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epinowcast - A Bayesian Framework for Real-time Infectious Disease Surveillance

A modular Bayesian framework for real-time infectious disease surveillance. Provides tools for nowcasting, reproduction number estimation, delay estimation, and forecasting from data subject to reporting delays, right-truncation, missing data, and incomplete ascertainment. Users can build models suited to their setting using a flexible formula interface supporting fixed effects, random effects, random walks, and time-varying parameters, with options including parametric and non-parametric delay distributions with optional modifiers (via discrete-time hazard models), renewal processes, observation models, missing data imputation, and stratified analyses with partial pooling. By jointly estimating disease dynamics and reporting patterns, our framework enables earlier and more reliable detection of trends. While designed with epidemiological applications in mind, the framework can be applied to any right-truncated time series count data.

Last updated

cmdstanreffective-reproduction-number-estimationepidemiologyinfectious-disease-surveillancenowcastingoutbreak-analysispandemic-preparednessreal-time-infectious-disease-modellingstan

9.30 score 67 stars 105 scripts

epinowcast - A Bayesian Framework for Real-time Infectious Disease Surveillance

A modular Bayesian framework for real-time infectious disease surveillance. Provides tools for nowcasting, reproduction number estimation, delay estimation, and forecasting from data subject to reporting delays, right-truncation, missing data, and incomplete ascertainment. Users can build models suited to their setting using a flexible formula interface supporting fixed effects, random effects, random walks, and time-varying parameters, with options including parametric and non-parametric delay distributions with optional modifiers (via discrete-time hazard models), renewal processes, observation models, missing data imputation, and stratified analyses with partial pooling. By jointly estimating disease dynamics and reporting patterns, our framework enables earlier and more reliable detection of trends. While designed with epidemiological applications in mind, the framework can be applied to any right-truncated time series count data.

Last updated

cmdstanreffective-reproduction-number-estimationepidemiologyinfectious-disease-surveillancenowcastingoutbreak-analysispandemic-preparednessreal-time-infectious-disease-modellingstan

9.30 score 67 stars 105 scripts

primarycensored - Primary Event Censored Distributions

Provides functions for working with primary event censored distributions and 'Stan' implementations for use in Bayesian modeling. Primary event censored distributions are useful for modeling delayed reporting scenarios in epidemiology and other fields (Charniga et al. (2024) <doi:10.48550/arXiv.2405.08841>). It also provides support for arbitrary delay distributions, a range of common primary distributions, and allows for truncation and secondary event censoring to be accounted for (Park et al. (2024) <doi:10.1101/2024.01.12.24301247>). A subset of common distributions also have analytical solutions implemented, allowing for faster computation. In addition, it provides multiple methods for fitting primary event censored distributions to data via optional dependencies.

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censoringdistributionsmc-stantruncation

9.07 score 9 stars 4 dependents 39 scripts 1.1k downloads

epidist - Estimate Epidemiological Delay Distributions with 'brms'

Understanding and accurately estimating epidemiological delay distributions is important for public health policy. These estimates influence epidemic situational awareness, control strategies, and resource allocation. This package provides methods to address the key challenges in estimating these distributions, including truncation, interval censoring, and dynamical biases. These issues are frequently overlooked, resulting in biased conclusions. Built on top of 'brms', it allows for flexible modelling including time-varying spatial components and partially pooled estimates of demographic characteristics.

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7.74 score 16 stars 1 dependents 28 scripts

EpiSoon - Forecast Cases Using Reproduction Numbers

To forecast the time-varying reproduction number and use this to forecast reported case counts. Includes tools to evaluate a range of models across samples and time series using proper scoring rules.

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case-forecastsforecasts

4.79 score 7 stars 25 scripts

epidist - Estimate Epidemiological Delay Distributions with 'brms'

Understanding and accurately estimating epidemiological delay distributions is important for public health policy. These estimates influence epidemic situational awareness, control strategies, and resource allocation. This package provides methods to address the key challenges in estimating these distributions, including truncation, interval censoring, and dynamical biases, following Park et al. (2024) <doi:10.1101/2024.01.12.24301247> and Charniga et al. (2024) <doi:10.1371/journal.pcbi.1012520>. These issues are frequently overlooked, resulting in biased conclusions. Built on top of 'brms', it allows for flexible modelling including time-varying and spatial effects and partially pooled estimates of demographic characteristics.

Last updated

4.49 score 1 dependents 23 scripts

idbrms - Infectious Disease Modelling Using brms

Infectious disease modelling using brms.

Last updated

brmsinfectious-disease-modelsopensource

2.70 score 5 stars 3 scripts