Modeling and computation of multistep batch testing for infectious diseases.
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ABSTRACT:
SUBMITTER: Ahn H
PROVIDER: S-EPMC8251120 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. Here, we introduce testing effectiveness (TE)-the fraction by which testing and post-diagnosis isolation reduce transmission at the population scale-and a model that incorporates test specifications and usage, within-host pathogen dynamics, and human behaviors to estimate TE. Using TE to guide recommendations, we show that today's rapid diagnostics should be used immediately upon symptom onset to control influenza A and respiratory syncytial virus (RSV), but delayed by up to 2d to control omicron-era SARS-CoV-2. Furthermore, while rapid tests are superior to RT-qPCR for control of founder-strain SARS-CoV-2, omicron-era changes in viral kinetics and rapid test sensitivity cause a reversal, with higher TE for RT-qPCR despite longer turnaround times. Finally, we illustrate the model's flexibility by quantifying tradeoffs in the use of post-diagnosis testing to shorten isolation times.
| S-EPMC10557819 | biostudies-literature
Project description:ObjectivesOur objective was to simulate the distribution of human papillomavirus (HPV) DNA test results from a 96-well microplate assay to identify results that may be consistent with well-to-well contamination, enabling programs to apply specific quality assurance parameters.Materials and methodsFor this modeling study, we designed an algorithm that generated the analysis population of 900,000 to simulate the results of 10,000 microplate assays, assuming discrete HPV prevalences of 12%, 13%, 14%, 15%, and 16%. Using binomial draws, the algorithm created a vector of results for each prevalence and reassembled them into 96-well matrices for results distribution analysis of the number of positive cells and number and size of cell clusters (≥2 positive cells horizontally or vertically adjacent) per matrix.ResultsFor simulation conditions of 12% and 16% HPV prevalence, 95% of the matrices displayed the following characteristics: 5 to 17 and 8 to 22 total positive cells, 0 to 4 and 0 to 5 positive cell clusters, and largest cluster sizes of up to 5 and up to 6 positive cells, respectively.ConclusionsOur results suggest that screening programs in regions with an oncogenic HPV prevalence of 12% to 16% can expect 5 to 22 positive results per microplate in approximately 95% of assays and 0 to 5 positive results clusters with no cluster larger than 6 positive results. Results consistently outside of these ranges deviate from what is statistically expected and could be the result of well-to-well contamination. Our results provide guidance that laboratories can use to identify microplates suspicious for well-to-well contamination, enabling improved quality assurance.
| S-EPMC6023602 | biostudies-literature
Project description:The progress of the SARS-CoV-2 pandemic requires the design of large-scale, cost-effective testing programs. Pooling samples provides a solution if the tests are sensitive enough. In this regard, the use of the gold standard, RT-qPCR, raises some concerns. Recently, droplet digital PCR (ddPCR) was shown to be 10-100 times more sensitive than RT-qPCR, making it more suitable for pooling. Furthermore, ddPCR quantifies the RNA content directly, a feature that, as we show, can be used to identify nonviable samples in pools. Cost-effective strategies require the definition of efficient deconvolution and re-testing procedures. In this paper we analyze the practical implementation of an efficient hierarchical pooling strategy for which we have recently derived the optimal, determining the best ways to proceed when there are impediments for the use of the absolute optimum or when multiple pools are tested simultaneously and there are restrictions on the throughput time. We also show how the ddPCR RNA quantification and the nested nature of the strategy can be combined to perform self-consistency tests for a better identification of infected individuals and nonviable samples. The studies are useful to those considering pool testing for the identification of infected individuals.
| S-EPMC8438083 | biostudies-literature
Project description:Background:Mathematical models are increasingly used to understand the dynamics of infectious diseases, including "chronic" infections with long generation times. Such models include features that are obscure to most clinicians and decision-makers. Methods:Using a model of a hypothetical active case-finding intervention for tuberculosis in India as an example, we illustrate the effects on model results of different choices for model structure, input parameters, and calibration process. Results:Using the same underlying data, different transmission models produced different estimates of the projected intervention impact on tuberculosis incidence by 2030 with different corresponding uncertainty ranges. We illustrate the reasons for these differences and present a simple guide for clinicians and decision-makers to evaluate models of infectious diseases. Conclusions:Mathematical models of chronic infectious diseases must be understood to properly inform policy decisions. Improved communication between modelers and consumers is critical if model results are to improve the health of populations.
