SARS-CoV-2 variant introduction following spring break travel and transmission mitigation strategies.

BACKGROUND: University spring break carries a two-pronged SARS-CoV-2 variant transmission risk. Circulating variants from universities can spread to spring break destinations, and variants from spring break destinations can spread to universities and surrounding communities. Therefore, it is critical to implement SARS-CoV-2 variant surveillance and testing strategies to limit community spread before and after spring break to mitigate virus transmission and facilitate universities safely returning to in-person teaching. METHODS: We examined the SARS-CoV-2 positivity rate and changes in variant lineages before and after the university spring break for two consecutive years. 155 samples were sequenced across four time periods: pre- and post-spring break 2021 and pre- and post-spring break 2022; following whole genome sequencing, samples were assigned clades. The clades were then paired with positivity and testing data from over 50,000 samples. RESULTS: In 2021, the number of variants in the observed population increased from four to nine over spring break, with variants of concern being responsible for most of the cases; Alpha percent composition increased from 22.2% to 56.4%. In 2022, the number of clades in the population increased only from two to three, all of which were Omicron or a sub-lineage of Omicron. However, phylogenetic analysis showed the emergence of distantly related sub-lineages. 2022 saw a greater increase in positivity than 2021, which coincided with a milder mitigation strategy. Analysis of social media data provided insight into student travel destinations and how those travel events may have impacted spread. CONCLUSIONS: We show the role that repetitive testing can play in transmission mitigation, reducing community spread, and maintaining in-person education. We identified that distantly related lineages were brought to the area after spring break travel regardless of the presence of a dominant variant of concern.

Health-related quality of life and impact of socioeconomic status among primary and secondary school students after the third COVID-19 wave in Berlin, Germany.

In the earlier phases of the COVID-19 pandemic, studies in Germany and elsewhere found an overall reduction in health-related quality of life (HRQoL) among students. However, there is little evidence on later pandemic stages as well as socioeconomic influencing factors. We aimed to (1) describe HRQoL in a Berlin student cohort at two time points in mid-2021, and to (2) analyze the effects of household income and education. We assessed HRQoL of students from 24 randomly selected primary and secondary schools in Berlin, Germany, with the KIDSCREEN-10 index in June and September 2021. To adjust for non-response bias, inverse probability weighting was applied. The potential effects of both household income and education (lower vs. higher) were estimated in generalized linear mixed models, based on prior assumptions presented in directed acyclic graphs. Our cohort comprised 660 students aged 7-19 years. In June 2021, 11.3% [95% CI = 9.0% - 14.0%] reported low HRQoL, whereas in September 2021, this increased to 13.7% [95% CI = 11.1% - 16.5%], with adolescent girls more frequently reporting low HRQoL at both time points (20% [95% CI = 17.1% - 23.3%] and 29% [95% CI = 25.5% - 32.5%]) compared to boys and younger children. While there was no statistically significant total effect of lower household income on HRQoL, a negative effect of lower household education was statistically significant (ß = -2.15, SE 0.95, 95% CI = -4.01 to -0.29, p = 0.024). In summary, students' HRQoL in mid-2021 was better than that documented in other studies conducted at pandemic onset using KIDSCREEN-10. Female adolescents reported low HRQoL more often, and lower household education significantly reduced children's HRQoL. Support strategies for psychosocial wellbeing should consider socioeconomically disadvantaged children as important target groups.

Factors affecting the number of influenza patients before and during COVID-19 pandemic, Thailand.

