Hypertension is a common comorbidity in COVID-19 patients. However, the association of hypertension with the severity and fatality of COVID-19 remain unclear. In the present meta-analysis, relevant studies reported the impacts of hypertension on SARS-CoV-2 infection were identified by searching PubMed, Elsevier Science Direct, Web of Science, Wiley Online Library, Embase and CNKI up to 20 March 2020. As the results shown, 12 publications with 2389 COVID-19 patients (674 severe cases) were included for the analysis of disease severity. The severity rate of COVID-19 in hypertensive patients was much higher than in non-hypertensive cases (37.58% vs 19.73%, pooled OR: 2.27, 95% CI: 1.80–2.86). Moreover, the pooled ORs of COVID-19 severity for hypertension vs. non-hypertension was 2.21 (95% CI: 1.58–3.10) and 2.32 (95% CI: 1.70–3.17) in age <50 years and ⩾50 years patients, respectively. Additionally, six studies with 151 deaths of 2116 COVID-19 cases were included for the analysis of disease fatality. The results showed that hypertensive patients carried a nearly 3.48-fold higher risk of dying from COVID-19 (95% CI: 1.72–7.08). Meanwhile, the pooled ORs of COVID-19 fatality for hypertension vs. non-hypertension was 6.43 (95% CI: 3.40–12.17) and 2.66 (95% CI: 1.27–5.57) in age <50 years and ⩾50 years patients, respectively. Neither considerable heterogeneity nor publication bias was observed in the present analysis. Therefore, our present results provided further evidence that hypertension could significantly increase the risks of severity and fatality of SARS-CoV-2 infection.

Student's t test is valid for statistical inference under the normality assumption or asymptotically. By contrast, although the bootstrap t test was proposed in 1993, it is seldom adopted in medical research. We aim to demonstrate that the bootstrap t test outperforms Student's t test under normality in data. Using random data samples from normal distributions, we evaluated the testing performance, in terms of true-positive rate (TPR) and false-positive rate and diagnostic abilities, in terms of the area under the curve (AUC), of the bootstrap t test and Student's t test. We explore the AUC of both tests with varying sample size and coefficient of variation. We compare the testing outcomes using the COVID-19 serial interval (SI) data in Shenzhen and Hong Kong, China, for demonstration. With fixed TPR, the bootstrap t test maintained the equivalent accuracy in TPR, but significantly improved the true-negative rate from the Student's t test. With varying TPR, the diagnostic ability of bootstrap t test outperformed or equivalently performed as Student's t test in terms of the AUC. The equivalent performances are possible but rarely occur in practice. We find that the bootstrap t test outperforms by successfully detecting the difference in COVID-19 SI, which is defined as the time interval between consecutive transmission generations, due to sex and non-pharmaceutical interventions against the Student's t test. We demonstrated that the bootstrap t test outperforms Student's t test, and it is recommended to replace Student's t test in medical data analysis regardless of sample size.

This study aimed to investigate the clinical characteristics and to analyse the epidemiological features of coronavirus disease 2019 (COVID-19) patients during convalescence. In this study, we enrolled 71 confirmed cases of COVID-19 who were discharged from hospital and transferred to isolation wards from 6 February to 26 March 2020. They were all employees of Zhongnan Hospital of Wuhan University or their family members of which three cases were <18 years of age. Clinical data were collected and analysed statistically. Forty-one cases (41/71, 57.7%) comprised medical faculty, young and middle-aged patients (aged ⩽60 years) accounted for 81.7% (58/71). The average isolation time period for all adult patients was 13.8 ± 6.1 days. During convalescence, RNA detection results of 35.2% patients (25/71) turned from negative to positive. The longest RNA reversed phase time was 7 days. In all, 52.9% of adult patients (36/68) had no obvious clinical symptoms, and the remaining ones had mild and non-specific clinical symptoms (e.g. cough, sputum, sore throat, disorders of the gastrointestinal tract etc.). Chest CT signs in 89.7% of adult patients (61/68) gradually improved, and in the others, the lesions were eventually absorbed and improved after short-term repeated progression. The main chest CT manifestations of adult patients were normal, GGO or fibre streak shadow, and six patients (8.8%) had extrapulmonary manifestations, but there was no significant correlation with RNA detection results (r = −0.008, P > 0.05). The drug treatment was mainly symptomatic support therapy, and antibiotics and antiviral drugs were ineffective. It is necessary to re-evaluate the isolation time and standard to terminate isolation for discharged COVID-19 patients.

