COVID-19 research has been produced at an unprecedented rate and managing what is currently known is in part being accomplished through synthesis research. Here we evaluated how the need to rapidly produce syntheses has impacted the quality of the synthesis research. Thus, we sought to identify, evaluate and map the synthesis research on COVID-19 published up to 10 July 2020. A COVID-19 literature database was created using pre-specified COVID-19 search algorithms carried out in eight databases. We identified 863 citations considered to be synthesis research for evaluation in this project. Four-hundred and thirty-nine reviews were fully assessed with A MeaSurement Tool to Assess systematic Reviews (AMSTAR-2) and rated as very low-quality (n = 145), low-quality (n = 80), medium-quality (n = 208) and high-quality (n = 151). The quality of these reviews fell short of what is expected for synthesis research with key domains being left out of the typical methodology. The increase in risk of bias due to non-adherence to systematic review methodology is unknown and prevents the reader from assessing the validity of the review. The responsibility to assure the quality is held by both producers and publishers of synthesis research and our findings indicate there is a need to equip readers with the expertise to evaluate the review conduct before using it for decision-making purposes.

The spread of COVID-19 is recent in India, which has within 4 months caused over 190 000 infections, as of 1 June 2020, despite four stringent lockdowns. With the current rate of the disease transmission in India, which is home to over 1.35 billion people, the infection spread is predicted to be worse than the USA in the upcoming months. To date, there is a major lack of understanding of the transmission dynamics and epidemiological characteristics of the disease in India, inhibiting effective measures to control the pandemic. We collected all the available data of the individual patients, cases and a range of parameters such as population distribution, testing and healthcare facilities, and weather, across all Indian states till May 2020. Numerical analysis was conducted to determine the effect of each parameter on the COVID-19 situation in India. A significant amount of local transmission in India initiated with travellers returning from abroad. Maharashtra, Tamil Nadu and Delhi are currently the top three infected states in India with doubling time of 14.5 days. The average recovery rate across Indian states is 42%, with a mortality rate below 3%. The rest 55% are currently active cases. In total, 88% of the patients experienced symptoms of high fever, 68% suffered from dry cough and 7.1% patients were asymptomatic. In total, 66.8% patients were males, 73% were in the age group of 20–59 years and over 83% recovered in 11–25 days. Approximately 3.4 million people were tested between 1 April and 25 May 2020, out of which 4% were detected COVID-19-positive. Given the current doubling time of infections, several states may face a major shortage of public beds and healthcare facilities soon. Weather has minimal effect on the infection spread in most Indian states. The study results will help policymakers to predict the trends of the disease spread in the upcoming months and devise better control measures.

Diagnostic testing for the novel coronavirus is an important tool to fight the coronavirus disease (Covid-19) pandemic. However, testing capacities are limited. A modified testing protocol, whereby a number of probes are ‘pooled’ (i.e. grouped), is known to increase the capacity for testing. Here, we model pooled testing with a double-average model, which we think to be close to reality for Covid-19 testing. The optimal pool size and the effect of test errors are considered. The results show that the best pool size is three to five, under reasonable assumptions. Pool testing even reduces the number of false positives in the absence of dilution effects.

The feasibility of non-pharmacological public health interventions (NPIs) such as physical distancing or isolation at home to prevent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in low-resource countries is unknown. Household survey data from 54 African countries were used to investigate the feasibility of SARS-CoV-2 NPIs in low-resource settings. Across the 54 countries, approximately 718 million people lived in households with ⩾6 individuals at home (median percentage of at-risk households 56% (95% confidence interval (CI), 51% to 60%)). Approximately 283 million people lived in households where ⩾3 people slept in a single room (median percentage of at-risk households 15% (95% CI, 13% to 19%)). An estimated 890 million Africans lack on-site water (71% (95% CI, 62% to 80%)), while 700 million people lacked in-home soap/washing facilities (56% (95% CI, 42% to 73%)). The median percentage of people without a refrigerator in the home was 79% (95% CI, 67% to 88%), while 45% (95% CI, 39% to 52%) shared toilet facilities with other households. Individuals in low-resource settings have substantial obstacles to implementing NPIs for mitigating SARS-CoV-2 transmission. These populations urgently need to be prioritised for coronavirus disease 2019 vaccination to prevent disease and to contain the global pandemic.

