In temperate regions, respiratory virus epidemics recur on a yearly basis, primarily during the winter season. This is believed to be induced by seasonal forcing, where the rate at which the virus can be transmitted varies cyclically across the course of each year. Seasonal epidemics can place substantial burden upon the healthcare system, with large numbers of infections and hospitalisations occurring across a short time period. However, the interactions between seasonal forcing and the factors necessary for epidemic resurgence - such as waning immunity, antigenic variation or demography - remain poorly understood. In this manuscript, we examine how the dynamics of antibody waning and antigenic variation can shape the seasonal recurrence of epidemics. We develop a novel susceptible-infectious-susceptible (SIS) immuno-epidemiological model of respiratory virus spread, where the susceptible population is stratified by their antibody level against the currently circulating strain of the virus, with this decaying as both antibody waning and antigenic drift occur. In the absence of seasonal forcing, we demonstrate the existence of two Hopf bifurcations over the effective antibody decay rate, with associated periodic model solutions. When seasonal forcing is introduced, we identify complex interactions between the strength of forcing and the effective antibody decay rate, yielding myriad dynamics including multi-year periodicity, quasiperiodicity and chaos. The timing and magnitude of seasonal epidemics is highly sensitive to this interaction, with the distribution of infection timing (by time of year) varying substantially across the parameter space. Finally, we show that seasonal forcing can produce resonant damping resulting in a cumulative infection incidence that is less than would otherwise be observed.

During the COVID-19 pandemic, many countries used real-time data analyses, predictive modelling, and COVID-19 case forecasts, to incorporate emerging evidence into their decisions. In Australia, national and jurisdictional public health responses were informed by weekly ensemble forecasts of daily COVID-19 case counts for each of Australia's eight states and territories, produced by a consortium of researchers under contract with the Australian Government. As members of this consortium, who produced these forecasts at each week, we now retrospectively evaluate approximately 100,000 predictions for daily case counts 1-28 days into the future, generated between July 2020 and December 2022, and report here (a) how the ensemble forecasts supported public health responses; (b) how well the ensemble forecast performed, relative to the forecasts produced by each contributing team; and (c) how we refined our reporting and visualisations to ensure that outputs were interpreted appropriately. Similar to COVID-19 forecasting studies in other countries, we found that the ensemble forecast consistently out-performed the individual model forecasts, and that performance was lowest when there were rapid changes in the epidemiology, such as periods around epidemic peaks. Our consortium's internal peer-review process allowed us to explain how features of each ensemble forecast related to the design of the individual models, and this helped enable public health stakeholders to interpret the forecasts appropriately. Ultimately, our forecasts provided information that supported public health responses during periods of different policy goals, and over a wide range of epidemic scenarios.

During winter months of temperate regions, concurrent epidemics of multiple respiratory pathogens can occur, causing periods of increased clinical burden. Case time series, which are predominantly used to monitor infection levels, can exhibit substantial noise and day-of-the-week effects, limiting the visual interpretation of trends in raw data. However, statistical methods can infer smoothed trends within case time series by quantifying and accounting for different sources of noise. Here we apply statistical models to estimate the epidemic dynamics of SARS-CoV-2, respiratory syncytial virus (RSV), and influenza subtypes (influenza A H3N2, influenza A H1N1, and influenza B) in Victoria, Australia, over the 2024 winter season. We model trends in daily reported cases and the daily growth rate over time for all pathogens/subtypes. We present: (1) retrospective analyses using the final dataset up to 10 September 2024 and (2) weekly real-time analyses from 19 March 2024 to 10 September 2024 using data up to each timepoint, including a retrospective performance evaluation. We estimated similar peak timing of SARS-CoV-2 and RSV epidemics in late May, followed by a H3N2-dominant influenza epidemic, which peaked in early July. Transient increases in SARS-CoV-2 activity coincided with the emergence of new variants and transient decreases in influenza activity corresponded to the timing of school holidays. Real-time estimates demonstrated good agreement with those produced at the end of the season, with significant overlap of the 95% credible intervals. Our findings demonstrate how statistical methods can be implemented in real time to synthesise noisy case time-series data into interpretable trends (including uncertainty), enabling quantification of the strength of evidence for whether epidemic activity is increasing, stable or declining. Our real-time outputs were reported weekly to the Department of Health, Victoria during June-September 2024, complementing other routine surveillance indicators.

