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1.
Euro Surveill ; 26(11)2021 03.
Article in English | MEDLINE | ID: covidwho-1181332

ABSTRACT

BackgroundA multi-tiered surveillance system based on influenza surveillance was adopted in the United Kingdom in the early stages of the coronavirus disease (COVID-19) epidemic to monitor different stages of the disease. Mandatory social and physical distancing measures (SPDM) were introduced on 23 March 2020 to attempt to limit transmission.AimTo describe the impact of SPDM on COVID-19 activity as detected through the different surveillance systems.MethodsData from national population surveys, web-based indicators, syndromic surveillance, sentinel swabbing, respiratory outbreaks, secondary care admissions and mortality indicators from the start of the epidemic to week 18 2020 were used to identify the timing of peaks in surveillance indicators relative to the introduction of SPDM. This timing was compared with median time from symptom onset to different stages of illness and levels of care or interactions with healthcare services.ResultsThe impact of SPDM was detected within 1 week through population surveys, web search indicators and sentinel swabbing reported by onset date. There were detectable impacts on syndromic surveillance indicators for difficulty breathing, influenza-like illness and COVID-19 coding at 2, 7 and 12 days respectively, hospitalisations and critical care admissions (both 12 days), laboratory positivity (14 days), deaths (17 days) and nursing home outbreaks (4 weeks).ConclusionThe impact of SPDM on COVID-19 activity was detectable within 1 week through community surveillance indicators, highlighting their importance in early detection of changes in activity. Community swabbing surveillance may be increasingly important as a specific indicator, should circulation of seasonal respiratory viruses increase.


Subject(s)
/prevention & control , Epidemiological Monitoring , /epidemiology , Humans , United Kingdom/epidemiology
2.
Lancet Respir Med ; 9(4): 407-418, 2021 04.
Article in English | MEDLINE | ID: covidwho-1180128

ABSTRACT

BACKGROUND: Most low-income and middle-income countries (LMICs) have little or no data integrated into a national surveillance system to identify characteristics or outcomes of COVID-19 hospital admissions and the impact of the COVID-19 pandemic on their national health systems. We aimed to analyse characteristics of patients admitted to hospital with COVID-19 in Brazil, and to examine the impact of COVID-19 on health-care resources and in-hospital mortality. METHODS: We did a retrospective analysis of all patients aged 20 years or older with quantitative RT-PCR (RT-qPCR)-confirmed COVID-19 who were admitted to hospital and registered in SIVEP-Gripe, a nationwide surveillance database in Brazil, between Feb 16 and Aug 15, 2020 (epidemiological weeks 8-33). We also examined the progression of the COVID-19 pandemic across three 4-week periods within this timeframe (epidemiological weeks 8-12, 19-22, and 27-30). The primary outcome was in-hospital mortality. We compared the regional burden of hospital admissions stratified by age, intensive care unit (ICU) admission, and respiratory support. We analysed data from the whole country and its five regions: North, Northeast, Central-West, Southeast, and South. FINDINGS: Between Feb 16 and Aug 15, 2020, 254 288 patients with RT-qPCR-confirmed COVID-19 were admitted to hospital and registered in SIVEP-Gripe. The mean age of patients was 60 (SD 17) years, 119 657 (47%) of 254 288 were aged younger than 60 years, 143 521 (56%) of 254 243 were male, and 14 979 (16%) of 90 829 had no comorbidities. Case numbers increased across the three 4-week periods studied: by epidemiological weeks 19-22, cases were concentrated in the North, Northeast, and Southeast; by weeks 27-30, cases had spread to the Central-West and South regions. 232 036 (91%) of 254 288 patients had a defined hospital outcome when the data were exported; in-hospital mortality was 38% (87 515 of 232 036 patients) overall, 59% (47 002 of 79 687) among patients admitted to the ICU, and 80% (36 046 of 45 205) among those who were mechanically ventilated. The overall burden of ICU admissions per ICU beds was more pronounced in the North, Southeast, and Northeast, than in the Central-West and South. In the Northeast, 1545 (16%) of 9960 patients received invasive mechanical ventilation outside the ICU compared with 431 (8%) of 5388 in the South. In-hospital mortality among patients younger than 60 years was 31% (4204 of 13 468) in the Northeast versus 15% (1694 of 11 196) in the South. INTERPRETATION: We observed a widespread distribution of COVID-19 across all regions in Brazil, resulting in a high overall disease burden. In-hospital mortality was high, even in patients younger than 60 years, and worsened by existing regional disparities within the health system. The COVID-19 pandemic highlights the need to improve access to high-quality care for critically ill patients admitted to hospital with COVID-19, particularly in LMICs. FUNDING: National Council for Scientific and Technological Development (CNPq), Coordinating Agency for Advanced Training of Graduate Personnel (CAPES), Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ), and Instituto de Salud Carlos III.


