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Introduction: Monitoring the evolution of COVID-19 has been essential for health decision-making. But this requires reliable numbers of the infected, dead and hospitalized, often difficult to have for multiple reasons. The use of fast and reliable estimation techniques, such as indirect surveys, is an option. Objectives: To present the CoronaSurveys project, which monitors COVID-19 by combining indirect surveys with the Network Scaleup Method (NSUM). Methodology: The system uses indirect online anonymous surveys to inquire about COVID-19, and the NSUM method to estimate cases. The system offers multi-language surveys with questions to track active cases, new cases, and fatalities, among others. Results: CoronaSurveys has been operating since March 2020, and continues to collect data, with over one hundred responses to date (over one million samples). The system has made good estimates for Spain and the United Kingdom, among other countries. Conclusion: This system is suitable in countries with limited health infrastructure or in situations of lack of knowledge about the pandemic, such as at the beginning of COVID-19, because its implementation cost is small and it requires simple devices to use it and few participants to obtain good estimates. [ABSTRACT FROM AUTHOR]

We conducted an updated meta-analysis to evaluate the 30-day mortality of hip fractures during the COVID-19 pandemic and assess mortality rates by country. We systematically searched Medline, EMBASE, and the Cochrane Library up to November 2022 for studies on the 30-day mortality of hip fractures during the pandemic. Two reviewers used the Newcastle–Ottawa tool to independently assess the methodological quality of the included studies. We conducted a meta-analysis and systematic review including 40 eligible studies with 17,753 patients with hip fractures, including 2280 patients with COVID-19 (12.8%). The overall 30-day mortality rate for hip fractures during the pandemic was 12.6% from published studies. The 30-day mortality of patients with hip fractures who had COVID-19 was significantly higher than those without COVID-19 (OR, 7.10; 95% CI, 5.51–9.15; I2 = 57%). The hip fracture mortality rate increased during the pandemic and varied by country, with the highest rates found in Europe, particularly the United Kingdom (UK) and Spain. COVID-19 may have contributed to the increased 30-day mortality rate in hip fracture patients. The mortality rate of hip fracture in patients without COVID-19 did not change during the pandemic. [ABSTRACT FROM AUTHOR]

Background and Aim: COVID-19 is a highly contagious infectious disease, and it has affected people's daily life and has raised great concern for governments and public health officials. Forecasting its future behavior may be useful for allocating medical resources and defining effective strategies for disease control, etc. Materials and Methods: The collected data was the cumulative and the absolute number of confirmed, death, and recovered cases of COVID-19 from February 20 to July 03, 2021. We used hierarchical cluster analysis. To forecast the future behavior of COVID-19, the Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Automatic Forecasting Procedure (Prophet), Naive, Seasonal Naive (s-Naive), boosted ARIMA, and boosted Prophet models were used. Results: The results of clustering showed a similar behavior of coronavirus in Iran and other countries such as France, Russia, Turkey, United Kingdom (UK), Argentina, Colombia, Italy, Spain, Germany, Poland, Mexico, and Indonesia. It also revealed similar patterns of SARS-CoV-2 for the same countries in six groups. Results showed that XGBoost models' family had higher accuracy than other models. Conclusion: In Iran, COVID-19 showed similar behavior patterns compared to the studied developed countries. The family of XGBoost models showed practical results and high precision in forecasting behavior patterns of the virus. Concerning the rapid spread of the virus worldwide, these models can be used to forecast the behavior patterns of SARS-CoV-2. Preventing the spread of the coronavirus, controlling the disease, and breaking down its chain necessitates community assistance, and in this mission, the role of statisticians cannot be neglected. [ABSTRACT FROM AUTHOR]

The new Coronavirus Disease 19, officially known as COVID-19, originated in China in 2019 and has since spread worldwide. We presented an age-structured Susceptible-Latent-Mild-Critical-Removed (SLMCR) compartmental model of COVID-19 disease transmission with nonlinear incidence during the pandemic period. We provided the model calibration to estimate parameters with day-wise COVID-19 data, i.e., reported cases by worldometer from 15th February to 30th March 2020 in six high-burden countries, including Australia, Italy, Spain, the USA, the UK, and Canada. We estimate transmission rates for each country and found that the country with the highest transmission rate is Spain, which may increase the new cases and deaths than the other countries. We found that saturation infection negatively impacted the dynamics of COVID-19 cases in all the six high-burden countries. The study used a sensitivity analysis to identify the most critical parameters through the partial rank correlation coefficient method. We found that the transmission rate of COVID-19 had the most significant influence on prevalence. The prediction of new cases in COVID-19 until 30th April 2020 using the developed model was also provided with recommendations to control strategies of COVID-19. We also found that adults are more susceptible to infection than both children and older people in all six countries. However, in Italy, Spain, the UK, and Canada, older people show more susceptibility to infection than children, opposite to the case in Australia and the USA. The information generated from this study would be helpful to the decision-makers of various organisations across the world, including the Ministry of Health in Australia, Italy, Spain, the USA, the UK, and Canada, to control COVID-19. [ABSTRACT FROM AUTHOR]