Purpose: The novel coronavirus (severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)) first appeared in Wuhan, China, in December 2019, and rapidly spread across the globe. Since most respiratory viruses are known to show a seasonal pattern of infection, it has been hypothesised that SARS-CoV-2 may be seasonally dependent as well. The present study looks at a possible effect of atmospheric temperature, which is one of the suspected factors influencing seasonality, on the evolution of the pandemic. Basic procedures: Since confirming a seasonal pattern would take several more months of observation, we conducted an innovative day-to-day micro-correlation analysis of nine outbreak locations, across four continents and both hemispheres, in order to examine a possible relationship between atmospheric temperature (used as a proxy for seasonality) and outbreak progression. Main findings: There was a negative correlation between atmospheric temperature variations and daily new cases growth rates, in all nine outbreaks, with a median lag of 10 days. Principal conclusions: The results presented here suggest that high temperatures might dampen SARS-CoV-2 propagation, while lower temperatures might increase its transmission. Our hypothesis is that this could support a potential effect of atmospheric temperature on coronavirus disease progression, and potentially a seasonal pattern for this virus, with a peak in the cold season and rarer occurrences in the summer. This could guide government policy in both the Northern and Southern hemispheres for the months to come.

During the last months and following the implementation of containment measures in the context of coronavirus disease 2019 (COVID-19) pandemic, the number of new human immunodeficiency virus (HIV) diagnoses radically decreased in Liege AIDS Reference Center, Belgium. The number of HIV screening tests has also dramatically dropped down to an unprecedented level. This decline of HIV diagnosis is caused by missed diagnoses of individuals infected before the establishment of such measures and to the reduction of high-risk sexual behaviours during the COVID-19 pandemic.

This study aimed to analyse the geographical distribution of coronavirus disease 2019 (COVID-19) and to identify high-risk areas in space and time for the occurrence of cases and deaths in the indigenous population of Brazil. This is an ecological study carried out between 24 March and 26 October 2020 whose units of analysis were the Special Indigenous Sanitary Districts. The Getis-Ord General G and Getis-Ord Gi* techniques were used to verify the spatial association of the phenomena and a retrospective space–time scan was performed. There were 32 041 confirmed cases of COVID-19 and 471 deaths. The non-randomness of cases (z score = 5.40; P < 0.001) and deaths (z score = 3.83; P < 0.001) were confirmed. Hotspots were identified for cases and deaths in the north and midwest regions of Brazil. Sixteen high-risk space–time clusters were identified for the occurrence of cases with a higher RR = 21.23 (P < 0.001) and four risk clusters for deaths with a higher RR = 80.33 (P < 0.001). These clusters were identified from 22 May and were active until 10 October 2020. The results indicate critical areas in the indigenous territories of Brazil and contribute to better directing the actions of control of COVID-19 in this population.

Healthcare workers (HCWs) have increased exposure and subsequent risk of infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This case-control study was conducted to investigate the contemporaneous risks associated with confirmed SARS-CoV-2 infection amongst HCWs following in-work exposure to a confirmed coronavirus disease-2019 (COVID-19) case. We assessed the influence of demographic (age, sex, nationality, high risk co-morbidities and vaccination status) and work-related factors (job role, exposure location, contact type, personal protective equipment (PPE) use) on infection risk following nosocomial SARS-CoV-2 exposure. All contact tracing records within the hospital site during waves 1–3 of the COVID-19 pandemic in Ireland were screened to identify exposure events, cases and controls. In total, 285 cases and 1526 controls were enrolled, as a result of 1811 in-work exposure events with 745 index cases. We demonstrate that male sex, Eastern European nationality, exposure location, PPE use and vaccination status all impact the likelihood of SARS-CoV-2 infection following nosocomial SARS-CoV-2 exposure. The findings draw attention to the need for continuing emphasis on PPE use and its persisting benefit in the era of COVID-19 vaccinations. We suggest that non-work-related factors may influence infection risk seen in certain ethnic groups and that infection risk in high-risk HCW roles (e.g. nursing) may be the result of repeated exposures rather than risks inherent to a single event.

