Implementation Science to Respond to the COVID-19 Pandemic.

Arianna Rubin Means,Bryan J Weiner,Eli Kern,Laura P Newman,Anjuli D Wagner

doi:10.3389/fpubh.2020.00462

Abstract

The COVID-19 pandemic continues to expand globally, requiring massive public health responses from national and local governments. These bodies have taken heterogeneous approaches to their responses, including when and how to introduce and enforce evidence-based interventions—such as social distancing, hand-washing, personal protective equipment (PPE), and testing. In this commentary, we reflect on opportunities for implementation science to contribute meaningfully to the COVID-19 pandemic response. We reflect backwards on missed opportunities in emergency preparedness planning, using the example of PPE stockpiling and supply management; this planning could have been strengthened through process mapping with consensus-building, microplanning with simulation, and stakeholder engagement. We propose current opportunities for action, focusing on enhancing the adoption, fidelity, and sustainment of hand washing and social distancing; we can combine qualitative data, policy analysis, and dissemination science to inform agile and rapid adjustment to social marketing strategies to enhance their penetration. We look to future opportunities to enhance the integration of new evidence in decision-making, focusing on serologic and virologic testing systems; we can leverage simulation and other systems engineering modeling to identify ideal system structures. Finally, we discuss the ways in which the COVID-19 pandemic challenges implementation science to become more rapid, rigorous, and nimble in its approach, and integrate with public health practice. In summary, we articulate the ways in which implementation science can inform, and be informed by, the COVID-19 pandemic, looking backwards, proposing actions for the moment, and approaches for the future.

Highlights

COVID-19 has emerged in early 2020 as a pandemic, marked by a classical exponential growth curve and global spread
State and local public health response efforts have focused on testing populations of public health significance, case and contact investigation, and infection prevention
Medical response to this novel virus has relied on limited supplies of personal protective equipment (PPE) for providers, ventilation assistance for severe cases, and testing

Summary

INTRODUCTION

COVID-19 has emerged in early 2020 as a pandemic, marked by a classical exponential growth curve and global spread. Even in the absence of an evidence-based intervention, IS can guide evidence-based decision-making as data become available This means having systems that help governments and health facilities incorporate evidence agilely: key attributes include strengthening stakeholder networks with clear communication channels, social mobilization mechanisms that can be quickly activated, and microplanning tools to rapidly guide resource allocation. As an effective vaccine becomes available, advanced use of these models can accelerate efficient distribution by prioritizing certain values—such as maximizing equity, or minimizing costs—and can compare a range of scenarios for decision-makers These modeling tools can be developed as new evidence-based interventions are either created or scaled up. IS will benefit from more purposeful integration within public health departments and health organizations, not exclusively within academic organizations

CONCLUSIONS

DATA AVAILABILITY STATEMENT