Economic Cost ◽  
Limited Capacity ◽  
Epidemic Dynamics ◽  
Capacity Level ◽  
Parametric Specification ◽  
The Impact ◽  
Second Wave ◽  
Short Transition

An extended SIR model, including several features of the recent COVID-19 outbreak, is considered: the infected and recovered individuals can either be detected or undetected and we also integrate an intensive care unit (ICU) capacity. We identify the optimal policy for controlling the epidemic dynamics using both lockdown and detection intervention levers, and taking into account the trade-off between the sanitary and the socio-economic cost of the pandemic, together with the limited capacity level of ICU. With parametric specification based on the COVID-19 literature, we investigate the sensitivities of various quantities on the optimal strategies. The optimal lockdown policy is structured into 4 phases: First a quick and strong lockdown intervention to stop the exponential growth of the contagion; second a short transition to reduce the prevalence of the virus; third a long period with full ICU capacity and stable virus prevalence; finally a return to normal social interactions with disappearance of the virus. The optimal scenario avoids the second wave of infection, provided the lockdown is released sufficiently slowly. Whenever massive resources are introduced to detect infected individuals, the pressure on social distancing can be released, whereas the impact of detection of immune individuals reveals to be more moderate.

scholarly journals Will the Large-scale Vaccination Succeed in Containing the COVID-19 Epidemic and How Soon?

2021 ◽  
Author(s):  
Shilei Zhao ◽  
Tong Sha ◽  
Yongbiao Xue ◽  
Chung-I Wu ◽  
Hua Chen
Keyword(s):  
United States Of America ◽  
Vaccine Efficacy ◽  
Large Scale ◽  
Herd Immunity ◽  
Vaccination Rate ◽  
Epidemic Dynamics ◽  
Intervention Measures ◽  
Vaccination Scheme ◽  
Mutant Strains ◽  
Promising Solution

The availability of vaccines provides a promising solution to containing the COVID-19 pandemic. Here, we develop an epidemiological model to quantitatively analyze and predict the epidemic dynamics of COVID-19 under vaccination. The model is applied to the daily released numbers of confirmed cases of Israel and United States of America to explore and predict the trend under vaccination based on their current epidemic status and intervention measures. For Israel, of which 53.83% of the population was fully vaccinated, under the current intensity of NPIs and vaccination scheme, the pandemic is predicted to end between May 14, 2021 to May 16, 2021 depending on an immunity duration between 180 days and 365 days; Assuming no NPIs after March 24, 2021, the pandemic will ends later, between July 4, 2021 to August 26, 2021. For USA, if we assume the current vaccination rate (0.268% per day) and intensity of NPIs, the pandemic will end between February 3, 2022 and August 17, 2029 depending on an immunity duration between 180 days and 365 days. However, assuming an immunity duration of 180 days and with no NPIs, the pandemic will not end, and instead reach an equilibrium state with a proportion of the population remaining actively infected. Overall the daily vaccination rate should be chosen according to the vaccine efficacy and the immunity duration to achieve herd immunity. In some situations, vaccination alone cannot stop the pandemic, and NPIs are necessary both to supplement vaccination and accelerate the end of the pandemic. Considering that vaccine efficacy and duration of immunity may be reduced for new mutant strains, it is necessary to remain cautiously optimistic about the prospect of the pandemic under vaccination.

scholarly journals Networks and epidemic models

2005 ◽  
Vol 2 (4) ◽  
pp. 295-307 ◽  
Author(s):  
Matt J Keeling ◽  
Ken T.D Eames
Keyword(s):  
Network Theory ◽  
Disease Dynamics ◽  
Approximation Techniques ◽  
Individual Level ◽  
Epidemic Dynamics ◽  
Intervention Measures ◽  
The Social ◽  
Finite Set ◽  
Random Mixing ◽  
The Individual

Networks and the epidemiology of directly transmitted infectious diseases are fundamentally linked. The foundations of epidemiology and early epidemiological models were based on population wide random-mixing, but in practice each individual has a finite set of contacts to whom they can pass infection; the ensemble of all such contacts forms a ‘mixing network’. Knowledge of the structure of the network allows models to compute the epidemic dynamics at the population scale from the individual-level behaviour of infections. Therefore, characteristics of mixing networks—and how these deviate from the random-mixing norm—have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures. Here, we review the basis of epidemiological theory (based on random-mixing models) and network theory (based on work from the social sciences and graph theory). We then describe a variety of methods that allow the mixing network, or an approximation to the network, to be ascertained. It is often the case that time and resources limit our ability to accurately find all connections within a network, and hence a generic understanding of the relationship between network structure and disease dynamics is needed. Therefore, we review some of the variety of idealized network types and approximation techniques that have been utilized to elucidate this link. Finally, we look to the future to suggest how the two fields of network theory and epidemiological modelling can deliver an improved understanding of disease dynamics and better public health through effective disease control.

