Jessica E. Stockdale

Decisions on public health interventions to control infectious diseases are often informed by computational models. Interpreting the predicted outcomes of a public health decision requires not only high-quality modelling but also an ethical framework for assessing the benefits and harms associated with different options. The design and specification of ethical frameworks matured independently of computational modelling, so many values recognized as important for ethical decision-making are missing from computational models. We demonstrate a proof-of-concept approach to incorporate multiple public health values into the evaluation of a simple computational model for vaccination against a pathogen such as SARS-CoV-2. By examining a bounded space of alternative prioritizations of three values relevant to public health ethics (aggregate clinical burden, equity in clinical burden, equity in adverse effects from vaccination), we identify value trade-offs, where the outcomes of optimal strategies differ depending on the ethical framework. This work demonstrates an approach to incorporating diverse values into decision criteria used to evaluate outcomes of models of infectious disease interventions.

Estimating the time-varying reproduction number ℛ _t during an epidemic is important. ℛ _t indicates whether an epidemic is growing or declining and can aid in assessing the impact of interventions. Recent advances have enhanced methods for estimating ℛ _t and other epidemiological parameters from surveillance and genomic data independently. The Birth-Death Skyline (BDSKY) in BEAST 2.5 and EpiEstim are two common methods used to estimate ℛ _t from these data sources. We introduce an outbreak simulation platform that generates pathogen sequence data and epidemiological linelists. We use this platform to to assess ℛ _t estimation methods’ accuracy under various sampling scenarios similar to what was observed during past epidemics. We identified biases and determined appropriate scenarios for improving the accuracy of ℛ _t estimation approaches based on multiple outbreak simulations. When data becomes sparse and unreliable, genomic sequence data provide reasonable ℛ _t estimates even when sampling is not uniform.

The continued evolution of the SARS-CoV-2 virus drove waves of infection worldwide throughout the pandemic. These evolutionary dynamics posed significant challenges for public health forecasting and, specifically, for predicting the size of COVID-19 waves. In this work we leverage a range of global public data, with a focus on features derived from pathogen genomic sequences, to model and predict the relative size of COVID-19 waves (as compared to the previous wave) across countries. Focusing on Omicron BA.1 and BA.2, we develop statistical models to assess the predictive power of these data in forecasting future variant-driven wave peaks. We find that, while forecasting wave size is a challenging task, variables such as genomic variant characteristics, prior wave dynamics, and demographic features e.g. life expectancy were informative, whereas seasonality was not. Our results show that the importance of features changed markedly between Omicron waves, reflecting the evolving epidemiological and genomic landscape. This work provides insights into improving predictive models for future outbreaks and pandemics, and prioritizing data collection efforts to enhance forecasting accuracy.

Almost all models used in analysis of infectious disease outbreaks contain some notion of population size, usually taken as the census population size of the community in question. In many settings, however, the census population is not equivalent to the population likely to be exposed, for example if there are population structures, outbreak controls or other heterogeneities. Although these factors may be taken into account in the model: adding compartments to a compartmental model, variable mixing rates and so on, this makes fitting more challenging, especially if the population complexities are not fully known. In this work we consider the concept of effective population size in outbreak modelling, which we define as the size of the population involved in an outbreak, as an alternative to use of more complex models. Effective population size is an important quantity in genetics for estimation of genetic diversity loss in populations, but it has not been widely applied in epidemiology. Through simulation studies and application to data from outbreaks of COVID-19 in China, we find that simple SIR models with effective population size can provide a good fit to data which are not themselves simple or SIR.

The seasonal influenza (flu) vaccine is designed to protect against those influenza viruses predicted to circulate during the upcoming flu season, but identifying which viruses are likely to circulate is challenging. We use features from phylogenetic trees reconstructed from hemagglutinin (HA) and neuraminidase (NA) sequences, together with a support vector machine, to predict future circulation. We obtain accuracies of 0.75 to 0.89 (AUC 0.83 to 0.91) over 2016–2020. We explore ways to select potential candidates for a seasonal vaccine and find that the machine learning model has a moderate ability to select strains that are close to future populations. However, consensus sequences among the most recent 3 years also do well at this task. We identify similar candidate strains to those proposed by the World Health Organization, suggesting that this approach can help inform vaccine strain selection.

Serial intervals – the time between symptom onset in infector and infectee – are a fundamental quantity in infectious disease control. However, their estimation requires knowledge of individuals’ exposures, typically obtained through resource-intensive contact tracing efforts. We introduce an alternate framework using virus sequences to inform who infected whom and thereby estimate serial intervals. We apply our technique to SARS-CoV-2 sequences from case clusters in the first two COVID-19 waves in Victoria, Australia. We find that our approach offers high resolution, cluster-specific serial interval estimates that are comparable with those obtained from contact data, despite requiring no knowledge of who infected whom and relying on incompletely-sampled data. Compared to a published serial interval, cluster-specific serial intervals can vary estimates of the effective reproduction number by a factor of 2–3. We find that serial interval estimates in settings such as schools and meat processing/packing plants are shorter than those in healthcare facilities.