| S-EPMC5716064 | biostudies-literature
Project description:The COVID-19 epidemic brought to the forefront the value of mathematical modelling for infectious diseases as a guide to help manage a formidable challenge for human health. A standard dynamic model widely used for a spreading epidemic separates a population into compartments-each comprising individuals at a similar stage before, during, or after infection-and keeps track of the population fraction in each compartment over time, by balancing compartment loading, discharge, and accumulation rates. The standard model provides valuable insight into when an epidemic spreads or what fraction of a population will have been infected by the epidemic's end. A subtle issue, however, with that model, is that it may misrepresent the peak of the infectious fraction of a population, the time to reach that peak, or the rate at which an epidemic spreads. This may compromise the model's usability for tasks such as "Flattening the Curve" or other interventions for epidemic management. Here we develop an extension of the standard model's structure, which retains the simplicity and insights of the standard model while avoiding the misrepresentation issues mentioned above. The proposed model relies on replacing a module of the standard model by a module resulting from Padé approximation in the Laplace domain. The Padé-approximation module would also be suitable for incorporation in the wide array of standard model variants used in epidemiology. This warrants a re-examination of the subject and could potentially impact model-based management of epidemics, development of software tools for practicing epidemiologists, and related educational resources.
| S-EPMC9056532 | biostudies-literature
Project description:Agent-based network modeling (ABNM) simulates each person at the individual-level as agents of the simulation, and uses network generation algorithms to generate the network of contacts between individuals. ABNM are suitable for simulating individual-level dynamics of infectious diseases, especially for diseases such as HIV that spread through close contacts within intricate contact networks. However, as ABNM simulates a scaled-version of the full population, consisting of all infected and susceptible persons, they are computationally infeasible for studying certain questions in low prevalence diseases such as HIV. We present a new simulation technique, agent-based evolving network modeling (ABENM), which includes a new network generation algorithm, Evolving Contact Network Algorithm (ECNA), for generating scale-free networks. ABENM simulates only infected persons and their immediate contacts at the individual-level as agents of the simulation, and uses the ECNA for generating the contact structures between these individuals. All other susceptible persons are modeled using a compartmental modeling structure. Thus, ABENM has a hybrid agent-based and compartmental modeling structure. The ECNA uses concepts from graph theory for generating scale-free networks. Multiple social networks, including sexual partnership networks and needle sharing networks among injecting drug-users, are known to follow a scale-free network structure. Numerical results comparing ABENM with ABNM estimations for disease trajectories of hypothetical diseases transmitted on scale-free contact networks are promising for application to low prevalence diseases.
| S-EPMC8459606 | biostudies-literature
Project description:Current modeling of infectious diseases allows for the study of realistic scenarios that include population heterogeneity, social structures, and mobility processes down to the individual level. The advances in the realism of epidemic description call for the explicit modeling of individual behavioral responses to the presence of disease within modeling frameworks. Here we formulate and analyze a metapopulation model that incorporates several scenarios of self-initiated behavioral changes into the mobility patterns of individuals. We find that prevalence-based travel limitations do not alter the epidemic invasion threshold. Strikingly, we observe in both synthetic and data-driven numerical simulations that when travelers decide to avoid locations with high levels of prevalence, this self-initiated behavioral change may enhance disease spreading. Our results point out that the real-time availability of information on the disease and the ensuing behavioral changes in the population may produce a negative impact on disease containment and mitigation.
