Integration of background knowledge for automatic detection of inconsistencies in gene ontology annotation.

MOTIVATION: Biological background knowledge plays an important role in the manual quality assurance (QA) of biological database records. One such QA task is the detection of inconsistencies in literature-based Gene Ontology Annotation (GOA). This manual verification ensures the accuracy of the GO annotations based on a comprehensive review of the literature used as evidence, Gene Ontology (GO) terms, and annotated genes in GOA records. While automatic approaches for the detection of semantic inconsistencies in GOA have been developed, they operate within predetermined contexts, lacking the ability to leverage broader evidence, especially relevant domain-specific background knowledge. This paper investigates various types of background knowledge that could improve the detection of prevalent inconsistencies in GOA. In addition, the paper proposes several approaches to integrate background knowledge into the automatic GOA inconsistency detection process. RESULTS: We have extended a previously developed GOA inconsistency dataset with several kinds of GOA-related background knowledge, including GeneRIF statements, biological concepts mentioned within evidence texts, GO hierarchy and existing GO annotations of the specific gene. We have proposed several effective approaches to integrate background knowledge as part of the automatic GOA inconsistency detection process. The proposed approaches can improve automatic detection of self-consistency and several of the most prevalent types of inconsistencies.This is the first study to explore the advantages of utilizing background knowledge and to propose a practical approach to incorporate knowledge in automatic GOA inconsistency detection. We establish a new benchmark for performance on this task. Our methods may be applicable to various tasks that involve incorporating biological background knowledge. AVAILABILITY AND IMPLEMENTATION: https://github.com/jiyuc/de-inconsistency.

Ethical frameworks should be applied to computational modelling of infectious disease interventions.

This perspective is part of an international effort to improve epidemiological models with the goal of reducing the unintended consequences of infectious disease interventions. The scenarios in which models are applied often involve difficult trade-offs that are well recognised in public health ethics. Unless these trade-offs are explicitly accounted for, models risk overlooking contested ethical choices and values, leading to an increased risk of unintended consequences. We argue that such risks could be reduced if modellers were more aware of ethical frameworks and had the capacity to explicitly account for the relevant values in their models. We propose that public health ethics can provide a conceptual foundation for developing this capacity. After reviewing relevant concepts in public health and clinical ethics, we discuss examples from the COVID-19 pandemic to illustrate the current separation between public health ethics and infectious disease modelling. We conclude by describing practical steps to build the capacity for ethically aware modelling. Developing this capacity constitutes a critical step towards ethical practice in computational modelling of public health interventions, which will require collaboration with experts on public health ethics, decision support, behavioural interventions, and social determinants of health, as well as direct consultation with communities and policy makers.

Propagation, detection and correction of errors using the sequence database network.

Nucleotide and protein sequences stored in public databases are the cornerstone of many bioinformatics analyses. The records containing these sequences are prone to a wide range of errors, including incorrect functional annotation, sequence contamination and taxonomic misclassification. One source of information that can help to detect errors are the strong interdependency between records. Novel sequences in one database draw their annotations from existing records, may generate new records in multiple other locations and will have varying degrees of similarity with existing records across a range of attributes. A network perspective of these relationships between sequence records, within and across databases, offers new opportunities to detect-or even correct-erroneous entries and more broadly to make inferences about record quality. Here, we describe this novel perspective of sequence database records as a rich network, which we call the sequence database network, and illustrate the opportunities this perspective offers for quantification of database quality and detection of spurious entries. We provide an overview of the relevant databases and describe how the interdependencies between sequence records across these databases can be exploited by network analyses. We review the process of sequence annotation and provide a classification of sources of error, highlighting propagation as a major source. We illustrate the value of a network perspective through three case studies that use network analysis to detect errors, and explore the quality and quantity of critical relationships that would inform such network analyses. This systematic description of a network perspective of sequence database records provides a novel direction to combat the proliferation of errors within these critical bioinformatics resources.

