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Register a new userStatistical techniques to estimate the SARS-CoV-2 infection fatality rate
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Added by
Mikael Mieskolainen
Publication date
2020
fields
Mathematical Statistics
and research's language is
English
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The determination of the infection fatality rate (IFR) for the novel SARS-CoV-2 coronavirus is a key aim for many of the field studies that are currently being undertaken in response to the pandemic. The IFR together with the basic reproduction number $R_0$, are the main epidemic parameters describing severity and transmissibility of the virus, respectively. The IFR can be also used as a basis for estimating and monitoring the number of infected individuals in a population, which may be subsequently used to inform policy decisions relating to public health interventions and lockdown strategies. The interpretation of IFR measurements requires the calculation of confidence intervals. We present a number of statistical methods that are relevant in this context and develop an inverse problem formulation to determine correction factors to mitigate time-dependent effects that can lead to biased IFR estimates. We also review a number of methods to combine IFR estimates from multiple independent studies, provide example calculations throughout this note and conclude with a summary and best practice recommendations. The developed code is available online.
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Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
This work is motivated by the Obepine French system for SARS-CoV-2 viral load monitoring in wastewater. The objective of this work is to identify, from time-series of noisy measurements, the underlying auto-regressive signals, in a context where the measurements present numerous missing data, censoring and outliers. We propose a method based on an auto-regressive model adapted to censored data with outliers. Inference and prediction are produced via a discretised smoother. This method is both validated on simulations and on real data from Obepine. The proposed method is used to denoise measurements from the quantification of the SARS-CoV-2 E gene in wastewater by RT-qPCR. The resulting smoothed signal shows a good correlation with other epidemiological indicators and an estimate of the whole system noise is produced.
Background: During 2021 several new variants of the SARS-CoV-2 virus appeared with both increased levels of transmissibility and virulence with respect to the original wild variant. The Delta (B.1.617.2) variation, first seen in India, dominates COVID-19 infections in several large countries including the United States and India. Most recently, the Lambda variant of interest with increased resistance to vaccines has spread through much of South America. Objective: This research explores the degree to which new variants of concern 1) generate spikes and waves of fluctuations in the daily case fatality rates (CFR) across countries in several regions in the face of increasing levels of vaccination of national populations and 2) may increase the vulnerability of persons with certain comorbidities. Methods: This study uses new, openly available, epidemiological statistics reported to the relevant national and international authorities for countries across the Americas, Europe, Africa, Asia and the Middle East. Daily CFRs and correlations of fatal COVID-19 infections with potential cofactors are computed for the first half of 2021 that has been dominated by the wide spread of several variants of concern as denoted by the World Health Organization. Results: The analysis yields a new quantitative measure of the temporal dynamics of mortality due to SARS-CoV-2 infections in the form of variations of a proxy case fatality rate compared on a country to-country basis in the same region. It also finds minimal variation of correlation between the cofactors based on WHO data and on the average apparent case fatality rate.
Near real-time monitoring of outbreak transmission dynamics and evaluation of public health interventions are critical for interrupting the spread of the novel coronavirus (SARS-CoV-2) and mitigating morbidity and mortality caused by coronavirus disease (COVID-19). Formulating a regional mechanistic model of SARS-CoV-2 transmission dynamics and frequently estimating parameters of this model using streaming surveillance data offers one way to accomplish data-driven decision making. For example, to detect an increase in new SARS-CoV-2 infections due to relaxation of previously implemented mitigation measures one can monitor estimates of the basic and effective reproductive numbers. However, parameter estimation can be imprecise, and sometimes even impossible, because surveillance data are noisy and not informative about all aspects of the mechanistic model, even for reasonably parsimonious epidemic models. To overcome this obstacle, at least partially, we propose a Bayesian modeling framework that integrates multiple surveillance data streams. Our model uses both COVID-19 incidence and mortality time series to estimate our model parameters. Importantly, our data generating model for incidence data takes into account changes in the total number of tests performed. We apply our Bayesian data integration method to COVID-19 surveillance data collected in Orange County, California. Our results suggest that California Department of Public Health stay-at-home order, issued on March 19, 2020, lowered the SARS-CoV-2 effective reproductive number $R_{e}$ in Orange County below 1.0, which means that the order was successful in suppressing SARS-CoV-2 infections. However, subsequent re-opening steps took place when thousands of infectious individuals remained in Orange County, so $R_{e}$ increased to approximately 1.0 by mid-June and above 1.0 by mid-July.
We investigate the treatment effect of the juvenile stay-at-home order (JSAHO) adopted in Saline County, Arkansas, from April 6 to May 7, in mitigating the growth of SARS-CoV-2 infection rates. To estimate the counterfactual control outcome for Saline County, we apply Difference-in-Differences and Synthetic Control design methodologies. Both approaches show that stay-at-home order (SAHO) significantly reduced the growth rate of the infections in Saline County during the period the policy was in effect, contrary to some of the findings in the literature that cast doubt on the general causal impact of SAHO with narrower scopes.
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