Extending EpiEstim to estimate the transmission advantage of pathogen variants in real-time: SARS-CoV-2 as a case-study

Simulation-based inference of the time-dependent reproduction number from temporally aggregated and under-reported disease incidence time series data

During infectious disease outbreaks, the time-dependent reproduction number ([Formula: see text]) can be estimated to monitor pathogen transmission. In previous work, we developed a simulation-based method for estimating [Formula: see text] from temporally aggregated disease incidence data (e.g. weekly case reports). While that approach is straightforward to use, it assumes implicitly that all cases are reported and the computation can be slow when applied to large datasets. In this article, we extend our previous approach and develop a computationally efficient simulation-based method for estimating [Formula: see text] in real-time accounting for both temporal aggregation of incidence data and under-reporting (with a fixed reporting probability per case). Using simulated data, we show that failing to consider stochastic under-reporting can lead to inappropriately precise estimates, including scenarios in which the true [Formula: see text] value lies outside inferred credible intervals more often than expected. We then apply our approach to data from the 2018 to 2020 Ebola outbreak in the Democratic Republic of the Congo (DRC), again exploring the effects of case under-reporting. Finally, we show how our method can be extended to account for temporal variations in reporting. Given information about the level of case reporting, our framework can be used to estimate [Formula: see text] during future outbreaks with under-reported and temporally aggregated case data.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

Evaluating a novel reproduction number estimation method: a comparative analysis

This paper presents practical methodologies for determining effective reproduction numbers, R(t), providing valuable insights for researchers and public health officials. It proposes multiple simplified approaches for estimating R(t) of infectious diseases and compares their effectiveness. These approaches include methods based on exponential, fixed (delta), normal, and gamma distributions for the generation time. The exponential and fixed generation time methods offer convenience as they rely solely on the mean generation time and the number of new infections. However, they are sensitive to the variance of the generation time distribution: the exponential method may underestimate R(t) when the variance is small, while the fixed generation time method may overestimate R(t) when the variance is large. The normal distribution method also risks underestimation, depending on the growth rate. In contrast, the gamma distribution method demonstrates greater robustness and accuracy across a variety of scenarios. A key contribution of this work is the consolidated presentation of these estimation methods, along with the novel derivation of an accurate R(t) formula based on the gamma distribution. This research offers practical guidance for selecting the most appropriate R(t) estimation method, emphasizing the importance of accounting for the specific characteristics of the infectious disease's generation time distribution.

Unveiling pandemic patterns: a detailed analysis of transmission and severity parameters across four COVID-19 waves in Bogotá, Colombia

BACKGROUND

Despite a wealth of data from high-income countries, there is limited information on the distinct epidemiological patterns observed in diverse, densely populated regions within Latin America. This retrospective analysis of COVID-19's four major waves in Bogotá, Colombia, evaluates 1.77 million cases in detail.

METHODS

A comprehensive suite of statistical methods was employed. Transmission dynamics were assessed by estimating the instantaneous reproduction number R ( t ) , while variant-specific transmission advantages were estimated using multinomial logistic regression models. Disease severity was assessed through a suite of indicators: Hospitalisation Case Ratio (HCR), intensive care unit case ratio (ICU-CR), case fatality ratio (CFR), hospitalisation fatality ratio (HFR), and ICU fatality ratio (ICU-FR). Additionally, we analysed the distribution of hospitalisations, ICU admissions, and fatalities by age group and wave. We employed a Bayesian hierarchical model to capture epidemiological delays-such as onset-to-death, hospitalisation, and ICU admission durations to estimate hospital and ICU stay durations.

RESULTS

Our findings reveal substantial variation in R ( t ) , with peaks exceeding 2.5 during the ancestral and Omicron waves. Over the course of the pandemic, we observed a 78% reduction in CFR, underscoring shifts in clinical severity. The third wave, associated with the Mu variant, recorded the highest case and death counts, alongside a decreased CFR, an elevated HFR, and a shift in the most affected age group towards younger populations. In contrast, the fourth wave, driven by the Omicron variant, exhibited the highest reproduction number and the lowest overall severity. This wave was characterised by a significant increase in pediatric hospitalisations. The study reveals a continued decline in the mean durations of hospital and ICU stays across the four waves, with hospital stays decreasing from 10.84 to 7.85 days and ICU stays dropping from 16.2 to 12.4 days.