A model-based assessment of social isolation practices for COVID-19 outbreak response in residential care facilities.

BACKGROUND: Residential aged-care facilities (RACFs, also called long-term care facilities, aged care homes, or nursing homes) have elevated risks of respiratory infection outbreaks and associated disease burden. During the COVID-19 pandemic, social isolation policies were commonly used in these facilities to prevent and mitigate outbreaks. We refer specifically to general isolation policies that were intended to reduce contact between residents, without regard to confirmed infection status. Such policies are controversial because of their association with adverse mental and physical health indicators and there is a lack of modelling that assesses their effectiveness. METHODS: In consultation with the Australian Government Department of Health and Aged Care, we developed an agent-based model of COVID-19 transmission in a structured population, intended to represent the salient characteristics of a residential care environment. Using our model, we generated stochastic ensembles of simulated outbreaks and compared summary statistics of outbreaks simulated under different mitigation conditions. Our study focuses on the marginal impact of general isolation (reducing social contact between residents), regardless of confirmed infection. For a realistic assessment, our model included other generic interventions consistent with the Australian Government's recommendations released during the COVID-19 pandemic: isolation of confirmed resident cases, furlough (mandatory paid leave) of staff members with confirmed infection, and deployment of personal protective equipment (PPE) after outbreak declaration. RESULTS: In the absence of any asymptomatic screening, general isolation of residents to their rooms reduced median cumulative cases by approximately 27%. However, when conducted concurrently with asymptomatic screening and isolation of confirmed cases, general isolation reduced the median number of cumulative infections by only 12% in our simulations. CONCLUSIONS: Under realistic sets of assumptions, our simulations showed that general isolation of residents did not provide substantial benefits beyond those achieved through screening, isolation of confirmed cases, and deployment of PPE. Our results also highlight the importance of effective case isolation, and indicate that asymptomatic screening of residents and staff may be warranted, especially if importation risk from the outside community is high. Our conclusions are sensitive to assumptions about the proportion of total contacts in a facility accounted for by casual interactions between residents.

COVID-19 vaccine coverage targets to inform reopening plans in a low incidence setting.

Since the emergence of SARS-CoV-2 in 2019 through to mid-2021, much of the Australian population lived in a COVID-19-free environment. This followed the broadly successful implementation of a strong suppression strategy, including international border closures. With the availability of COVID-19 vaccines in early 2021, the national government sought to transition from a state of minimal incidence and strong suppression activities to one of high vaccine coverage and reduced restrictions but with still-manageable transmission. This transition is articulated in the national 're-opening' plan released in July 2021. Here, we report on the dynamic modelling study that directly informed policies within the national re-opening plan including the identification of priority age groups for vaccination, target vaccine coverage thresholds and the anticipated requirements for continued public health measures-assuming circulation of the Delta SARS-CoV-2 variant. Our findings demonstrated that adult vaccine coverage needed to be at least 60% to minimize public health and clinical impacts following the establishment of community transmission. They also supported the need for continued application of test-trace-isolate-quarantine and social measures during the vaccine roll-out phase and beyond.

MPVNN: Mutated Pathway Visible Neural Network architecture for interpretable prediction of cancer-specific survival risk.

MOTIVATION: Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with a lack of interpretability. More interpretable visible neural network architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types. RESULTS: We propose a novel Mutated Pathway Visible Neural Network (MPVNN) architecture, designed using prior signaling pathway knowledge and random replacement of known pathway edges using gene mutation data simulating signal flow disruption. As a case study, we use the PI3K-Akt pathway and demonstrate overall improved cancer-specific survival risk prediction of MPVNN over other similar-sized NN and standard survival analysis methods. We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that is important in risk prediction for particular cancer types, is reliable. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/gourabghoshroy/MPVNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploring automatic inconsistency detection for literature-based gene ontology annotation.

