Strengthening Pandemic Preparedness and Response through Integrated Modelling

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Table of Contents

• Acknowledgements 11
• Abbreviations 13
• About this guide 14
• Key messages 16
• 1. Understanding the challenges of policy-making during epidemics and pandemics 19
• 1.1 Policy-making in a changing and uncertain environment 20
• 1.1.1 Understanding the complexity of pathogen transmission and disease severity 20
• 1.1.2 Identifying optimal policy responses in the face of uncertainty 21
• 1.2 Manage intense political and time pressure 23
• 1.2.1 Establishing whole-of-government and whole-of-society approaches 23
• 1.2.2 Identifying the best available evidence 24
• 1.2.3 Overcoming disciplinary silos 25
• 2. Understanding the role of integrated modelling in pandemic preparedness and response 29
• 2.1 A common framework for epidemiological and economic considerations in policy-making 30
• 2.1.1 Capturing a wider set of factors influencing the course and impacts of epidemics 30
• 2.1.2 Capturing key basic factors that drive differential impacts across populations 32
• 2.1.3 Formalizing ways to weigh the benefits and costs of various policies 32
• 2.2 A tool to compare the magnitude and uncertainty of effects of various policies 33
• 2.2.1 Designing and optimizing epidemic or pandemic control policies 33
• 2.2.2 Evaluating crisis-related economic policies 36
• 2.2.3 Making explicit assumptions about key uncertain elements 36
• 2.3 A tool to help strengthen pandemic preparedness and response in policy-making 37
• 2.3.1 Building, using and refining integrated models through an iterative process 37
• 2.3.2 Populating, calibrating and validating integrated models 39
• 3. Enhancing the production and use of integrated modelling to strengthen pandemic preparedness and response 42
• 3.1 Initiative 1: Formally incorporate integrated modelling into policy-making 43
• 3.1.1 Institutionalizing integrated modelling to inform policy-making 43
• 3.1.2 Assigning clear modelling-related responsibilities 44
• 3.1.3 Establishing procedures on the production of adequate integrated models 45
• 3.1.4 Creating an ecosystem of key data and data sources for policy-making 46
• 3.2 Initiative 2: Establish and maintain active communications 50
• 3.2.1 Creating formal communications channels for before, during and after epidemics 50
• 3.2.2 Communicating policy questions and how integrated modelling can help 50
• 3.2.3 Facilitating interactions at all stages of new model development 53
• 3.2.4 Creating active networks of policy-makers and modelling groups 53
• 3.2.5 Increasing integrated modelling literacy and communication across society 59
• 3.3 Initiative 3: Match the appropriate integrated model with the right question 60
• 3.3.1 Building base-modelling structures before epidemics and pandemics or during interepidemic or pandemic times 60
• 3.3.2 Building capabilities to determine how to match the appropriate models with the right policy questions 61
• 3.3.3 Ensuring integrated modelling can adapt to policy priorities during epidemics and pandemics 61
• 3.3.4 Creating procedures and systems for comparative integrated modelling 62
• 3.4 Initiative 4: Build local integrated modelling production capacity 62
• 3.4.1 Establishing networks that facilitate cooperation and collaboration between integrated modelling groups 63
• 3.4.2 Incorporating integrated modelling and real world decision-making into educational programmes 65
• 3.4.3 Creating new interdisciplinary modelling positions along with appropriate incentives to fill those positions 67
• Concluding remarks 69
• References 70
• Annex 1  Methods and declarations of interest 82
• Annex 2  COVID-19 integrated modelling 84
• Annex 3  Key data sources used in COVID-19 integrated models 87
• Annex 4  A framework to contextualize the four initiatives proposed to enhance the production and use of integrated modelling 92
• Annex 5 Ten key principles to help effective communication about modelling 98
• Fig. 1. Integration of epidemiological and economic considerations through in-person contacts, workforce productivity and behavioural change due to infection avoidance 31
• Fig. 2. Iterative process to build, use and refine integrated models 38
• Fig. 3. Four-step approach to engage policy-makers in model development and evolution 52
• Fig. 4. Collaboratory, an interactive knowledge-sharing space for the epidemic and pandemic intelligence community 59
• Fig. A2.1. Simplified representation of COVID-19 integrated modelling 84
• Fig. A4.1. A framework of country types along two dimensions: the degree to which modelling is currently used to inform policy-making and the current availability of modelling and integrated modelling 94
• Table 1. Projected health and short-term economic costs across all scenarios and additional GDP loss per death averted for each scenario compared to the next-less-stringent scenario 34
• Table 2. Steps in integrated modelling and how policy-makers and those supporting them can be involved 54
• Table A4.1. Summary of short-term activities by country type to formally incorporate integrated modelling into policy-making (Initiative 1) 93
• Table A4.2. Summary of short-term activities by country type to establish and maintain active communications (Initiative 2) 95
• Table A4.3. Summary of short-term activities by country type to match the appropriate integrated model with the right policy question (Initiative 3) 96
• Table A4.4. Summary of short-term activities by country type to build local integrated modelling production capacity (Initiative 4) 97
• Table A5.1. Ten key principles to help effective communication about modelling 98
• Box 1. PHSM 21
• Box 2. A decision framework to inform policy-making during the COVID-19 pandemic 24
• Box 3. Modelling the COVID-19 pandemic at the Bank of Italy 26
• Box 4. Integrated modelling to inform pandemic control strategies in Norway 28
• Box 5. Illustrative application of integrated modelling in a lower-middle-income country 34
• Box 6. The G20 Joint Finance and Health Task Force for pandemic prevention, preparedness and response 35
• Box 7. Approaches to calibrate the locally developed Tekanelo integrated model in South Africa 41
• Box 8. Calibration of an existing integrated model to inform policy-making in Sri Lanka 47
• Box 9. The FASSSTER data ecosystem and modelling platform in the Philippines 48
• Box 10. The Integrated Outbreak Analytics (IOA) initiative 49
• Box 11. A four-step approach to engage policy-makers during model development and evolution in Argentina 51
• Box 12. Sustained communication and collaboration between local and international modelling institutes, policy-makers and the WHO: the experience of the Philippines’ FASSSTER team 56
• Box 13. Collaboratory, an initiative of the WHO Hub for Pandemic and Epidemic Intelligence 58
• Box 14. The MIDAS network 64
• Box 15. Key success factors in translating modelled evidence into effective policy-making and policy impact 66