Phylogenetic analysis of 17271 Indian SARS-CoV-2 genomes to identify temporal and spatial hotspot mutations.

The second wave of SARS-CoV-2 has hit India hard and though the vaccination drive has started, moderate number of COVID affected patients is still present in the country, thereby leading to the analysis of the evolving virus strains. In this regard, multiple sequence alignment of 17271 Indian SARS-CoV-2 sequences is performed using MAFFT followed by their phylogenetic analysis using Nextstrain. Subsequently, mutation points as SNPs are identified by Nextstrain. Thereafter, from the aligned sequences temporal and spatial analysis are carried out to identify top 10 hotspot mutations in the coding regions based on entropy. Finally, to judge the functional characteristics of all the non-synonymous hotspot mutations, their changes in proteins are evaluated as biological functions considering the sequences by using PolyPhen-2 while I-Mutant 2.0 evaluates their structural stability. For both temporal and spatial analysis, there are 21 non-synonymous hotspot mutations which are unstable and damaging.

Providing early indication of regional anomalies in COVID-19 case counts in England using search engine queries.

Prior work has shown the utility of using Internet searches to track the incidence of different respiratory illnesses. Similarly, people who suffer from COVID-19 may query for their symptoms prior to accessing the medical system (or in lieu of it). To assist in the UK government's response to the COVID-19 pandemic we analyzed searches for relevant symptoms on the Bing web search engine from users in England to identify areas of the country where unexpected rises in relevant symptom searches occurred. These were reported weekly to the UK Health Security Agency to assist in their monitoring of the pandemic. Our analysis shows that searches for "fever" and "cough" were the most correlated with future case counts during the initial stages of the pandemic, with searches preceding case counts by up to 21 days. Unexpected rises in search patterns were predictive of anomalous rises in future case counts within a week, reaching an Area Under Curve of 0.82 during the initial phase of the pandemic, and later reducing due to changes in symptom presentation. Thus, analysis of regional searches for symptoms can provide an early indicator (of more than one week) of increases in COVID-19 case counts.

A hierarchical study for urban statistical indicators on the prevalence of COVID-19 in Chinese city clusters based on multiple linear regression (MLR) and polynomial best subset regression (PBSR) analysis.

With evidence-based measures, COVID-19 can be effectively controlled by advanced data analysis and prediction. However, while valuable insights are available, there is a shortage of robust and rigorous research on what factors shape COVID-19 transmissions at the city cluster level. Therefore, to bridge the research gap, we adopted a data-driven hierarchical modeling approach to identify the most influential factors in shaping COVID-19 transmissions across different Chinese cities and clusters. The data used in this study are from Chinese officials, and hierarchical modeling conclusions drawn from the analysis are systematic, multifaceted, and comprehensive. To further improve research rigor, the study utilizes SPSS, Python and RStudio to conduct multiple linear regression and polynomial best subset regression (PBSR) analysis for the hierarchical modeling. The regression model utilizes the magnitude of various relative factors in nine Chinese city clusters, including 45 cities at a different level of clusters, to examine these aspects from the city cluster scale, exploring the correlation between various factors of the cities. These initial 12 factors are comprised of 'Urban population ratio', 'Retail sales of consumer goods', 'Number of tourists', 'Tourism Income', 'Ratio of the elderly population (> 60 year old) in this city', 'population density', 'Mobility scale (move in/inbound) during the spring festival', 'Ratio of Population and Health facilities', 'Jobless rate (%)', 'The straight-line distance from original epicenter Wuhan to this city', 'urban per capita GDP', and 'the prevalence of the COVID-19'. The study's results provide rigorously-tested and evidence-based insights on most instrumental factors that shape COVID-19 transmissions across cities and regions in China. Overall, the study findings found that per capita GDP and population mobility rates were the most affected factors in the prevalence of COVID-19 in a city, which could inform health experts and government officials to design and develop evidence-based and effective public health policies that could curb the spread of the COVID-19 pandemic.

Assessing the impact of SARS-CoV-2 prevention measures in Austrian schools using agent-based simulations and cluster tracing data.

We aim to identify those measures that effectively control the spread of SARS-CoV-2 in Austrian schools. Using cluster tracing data we calibrate an agent-based epidemiological model and consider situations where the B1.617.2 (delta) virus strain is dominant and parts of the population are vaccinated to quantify the impact of non-pharmaceutical interventions (NPIs) such as room ventilation, reduction of class size, wearing of masks during lessons, vaccinations, and school entry testing by SARS-CoV2-antigen tests. In the data we find that 40% of all clusters involved no more than two cases, and 3% of the clusters only had more than 20 cases. The model shows that combinations of NPIs together with vaccinations are necessary to allow for a controlled opening of schools under sustained community transmission of the SARS-CoV-2 delta variant. For plausible vaccination rates, primary (secondary) schools require a combination of at least two (three) of the above NPIs.