MOTIVATION: Literature-based gene ontology annotations (GOA) are biological database records that use controlled vocabulary to uniformly represent gene function information that is described in the primary literature. Assurance of the quality of GOA is crucial for supporting biological research. However, a range of different kinds of inconsistencies in between literature as evidence and annotated GO terms can be identified; these have not been systematically studied at record level. The existing manual-curation approach to GOA consistency assurance is inefficient and is unable to keep pace with the rate of updates to gene function knowledge. Automatic tools are therefore needed to assist with GOA consistency assurance. This article presents an exploration of different GOA inconsistencies and an early feasibility study of automatic inconsistency detection. RESULTS: We have created a reliable synthetic dataset to simulate four realistic types of GOA inconsistency in biological databases. Three automatic approaches are proposed. They provide reasonable performance on the task of distinguishing the four types of inconsistency and are directly applicable to detect inconsistencies in real-world GOA database records. Major challenges resulting from such inconsistencies in the context of several specific application settings are reported. This is the first study to introduce automatic approaches that are designed to address the challenges in current GOA quality assurance workflows. The data underlying this article are available in Github at https://github.com/jiyuc/AutoGOAConsistency.

Rapid prototyping of models for COVID-19 outbreak detection in workplaces.

Early case detection is critical to preventing onward transmission of COVID-19 by enabling prompt isolation of index infections, and identification and quarantining of contacts. Timeliness and completeness of ascertainment depend on the surveillance strategy employed. This paper presents modelling used to inform workplace testing strategies for the Australian government in early 2021. We use rapid prototype modelling to quickly investigate the effectiveness of testing strategies to aid decision making. Models are developed with a focus on providing relevant results to policy makers, and these models are continually updated and improved as new questions are posed. Developed to support the implementation of testing strategies in high risk workplace settings in Australia, our modelling explores the effects of test frequency and sensitivity on outbreak detection. We start with an exponential growth model, which demonstrates how outbreak detection changes depending on growth rate, test frequency and sensitivity. From the exponential model, we learn that low sensitivity tests can produce high probabilities of detection when testing occurs frequently. We then develop a more complex Agent Based Model, which was used to test the robustness of the results from the exponential model, and extend it to include intermittent workplace scheduling. These models help our fundamental understanding of disease detectability through routine surveillance in workplaces and evaluate the impact of testing strategies and workplace characteristics on the effectiveness of surveillance. This analysis highlights the risks of particular work patterns while also identifying key testing strategies to best improve outbreak detection in high risk workplaces.

Modelling mass drug administration strategies for reducing scabies burden in Monrovia, Liberia.

Scabies is a parasitic infestation with high global burden. Mass drug administrations (MDAs) are recommended for communities with a scabies prevalence of >10%. Quantitative analyses are needed to demonstrate the likely effectiveness of MDA recommendations. In this study, we developed an agent-based model of scabies transmission calibrated to demographic and epidemiological data from Monrovia. We used this model to compare the effectiveness of MDA scenarios for achieving scabies elimination and reducing scabies burden, as measured by time until recrudescence following delivery of an MDA and disability-adjusted-life-years (DALYs) averted. Our model showed that three rounds of MDA delivered at six-month intervals and reaching 80% of the population could reduce prevalence below 2% for three years following the final round, before recrudescence. When MDAs were followed by increased treatment uptake, prevalence was maintained below 2% indefinitely. Increasing the number of and coverage of MDA rounds increased the probability of achieving elimination and the number of DALYs averted. Our results suggest that acute reduction of scabies prevalence by MDA can support a transition to improved treatment access. This study demonstrates how modelling can be used to estimate the expected impact of MDAs by projecting future epidemiological dynamics and health gains under alternative scenarios.

Value of information analysis for pandemic response: intensive care unit preparedness at the onset of COVID-19.

