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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-314458

ABSTRACT

Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-314457

ABSTRACT

The objective of this work was to estimate the diagnostic accuracy of RT-PCR and Lateral flow immunoassay tests (LFIA) for COVID-19, depending on the time post symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent class models (BLCMs), which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (IgG and/or IgM) assays using RT-PCR as the reference method. The cross-classified results of LFIA and RT-PCR were analysed separately for the first, second and third week post symptom onset. The Se RT-PCR was 0.695 (95% probability intervals: 0.563;0.837) for the first week and remained similar for the second and the third week. The Se IgG/M was 0.318 (0.229;0.416) for the first week and increased steadily. It was 0.755 (0.673;0.829) and 0.927 (0.881;0.965) for the second and third week, respectively. Both tests had a high to absolute Sp , with point median estimates for Sp RT-PCR being consistently higher. Sp RT-PCR was 0.990 (0.980;0.998) for the first week. The corresponding value for Sp IgG/M was 0.962 (0.905;0.998). Further, Sp estimates for each test did not differ between weeks. BLCMs provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and for different risk profiles.

3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-306190

ABSTRACT

We have been experiencing a global pandemic with baleful consequences for mankind, since the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was first identified in Wuhan of China, in December 2019.  So far, several potential risk factors for SARS-CoV-2 infection have been identified. Among them, the role of ABO blood group polymorphisms has been studied with results that are still unclear. The aim of this study was to collect and meta-analyze available studies on the relationship between SARS-CoV-2 infection and different blood groups, as well as Rhesus state. We performed a systematic search on PubMed/MEDLINE and Scopus databases for published articles and preprints. Twenty-two studies, after the removal of duplicates, met the inclusion criteria for meta-analysis with ten of them also including information on Rhesus factor. The odds ratios (OR) and 95% confidence intervals (CI) were calculated for the extracted data. Random-effects models were used to obtain the overall pooled ORs. Publication bias and sensitivity analysis were also performed. Our results indicate that blood groups A, B and AB have a higher risk for COVID-19 infection compared to blood group O, which appears to have a protective effect. An association between Rhesus state and COVID-19 infection could not be estabished.

4.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-304769

ABSTRACT

Background: Seroprevalence of SARS Cov-2 provides a good indication of the extent of exposure and spread in the population, as well as those likely to benefit from a vaccine candidate. To date, there is no published or ongoing systematic review on the seroprevalence of COVID-19 in Low- and Middle-Income Countries (LMICs). This systematic review and meta-analysis will estimate SARS Cov-2 seroprevalence and the risk factors for SARS Cov-2 infection in LMICs. Methods: We will search PubMed, EMBASE, WHO COVID-19 Global research database, Google Scholar, the African Journals Online, LILAC, HINARI, medRxiv, bioRxiv and Cochrane Library for potentially useful studies on seroprevalence of COVID-19 in LMICs from December 2019 to December 2020 without language restriction. Two authors will independently screen all the articles, select studies based on pre-specified eligibility criteria and extract data using a pre-tested data extraction form. Any disagreements will be resolved through discussion between the authors. The pooled seroprevalence of SARS CoV-2 for people from LMICs will be calculated. Random effects model will be used in case of substantial heterogeneity in the included studies, otherwise fixed-effect model will be used. A planned subgroup, sensitivity and meta-regression analyses will be performed. For comparative studies, the analyses will be performed using Review Manager v 5.4;otherwise, STATA 16 will be used. All effect estimates will be presented with their confidence intervals. Discussion: The study will explore and systematically review empirical evidence on SARS Cov-2 seroprevalence in LMICs, and to assess the risk factors for SARS Cov-2 infection in Low Middle Income Countries in the context of rolling out vaccines in these countries. Finally, explore risk classifications to help with the rolling out of vaccines in LMICs. Systematic review registration : The protocol for this review has been registered in PROSPERO (CRD422020221548).

5.
Sci Rep ; 11(1): 23775, 2021 12 10.
Article in English | MEDLINE | ID: covidwho-1565730

ABSTRACT

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks.