Epidemiological characteristics of COVID-19 clusters in Hainan, China.

ABSTRACT: To analyze the epidemiological characteristics of coronavirus disease 2019 (COVID-19) clusters in Hainan, and to provide a basis for the prevention and control of disease clusters.Descriptive epidemiology was used to retrospectively analyze the characteristics of disease clusters in 168 cases of COVID-19.Of the 168 COVID-19 cases, 99 (58.93%) comprised 29 clusters, 22 (75.86%) of which were imported and included 63 cases (63.64%), while 7 clusters (24.14%) were local and included 36 cases (36.36%). Of the cluster cases, 49 were men (49.49%) and 50 were women (50.50%), the median age was 52 years, and the maximum number of cases from 41 to 60 was at 37 years (37.37%). There were 67 first generation cases (67.68%), 28 (28.28%) second generation, and 4 (4.04%) third generation. Of the clusters, 68.97% occurred from January 31 to February 7, with the highest peak on February 6. The local disease clusters occurred with a time lag. The 2 cities with the most reported incidents were Sanya (10 cases, 34.48%) and Haikou (5 cases, 17.24%). Family clusters were most frequent, with 18 clusters (62.07%) involving 62 cases (62.63%), followed by social clusters, with 3 clusters (10.34%). The most complex clusters involved 3 cluster types (family, travel, and community). There was a statistically significant difference in the infectivity of the imported clusters versus the local clusters, with imported clusters being lower (Z = -2.851, P = .004). The infectivity of all cases or family members was highest in Haikou and lowest in Sanya. The infectivity of all cases with an incubation period of ≤7 days was 1.53 ±â€Š1.01, in which the infectivity of family members was 1.29 ±â€Š1.10. The infectivity of all cases with an incubation period of ≤14 days was 1.89 ±â€Š1.23, in which the infectivity of family members was 1.43 ±â€Š1.37.COVID-19 clusters in Hainan mainly occurred in families, and local clusters had high infectivity. Therefore, key populations and regions should be monitored, and targeted preventive measures should be carried out to provide a reference for the prevention and control of disease clusters.

Analysis of SARS-CoV-2 Genomes from West Java, Indonesia.

West Java Health Laboratory (WJHL) is one of the many institutions in Indonesia that have sequenced SARS-CoV-2 genome. Although having submitted a large number of sequences since September 2020, however, these submitted data lack advanced analyses. Therefore, in this study, we analyze the variant distribution, hotspot mutation, and its impact on protein structure and function of SARS-CoV-2 from the collected samples from WJHL. As many as one hundred sixty-three SARS-CoV-2 genome sequences submitted by West Java Health Laboratory (WJHL), with collection dates between September 2020 and June 2021, were retrieved from GISAID. Subsequently, the frequency and distribution of non-synonymous mutations across different cities and regencies from these samples were analyzed. The effect of the most prevalent mutations from dominant variants on the stability of their corresponding proteins was examined. The samples mostly consisted of people of working-age, and were distributed between female and male equally. All of the sample sequences showed varying levels of diversity, especially samples from West Bandung which carried the highest diversity. Dominant variants are the VOC B.1.617.2 (Delta) variant, B.1.466.2 variant, and B.1.470 variant. The genomic regions with the highest number of mutations are the spike, NSP3, nucleocapsid, NSP12, and ORF3a protein. Mutation analysis showed that mutations in structural protein might increase the stability of the protein. Oppositely, mutations in non-structural protein might lead to a decrease in protein stability. However, further research to study the impact of mutations on the function of SARS-CoV-2 proteins are required.

An Opportunistic Survey Reveals an Unexpected Coronavirus Diversity Hotspot in North America.

