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Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic.

ABSTRACT: Introduction The COVID-19 pandemic has highlighted the need for robust data linkage systems and methods for identifying outbreaks of disease in near real-time. Objectives The primary objective of this study was to develop a real-time geospatial surveillance system to monitor the spread of COVID-19 across the UK. Methods Using self-reported app data and the Secure Anonymised Information Linkage (SAIL) Databank, we demonstrate the use of sophisticated spatial modelling for near-real-time prediction of COVID-19 prevalence at small-area resolution to inform strategic government policy areas. Results We demonstrate that using a combination of crowd-sourced app data and sophisticated geo-statistical techniques it is possible to predict hot spots of COVID-19 at fine geographic scales, nationally. We are also able to produce estimates of their precision, which is an important pre-requisite to an effective control strategy to guard against over-reaction to potentially spurious features of 'best guess' predictions. Conclusion In the UK, important emerging risk-factors such as social deprivation or ethnicity vary over small distances, hence risk needs to be modelled at fine spatial resolution to avoid aggregation bias. We demonstrate that existing geospatial statistical methods originally developed for global health applications are well-suited to this task and can be used in an anonymised databank environment, thus preserving the privacy of the individuals who contribute their data.

SUBMITTER: Fry R

PROVIDER: S-EPMC7843148 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThere have been concerns that the COVID-19 pandemic may lead to an increase in suicide. The coronial system in England is not suitable for timely monitoring of suicide because of the delay of several months before inquests are held.MethodsWe used data from established systems of "real time surveillance" (RTS) of suspected suicides, in areas covering a total population of around 13 million, to test the hypothesis that the suicide rate rose after the first national lockdown began in England.FindingsThe number of suicides in April-October 2020, after the first lockdown began, was 121•3 per month, compared to 125•7 per month in January-March 2020 (-4%; 95% CI-19% to 13%, p = 0•59). Incidence rate ratios did not show a significant rise in individual months after lockdown began and were not raised during the 2-month lockdown period April-May 2020 (IRR: 1•01 [0•81-1•25]) or the 5-month period after the easing of lockdown, June-October 2020 (0•94 [0•81-1•09]). Comparison of the suicide rates after lockdown began in 2020 for the same months in selected areas in 2019 showed no difference.InterpretationWe did not find a rise in suicide rates in England in the months after the first national lockdown began in 2020, despite evidence of greater distress. However, a number of caveats apply. These are early figures and may change. Any effect of the pandemic may vary by population group or geographical area. The use of RTS in this way is new and further development is needed before it can provide full national data.FundingThis study was funded by the Healthcare Quality Improvement Partnership (HQIP).The HQIP is led by a consortium of the Academy of Medical Royal Colleges, the Royal College of Nursing, and National Voices. Its aim is to promote quality improvement in patient outcomes, and in particular, to increase the impact that clinical audit, outcome review programs and registries have on healthcare quality in England and Wales. HQIP holds the contract to commission, manage, and develop the National Clinical Audit and Patient Outcomes Program (NCAPOP), comprising around 40 projects covering care provided to people with a wide range of medical, surgical and mental health conditions. The program is funded by NHS England, the Welsh Government and, with some individual projects, other devolved administrations, and crown dependencies.

Project description:The initial cluster of severe pneumonia cases that triggered the COVID-19 epidemic was identified in Wuhan, China in December 2019. While early cases of the disease were linked to a wet market, human-to-human transmission has driven the rapid spread of the virus throughout China. The Chinese government has implemented containment strategies of city-wide lockdowns, screening at airports and train stations, and isolation of suspected patients; however, the cumulative case count keeps growing every day. The ongoing outbreak presents a challenge for modelers, as limited data are available on the early growth trajectory, and the epidemiological characteristics of the novel coronavirus are yet to be fully elucidated. We use phenomenological models that have been validated during previous outbreaks to generate and assess short-term forecasts of the cumulative number of confirmed reported cases in Hubei province, the epicenter of the epidemic, and for the overall trajectory in China, excluding the province of Hubei. We collect daily reported cumulative confirmed cases for the 2019-nCoV outbreak for each Chinese province from the National Health Commission of China. Here, we provide 5, 10, and 15 day forecasts for five consecutive days, February 5th through February 9th, with quantified uncertainty based on a generalized logistic growth model, the Richards growth model, and a sub-epidemic wave model. Our most recent forecasts reported here, based on data up until February 9, 2020, largely agree across the three models presented and suggest an average range of 7409-7496 additional confirmed cases in Hubei and 1128-1929 additional cases in other provinces within the next five days. Models also predict an average total cumulative case count between 37,415 and 38,028 in Hubei and 11,588-13,499 in other provinces by February 24, 2020. Mean estimates and uncertainty bounds for both Hubei and other provinces have remained relatively stable in the last three reporting dates (February 7th - 9th). We also observe that each of the models predicts that the epidemic has reached saturation in both Hubei and other provinces. Our findings suggest that the containment strategies implemented in China are successfully reducing transmission and that the epidemic growth has slowed in recent days.

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Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic.

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