We present two complementary model-based methods for calculating the risk of international spread of the novel coronavirus SARS-CoV-2 from the outbreak epicentre. One model aims to calculate the number of cases that would be exported from an endemic country to disease-free regions by travellers. The second model calculates the probability that an infected traveller will generate at least one secondary autochthonous case in the visited country. Although this paper focuses on the data from China, our methods can be adapted to calculate the risk of importation and subsequent outbreaks. We found an average R0 = 5.31 (ranging from 4.08 to 7.91) and a risk of spreading of 0.75 latent individuals per 1000 travellers. In addition, one infective traveller would be able to generate at least one secondary autochthonous case in the visited country with a probability of 23%.

Conflicting results have been obtained through meta-analyses for the role of obesity as a risk factor for adverse outcomes in patients with coronavirus disease-2019 (COVID-19), possibly due to the inclusion of predominantly multimorbid patients with severe COVID-19. Here, we aimed to study obesity alone or in combination with other comorbidities as a risk factor for short-term all-cause mortality and other adverse outcomes in Mexican patients evaluated for suspected COVID-19 in ambulatory units and hospitals in Mexico. We performed a retrospective observational analysis in a national cohort of 71 103 patients from all 32 states of Mexico from the National COVID-19 Epidemiological Surveillance Study. Two statistical models were applied through Cox regression to create survival models and logistic regression models to determine risk of death, hospitalisation, invasive mechanical ventilation, pneumonia and admission to an intensive care unit, conferred by obesity and other comorbidities (diabetes mellitus (DM), chronic obstructive pulmonary disease, asthma, immunosuppression, hypertension, cardiovascular disease and chronic kidney disease). Models were adjusted for other risk factors. From 24 February to 26 April 2020, 71 103 patients were evaluated for suspected COVID-19; 15 529 (21.8%) had a positive test for SARS-CoV-2; 46 960 (66.1%), negative and 8614 (12.1%), pending results. Obesity alone increased adjusted mortality risk in positive patients (hazard ratio (HR) = 2.7, 95% confidence interval (CI) 2.04–2.98), but not in negative and pending-result patients. Obesity combined with other comorbidities further increased risk of death (DM: HR = 2.79, 95% CI 2.04–3.80; immunosuppression: HR = 5.06, 95% CI 2.26–11.41; hypertension: HR = 2.30, 95% CI 1.77–3.01) and other adverse outcomes. In conclusion, obesity is a strong risk factor for short-term mortality and critical illness in Mexican patients with COVID-19; risk increases when obesity is present with other comorbidities.

The duration of immunity after first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the extent to which prior immunity prevents reinfection is uncertain and remains an important question within the context of new variants. This is a retrospective population-based matched observational study where we identified the first polymerase chain reaction (PCR) positive of primary SARS-CoV-2 infection case tests between 1 March 2020 and 30 September 2020. Each case was matched by age, sex, upper tier local authority of residence and testing route to one individual testing negative in the same week (controls) by PCR. After a 90-day pre-follow-up period for cases and controls, any subsequent positive tests up to 31 December 2020 and deaths within 28 days of testing positive were identified, this encompassed an essentially vaccine-free period. We used a conditional logistic regression to analyse the results. There were 517 870 individuals in the matched cohort with 2815 reinfection cases and 12 098 first infections. The protective effect of a prior SARS-CoV-2 PCR-positive episode was 78% (odds ratio (OR) 0.22, 0.21–0.23). Protection rose to 82% (OR 0.18, 0.17–0.19) after a sensitivity analysis excluded 933 individuals with a first test between March and May and a subsequent positive test between June and September 2020. Amongst individuals testing positive by PCR during follow-up, reinfection cases had 77% lower odds of symptoms at the second episode (adjusted OR 0.23, 0.20–0.26) and 45% lower odds of dying in the 28 days after reinfection (adjusted OR 0.55, 0.42–0.71). Prior SARS-CoV-2 infection offered protection against reinfection in this population. There was some evidence that reinfections increased with the alpha variant compared to the wild-type SARS-CoV-2 variant highlighting the importance of continued monitoring as new variants emerge.