Subject(s)
COVID-19/epidemiology , Pandemics , Humans , Italy/epidemiology , New York/epidemiology , Predictive Value of Tests , Time Factors
6.
Int J Stroke ; 16(7): 771-783, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1374086

ABSTRACT

BACKGROUND: The effect of the COVID pandemic on stroke network performance is unclear, particularly with consideration of drip&ship vs. mothership models. AIMS: We systematically reviewed and meta-analyzed variations in stroke admissions, rate and timing of reperfusion treatments during the first wave COVID pandemic vs. the pre-pandemic timeframe depending on stroke network model adopted. SUMMARY OF FINDINGS: The systematic review followed registered protocol (PROSPERO-CRD42020211535), PRISMA and MOOSE guidelines. We searched MEDLINE, EMBASE, and CENTRAL until 9 October 2020 for studies reporting variations in ischemic stroke admissions, treatment rates, and timing in COVID (first wave) vs. control-period. Primary outcome was the weekly admission incidence rate ratio (IRR = admissions during COVID-period/admissions during control-period). Secondary outcomes were (i) changes in rate of reperfusion treatments and (ii) time metrics for pre- and in-hospital phase. Data were pooled using random-effects models, comparing mothership vs. drip&ship model. Overall, 29 studies were included in quantitative synthesis (n = 212,960). COVID-period was associated with a significant reduction in stroke admission rates (IRR = 0.69, 95%CI = 0.61-0.79), with higher relative presentation of large vessel occlusion (risk ratio (RR) = 1.62, 95% confidence interval (CI) = 1.24-2.12). Proportions of patients treated with endovascular treatment increased (RR = 1.14, 95%CI = 1.02-1.28). Intravenous thrombolysis decreased overall (IRR = 0.72, 95%CI = 0.54-0.96) but not in the mothership model (IRR = 0.81, 95%CI = 0.43-1.52). Onset-to-door time was longer for the drip&ship in COVID-period compared to the control-period (+32 min, 95%CI = 0-64). Door-to-scan was longer in COVID-period (+5 min, 95%CI = 2-7). Door-to-needle and door-to-groin were similar in COVID-period and control-period. CONCLUSIONS: Despite a 35% drop in stroke admissions during the first pandemic wave, proportions of patients receiving reperfusion and time-metrics were not inferior to control-period. Mothership preserved the weekly rate of intravenous thrombolysis and the onset-to-door timing to pre-pandemic standards.


Subject(s)
COVID-19 , Hospitalization/statistics & numerical data , Stroke/therapy , Thrombolytic Therapy , Humans , Incidence , Pandemics , Reperfusion , Time-to-Treatment
7.
Am J Epidemiol ; 190(8): 1689-1695, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1337252

ABSTRACT

Our objective was to estimate the diagnostic accuracy of real-time polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA) tests for coronavirus disease 2019 (COVID-19), depending on the time after symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent-class models, which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (immunoglobulin G (IgG) and/or immunoglobulin M (IgM)) assays using RT-PCR as the reference method. The sensitivity of RT-PCR was 0.68 (95% probability interval (PrI): 0.63, 0.73). IgG/M sensitivity was 0.32 (95% PrI :0.23; 0.41) for the first week and increased steadily. It was 0.75 (95% PrI: 0.67; 0.83) and 0.93 (95% PrI: 0.88; 0.97) for the second and third weeks after symptom onset, respectively. Both tests had a high to absolute specificity, with higher point median estimates for RT-PCR specificity and narrower probability intervals. The specificity of RT-PCR was 0.99 (95% PrI: 0.98; 1.00). and the specificity of IgG/IgM was 0.97 (95% PrI: 0.92, 1.00), 0.98 (95% PrI: 0.95, 1.00) and 0.98 (95% PrI: 0.94, 1.00) for the first, second, and third weeks after symptom onset. The diagnostic accuracy of LFIA varies with time after symptom onset. Bayesian latent-class models provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and different risk profiles.


