evaluating the use of the reproduction number as an epidemiological tool using spatio temporal CORD-Papers-2022-06-02 (Version 1)

Title: Evaluating the use of the reproduction number as an epidemiological tool using spatio-temporal trends of the Covid-19 outbreak in England
Abstract: The time-varying reproduction number (Rt: the average number secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. Since new infections usually are not observed directly it can only be estimated from delayed and potentially biased data. We estimated Rt using a model that mapped unobserved infections to observed test-positive cases hospital admissions and deaths with confirmed Covid-19 in seven regions of England over March through August 2020. We explored the sensitivity of Rt estimates of Covid-19 in England to different data sources and investigated the potential of using differences in the estimates to track epidemic dynamics in population sub-groups. Our estimates of transmission potential varied for each data source. The divergence between estimates from each source was not consistent within or across regions over time although estimates based on hospital admissions and deaths were more spatio-temporally synchronous than compared to estimates from all test-positives. We compared differences in Rt with the demographic and social context of transmission and found the differences between Rt may be linked to biased representations of sub-populations in each data source: from uneven testing rates or increasing severity of disease with age seen via outbreaks in care home populations and changing age distributions of cases. We highlight that policy makers should consider the source populations of Rt estimates. Further work should clarify the best way to combine and interpret Rt estimates from different data sources based on the desired use.
Published: 2020-10-20
DOI: 10.1101/2020.10.18.20214585
DOI_URL: http://doi.org/10.1101/2020.10.18.20214585
Author Name: Sherratt K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sherratt_k
Author Name: Abbott S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/abbott_s
Author Name: Meakin S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/meakin_s
Author Name: Hellewell J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hellewell_j
Author Name: Munday J D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/munday_j_d
Author Name: Bosse N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bosse_n
Author Name: CMMID Covid working group
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cmmid_covid_working_group
Author Name: Jit M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/jit_m
Author Name: Funk S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/funk_s
sha: 6b25ae9198db861f7c27fa3393afb55ecc4e2a38
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2020.10.18.20214585 http://medrxiv.org/cgi/content/short/2020.10.18.20214585v1?rss=1
has_full_text: TRUE
Keywords Extracted from Text Content: test-positives Covid-19 Stan [26] linelist SFunk SARS-Cov-2 Rts Covid-19 compartmentalise Covid-19 UK CrIs NIB patient 4,798 KS N=19 UK Government's [ Figure 2 spatially-or mis-specified Philippines 1,929 medRxiv preprint medRxiv Bill N=6,733 coronavirus SARS-CoV-2 5,017 https://doi.org/10.1101/2020. National Health Service SI figure 2 SI2 N=690 SI2B [ Figure 1 https://doi.org/10.1101/2020.10.18.20214585 doi N=16,185 SI3 NHS UK Centre Gwenan M Knight Amy Gimma Infectious Disease COVID-19 Emily S Nightingale
Extracted Text Content in Record: First 5000 Characters:The time-varying reproduction number (Rt: the average number secondary infections caused by each infected person) may be used to assess changes in transmission potential during an epidemic. Since new infections usually are not observed directly, it can only be estimated from delayed and potentially biased data. We estimated Rt using a model that mapped unobserved infections to observed testpositive cases, hospital admissions, and deaths with confirmed Covid-19, in seven regions of England over March through August 2020. We explored the sensitivity of Rt estimates of Covid-19 in England to different data sources, and investigated the potential of using differences in the estimates to track epidemic dynamics in population sub-groups. Our estimates of transmission potential varied for each data source. The divergence between estimates from each source was not consistent within or across regions over time, although estimates based on hospital admissions and deaths were more spatio-temporally synchronous than compared to estimates from all test-positives. We compared differences in Rt with the demographic and social context of transmission, and found the differences between Rt may be linked to biased representations of subpopulations in each data source: from uneven testing rates, or increasing severity of disease with age, seen via outbreaks in care home populations and changing age distributions of cases. We highlight that policy makers should consider the source populations of Rt estimates. Further work should clarify the best way to combine and interpret Rt estimates from different data sources based on the desired use. Since its emergence in 2019, the novel coronavirus SARS-CoV-2 has caused over six million cases of disease worldwide within six months [1] ). Its rapid initial spread and high death rate prompted global policy interventions to prevent continued transmission, with widespread temporary bans on social interaction outside the household [2] . Introducing and adjusting such policy measures depends on a judgement in balancing continued transmission potential with the multidimensional consequences of interventions. It is therefore critical to inform the implementation of policy measures with a clear and timely understanding of ongoing epidemic dynamics [3, 4] . In principle, transmission could be tracked by directly recording all new infections. In practice, real-time monitoring of the Covid-19 epidemic relies on surveillance of indicators that are subject to different levels of bias and delay. In England, widely available surveillance data across the population includes: 1) the number of positive tests, biased by changing test availability and practice, and delayed by the time from infection to symptom onset (if testing is symptom-based), from symptom onset to a decision to be tested and from test to test result; 2) the number of new hospital admissions, biased by differential severity that triggers care seeking and hospitalisation, and additionally delayed by the time to develop severe diseases; and 3) the number of new deaths due to Covid-19, biased by differential risk of death and the exact definition of a Covid-19 death, and further delayed by the time to death. Each of these indicators provides a different view on the epidemic and therefore contains potentially useful information. However, any interpretation of their behaviour needs to reflect these biases and lags and is best done in combination with the other indicators. One approach that allows this in a principled manner is to use the different data sets to separately track the time-varying reproduction number, Rt, the average number of secondary infections generated by each new infected person [5] . Because Rt quantifies changes in infection levels, it is independent of the level of overall ascertainment as long as this does not change over time or is explicitly accounted for [6] . At the same time, the underlying observations in each data source may result from different lags from infection to observation. However, if these delays are correctly specified then transmission behaviour over time can be consistently compared via estimates of Rt. Different methods exist to estimate the time-varying reproduction number, and in the UK a number of mathematical and statistical methods have been used to produce estimates used to inform policy [7, 8] . Empirical estimates of Rt can be achieved by estimating time-varying patterns in transmission events from mapping to a directly observed time-series indicator of infection such as reported symptomatic cases. This can be based on the the probabilistic assignment of transmission pairs [9] , the exponential growth rate [10] , or the renewal equation [11, 12] . Alternatively, Rt can be estimated via mechanistic models which explicitly compartmentalise the disease transmission cycle into stages from susceptible through exposed, infectious and recovered [13, 14] . This can include accounting for varying pop
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