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International travel poses substantial risks for continued introduction of SARS-CoV-2. As of the 17th August 2020, travellers from 12 of the top 25 countries flying into the UK are required to self-isolate for 14 days. We estimate that 895 (CI: 834–958) infectious travellers arrive in a single week, of which 87% (779, CI: 722–837) originate from countries on the UK quarantine list. We compare alternative measures to the 14 day self-isolation (78.0% effective CI: 74.4–81.6) which could be more feasible long-term. A single RT-PCR taken upon arrival at the airport is 39.6% (CI: 35.2–43.7) effective, or equivalently, it would only detect 2 in 5 infectious passengers. Alternatively, testing four days after arrival is 64.3% (CI: 60.0–68.3) effective whereas a test at the airport plus additional test four days later is 68.9% (CI: 64.9–73.0) effective. Rapidly implementing control measures for travellers from risky countries is vital to protect public health; this methodology can be quickly updated to assess the impact of any further changes to international travel policy or disease occurrence.

Since the identification of Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China in December 2019, there have been more than 17 million cases of the disease in 216 countries worldwide. Comparisons of prevalence estimates between different communities can inform policy decisions regarding safe travel between countries, help to assess when to implement (or remove) disease control measures and identify the risk of over-burdening healthcare providers. Estimating the true prevalence can, however, be challenging because officially reported figures are likely to be significant underestimates of the true burden of COVID-19 within a community. Previous methods for estimating the prevalence fail to incorporate differences between populations (such as younger populations having higher rates of asymptomatic cases) and so comparisons between, for example, countries, can be misleading. Here, we present an improved methodology for estimating COVID-19 prevalence. We take the reported number of cases and deaths (together with population size) as raw prevalence for the population. We then apply an age-adjustment to this which allows the age-distribution of that population to influence the case-fatality rate and the proportion of asymptomatic cases. Finally, we calculate the likely underreporting factor for the population and use this to adjust our prevalence estimate further. We use our method to estimate the prevalence for 166 countries (or the states of the United States of America, hereafter referred to as US state) where sufficient data were available. Our estimates show that as of the 30th July 2020, the top three countries with the highest estimated prevalence are Brazil (1.26%, 95% CI: 0.96 – 1.37), Kyrgyzstan (1.10%, 95% CI: 0.82 – 1.19) and Suriname (0.58%, 95% CI: 0.44 – 0.63). Brazil is predicted to have the largest proportion of all the current global cases (30.41%, 95%CI: 27.52 – 30.84), followed by the USA (14.52%, 95%CI: 14.26 – 16.34) and India (11.23%, 95%CI: 11.11 – 11.24). Amongst the US states, the highest prevalence is predicted to be in Louisiana (1.07%, 95% CI: 1.02 – 1.12), Florida (0.90%, 95% CI: 0.86 – 0.94) and Mississippi (0.77%, 95% CI: 0.74 – 0.81) whereas amongst European countries, the highest prevalence is predicted to be in Montenegro (0.47%, 95% CI: 0.42 - 0.50), Kosovo (0.35%, 95% CI: 0.29 - 0.37) and Moldova (0.28%, 95% CI: 0.23 - 0.30). Our results suggest that Kyrgyzstan (0.04 tests per predicted case), Brazil (0.04 tests per predicted case) and Suriname (0.29 tests per predicted case) have the highest underreporting out of the countries in the top 25 prevalence. In comparison, Israel (34.19 tests per predicted case), Bahrain (19.82 per predicted case) and Palestine (9.81 tests per predicted case) have the least underreporting. The results of this study may be used to understand the risk between different geographical areas and highlight regions where the prevalence of COVID-19 is increasing most rapidly. The method described is quick and easy to implement. Prevalence estimates should be updated on a regular basis to allow for rapid fluctuations in disease patterns.

Purpose: To evaluate the clinical features, and visual outcomes of sterile uveitis after iris-fixated phakic intraocular lens implantation (pIOLs) (Artisan-Artiflex® and Verisyse-Veriflex® ). Material and Methods: In this retrospective non-comparative case series, the medical records of 117 eyes implanted with Artisan-Artiflex® (Ophtec BV, Groningen, Netherlands) and Verisyse-Veriflex® (AMO, Santa Ana, CA) iris claw phakic IOLs were analyzed for postimplantation sterile uveitis. The mean age of the 87 patients included in the study was 27.2±7.4 years. Of these patients, 56 (64.3%) were men and 31 (35.6%) were women. Patient age, gender, IOL brand type, refractive error, optic diameter, unilateral vs bilateral implantation, and anterior chamber depth were analyzed. Features of uveitis, uncorrected and best corrected visual acuity (VA) at presentation and at follow-up visits were examined. The mean follow-up time was 14.6 months (range: 6-37 months). Results: Clinically significant uveitis was observed in 12 of 117 eyes (10.3%) in a total of ten patients. Of these ten patients, four (40%) were women and six (60%) were men, and the mean age was 25.1±5.3 years (range: 18-36 years). Among those with bilateral implantation, 6.9% of patients developed bilateral uveitis, while 13.8% (4 of the 29 implanted bilaterally) developed unilateral uveitis. Foldable pIOL implantation was the only variable associated with the development of uveitis (P=.03). Conclusion: Although the prognosis is usually benign, sterile uveitis occurred in 10.3% of patients after iris-fixated pIOL implantation. The implantation of a foldable pIOL was the only variable associated with sterile uveitis. Appropriate medical management can be effective treatment, without the need for pIOL replacement.