The outbreak of pneumonia-like respiratory disorder at China and its rapid transmission world-wide resulted in public health emergency, which brought lineage B betacoronaviridae SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) into spotlight. The fairly high mutation rate, frequent recombination and interspecies transmission in betacoronaviridae are largely responsible for their temporal changes in infectivity and virulence. Investigation of global SARS-CoV-2 genotypes revealed considerable mutations in structural, non-structural, accessory proteins as well as untranslated regions. Among the various types of mutations, single-nucleotide substitutions are the predominant ones. In addition, insertion, deletion and frame-shift mutations are also reported, albeit at a lower frequency. Among the structural proteins, spike glycoprotein and nucleocapsid phosphoprotein accumulated a larger number of mutations whereas envelope and membrane proteins are mostly conserved. Spike protein and RNA-dependent RNA polymerase variants, D614G and P323L in combination became dominant world-wide. Divergent genetic variants created serious challenge towards the development of therapeutics and vaccines. This review will consolidate mutations in different SARS-CoV-2 proteins and their implications on viral fitness.

The current coronavirus (COVID-19) pandemic offers a unique opportunity to conduct an infodemiological study examining patterns in online searching activity about a specific disease and how this relates to news media within a specific country. Google Trends quantifies volumes of online activity. The relative search volume was obtained for ‘Coronavirus’, ‘handwashing’, ‘face mask’ and symptom related keywords, for the United Kingdom, from the date of the first confirmed case until numbers peaked in April. The relationship between online search traffic and confirmed case numbers was examined. Search volumes varied over time; peaks appear related to events in the progression of the epidemic which were reported in the media. Search activity on ‘Coronavirus’ correlated well against confirmed case number as did ‘face mask’ and symptom-related keywords. User-generated online data sources such as Google Trends may aid disease surveillance, being more responsive to changes in disease occurrence than traditional disease reporting. The relationship between media coverage and online searching activity is rarely examined, but may be driving online behavioural patterns.

The explosive outbreak of COVID-19 led to a shortage of medical resources, including isolation rooms in hospitals, healthcare workers (HCWs) and personal protective equipment. Here, we constructed a new model, non-contact community treatment centres to monitor and quarantine asymptomatic and mildly symptomatic COVID-19 patients who recorded their own vital signs using a smartphone application. This new model in Korea is useful to overcome shortages of medical resources and to minimise the risk of infection transmission to HCWs.

India has the third-largest burden of human immunodeficiency virus (HIV) infection in the world. The coronavirus disease 2019 (COVID-19) pandemic has only exposed the cracks in the Indian healthcare infrastructure concerning HIV. The prevalence of HIV in India is more among the destitute or sections of society shrouded by years of social stigma such as prostitutes, truck drivers, transsexuals and intravenous drug users. National AIDS Control Organisation and The Joint United Nations Programme on HIV/AIDS (UNAIDS) organisation have many several efforts over the years to set up counselling and testing centres all over the country and spread awareness about HIV among the masses. COVID-19 pandemic has reversed years of progress made by the same. HIV patients are biologically more susceptible to COVID-19, and the lockdown has led to the loss of jobs, economic crises, shortage of drugs and necessities such as food and housing among this vulnerable population, which can result in lowered CD4-T cell counts in the coming months and make way for dangerous opportunistic infection outbreaks in this population increasing the overall HIV burden of India. This article explores how COVID-19 has impacted India's already existing HIV epidemic and tries to put forth recommendations based on the evidence found to be better prepared in treating the HIV-positive population in India in the face of another catastrophe like the COVID-19.

Mathematical modelling studies predicting the spread of the coronavirus disease 2019 (COVID-19) have been used worldwide, but precisions are limited. Thus, continuous evaluation of the modelling studies is crucial. We investigated situations of virus importation in sub-Saharan Africa (SSA) to assess effectiveness of a modelling study by Haider N et al. titled ‘Passengers’ destinations from China: low risk of novel coronavirus (2019-nCoV) transmission into Africa and South America’. We obtained epidemiological data of 2417 COVID-19 cases reported by 40 countries in SSA within 30 days of the first case confirmed in Nigeria on 27 February. Out of 442 cases which had travel history available, only one (0.2%) had a travel history to China. These findings underline the result of the model. However, the fact that there were numbers of imported cases from other regions shows the limits of the model. The limits could be attributed to the characteristics of the COVID-19 which is infectious even when the patients do not express any symptoms. Therefore, there is a profound need for all modelling researchers to take asymptomatic cases into account when they establish modelling studies.

