Current crisis or artifact of surveillance: insights into rebound chlamydia rates from dynamic modelling

<p>Abstract</p> <p>Background</p> <p>After initially falling in the face of intensified control efforts, reported rates of sexually transmitted chlamydia in many developed countries are rising. Recent hypotheses for this phenomenon have broadly focused on improved case finding or an increase in the prevalence. Because of many complex interactions behind the spread of infectious diseases, dynamic models of infection transmission are an effective means to guide learning, and assess quantitative conjectures of epidemiological processes. The objective of this paper is to bring a unique and robust perspective to observed chlamydial patterns through analyzing surveillance data with mathematical models of infection transmission.</p> <p>Methods</p> <p>This study integrated 25-year testing volume data from the Canadian province of Saskatchewan with one susceptible-infected-treated-susceptible and three susceptible-infected-treated-removed compartmental models. Calibration of model parameters to fit observed 25-year case notification data, after being combined with testing records, placed constraints on model behaviour and allowed for an approximation of chlamydia prevalence to be estimated. Model predictions were compared to observed case notification trends, and extensive sensitivity analyses were performed to confirm the robustness of model results.</p> <p>Results</p> <p>Model predictions accurately mirrored historic chlamydial trends including an observed rebound in the mid 1990s. For all models examined, the results repeatedly highlighted that increased testing volumes, rather than changes in the sensitivity and specificity of testing technologies, sexual behaviour, or truncated immunological responses brought about by treatment can, explain the increase in observed chlamydia case notifications.</p> <p>Conclusions</p> <p>Our results highlight the significant impact testing volume can have on observed incidence rates, and that simple explanations for these observed increases appear to have been dismissed in favor of changes to the underlying prevalence. These simple methods not only demonstrate geographic portability, but the results reassure the public health effort towards monitoring and controlling chlamydia.</p

Agent-Based Modeling and its Tradeoffs: An Introduction & Examples

Agent-based modeling is a computational dynamic modeling technique that may be less familiar to some readers. Agent-based modeling seeks to understand the behaviour of complex systems by situating agents in an environment and studying the emergent outcomes of agent-agent and agent-environment interactions. In comparison with compartmental models, agent-based models offer simpler, more scalable and flexible representation of heterogeneity, the ability to capture dynamic and static network and spatial context, and the ability to consider history of individuals within the model. In contrast, compartmental models offer faster development time with less programming required, lower computational requirements that do not scale with population, and the option for concise mathematical formulation with ordinary, delay or stochastic differential equations supporting derivation of properties of the system behaviour. In this chapter, basic characteristics of agent-based models are introduced, advantages and disadvantages of agent-based models, as compared with compartmental models, are discussed, and two example agent-based infectious disease models are reviewed

A unified framework of immunological and epidemiological dynamics for the spread of viral infections in a simple network-based population

<p>Abstract</p> <p>Background</p> <p>The desire to better understand the immuno-biology of infectious diseases as a broader ecological system has motivated the explicit representation of epidemiological processes as a function of immune system dynamics. While several recent and innovative contributions have explored unified models across cellular and organismal domains, and appear well-suited to describing particular aspects of intracellular pathogen infections, these existing immuno-epidemiological models lack representation of certain cellular components and immunological processes needed to adequately characterize the dynamics of some important epidemiological contexts. Here, we complement existing models by presenting an alternate framework of anti-viral immune responses within individual hosts and infection spread across a simple network-based population.</p> <p>Results</p> <p>Our compartmental formulation parsimoniously demonstrates a correlation between immune responsiveness, network connectivity, and the natural history of infection in a population. It suggests that an increased disparity between people's ability to respond to an infection, while maintaining an average immune responsiveness rate, may worsen the overall impact of an outbreak within a population. Additionally, varying an individual's network connectivity affects the rate with which the population-wide viral load accumulates, but has little impact on the asymptotic limit in which it approaches. Whilst the clearance of a pathogen in a population will lower viral loads in the short-term, the longer the time until re-infection, the more severe an outbreak is likely to be. Given the eventual likelihood of reinfection, the resulting long-run viral burden after elimination of an infection is negligible compared to the situation in which infection is persistent.</p> <p>Conclusion</p> <p>Future infectious disease research would benefit by striving to not only continue to understand the properties of an invading microbe, or the body's response to infections, but how these properties, jointly, affect the propagation of an infection throughout a population. These initial results offer a refinement to current immuno-epidemiological modelling methodology, and reinforce how coupling principles of immunology with epidemiology can provide insight into a multi-scaled description of an ecological system. Overall, we anticipate these results to as a further step towards articulating an integrated, more refined epidemiological theory of the reciprocal influences between host-pathogen interactions, epidemiological mixing, and disease spread.</p