This study was aimed to explore the association between potential factors including public health and social measures and the number of influenza patients in Thailand between 2014-2021. Secondary data from relevant agencies were collected. Generalized Estimating Equation (GEE) and regression coefficient (ß) were performed at a significance level of 0.05. We found factors associated with number of influenza patients during the time prior to COVID-19 pandemic were monthly income per household (Adjusted ß = -0.02; 95% CI: -0.03, -0.01), population density (Adjusted ß = 1.00; 95% CI: 0.82, 1.18), rainy season (Adjusted ß = 137.15; 95% CI: 86.17, 188.13) and winter time (Adjusted ß = 56.46; 95% CI: 3.21, 109.71). During the time of COVID-19 pandemic, population density (Adjusted ß = 0.20; 95% CI: 0.15, 0.26), rainy season (Adjusted ß = -164.23; 95% CI: -229.93, -98.52), winter time (Adjusted ß = 61.06; 95% CI: 0.71, 121.41), public health control measures (prohibition of entering to into an area with high number of COVID-19 infections (Adjusted ß = -169.34; 95% CI: -233.52, -105.16), and restriction of travelling also reduced the number of influenza patients (Adjusted ß = -66.88; 95% CI: -125.15, -8.62) were associated with number of influenza patients. This study commends strategies in monitoring influenza patients to focus on the areas with low income, high population density, and in specific seasons. Public health and social measures which can be implemented are prohibition of entering to risk-areas (lock down), and restriction of travelling across provinces which their effectiveness in reducing influenza infections.

A surge in food insecurity during the COVID-19 pandemic in a cohort in Mexico City.

BACKGROUND: The COVID-19 pandemic has not only caused tremendous loss of life and health but has also greatly disrupted the world economy. The impact of this disruption has been especially harsh in urban settings of developing countries. We estimated the impact of the pandemic on the occurrence of food insecurity in a cohort of women living in Mexico City, and the socioeconomic characteristics associated with food insecurity severity. METHODS: We analyzed data longitudinally from 685 women in the Mexico City-based ELEMENT cohort. Food insecurity at the household level was gathered using the Latin American and Caribbean Food Security Scale and measured in-person during 2015 to 2019 before the pandemic and by telephone during 2020-2021, in the midst of the pandemic. Fluctuations in the average of food insecurity as a function of calendar time were modeled using kernel-weighted local polynomial regression. Fixed and random-effects ordinal logistic regression models of food insecurity were fitted, with timing of data collection (pre-pandemic vs. during pandemic) as the main predictor. RESULTS: Food insecurity (at any level) increased from 41.6% during the pre-pandemic period to 53.8% in the pandemic stage. This increase was higher in the combined severe-moderate food insecurity levels: from 1.6% pre-pandemic to 16.8% during the pandemic. The odds of severe food insecurity were 3.4 times higher during the pandemic relative to pre-pandemic levels (p<0.01). Socioeconomic status quintile (Q) was significantly related to food insecurity (Q2 OR = 0.35 p<0.1, Q3 OR = 0.48 p = 0.014, Q4 OR = 0.24 p<0.01, and Q5 OR = 0.17 p<0.01), as well as lack of access to social security (OR = 1.69, p = 0.01), and schooling (OR = 0.37, p<0.01). CONCLUSIONS: Food insecurity increased in Mexico City households in the ELEMENT cohort as a result of the COVID-19 pandemic. These results contribute to the body of evidence suggesting that governments should implement well-designed, focalized programs in the context of economic crisis such as the one caused by COVID-19 to prevent families from the expected adverse health and well-being consequences associated to food insecurity, especially for the most vulnerable.

Open-source data pipeline for street-view images: A case study on community mobility during COVID-19 pandemic.

Street View Images (SVI) are a common source of valuable data for researchers. Researchers have used SVI data for estimating pedestrian volumes, demographic surveillance, and to better understand built and natural environments in cityscapes. However, the most common source of publicly available SVI data is Google Street View. Google Street View images are collected infrequently, making temporal analysis challenging, especially in low population density areas. Our main contribution is the development of an open-source data pipeline for processing 360-degree video recorded from a car-mounted camera. The video data is used to generate SVIs, which then can be used as an input for longitudinal analysis. We demonstrate the use of the pipeline by collecting an SVI dataset over a 38-month longitudinal survey of Seattle, WA, USA during the COVID-19 pandemic. The output of our pipeline is validated through statistical analyses of pedestrian traffic in the images. We confirm known results in the literature and provide new insights into outdoor pedestrian traffic patterns. This study demonstrates the feasibility and value of collecting and using SVI for research purposes beyond what is possible with currently available SVI data. Our methods and dataset represent a first of its kind longitudinal collection and application of SVI data for research purposes. Limitations and future improvements to the data pipeline and case study are also discussed.

