MEPI-MS11

Models of COVID-19 Vaccination, Non-Pharmaceutical Interventions, and Relaxation

Wednesday, June 16 at 02:15am (PDT)
Wednesday, June 16 at 10:15am (BST)
Wednesday, June 16 06:15pm (KST)

SMB2021 Follow Wednesday (Thursday) during the "MS11" time block.

Note: this minisymposia has multiple sessions. The second session is MS16-MEPI (click here). The third session is MS20-MEPI (click here).

Jane Heffernan (York University, Canada), Miranda Teboh Ewungkem (Lehigh University, USA), Zhilan Feng (Purdue University, USA), John Glasser (Centres for Disease Control, USA)

Description:

At present, health policymakers are focused on allocating available vaccine among healthcare, other essential workers, and vulnerable segments of their populations. As immunity to SARS-CoV-2 increases, however, their attention will turn increasingly to assessments of the relative effectiveness of non-pharmaceutical interventions so that the least effective ones, especially those with adverse economic impact, and possibly eventually all, can be relaxed. As transmission modeling can inform such decisions, we will invite several speakers to share their recent work to address question on vaccination, NPI interventions, and relaxation.

Bruce Mellado

(University of Witwatersand, South Africa)

"Modelling the COVID-19 pandemic in South Africa: the role of AI"

In this presentation work performed by the Gauteng Province Premier COVID-19 Advisory Committee in data analysis, modelling, predictions and vaccine roll-out straggles. The use of Artificial Intelligence through Machine Learning in devising smart algorithms will be highlighted. The challenges of interfacing advanced analytics with advising policy-makers will also be discussed.

Ellen Brooks Pollock

(University of Bristol, England)

"Mapping social distancing measures to the reproduction number during vaccine rollout"

Background: In the absence of a vaccine, SARS-CoV-2 transmission has been controlled by preventing person-to-person interactions via social distancing measures. As vaccination is rolled out and social distancing restrictions are lifted, policy-makers need to consider how combinations of measures will affect transmission and understand the trade-offs between them. Methods: We use age-specific social contact data collect in the UK in 2010, together with epidemiological data, to quantify the components of the COVID-19 reproduction number. We estimate the impact of social distancing policies and vaccination rollout on the reproduction number in the UK. Results: We demonstrate that pre-collected social contact data can be used to provide a time-varying estimate of the reproduction number (R). Transmission associated with primary schools is predicted to have a smaller impact on community prevalence than transmission in secondary schools. Prioritising older age groups for vaccination leads to modest initial indirect benefits of vaccination. Some levels of contact tracing and COVID security are required until the majority of the adult population are vaccinated. The results can be explored at https://ellenbp.shinyapps.io/reckoners/. Conclusions: Our approach has been widely used by policy-makers to project the impact of social distancing measures and assess the trade-offs between them. Effective social distancing, contact tracing and COVID-security are required while vaccination is rolled out.

Nick Golding

(Curtin University, Australia)

"Real-time tracking and forecasting of COVID-19 transmission potential in Australia"

The Australian COVID-19 pandemic experience has been characterised by long periods of no transmission interspersed with localised, and mostly small outbreaks linked to spillover from quarantine facilities for international arrivals. COVID-19 preparedness and response decision making in Australia has therefore been focused on the potential for outbreaks to take-off and the likely impact of interventions on preventing that. However in the absence of cases, standard models for estimating the reproduction rate of the virus cannot be used. We will detail a novel semi-mechanistic Bayesian statistical model developed to track COVID-19 transmission potential in Australia over time. This quantity can be tracked even in the absence of cases by drawing on mobility data streams, behavioural surveys, and data on health surveillance systems. The effects of lockdowns, varying adherence to hygiene measures, age-structured vaccination roll-out, variants with different transmissibility, and the effectiveness of health surveillance systems are all explicitly considered. This model is able to track the potential rate of transmission in the absence of cases, the realised rate of transmission in the presence of cases, and to move smoothly between these metrics. A particular advantage of this approach for the Australian context is the ability to derive estimates of transmission rates in the very early stages of an outbreak, when numbers of cases are still in the single figures. This model has informed the Australian response to COVID-19 throughout the pandemic - as discussed in a separate talk by Dr Freya Shearer at this conference.

Wilfred Ndifon

(African Institute for the Mathematical Sciences, South Africa)

"Vaccinating to Minimize COVID-19 Morbidity"

The morbidity caused by an acute infectious disease like COVID-19 is frequently measured only with respect to the short-term effects of infection. We consider an important longer-term effect, namely the deterioration of immune functioning due to accelerated senescence of pathogen-responsive T cells. Using both mathematics and data, we argue that this type of immune deterioration is negligible in young adults but substantial in older adults. A consideration that emerges in the current context of a limited COVID-19 vaccine supply is how to optimize vaccination in order to minimize such immune deterioration. We show that, compared to alternative vaccination strategies, prioritizing older adults as well as individuals who have an already significantly deteriorated immune functioning is optimal. Because, as we argue using data, the severity of SARS-CoV-2 infections increases with immune deterioration, this vaccination strategy would also save the most lives. Our mathematical framework offers a natural explanation for the higher risk of SARS-CoV-2 infection-induced death that has been observed in men compared to women.

Hosted by SMB2021 Follow

Virtual conference of the Society for Mathematical Biology, 2021.