Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number
Keeling MJ., Dyson L., Guyver-Fletcher G., Holmes A., Semple MG., Tildesley MJ., Hill EM., Brito A., Earnest R., Fauver J., Kalinich C., Petrone M., Rothman J., Vogels C., Watkins A., Ferguson D., Schulz W., Kerantzas N., Landry M., Peaper D., Baillie JK., Openshaw PJM., Carson G., Alex B., Bach B., Barclay WS., Bogaert D., Chand M., Cooke GS., Docherty AB., Dunning J., da Silva Filipe A., Fletcher T., Green CA., Harrison EM., Hiscox JA., Ho AYW., Horby PW., Ijaz S., Khoo S., Klenerman P., Lim WS., Mentzer AJ., Merson L., Meynert AM., Noursadeghi M., Palmarini M., Paxton WA., Pollakis G., Price N., Rambaut A., Robertson DL., Russell CD., Sancho-Shimizu V., Scott JT., Sigfrid L., Solomon T., Sriskandan S., Stuart D., Summers C., Tedder RS., Thomson EC., Thwaites RS., Turtle LCW., Zambon M., Hardwick H., Donohue C., Ewins J., Oosthuyzen W., Griffiths F., Norman L., Pius R., Drake TM., Fairfield CJ., Knight S., Mclean KA., Murphy D., Shaw CA., Dalton J., Girvan M., Saviciute E., Harrison SRJ., Marsh L., Connor M., Leeming G., Law A., Hendry R., Greenhalf W., Shaw V., Ahmed KA., Armstrong JA., Ashworth M., Asiimwe IG., Bakshi S., Barlow SL., Booth L., Brennan B., Bullock K., Catterall BWA., Clark JJ.
The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, (Formula presented.), has taken on special significance in terms of the general understanding of whether the epidemic is under control ((Formula presented.)). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March–June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.