The potential of tailed amplicons for SARS-CoV-2 detection in Nucleic Acid Lateral Flow Assays.

Nucleic Acid Lateral Flow Assays (NALFAs) are a promising solution for the point-of-care detection of viruses like SARS-CoV-2. However, they show some drawbacks, such as the great dependency on the use of antibodies and the need for post-amplification protocols that enable the preparation of amplicons for effective readings, as well as low sensitivity. Here, we developed amplicons of a specific SARS-CoV-2 gene tailed with single-strand DNA (ssDNA) sequences to hybridize with DNA probes immobilized on the NALFA strips, thus overcoming the aforementioned problems. Results have shown that tailed primers have not compromised the amplification efficiency and allowed the correct detection of the amplicons in the lateral flow strip. This approach has presented a limit of detection (LOD) of 25 RNA copies /reaction mix (1 copy/µL) and the test of cross-reactivity with other related viruses has not shown any cross-reactivity. Twenty clinical samples were evaluated by NALFA and simultaneously compared with the gold standard RT-qPCR protocol, originating equal results. Although the number of clinical specimens tested being relatively small, this indicates a sensitivity and specificity both of 100%. In short, an alternative NALFA was successfully implemented, rendering an accurate route for SARS-CoV-2 diagnosis, compatible with low-resource settings.

Mode of delivery and birth outcomes before and during COVID-19 -A population-based study in Ontario, Canada.

There is lack of clarity on whether pregnancies during COVID-19 resulted in poorer mode of delivery and birth outcomes in Ontario, Canada. We aimed to compare mode of delivery (C-section), birth (low birthweight, preterm birth, NICU admission), and health services use (HSU, hospitalizations, ED visits, physician visits) outcomes in pregnant Ontario women before and during COVID-19 (pandemic periods). We further stratified for pre-existing chronic diseases (asthma, eczema, allergic rhinitis, diabetes, hypertension). Deliveries before (Jun 2018-Feb 2020) and during (Jul 2020-Mar 2022) pandemic were from health administrative data. We used multivariable logistic regression analyses to estimate adjusted odds ratios (aOR) of delivery and birth outcomes, and negative binomial regression for adjusted rate ratios (aRR) of HSU. We compared outcomes between pre-pandemic and pandemic periods. Possible interactions between study periods and covariates were also examined. 323,359 deliveries were included (50% during pandemic). One in 5 (18.3%) women who delivered during the pandemic had not received any COVID-19 vaccine, while one in 20 women (5.2%) lab-tested positive for COVID-19. The odds of C-section delivery during the pandemic was 9% higher (aOR = 1.09, 95% CI: 1.08-1.11) than pre-pandemic. The odds of preterm birth and NICU admission were 15% (aOR = 0.85, 95% CI: 0.82-0.87) and 10% lower (aOR = 0.90, 95% CI: 0.88-0.92), respectively, during COVID-19. There was a 17% reduction in ED visits but a 16% increase in physician visits during the pandemic (aRR = 0.83, 95% CI: 0.81-0.84 and aRR = 1.16, 95% CI: 1.16-1.17, respectively). These aORs and aRRs were significantly higher in women with pre-existing chronic conditions. During the pandemic, healthcare utilization, especially ED visits (aRR = 0.83), in pregnant women was lower compared to before. Ensuring ongoing prenatal care during the pandemic may reduce risks of adverse mode of delivery and the need for acute care during pregnancy.