In summer 2020, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was detected on mink farms in Utah. An interagency One Health response was initiated to assess the extent of the outbreak and included sampling animals from on or near affected mink farms and testing them for SARS-CoV-2 and non-SARS coronaviruses. Among the 365 animals sampled, including domestic cats, mink, rodents, raccoons, and skunks, 261 (72%) of the animals harbored at least one coronavirus. Among the samples that could be further characterized, 127 alphacoronaviruses and 88 betacoronaviruses (including 74 detections of SARS-CoV-2 in mink) were identified. Moreover, at least 10% (n = 27) of the coronavirus-positive animals were found to be co-infected with more than one coronavirus. Our findings indicate an unexpectedly high prevalence of coronavirus among the domestic and wild free-roaming animals tested on mink farms. These results raise the possibility that mink farms could be potential hot spots for future trans-species viral spillover and the emergence of new pandemic coronaviruses.

Turnover of SARS-CoV-2 Lineages Shaped the Pandemic and Enabled the Emergence of New Variants in the State of Rio de Janeiro, Brazil.

In the present study, we provide a retrospective genomic epidemiology analysis of the SARS-CoV-2 pandemic in the state of Rio de Janeiro, Brazil. We gathered publicly available data from GISAID and sequenced 1927 new genomes sampled periodically from March 2021 to June 2021 from 91 out of the 92 cities of the state. Our results showed that the pandemic was characterized by three different phases driven by a successive replacement of lineages. Interestingly, we noticed that viral supercarriers accounted for the overwhelming majority of the circulating virus (>90%) among symptomatic individuals in the state. Moreover, SARS-CoV-2 genomic surveillance also revealed the emergence and spread of two new variants (P.5 and P.1.2), firstly reported in this study. Our findings provided important lessons learned from the different epidemiological aspects of the SARS-CoV-2 dynamic in Rio de Janeiro. Altogether, this might have a strong potential to shape future decisions aiming to improve public health management and understanding mechanisms underlying virus dispersion.

Explaining COVID-19 outbreaks with reactive SEIRD models.

COVID-19 epidemics have varied dramatically in nature across the United States, where some counties have clear peaks in infections, and others have had a multitude of unpredictable and non-distinct peaks. Our lack of understanding of how the pandemic has evolved leads to increasing errors in our ability to predict the spread of the disease. This work seeks to explain this diversity in epidemic progressions by considering an extension to the compartmental SEIRD model. The model we propose uses a neural network to predict the infection rate as a function of both time and the disease's prevalence. We provide a methodology for fitting this model to available county-level data describing aggregate cases and deaths. Our method uses Expectation-Maximization to overcome the challenge of partial observability, due to the fact that the system's state is only partially reflected in available data. We fit a single model to data from multiple counties in the United States exhibiting different behavior. By simulating the model, we show that it can exhibit both single peak and multi-peak behavior, reproducing behavior observed in counties both in and out of the training set. We then compare the error of simulations from our model with a standard SEIRD model, and show that ours substantially reduces errors. We also use simulated data to compare our methodology for handling partial observability with a standard approach, showing that ours is significantly better at estimating the values of unobserved quantities.

Patients' treatment limitations as predictive factor for mortality in COVID-19: results from hospitalized patients of a hotspot region for SARS-CoV-2 infections.

BACKGROUND: In hospitalized patients with SARS-CoV-2 infection, outcomes markedly differ between locations, regions and countries. One possible cause for these variations in outcomes could be differences in patient treatment limitations (PTL) in different locations. We thus studied their role as predictor for mortality in a population of hospitalized patients with COVID-19. METHODS: In a region with high incidence of SARS-CoV-2 infection, adult hospitalized patients with PCR-confirmed SARS-CoV-2 infection were prospectively registered and characterized regarding sex, age, vital signs, symptoms, comorbidities (including Charlson comorbidity index (CCI)), transcutaneous pulse oximetry (SpO2) and laboratory values upon admission, as well as ICU-stay including respiratory support, discharge, transfer to another hospital and death. PTL assessed by routine clinical procedures comprised the acceptance of ICU-therapy, orotracheal intubation and/or cardiopulmonary resuscitation. RESULTS: Among 526 patients included (median [quartiles] age 73 [57; 82] years, 47% female), 226 (43%) had at least one treatment limitation. Each limitation was associated with age, dementia and eGFR (p < 0.05 each), that regarding resuscitation additionally with Charlson comorbidity index (CCI) and cardiac disease. Overall mortality was 27% and lower (p < 0.001) in patients without treatment limitation (12%) compared to those with any limitation (47%). In univariate analyses, age and comorbidities (diabetes, cardiac, cerebrovascular, renal, hepatic, malignant disease, dementia), SpO2, hemoglobin, leucocyte numbers, estimated glomerular filtration rate (eGFR), C-reactive protein (CRP), Interleukin-6 and LDH were predictive for death (p < 0.05 each). In multivariate analyses, the presence of any treatment limitation was an independent predictor of death (OR 4.34, 95%-CI 2.10-12.30; p = 0.001), in addition to CCI, eGFR < 55 ml/min, neutrophil number > 5 G/l, CRP > 7 mg/l and SpO2 < 93% (p < 0.05 each). CONCLUSION: In hospitalized patients with SARS-CoV-2, the percentage of patients with treatment limitations was high. PTL were linked to age, comorbidities and eGFR assessed upon admission and strong, independent risk factors for mortality. These findings might be useful for further understanding of COVID-19 mortality and its regional variations. Clinical trial registration ClinicalTrials.gov Identifier: NCT04344171.