BACKGROUND: During the early stages of the COVID-19 pandemic, there was considerable uncertainty surrounding epidemiological and clinical aspects of SARS-CoV-2. Governments around the world, starting from varying levels of pandemic preparedness, needed to make decisions about how to respond to SARS-CoV-2 with only limited information about transmission rates, disease severity and the likely effectiveness of public health interventions. In the face of such uncertainties, formal approaches to quantifying the value of information can help decision makers to prioritise research efforts. METHODS: In this study we use Value of Information (VoI) analysis to quantify the likely benefit associated with reducing three key uncertainties present in the early stages of the COVID-19 pandemic: the basic reproduction number ([Formula: see text]), case severity (CS), and the relative infectiousness of children compared to adults (CI). The specific decision problem we consider is the optimal level of investment in intensive care unit (ICU) beds. Our analysis incorporates mathematical models of disease transmission and clinical pathways in order to estimate ICU demand and disease outcomes across a range of scenarios. RESULTS: We found that VoI analysis enabled us to estimate the relative benefit of resolving different uncertainties about epidemiological and clinical aspects of SARS-CoV-2. Given the initial beliefs of an expert, obtaining more information about case severity had the highest parameter value of information, followed by the basic reproduction number [Formula: see text]. Resolving uncertainty about the relative infectiousness of children did not affect the decision about the number of ICU beds to be purchased for any COVID-19 outbreak scenarios defined by these three parameters. CONCLUSION: For the scenarios where the value of information was high enough to justify monitoring, if CS and [Formula: see text] are known, management actions will not change when we learn about child infectiousness. VoI is an important tool for understanding the importance of each disease factor during outbreak preparedness and can help to prioritise the allocation of resources for relevant information.

PoLoBag: Polynomial Lasso Bagging for signed gene regulatory network inference from expression data.

MOTIVATION: Inferring gene regulatory networks (GRNs) from expression data is a significant systems biology problem. A useful inference algorithm should not only unveil the global structure of the regulatory mechanisms but also the details of regulatory interactions such as edge direction (from regulator to target) and sign (activation/inhibition). Many popular GRN inference algorithms cannot infer edge signs, and those that can infer signed GRNs cannot simultaneously infer edge directions or network cycles. RESULTS: To address these limitations of existing algorithms, we propose Polynomial Lasso Bagging (PoLoBag) for signed GRN inference with both edge directions and network cycles. PoLoBag is an ensemble regression algorithm in a bagging framework where Lasso weights estimated on bootstrap samples are averaged. These bootstrap samples incorporate polynomial features to capture higher-order interactions. Results demonstrate that PoLoBag is consistently more accurate for signed inference than state-of-the-art algorithms on simulated and real-world expression datasets. AVAILABILITY AND IMPLEMENTATION: Algorithm and data are freely available at https://github.com/gourabghoshroy/PoLoBag. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modelling the effect of within-host dynamics on the diversity of a multi-strain pathogen.

Multi-strain pathogens such as Group A Streptococcus, Streptococcus pneumoniae, and Staphylococcus aureus cause millions of infections each year with a substantial health burden. Control of multi-strain pathogens can be complicated by the high strain diversity often observed in endemic settings. It is not well understood how high strain diversity is maintained in populations, given that they compete with each other both directly (within an individual host) and indirectly (via host immunity). Previous modelling studies have investigated how indirect competition affects the prevalence and diversity of strains. However, these studies often make simplifying assumptions about the direct competition that occurs within hosts. Currently, little data is available to validate these assumptions, hence there is a need to clarify how sensitive model outputs are to these assumptions. In this study, we compare the dynamics of multi-strain pathogens under different assumptions about direct competition between strains using an agent-based model. We find that the assumptions made about direct competition can affect the epidemiological dynamics, particularly when there is no long-term immunity following infections and a low rate of importation of non-circulating strains. Our results suggest that while direct and indirect competition can each decrease strain diversity when they act in isolation, they may increase strain diversity when they act together. This finding highlights the importance of examining sensitivity to assumptions about strain competition. In particular, omitting consideration of direct competition can lead to inaccurate estimates of the likely effectiveness of control strategies as changes in strain diversity shift the level of direct strain competition.

