Objectives The aim of this study was to evaluate the differences in surgical capacity for head and neck cancer in the UK between the first wave (March‐June 2020) and the current wave (Jan‐Feb 2021) of the COVID‐19 pandemic. Design REDcap online‐based survey of hospital capacity. Setting UK secondary and tertiary hospitals providing head and neck cancer surgery. Participants One representative per hospital was asked to report the capacity for head and neck cancer surgery in that institution. Main outcome measures The principal measures of interests were new patient referrals, capacity in outpatients, theatres and critical care; therapeutic compromises constituting delay to surgery, de‐escalated surgery and therapeutic migration to non‐surgical primary modality. Results Data were returned from approximately 95% of UK hospitals with a head and neck cancer surgery specialist service. 50% of UK head and neck cancer patients requiring surgery have significantly compromised treatments during the second wave: 28% delayed, 10% have received radiotherapy‐based treatment instead of surgery, and 12% have received de‐escalated surgery. Surgical capacity has been more severely constrained in the second wave (58% of pre‐pandemic level) compared with the first wave (62%) despite the time to prepare. Conclusions Some hospitals are overwhelmed by COVID‐19 and unable to offer essential cancer surgery, but all have neighbouring hospitals in their region retaining good (or even normal) capacity. It is noteworthy that very few patients have been appropriately redirected away from the hospitals most constrained by their burden of COVID‐19. The paucity of an effective central or regional strategic response to this evident mismatch between demand and surgical capacity is to the detriment of our head and neck cancer patients.

The mechanically stable layered perovskite Bi2LaO4I, a non-magnetic insulator, as a possible candidate for optoelectronic and thermoelectric applications.

Background: COVID-19, caused by SARS-CoV-2, is a newly identified highly infectious disease. It has affected almost every country including Nepal causing a pandemic situation. Most of the properties of SARS-CoV-2 are not known and still under intense investigation. Due to high mutation rate, it reappears in many countries in the form of new variant. In Nepal, second wave impact of COVID-19 is mainly caused by newly found delta variant of SARS-CoV-2. In this case, the mathematical modelling is noted to play important role to understand control strategies for the spread of coronavirus. Aims and Objective: To analyze the second wave impact by modelling the data of COVID-19 cases in Nepal. Materials and Methods: We have analyzed COVID-19 daily cases and deaths reported by Ministry of Health and Population, Government of Nepal from April 1 to May 31, 2021. A logistic model has been used to present the trend line of COVID-19 infection in Nepal, based on the law of population growth developed by Verhulst. Results: The results show a good fit between observed and predicted data by logistic model as indicated by coefficient of determination having value near to unity. The point of inflection from the logistic model predicted a maximum of 9951 daily new cases. The maximum number of cumulative cases estimated at the end of second wave was found to be 307293 with 95% confidence interval. Conclusion: Logistic model properly describes the growth of COVID-19 cases with time. This type of data modelling and analysis will be very useful in predicting the upcoming trend of COVID-19 in Nepal as a basis for making health policy management by the government.

This paper aims to integrate novel coronavirus daily cases in SAARC countries; India, Pakistan, Bangladesh, Nepal, Sri Lanka, Afghanistan, Maldives and Bhutan to forecast the epidemic trend of COVID-19 by using logistic model. The recent trend of coronavirus cases were analyzed from the COVID-19 epidemiological data for SAARC countries from 23 January 2020 to 31 May 2021. The final size, growth rate parameter and point of inflection of COVID-19 for each countries were calculated by fitting the logistic curve with the cumulative cases. The graphical patterns of COVID-19 daily cases reflect that its second wave impact is more devastating than the first wave in SAARC countries. The increasing trend of COVID-19 cases in these countries was well described by logistic model with coefficient of determination greater than 0.96. The predictive final size of the second wave infections is maximum for India which is 19.8 million with growth rate parameter of 0.08 and inflection time of 68 days whereas the predictive final size is minimum for Afghanistan which is 0.041 million with growth rate parameter of 0.06 and inflection time of 71 days. The logistic model is helpful in predicting the trajectory of the infected cases in a country if the current scenario of this type of infectious disease remains same. Also, it helps the government to frame policy decisions and necessary actions that controls the transmission of COVID-19 in the South Asian region.

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