SARS-CoV-2 clade dynamics and their associations with hospitalisations during the first two years of the COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic was characterised by rapid waves of disease, carried by the emergence of new and more infectious SARS-CoV-2 virus variants. How the pandemic unfolded in various locations during its first two years has yet to be sufficiently covered. To this end, here we are looking at the circulating SARS-CoV-2 variants, their diversity, and hospitalisation rates in Estonia in the period from March 2000 to March 2022. METHODS: We sequenced a total of 27,550 SARS-CoV-2 samples in Estonia between March 2020 and March 2022. High-quality sequences were genotyped and assigned to Nextstrain clades and Pango lineages. We used regression analysis to determine the dynamics of lineage diversity and the probability of clade-specific hospitalisation stratified by age and sex. RESULTS: We successfully sequenced a total of 25,375 SARS-CoV-2 genomes (or 92%), identifying 19 Nextstrain clades and 199 Pango lineages. In 2020 the most prevalent clades were 20B and 20A. The various subsequent waves of infection were driven by 20I (Alpha), 21J (Delta) and Omicron clades 21K and 21L. Lineage diversity via the Shannon index was at its highest during the Delta wave. About 3% of sequenced SARS-CoV-2 samples came from hospitalised individuals. Hospitalisation increased markedly with age in the over-forties, and was negligible in the under-forties. Vaccination decreased the odds of hospitalisation in over-forties. The effect of vaccination on hospitalisation rates was strongly dependent upon age but was clade-independent. People who were infected with Omicron clades had a lower hospitalisation likelihood in age groups of forty and over than was the case with pre-Omicron clades regardless of vaccination status. CONCLUSIONS: COVID-19 disease waves in Estonia were driven by the Alpha, Delta, and Omicron clades. Omicron clades were associated with a substantially lower hospitalisation probability than pre-Omicron clades. The protective effect of vaccination in reducing hospitalisation likelihood was independent of the involved clade.

Determinants of post-acute COVID-19 syndrome among hospitalized severe COVID-19 patients: A 2-year follow-up study.

BACKGROUND: Post-acute COVID-19 syndrome is a condition where individuals experience persistent symptoms after the acute phase of the COVID-19 infection has resolved, which lowers their quality of life and ability to return to work. This study assessed the prevalence and associated risk factors of post-acute COVID-19 syndrome (PACS) among severe COVID-19 patients who were discharged from Millennium COVID-19 Care Center, Addis Ababa, Ethiopia. METHODS: A cross-sectional study using data collected from patient charts and a follow-up telephone interview after two years of discharge. Systematic random sampling was used to select a total of 400 patients. A structured questionnaire developed from the case report form for PACS of the World Health Organization (WHO) was used. Frequency and cross-tabulation were used for descriptive statistics. Predictor variables with a p-value <0.25 in bivariate analyses were included in the logistic regression. RESULT: Out of the 400 patients, 20 patients were dead, 14 patients refused to give consent, and 26 patients couldn't be reached because their phones weren't working. Finally, 340 were included in the study. The majority (68.5%) were males and the mean age was 53.9 (±13.3 SD) years. More than a third (38.1%) of the patients reported the persistence of at least one symptom after hospital discharge. The most common symptoms were fatigue (27.5%) and Cough (15.3%). Older age (AOR 1.04, 95% CI 1.02-1.07), female sex (AOR 1.82, 95% CI 1.00-3.29), presence of comorbidity (AOR 2.38, 95% CI 1.35-4.19), alcohol use (AOR 3.05, 95% CI 1.49-6.26), fatigue at presentation (AOR 2.18, 95% CI 1.21-3.95), and longer hospital stay (AOR 1.06, 95% CI 1.02-1.10) were found to increase the odds of developing post-acute COVID-19 syndrome. Higher hemoglobin level was found to decrease the risk of subsequent post-acute COVID-19 syndrome (AOR 0.84, 95% CI 0.71-0.99). CONCLUSION: The prevalence of post-acute COVID-19 syndrome is high, with a wide range of persistent symptoms experienced by patients. COVID-19 survivors with the identified risk factors are more susceptible to post-acute COVID-19 and require targeted monitoring and care in a multidisciplinary approach.

Long-term health conditions and UK labour market outcomes during the COVID-19 pandemic.