Abstract Temperate regions of the world, such as southern Australia, often experience increased health burden from respiratory pathogens during winter. The ability to forecast short-term trends in cases of these pathogens is of significant interest to public health. Across the 2024 southern hemisphere winter period, the Australia–Aotearoa Consortium for Epidemic Forecasting and Analytics (ACEFA) ran a pilot respiratory virus forecasting initiative in collaboration with the Victorian Department of Health. Each week from the 9th of May 2024 through to 12th September 2024, the consortium solicited 28-day forecasts of daily case incidence for influenza, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and respiratory syncytial virus (RSV) from multiple research groups. Four component model forecasts were contributed by three different research groups, with a fourth group utilising the component forecasts to generate ensemble forecasts (making a total of six models, four component models and two ensembles). Here we statistically evaluated the performance of each forecast and a baseline model against the observed case data. The two ensemble models were found to be frequently the top performing models. All models performed worse than the baseline model around the epidemic peaks for each pathogen.

Abstract The purpose of electronic disease syndromic surveillance (EDSyS) systems is to detect hazardous pathogens and other unusual signals in health surveillance data before such events are identified by an individual clinician or healthcare facility. However, EDSyS systems have primarily been evaluated using simulated health surveillance data, which do not necessarily capture the richness and complexities of real-world health data. We have updated and extended an existing EDSyS system, EpiDefend, which combines ensemble forecasting and recursive Bayesian estimation in a particle filter framework that supports demographic and spatial structure. We simulated the release of several pathogens, both infectious and non-infectious, and injected the resulting cases into a real-world health data set. Here we evaluate EpiDefend’s sensitivity and specificity in detecting these simulated releases, and measure the time to detection against pathogen-specific estimates of the time to clinical detection, as informed by clinicians and microbiologists. We show that for diseases where clinical diagnosis can be challenging, such as Q fever (Coxiella burnetii) and tularaemia (Francisella tularensis), EpiDefend can reliably beat the time to clinical detection. In contrast, for pathogens that can be clinically diagnosed relatively quickly, such as inhalational anthrax and pneumonic plague, it is extremely difficult to beat the time to clinical detection. Our results suggest that EpiDefend may be able to reliably detect real-world introductions or releases of some pathogens at low false-alarm rates before a clinical diagnosis would be confirmed, and this would represent a landmark achievement for EDSyS systems.

Abstract Cell tropism, or the preference of a virus for particular cell types, has major implications for viral transmission, pathogenesis, and evolution. An increase in viral fitness — increased within-host replication, also leading to increased transmission between hosts — can result from a virus changing its cell tropism. This is illustrated in the context of influenza, where adaptation to infect cells expressing α 2-6 linked sialic acid receptors enhances human-to-human transmissibility. Target cell populations differ not only in abundance but also in intrinsic properties such as susceptibility, viral production, and interferon responses, rendering the relationship between tropism and viral fitness multi-faceted and complex. Understanding how different cell tropisms quantitatively change fitness remains an important open question in virology and quantitative biology. Here, we present a within-host mathematical model that incorporates distinct target cell types differing in key properties, and examine how cell tropism affects viral fitness, as measured by metrics such as peak viral load, infection duration, or total virus produced. Our analysis reveals that tradeoffs may arise when cell types differ by multiple characteristics. We further demonstrate that model parameters describing heterogeneity between cell types can be more accurately inferred when cell type proportions are measured alongside viral load. Our findings provide a framework for assessing the links between viral evolution, cell tropism, and within-host fitness, and motivate the design of experiments to collect quantitative data on between-cell heterogeneity.

Introductions of H5N1 clade 2.3.4.4b into dairy cattle have resulted in outbreaks on dairy farms across the United States since early-2024. Outbreaks have significant consequences for animal health, result in economic losses for the dairy industry, and pose a threat to human health. Though the relative contributions of different on-farm transmission pathways remain a key uncertainty, a major route is considered to be through repeated contamination of milking stalls (i.e. the equipment and area where an individual cow is milked) due to the milking of infected animals. Here we develop mathematical models of H5N1 transmission dynamics on dairy farms, considering multiple possible transmission pathways, and identify factors that contribute to outbreak risk and on-farm interventions for mitigating risk. In particular, we demonstrate that dividing cattle into 'milking cohorts', with cohorts kept in separate pens or paddocks and milked in the same order every day, would be highly effective at mitigating outbreaks irrespective of the dominant transmission pathway. Cohorting cattle is most effective when implemented pre-emptively (i.e. before an outbreak) and when newly introduced cattle are kept in the final milking cohort. Additionally, we demonstrate that frequent bulk milk sample testing (e.g. weekly) would enable the rapid detection of outbreaks and implementation of reactive interventions (or scaling up of existing interventions). Our findings can support the development of management guidelines for effectively responding to H5N1 outbreaks in dairy cattle.

