models

Various tools for investigate the data in this data portal or models

🩠 Vector Control and Policy: The Ross–Macdonald Model with Quarantine

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The Ross–Macdonald Model with Quarantine (RM-Q) is a deterministic compartmental framework developed for vector-borne diseases, particularly malaria, that explicitly incorporates public health isolation strategies for infected humans. By introducing a quarantined human compartment, the model quantifies how reducing mosquito–human contact through isolation policies alters transmission dynamics. This extension is especially relevant for elimination settings, where … Read more

🩟 Dynamics with Memory: The Delayed Ross–Macdonald Model

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The Delayed Ross–Macdonald Model represents a major refinement of classical mechanistic models for vector-borne diseases such as malaria. Its defining innovation is the explicit incorporation of fixed pathogen incubation periods in both the human host and the insect vector. These intrinsic and extrinsic incubation periods are modeled as time delays rather than exponentially distributed transition … Read more

💉 Epidemiological Planning: The SEIVRD Compartmental Model

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The Susceptible–Exposed–Symptomatic–Vaccinated–Recovered–Deceased (SEIVRD) compartmental model is an enhanced deterministic framework developed to study epidemic dynamics in settings where latency, vaccination, and disease-induced mortality must be explicitly represented. By extending classical SEIR-type models to include vaccination and death as distinct epidemiological states, this framework supports strategic public health planning in contexts where vaccine deployment, immunity loss, … Read more

📊 Modeling Detection Errors: The SIQRD Framework for False Positives

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The Susceptible–Infectious–Quarantined–Recovered–Dead (SIQRD) model is a compartmental framework in mathematical epidemiology designed to analyze infectious disease dynamics under testing and isolation policies. A defining feature of this model is its explicit treatment of quarantine following detection, including the epidemiological and operational consequences of false-positive test results. By allowing quarantined but uninfected individuals to return to … Read more

🧬 SEAIRD Model: Dissecting Asymptomatic Spread and Mortality

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The Susceptible–Exposed–Asymptomatic–Infectious–Recovered–Dead (SEAIRD) model is an advanced compartmental framework in mathematical epidemiology designed to capture the full spectrum of infection dynamics in viral diseases characterized by asymptomatic transmission and non-negligible mortality. By explicitly modeling both a latent incubation phase and a distinct asymptomatic infectious class, the SEAIRD model provides a refined representation of epidemic progression … Read more

💀 Tracking Outcomes: The SEIRD Compartmental Model

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The Susceptible–Exposed–Infectious–Recovered–Dead (SEIRD) model is a deterministic compartmental framework widely used in mathematical epidemiology to analyze infectious disease dynamics while explicitly accounting for disease-induced mortality. By extending the classical SEIR structure to include a distinct death compartment, the SEIRD model enables direct quantification of epidemic severity and overall population impact. This feature is particularly important … Read more

📈 High-Granularity Control: Analyzing Disease Dynamics with the SICARQD Model

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The Susceptible–Incubating–Contagious–Aware–Quarantined–Recovered–Deceased (SICARQD) model is an advanced compartmental framework developed to explicitly evaluate the epidemiological impact of public health interventions, particularly detection and quarantine policies. By distinguishing multiple infection stages and isolation processes, the model captures critical features of modern viral epidemics, such as pre-symptomatic transmission, delayed awareness, and compliance-driven isolation. This level of granularity … Read more

đŸ„ SAIHRD Model: Capturing the Granularity of Viral Disease

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The Susceptible–Asymptomatic–Ill–Hospitalized–Recovered–Deceased (SAIHRD) model is a high-granularity extension of classical compartmental modeling frameworks in mathematical epidemiology. It is specifically designed to represent the epidemiological complexity of modern viral pandemics characterized by substantial asymptomatic transmission and heterogeneous disease severity. By explicitly tracking hospitalization and mortality, the SAIHRD model provides a powerful analytical tool for quantifying disease … Read more

📈 Phenomenological Forecasting: Leveraging Sigmoidal Growth Models

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Sigmoidal growth models constitute an important class of phenomenological forecasting tools in mathematical epidemiology, designed to project the propagation and temporal trajectory of infectious populations during epidemic outbreaks. Unlike classical compartmental models, these approaches are primarily statistical rather than mechanistic. They capture the characteristic S-shaped epidemic curve, describing how case counts evolve from an initial … Read more

🧠 The Neural SIR Model: Mechanistic Modeling Meets Deep Learning

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The Neural SIR Model represents a major methodological advance in mathematical epidemiology by integrating the classical mechanistic Susceptible–Infectious–Recovered (SIR) framework with modern deep learning techniques, particularly Physics-Informed Neural Networks. This hybrid paradigm preserves the interpretability and biological grounding of differential equation–based models while exploiting the expressive power of neural networks for accurate parameter inference and … Read more