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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.

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

8.88 score 64 stars 88 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

8.88 score 64 stars 88 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

8.46 score 9 stars 2 dependents 25 scripts 661 downloads

baselinenowcast - Baseline Nowcasting for Right-Truncated Epidemiological Data

Nowcasting right-truncated epidemiological data is critical for timely public health decision-making, as reporting delays can create misleading impressions of declining trends in recent data. This package provides nowcasting methods based on using empirical delay distributions and uncertainty from past performance. It is also designed to be used as a baseline method for developers of new nowcasting methods. For more details on the performance of the method(s) in this package applied to case studies of COVID-19 and norovirus, see our recent paper at <https://wellcomeopenresearch.org/articles/10-614>. The package supports standard data frame inputs with reference date, report date, and count columns, as well as the direct use of reporting triangles, and is compatible with 'epinowcast' objects. Alongside an opinionated default workflow, it has a low-level pipe-friendly modular interface, allowing context-specific workflows. It can accommodate a wide spectrum of reporting schedules, including mixed patterns of reference and reporting (daily-weekly, weekly-daily). It also supports sharing delay distributions and uncertainty estimates between strata, as well as custom uncertainty models and delay estimation methods.

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7.67 score 10 stars 15 scripts 151 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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4.82 score 16 stars 13 scripts

hashprng - Hash-Based Matching Pseudo-Random Number Generation

Provides helper functions for use of hash-based matching (HBM) for pseudo-random number generation (PRNG) in stochastic simulations. HBM-PRNG is an approach to simplify matching synthetic experiment samples, which ensures that matched runs different only in the focal parameters, not in their chance events.

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stochastic-simulationcpp

3.00 score 2 stars 5 scripts