ABSTRACT
With the COVID-19 pandemic leading to radical political control of social behaviour, including restricted movement outsides homes. Can more detailed analysis of the published confirmed local case data from the pandemic in England using infection ratio and comparing local level data provide a deeper understanding of the wider community infection and inform the future unlocking process. The historic daily published 78,842 confirmed cases in England up to 13/4/2020 in each of 149 Upper Tier Local Authority (UTLA) were converted to Average Daily Infection Rate (RADIR), an R-value - the number of further people infected by one infected person after their 5-day incubation and during their 5-day infectious phase, and the associated Rate of Change of Infection Rate ({Delta}IR) also calculated. Results compared to look for significant variances between regions. Stepwise regression was carried out to see what local factors could be linked to the difference in local infection rates. The peak of COVID-19 infection has passed. The current RADIR is now below 1. The rate of decline is such that within 14 days it may be below 0.5. There are significant variations in the current RADIR and {Delta}IR between the UTLAs, suggesting that the disease locally may be at different stages. Regression analysis across UTLAs found that the only factor that could be related to the fall in RADIR was an increase in the number of confirmed infection/1,000 population. Extrapolation of these results showed that based on assuming a link to increased immunity, unreported community infection may be over 200 times higher than the reported confirmed cases providing evidence that by the end of the second week in April 26% of the population may already have had the disease and so now have increased immunity. Linking these increased estimated infected numbers to recorded deaths indicates a possible mortality rate of 0.14%. Analysis of the current reported local case data using the infectious ratio does provide greater insight into the current levels of community infection and can be used to make better-informed decisions about the future management of restricted social behaviour and movement
ABSTRACT
IntroductionSevere Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is the name given to the 2019 novel coronavirus. COVID-19 is the name given to the disease associated with the virus. SARS-CoV-2 is a new strain of coronavirus that has not been previously identified in humans. MethodsTwo key factors were analysed which when multiplied together would give an estimate of relative demand on healthcare utilisation. These factors were case incidence and case morbidity. GP Practice data was used as this provided the most geographically granular source of published public population data. To analyse case incidence, the latest values for indicators that could be associated with infection transmission rates were collected from the Office of National Statistics (ONS) and Quality Outcome Framework (QOF) sources. These included population density, % age >16 at fulltime work/education, % age over 60, % BME ethnicity, social deprivation as IMD 2019, Location as latitude/longitude, and patient engagement as % self-confident in their own long term condition management. Average case morbidity was calculated by applying the international mortality Odds Ratio to the local population relevant age and disease prevalences and then summing and dividing by the equivalent national figure. To provide a comparative measure of overall healthcare resource impact, individual GP practice impact scores were compared against the median practice. ResultsThe case incidence regression is a dynamic situation with the significance of specific factors moderating over time as the balance between external infection, community transmission and impact of mitigation measures feeds through to the number of cases. It showed that currently Urban, % Working and age >60 were the strongest determinants of case incidence. The local population comorbidity remains unchanged. The range of relative HC impact was wide with 80% of practices falling between 20%-250% of the national median. Once practice population numbers were included it showed that the top 33% of GP practices supporting 45% of the patient population would require 68% of COVID-19 healthcare resources. The model provides useful information about the relative impact of Covid-19 on healthcare workload at GP practice granularity in all parts of England. ConclusionCovid-19 is impacting on the utilisation of health and social care resources across the country. This model provides a method for predicting relative local levels of disease burden based on defined criteria and thereby providing a method for targeting limited (and perhaps soon to be scarce) care resources to optimise national, regional and local responses to the COVID-19 outbreak..
