About this Research Topic
Three years into the COVID-19 pandemic, we have entered a persistent pandemic stage caused by the Omicron variant of SARS-CoV-2 that emerged in late 2021. The rapid spread of the Omicron variant, its diverse epidemiological profile, and relaxed public health control measures in most countries, has resulted in its establishment as the predominant variant globally. Co-circulation of multiple Omicron sub-variants, reinfections associated with immune escape, and changes in pharmaceutical interventions have transformed the epidemiology of and evolutionary pressures on the virus. The implementation of health policies and surveillance, including non-pharmaceutical interventions (NPIs), vaccination (as well as booster) rollout, diagnostic testing, and whole genomic sequencing, has become more challenging than ever due to pandemic fatigue and the reallocation of resources over time.
Living with COVID-19 poses a unique public health challenge: how do we cope with the ever-evolving virus, while minimising disruptions to commercial and social activities? This is a question that requires further investigation. Epidemic modelling has played a crucial role in different fronts since the start of the pandemic, including predicting the severity of the pandemic, evaluating the effects of different NPIs and vaccination coverage, as well as estimating disease burdens in health care and social systems. These fronts are further complicated in the Omicron era due to several factors including waning immunity and immune escape, varying public adherence to voluntary NPIs (e.g. isolation, social distancing), and increased social and spatial interactions. Exploring the impacts of these factors through the lens of modelling will help us better prepare for the next phase of the pandemic.
This research topic aims to gain insights in the emergence and persistence of COVID-19 from a modelling perspective, shedding light on how to shape more effective and agile public health policies and interventions. We welcome original research articles and reviews to contribute to this topic based on, but not limited to, the following aspects:
• epidemiological characteristics (e.g. size, duration, and severity of waves)
• disease burdens (e.g. impacts on infrastructure systems and healthcare systems)
• societal and behavioural impacts (e.g. adherence to NPIs and booster rollout)
• spatiotemporal considerations (e.g. mobility, heterogeneous mixing in population).
• surveillance systems (e.g. genomic surveillance, diagnostic testing, representative sampling)
Living with COVID-19 poses a unique public health challenge: how do we cope with the ever-evolving virus, while minimising disruptions to commercial and social activities? This is a question that requires further investigation. Epidemic modelling has played a crucial role in different fronts since the start of the pandemic, including predicting the severity of the pandemic, evaluating the effects of different NPIs and vaccination coverage, as well as estimating disease burdens in health care and social systems. These fronts are further complicated in the Omicron era due to several factors including waning immunity and immune escape, varying public adherence to voluntary NPIs (e.g. isolation, social distancing), and increased social and spatial interactions. Exploring the impacts of these factors through the lens of modelling will help us better prepare for the next phase of the pandemic.
This research topic aims to gain insights in the emergence and persistence of COVID-19 from a modelling perspective, shedding light on how to shape more effective and agile public health policies and interventions. We welcome original research articles and reviews to contribute to this topic based on, but not limited to, the following aspects:
• epidemiological characteristics (e.g. size, duration, and severity of waves)
• disease burdens (e.g. impacts on infrastructure systems and healthcare systems)
• societal and behavioural impacts (e.g. adherence to NPIs and booster rollout)
• spatiotemporal considerations (e.g. mobility, heterogeneous mixing in population).
• surveillance systems (e.g. genomic surveillance, diagnostic testing, representative sampling)
Keywords: Epidemic modelling, pandemic modelling, COVID-19, social distancing, computational epidemiology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