Thirteen men wintering on an Antarctic base were isolated from other human contact for 10 months. During this period Coxsackievirus A21 and later influenza A2 virus were administered to some of the men. Serum samples were collected from each of the men at monthly intervals.

Coxsackievirus A21 produced symptoms and apparently spread to uninoculated men. It also appears that repeated re-infections occurred and that the virus persisted in this small community for most of the period of isolation. HI antibody responses in the absence of neutralizing antibody responses seem to be transient.

The vaccine strain of influenza virus induced antibody responses but did not cause symptoms. There was no evidence of spread to uninoculated men.

Antibody titres against influenza C, parainfluenzaviruses 1 and 2 and coronavirus OC43 did not fall significantly during isolation.

An outbreak of respiratory illness occurred at the end of isolation and its origin was traced. No causative agent was detected.

We update our previous insights into COVID-19 vaccine acceptance and hesitancy in Finland. Vaccine acceptance increased from 64% (November/December 2020) to 74% (April 2021). However, there was a group of participants that were preferring to wait to get vaccinated ranging from 6% of over-64-years-olds to 29% of under-30-years-olds. The previously identified enablers convenience (below-50-years-olds), worry about severe disease and protection for oneself (above-50-years-olds) were no longer significantly associated with increased vaccine acceptance. Understanding barriers and enablers behind vaccine acceptance is decisive in ensuring a successful implementation of COVID-19 vaccination programs, which will be key to ending the pandemic.

The SARS-CoV-2 Omicron variant has increased infectivity and immune escape compared with previous variants, and caused the surge of massive COVID-19 waves globally. Despite a vast majority (~90%) of the population of Santa Fe city, Argentina had been vaccinated and/or had been infected by SARS-CoV-2 when Omicron emerged, the epidemic wave that followed its arrival was by far the largest one experienced in the city. A serosurvey conducted prior to the arrival of Omicron allowed to assess the acquired humoral defences preceding the wave and to conduct a longitudinal study to provide individual-level real-world data linking antibody levels and protection against COVID-19 during the wave. A very large proportion of 1455 sampled individuals had immunological memory against COVID-19 at the arrival of Omicron (almost 90%), and about half (48.9%) had high anti-spike immunoglobulin G levels (>200 UI/ml). However, the antibody titres varied greatly among the participants, and such variability depended mainly on the vaccine platform received, on having had COVID-19 previously and on the number of days elapsed since last antigen exposure (vaccine shot or natural infection). A follow-up of 514 participants provided real-world evidence of antibody-mediated protection against COVID-19 during a period of high risk of exposure to an immune-escaping highly transmissible variant. Pre-wave antibody titres were strongly negatively associated with COVID-19 incidence and severity of symptoms during the wave. Also, receiving a vaccine shot during the follow-up period reduced the COVID-19 risk drastically (15-fold). These results highlight the importance of maintaining high defences through vaccination at times of high risk of exposure to immune-escaping variants.

This study aimed to analyse the trend and spatial–temporal clusters of risk of transmission of COVID-19 in northeastern Brazil. We conducted an ecological study using spatial and temporal trend analysis. All confirmed cases of COVID-19 in the Northeast region of Brazil were included, from 7 March to 22 May 2020. We used the segmented log-linear regression model to assess time trends, and the local empirical Bayesian estimator, the global and local Moran indexes for spatial analysis. The prospective space–time scan statistic was performed using the Poisson probability distribution model. There were 113 951 confirmed cases of COVID-19. The average incidence rate was 199.73 cases/100 000 inhabitants. We observed an increasing trend in the incidence rate in all states. Spatial autocorrelation was reported in metropolitan areas, and 178 municipalities were considered a priority, especially in the states of Ceará and Maranhão. We identified 11 spatiotemporal clusters of COVID-19 cases; the primary cluster included 70 municipalities from Ceará state. COVID-19 epidemic is increasing rapidly throughout the Northeast region of Brazil, with dispersion towards countryside. It was identified high risk clusters for COVID-19, especially in the coastal side.

