INTRODUCTION: Loss of smell and taste is now recognised as amongst the most common symptoms of COVID-19 and the best predictor of COVID-19 positivity. Long term outcomes are unknown. This study aims to investigate recovery of loss of smell and the prevalence of parosmia. METHODOLOGY: 6-month follow-up of respondents to an online surgery who self-reported loss of smell at the onset of the CO- VID-19 pandemic in the UK. Information of additional symptoms, recovery of loss of smell and the development of parosmia was collected. RESULTS: 44% of respondents reported at least one other ongoing symptom at 6 months, of which fatigue (n=106) was the most prevalent. There was a significant improvement in self-rating of severity of olfactory loss where 177 patients stated they had a normal smell of smell while 12 patients reported complete loss of smell. The prevalence of parosmia is 43.1% with median interval of 2.5 months (range 0-6) from the onset of loss of smell. CONCLUSIONS: While many patients recover quickly, some experience long-term deficits with no self-reported improvement at 6 months. Furthermore, there is a high prevalence of parosmia even in those who report at least some recovery of olfactory func- tion. Longer term evaluation of recovery is required.

As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.