Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil

Tarrataca, Luís; Dias, Claudia Mazza; Haddad, Diego B.; Arruda, Edilson F.

doi:10.1186/s13362-020-00098-w

Cited by 23 publications

(21 citation statements)

References 57 publications

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“…(Kompella et al, 2020) applied the Soft-Actor-Critic algorithm (Haarnoja et al, 2018) on an agent-based epidemiological model with community interactions allowing the spread of the disease to be an emergent property of people's behaviors and the government's policies. Other contributions applied non-rl optimization methods such as deterministic rules (Tarrataca et al, 2020), stochastic approximation algorithms (Yaesoubi et al, 2020), optimal control (Charpentier et al, 2020) or Bayesian optimization (Chandak et al, 2020). This latter paper also proposes a stochastic agent-based model called VIPER (Virus-Individual-Policy-EnviRonment) allowing to compare the optimization results on variations of the demographics and geographical distribution of population.…”

Section: Discussionmentioning

confidence: 99%

“…seir: Yaesoubi et al, 2020, agent-based models: Chandak et al, 2020 or a combination of both: Kompella et al, 2020), of optimization methods (e.g. deterministic rules: Tarrataca et al, 2020, Bayesian optimization: Chandak et al, 2020, Deep rl: Arango & Pelov, 2020Kompella et al, 2020, optimal control: Charpentier et al, 2020, evolutionary optimization: Miikkulainen et al, 2020 or game-theoretic analysis: Elie et al, 2020), of cost functions (e.g. fixed weighted sum of health and economic costs: Arango & Pelov, 2020;Kompella et al, 2020, possibly adding constraints on the school closure budget: Libin et al, 2020, or multi-objective optimization: Miikkulainen et al, 2020, of state and action spaces (e.g.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

Colas¹,

Hejblum²,

Rouillon³

et al. 2021

jair

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Modeling the dynamics of epidemics helps to propose control strategies based on pharmaceuticaland non-pharmaceutical interventions (contact limitation, lockdown, vaccination,etc). Hand-designing such strategies is not trivial because of the number of possibleinterventions and the difficulty to predict long-term effects. This task can be cast as an optimization problem where state-of-the-art machine learning methods such as deep reinforcement learning might bring significant value. However, the specificity of each domain|epidemic modeling or solving optimization problems|requires strong collaborationsbetween researchers from different fields of expertise. This is why we introduce EpidemiOptim, a Python toolbox that facilitates collaborations between researchers inepidemiology and optimization. EpidemiOptim turns epidemiological models and cost functions into optimization problems via a standard interface commonly used by optimization practitioners (OpenAI Gym). Reinforcement learning algorithms based on QLearning with deep neural networks (DQN) and evolutionary algorithms (NSGA-II) are already implemented. We illustrate the use of EpidemiOptim to find optimal policies fordynamical on-o lockdown control under the optimization of the death toll and economic recess using a Susceptible-Exposed-Infectious-Removed (SEIR) model for COVID-19. Using EpidemiOptim and its interactive visualization platform in Jupyter notebooks, epidemiologists, optimization practitioners and others (e.g. economists) can easily compare epidemiological models, costs functions and optimization algorithms to address important choicesto be made by health decision-makers. Trained models can be explored by experts and non-experts via a web interface. This article is part of the special track on AI and COVID-19.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

Colas¹,

Hejblum²,

Rouillon³

et al. 2021

jair

View full text Add to dashboard Cite

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“…A clustering framework was used to study the economic impact during lockdown in (Ghamizi et al, 2020). Economic crisis of various countries was studied using data driven models in (Tarrataca et al, 2021). Epidemiological forecasting was also made with the aid of a deep learning method.…”

Section: Other Impactsmentioning

confidence: 99%

Battling COVID-19 using machine learning: A review

Chadaga

Prabhu

et al. 2021

Cogent Engineering

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Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2) known as Coronavirus surfaced in late 2019. It turned out to be a life-threatening disease and is causing chaos all around the world. The World Health Organisation (WHO) declared it a pandemic in March 2020. To handle COVID-19 related problems, research in many areas of science was introduced. Machine learning (ML), being one of the most successful stories in recent times is widely used to solve a variety of problems in our everyday life. Here, an overview of machine learning that tackles the pandemic is discussed in the beginning. Various datasets related to COVID-19 are also explored. Diagnosis of this viral disease using CT-Scans, X-ray images, sound analysis and blood tests using machine learning are presented in-depth. Drug and vaccine development using machine learning for COVID-19 are also discussed. Pandemic management and control were also examined. The main objective of this paper is to conduct a systematic review of machine learning applications that fight the deadly virus. This paper helps the researchers to understand and analyse the data trends related to COVID-19 and also

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“…Lockdown, curfews have been used in several countries, with different schedules and durations. The efficacy and impact of these approaches on global population health should be carefully assessed to be prepared in the case of a new pandemic [34][35][36].…”

Section: Personal Protective Equipment (Ppe)mentioning

confidence: 99%

Airborne Coronaviruses: Observations from Veterinary Experience

et al. 2021

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The virus responsible for the pandemic that has affected 152 countries worldwide is a new strain of coronavirus (CoV), which belongs to a family of viruses widespread in many animal species, including birds, and mammals including humans. Indeed, CoVs are known in veterinary medicine affecting several species, and causing respiratory and/or enteric, systemic diseases and reproductive disease in poultry. Animal diseases caused by CoV may be considered from the following different perspectives: livestock and poultry CoVs cause mainly “population disease”; while in companion animals they are a source of mainly “individual/single subject disease”. Therefore, respiratory CoV diseases in high-density, large populations of livestock or poultry may be a suitable example for the current SARS-CoV-2/COVID-19 pandemic. In this review we describe some strategies applied in veterinary medicine to control CoV and discuss if they may help to develop practical and useful strategies to control the SARS-CoV-2/COVID-19 pandemic.

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Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil

Cited by 23 publications

References 57 publications

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models

Battling COVID-19 using machine learning: A review

Airborne Coronaviruses: Observations from Veterinary Experience

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