Between 21 November and 22 December 2020, a SARS-CoV-2 community testing pilot took place in the South Wales Valleys. We conducted a case-control study in adults taking part in the pilot using an anonymous online questionnaire. Social, demographic and behavioural factors were compared in people with a positive lateral flow test (cases) and a sample of negatives (controls). A total of 199 cases and 2621 controls completed a questionnaire (response rates: 27.1 and 37.6% respectively). Following adjustment, cases were more likely to work in the hospitality sector (aOR 3.39, 95% CI 1.43–8.03), social care (aOR 2.63, 1.22–5.67) or healthcare (aOR 2.31, 1.29–4.13), live with someone self-isolating due to contact with a case (aOR 3.07, 2.03–4.62), visit a pub (aOR 2.87, 1.11–7.37) and smoke or vape (aOR 1.54, 1.02–2.32). In this community, and at this point in the epidemic, reducing transmission from a household contact who is self-isolating would have the biggest public health impact (population-attributable fraction: 0.2). As restrictions on social mixing are relaxed, hospitality venues will become of greater public health importance, and those working in this sector should be adequately protected. Smoking or vaping may be an important modifiable risk factor.

The aim of this study was to estimate the basic reproduction number (R0) of COVID-19 in the early stage of the epidemic and predict the expected number of new cases in Shahroud in Northeastern Iran. The R0 of COVID-19 was estimated using the serial interval distribution and the number of incidence cases. The 30-day probable incidence and cumulative incidence were predicted using the assumption that daily incidence follows a Poisson distribution determined by daily infectiousness. Data analysis was done using ‘earlyR’ and ‘projections’ packages in R software. The maximum-likelihood value of R0 was 2.7 (95% confidence interval (CI): 2.1−3.4) for the COVID-19 epidemic in the early 14 days and decreased to 1.13 (95% CI 1.03–1.25) by the end of day 42. The expected average number of new cases in Shahroud was 9.0 ± 3.8 cases/day, which means an estimated total of 271 (95% CI: 178–383) new cases for the period between 02 April to 03 May 2020. By day 67 (27 April), the effective reproduction number (Rt), which had a descending trend and was around 1, reduced to 0.70. Based on the Rt for the last 21 days (days 46–67 of the epidemic), the prediction for 27 April to 26 May is a mean daily cases of 2.9 ± 2.0 with 87 (48–136) new cases. In order to maintain R below 1, we strongly recommend enforcing and continuing the current preventive measures, restricting travel and providing screening tests for a larger proportion of the population.

Surveillance data shows a geographical overlap between the early coronavirus disease 2019 (COVID-19) pandemic and the past Q fever epidemic (2007–2010) in the Netherlands. We investigated the relationship between past Q fever and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in 2020/2021, using a retrospective matched cohort study.

In January 2021, former Q fever patients received a questionnaire on demographics, SARS-CoV-2 test results and related hospital/intensive care unit (ICU) admissions. SARS-CoV-2 incidence with 95% confidence intervals (CI) in former Q fever patients and standardised incidence ratios (SIR) to compare to the age-standardised SARS-CoV-2 incidence in the general regional population were calculated.

Among 890 former Q fever patients (response rate: 68%), 66 had a PCR-confirmed SARS-CoV-2 infection. Of these, nine (14%) were hospitalised and two (3%) were admitted to ICU. From February to June 2020 the SARS-CoV-2 incidence was 1573/100 000 (95% CI 749–2397) in former Q fever patients and 695/100 000 in the general population (SIR 2.26; 95% CI 1.24–3.80). The incidence was not significantly higher from September 2020 to February 2021.

We found no sufficient evidence for a difference in SARS-CoV-2 incidence or an increased severity in former Q fever patients vs. the general population during the period with widespread SARS-CoV-2 testing availability (September 2020–February 2021). This indicates that former Q fever patients do not have a higher risk of SARS-CoV-2 infection.