COVID-19: Hotspot hospital?- seroprevalence of SARS-CoV-2 antibodies in hospital employees in a secondary care hospital network in Germany: Intermediate results of a prospective surveillance study.

PURPOSE: The objective of the ongoing study was to investigate how SARS-CoV-2 infection spread within two hospitals in North Rhine-Westphalia, Germany by testing the employees working in high-risk, intermediate-risk and low-risk-areas for the presence of SARS-CoV-2 IgG antibodies. Presented intermediate results evaluate the first infection period until the end of September 2020. METHODS: The study "COVID-19: Hotspot hospital?- Seroprevalence of SARS-CoV-2 antibodies in hospital employees in a secondary care hospital network in Germany " is a prospective, single centre observational cohort study conducted at the St. Vincenz Hospital Datteln with 316 beds. The presented data include one other hospital: St. Laurentius Stift Waltrop, Germany with 172 beds. RESULTS: Between June 2020 and September 2020 we analyzed serum samples of 907 employees which represents 62.1% of all employees. Thirteen employees (1.4%), respectively 13/696 healthcare workers (HCWs) (1.9%) had detectable SARS-CoV-2 IgG antibodies. Among them, 4 (30.8%) were aware of COVID-19 exposure, and 5 (38.5%) reported clinical symptoms. HCWs working in high-risk areas had a seroprevalence rate of 1.6% (1/64), HCWs working in intermediate-risk area 1.7% (11/632) and 0.5% employees (1/211) in low-risk areas with no contact to patients were seropositive. CONCLUSION: Even if we treated COVID-19 positive patients, we found no clear evidence that infection was transmitted to HCWs in contact to these patients. As knowledge about SARS-CoV-2 transmission evolves, the concept of infection prevention must be continuously reviewed and adapted as needed to keep hospitals a safe place.

Multiple detection and spread of novel strains of the SARS-CoV-2 B.1.177 (B.1.177.75) lineage that test negative by a commercially available nucleocapsid gene real-time RT-PCR.

Several lineages of SARS-CoV-2 are currently circulating worldwide. During SARS-CoV-2 diagnostic activities performed in Abruzzo region (central Italy) several strains belonging to the B.1.177.75 lineage tested negative for the N gene but positive for the ORF1ab and S genes (+/+/- pattern) by the TaqPath COVID-19 CE-IVD RT-PCR Kit manufactured by Thermofisher. By sequencing, a unique mutation, synonymous 28948C > T, was found in the N-negative B.1.177.75 strains. Although we do not have any knowledge upon the nucleotide sequences of the primers and probe adopted by this kit, it is likely that N gene dropout only occurs when 28948C > T is coupled with 28932C > T, this latter present, in turn, in all B.1.177.75 sequences available on public databases. Furthermore, epidemiological analysis was also performed. The majority of the N-negative B.1.177.75 cases belonged to two clusters apparently unrelated to each other and both clusters involved young people. However, the phylogeny for sequences containing the +/+/- pattern strongly supports a genetic connection and one common source for both clusters. Though, genetic comparison suggests a connection rather than indicating the independent emergence of the same mutation in two apparently unrelated clusters. This study highlights once more the importance of sharing genomic data to link apparently unrelated epidemiological clusters and to, remarkably, update molecular tests.

Behavioral manipulation-key to the successful global spread of the new coronavirus SARS-CoV-2?