CONCLUSIONS

This study reveals significant shifts in transmission and severity metrics-including mortality, hospitalisation, and ICU rates and stays-across age groups during Bogotá's four COVID-19 waves. These insights underscore the value of retrospective analyses to understand the pandemic's varied impact and inform public health strategies in diverse urban settings.

A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates

During pandemics, countries, regions, and communities develop various epidemic models to evaluate spread and guide mitigation policies. However, model uncertainties caused by complex transmission behaviors, contact-tracing networks, time-varying parameters, human factors, and limited data present significant challenges to model-based approaches. To address these issues, we propose a novel framework that centers around reproduction number estimates to perform counterfactual analysis, strategy evaluation, and feedback control of epidemics. The framework 1) introduces a mechanism to quantify the impact of the testing-for-isolation intervention strategy on the basic reproduction number. Building on this mechanism, the framework 2) proposes a method to reverse engineer the effective reproduction number under different strengths of the intervention strategy. In addition, based on the method that quantifies the impact of the testing-for-isolation strategy on the basic reproduction number, the framework 3) proposes a closed-loop control algorithm that uses the effective reproduction number both as feedback to indicate the severity of the spread and as the control goal to guide adjustments in the intensity of the intervention. We illustrate the framework, along with its three core methods, by addressing three key questions and validating its effectiveness using data collected during the COVID-19 pandemic at the University of Illinois Urbana-Champaign (UIUC) and Purdue University: 1) How severe would an outbreak have been without the implemented intervention strategies? 2) What impact would varying the intervention strength have had on an outbreak? 3) How can we adjust the intervention intensity based on the current state of an outbreak?

A simulation-based approach for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data

Tracking pathogen transmissibility during infectious disease outbreaks is essential for assessing the effectiveness of public health measures and planning future control strategies. A key measure of transmissibility is the time-dependent reproduction number, which has been estimated in real-time during outbreaks of a range of pathogens from disease incidence time series data. While commonly used approaches for estimating the time-dependent reproduction number can be reliable when disease incidence is recorded frequently, such incidence data are often aggregated temporally (for example, numbers of cases may be reported weekly rather than daily). As we show, commonly used methods for estimating transmissibility can be unreliable when the timescale of transmission is shorter than the timescale of data recording. To address this, here we develop a simulation-based approach involving Approximate Bayesian Computation for estimating the time-dependent reproduction number from temporally aggregated disease incidence time series data. We first use a simulated dataset representative of a situation in which daily disease incidence data are unavailable and only weekly summary values are reported, demonstrating that our method provides accurate estimates of the time-dependent reproduction number under such circumstances. We then apply our method to two outbreak datasets consisting of weekly influenza case numbers in 2019-20 and 2022-23 in Wales (in the United Kingdom). Our simple-to-use approach will allow accurate estimates of time-dependent reproduction numbers to be obtained from temporally aggregated data during future infectious disease outbreaks.

The Ways of the Virus: Interactions of Platelets and Red Blood Cells with SARS-CoV-2, and Their Potential Pathophysiological Significance in COVID-19

The hematological effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are important in COVID-19 pathophysiology. However, the interactions of SARS-CoV-2 with platelets and red blood cells are still poorly understood. There are conflicting data regarding the mechanisms and significance of these interactions. The aim of this review is to put together available data and discuss hypotheses, the known and suspected effects of the virus on these blood cells, their pathophysiological and diagnostic significance, and the potential role of platelets and red blood cells in the virus's transport, propagation, and clearance by the immune system. We pay particular attention to the mutual activation of platelets, the immune system, the endothelium, and blood coagulation and how this changes with the evolution of SARS-CoV-2. There is now convincing evidence that platelets, along with platelet and erythroid precursors (but not mature erythrocytes), are frequently infected by SARS-CoV-2 and functionally changed. The mechanisms of infection of these cells and their role are not yet entirely clear. Still, the changes in platelets and red blood cells in COVID-19 are significantly associated with disease severity and are likely to have prognostic and pathophysiological significance in the development of thrombotic and pulmonary complications.