Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning

Khadilkar, Harshad; Ganu, Tanuja; Seetharam, Deva P.

doi:10.1007/s41403-020-00129-3

Cited by 46 publications

(38 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Essential services, supply chains, and some parts of the market must remain active since households cannot retain supplies and remain self-sufficient for such extended periods. Therefore, a practical PWQ can at most be implemented at a level of η = 70-75% 49 .…”

Section: Resultsmentioning

confidence: 99%

Alternating quarantine for sustainable epidemic mitigation

et al. 2021

View full text Add to dashboard Cite

Absent pharmaceutical interventions, social distancing, lock-downs and mobility restrictions remain our prime response in the face of epidemic outbreaks. To ease their potentially devastating socioeconomic consequences, we propose here an alternating quarantine strategy: at every instance, half of the population remains under lockdown while the other half continues to be active - maintaining a routine of weekly succession between activity and quarantine. This regime minimizes infectious interactions, as it allows only half of the population to interact for just half of the time. As a result it provides a dramatic reduction in transmission, comparable to that achieved by a population-wide lockdown, despite sustaining socioeconomic continuity at ~50% capacity. The weekly alternations also help address the specific challenge of COVID-19, as their periodicity synchronizes with the natural SARS-CoV-2 disease time-scales, allowing to effectively isolate the majority of infected individuals precisely at the time of their peak infection.

show abstract

Section: Resultsmentioning

confidence: 99%

Alternating quarantine for sustainable epidemic mitigation

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Permasalahan yang kemudian terjadi setelah diterapkannya physical distancing dan social distancing adalah merubah proses pembelajaran di sekolah menjadi belajar di rumah ini kemudian memberikan dampak yang signifikan terhadap perkembangan Pendidikan siswa. Pembelajaran yang selalu di kelas dengan berbagai metode pembelajaran baik konvensional maupun inovasi metode menjadi terhambat (Khadilkar et al, 2020;Mussardo, 2019;Uscher-Pines et al, 2018). salah satu jenjang Pendidikan yang paling dipengaruhi oleh penerapan lockdown adalah jenjang Pendidikan Anak Usia Dini (PAUD).…”

Section: Pendahuluanunclassified

“…Biasanya pembelajaran diberikan sepenuhnya kepada guru di sekolah dalam mendidik anak. Akan tetapi sekarang setelah adanya wabah covid-19 kemudian mengharuskan orang tua sebagai guru dan juga sekolah bagi anak (Khadilkar et al, 2020). Proses aktivitas anak pada masa lockdown bervariasi, hal ini dikarenakan setiap anak memiliki aktivitasnya sendiri Ketika di rumah.…”

Section: Pendahuluanunclassified

Implementasi Pembelajaran pada Masa Lockdown bagi Lembaga PAUD di Kabupaten Lombok Timur

Nahdi¹,

Ramdhani²,

Yuliatin³

et al. 2020

View full text Add to dashboard Cite

Lembaga PAUD sebagai pelaksana proses pembelajaran berperan penting dalam pelaksanaan pembelajaran di rumah selama pandemi. Tujuan dari penelitian ini adalah untuk melihat implementasi pembelajaran di PAUD selama Pandemi Covid-19 dari Lembaga PAUD yang ada di Kabupaten Lombok Timur. Terdapat 30 sampel Lembaga PAUD dengan jenis penelitian kualitatif. Instrumen yang digunakan adalah wawancara, observasi, angket dan dokumentasi. proses analisis data menggunakan analisis miles & Hubberman dengan triangulasi data. Tiga aktivitas utama yang digunakan dalam penerapan pembelajaran yaitu penugasan (A), Home Visit (B), Laporan Kegiatan Harian Anak (3). Ketiga aktivitas utama ini yang paling banyak digunakan oleh pihak Lembaga PAUD adalah Penugasan (A) dan kolaborasi Penugasan dengan Home Visit (AB), dan terdapat pula Lembaga yang menggunakan ketiga aktivitas kegiatan pembelajaran tersebut yaitu kolaborasi Penugasan , Home Visit dan Laporan Kegiatan Anak (ABC)

show abstract

“…They demonstrated that DQL can be useful in the context of epidemiological decision-making. Khadilkar et al [21] proposed a methodology to compute optimal lockdown/release policies learnt with a RL algorithm in the context of COVID-19 pandemic. They considered the function of disease parameters (infectiousness, gestation period, duration of symptoms, and probability of death) and population characteristics (density, movement propensity).…”

Section: Artificial Intelligence To Fight Covid-19mentioning

confidence: 99%

A Methodology Based on Deep Q-Learning/Genetic Algorithms for Optimizing COVID-19 Pandemic Government Actions

Miralles-Pechuán

Jiménez

Ponce

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

Whenever countries are threatened by a pandemic, as is the case with the COVID-19 virus, governments need help to take the right actions to safeguard public health as well as to mitigate the negative effects on the economy. A restrictive approach can seriously damage the economy. Conversely, a relaxed one may put at risk a high percentage of the population. Other investigations in this area are focused on modelling the spread of the virus or estimating the impact of the different measures on its propagation. However, in this paper, we propose a new methodology for helping governments in planning the phases to combat the pandemic based on their priorities. To this end, we implement the SEIR epidemiological model to represent the evolution of the COVID-19 virus on the population. To optimize the best sequences of actions governments can take, we propose a methodology with two approaches, one based on Deep Q-Learning and another one based on Genetic Algorithms. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system focused on meeting two objectives: firstly, getting few people infected so that hospitals are not overwhelmed, and secondly, avoiding taking drastic measures which could cause serious damage to the economy. The conducted experiments evaluate our methodology based on the accumulated rewards during the established period. The experiments also prove that it is a valid tool for governments to reduce the negative effects of a pandemic by optimizing the planning of the phases. According to our results, the approach based on Deep Q-Learning outperforms the one based on Genetic Algorithms.

show abstract

Optimising Lockdown Policies for Epidemic Control using Reinforcement Learning

Cited by 46 publications

References 5 publications

Alternating quarantine for sustainable epidemic mitigation

Alternating quarantine for sustainable epidemic mitigation

Implementasi Pembelajaran pada Masa Lockdown bagi Lembaga PAUD di Kabupaten Lombok Timur

A Methodology Based on Deep Q-Learning/Genetic Algorithms for Optimizing COVID-19 Pandemic Government Actions

Contact Info

Product

Resources

About