Background: Since December 2019, the world has been facing one of the most critical health threats of the last decades. In March 2021, the official number of deaths passed 2.6 million worldwide. Most countries have developed policies to control the disease. Nevertheless, countries have experienced different outcomes related to their various adapted policies. Complementing our first report, in this article, we report our findings of comparing the policies adopted to combat coronavirus disease 2019 (COVID-19) in Iran, with those from nine selected countries, including China, Japan, South Korea, Singapore, Germany, the United States, the United Kingdom, Spain, and Italy, to draw evidence-informed policy lessons. Methods: This is a qualitative study conducted based on document analysis related to COVID-19 policies in Iran and nine selected countries. Using a deductive approach, data were extracted and analyzed based on the components of the WHO Building Blocks Framework. Finally, we compared the Iranian policies with the nine selected countries. Results: Documents analysis revealed a spectrum of policies, which have led to a variety of outcomes. Based on our findings, three main strategies (widespread testing, comprehensive contact tracing, and timely measures) were the most effective directions to combat COVID-19. Conclusion: The experience of the first and second waves of COVID-19 showed that the risk of coronavirus is serious and will continue until a vaccine or decisive treatment is achieved. Therefore, countries are required to adopt appropriate and tailored policies to deal with this crisis effectively. Applying the experiences from the previous waves is essential for more efficient performance in the likely upcoming waves. INSET: 260. [ABSTRACT FROM AUTHOR]

Background To stop the spread of the new coronavirus disease in 2019 (COVID-19), many countries had completely locked down. This lockdown restricted the everyday life of the affected residents and changed their mobility pattern, but its effects on sleep pattern were largely unknown. Methods Here, utilizing one of the largest crowdsourced database (Sleep as Android), we analyzed the sleep pattern of 25 217 users with 1 352 513 sleep records between 1 January and 29 April 2020 in the US and 16 European countries (Germany, UK, Spain, France, Italy, The Netherlands, Belgium, Hungary, Denmark, Finland, Norway, Czech, Sweden, Austria, Poland and Switzerland) with more than 100 records in all days of 2020. Results During the COVID-19 pandemic, the sleeping pattern before and after the country-level lockdown largely differed. The subjects increased their sleep duration by an average of 11.3 to 18.6 min on weekday nights, except Denmark (4.9 min) and Finland (7.1 min). In addition, subjects form all 16 European countries delayed their sleep onset from 10.7 min (Sweden) to 29.6 min (Austria). Conclusion During the COVID-19 pandemic, residents in the US and 16 European countries delayed their bedtime and slept longer than usual. [ABSTRACT FROM AUTHOR]

As the number of cases of COVID-19 continues to grow, local health services are at risk of being overwhelmed with patients requiring intensive care. We develop and implement an algorithm to provide optimal re-routing strategies to either transfer patients requiring Intensive Care Units (ICU) or ventilators, constrained by feasibility of transfer. We validate our approach with realistic data from the United Kingdom and Spain. In the UK, we consider the National Health Service at the level of trusts and define a 4-regular geometric graph which indicates the four nearest neighbours of any given trust. In Spain we coarse-grain the healthcare system at the level of autonomous communities, and extract similar contact networks. Through random search optimisation we identify the best load sharing strategy, where the cost function to minimise is based on the total number of ICU units above capacity. Our framework is general and flexible allowing for additional criteria, alternative cost functions, and can be extended to other resources beyond ICU units or ventilators. Assuming a uniform ICU demand, we show that it is possible to enable access to ICU for up to 1000 additional cases in the UK in a single step of the algorithm. Under a more realistic and heterogeneous demand, our method is able to balance about 600 beds per step in the Spanish system only using local sharing, and over 1300 using countrywide sharing, potentially saving a large percentage of these lives that would otherwise not have access to ICU. [ABSTRACT FROM AUTHOR]