Automatic consistency assurance for literature-based gene ontology annotation.

BACKGROUND: Literature-based gene ontology (GO) annotation is a process where expert curators use uniform expressions to describe gene functions reported in research papers, creating computable representations of information about biological systems. Manual assurance of consistency between GO annotations and the associated evidence texts identified by expert curators is reliable but time-consuming, and is infeasible in the context of rapidly growing biological literature. A key challenge is maintaining consistency of existing GO annotations as new studies are published and the GO vocabulary is updated. RESULTS: In this work, we introduce a formalisation of biological database annotation inconsistencies, identifying four distinct types of inconsistency. We propose a novel and efficient method using state-of-the-art text mining models to automatically distinguish between consistent GO annotation and the different types of inconsistent GO annotation. We evaluate this method using a synthetic dataset generated by directed manipulation of instances in an existing corpus, BC4GO. We provide detailed error analysis for demonstrating that the method achieves high precision on more confident predictions. CONCLUSIONS: Two models built using our method for distinct annotation consistency identification tasks achieved high precision and were robust to updates in the GO vocabulary. Our approach demonstrates clear value for human-in-the-loop curation scenarios.

Epidemiological consequences of enduring strain-specific immunity requiring repeated episodes of infection.

Group A Streptococcus (GAS) skin infections are caused by a diverse array of strain types and are highly prevalent in disadvantaged populations. The role of strain-specific immunity in preventing GAS infections is poorly understood, representing a critical knowledge gap in vaccine development. A recent GAS murine challenge study showed evidence that sterilising strain-specific and enduring immunity required two skin infections by the same GAS strain within three weeks. This mechanism of developing enduring immunity may be a significant impediment to the accumulation of immunity in populations. We used an agent-based mathematical model of GAS transmission to investigate the epidemiological consequences of enduring strain-specific immunity developing only after two infections with the same strain within a specified interval. Accounting for uncertainty when correlating murine timeframes to humans, we varied this maximum inter-infection interval from 3 to 420 weeks to assess its impact on prevalence and strain diversity, and considered additional scenarios where no maximum inter-infection interval was specified. Model outputs were compared with longitudinal GAS surveillance observations from northern Australia, a region with endemic infection. We also assessed the likely impact of a targeted strain-specific multivalent vaccine in this context. Our model produced patterns of transmission consistent with observations when the maximum inter-infection interval for developing enduring immunity was 19 weeks. Our vaccine analysis suggests that the leading multivalent GAS vaccine may have limited impact on the prevalence of GAS in populations in northern Australia if strain-specific immunity requires repeated episodes of infection. Our results suggest that observed GAS epidemiology from disease endemic settings is consistent with enduring strain-specific immunity being dependent on repeated infections with the same strain, and provide additional motivation for relevant human studies to confirm the human immune response to GAS skin infection.

Modelling testing and response strategies for COVID-19 outbreaks in remote Australian Aboriginal communities.

BACKGROUND: Remote Australian Aboriginal and Torres Strait Islander communities have potential to be severely impacted by COVID-19, with multiple factors predisposing to increased transmission and disease severity. Our modelling aims to inform optimal public health responses. METHODS: An individual-based simulation model represented SARS-CoV2 transmission in communities ranging from 100 to 3500 people, comprised of large, interconnected households. A range of strategies for case finding, quarantining of contacts, testing, and lockdown were examined, following the silent introduction of a case. RESULTS: Multiple secondary infections are likely present by the time the first case is identified. Quarantine of close contacts, defined by extended household membership, can reduce peak infection prevalence from 60 to 70% to around 10%, but subsequent waves may occur when community mixing resumes. Exit testing significantly reduces ongoing transmission. Concurrent lockdown of non-quarantined households for 14 days is highly effective for epidemic control and reduces overall testing requirements; peak prevalence of the initial outbreak can be constrained to less than 5%, and the final community attack rate to less than 10% in modelled scenarios. Lockdown also mitigates the effect of a delay in the initial response. Compliance with lockdown must be at least 80-90%, however, or epidemic control will be lost. CONCLUSIONS: A SARS-CoV-2 outbreak will spread rapidly in remote communities. Prompt case detection with quarantining of extended-household contacts and a 14 day lockdown for all other residents, combined with exit testing for all, is the most effective strategy for rapid containment. Compliance is crucial, underscoring the need for community supported, culturally sensitive responses.

