scholarly journals Estimating COVID-19 Virus Prevalence from Records of Testing Rate and Test Positivity

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Arnout JW Everts
Keyword(s):  
Virus Prevalence ◽  
Testing Rate

scholarly journals Estimating COVID-19 Virus Prevalence from Records of Testing Rate and Test Positivity

2020 ◽  
Author(s):  
Arnout JW Everts
Keyword(s):  
Western Europe ◽  
Estimation Method ◽  
Virus Prevalence ◽  
Testing Rate ◽  
Community Model ◽  
Unbiased Test ◽  
Prevalence Estimates ◽  
Initial Dataset ◽  
Relationship Of ◽  
Epidemic Wave

ABSTRACTIntroductionPCR testing for COVID-19 is not done at random but selectively on suspected cases. This paper presents a method to estimate a “genuine Virus Prevalence” by quantifying and removing the bias related to selective testing.MethodsData used are from nine (9) neighbouring countries in Western Europe that record similar epidemic trends despite differences in Testing Rate. Regression analysis is used to establish a relationship of declining Test Positivity with increased Testing Rate. By extrapolating this trend to an “infinitely complete” Testing Rate, an unbiased Test Positivity or “genuine Virus Prevalence” is computed. Via pairing of “genuine Virus Prevalence” with Excess-Deaths, a “genuine Infection Fatality Rate (IFR) is also derived.ResultsPeak levels of “genuine Virus Prevalence” were around 0.5 to 2% during the 1st epidemic “wave” (week 10 to week 20) and are approaching similar levels in the ongoing 2nd “wave” (week 34 onward). “Genuine Virus Prevalence” estimates are close to reported Seroprevalence in the studied countries with a correlation coefficient of 0.58. “Genuine” IFR is found comparable to closed-community model IFR. Finally, results of community mass-testing in Slovakia are within the estimated range of “genuine Virus Prevalence”.ConclusionsEstimates of “genuine Virus Prevalence” benchmark favourably to other indications of virus prevalence suggesting the estimation method is robust and potentially deployable beyond this initial dataset of countries. “Genuine Virus Prevalence” curves suggest that during the 1st epidemic “wave”, curve flattening and waning happened at very modest levels of infection spread, either naturally or facilitated by government measures.

scholarly journals Seasonal pattern of hepatitis E virus prevalence in swine in two different geographical areas of China

2013 ◽  
Vol 141 (11) ◽  
pp. 2403-2409 ◽  
Author(s):  
Y. H. LU ◽  
H. Z. QIAN ◽  
A. Q. HU ◽  
X. QIN ◽  
Q. W. JIANG ◽  
...  
Keyword(s):  
Hepatitis E Virus ◽  
Seasonal Pattern ◽  
Eastern China ◽  
Geographical Area ◽  
Hepatitis E ◽  
Southwestern China ◽  
Seasonal Patterns ◽  
Virus Prevalence ◽  
Swine Hepatitis ◽  
A Minor

SUMMARYWe studied seasonal patterns of swine hepatitis E virus (HEV) infection in China. From 2008 to 2011, 4200 swine bile specimens were collected for the detection of HEV RNA. A total of 92/2400 (3·83%) specimens in eastern China and 47/1800 (2·61%) specimens in southwestern China were positive for HEV. Seasonal patterns differing by geographical area were suggested. In eastern China, the major peak of HEV RNA prevalence was during March–April, with a minor peak during September–October, and a dip during July–August. In southwestern China, the peak was during September–October and the dip during March–April. The majority of subtype 4a cases (63/82, 76·83%) were detected in the first half of the year, while the majority of subtype 4b cases (26/29, 89·66%) were concentrated in the second half of the year, suggesting that different subtypes contribute to different peaks. Our results indicate that the distribution of HEV subtypes is associated with seasonal patterns.

AR-Tri-Training: Tri-Training with Assistant Strategy

2014 ◽  
Vol 513-517 ◽  
pp. 1840-1844 ◽  
Author(s):  
Long Jie Cui ◽  
Hong Li Wang ◽  
Rong Yi Cui
Keyword(s):  
Learning Strategy ◽  
Voice Recognition ◽  
Classification Performance ◽  
Experimental Results ◽  
Training Algorithm ◽  
Information Strategy ◽  
Testing Rate ◽  
Rich Information ◽  
Validation Set

The classification performance of the classifier is weakened because the noise samples are introduced for the use of unlabeled samples in Tri-training. In this paper a new Tri-training style algorithm named AR-Tri-training (Tri-training with assistant and rich strategy) is proposed. Firstly, the assistant learning strategy is posed. Then the supporting learner is designed by combining the assistant learning strategy with rich information strategy. The number of mislabeled samples produced in the iterations of three classifiers mutually labeling are reduced by use of the supporting learner, moreover the unlabeled samples and the misclassified samples of validation set can be fully used. The proposed algorithm is applied to voice recognition. The experimental results show that AR-Tri-training algorithm can compensate for the shortcomings of Tri-training algorithm, further improve the testing rate.

The hepatitis E virus prevalence in autoimmune hepatitis

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Parastoo Yousefi ◽  
Saber Soltani ◽  
Ali Gholami ◽  
Maryam Esghaei ◽  
Hossin Keyvani ◽  
...  
Keyword(s):  
Autoimmune Hepatitis ◽  
Hepatitis E Virus ◽  
Hepatitis E ◽  
Virus Prevalence

scholarly journals Long-term variation in influenza A virus prevalence and subtype diversity in migratory mallards in northern Europe

2014 ◽  
Vol 281 (1781) ◽  
pp. 20140098 ◽  
Author(s):  
Neus Latorre-Margalef ◽  
Conny Tolf ◽  
Vladimir Grosbois ◽  
Alexis Avril ◽  
Daniel Bengtsson ◽  
...  
Keyword(s):  
Influenza A Virus ◽  
Influenza A ◽  
Herd Immunity ◽  
Stopover Site ◽  
Virus Prevalence ◽  
Early Autumn ◽  
Temporal Occurrence ◽  
Wildlife Populations ◽  
Pronounced Peak

Data on long-term circulation of pathogens in wildlife populations are seldom collected, and hence understanding of spatial–temporal variation in prevalence and genotypes is limited. Here, we analysed a long-term surveillance series on influenza A virus (IAV) in mallards collected at an important migratory stopover site from 2002 to 2010, and characterized seasonal dynamics in virus prevalence and subtype diversity. Prevalence dynamics were influenced by year, but retained a common pattern for all years whereby prevalence was low in spring and summer, but increased in early autumn with a first peak in August, and a second more pronounced peak during October–November. A total of 74 haemagglutinin (HA)/neuraminidase (NA) combinations were isolated, including all NA and most HA (H1–H12) subtypes. The most common subtype combinations were H4N6, H1N1, H2N3, H5N2, H6N2 and H11N9, and showed a clear linkage between specific HA and NA subtypes. Furthermore, there was a temporal structuring of subtypes within seasons based on HA phylogenetic relatedness. Dissimilar HA subtypes tended to have different temporal occurrence within seasons, where the subtypes that dominated in early autumn were rare in late autumn, and vice versa. This suggests that build-up of herd immunity affected IAV dynamics in this system.

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