Despite SARS-CoV-19 infection has a stereotypical clinical picture, isolated cases with unusual manifestations have been reported, some of them being well-known to be triggered by viral infections. However, the real frequency in COVID-19 is unknown. Analysing data of 63 822 COVID patients attending 50 Spanish emergency department (ED) during the COVID outbreak, before hospitalisation, we report frequencies of (myo)pericarditis (0.71‰), meningoencephalitis (0.25‰), Guillain–Barré syndrome (0.13‰), acute pancreatitis (0.71‰) and spontaneous pneumothorax (0.57‰). Compared with general ED population, COVID patients developed more frequently Guillain–Barré syndrome (odds ratio (OR) 4.55, 95% confidence interval (CI) 2.09–9.90), spontaneous pneumothorax (OR 1.98, 95% CI 1.40–2.79) and (myo)pericarditis (OR 1.45, 95% CI 1.07–1.97), but less frequently pancreatitis (OR 0.44, 95% CI 0.33–0.60).

The risk factors specific to the elderly population for severe coronavirus disease 2019 (COVID-19) caused by the Omicron variant of concern (VOC) are not yet clear. We performed an exploratory analysis using logistic regression to identify risk factors for severe COVID-19 illness among 4,868 older adults with a positive severe acute respiratory coronavirus 2 (SARS-CoV-2) test result who were admitted to a healthcare facility between 1 January 2022 and 16 May 2022. We then conducted one-to-one propensity score (PS) matching for three factors – dementia, admission from a long-term care facility and poor physical activity status – and used Fisher's exact test to compare the proportion of severe COVID-19 cases in the matched data. We also estimated the average treatment effect on treated (ATT) in each PS matching analysis. Of the 4,868 cases analysed, 1,380 were severe. Logistic regression analysis showed that age, male sex, cardiovascular disease, cerebrovascular disease, chronic lung disease, renal failure and/or dialysis, physician-diagnosed obesity, admission from a long-term care facility and poor physical activity status were risk factors for severe disease. Vaccination and dementia were identified as factors associated with non-severe illness. The ATT for dementia, admission from a long-term care facility and poor physical activity status was −0.04 (95% confidence interval −0.07 to −0.01), 0.09 (0.06 to 0.12) and 0.17 (0.14 to 0.19), respectively. Our results suggest that poor physical activity status and living in a long-term care facility have a substantial association with the risk of severe COVID-19 caused by the Omicron VOC, while dementia may be associated with non-severe illness.

The aim of this study was to apply a back-calculation model to Great Britain (GB) classical scrapie surveillance data, and use this model to estimate how many more cases might be expected, and over what time frame these cases might occur. A back-calculation model was applied to scrapie surveillance data between 2005 and 2019 to estimate the annual rate of decline of classical scrapie. This rate was then extrapolated to predict the number of future cases each year going forward. The model shows that there may be yet further cases of classical scrapie in GB. These will most likely occur in the fallen stock scheme, with approximately a 25% probability of at least 1 further scrapie positive, with a very low probability (~0.2%) of having up to three additional scrapie positives. This highlights the difficulty of completely eliminating all further cases, even in the presence of very effective control measures.

Given the fast spread of the novel coronavirus (COVID-19) worldwide and its classification by the World Health Organization (WHO) as being one of the worst pandemics in history, several scientific studies are carried out using various statistical and mathematical models to predict and study the likely evolution of this pandemic in the world. In the present research paper, we present a brief study aiming to predict the probability of reaching a new record number of COVID-19 cases in Lebanon, based on the record theory, giving more insights about the rate of its quick spread in Lebanon. The main advantage of the records theory resides in avoiding several statistical constraints concerning the choice of the underlying distribution and the quality of the residuals. In addition, this theory could be used, in cases where the number of available observations is somehow small. Moreover, this theory offers an alternative solution in case where machine learning techniques and long-term memory models are inapplicable because they need a considerable amount of data to be performant. The originality of this paper lies in presenting a new statistical approach allowing the early detection of unexpected phenomena such as the new pandemic COVID-19. For this purpose, we used epidemiological data from Johns Hopkins University to predict the trend of COVID-2019 in Lebanon. Our method is useful in calculating the probability of reaching a new record as well as studying the propagation of the disease. It also computes the probabilities of the waiting time to observe the future COVID-19 record. Our results obviously confirm the quick spread of the disease in Lebanon over a short time.