Longitudinal Analysis of Group A Streptococcus emm Types and emm Clusters in a High-Prevalence Setting: Relationship between Past and Future Infections.

Group A Streptococcus is a pathogen of global importance, but despite the ubiquity of group A Streptococcus infections, the relationship between infection, colonization, and immunity is still not completely understood. The M protein, encoded by the emm gene, is a major virulence factor and vaccine candidate and forms the basis of a number of classification systems. Longitudinal patterns of emm types collected from 457 Fijian schoolchildren over a 10-month period were analyzed. No evidence of tissue tropism was observed, and there was no apparent selective pressure or constraint of emm types. Patterns of emm type acquisition suggest limited, if any, modification of future infection based on infection history. Where impetigo is the dominant mode of transmission, circulating emm types either may not be constrained by ecological niches or population immunity to the M protein, or they may require several infections over a longer period of time to induce such immunity.

Modelling the household-level impact of a maternal respiratory syncytial virus (RSV) vaccine in a high-income setting.

BACKGROUND: Respiratory syncytial virus (RSV) infects almost all children by the age of 2 years, with the risk of hospitalisation highest in the first 6 months of life. Development and licensure of a vaccine to prevent severe RSV illness in infants is a public health priority. A recent phase 3 clinical trial estimated the efficacy of maternal vaccination at 39% over the first 90 days of life. Households play a key role in RSV transmission; however, few estimates of population-level RSV vaccine impact account for household structure. METHODS: We simulated RSV transmission within a stochastic, individual-based model framework, using an existing demographic model, structured by age and household and parameterised with Australian data, as an exemplar of a high-income country. We modelled vaccination by immunising pregnant women and explicitly linked the immune status of each mother-infant pair. We quantified the impact on children for a range of vaccine properties and uptake levels. RESULTS: We found that a maternal immunisation strategy would have the most substantial impact in infants younger than 3 months, reducing RSV infection incidence in this age group by 16.6% at 70% vaccination coverage. In children aged 3-6 months, RSV infection was reduced by 5.3%. Over the first 6 months of life, the incidence rate for infants born to unvaccinated mothers was 1.26 times that of infants born to vaccinated mothers. The impact in older age groups was more modest, with evidence of infections being delayed to the second year of life. CONCLUSIONS: Our findings show that while individual benefit from maternal RSV vaccination could be substantial, population-level reductions may be more modest. Vaccination impact was sensitive to the extent that vaccination prevented infection, highlighting the need for more vaccine trial data.

Profiling Mycobacterium tuberculosis transmission and the resulting disease burden in the five highest tuberculosis burden countries.

BACKGROUND: Tuberculosis (TB) control efforts are hampered by an imperfect understanding of TB epidemiology. The true age distribution of disease is unknown because a large proportion of individuals with active TB remain undetected. Understanding of transmission is limited by the asymptomatic nature of latent infection and the pathogen's capacity for late reactivation. A better understanding of TB epidemiology is critically needed to ensure effective use of existing and future control tools. METHODS: We use an agent-based model to simulate TB epidemiology in the five highest TB burden countries-India, Indonesia, China, the Philippines and Pakistan-providing unique insights into patterns of transmission and disease. Our model replicates demographically realistic populations, explicitly capturing social contacts between individuals based on local estimates of age-specific contact in household, school and workplace settings. Time-varying programmatic parameters are incorporated to account for the local history of TB control. RESULTS: We estimate that the 15-19-year-old age group is involved in more than 20% of transmission events in India, Indonesia, the Philippines and Pakistan, despite representing only 5% of the local TB incidence. According to our model, childhood TB represents around one fifth of the incident TB cases in these four countries. In China, three quarters of incident TB were estimated to occur in the ≥ 45-year-old population. The calibrated per-contact transmission risk was found to be similar in each of the five countries despite their very different TB burdens. CONCLUSIONS: Adolescents and young adults are a major driver of TB in high-incidence settings. Relying only on the observed distribution of disease to understand the age profile of transmission is potentially misleading.