Subject(s)
/epidemiology , Epidemiological Monitoring , Healthcare Disparities/statistics & numerical data , Hospital Mortality/trends , Pandemics/statistics & numerical data , Adult , Aged , Aged, 80 and over , Brazil/epidemiology , /therapy , Comorbidity , Female , Geography , Health Services Accessibility/organization & administration , Health Services Accessibility/statistics & numerical data , Health Services Needs and Demand/statistics & numerical data , Humans , Intensive Care Units/statistics & numerical data , Male , Middle Aged , Patient Admission/statistics & numerical data , Respiration, Artificial/statistics & numerical data , Retrospective Studies , Young Adult
4.
MMWR Morb Mortal Wkly Rep ; 70(13): 483-489, 2021 Apr 02.
Article in English | MEDLINE | ID: covidwho-1168278

ABSTRACT

Long-standing systemic social, economic, and environmental inequities in the United States have put many communities of color (racial and ethnic minority groups) at increased risk for exposure to and infection with SARS-CoV-2, the virus that causes COVID-19, as well as more severe COVID-19-related outcomes (1-3). Because race and ethnicity are missing for a proportion of reported COVID-19 cases, counties with substantial missing information often are excluded from analyses of disparities (4). Thus, as a complement to these case-based analyses, population-based studies can help direct public health interventions. Using data from the 50 states and the District of Columbia (DC), CDC identified counties where five racial and ethnic minority groups (Hispanic or Latino [Hispanic], non-Hispanic Black or African American [Black], non-Hispanic Asian [Asian], non-Hispanic American Indian or Alaska Native [AI/AN], and non-Hispanic Native Hawaiian or other Pacific Islander [NH/PI]) might have experienced high COVID-19 impact during April 1-December 22, 2020. These counties had high 2-week COVID-19 incidences (>100 new cases per 100,000 persons in the total population) and percentages of persons in five racial and ethnic groups that were larger than the national percentages (denoted as "large"). During April 1-14, a total of 359 (11.4%) of 3,142 U.S. counties reported high COVID-19 incidence, including 28.7% of counties with large percentages of Asian persons and 27.9% of counties with large percentages of Black persons. During August 5-18, high COVID-19 incidence was reported by 2,034 (64.7%) counties, including 92.4% of counties with large percentages of Black persons and 74.5% of counties with large percentages of Hispanic persons. During December 9-22, high COVID-19 incidence was reported by 3,114 (99.1%) counties, including >95% of those with large percentages of persons in each of the five racial and ethnic minority groups. The findings of this population-based analysis complement those of case-based analyses. In jurisdictions with substantial missing race and ethnicity information, this method could be applied to smaller geographic areas, to identify communities of color that might be experiencing high potential COVID-19 impact. As areas with high rates of new infection change over time, public health efforts can be tailored to the needs of communities of color as the pandemic evolves and integrated with longer-term plans to improve health equity.


Subject(s)
/epidemiology , Continental Population Groups/statistics & numerical data , Ethnic Groups/statistics & numerical data , Minority Groups/statistics & numerical data , /ethnology , Epidemiological Monitoring , Health Status Disparities , Humans , Incidence , Risk Assessment , United States/epidemiology
5.
BMJ Open ; 11(3): e046764, 2021 03 29.
Article in English | MEDLINE | ID: covidwho-1158113