During 6 weeks in February–March 2021, the Dutch municipal health service Utrecht studied the epidemiological effects on test incidence and the detection of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with mass testing (MT). During MT, inhabitants of Bunschoten could repeatedly test regardless of symptoms and as often as desired at the close-by test facilities in the municipality. Data from the regular COVID-19 registration was used for analysis. In Bunschoten, MT caused a significant increase in test incidence and an immediate increase in the number of detected active infections, in contrast to a stabilisation in the rest of the province of Utrecht. Age distribution of test incidence shifted to the older population in Bunschoten during MT. During MT, there was a 6.8 percentage point increase in detected asymptomatic cases, a 0.4 percentage point increase in pre-symptomatic cases and a decrease of 0.5 days between onset of symptoms and test date. This study has shown that MT increases test incidence and helps to obtain a more complete view of the presence of SARS-CoV-2 in a community, which can be useful in specific situations with a defined target group or goal. However, the question remains open whether the use of MT is proportionate to the overall gain.

There is a paucity of evidence about the prevalence and risk factors for symptomatic infection among children. This study aimed to describe the prevalence of symptomatic coronavirus disease 2019 (COVID-19) and its risk factors in children and adolescents aged 0–18 years in Qatar. We conducted a cross-sectional study of all children aged 0–18 years diagnosed with COVID-19 using polymerase chain reaction in Qatar during the period 1st March to 31st July 2020. A generalised linear model with a binomial family and identity link was used to assess the association between selected factors and the prevalence of symptomatic infection. A total of 11 445 children with a median age of 8 years (interquartile range (IQR) 3–13 years) were included in this study. The prevalence of symptomatic COVID-19 was 36.6% (95% confidence interval (CI) 35.7–37.5), and it was similar between children aged <5 years (37.8%), 5–9 years (34.3%) and 10 + years (37.3%). The most frequently reported symptoms among the symptomatic group were fever (73.5%), cough (34.8%), headache (23.2%) and sore throat (23.2%). Fever (82.8%) was more common in symptomatic children aged <5 years, while cough (38.7%) was more prevalent in those aged 10 years or older, compared to other age groups. Variables associated with an increased risk of symptomatic infection were; contact with confirmed cases (RD 0.21; 95% CI 0.20–0.23; P = 0.001), having visited a health care facility (RD 0.54; 95% CI 0.45–0.62; P = 0.001), and children aged under 5 years (RD 0.05; 95% CI 0.02–0.07; P = 0.001) or aged 10 years or older (RD 0.04; 95% CI 0.02–0.06; P = 0.001). A third of the children with COVID-19 were symptomatic with a higher proportion of fever in very young children and a higher proportion of cough in those between 10 and 18 years of age.

Identification of geographical areas with high burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission in schools using spatial analyses has become an important tool to guide targeted interventions in educational setting. In this study, we aimed to explore the spatial distribution and determinants of coronavirus disease 2019 (COVID-19) among students aged 3–18 years in South Korea. We analysed the nationwide epidemiological data on laboratory-confirmed COVID-19 cases in schools and in the communities between January 2020 and October 2021 in South Korea. To explore the spatial distribution, the global Moran's I and Getis-Ord's G using incidence rates among the districts of aged 3–18 years and 30–59 years. Spatial regression analysis was performed to find sociodemographic predictors of the COVID-19 attack rate in schools and in the communities. The global spatial correlation estimated by Moran's I was 0.647 for the community population and 0.350 for the student population, suggesting that the students were spatially less correlated than the community-level outbreak of SARS-CoV-2. In schools, attack rate of adults aged 30–59 years in the community was associated with increased risk of transmission (P < 0.0001). Number of students per class (in kindergartens, primary schools, middle schools and high schools) did not show significant association with the school transmission of SARS-CoV-2. In South Korea, COVID-19 in students had spatial variations across the country. Statistically significant high hotspots of SARS-CoV-2 transmission among students were found in the capital area, with dense population level and high COVID-19 burden among adults aged 30–59 years. Our finding suggests that controlling community-level burden of COVID-19 can help in preventing SARS-CoV-2 infection in school-aged children.