Strengthening health systems and maintaining essential service delivery during health emergencies response is critical for early detection and diagnosis, prompt treatment, and effective control of pandemics, including the novel coronavirus disease 2019 (COVID-19). Health information systems (HIS) developed during recent Ebola outbreaks in West Africa and the Democratic Republic of the Congo (DRC) provided opportunities to collect, analyze, and distribute data to inform both day-to-day and long-term policy decisions on outbreak preparedness. As COVID-19 continues to sweep across the globe, HIS and related technological advancements remain vital for effective and sustained data sharing, contact tracing, mapping and monitoring, community risk sensitization and engagement, preventive education, and timely preparedness and response activities. In reviewing literature of how HIS could have further supported mitigation of these Ebola outbreaks and the ongoing COVID-19 pandemic, 3 key areas were identified: governance and coordination, health systems infrastructure and resources, and community engagement. In this concept study, we outline scalable HIS lessons from recent Ebola outbreaks and early COVID-19 responses along these 3 domains, synthesizing recommendations to offer clear, evidence-based approaches on how to leverage HIS to strengthen the current pandemic response and foster community health systems resilience moving forward.

Despite growing international attention, the anthropological and socio-behavioral elements of epidemics continue to be understudied and under resourced and lag behind the traditional outbreak response infrastructure. As seen in the current 2019 coronavirus disease (COVID-19) pandemic, the importance of socio-behavioral elements in understanding transmission and facilitating control of many outbreak-prone pathogens, this is problematic. Beyond the recent strengthening of global outbreak response capacities and global health security measures, a greater focus on the socio-behavioral components of outbreak response is required. We add to the current discussion by briefly highlighting the importance of socio-behavior in the Ebola virus disease (EVD) response, and describe vital areas of future development, including methods for community engagement and validated frameworks for behavioral modeling and change in outbreak settings.

The response to the novel coronavirus outbreak in China suggests that many of the lessons from the 2003 SARS epidemic have been implemented and the response improved as a consequence. Nevertheless some questions remain and not all lessons have been successful. The national and international response demonstrates the complex link between public health, science and politics when an outbreak threatens to impact on global economies and reputations. The unprecedented measures implemented in China are a bold attempt to control the outbreak – we need to understand their effectiveness to balance costs and benefits for similar events in the future.

The capacity to rapidly distinguish Ebola virus disease from other infectious diseases and to monitor biochemistry and viremia levels is crucial to the clinical management of suspected Ebola virus disease cases. This article describes the design and practical considerations of a laboratory straddling the high- and low-risk zones of an Ebola treatment center to produce timely diagnostic and clinical results for informed case management of Ebola virus disease in real-life conditions. This innovation may be of relevance for actors requiring flexible laboratory implementation in contexts of high-communicability, high-lethality disease outbreaks.

We developed an individual-based (IB) model to explore the stochastic attributes of state transitions, the heterogeneity of the individual interactions, and the impact of different network structure choices on the poliovirus transmission process in the context of understanding the dynamics of outbreaks. We used a previously published differential equation-based model to develop the IB model and inputs. To explore the impact of different types of networks, we implemented a total of 26 variations of six different network structures in the IB model. We found that the choice of network structure plays a critical role in the model estimates of cases and the dynamics of outbreaks. This study provides insights about the potential use of an IB model to support policy analyses related to managing the risks of polioviruses and shows the importance of assumptions about network structure.