At the present time, COVID-19 is spreading rapidly [1]. The global prevention and control of COVID-19 is focused on the estimation of the relevant incubation period, basic reproduction number (R0), effective reproduction number (Rt) and death risk. Although the prevention and control of COVID-19 requires a reliable estimation of the relevant incubation period, R0, Rt and death risk. Another key epidemiological parameter-asymptomatic ratio that provides strength and range for social alienation strategies of COVID-19, which is widely defined as the proportion of asymptomatic infections among all disease infections. In fact, the ratio of asymptomatic infection is a useful indicator of the burden of disease and a better measurement of the transmissibility of the virus. So far, people have not paid enough attention to asymptomatic carriers. The asymptomatic carriers discussed in this study are recessive infections, that is, those who have never shown symptoms after onset of infection. We will discuss three aspects: detection, infectivity and proportion of healthy carriers.

A novel coronavirus disease, designated as COVID-19, has become a pandemic worldwide. This study aims to estimate the incubation period and serial interval of COVID-19. We collected contact tracing data in a municipality in Hubei province during a full outbreak period. The date of infection and infector–infectee pairs were inferred from the history of travel in Wuhan or exposed to confirmed cases. The incubation periods and serial intervals were estimated using parametric accelerated failure time models, accounting for interval censoring of the exposures. Our estimated median incubation period of COVID-19 is 5.4 days (bootstrapped 95% confidence interval (CI) 4.8–6.0), and the 2.5th and 97.5th percentiles are 1 and 15 days, respectively; while the estimated serial interval of COVID-19 falls within the range of −4 to 13 days with 95% confidence and has a median of 4.6 days (95% CI 3.7–5.5). Ninety-five per cent of symptomatic cases showed symptoms by 13.7 days (95% CI 12.5–14.9). The incubation periods and serial intervals were not significantly different between male and female, and among age groups. Our results suggest a considerable proportion of secondary transmission occurred prior to symptom onset. And the current practice of 14-day quarantine period in many regions is reasonable.

Even though the impact of COVID-19 in metropolitan areas has been extensively studied, the geographic spread to smaller cities is also of great concern. We conducted an ecological study aimed at identifying predictors of early introduction, incidence rates of COVID-19 and mortality (up to 8 May 2020) among 604 municipalities in inner São Paulo State, Brazil. Socio-demographic indexes, road distance to the state capital and a classification of regional relevance were included in predictive models for time to COVID-19 introduction (Cox regression), incidence and mortality rates (zero-inflated binomial negative regression). In multivariable analyses, greater demographic density and higher classification of regional relevance were associated with both early introduction and increased rates of COVID-19 incidence and mortality. Other predictive factors varied, but distance from the State Capital (São Paulo City) was negatively associated with time-to-introduction and with incidence rates of COVID-19. Our results reinforce the hypothesis of two patterns of geographical spread of SARS-Cov-2 infection: one that is spatial (from the metropolitan area into the inner state) and another which is hierarchical (from urban centres of regional relevance to smaller and less connected municipalities). Those findings may apply to other settings, especially in developing and highly heterogeneous countries, and point to a potential benefit from strengthening non-pharmaceutical control strategies in areas of greater risk.

During the coronavirus disease 2019 (COVID-19) pandemic, many countries opted for strict public health measures, including closing schools. After some time, they have started relaxing some of those restrictions. To avoid overwhelming health systems, predictions for the number of new COVID-19 cases need to be considered when choosing a school reopening strategy. Using a computer simulation based on a stochastic compartmental model that includes a heterogeneous and dynamic network, we analyse different strategies to reopen schools in the São Paulo Metropolitan Area, including one similar to the official reopening plan. Our model allows us to describe different types of relations between people, each type with a different infectiousness. Based on our simulations and model assumptions, our results indicate that reopening schools with all students at once has a big impact on the number of new COVID-19 cases, which could cause a collapse of the health system. On the other hand, our results also show that a controlled school reopening could possibly avoid the collapse of the health system, depending on how people follow sanitary measures. We estimate that postponing the schools' return date for after a vaccine becomes available may save tens of thousands of lives just in the São Paulo Metropolitan Area compared to a controlled reopening considering a worst-case scenario. We also discuss our model constraints and the uncertainty of its parameters.