ABSTRACT

INTRODUCTION: Despite unrelenting efforts to contain its spread, COVID-19 is still causing unprecedented global crises. Ethiopia reported its first case on 13 March 2020 but has an accelerated case load and geographical distribution recently. In this article, we described the epidemiology of COVID-19 in Oromia Region, the largest and most populous region in Ethiopia, during the early months of the outbreak. METHODS: We analysed data from the COVID-19 surveillance database of the Oromia Regional Health Bureau. We included all reverse transcription-PCR-confirmed cases reported from the region between 13 March and 13 September 2020. RESULTS: COVID-19 was confirmed in 8955 (5.5%) of 164 206 tested individuals. The test positivity rate increased from an average of 1.0% in the first 3 months to 6.3% in August and September. About 70% (6230) of the cases were men; the mean age was 30.0 years (SD=13.3), and 90.5% were <50 years of age. Only 64 (0.7%) of the cases had symptoms at diagnosis. Cough was the most common among symptomatic cases reported in 48 (75.0%), while fever was the least. Overall, 4346 (48.5%) have recovered from the virus; and a total of 52 deaths were reported with a case fatality rate of 1.2%. However, we should interpret the reported case fatality rate cautiously since in 44 (84.6%) of those reported as COVID-19 death, the virus was detected from dead bodies. CONCLUSION: Despite the steady increase in the number of reported COVID-19 cases, Ethiopia has so far avoided the feared catastrophe from the pandemic due to the milder and asymptomatic nature of the disease. However, with the current pattern of widespread community transmission, the danger posed by the pandemic remains real. Thus, the country should focus on averting COVID-19-related humanitarian crisis through strengthening COVID-19 surveillance and targeted testing for the most vulnerable groups.


Subject(s)
/epidemiology , Adult , Epidemiological Monitoring , Ethiopia/epidemiology , Female , Humans , Male , Middle Aged , Pandemics
6.
Euro Surveill ; 26(8)2021 02.
Article in English | MEDLINE | ID: covidwho-1150674

ABSTRACT

BackgroundTimely monitoring of COVID-19 impact on mortality is critical for rapid risk assessment and public health action.AimBuilding upon well-established models to estimate influenza-related mortality, we propose a new statistical Attributable Mortality Model (AttMOMO), which estimates mortality attributable to one or more pathogens simultaneously (e.g. SARS-CoV-2 and seasonal influenza viruses), while adjusting for seasonality and excess temperatures.MethodsData from Nationwide Danish registers from 2014-week(W)W27 to 2020-W22 were used to exemplify utilities of the model, and to estimate COVID-19 and influenza attributable mortality from 2019-W40 to 2020-W20.ResultsSARS-CoV-2 was registered in Denmark from 2020-W09. Mortality attributable to COVID-19 in Denmark increased steeply, and peaked in 2020-W14. As preventive measures and national lockdown were implemented from 2020-W12, the attributable mortality started declining within a few weeks. Mortality attributable to COVID-19 from 2020-W09 to 2020-W20 was estimated to 16.2 (95% confidence interval (CI): 12.0 to 20.4) per 100,000 person-years. The 2019/20 influenza season was mild with few deaths attributable to influenza, 3.2 (95% CI: 1.1 to 5.4) per 100,000 person-years.ConclusionAttMOMO estimates mortality attributable to several pathogens simultaneously, providing a fuller picture of mortality by COVID-19 during the pandemic in the context of other seasonal diseases and mortality patterns. Using Danish data, we show that the model accurately estimates mortality attributable to COVID-19 and influenza, respectively. We propose using standardised indicators for pathogen circulation in the population, to make estimates comparable between countries and applicable for timely monitoring.


Subject(s)
/mortality , Epidemiological Monitoring , Influenza, Human/mortality , Models, Statistical , Communicable Disease Control , Denmark/epidemiology , Humans , Seasons
8.
Virol J ; 18(1): 59, 2021 03 20.
Article in English | MEDLINE | ID: covidwho-1143228

ABSTRACT

The sample collection procedure for SARS-CoV-2 has a strong impact on diagnostic capability, contact tracing approach, ultimately affecting the infection containment performance. This study demonstrates that self-collected nasal-swab has shown to be a valid and well tolerated procedure to SARS-CoV-2 surveillance in a healthcare system. More significantly, no performance adequacy difference was detected in self-administered swabs between healthcare worker (HCW) and non-HCW which allows to speculate that this procedure could be successfully extended to the entire population for mass screening.


