scholarly journals Impact of the accuracy of case-based surveillance data on the estimation of time-varying reproduction numbers

2020 ◽  
Author(s):  
Michele Starnini ◽  
Alberto Aleta ◽  
Michele Tizzoni ◽  
Yamir Moreno
Keyword(s):  
Time Series ◽  
Onset Time ◽  
Surveillance Data ◽  
Time Varying ◽  
Inaccurate Data ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Containment Measures ◽  
Estimation Of Time ◽  
Case Based

AbstractStudies aimed at characterizing the evolution of COVID-19 disease often rely on case-based surveillance data publicly released by health authorities, that can be incomplete and prone to errors. Here, we quantify the biases caused by the use of inaccurate data in the estimation of the Time-Varying Reproduction Number R(t). By focusing on Italy and Spain, two of the hardest-hit countries in Europe and worldwide, we show that if the symptoms’ onset time-series is inferred from the notification date series, the R(t) curve cannot capture nor describe accurately the early dynamics of the epidemic. Furthermore, the effectiveness of the containment measures that were implemented, such as national lockdowns, can be properly evaluated only when R(t) is estimated using the real time-series of dates of symptoms’ onset. Our findings show that extreme care should be taken when a pivotal quantity like R(t) is used to make decisions and to evaluate different alternatives.

scholarly journals Impact of data accuracy on the evaluation of COVID-19 mitigation policies

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Michele Starnini ◽  
Alberto Aleta ◽  
Michele Tizzoni ◽  
Yamir Moreno
Keyword(s):  
Epidemiological Data ◽  
Surveillance Data ◽  
Inaccurate Data ◽  
Health Authorities ◽  
The Social ◽  
Containment Measures ◽  
Public Health Authorities ◽  
Nonpharmaceutical Interventions ◽  
The Impact ◽  
Extreme Care

Abstract Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number $ R(t) $ , a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of $ R(t) $ could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.

scholarly journals The Estimated Time-Varying Reproduction Numbers during the Ongoing Epidemic of the Coronavirus Disease 2019 (COVID-19) in China

2020 ◽  
Author(s):  
Fu-Chang Hu ◽  
Fang-Yu Wen
Keyword(s):  
Infectious Disease ◽  
Reproduction Number ◽  
Control Measures ◽  
Time Varying ◽  
Epidemic Curve ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Public Health Authorities ◽  
Control And Prevention ◽  
Over Time

AbstractBackgroundHow could we anticipate the progression of the ongoing epidemic of the coronavirus disease 2019 (COVID-19) in China? As a measure of transmissibility, the value of basic reproduction number varies over time during an epidemic of infectious disease. Hence, this study aimed to estimate concurrently the time-varying reproduction number over time during the COVID-19 epidemic in China.MethodsWe extracted the epidemic data from the “Tracking the Epidemic” website of the Chinese Center for Disease Control and Prevention for the duration of January 19, 2020 and March 14, 2020. Then, we applied the novel method implemented in the incidence and EpiEstim packages to the data of daily new confirmed cases for robustly estimating the time-varying reproduction number in the R software.ResultsThe epidemic curve of daily new confirmed cases in China peaked around February 4−6, 2020, and then declined gradually, except the very high peak on February 12, 2020 owing to the added clinically diagnosed cases (Hubei Province only). Under two specified plausible scenarios for the distribution of serial interval, both curves of the estimated time-varying reproduction numbers fell below 1.0 around February 17−18, 2020. Finally, the COVID-19 epidemic in China abated around March 7−8, 2020, indicating that the prompt and aggressive control measures of China were effective.ConclusionSeeing the estimated time-varying reproduction number going downhill was more informative than looking for the drops in the daily number of new confirmed cases during an ongoing epidemic of infectious disease. We urged public health authorities and scientists to estimate time-varying reproduction numbers routinely during epidemics of infectious diseases and to report them daily to the public until the end of the COVID-19 epidemic.

scholarly journals Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves

2020 ◽  
Author(s):  
Kris Varun Parag
Keyword(s):  
Real Time ◽  
Change Point ◽  
Reproduction Number ◽  
Window Size ◽  
Time Varying ◽  
Markov State ◽  
State Process ◽  
Reproduction Numbers ◽  
Low Incidence ◽  
Estimation Of Time

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. This combination of maximising information and minimising assumptions, makes EpiFilter more statistically robust in periods of low incidence, where existing methods can struggle. As a result, we find EpiFilter to be particularly suited for assessing the risk of second waves of infection, in real time.

scholarly journals Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves

2021 ◽  
Vol 17 (9) ◽  
pp. e1009347
Author(s):  
Kris V. Parag
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Spatial Scales ◽  
Window Size ◽  
Time Varying ◽  
Markov State ◽  
State Process ◽  
Reproduction Numbers ◽  
Low Incidence ◽  
Estimation Of Time

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.

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