| S-EPMC3216549 | biostudies-literature
Project description:BackgroundMany cost-effectiveness analyses (CEAs) only consider outcomes for a single disease when comparing interventions that prevent or treat 1 disease (e.g., vaccination) to interventions that prevent or treat multiple diseases (e.g., vector control to prevent mosquito-borne diseases). An intervention targeted to a single disease may be preferred to a broader intervention in a single-disease model, but this conclusion might change if outcomes from the additional diseases were included. However, multidisease models are often complex and difficult to construct.MethodsWe present conditions for when multiple diseases should be considered in such a CEA. We propose methods for estimating health outcomes and costs associated with control of additional diseases using parallel single-disease models. Parallel modeling can incorporate competing mortality and coinfection from multiple diseases while maintaining model simplicity. We illustrate our approach with a CEA that compares a dengue vaccine, a chikungunya vaccine, and mosquito control via insecticide and mosquito nets, which can prevent dengue, chikungunya, Zika, and yellow fever.ResultsThe parallel models and the multidisease model generated similar estimates of disease incidence and deaths with much less complexity. When using this method in our case study, considering only chikungunya and dengue, the preferred strategy was insecticide. A broader strategy-insecticide plus long-lasting insecticide-treated nets-was not preferred when Zika and yellow fever were included, suggesting the conclusion is robust even without the explicit inclusion of all affected diseases.LimitationsParallel modeling assumes independent probabilities of infection for each disease.ConclusionsWhen multidisease effects are important, our parallel modeling method can be used to model multiple diseases accurately while avoiding additional complexity.
| S-EPMC8831671 | biostudies-literature
Project description:Modeling the global dynamics of emerging infectious diseases (EIDs) like COVID-19 can provide important guidance in the preparation and mitigation of pandemic threats. While age-structured transmission models are widely used to simulate the evolution of EIDs, most of these studies focus on the analysis of specific countries and fail to characterize the spatial spread of EIDs across the world. Here, we developed a global pandemic simulator that integrates age-structured disease transmission models across 3,157 cities and explored its usage under several scenarios. We found that without mitigations, EIDs like COVID-19 are highly likely to cause profound global impacts. For pandemics seeded in most cities, the impacts are equally severe by the end of the first year. The result highlights the urgent need for strengthening global infectious disease monitoring capacity to provide early warnings of future outbreaks. Additionally, we found that the global mitigation efforts could be easily hampered if developed countries or countries near the seed origin take no control. The result indicates that successful pandemic mitigations require collective efforts across countries. The role of developed countries is vitally important as their passive responses may significantly impact other countries.
| S-EPMC10153731 | biostudies-literature
Project description:BackgroundMost people around the world do not have access to facility-based diagnostic testing, and the gap in availability of diagnostic tests is a major public health challenge. Self-testing, self-sampling, and institutional testing outside conventional clinical settings are transforming infectious disease diagnostic testing in a wide range of low- and middle-income countries (LMICs). We examined the delivery models of infectious disease diagnostic testing outside clinics to assess the impact on test uptake and linkage to care.MethodsWe conducted a systematic review and meta-analysis, searching 6 databases and including original research manuscripts comparing testing outside clinics with conventional testing. The main outcomes were test uptake and linkage to care, delivery models, and adverse outcomes. Data from studies with similar interventions and outcomes within thematic areas of interest were pooled, and the quality of evidence was assessed using GRADE. This study was registered in PROSPERO (CRD42019140828).We identified 10 386 de-duplicated citations, and 76 studies were included. Data from 18 studies were pooled in meta-analyses. Studies focused on HIV (48 studies), chlamydia (8 studies), and multiple diseases (20 studies). HIV self-testing increased test uptake compared with facility-based testing (9 studies: pooled odds ratio [OR], 2.59; 95% CI, 1.06-6.29; moderate quality). Self-sampling for sexually transmitted infections increased test uptake compared with facility-based testing (7 studies: pooled OR, 1.74; 95% CI, 0.97-3.12; moderate quality). Conclusions. Testing outside of clinics increased test uptake without significant adverse outcomes. These testing approaches provide an opportunity to expand access and empower patients. Further implementation research, scale-up of effective service delivery models, and policies in LMIC settings are needed.
| S-EPMC7545117 | biostudies-literature