Since early-2024 unprecedented outbreaks of highly pathogenic avian influenza H5N1 clade 2.3.4.4b have been ongoing in dairy cattle in the United States with significant consequences for the dairy industry and public health. Estimation of key epidemiological parameters is required to support outbreak response, including predicting the likely effectiveness of interventions and testing strategies. Here, we pool limited publicly available data from four studies of naturally and experimentally infected dairy cattle. We quantify Ct value trajectories of infected dairy cattle and the relationship between Ct value and the log-titer of infectious virus, a proxy for infectiousness. We estimate that following infection minimum Ct values are rapidly reached within 1-2 days with a population mean Ct value of 15.7 (12.9, 18.4). We identify a threshold Ct value of 21.8 (19.9, 24.6), with values of Ct value above this threshold representing little-to-no infectious viral load. Finally, assuming a direct relationship between Ct value and infectiousness, we estimate the distribution of the duration of infectiousness for dairy cattle (i.e., the duration their Ct value remains below the critical threshold) with a population median of 7.8 (4.1, 13.9) days. Our estimates will be critical inputs to the development of outbreak management guidelines and modeling analyses informing response strategies.

A characteristic of malaria in all its forms is the potential for superinfection (that is, multiple concurrent blood-stage infections). An additional characteristic of Plasmodium vivax malaria is a reservoir of latent parasites (hypnozoites) within the host liver, which activate to cause (blood-stage) relapses. Here, we present a model of hypnozoite accrual and superinfection for P. vivax. To couple host and vector dynamics for a homogeneously-mixing population, we construct a density-dependent Markov population process with countably many types, for which disease extinction is shown to occur almost surely. We also establish a functional law of large numbers, taking the form of an infinite-dimensional system of ordinary differential equations that can also be recovered by coupling expected host and vector dynamics (i.e. a hybrid approximation) or through a standard compartment modelling approach. Recognising that the subset of these equations that model the infection status of the human hosts has precisely the same form as the Kolmogorov forward equations for a Markovian network of infinite server queues with an inhomogeneous batch arrival process, we use physical insight into the evolution of the latter process to write down a time-dependent multivariate generating function for the solution. We use this characterisation to collapse the infinite-compartment model into a single integrodifferential equation (IDE) governing the intensity of mosquito-to-human transmission. Through a steady state analysis, we recover a threshold phenomenon for this IDE in terms of a parameter R_0 expressible in terms of the primitives of the model, with the disease-free equilibrium shown to be uniformly asymptotically stable if R_0<1 and an endemic equilibrium solution emerging if R_0>1 . Our work provides a theoretical basis to explore the epidemiology of P. vivax, and introduces a strategy for constructing tractable population-level models of malarial superinfection that can be generalised to allow for greater biological realism in a number of directions.

We developed a flexible infectious disease model framework that combines a detailed individual-based model of arrival pathways (quarantine model) and an individual-based model of the arrivals environment (community model) to inform border risk assessments. The work was motivated by Australia's desire to safely increase international arrival volumes, which had been heavily constrained since early 2020 as a result of the COVID-19 pandemic. These analyses supported decisions on quarantine and border policy in the context of the Australian government's national reopening plan in late 2021. The quarantine model provides a detailed representation of transmission within quarantine and time-varying infectiousness and test sensitivity within individuals, to characterize the likelihood and infectiousness of breaches from quarantine. The community model subsequently captures the impact on these infectious individuals in the presence of varying vaccination coverage, arrival volumes, public health and social measures (PHSMs) and test-trace-isolate-quarantine system effectiveness in the Australian context. Our results showed that high vaccination coverage would be required to safely reopen with support from ongoing PHSMs, and quarantine pathways have minimal impact on infection dynamics in the presence of existing local transmission. The modelling pipeline we present can be flexibly adapted to a range of scenarios, and thus provides a useful framework for generating timely risk assessments in the event of future pandemics.

