Predictive Capacity of COVID-19 Test Positivity Rate

Fenga, Livio; Gáspari, Mauro

doi:10.3390/s21072435

Cited by 18 publications

(16 citation statements)

References 41 publications

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“…by year groups or location (e.g. rural or urban) – is thus an option, as proposed in 38 . However, it is a fact that in many cases, such measures might not be a viable option, at least in the short run, nor be reasonably applicable.…”

Section: Discussionmentioning

confidence: 99%

Impact estimation on COVID-19 infections following school reopening in September 2020 in Italy

Fenga

Galli

2021

Preprint

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Background Since its outbreak, CoViD-19 (formally known as 2019-nCoV) has been triggering many questions among public authorities, social organisms and school officials, as to when students should be allowed to return to school. Such a decision is critical and must take into account, other than its beneficial effects, also those associated with an increased exposition of the students to the virus, which, as a result, might spread at a faster rate. To date, in Italy, a few studies have rigorously investigated the correlation between school reopening and number of people tested positive to CoViD-19. Therefore, this paper aims to provide an assessment of such an impact as well as to illustrate the methodology followed. Methods Official daily data on the cumulative number of people tested positive to CoViD-19, in conjunction with external information accounting for the different points in time schools reopened in the various Italian regions, have been employed to build a stochastic model of the type Seasonal Autoregressive Moving Average embodying external information. Results There was a statistically significant increase in the number of positive cases in all the Italian regions related to schools reopening. Such an increase occurred, in average, about 18.9 days after the schools have been reopened. Schools reopening have been significantly contributed to the diffusion of the pandemic, with an overall estimated impact of about 228,724 positive cases. Conclusions The results suggest the need for strict control of all in-school activities. This could be done by using, to a variable extent, all the non-pharmaceutical interventions available, such as limited access to school spaces, no overlapping practices between different sports in the same space, universal masking, bubble-size classroom. However, in many cases, such measures might not be a viable option, at least in the short run, nor be reasonably applicable. Therefore, whenever the established safety criteria could not be met, school buildings should remain closed.

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Section: Discussionmentioning

confidence: 99%

Impact estimation on COVID-19 infections following school reopening in September 2020 in Italy

Fenga

Galli

2021

Preprint

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“…A large-scale COVID-19 TPR is often used globally to control the pandemic as the SARS-CoV-2 transmission indicator and may estimate COVID-19 prevalence, predicting health care requirements, and monitoring the severity of cases in a country (12)(13)(14). Here we determined the COVID-19 q-RT-PCR TPR in West Sumatra Province of Indonesia from April 2020 to March 2021 by using large retrospective data from the two labs.…”

Section: Discussionmentioning

confidence: 99%

“…This data is in line with a cumulative number of COVID-19 new cases recorded in West Sumatra at the same time (3). However, daily TPR might predict the number of hospitalized patients after 15 days, much longer than daily incidence cases with only 4 days, so further research is needed to con rm (13).…”

Section: Discussionmentioning

confidence: 99%

Analysis of COVID-19 Test Positivity Rate in West Sumatra, Indonesia: A Cross-sectional Study of One-year Observation

Putra

Lailani

Mahata

et al. 2021

Preprint

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Background: The test positivity rate (TPR) of COVID-19 is an epidemiological indicator used to estimate SARS-CoV-2 transmission in a population at a certain time. However, large data analysis on the TPR in Indonesia is still limited. In this study, we determined COVID-19 TPR dynamics of Indonesian West Sumatra Province in the first year of cases were recorded.Method: We conducted an observational study with a cross-sectional approach from one-year secondary data of COVID-19 test using qualitative reverse transcription polymerase chain reaction (q-RT-PCR) in West Sumatra collected from April 2020 until March 2021. The TPR(s) in the province, its regions (cities/ regencies), and districts were determined annually, quarterly, and monthly to analyze their trends.Results: From a total of 410,424 individuals taking COVID-19 q-RT-PCR examination during one-year observation, the provincial TPR was 8.11%. The highest TPR quarterly and monthly was detected in the third quarter (October 2020 – December 2020, 12.18%) and October 2020 (15.62%) respectively. The TPR of cities was likely two times higher than regencies. There were significant differences in annual TPR between regions, districts, and any period of times detected in this study.Conclusion: We have shown the COVID-19 q-RT-PCR TPR dynamics to describe SARS-CoV-2 transmission control among different areas in West Sumatra. This study should be beneficial to ensure an effective COVID-19 preventive strategy in the future.

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“…On a dataset consisting of 224 validated COVID-19 images, Apostolopoulos and Mpesiana [ 30 ] have used various pre-trained DL algorithms and obtained 98.75 percent and 93.48 percent precision, respectively, for two and three classes. Livio Fenga [ 31 ] developed a statistical analysis tool known as seasonal auto regressive moving average to identify the positivity test rate of the COVID-19. It was reported in [ 32 ] that facial recognition using deep learning could be a useful tool for the detection and diagnostics of the COVID infected patients from the crowded places.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

Almalki

Qayyum²,

Irfan

et al. 2021

Healthcare

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The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

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Predictive Capacity of COVID-19 Test Positivity Rate

Cited by 18 publications

References 41 publications

Impact estimation on COVID-19 infections following school reopening in September 2020 in Italy

Impact estimation on COVID-19 infections following school reopening in September 2020 in Italy

Analysis of COVID-19 Test Positivity Rate in West Sumatra, Indonesia: A Cross-sectional Study of One-year Observation

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images

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