Human-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interaction can have an array of various outcomes-it could be mortal, morbid or merely carrying minor health consequences. The very rapid global spread has raised the issue whether there are further multi-dimensional consequences of SARS-CoV-2 infection on human behavior, the key of its transmission. During the coronavirus crisis, odd, abnormal, and irresponsible behavior has been reported in coronavirus disease 2019 (COVID-19) individuals, particularly in super-spreaders, that is, persons with a high viral load, thus constituting also super-emitters. Indeed, cases of infected persons ignoring self-confinement orders, intentionally disregarding physical distancing and multiplying social interactions, or even deliberately sneezing, spitting or coughing were reported. While it is known that some other viruses, such as rabies and even influenza do change human behavior, this remains unclear for SARS-CoV-2. In this perspective, we highlight the possibility that COVID-19 is facilitated by altered human social behavior that benefits SARS-CoV-2 transmission, through showcasing similar virus-induced changed behavior by other pathogens and relating this to reports from the gray literature.

Transmission dynamics of SARS-CoV-2 within families with children in Greece: A study of 23 clusters.

There is limited information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection clustering within families with children. We aimed to study the transmission dynamics of SARS-CoV-2 within families with children in Greece. We studied 23 family clusters of coronavirus disease 2019 (COVID-19). Infection was diagnosed by reverse-transcriptase polymerase chain reaction in respiratory specimens. The level of viral load was categorized as high, moderate, or low based on the cycle threshold values. There were 109 household members (66 adults and 43 children). The median attack rate per cluster was 60% (range: 33.4%-100%). An adult member with COVID-19 was the first case in 21 (91.3%) clusters. Transmission of infection occurred from an adult to a child in 19 clusters and/or from an adult to another adult in 12 clusters. There was no evidence of child-to-adult or child-to-child transmission. In total 68 household members (62.4%) tested positive. Children were more likely to have an asymptomatic SARS-CoV-2 infection compared to adults (40% vs 10.5%; P = .021). In contrast, adults were more likely to develop a severe clinical course compared with children (8.8% vs 0%; P = .021). In addition, infected children were significantly more likely to have a low viral load while adults were more likely to have a moderate viral load (40.7% and 18.6% vs 13.8% and 51.7%, respectively; P = .016). In conclusion, while children become infected by SARS-CoV-2, they do not appear to transmit infection to others. Furthermore, children more frequently have an asymptomatic or mild course compared to adults. Further studies are needed to elucidate the role of viral load on these findings.

Screening around a COVID-19 cluster: Exploring its impact on a local virus outbreak.

OBJECTIVE: In March 2020, we implemented screening of the contacts of a COVID-19 cluster having occurred in the Lot-et-Garonne department, the first department of the Nouvelle-Aquitaine region to be affected by the active circulation of SARS-CoV-2. We aimed to describe the impact of this screening on the local SARS-CoV-2 outbreak. METHODS: All high-risk contacts, as well as the individuals living in their households, were screened. We detailed the evolution of the number of confirmed COVID-19 cases in the Lot-et-Garonne department and the rest of the Nouvelle-Aquitaine region. RESULTS: Among the 89 screened individuals, 10 new cases were confirmed, including 4 asymptomatic persons. In Lot-et-Garonne, the number of confirmed COVID-19 cases immediately decreased after this screening and no epidemic peak occurred, contrary to what was observed in the rest of the region. CONCLUSION: The early screening of high-risk contacts of COVID-19 cases and members of their household implemented a few days before the first lockdown probably helped to prevent the spread of the virus in the department.

SARS-CoV-2 molecular testing in Greek hospital paediatric departments: a nationwide study.

As most children infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) present with mild symptoms or they are asymptomatic, the optimal strategy for molecular testing it is not well defined. The aim of the study was to determine the extent and aetiology of molecular testing for SARS-CoV-2 in Greek paediatric departments during the first phase of the pandemic and identify possible differences in incidence, depending on the age group and geographical area. We conducted a nationwide study of molecular testing for SARS-CoV-2 of children in paediatric departments between March and June 2020. A total of 65 paediatric departments participated in the study, representing 4901 children who were tested for SARS-CoV-2 and 90 (1.8%) were positive. Most paediatric cases were associated with topical outbreaks. Adolescents 11-16 years had the highest positivity rate (3.6%) followed by children 6-10 years (1.9%). However, since the testing rate significantly differed between age groups, the modified incidence of SARS-CoV-2 infection per age group was highest in infants <1 year (19.25/105 population). Most children tested presented with fever (70.9%), respiratory (50.1%) or gastrointestinal symptoms (28.1%). Significant differences were detected between public and private hospitals regarding the positivity rate (2.34% vs. 0.39%, P-value <0.001). Significant variation in SARS-CoV-2 molecular testing positivity rate and incidence between age groups indicate discrepancies in risk factors among different age groups that shall be considered when ordering molecular testing.