Monitoring the number of COVID-19 patients in hospital beds was a critical component of Australia’s real-time surveillance strategy for the disease. From 2021 to 2023, we produced short-term forecasts of bed occupancy to support public health decision-making. We present a model for forecasting the number of ward and intensive care unit (ICU) beds occupied by COVID-19 cases. The model simulates the stochastic progression of COVID-19 patients through the hospital system and is fit to reported occupancy counts using an approximate Bayesian method. We do not directly model infection dynamics—instead, taking independently produced forecasts of case incidence as an input—enabling the independent development of our model from that of the underlying case forecast(s). Here, we evaluate the performance of 21-day forecasts of ward and ICU occupancy across Australia’s eight states and territories produced across the period March and September 2022. We find forecasts are on average biased downwards immediately prior to epidemic peaks and biased upwards post-peak. Forecast performance is best in jurisdictions with the largest population sizes. Our forecasts of COVID-19 hospital burden were reported weekly to national decision-making committees to support Australia’s public health response. Our modular approach for forecasting clinical burden is found to enable both the independent development of our model from that of the underlying case forecast(s) and the performance benefits of an ensemble case forecast to be leveraged by our occupancy forecasts. During the COVID-19 pandemic, predicting the potential future impact of the disease on hospitals was crucial. In this study, we aimed to predict how many hospital ward and intensive care unit (ICU) beds would be needed for COVID-19 patients in Australia with the aim of supporting public health decision-makers. Our approach used forecasts of new COVID-19 cases as input and factored in real-time information such as how likely cases were to be hospitalised or require ICU care. We found that, while generally accurate, our forecasts tended to underpredict just before a wave of infections peaked, and overpredict after the peak had passed. Our flexible modelling method could be adapted to predict hospital needs for other infectious diseases in the future, helping to prepare for epidemics of illnesses like influenza or RSV. Tobin et al. present a model for forecasting COVID-19 ward and intensive care unit (ICU) bed occupancy in Australia, using independently produced case incidence forecasts as input. Evaluating 21-day forecasts produced between March to September 2022, performance varied with jurisdiction and epidemiological context.

Quantifying the extent to which previous infections and vaccinations confer protection against future infection or disease outcomes is critical to managing the transmission and consequences of infectious diseases. We present a general statistical model for predicting the strength of protection conferred by different immunising exposures (numbers, types, and strains of both vaccines and infections), against multiple outcomes of interest, whilst accounting for immune waning. We predict immune protection against key clinical outcomes: developing symptoms, hospitalisation, and death. We also predict transmission-related outcomes: acquisition of infection and onward transmission in breakthrough infections. These enable quantification of the impact of immunity on population-level transmission dynamics. Our model calibrates the level of immune protection, drawing on both population-level data, such as vaccine effectiveness estimates, and neutralising antibody levels as a correlate of protection. This enables the model to learn realised immunity levels beyond those which can be predicted by antibody kinetics or other correlates alone. We demonstrate an application of the model for SARS-CoV-2, and predict the individual-level protective effectiveness conferred by natural infections with the Delta and the Omicron B.1.1.529 variants, and by the BioNTech-Pfizer (BNT162b2), Oxford-AstraZeneca (ChAdOx1), and 3rd-dose mRNA booster vaccines, against outcomes for both Delta and Omicron. We also demonstrate a use case of the model in late 2021 during the emergence of Omicron, showing how the model can be rapidly updated with emerging epidemiological data on multiple variants in the same population, to infer key immunogenicity and intrinsic transmissibility characteristics of the new variant, before the former can be more directly observed via vaccine effectiveness data. This model provided timely inference on rapidly evolving epidemic situations of significant concern during the early stages of the COVID-19 pandemic. The general nature of the model enables it to be used to support management of a range of infectious diseases.

