A renewal-equation approach to estimating R _t and infectious disease case counts in the presence of reporting delays

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Год журнала: 2025, Номер 383(2292)

Опубликована: Март 13, 2025

During infectious disease outbreaks, delays in case reporting mean that the time series of cases is unreliable, particularly for those occurring most recently. This means real-time estimates time-varying reproduction number, R t , are often made using a only up until period sufficiently far past there some confidence counts. recent usually out date, inducing lags response public health authorities. Here, we introduce an estimation method, which makes use retrospective updates to happen as more occurred historically enter system; these data encode within them information about delays, our method also estimates. These estimates, turn, allow us estimate true count recently allowing up-to-date . Our simultaneously historical counts and single Bayesian framework, uncertainty each quantities be accounted for. We apply both simulated real outbreak data, shows substantially improves upon naive do not account delays. available open-source fully tested R package, incidenceinflation research highlights value keeping since changes can help characterize nuisance processes, such when estimating key epidemic quantities. article part theme issue ‘Uncertainty quantification healthcare biological systems (Part 1)’.

Язык: Английский

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

Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, Год журнала: 2025, Номер 383(2293)

Опубликована: Апрель 2, 2025

During infectious disease outbreaks, the time-dependent reproduction number ( R t ) can be estimated to monitor pathogen transmission. In previous work, we developed a simulation-based method for estimating from temporally aggregated incidence data (e.g. weekly case reports). While that approach is straightforward use, it assumes implicitly all cases are reported and computation slow when applied large datasets. this article, extend our develop computationally efficient in real-time accounting both temporal aggregation of under-reporting (with fixed reporting probability per case). Using simulated data, show failing consider stochastic lead inappropriately precise estimates, including scenarios which true value lies outside inferred credible intervals more often than expected. We then apply 2018 2020 Ebola outbreak Democratic Republic Congo (DRC), again exploring effects under-reporting. Finally, how extended account variations reporting. Given information about level reporting, framework used estimate during future outbreaks with under-reported data. This article part theme issue ‘Uncertainty quantification healthcare biological systems (Part 2)’.

Язык: Английский

Real-time inference of the end of an outbreak: Temporally aggregated disease incidence data and under-reporting

Infectious Disease Modelling, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Professor Pierre Magal made important contributions to the field of mathematical biology before his death on February 20, 2024, including research in which epidemiological models were used study ends infectious disease outbreaks. In related work, there has been interest inferring (in real-time) when outbreaks have ended and control interventions can be relaxed. Here, we analyse data from 2018 Ebola outbreak Équateur Province, Democratic Republic Congo, during an Response Team (ERT) was deployed implement public health measures. We use a renewal equation transmission model perform quasi real-time investigation into ERT could withdrawn safely at tail end outbreak. Specifically, each week following arrival ERT, calculate probability future cases if is withdrawn. First, show that similar estimates obtained either daily or weekly case reports. This demonstrates high temporal resolution reporting may not always necessary determine Second, demonstrate how under-reporting accounted for rigorously estimating cases. find that, lower level reporting, longer it wait after apparent final removed (with only small additional cases). Finally, uncertainty extent included Our highlights importance accounting deciding remove

Язык: Английский