What can urban mobility data reveal about the spatial distribution of infection in a single city?

BACKGROUND: Infectious diseases spread through inherently spatial processes. Road and air traffic data have been used to model these processes at national and global scales. At metropolitan scales, however, mobility patterns are fundamentally different and less directly observable. Estimating the spatial distribution of infection has public health utility, but few studies have investigated this at an urban scale. In this study we address the question of whether the use of urban-scale mobility data can improve the prediction of spatial patterns of influenza infection. We compare the use of different sources of urban-scale mobility data, and investigate the impact of other factors relevant to modelling mobility, including mixing within and between regions, and the influence of hub and spoke commuting patterns. METHODS: We used journey-to-work (JTW) data from the Australian 2011 Census, and GPS journey data from the Sygic GPS Navigation & Maps mobile app, to characterise population mixing patterns in a spatially-explicit SEIR (susceptible, exposed, infectious, recovered) meta-population model. RESULTS: Using the JTW data to train the model leads to an increase in the proportion of infections that arise in central Melbourne, which is indicative of the city's spoke-and-hub road and public transport networks, and of the commuting patterns reflected in these data. Using the GPS data increased the infections in central Melbourne to a lesser extent than the JTW data, and produced a greater heterogeneity in the middle and outer regions. Despite the limitations of both mobility data sets, the model reproduced some of the characteristics observed in the spatial distribution of reported influenza cases. CONCLUSIONS: Urban mobility data sets can be used to support models that capture spatial heterogeneity in the transmission of infectious diseases at a metropolitan scale. These data should be adjusted to account for relevant urban features, such as highly-connected hubs where the resident population is likely to experience a much lower force of infection that the transient population. In contrast to national and international scales, the relationship between mobility and infection at an urban level is much less apparent, and requires a richer characterisation of population mobility and contact.

Determining the Best Strategies for Maternally Targeted Pertussis Vaccination Using an Individual-Based Model.

Rising pertussis incidence has prompted a number of countries to implement maternally targeted vaccination strategies to protect vulnerable infants, but questions remain about the optimal design of such strategies. We simulated pertussis transmission within an individual-based model parameterized to match Australian conditions, explicitly linking infants and their mothers to estimate the effectiveness of alternative maternally targeted vaccination strategies (antenatal delivery vs. postnatal delivery) and the benefit of revaccination over the course of multiple pregnancies. For firstborn infants aged less than 2 months, antenatal immunization reduced annual pertussis incidence by 60%, from 780 per 100,000 firstborn children under age 2 months (interquartile range (IQR), 682-862) to 315 per 100,000 (IQR, 260-370), while postnatal vaccination produced a minimal reduction, with an incidence of 728 per 100,000 (IQR, 628-789). Subsequent infants obtained limited protection from a single antenatal dose, but revaccinating mothers during every pregnancy decreased incidence for these infants by 58%, from 1,878 per 100,000 subsequent children under age 2 months (IQR, 1,712-2,076) to 791 per 100,000 (IQR, 683-915). Subsequent infants also benefited from household-level herd immunity when antenatal vaccination for every pregnancy was combined with a toddler booster dose at age 18 months; incidence was reduced to 626 per 100,000 (IQR, 548-691). Our approach provides useful information to aid consideration of alternative maternally targeted vaccination strategies and can inform development of outcome measures for program evaluation.