scholarly journals Cost-effectiveness Analysis with Influence Diagrams

2015 ◽  
Vol 54 (04) ◽  
pp. 353-358 ◽  
Author(s):  
M. Arias ◽  
F. J. Díez
Keyword(s):  
Cost Effectiveness ◽  
Decision Tree ◽  
Decision Trees ◽  
Health Benefit ◽  
Economic Cost ◽  
Software Tool ◽  
Cost Effectiveness Analysis ◽  
Influence Diagrams ◽  
Effectiveness Analysis ◽  
Optimal Intervention

Summary Background: Cost-effectiveness analysis (CEA) is used increasingly in medicine to determine whether the health benefit of an intervention is worth the economic cost. Decision trees, the standard decision modeling technique for non-temporal domains, can only perform CEA for very small problems. Objective: To develop a method for CEA in problems involving several dozen variables. Methods: We explain how to build influence diagrams (IDs) that explicitly represent cost and effectiveness. We propose an algorithm for evaluating cost-effectiveness IDs directly, i.e., without expanding an equivalent decision tree. Results: The evaluation of an ID returns a set of intervals for the willingness to pay – separated by cost-effectiveness thresholds – and, for each interval, the cost, the effectiveness, and the optimal intervention. The algorithm that evaluates the ID directly is in general much more efficient than the brute-force method, which is in turn more efficient than the expansion of an equivalent decision tree. Using OpenMarkov, an open-source software tool that implements this algorithm, we have been able to perform CEAs on several IDs whose equivalent decision trees contain millions of branches. Conclusion: IDs can perform CEA on large problems that cannot be analyzed with decision trees.

scholarly journals The ecological dynamics of the coronavirus epidemics during transmission from outside sources when R 0 is successfully managed below one

2021 ◽  
Vol 8 (6) ◽  
pp. 202234
Author(s):  
Steinar Engen ◽  
Huaiyu Tian ◽  
Ruifu Yang ◽  
Ottar N. Bjørnstad ◽  
Jason D. Whittington ◽  
...  
Keyword(s):  
World Health ◽  
Reproductive Number ◽  
Model Framework ◽  
Epidemic Dynamics ◽  
Intervention Measures ◽  
Individual Based Models ◽  
Rapid Prediction ◽  
Asymptomatic Infections ◽  
Age Structured ◽  
Health Organization

Since COVID-19 spread globally in early 2020 and was declared a pandemic by the World Health Organization (WHO) in March, many countries are managing the local epidemics effectively through intervention measures that limit transmission. The challenges of immigration of new infections into regions and asymptomatic infections remain. Standard deterministic compartmental models are inappropriate for sub- or peri-critical epidemics (reproductive number close to or less than one), so individual-based models are often used by simulating transmission from an infected person to others. However, to be realistic, these models require a large number of parameters, each with its own set of uncertainties and lack of analytic tractability. Here, we apply stochastic age-structured Leslie theory with a long history in ecological research to provide some new insights to epidemic dynamics fuelled by external imports. We model the dynamics of an epidemic when R 0 is below one, representing COVID-19 transmission following the successful application of intervention measures, and the transmission dynamics expected when infections migrate into a region. The model framework allows more rapid prediction of the shape and size of an epidemic to improve scaling of the response. During an epidemic when the numbers of infected individuals are rapidly changing, this will help clarify the situation of the pandemic and guide faster and more effective intervention.

scholarly journals Control of COVID-19 outbreak using an extended SEIR model

2021 ◽  
pp. 1-26
Author(s):  
Sean T. McQuade ◽  
Ryan Weightman ◽  
Nathaniel J. Merrill ◽  
Aayush Yadav ◽  
Emmanuel Trélat ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Optimal Strategies ◽  
Economic Cost ◽  
Contact Tracing ◽  
Economic Activities ◽  
Seir Model ◽  
Proposed Model ◽  
High Death ◽  
Containment Measures ◽  
The Cost

The outbreak of COVID-19 resulted in high death tolls all over the world. The aim of this paper is to show how a simple SEIR model was used to make quick predictions for New Jersey in early March 2020 and call for action based on data from China and Italy. A more refined model, which accounts for social distancing, testing, contact tracing and quarantining, is then proposed to identify containment measures to minimize the economic cost of the pandemic. The latter is obtained taking into account all the involved costs including reduced economic activities due to lockdown and quarantining as well as the cost for hospitalization and deaths. The proposed model allows one to find optimal strategies as combinations of implementing various non-pharmaceutical interventions and study different scenarios and likely initial conditions.

scholarly journals Modeling partial lockdowns in multiplex networks using partition strategies

