Department of Biosystems Science and Engineering, ETH Zurich, Swiss Federal Institute of Technology, Basel, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
BMC Bioinformatics. 2023 Aug 11;24(1):310. doi: 10.1186/s12859-023-05428-4.
Accurate estimation of the effective reproductive number ([Formula: see text]) of epidemic outbreaks is of central relevance to public health policy and decision making. We present estimateR, an R package for the estimation of the reproductive number through time from delayed observations of infection events. Such delayed observations include confirmed cases, hospitalizations or deaths. The package implements the methodology of Huisman et al. but modularizes the [Formula: see text] estimation procedure to allow easy implementation of new alternatives to the currently available methods. Users can tailor their analyses according to their particular use case by choosing among implemented options.
The estimateR R package allows users to estimate the effective reproductive number of an epidemic outbreak based on observed cases, hospitalization, death or any other type of event documenting past infections, in a fast and timely fashion. We validated the implementation with a simulation study: estimateR yielded estimates comparable to alternative publicly available methods while being around two orders of magnitude faster. We then applied estimateR to empirical case-confirmation incidence data for COVID-19 in nine countries and for dengue fever in Brazil; in parallel, estimateR is already being applied (i) to SARS-CoV-2 measurements in wastewater data and (ii) to study influenza transmission based on wastewater and clinical data in other studies. In summary, this R package provides a fast and flexible implementation to estimate the effective reproductive number for various diseases and datasets.
The estimateR R package is a modular and extendable tool designed for outbreak surveillance and retrospective outbreak investigation. It extends the method developed for COVID-19 by Huisman et al. and makes it available for a variety of pathogens, outbreak scenarios, and observation types. Estimates obtained with estimateR can be interpreted directly or used to inform more complex epidemic models (e.g. for forecasting) on the value of [Formula: see text].
准确估计传染病暴发的有效繁殖数 ([Formula: see text]) 对公共卫生政策和决策至关重要。我们提出了 estimateR，这是一个 R 包，用于通过延迟观察感染事件来估计随时间变化的繁殖数。这种延迟观察包括确诊病例、住院或死亡。该包实现了 Huisman 等人的方法，但将 [Formula: see text] 估计过程模块化，以允许轻松实现当前可用方法的新替代方案。用户可以根据自己的特定用例，通过选择已实现的选项来定制自己的分析。
estimateR R 包允许用户根据观察到的病例、住院、死亡或任何其他类型记录过去感染的事件，快速及时地估计传染病暴发的有效繁殖数。我们通过模拟研究验证了实现：estimateR 产生的估计值与替代的公开可用方法相当，而速度快两个数量级。然后，我们将 estimateR 应用于九个国家的 COVID-19 病例确诊发病率数据和巴西的登革热数据；同时，estimateR 已经在 SARS-CoV-2 测量的废水数据中应用 (i)，并在其他研究中基于废水和临床数据研究流感传播 (ii)。总之，这个 R 包为各种疾病和数据集提供了一种快速灵活的有效繁殖数估计方法。
estimateR R 包是一个模块化和可扩展的工具，用于暴发监测和回顾性暴发调查。它扩展了 Huisman 等人针对 COVID-19 开发的方法，并使其可用于各种病原体、暴发场景和观察类型。estimateR 获得的估计值可以直接解释，也可以用于为更复杂的流行模型（例如预测）提供信息，以了解 [Formula: see text] 的价值。