Subject(s)
/diagnosis , Nasal Cavity/virology , Specimen Handling/methods , Adult , Cross-Sectional Studies , Epidemiological Monitoring , Female , France/epidemiology , Hospitals , Humans , Male , Middle Aged , Surveys and Questionnaires
10.
Int J Qual Health Care ; 33(Supplement_1): 51-55, 2021 Jan 12.
Article in English | MEDLINE | ID: covidwho-1139994

ABSTRACT

BACKGROUND: In response to the coronavirus disease of 2019 (COVID-19) pandemic, healthcare systems worldwide have stepped up their infection prevention and control efforts in order to reduce the spread of the infection. Behaviours, such as hand hygiene, screening and cohorting of patients, and the appropriate use of antibiotics have long been recommended in surgery, but their implementation has often been patchy. METHODS: The current crisis presents an opportunity to learn about how to improve infection prevention and control and surveillance (IPCS) behaviours. The improvements made were mainly informal, quick and stemming from the frontline rather than originating from formal organizational structures. The adaptations made and the expertise acquired have the potential for triggering deeper learning and to create enduring improvements in the routine identification and management of infections relating to surgery. RESULTS: This paper aims to illustrate how adopting a human factors and ergonomics perspective can provide insights into how clinical work systems have been adapted and reconfigured in order to keep patients and staff safe. CONCLUSION: For achieving sustainable change in IPCS practices in surgery during COVID-19 and beyond we need to enhance organizational learning potentials.


Subject(s)
Infection Control/methods , Surgical Procedures, Operative/standards , Anti-Bacterial Agents/therapeutic use , Cross Infection/prevention & control , Epidemiological Monitoring , Ergonomics/methods , Hand Hygiene , Humans , Infection Control/standards
11.
Sci Transl Med ; 13(584)2021 03 10.
Article in English | MEDLINE | ID: covidwho-1127537

ABSTRACT

Acute flaccid myelitis (AFM) recently emerged in the United States as a rare but serious neurological condition since 2012. Enterovirus D68 (EV-D68) is thought to be a main causative agent, but limited surveillance of EV-D68 in the United States has hampered the ability to assess their causal relationship. Using surveillance data from the BioFire Syndromic Trends epidemiology network in the United States from January 2014 to September 2019, we characterized the epidemiological dynamics of EV-D68 and found latitudinal gradient in the mean timing of EV-D68 cases, which are likely climate driven. We also demonstrated a strong spatiotemporal association of EV-D68 with AFM. Mathematical modeling suggested that the recent dominant biennial cycles of EV-D68 dynamics may not be stable. Nonetheless, we predicted that a major EV-D68 outbreak, and hence an AFM outbreak, would have still been possible in 2020 under normal epidemiological conditions. Nonpharmaceutical intervention efforts due to the ongoing COVID-19 pandemic are likely to have reduced the sizes of EV-D68 and AFM outbreaks in 2020, illustrating the broader epidemiological impact of the pandemic.


Subject(s)
Central Nervous System Viral Diseases/epidemiology , Central Nervous System Viral Diseases/virology , Enterovirus D, Human/physiology , Myelitis/epidemiology , Myelitis/virology , Neuromuscular Diseases/epidemiology , Neuromuscular Diseases/virology , Disease Susceptibility , Epidemiological Monitoring , Humans , Models, Biological , Spatio-Temporal Analysis , United States/epidemiology
12.
Sci Rep ; 11(1): 5372, 2021 03 08.
Article in English | MEDLINE | ID: covidwho-1123151

ABSTRACT

Wastewater-based epidemiology (WBE) is a great approach that enables us to comprehensively monitor the community to determine the scale and dynamics of infections in a city, particularly in metropolitan cities with a high population density. Therefore, we monitored the time course of the SARS-CoV-2 RNA concentration in raw sewage in the Frankfurt metropolitan area, the European financial center. To determine the SARS-CoV-2 RNA concentration in sewage, we continuously collected 24 h composite samples twice a week from two wastewater treatment plant (WWTP) influents (Niederrad and Sindlingen) serving the Frankfurt metropolitan area and performed RT-qPCR analysis targeting three genes (N gene, S gene, and ORF1ab gene). In August, a resurgence in the SARS-CoV-2 RNA load was observed, reaching 3 × 1013 copies/day, which represented similar levels compared to April with approx. 2 × 1014 copies/day. This corresponds to a continuous increase again in COVID-19 cases in Frankfurt since August, with an average of 28.6 incidences, compared to 28.7 incidences in April. Different temporal dynamics were observed between different sampling points, indicating local dynamics in COVID-19 cases within the Frankfurt metropolitan area. The SARS-CoV-2 RNA load to the WWTP Niederrad ranged from approx. 4 × 1011 to 1 × 1015 copies/day, the load to the WWTP Sindlingen from approx. 1 × 1011 to 2 × 1014 copies/day, which resulted in a preceding increase in these loading in July ahead of the weekly averaged incidences. The study shows that WBE has the potential as an early warning system for SARS-CoV-2 infections and a monitoring system to identify global hotspots of COVID-19.


