Browsing by Author "Bingham, Jeremy"

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    Interpreting HIV diagnostic histories into infection time estimates : analytical framework and online tool
    (BMC (part of Springer Nature), 2019-10-26) Grebe, Eduard; Facente, Shelley N.; Bingham, Jeremy; Pilcher, Christopher D.; Powrie, Andrew; Gerber, Jarryd; Priede, Gareth; Chibawara, Trust; Busch, Michael P.; Murphy, Gary; Kassanjee, Reshma; Welte, Alex
    Background: It is frequently of epidemiological and/or clinical interest to estimate the date of HIV infection or time-since-infection of individuals. Yet, for over 15 years, the only widely-referenced infection dating algorithm that utilises diagnostic testing data to estimate time-since-infection has been the ‘Fiebig staging’ system. This defines a number of stages of early HIV infection through various standard combinations of contemporaneous discordant diagnostic results using tests of different sensitivity. To develop a new, more nuanced infection dating algorithm, we generalised the Fiebig approach to accommodate positive and negative diagnostic results generated on the same or different dates, and arbitrary current or future tests – as long as the test sensitivity is known. For this purpose, test sensitivity is the probability of a positive result as a function of time since infection. Methods: The present work outlines the analytical framework for infection date estimation using subject-level diagnostic testing histories, and data on test sensitivity. We introduce a publicly-available online HIV infection dating tool that implements this estimation method, bringing together 1) curatorship of HIV test performance data, and 2) infection date estimation functionality, to calculate plausible intervals within which infection likely became detectable for each individual. The midpoints of these intervals are interpreted as infection time ‘point estimates’ and referred to as Estimated Dates of Detectable Infection (EDDIs). The tool is designed for easy bulk processing of information (as may be appropriate for research studies) but can also be used for individual patients (such as in clinical practice). Results: In many settings, including most research studies, detailed diagnostic testing data are routinely recorded, and can provide reasonably precise estimates of the timing of HIV infection. We present a simple logic to the interpretation of diagnostic testing histories into infection time estimates, either as a point estimate (EDDI) or an interval (earliest plausible to latest plausible dates of detectable infection), along with a publicly-accessible online tool that supports wide application of this logic. Conclusions: This tool, available at https://tools.incidence-estimation.org/idt/, is readily updatable as test technology evolves, given the simple architecture of the system and its nature as an open source project.
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    Prejudice, privilege, and power : conflicts and cooperation between recognizable groups
    (AIMS Press, 2019) Bingham, Jeremy; Landi, Pietro; Hui, Cang
    The problem of cooperation remains one of the fundamental questions in the fields of biology, sociology, and economics. The emergence and maintenance of cooperation are naturally affected by group dynamics, since individuals are likely to behave differently based on shared group membership. We here formulate a model of socio-economic power between two prejudiced groups, and explore the conditions for their cooperative coexistence under two social scenarios in a well-mixed environment. Each scenario corresponds to an asymmetrical increase in the payoffs for mutual cooperation in either cross-group or within-group interactions. In the 'inter-dependence' scenario payoffs of cross-group cooperation are enhanced, while in the 'group-cohesion' scenario payoffs of within-group cooperation are enhanced. We find that stable cooperative coexistence is possible only in the inter-dependence scenario. The conditions for such coexistence are highly sensitive to prejudice, defined as the reduction in probability for cross-group cooperation, and less sensitive to privilege, defined as the enhancements to payoffs of cross-group cooperation.
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    Towards a nuanced view of diagnostic test properties: an application to transfusion transmitted risk estimation
    (Stellenbosch : Stellenbosch University, 2021-03) Bingham, Jeremy; Welte, Alex; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences.
    ENGLISH ABSTRACT: Laboratory screening (rather than pathogen inactivation) is likely to remain, for the foreseeable future, the primary means of ensuring the safety of blood products from transfusion transmissible viruses such as Hepatitis B, Hepatitis C, and Human Immunodeficiency Virus (HIV). Depending on the tests used, there is generically some ‘residual risk’ of transfusion transmitted infection, as no test can guarantee detection of all potentially infectious material. Previouslydescribed risk estimation approaches 1) mostly treat detectability and infectiousness as categories rather than continuously tunable; 2) disregard sources of variability and their correlation; and 3) are not generalizable to arbitrary detection biomarkers – making it difficult to generate estimates of residual risk without extensive programmatic monitoring. We describe a broad framework for modelling test performance which incorporates hitherto neglected sources of variability in parameters governing the infectiousness and detectability of transfusion-transmissible pathogens. We utilise models based on this framework to demonstrate the relationship between test performance and residual risk for various assumptions about the biomarker/infectiousness relationship, and illustrate how the same framework may be used to inform modelling efforts in related fields - such as infection dating and incidence estimation - which rely on realistic representations of test performance. The key findings from our scenario modelling demonstrate: 1) Diminishing returns on increased screening sensitivity not evident in less flexible models; 2) increasing inter-subject variability in detectability and infectiousness leads to increasing residual risk in our general model, but lower risk estimates than in a previously described and widely used semi-mechanistic model. These effects are stronger when the average delay between infectiousness and detectability is short. Planning blood product screening algorithms in light of simulations using our models can generate robust expectations of residual risk over a wide range of test performance and product risk levels. We outline when simpler models may be relied upon, and when additional nuance must be considered.