BACKGROUND: Long-term health conditions can affect labour market outcomes. COVID-19 may have increased labour market inequalities, e.g. due to restricted opportunities for clinically vulnerable people. Evaluating COVID-19's impact could help target support. AIM: To quantify the effect of several long-term conditions on UK labour market outcomes during the COVID-19 pandemic and compare them to pre-pandemic outcomes. METHODS: The Understanding Society COVID-19 survey collected responses from around 20,000 UK residents in nine waves from April 2020-September 2021. Participants employed in January/February 2020 with a variety of long-term conditions were matched with people without the condition but with similar baseline characteristics. Models estimated probability of employment, hours worked and earnings. We compared these results with results from a two-year pre-pandemic period. We also modelled probability of furlough and home-working frequency during COVID-19. RESULTS: Most conditions (asthma, arthritis, emotional/nervous/psychiatric problems, vascular/pulmonary/liver conditions, epilepsy) were associated with reduced employment probability and/or hours worked during COVID-19, but not pre-pandemic. Furlough was more likely for people with pulmonary conditions. People with arthritis and cancer were slower to return to in-person working. Few effects were seen for earnings. CONCLUSION: COVID-19 had a disproportionate impact on people with long-term conditions' labour market outcomes.

Development and evaluation of a lyophilization protocol for colorimetric RT-LAMP diagnostic assay for COVID-19.

Molecular diagnostics involving nucleic acids (DNA and RNA) are regarded as extremely functional tools. During the 2020 global health crisis, efforts intensified to optimize the production and delivery of molecular diagnostic kits for detecting SARS-CoV-2. During this period, RT-LAMP emerged as a significant focus. However, the thermolability of the reagents used in this technique necessitates special low-temperature infrastructure for transport, storage, and conservation. These requirements limit distribution capacity and necessitate cost-increasing adaptations. Consequently, this report details the development of a lyophilization protocol for reagents in a colorimetric RT-LAMP diagnostic kit to detect SARS-CoV-2, facilitating room-temperature transport and storage. We conducted tests to identify the ideal excipients that maintain the molecular integrity of the reagents and ensure their stability during room-temperature storage and transport. The optimal condition identified involved adding 5% PEG 8000 and 75 mM trehalose to the RT-LAMP reaction, which enabled stability at room temperature for up to 28 days and yielded an analytical and diagnostic sensitivity and specificity of 83.33% and 90%, respectively, for detecting SARS-CoV-2. This study presents the results of a lyophilized colorimetric RT-LAMP COVID-19 detection assay with diagnostic sensitivity and specificity comparable to RT-qPCR, particularly in samples with high viral load.

Rapid expansion and international spread of M1UK in the post-pandemic UK upsurge of Streptococcus pyogenes.

The UK observed a marked increase in scarlet fever and invasive group A streptococcal infection in 2022 with severe outcomes in children and similar trends worldwide. Here we report lineage M1UK to be the dominant source of invasive infections in this upsurge. Compared with ancestral M1global strains, invasive M1UK strains exhibit reduced genomic diversity and fewer mutations in two-component regulator genes covRS. The emergence of M1UK is dated to 2008. Following a bottleneck coinciding with the COVID-19 pandemic, three emergent M1UK clades underwent rapid nationwide expansion, despite lack of detection in previous years. All M1UK isolates thus-far sequenced globally have a phylogenetic origin in the UK, with dispersal of the new clades in Europe. While waning immunity may promote streptococcal epidemics, the genetic features of M1UK point to a fitness advantage in pathogenicity, and a striking ability to persist through population bottlenecks.

Adolescents' trajectories of depression and anxiety symptoms prior to and during the COVID-19 pandemic and their association with healthy sleep patterns.