As demonstrated during the COVID-19 pandemic, non-pharmaceutical interventions, such as case isolation, are an important element of pandemic response. The overall impact of case isolation on epidemic dynamics depends on a number of factors, including the timing of isolation relative to the onset of contagiousness for each individual instructed to isolate by public health authorities. While there is an extensive literature examining the importance of minimising the delay from exposure to direction to isolate in determining the impact of case isolation policy, less is known about how underlying epidemic dynamics may also contribute to that impact. Empirical observation and modelling studies have shown that, as an epidemic progresses, the distribution of viral loads among cases changes systematically. In principle, this may allow for more targeted and efficient isolation strategies to be implemented. Here, we describe a multi-scale agent-based model developed to investigate how isolation strategies that account for cases viral loads could be incorporated into policy. We compare the impact and efficiency of isolation strategies in which all cases, regardless of their viral load, are required to isolate to strategies in which some cases may be exempt from isolation. Our findings show that, following the epidemic peak, the vast majority of cases identified with a low viral load are in the declining phase of their infection and so contribute less to overall contagiousness. This observation prompts the question about the potential public health value of discontinuing isolation for such individuals. Our numerical investigation of this 'adaptive' strategy shows that exempting individuals with low viral loads from isolation following the epidemic peak leads to a modest increase in new infections. Surprisingly, it also leads to a drop in efficiency, as measured by the average number of infections averted per isolated case. Our findings therefore suggest caution in adopting such flexible or adaptive isolation policies. Our multi-scale modelling framework is sufficiently flexible to enable extensive numerical evaluation of more complex isolation strategies that incorporate more disease-specific biological and epidemiological features, supporting the development and evaluation of future public health pandemic response plans.

Since its emergence in 1968, influenza A H3N2 has caused yearly epidemics in temperate regions. While infection confers immunity against antigenically similar strains, new antigenically distinct strains that evade existing immunity regularly emerge ('antigenic drift'). Immunity at the individual level is complex, depending on an individual's lifetime infection history. An individual's first infection with influenza typically elicits the greatest response with subsequent infections eliciting progressively reduced responses ('antigenic seniority'). The combined effect of individual-level immune responses and antigenic drift on the epidemiological dynamics of influenza are not well understood. Here we develop an integrated modelling framework of influenza transmission, immunity, and antigenic drift to show how individual-level exposure, and the build-up of population level immunity, shape the long-term epidemiological dynamics of H3N2. Including antigenic seniority in the model, we observe that following an initial decline after the pandemic year, the average annual attack rate increases over the next 80 years, before reaching an equilibrium, with greater increases in older age-groups. Our analyses suggest that the average attack rate of H3N2 is still in a growth phase. Further increases, particularly in the elderly, may be expected in coming decades, driving an increase in healthcare demand due to H3N2 infections.

Since early-2024 unprecedented outbreaks of highly pathogenic avian influenza H5N1 clade 2.3.4.4b have been ongoing in dairy cattle in the United States with significant consequences for the dairy industry and public health. Estimation of key epidemiological parameters is required to support outbreak response, including predicting the likely effectiveness of interventions and testing strategies. Here we pool limited publicly available data from three studies of naturally and experimentally infected dairy cattle. We quantify Ct value trajectories of infected dairy cattle and the relationship between Ct value and the log-titre of infectious virus, a proxy for infectiousness. We estimate that following infection peak Ct values are rapidly reached within 1--2 days with a population mean Ct value of 16.9 (13.2, 20.5). We identify a critical threshold Ct value of 21.5 (20.1, 23.6), with values of Ct value above this threshold representing little-to-no infectious viral load. Finally, we estimate the distribution of the duration of infectiousness for dairy cattle (i.e. the duration their Ct value remains above the critical threshold) with a population median of 6.2 (2.8, 13.1) days. ### Competing Interest Statement The authors have declared no competing interest.

Increasingly, experimentalists and modellers alike have come to recognise the important role of spatial structure in infection dynamics. Almost invariably, spatial computational models of viral infections — as with in vitro experimental systems — represent the tissue as wide and flat, which is often assumed to be representative of the entire affected tissue within the host. However, this assumption fails to take into account the distinctive geometry of the respiratory tract in the context of viral infections. The respiratory tract is characterised by a tubular, branching structure, and moreover is spatially heterogeneous: deeper regions of the lung are composed of far narrower airways and are associated with more severe infection. Here, we extend a typical multicellular model of viral dynamics to account for two essential features of the geometry of the respiratory tract: the tubular structure of airways, and the branching process between airway generations. We show that, with this more realistic tissue geometry, the dynamics of infection are substantially changed compared to standard computational and experimental approaches, and that the resulting model is equipped to tackle important biological phenomena that do not arise in a flat host tissue, including viral lineage dynamics, and heterogeneity in immune responses to infection in different regions of the respiratory tree. Our findings suggest aspects of viral dynamics which current in vitro systems may be insufficient to describe, and points to several features of respiratory infections which can be experimentally assessed.

