Optimal Lockdown Policy for Covid-19: A Modelling Study

As the COVID-19 spreads across the world, prevention measures are becoming the essential weapons to combat against the pandemic in the period of crisis. The lockdown measure is the most controversial one as it imposes an overwhelming impact on our economy and society. Especially when and how to enforce the lockdown measures are the most challenging questions considering both economic and epidemiological costs. In this paper, we extend the classic SIR model to find optimal decision making to balance between economy and people’s health during the outbreak of COVID-19. In our model, we intend to solve a two phases optimisation problem: policymakers control the lockdown rate to maximise the overall welfare of the society; people in different health statuses take different decisions on their working hours and consumption to maximise their utility. We develop a novel method to estimate parameters for the model through various additional sources of data. We use the Cournot equilibrium to model people’s behaviour and also consider the cost of death in order to leverage between economic and epidemic costs. The analysis of simulation results provides scientific suggestions for policymakers to make critical decisions on when to start the lockdown and how strong it should be during the whole period of the outbreak. Although the model is originally proposed for the COVID-19 pandemic, it can be generalised to address similar problems to control the outbreak of other infectious diseases with the lockdown measures.

[1]  Fausto Gozzi,et al.  A Simple Planning Problem for COVID-19 Lockdown , 2020, SSRN Electronic Journal.

[2]  J. Rocklöv,et al.  The reproductive number of COVID-19 is higher compared to SARS coronavirus , 2020, Journal of travel medicine.

[3]  P. Klepac,et al.  Early dynamics of transmission and control of COVID-19: a mathematical modelling study , 2020, The Lancet Infectious Diseases.

[4]  S. Bhatt,et al.  Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe , 2020, Nature.

[5]  Dirk Niepelt,et al.  On the Optimal "Lockdown" During an Epidemic , 2020, SSRN Electronic Journal.

[6]  G. Leung,et al.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.

[7]  Guido Lorenzoni,et al.  Macroeconomic Implications of Covid-19: Can Negative Supply Shocks Cause Demand Shortages? , 2020, SSRN Electronic Journal.

[8]  C. Gollier Cost–benefit analysis of age‐specific deconfinement strategies , 2020 .

[9]  Patrick Jenny,et al.  Dynamic Modeling to Identify Mitigation Strategies for Covid-19 Pandemic , 2020, medRxiv.

[10]  M. K. Mak,et al.  Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates , 2014, Appl. Math. Comput..

[11]  D. Cummings,et al.  Strategies for containing an emerging influenza pandemic in Southeast Asia , 2005, Nature.

[12]  Ruifu Yang,et al.  An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China , 2020, Science.

[13]  Victor Chernozhukov,et al.  Optimal Targeted Lockdowns in a Multi-Group Sir Model , 2020 .

[14]  S. Eubank,et al.  Commentary on Ferguson, et al., “Impact of Non-pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand” , 2020, Bulletin of Mathematical Biology.

[15]  Kyle F. Herkenhoff,et al.  An Seir Infectious Disease Model with Testing and Conditional Quarantine , 2020, SSRN Electronic Journal.

[16]  M. Eichenbaum,et al.  The Macroeconomics of Epidemics , 2020, The Review of Financial Studies.

[17]  Hu Zhang,et al.  An evaluation of mathematical models for the outbreak of COVID-19 , 2020, Precision clinical medicine.

[18]  HighWire Press Proceedings of the Royal Society of London. Series A, Containing papers of a mathematical and physical character , 1934 .

[19]  A. Vespignani,et al.  Modelling the impact of testing, contact tracing and household quarantine on second waves of COVID-19 , 2020, Nature Human Behaviour.

[20]  Facundo Piguillem,et al.  Optimal Covid-19 Quarantine and Testing Policies , 2020, The Economic Journal.

[21]  W. O. Kermack,et al.  A contribution to the mathematical theory of epidemics , 1927 .

[22]  Venky Venkateswaran,et al.  Optimal Mitigation Policies in a Pandemic: Social Distancing and Working from Home , 2020, The Review of Financial Studies.

[23]  Maryam Farboodi,et al.  Internal and external effects of social distancing in a pandemic , 2020, Journal of Economic Theory.

[24]  M. Tertilt,et al.  An Economic Model of the Covid-19 Epidemic: The Importance of Testing and Age-Specific Policies , 2020, SSRN Electronic Journal.

[25]  Yang Liu,et al.  Early dynamics of transmission and control of COVID-19: a mathematical modelling study , 2020, The Lancet Infectious Diseases.

[26]  Miguel Faria-e-Castro,et al.  Fiscal policy during a pandemic , 2020, Journal of Economic Dynamics and Control.

[27]  H. Hethcote Three Basic Epidemiological Models , 1989 .