The COVID-19 pandemic has seen a rise in anxiety and depression among adolescents. This study aimed to investigate the longitudinal associations between sleep and mental health among a large sample of Australian adolescents and examine whether healthy sleep patterns were protective of mental health in the context of the COVID-19 pandemic. We used three waves of longitudinal control group data from the Health4Life cluster-randomized trial (N = 2781, baseline Mage = 12.6, SD = 0.51; 47% boys and 1.4% 'prefer not to say'). Latent class growth analyses across the 2 years period identified four trajectories of depressive symptoms: low-stable (64.3%), average-increasing (19.2%), high-decreasing (7.1%), moderate-increasing (9.4%), and three anxiety symptom trajectories: low-stable (74.8%), average-increasing (11.6%), high-decreasing (13.6%). We compared the trajectories on sociodemographic and sleep characteristics. Adolescents in low-risk trajectories were more likely to be boys and to report shorter sleep latency and wake after sleep onset, longer sleep duration, less sleepiness, and earlier chronotype. Where mental health improved or worsened, sleep patterns changed in the same direction. The subgroups analyses uncovered two important findings: (1) the majority of adolescents in the sample maintained good mental health and sleep habits (low-stable trajectories), (2) adolescents with worsening mental health also reported worsening sleep patterns and vice versa in the improving mental health trajectories. These distinct patterns of sleep and mental health would not be seen using mean-centred statistical approaches.

Maximally informative feature selection using Information Imbalance: Application to COVID-19 severity prediction.

Clinical databases typically include, for each patient, many heterogeneous features, for example blood exams, the clinical history before the onset of the disease, the evolution of the symptoms, the results of imaging exams, and many others. We here propose to exploit a recently developed statistical approach, the Information Imbalance, to compare different subsets of patient features and automatically select the set of features that is maximally informative for a given clinical purpose, especially in minority classes. We adapt the Information Imbalance approach to work in a clinical framework, where patient features are often categorical and are generally available only for a fraction of the patients. We apply this algorithm to a data set of ∼ 1300 patients treated for COVID-19 in Udine hospital before October 2021. Using this approach, we find combinations of features which, if used in combination, are maximally informative of the clinical fate and of the severity of the disease. The optimal number of features, which is determined automatically, turns out to be between 10 and 15. These features can be measured at admission. The approach can be used also if the features are available only for a fraction of the patients, does not require imputation and, importantly, is able to automatically select features with small inter-feature correlation. Clinical insights deriving from this study are also discussed.

Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model.

Accurate short-term predictions of COVID-19 cases with empirical models allow Health Officials to prepare for hospital contingencies in a two-three week window given the delay between case reporting and the admission of patients in a hospital. We investigate the ability of Gompertz-type empiric models to provide accurate prediction up to two and three weeks to give a large window of preparation in case of a surge in virus transmission. We investigate the stability of the prediction and its accuracy using bi-weekly predictions during the last trimester of 2020 and 2021. Using data from 2020, we show that understanding and correcting for the daily reporting structure of cases in the different countries is key to accomplish accurate predictions. Furthermore, we found that filtering out predictions that are highly unstable to changes in the parameters of the model, which are roughly 20%, reduces strongly the number of predictions that are way-off. The method is then tested for robustness with data from 2021. We found that, for this data, only 1-2% of the one-week predictions were off by more than 50%. This increased to 3% for two-week predictions, and only for three-week predictions it reached 10%.

Faecal shedding of SARS-CoV-2 from patients with asymptomatic and mild COVID-19 without gastrointestinal symptoms in Ghana.

OBJECTIVE: In this study, we sought to determine whether faecal shedding occurs among SARS-COV-2 positive Ghanaians, as reported elsewhere. Hence we assayed for SARS-COV-2 in the stools of 48 SARS-COV-2 confirmed patients at the Ho Municipal Hospital in Ghana. RESULTS: Of the 48 COVID-19 patients, 45 (93.8%) had positive tests for SARS-CoV-2 faecal shedding. About 60% reported no respiratory symptoms, while only 2% (1 patient) reported gastrointestinal (GI) symptoms in the form of nausea. Other symptoms reported included headache (57.9%), weakness (57.9%), cough (52.6%), blocked/runny nose (47.4%), fever (31.6%), sore throat (31.6%), and shortness of breath (21.1%). One person complained of nausea (5.3%) Semi-quantitative comparison of the SARS COV-2 viral loads in matched respiratory and faecal samples using the cycle threshold (CT) values revealed no statistical differences. Furthermore, the duration between collection of respiratory and faecal samples did not have any direct influence on the differences in the CT values. This suggests that treatment and use of sewage for environmental surveillance of SARS COV-2 could be a potential public health countermeasure.