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Adrià Plazas ◽  
Irene Malvestio ◽  
Michele Starnini ◽  
Albert Díaz-Guilera
Keyword(s):  
Social Interactions ◽  
Economic Burden ◽  
Economic Cost ◽  
Network Approach ◽  
Epidemic Outbreak ◽  
Epidemic Spreading ◽  
Network Partition ◽  
Multiplex Network ◽  
The Social ◽  
Containment Measures

AbstractNational stay-at-home orders, or lockdowns, were imposed in several countries to drastically reduce the social interactions mainly responsible for the transmission of the SARS-CoV-2 virus. Despite being essential to slow down the COVID-19 pandemic, these containment measures are associated with an economic burden. In this work, we propose a network approach to model the implementation of a partial lockdown, breaking the society into disconnected components, or partitions. Our model is composed by two main ingredients: a multiplex network representing human contacts within different contexts, formed by a Household layer, a Work layer, and a Social layer including generic social interactions, and a Susceptible-Infected-Recovered process that mimics the epidemic spreading. We compare different partition strategies, with a twofold aim: reducing the epidemic outbreak and minimizing the economic cost associated to the partial lockdown. We also show that the inclusion of unconstrained social interactions dramatically increases the epidemic spreading, while different kinds of restrictions on social interactions help in keeping the benefices of the network partition.

scholarly journals Modeling of suppression and mitigation interventions in the COVID-19 epidemics

2021 ◽  
Author(s):  
YueXing Han ◽  
ZeYang Xie ◽  
YiKe Guo ◽  
Bing Wang
Keyword(s):  
Intervention Strategy ◽  
Periodic Wave ◽  
Periodic Waves ◽  
Infection Level ◽  
Medical Resources ◽  
Daily Lives ◽  
Epidemic Dynamics ◽  
Intervention Measures ◽  
Negative Impacts ◽  
Short Time

Abstract Background: The global spread of the COVID-19 pandemic has become the most fundamental threat to humanealth. In the absence of vaccines and effective therapeutical solutions, non-pharmaceutic interventions have become a major way for controlling the epidemics. Soft mitigation interventions are able to slow down the epidemic but not to halt it well. While strict suppression interventions are efficient for controlling the epidemics, long-term measures are likely to have negative impacts on economics and people’s daily lives. Hence, dynamically balancing the interventions of suppression and mitigation plays a fundamental role in manipulating the epidemic curves.Methods: We collected data of the number of infections for several countries during the COVID-19 pandemics and found a clear phenomenon of periodic waves of infections. Based on the observation, by connecting the infection level with the medical resources and a tolerance parameter, we propose a mathematical model by combining intervention measures to understand the epidemic dynamics.Results: Depending on the parameters of the medical resources, tolerance level, and the starting time of interventions, the combined intervention measure dynamically changes with the infection level, resulting in a periodic wave of infections con-trolled within an accepted level. The study reveals that, (a) with an immediate, strict suppression, the number of infections and deaths is well controlled with a significant reduction in very short time period; (b) an appropriate, dynamical combination of suppression and mitigation may find a feasible way in reducing the impacts of epidemics on people’s lives and economics.Conclusions: While the assumption of interventions deployed with a cycle of period in the model is limited and unrealistic, the phenomenon of periodic waves of infections in reality is captured by our model. These results provide helpful insights for policy-makers to dynamically deploy an appropriate intervention strategy to effectively battle against the COVID-19.

scholarly journals Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States

2021 ◽  
Vol 18 (14) ◽  
pp. 7594
Author(s):  
Derek Huang ◽  
Huanyu Tao ◽  
Qilong Wu ◽  
Sheng-You Huang ◽  
Yi Xiao
Keyword(s):  
United States ◽  
Epidemic Model ◽  
The United States ◽  
Transmission Dynamics ◽  
Epidemic Dynamics ◽  
Intervention Measures ◽  
The Us ◽  
The World ◽  
Effective Drugs

Coronavirus 2019 (COVID-19) is causing a severe pandemic that has resulted in millions of confirmed cases and deaths around the world. In the absence of effective drugs for treatment, non-pharmaceutical interventions are the most effective approaches to control the disease. Although some countries have the pandemic under control, all countries around the world, including the United States (US), are still in the process of controlling COVID-19, which calls for an effective epidemic model to describe the transmission dynamics of COVID-19. Meeting this need, we have extensively investigated the transmission dynamics of COVID-19 from 22 January 2020 to 14 February 2021 for the 50 states of the United States, which revealed the general principles underlying the spread of the virus in terms of intervention measures and demographic properties. We further proposed a time-dependent epidemic model, named T-SIR, to model the long-term transmission dynamics of COVID-19 in the US. It was shown in this paper that our T-SIR model could effectively model the epidemic dynamics of COVID-19 for all 50 states, which provided insights into the transmission dynamics of COVID-19 in the US. The present study will be valuable to help understand the epidemic dynamics of COVID-19 and thus help governments determine and implement effective intervention measures or vaccine prioritization to control the pandemic.

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