Background During the COVID-19 pandemic, many countries implemented mass community testing programs, where individuals would seek tests due to (primarily) the onset of symptoms. The cases recorded by mass testing programs represent only a fraction of infected individuals, and depend on how many people seek testing. If test-seeking behaviour exhibits heterogeneities or changes over time, and this is not accounted for when analysing case data, then inferred epidemic dynamics used to inform public health decision-making can be biased.Methods Here we describe temporal trends in COVID-19 test-seeking behaviour in Australia by symptoms, age group, test type, and jurisdiction from November 2021–September 2023. We use data from two surveillance systems: a weekly nationwide behavioural survey (NBS), established by the Australian Government to monitor a range of behavioural responses to COVID-19; and Australia’s FluTracking system, a ‘participatory surveillance system’ designed for monitoring influenza-like illness and health-care seeking behaviour, which was adapted in early 2020 to include questions relevant to COVID-19.Results We found that peaks in test-seeking behaviour generally aligned with peaks in the rate of reported cases. Test-seeking behaviour rapidly increased in early-2022 coinciding with greater availability of rapid antigen tests. There were heterogeneities in test-seeking behaviour by jurisdiction and age-group, which were dynamic through time. Test-seeking behaviour was lowest in older individuals (60+ years) until July 2022, after which there was greater homogeneity across age-groups. Test-seeking behaviour was highest in the Australian Capital Territory and Tasmania and consistently lowest in Queensland. Over the course of the study test-seeking behaviour was highest in individuals who reported symptoms more predictive of COVID-19 infection. There was a greater probability of seeking a test for individuals in FluTracking compared to the NBS, suggesting that participatory surveillance systems such as FluTracking may include a health-conscious subset of the population.Conclusions Our findings demonstrate the dynamism of test-seeking behaviour, highlighting the importance of the continued collection of behavioural data through dedicated surveillance systems.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementFunding for this work was provided by the Australian Government Department of Health and Aged Care and the National Health and Medical Research Council of Australia through the Investigator Grant Scheme (FMS Emerging Leader Fellowship, 2021/GNT2010051)### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Human Research Ethics Committee of the University of Melbourne gave ethical approval for this work (reference number 2023-26949-40340-2)I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAccess to the individual-level survey data from the NBS and FluTracking is restricted to protect participants’ anonymity. The aggregated values and confidence intervals for all figures and supplementary figures are provided in the supplementary materials.

There is growing recognition in both the experimental and modelling literature of the importance of spatial structure to the dynamics of viral infections in tissues. Aided by the evolution of computing power and motivated by recent biological insights, there has been an explosion of new, spatially-explicit models for within-host viral dynamics in recent years. This development has only been accelerated in the wake of the COVID-19 pandemic. Spatially-structured models offer improved biological realism and can account for dynamics which cannot be well-described by conventional, mean-field approaches. However, despite their growing popularity, spatially-structured models of viral dynamics are underused in biological applications. One major obstacle to the wider application of such models is the huge variety in approaches taken, with little consensus as to which features should be included and how they should be implemented for a given biological context. Previous reviews of the field have focused on specific modelling frameworks or on models for particular viral species. Here, we instead apply a scoping review approach to the literature of spatially-structured viral dynamics models as a whole to provide an exhaustive update of the state of the field. Our analysis is structured along two axes, methodology and viral species, in order to examine the breadth of techniques used and the requirements of different biological applications. We then discuss the contributions of mathematical and computational modelling to our understanding of key spatially-structured aspects of viral dynamics, and suggest key themes for future model development to improve robustness and biological utility.

Contact tracing is an important public health measure used to reduce transmission of infectious diseases. Contact tracers typically conduct telephone interviews with cases to identify contacts and direct them to quarantine, with the aim of preventing onward transmission. However, in situations where caseloads exceed the capacity of the public health system, timely interviews may not be feasible for all cases. Here we present a modelling framework for assessing the impact of different case interview prioritisation strategies on disease transmission. Our model is based on Australian contact tracing procedures and informed by contact tracing data on COVID-19 cases notified in Australia from 2020 to 2021. Our results demonstrate that last-in-first-out strategies (where cases with the most recent swab or notification dates are interviewed first) are more effective at reducing transmission than first-in-first-out strategies (where cases with the oldest swab or notification dates are interviewed first) or strategies with no explicit prioritisation. To maximise the public health benefit from a given case interview capacity, public health practitioners may consider our findings when designing case interview prioritisation protocols for outbreak response.