Analyzing factors affecting positivity in drive-through COVID-19 testing: a cross-sectional study.

BACKGROUND: Demand for COVID-19 testing prompted the implementation of drive-through testing systems. However, limited research has examined factors influencing testing positivity in this setting. METHODS: From October 2020 to March 2023, a total of 1,341 patients, along with their clinical information, were referred from local clinics to the Sasebo City COVID-19 drive-through PCR center for testing. Association between clinical information or factors related to the drive-through center and testing results was analyzed by Fisher's exact test and logistic regression models. RESULTS: Individuals testing positive exhibited higher frequencies of upper respiratory symptoms; cough (OR 1.5 (95% CI 1.2-1.8), p < 0.001, q = 0.005), sore throat (OR 2.4 (95% CI 1.9-3.0), p < 0.001, q < 0.001), runny nose (OR 1.4 (95% CI 1.1-1.8), p = 0.002, q = 0.009), and systemic symptoms; fever (OR 1.5 (95% CI 1.1-2.0), p = 0.006, q = 0.02), headache (OR 1.9 (95% CI 1.4-2.5), p < 0.001, q < 0.001), and joint pain (OR 2.7 (95% CI 1.8-4.1), p < 0.001, q < 0.001). Conversely, gastrointestinal symptoms; diarrhea (OR 0.2 (95% CI 0.1-0.4), p < 0.001, q < 0.001) and nausea (OR 0.3 (95% CI 0.1-0.6), p < 0.001, q < 0.001) were less prevalent among positives. During omicron strain predominant period, higher testing positivity rate (OR 20 (95% CI 13-31), p < 0.001) and shorter period from symptom onset to testing (3.2 vs. 6.0 days, p < 0.001) were observed compared to pre-omicron period. Besides symptoms, contact history with infected persons at home (OR 4.5 (95% CI 3.1-6.5), p < 0.001, q < 0.001) and in office or school (OR 2.9 (95% CI 2.1-4.1), p < 0.001, q < 0.001), as well as the number of sample collection experiences by collectors (B 7.2 (95% CI 2.8-12), p = 0.002) were also associated with testing results. CONCLUSIONS: These findings underscore the importance of factors related to drive-through centers, especially contact history interviews and sample collection skills, for achieving higher rates of COVID-19 testing positivity. They also contribute to enhanced preparedness for next infectious disease pandemics.

Yeast species in the respiratory samples of COVID-19 patients; molecular tracking of Candida auris.

Introduction: Although the existence of Candida species in the respiratory tract is often considered commensal, it is crucial to recognize the significance of Candida colonization in immunocompromised or COVID-19 patients. The emergence of Candida auris as an emerging pathogen further emphasizes the importance of monitoring yeast infection/colonization, particularly in COVID-19 patients. Methods: In this study, respiratory samples mainly from COVID-19 patients, primarily those suspected of having a fungal infection, were cultured on Sabouraud dextrose agar plates and the yeast colonies were identified using a two-step multiplex PCR method. The samples suspected of C. auris underwent specific nested PCR followed by sequence analysis. Results: A total of 199 respiratory samples were collected from 73 women and 126 men, ranging in age from 1.6 to 88 years. Among the patients, 141 had COVID-19, 32 had cancer, 5 were hospitalized in ICU, 2 had chronic obstructive pulmonary disease)COPD(, and others were patients with combination diseases. From these samples, a total of 334 yeast strains were identified. C. albicans (n=132, 39.52%) was the most common species, followed by C. tropicalis (n=67, 20%), C. glabrata (n=56, 16.76%), C. krusei (n=18, 5.4%), C. parapsilosis (n=17, 5.08%), Saccharomyces cerevisiae (n=10, 3%), C. kefyr (n=9, 2.6%), C. dubliniensis (n=7, 2.1%), C. lusitaniae (n=5, 1.5%), C. auris (n=3, 0.9%), C. guilliermondii (n=2, 0.6%), C. rugosa (n=1, 0.3%), C. intermedia (n=1, 0.3%), and Trichosporon spp. (n=1, 0.3%). C. auris was detected in a patient in ICU and two COVID-19 patients. While its presence was confirmed through sequence analysis, our extensive efforts to isolate C. auris were unsuccessful. Conclusion: While C. albicans colonization remains prevalent, our study found no evidence of Candida lung infection. Since the role of Candida colonization in airway secretions remains ambiguous due to limited research, further studies are imperative to shed light on this matter.