Compositional Modeling with Stock and Flow Diagrams

Stock and flow diagrams are widely used in epidemiology to model the dynamics of populations. Although tools already exist for building these diagrams and simulating the systems they describe, we have created a new package called StockFlow, part of the AlgebraicJulia ecosystem, which uses ideas from category theory to overcome notable limitations of existing software. Compositionality is provided by the theory of decorated cospans: stock and flow diagrams can be composed to form larger ones in an intuitive way formalized by the operad of undirected wiring diagrams. Our approach also cleanly separates the syntax of stock and flow diagrams from the semantics they can be assigned. We consider semantics in ordinary differential equations, although others are possible. As an example, we explain code in StockFlow that implements a simplified version of a COVID-19 model used in Canada.Comment: In Proceedings ACT 2022, arXiv:2307.1551

An Algebraic Framework for Stock & Flow Diagrams and Dynamical Systems Using Category Theory

Mathematical modeling of infectious disease at scale is important, but challenging. Some of these difficulties can be alleviated by an approach that takes diagrams seriously as mathematical formalisms in their own right. Stock & flow diagrams are widely used as broadly accessible building blocks for infectious disease modeling. In this chapter, rather than focusing on the underlying mathematics, we informally use communicable disease examples created by the implemented software of StockFlow.jl to explain the basics, characteristics, and benefits of the categorical framework. We first characterize categorical stock & flow diagrams, and note the clear separation between the syntax of stock & flow diagrams and their semantics, demonstrating three examples of semantics already implemented in the software: ODEs, causal loop diagrams, and system structure diagrams. We then establish composition and stratification frameworks and examples for stock & flow diagrams. Applying category theory, these frameworks can build large diagrams from smaller ones in a modular fashion. Finally, we introduce the open-source ModelCollab software for diagram-centric real-time collaborative modeling. Using the graphical user interface, this web-based software allows the user to undertake the types of categorically-rooted operations discussed above, but without any knowledge of their categorical foundations

Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy

Background: System science approaches are increasingly used to explore complex public health problems. Quantitative methods, such as participatory dynamic simulation modelling, can mobilise knowledge to inform health policy decisions. However, the analytic and practical steps required to turn collaboratively developed, qualitative system maps into rigorous and policy relevant quantified dynamic simulation models are not well described. This paper reports on the processes, interactions and decisions that occurred at the interface between modellers and end-user participants in an applied health sector case study focusing on diabetes in pregnancy. Methods: An analysis was conducted using qualitative data from a participatory dynamic simulation modelling case study in an Australian health policy setting. Recordings of participatory model development workshops and subsequent meetings were analysed and triangulated with field notes and other written records of discussions and decisions. Case study vignettes were collated to illustrate the deliberations and decisions made throughout the model development process. Results: The key analytic objectives and decision-making processes included: defining the model scope; analysing and refining the model structure to maximise local relevance and utility; reviewing and incorporating evidence to inform model parameters and assumptions; focusing the model on priority policy questions; communicating results and applying the models to policy processes. These stages did not occur sequentially; the model development was cyclical and iterative with decisions being re-visited and refined throughout the process. Storytelling was an effective strategy to both communicate and resolve concerns about the model logic and structure, and to communicate the outputs of the model to a broader audience. Conclusion: The in-depth analysis reported here examined the application of participatory modelling methods to move beyond qualitative conceptual mapping to the development of a rigorously quantified and policy relevant, complex dynamic simulation model. The analytic objectives and decision-making themes identified provide guidance for interpreting, understanding and reporting future participatory modelling projects and methods

Evaluation of recreational health risk in coastal waters based on enterococcus densities and bathing patterns.

We constructed a simulation model to compute the incidences of highly credible gastrointestinal illness (HCGI) in recreational bathers at two intermittently contaminated beaches of Orange County, California. Assumptions regarding spatial and temporal bathing patterns were used to determine exposure levels over a 31-month study period. Illness rates were calculated by applying previously reported relationships between enterococcus density and HCGI risk to the exposure data. Peak enterococcus concentrations occurred in late winter and early spring, but model results showed that most HCGI cases occurred during summer, attributable to elevated number of exposures. Approximately 99% of the 95,010 illness cases occurred when beaches were open. Model runs were insensitive to 0-10% swimming activity assumed during beach closure days. Comparable illness rates resulted under clustered and uniform bather distribution scenarios. HCGI attack rates were within federal guidelines of tolerable risk when averaged over the study period. However, tolerable risk thresholds were exceeded for 27 total days and periods of at least 6 consecutive days. Illness estimates were sensitive to the functional form and magnitude of the enterococcus density-HCGI relationships. The results of this study contribute to an understanding of recreational health risk in coastal waters