Subject(s)
Environmental Monitoring , RNA, Viral/analysis , Waste Water/virology , /epidemiology , Cities , Epidemiological Monitoring , Genes, Viral , Germany , Sewage/virology , Time Factors , Viral Load , Water Purification
13.
Nat Commun ; 12(1): 1501, 2021 03 08.
Article in English | MEDLINE | ID: covidwho-1123130

ABSTRACT

Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models.


Subject(s)
Basic Reproduction Number , Epidemiological Monitoring , Models, Theoretical , /transmission , Forecasting , Hong Kong/epidemiology , Humans , Pandemics , Travel
14.
Sci Adv ; 7(10)2021 03.
Article in English | MEDLINE | ID: covidwho-1119270

ABSTRACT

Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.


Subject(s)
/diagnosis , Epidemiological Monitoring , /physiology , /virology , Disease Outbreaks , Humans , Probability , Time Factors , United States/epidemiology
15.
Psychol Med ; 51(4): 529-537, 2021 03.
Article in English | MEDLINE | ID: covidwho-1118760

ABSTRACT

Suicide in the US has increased in the last decade, across virtually every age and demographic group. Parallel increases have occurred in non-fatal self-harm as well. Research on suicide across the world has consistently demonstrated that suicide shares many properties with a communicable disease, including person-to-person transmission and point-source outbreaks. This essay illustrates the communicable nature of suicide through analogy to basic infectious disease principles, including evidence for transmission and vulnerability through the agent-host-environment triad. We describe how mathematical modeling, a suite of epidemiological methods, which the COVID-19 pandemic has brought into renewed focus, can and should be applied to suicide in order to understand the dynamics of transmission and to forecast emerging risk areas. We describe how new and innovative sources of data, including social media and search engine data, can be used to augment traditional suicide surveillance, as well as the opportunities and challenges for modeling suicide as a communicable disease process in an effort to guide clinical and public health suicide prevention efforts.


Subject(s)
Communicable Diseases/transmission , Epidemiological Monitoring , Models, Theoretical , Suicide/statistics & numerical data , /transmission , Humans
16.
PLoS Comput Biol ; 17(3): e1008726, 2021 03.
Article in English | MEDLINE | ID: covidwho-1117464

ABSTRACT

We propose an analysis and applications of sample pooling to the epidemiologic monitoring of COVID-19. We first introduce a model of the RT-qPCR process used to test for the presence of virus in a sample and construct a statistical model for the viral load in a typical infected individual inspired by large-scale clinical datasets. We present an application of group testing for the prevention of epidemic outbreak in closed connected communities. We then propose a method for the measure of the prevalence in a population taking into account the increased number of false negatives associated with the group testing method.


Subject(s)
/methods , /epidemiology , Epidemiological Monitoring , Group Processes , Population Surveillance/methods , /isolation & purification , /virology , Datasets as Topic , Humans , Luxembourg/epidemiology , Prevalence , Sensitivity and Specificity
17.
CMAJ Open ; 9(1): E149-E156, 2021.
Article in English | MEDLINE | ID: covidwho-1115549

