scholarly journals Are epidemic growth rates more informative than reproduction numbers?

2021 ◽  
Author(s):  
Kris V Parag ◽  
Robin N Thompson ◽  
Christl A. Donnelly
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Pathogen Transmission ◽  
Rate Of Change ◽  
Summary Statistics ◽  
Epidemic Spread ◽  
Average Ability ◽  
Transmission Mechanisms ◽  
Reproduction Numbers ◽  
Modelling Assumptions

Summary statistics, often derived from simplified models of epidemic spread, inform public health policy in real time. The instantaneous reproduction number, Rt , is predominant among these statistics, measuring the average ability of an infection to multiply. However, Rt encodes no temporal information and is sensitive to modelling assumptions. Consequently, some have proposed the epidemic growth rate, rt , i.e., the rate of change of the log-transformed case incidence, as a more temporally meaningful and model-agnostic policy guide. We examine this assertion, identifying if and when estimates of rt are more informative than those of Rt . We assess their relative strengths both for learning about pathogen transmission mechanisms and for guiding epidemic interventions in real time.

scholarly journals Fundamental limits on inferring epidemic resurgence in real time

2021 ◽  
Author(s):  
Kris V Parag ◽  
Christl A. Donnelly
Keyword(s):  
Mathematical Models ◽  
Real Time ◽  
Syndromic Surveillance ◽  
Reproduction Number ◽  
Pathogen Transmission ◽  
False Alarms ◽  
Surveillance Systems ◽  
Epidemic Spread ◽  
Fundamental Limits

We find that epidemic resurgence, defined as an upswing in the effective reproduction number (R) of the contagion from subcritical to supercritical values, is fundamentally difficult to detect in real time. Intrinsic latencies in pathogen transmission, coupled with often smaller incidence across periods of subcritical spread mean that resurgence cannot be reliably detected without significant delays, even if case reporting is perfect. This belies epidemic suppression (where R falls from supercritical to subcritical values), which can be ascertained 5-10 times more rapidly. These innate limits on detecting resurgence only worsen when spatial or demographic heterogeneities are incorporated. Consequently, we argue that resurgence is more effectively handled proactively, at the expense of false alarms. Responses to recrudescent infections or emerging variants of concern will more likely be timely if informed by improved syndromic surveillance systems than by optimised mathematical models of epidemic spread.

scholarly journals Real-time Nowcasting and Forecasting of COVID-19 Dynamics in England: the first wave?

2020 ◽  
Author(s):  
Paul J Birrell ◽  
Joshua Blake ◽  
Edwin van Leeuwen ◽  
Nick Gent ◽  
Daniela De Angelis ◽  
...  
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Mitigation Measures ◽  
Contact Tracing ◽  
The Real ◽  
South West ◽  
Effective Contact ◽  
Ongoing Work ◽  
Reproduction Numbers ◽  
Second Wave

England has been heavily affected by the SARS-CoV-2 pandemic, with severe 'lock-down' mitigation measures now gradually being lifted. The real-time pandemic monitoring presented here has contributed to the evidence informing this pandemic management. Estimates on the 10th May showed lock-down had reduced transmission by 75%, the reproduction number falling from 2.6 to 0.61. This regionally-varying impact was largest in London of 81% (95% CrI: 77%-84%). Reproduction numbers have since slowly increased, and on 19th June the probability that the epidemic is growing was greater than 50% in two regions, South West and London. An estimated 8% of the population had been infected, with a higher proportion in London (17%). The infection-to-fatality ratio is 1.1% (0.9%-1.4%) overall but 17% (14%-22%) among the over-75s. This ongoing work will be key to quantifying any widespread resurgence should accrued immunity and effective contact tracing be insufficient to preclude a second wave.

scholarly journals Simple Forward Finite Difference for Computing Reproduction Number of COVID-19 in Indonesia During the New Normal

2021 ◽  
Vol 5 (1) ◽  
pp. 88
Author(s):  
Suryasatriya Trihandaru ◽  
Hanna Arini Parhusip ◽  
Bambang Susanto ◽  
Yohanes Sardjono
Keyword(s):  
Finite Difference ◽  
Reproduction Number ◽  
Research Result ◽  
Sir Model ◽  
Rate Of Change ◽  
Time Dependent ◽  
Normal System ◽  
New Normal ◽  
Reproduction Numbers ◽  
Normal Period

The research purpose shown in this article is describing the time dependent reproduction number of coronavirus called by COVID-19 in the new normal period  for 3 types areas, i.e. small, medium and global areas by considering the number of people in these areas.  It is known that in early June 2020, Indonesia has claimed to open activities during the pandemic with the new normal system. Though the number of COVID-19 cases is still increasing in almost infected areas, normal activities are coming back with healty care protocols where public areas are opened as usual with certain restrictions. In order to have observations of spreading impact of COVID-19, the basic reproduction number (Ro)  i.e. the reproduction number (Ro) is the ratio between 2 parameters of SIR model where SIR stands for Susceptible individuals, Infected individuals, and Recovered individuals respectively. The reproduction numbers  are computed as discrete values depending on time. The used research method is  finite difference scheme for computing rate of change parameters in SIR models based on the COVID-19 cases in Indonesia (global area), Jakarta (medium area) and Salatiga (small area) by considering the number of people in these areas respectively. The simple forward finite difference is employed to the SIR model to have time dependent of parameters. The second approach is using the governing linear system to obtain the values of parameter daily. These parameters are computed for each day such that the values of Ro are obtained as function of time. The research result shows that 3 types areas give the same profiles of parameters that the rate of changes of reproduction numbers are decreasing with respect to time. This concludes that the reproduction numbers are most likely decreasing.

