Living With COVID-19: A Systemic and Multi-Criteria Approach to Enact Evidence-Based Health Policy

The lifting of COVID-19 (coronavirus disease 2019) lockdown requires, in the short and medium terms, a holistic and evidence-based approach to population health management based on combining risk factors and bio-economic outcomes, including actors' behaviors. This dynamic and global approach to health control is necessary to deal with the new paradigm of living with an infectious disease, which disrupts our individual freedom and behaviors. The challenge for policymakers consists of defining methods of lockdown-lifting and follow-up (middle-term rules) that best meet the needs for resumption of economic activity, societal wellbeing, and containment of the outbreak. There is no simple and ready-to-use way to do this since it means considering several competing objectives at the same time and continuously adapting the strategy and rules, ideally at local scale. We propose a framework for creating a precision evidence-based health policy that simultaneously considers public health, economic, and societal dimensions while accounting for constraints and uncertainty. It is based on the four following principles: integrating multiple and heterogeneous information, accepting navigation with uncertainty, adjusting the strategy dynamically with feedback mechanisms, and managing clusters through a multi-scalar conception. The evidence-based policy intervention for COVID-19 obtained includes scientific background via epidemiological modeling and bio-economic modeling. A set of quantitative and qualitative indicators are used as feedback to precisely monitor the societal-economic-epidemiological dynamics, allowing tightening or loosening of measures before epidemic damage (re-)occurs. Altogether, this allows an evidence-based policy that steers the strategy with precision and avoids any political shock.

[1]  V. Colizza,et al.  Expected impact of lockdown in Île-de-France and possible exit strategies , 2020, medRxiv.

[2]  Vittoria Colizza,et al.  Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies , 2020, BMC Medicine.

[3]  E. Ostrom,et al.  Insight, part of a Special Feature on A Framework for Analyzing, Comparing, and Diagnosing Social-Ecological Systems Social-ecological system framework: initial changes and continuing challenges , 2014 .

[4]  H. Eakin,et al.  Promoting agency for social-ecological transformation: a transformation-lab in the Xochimilco social-ecological system , 2018 .

[5]  K. Arrow,et al.  Social-ecological systems as complex adaptive systems: modeling and policy implications , 2012, Environment and Development Economics.

[6]  J. Glazer,et al.  Multiple payers, commonality and free-riding in health care: Medicare and private payers. , 2002, Journal of health economics.

[7]  J. Xiang,et al.  Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study , 2020, The Lancet.

[8]  C. Murray Forecasting the impact of the first wave of the COVID-19 pandemic on hospital demand and deaths for the USA and European Economic Area countries , 2020, medRxiv.

[9]  F. Macciardi,et al.  Systems healthcare: a holistic paradigm for tomorrow , 2017, BMC Systems Biology.

[10]  John W. Kingdon Agendas, alternatives, and public policies , 1984 .

[11]  D. Studdert,et al.  Disease Control, Civil Liberties, and Mass Testing - Calibrating Restrictions during the Covid-19 Pandemic. , 2020, The New England journal of medicine.

[12]  G Hardin,et al.  The tragedy of the commons. The population problem has no technical solution; it requires a fundamental extension in morality. , 1968, Science.

[13]  J. Earp,et al.  Social Ecological Approaches to Individuals and Their Contexts , 2012, Health education & behavior : the official publication of the Society for Public Health Education.

[14]  Dennis Andersson,et al.  A retrospective cohort study , 2018 .

[15]  P. Hassenteufel Sociologie politique : l'action publique , 2011 .

[16]  R. Veerkamp,et al.  Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle. , 2019, Journal of dairy science.

[17]  E. Ostrom A diagnostic approach for going beyond panaceas , 2007, Proceedings of the National Academy of Sciences.

[18]  Maxwell Pinz,et al.  Explaining the Bomb-Like Dynamics of COVID-19 with Modeling and the Implications for Policy , 2020, medRxiv.

[19]  M. M. Álvarez,et al.  Modeling COVID-19 epidemics in an Excel spreadsheet: Democratizing the access to first-hand accurate predictions of epidemic outbreaks , 2020, medRxiv.

[20]  C. Whittaker,et al.  Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand , 2020 .

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

[22]  K. Shadan,et al.  Available online: , 2012 .

[23]  Jan Rotmans,et al.  Conceptualizing, Observing, and Influencing Social-Ecological Transitions , 2009 .

[24]  G. Hardin,et al.  The Tragedy of the Commons , 1968, Green Planet Blues.

[25]  Navonil Mustafee,et al.  Rethinking health systems strengthening: key systems thinking tools and strategies for transformational change. , 2016, Health policy and planning.

[26]  M. Lipsitch,et al.  Projecting the transmission dynamics of SARS-CoV-2 through the postpandemic period , 2020, Science.

[27]  J. Sallis,et al.  Ecological models of health behavior. , 2008 .

[28]  D. Hennessy,et al.  Asymmetric Information, Externalities and Incentives in Animal Disease Prevention and Control , 2018 .

[29]  V. Dubois L'action publique , 2009 .