ABSTRACT

BACKGROUND: Information on the epidemiology of patients in hospital with laboratory-confirmed coronavirus disease 2019 (COVID-19) in Canadian acute care hospitals is needed to inform infection prevention and control strategies and public health measures. The aim of this surveillance was to describe the epidemiology of patients in hospital with laboratory-confirmed COVID-19 in a network of Canadian acute care hospitals between Mar. 1 and Aug. 31, 2020. METHODS: Through prospective surveillance, we identified adult and pediatric patients in hospital with laboratory-confirmed COVID-19 using a standard definition between Mar. 1 and Aug. 31, 2020, through the Canadian Nosocomial Infection Surveillance Program (CNISP), a network of 78 hospitals. Patient demographic and clinical characteristics and data on treatment, interventions and outcomes were reviewed and described. RESULTS: As of Aug. 31, 2020, the CNISP had received data for 1906 patients in hospital with COVID-19 in 49 sentinel hospitals in 9 provinces. The majority of patients in hospital with COVID-19 were older (median age 71 yr) and had underlying medical conditions (85.8%). Few children with COVID-19 were admitted to a participating hospital (n = 37, 1.9%). Acquisition of COVID-19 in hospitals was infrequent (6.4% of all cases). A total of 32.8% of patients were admitted from a long-term care facility or retirement home. Health care workers constituted 10.6% of adult patients aged 18-65 years in hospital with COVID-19. Thirty-day attributable mortality was 16.2%. Hospital admission rates peaked in mid-April and were highest in Ontario and Quebec. INTERPRETATION: Surveillance findings indicate that a high proportion of Canadian patients in hospital with COVID-19 during the first 6 months of the pandemic were older adults with underlying medical conditions. Active surveillance of patients in hospital with COVID-19 is critical to enhancing our knowledge of the epidemiology of COVID-19 and to identifying populations at risk for severe outcomes, which will help guide Canada's response in the coming months.


Subject(s)
/diagnosis , Outpatient Clinics, Hospital/statistics & numerical data , /genetics , Adult , Aged , Aged, 80 and over , /mortality , Child , Child, Preschool , Cross Infection/epidemiology , Cross Infection/prevention & control , Epidemiological Monitoring , Female , Health Personnel/statistics & numerical data , Hospitalization/statistics & numerical data , Humans , Infant , Male , Middle Aged , Mortality/trends , Ontario/epidemiology , Prospective Studies , Quebec/epidemiology
18.
PLoS One ; 16(2): e0247854, 2021.
Article in English | MEDLINE | ID: covidwho-1102388

ABSTRACT

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people "overcrowded" social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis.


Subject(s)
/epidemiology , Epidemiological Monitoring , Social Media , Emergency Medical Services , Forecasting , Humans , Italy/epidemiology , Pandemics
19.
Biochem Biophys Res Commun ; 550: 8-14, 2021 04 23.
Article in English | MEDLINE | ID: covidwho-1101113

ABSTRACT

The SARS-CoV-2 Variant of Concern 202012/01 (VOC-202012/01) emerged in southeast England and rapidly spread worldwide. This variant is believed to be more transmissible, with all attention being given to its spike mutations. However, VOC-202012/01 has also a mutation (Q27stop) that truncates the ORF8, a likely immune evasion protein. Removal of ORF8 changes the clinical outset of the disease, which may affect the virus transmissibility. Here I provide a detailed analysis of all reported ORF8-deficient lineages found in the background of relevant spike mutations, identified among 231,433 SARS-CoV-2 genomes. I found 19 ORF8 nonsense mutations, most of them occurring in the 5' half of the gene. The ORF8-deficient lineages were rare, representing 0.67% of sequenced genomes. Nevertheless, I identified two clusters of related sequences that emerged recently and spread in different countries. The widespread D614G spike mutation was found in most ORF-deficient lineages. Although less frequent, HV69-70del and L5F spike mutations occurred in the background of six different ORF8 nonsense mutations. I also confirmed that VOC-202012/01 is the ORF8-deficient variant with more spike mutations reported to date, although other variants could have up to six spike mutations, some of putative biological relevance. Overall, these results suggest that monitoring ORF8-deficient lineages is important for the progression of the COVID-19 pandemic, particularly when associated with relevant spike mutations.


Subject(s)
/transmission , Epidemiological Monitoring , Gene Deletion , Spike Glycoprotein, Coronavirus/genetics , Viral Proteins/genetics , /epidemiology , Codon, Nonsense , Codon, Terminator/genetics , Evolution, Molecular , Genes, Viral/genetics , Humans , Phylogeny , Selection, Genetic , Time Factors , United Kingdom/epidemiology
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