Subject(s)
COVID-19 Nucleic Acid Testing/statistics & numerical data , COVID-19 Serological Testing/statistics & numerical data , COVID-19/diagnosis , Immunoassay/statistics & numerical data , Real-Time Polymerase Chain Reaction/statistics & numerical data , Antibodies, Viral/blood , Bayes Theorem , COVID-19/immunology , Humans , Latent Class Analysis , SARS-CoV-2/genetics , SARS-CoV-2/immunology , Sensitivity and Specificity , Time Factors
8.
Am J Epidemiol ; 190(8): 1689-1695, 2021 08 01.
Article in English | MEDLINE | ID: covidwho-1171942

ABSTRACT

Our objective was to estimate the diagnostic accuracy of real-time polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA) tests for coronavirus disease 2019 (COVID-19), depending on the time after symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent-class models, which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (immunoglobulin G (IgG) and/or immunoglobulin M (IgM)) assays using RT-PCR as the reference method. The sensitivity of RT-PCR was 0.68 (95% probability interval (PrI): 0.63, 0.73). IgG/M sensitivity was 0.32 (95% PrI :0.23; 0.41) for the first week and increased steadily. It was 0.75 (95% PrI: 0.67; 0.83) and 0.93 (95% PrI: 0.88; 0.97) for the second and third weeks after symptom onset, respectively. Both tests had a high to absolute specificity, with higher point median estimates for RT-PCR specificity and narrower probability intervals. The specificity of RT-PCR was 0.99 (95% PrI: 0.98; 1.00). and the specificity of IgG/IgM was 0.97 (95% PrI: 0.92, 1.00), 0.98 (95% PrI: 0.95, 1.00) and 0.98 (95% PrI: 0.94, 1.00) for the first, second, and third weeks after symptom onset. The diagnostic accuracy of LFIA varies with time after symptom onset. Bayesian latent-class models provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and different risk profiles.


Subject(s)
COVID-19 Nucleic Acid Testing/statistics & numerical data , COVID-19 Serological Testing/statistics & numerical data , COVID-19/diagnosis , Immunoassay/statistics & numerical data , Real-Time Polymerase Chain Reaction/statistics & numerical data , Antibodies, Viral/blood , Bayes Theorem , COVID-19/immunology , Humans , Latent Class Analysis , SARS-CoV-2/genetics , SARS-CoV-2/immunology , Sensitivity and Specificity , Time Factors
9.
Stroke ; 51(12): 3746-3750, 2020 12.
Article in English | MEDLINE | ID: covidwho-1021185

ABSTRACT

BACKGROUND AND PURPOSE: We aimed to investigate the rate of hospital admissions for cerebrovascular events and of revascularization treatments for acute ischemic stroke in Italy during the coronavirus disease 2019 (COVID-19) outbreak. METHODS: The Italian Stroke Organization performed a multicenter study involving 93 Italian Stroke Units. We collected information on hospital admissions for cerebrovascular events from March 1 to March 31, 2020 (study period), and from March 1 to March 31, 2019 (control period). RESULTS: Ischemic strokes decreased from 2399 in 2019 to 1810 in 2020, with a corresponding hospitalization rate ratio (RR) of 0.75 ([95% CI, 0.71-0.80] P<0.001); intracerebral hemorrhages decreased from 400 to 322 (hospitalization RR, 0.81 [95% CI, 0.69-0.93]; P=0.004), and transient ischemic attacks decreased from 322 to 196 (hospitalization RR, 0.61 [95% CI, 0.51-0.73]; P<0.001). Hospitalizations decreased in Northern, Central, and Southern Italy. Intravenous thrombolyses decreased from 531 (22.1%) in 2019 to 345 in 2020 (19.1%; RR, 0.86 [95% CI, 0.75-0.99]; P=0.032), while primary endovascular procedures increased in Northern Italy (RR, 1.61 [95% CI, 1.13-2.32]; P=0.008). We found no correlation (P=0.517) between the hospitalization RRs for all strokes or transient ischemic attack and COVID-19 incidence in the different areas. CONCLUSIONS: Hospitalizations for stroke or transient ischemic attacks across Italy were reduced during the worst period of the COVID-19 outbreak. Intravenous thrombolytic treatments also decreased, while endovascular treatments remained unchanged and even increased in the area of maximum expression of the outbreak. Limited hospitalization of the less severe patients and delays in hospital admission, due to overcharge of the emergency system by COVID-19 patients, may explain these data.


Subject(s)
COVID-19/epidemiology , Cerebral Hemorrhage/epidemiology , Hospitalization/statistics & numerical data , Ischemic Attack, Transient/epidemiology , Ischemic Stroke/epidemiology , Thrombectomy/statistics & numerical data , Thrombolytic Therapy/statistics & numerical data , Aged , Aged, 80 and over , Endovascular Procedures/statistics & numerical data , Female , Humans , Ischemic Stroke/therapy , Italy/epidemiology , Male , Middle Aged
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