Large environmental changes reduce valence-dependent belief updating.

When updating beliefs, humans tend to integrate more desirable information than undesirable information. In stable environments (low uncertainty and high predictability), this asymmetry favors motivation towards action and perceived self-efficacy. However, in changing environments (high uncertainty and low predictability), this process can lead to risk underestimation and increase unwanted costs. Here, we examine how people (n = 388) integrate threatening information during an abrupt environmental change (mandatory quarantine during the COVID-19 pandemic). Given that anxiety levels are associated with the magnitude of the updating belief asymmetry; we explore its relationship during this particular context. We report a significant reduction in asymmetrical belief updating during a large environmental change as individuals integrated desirable and undesirable information to the same extent. Moreover, this result was supported by computational modeling of the belief update task. However, we found that the reduction in asymmetrical belief updating was not homogeneous among people with different levels of Trait-anxiety. Individuals with higher levels of Trait-anxiety maintained a valence-dependent updating, as it occurs in stable environments. On the other hand, updating behavior was not associated with acute anxiety (State-Anxiety), health concerns (Health-Anxiety), or having positive expectations (Trait-Optimism). These results suggest that highly uncertain environments can generate adaptive changes in information integration. At the same time, it reveals the vulnerabilities of individuals with higher levels of anxiety to adapt the way they learn.

Common cold viruses circulating in children threaten wild chimpanzees through asymptomatic adult carriers.

Reverse zoonotic respiratory diseases threaten great apes across Sub-Saharan Africa. Studies of wild chimpanzees have identified the causative agents of most respiratory disease outbreaks as "common cold" paediatric human pathogens, but reverse zoonotic transmission pathways have remained unclear. Between May 2019 and August 2021, we conducted a prospective cohort study of 234 children aged 3-11 years in communities bordering Kibale National Park, Uganda, and 30 adults who were forest workers and regularly entered the park. We collected 2047 respiratory symptoms surveys to quantify clinical severity and simultaneously collected 1989 nasopharyngeal swabs approximately monthly for multiplex viral diagnostics. Throughout the course of the study, we also collected 445 faecal samples from 55 wild chimpanzees living nearby in Kibale in social groups that have experienced repeated, and sometimes lethal, epidemics of human-origin respiratory viral disease. We characterized respiratory pathogens in each cohort and examined statistical associations between PCR positivity for detected pathogens and potential risk factors. Children exhibited high incidence rates of respiratory infections, whereas incidence rates in adults were far lower. COVID-19 lockdown in 2020-2021 significantly decreased respiratory disease incidence in both people and chimpanzees. Human respiratory infections peaked in June and September, corresponding to when children returned to school. Rhinovirus, which caused a 2013 outbreak that killed 10% of chimpanzees in a Kibale community, was the most prevalent human pathogen throughout the study and the only pathogen present at each monthly sampling, even during COVID-19 lockdown. Rhinovirus was also most likely to be carried asymptomatically by adults. Although we did not detect human respiratory pathogens in the chimpanzees during the cohort study, we detected human metapneumovirus in two chimpanzees from a February 2023 outbreak that were genetically similar to viruses detected in study participants in 2019. Our data suggest that respiratory pathogens circulate in children and that adults become asymptomatically infected during high-transmission times of year. These asymptomatic adults may then unknowingly carry the pathogens into forest and infect chimpanzees. This conclusion, in turn, implies that intervention strategies based on respiratory symptoms in adults are unlikely to be effective for reducing reverse zoonotic transmission of respiratory viruses to chimpanzees.