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.

COVID-19: Time-Dependent Effective Reproduction Number and Sub-notification Effect Estimation Modeling (Preprint)

2020 ◽  
Author(s):  
Eduardo Atem De Carvalho ◽  
Rogerio Atem De Carvalho
Keyword(s):  
New York ◽  
Reproduction Number ◽  
Moving Average ◽  
Real Data ◽  
Time Dependent ◽  
Initial Value ◽  
Health Authorities ◽  
Reproduction Numbers ◽  
Effect Estimation ◽  
The Impact

BACKGROUND Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that govern their infection and death cycles, in order to be able to make better decisions. In particular, a series of reproduction number estimation models have been presented, with different practical results. OBJECTIVE This article aims to present an effective and efficient model for estimating the Reproduction Number and to discuss the impacts of sub-notification on these calculations. METHODS The concept of Moving Average Method with Initial value (MAMI) is used, as well as a model for Rt, the Reproduction Number, is derived from experimental data. The models are applied to real data and their performance is presented. RESULTS Analyses on Rt and sub-notification effects for Germany, Italy, Sweden, United Kingdom, South Korea, and the State of New York are presented to show the performance of the methods here introduced. CONCLUSIONS We show that, with relatively simple mathematical tools, it is possible to obtain reliable values for time-dependent, incubation period-independent Reproduction Numbers (Rt). We also demonstrate that the impact of sub-notification is relatively low, after the initial phase of the epidemic cycle has passed.

scholarly journals PIN50 Leveraging Publicly Available Data to Estimate a Real-Time Reproduction Number (Rt) for Covid-19: A Comparison of Two Methods

2021 ◽  
Vol 24 ◽  
pp. S114
Author(s):  
S. Emerson ◽  
K. Johnston ◽  
A. Howarth ◽  
J. Schneider ◽  
M. Friesen ◽  
...  
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Time Reproduction

scholarly journals Modelling the impact of the tier system on SARS-CoV-2 transmission in the UK between the first and second national lockdowns

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e050346
Author(s):  
Daniel J Laydon ◽  
Swapnil Mishra ◽  
Wes R Hinsley ◽  
Pantelis Samartsidis ◽  
Seth Flaxman ◽  
...  
Keyword(s):  
Real Time ◽  
Reproduction Number ◽  
Secondary Outcome ◽  
Bayesian Hierarchical ◽  
Time Reproduction ◽  
Tier System ◽  
The Uk ◽  
National Trends ◽  
Tier 3 ◽  
The Impact

ObjectiveTo measure the effects of the tier system on the COVID-19 pandemic in the UK between the first and second national lockdowns, before the emergence of the B.1.1.7 variant of concern.DesignThis is a modelling study combining estimates of real-time reproduction number Rt (derived from UK case, death and serological survey data) with publicly available data on regional non-pharmaceutical interventions. We fit a Bayesian hierarchical model with latent factors using these quantities to account for broader national trends in addition to subnational effects from tiers.SettingThe UK at lower tier local authority (LTLA) level. 310 LTLAs were included in the analysis.Primary and secondary outcome measuresReduction in real-time reproduction number Rt.ResultsNationally, transmission increased between July and late September, regional differences notwithstanding. Immediately prior to the introduction of the tier system, Rt averaged 1.3 (0.9–1.6) across LTLAs, but declined to an average of 1.1 (0.86–1.42) 2 weeks later. Decline in transmission was not solely attributable to tiers. Tier 1 had negligible effects. Tiers 2 and 3, respectively, reduced transmission by 6% (5%–7%) and 23% (21%–25%). 288 LTLAs (93%) would have begun to suppress their epidemics if every LTLA had gone into tier 3 by the second national lockdown, whereas only 90 (29%) did so in reality.ConclusionsThe relatively small effect sizes found in this analysis demonstrate that interventions at least as stringent as tier 3 are required to suppress transmission, especially considering more transmissible variants, at least until effective vaccination is widespread or much greater population immunity has amassed.

Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

2021 ◽  
Keyword(s):  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Reproduction Number ◽  
Healthcare Systems ◽  
Regional Level ◽  
Data Driven ◽  
Effective Response ◽  
Reproduction Numbers ◽  
Exploratory Data ◽  
Using Data

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.

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