key: cord-0889451-9p8g34y3 authors: Liu, Yang; Tang, Julian W.; Lam, Tommy T.Y. title: Transmission dynamics of the COVID-19 epidemics in England date: 2020-12-23 journal: Int J Infect Dis DOI: 10.1016/j.ijid.2020.12.055 sha: c1319ef382274342adda4dc9df14bcd0b629fcb3 doc_id: 889451 cord_uid: 9p8g34y3 BACKGROUND: The ongoing COVID-19 pandemic has caused a tremendous health burden and impact on the world economy. As one of the European countries experiencing one of the worst COVID-19 epidemics, the UK government at the end of March 2020 implemented the biggest lockdown of society during peacetime in British history, aiming to contain the rapid spread of the virus. While the lockdown has been maintained for seven weeks in UK, the effectiveness of the control measures in suppressing the transmission of the disease remains incompletely understood. METHOD: We applied a Bayesian SEIR (susceptible-exposed-infected-removed) epidemiological model to rebuild the local transmission dynamics of the spread of COVID-19 in nine regions of England. RESULTS: We found that the basic reproduction number (R(0)) in England is relatively high compared with China. Our estimation of the temporally varying effective reproduction number (R(t)) suggests that the control measures, especially the forceful lockdown, were effective to reduce the transmissibility and curb the COVID-19 epidemic. Although the overall incidence rate in the UK has declined, our forecasting highlights the possibility of a second wave of the disease in several regions, which may be currently underway in one of the cities there (e.g. Leicester, East Midlands). CONCLUSION: This study enhances our understanding of the current outbreak and effectiveness of control measures in the UK. The unexpected emergence and outbreak of the coronavirus disease 2019 (COVID- Nevertheless, given the ongoing COVID-19 activities in tropical regions, it is now very J o u r n a l P r e -p r o o f unlikely that the current UK epidemic will naturally end during summer. Therefore, identifying effective, practical and economic public health interventions both for now and in the future will be critical to rapidly contain the spread of the virus and alleviate the pressure on healthcare systems. Here we applied a Bayesian SEIR (susceptible-exposed-infected-removed) epidemiological model that incorporates internal migration data and the regional daily number of laboratory-confirmed cases to reveal the local epidemic progression of the COVID-19 in nine regions of England, including East Midlands, East of England, London, North East, North West, South East, South West, West Midlands, and Yorkshire and the Humber. The regional basic reproduction number (R0) and temporally varying effective reproduction number (Rt) were estimated by a sequential Monte Carlo method to identify the effectiveness of control measures. We also provide forecasts for the number of daily cases for nine regions of England. The laboratory-confirmed cases were identified in local laboratories of the National Health System (NHS) by testing specimens from people eligible for SARS-CoV-2 testing, according to the national guidance active at that time. The geographical location of each specimen was tracked by the home postcode of the person being tested. If repeat tests were conducted, the date when the first positive test occurred was recorded. Redundant tests from the same person were removed so there was no double record. Cases were aggregated according to the corresponding administrative area. Not all local authorities had complete records, and some administrative regions were too small and did not seem to have significant or continuous outbreaks. Therefore, we focused on regional level data in this study. In addition, our model only considers local transmissions, therefore cases reported prior to 27 February 2020 (the date when the local transmission was considered to have begun) were excluded. Note that, data was not always up-to-date as some community test results would have been delayed (including care home figures). Therefore, the daily number of laboratory-confirmed cases from all nine regions were collated on 7 June 2020 (one week after the last date of our collected data) and shown in Figure 1 . It is likely that the epidemic peak in England was reached on 8 April 2020. COVID-19 cases in London and the North West accounted for the first (18.4%) and second largest (17.6%) number of cumulative cases by 1 June 2020. To account for the movement of population between regions in modelling the disease transmission, we used the annual mid-year internal migration data, where J o u r n a l P r e -p r o o f 8 internal migration was defined as residential moves across the boundaries of the nine English regions. We used the latest available annual data, from 2018. Inflow and outflow data were aggregated across sex and age. To have a constant population size in the model, the inflow and outflow data involved in the model were transformed so that they were both equal to the mean of the observed inflow and outflow data. A SEIR compartmental model, which is widely used in infectious disease modeling to describe the transmission dynamics within a community, was applied here. The model divides the population into susceptible (S), exposed (E; but not infectious), infected (I) and removed (R) compartments and people progress between these disease states, which have been clinically described elsewhere (Huang et We assume that a proportion δ of the exposed population will enter Ia and a Uniform prior distribution U[0,1] is used to account for the uncertainty about δ. The equations of changes in each compartment are set as follows where we assume there is no imported case. In addition to the compartment model, a testing module is added to account for the reporting delay: The model is depicted in Figure 2 . To account for the early period when the virus started to seed in each English region before public awareness, we run a preliminary model that is similar to Equation ( The basic reproduction number R0 is an important parameter that quantifies the disease transmissibility at the start of epidemic. The estimated basic reproduction numbers R0 for each of the nine English regions are listed in Table 1 (histograms of the posterior samples are shown in Figure S1 ). All these regions have R0 between 2.8-3.9, which are significantly higher than 1. Notably, we find that the estimated R0 is positively correlated with the population size in each region. Spearman's rank correlation is 0.77 which is significantly higher In 25 February 2020, the UK government announced its general strategy, which aims to reduce the impact of the disease by four successive phase. Contain: detect, trace and isolate early cases. Delay: slow the spread and delay the peak until warmer months. Research: develop diagnostic tests, drugs and vaccines. Mitigate: save lives and maintain nationwide order once the disease is widespread. This announcement was only followed by advice that travelers from heavily hit countries should self-isolate. On 12 March, the government started to issue policies for local residents, advising those with respiratory symptoms to self-isolate at home. After the release of the controversial herd immunity strategy, the UK government advised people against "non-essential" travel and public entertainment venues on 16 March. This was followed by the closure of all pubs, cafes, restaurants, bars, gyms, etc. Our study demonstrated the use of a Bayesian SEIR model to Since no individual patient's data is identifiable in this study, the ethical approval or individual consent is not applicable. 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Goudie for helpful discussions of this work. The authors declare that they have no competing interests.