An analysis of Deep Learning Models for Detection of COVID-19 Diseases

The World Health Organization has classified COVID-19 as a pandemic virus at this time. The conditions posed significant challenges for every nation on the planet, notably with the preparations made for health care and the lengthy reactions required. Because of the sudden rise in the number of infections due to COVID-19 disease and the limited resources for detecting it has become requisite to develop an artificial intelligence-based system for determining the COVID-19 disease. An increasing number of people throughout the world are testing positive for COVID-19 every day. A rapid and accurate identification of COVID-19 is a time-sensitive prerequisite for preventing and controlling the pandemic by means of appropriate isolation and medical treatment. The significance of the current work lies in its discussion of the overview of the deep learning approaches with diagnostic imaging. This includes topics such as various deep learning models and its impact in efficiently detecting the virus transmitted indications. © 2022 IEEE.

COVID DISEASE DETECTION USING ReLU VARIANTS

Since long, the unexpected corona cases are being reported starting from Wuhan to all parts of the world. COVID-19 epidemic is spreading all over the world and became mysterious to track its root cause. The purpose of the research is to identify the highly affected areas and the cause for spreading the disease based on the current day statistics. The root cause of the disease is detected based on test reports and epidemiology is estimated using ReLU variants. This research is useful to the society or Government in analyzing the health status of Corona patients.

Pandemic Measures for the COVID19 Outbreak Control in Malaysia: A Correlative Comparison with China

The 2019 novel coronavirus (2019-nCoV) outbreak is declared as a pandemic by the World Health Organization. This chapter presents a simplified approach of the 2019-nCoV outbreak in Malaysia, based on a simple mathematical model and limited reference data. The profound model predictions is based on the actual data on the date of confirmation excluding deaths, considering the recovered will have the possibility to get infected again. The 14 days incubation characteristics are used in the computations as pronounced by CDC to improve the prediction characteristics. This includes the four stages of recovery characteristics in any pandemic cases towards cluster segregation, contact tracing towards flattening the growth curve. The model from china was taken as reference and the Malaysian recovery phase analyses and compared with the measures in place. The computational approach for the dataset available is presented and the similarity measure is a good reference point in handling the pandemic of this size in future. © 2022, Institute of Technology PETRONAS Sdn Bhd.

The burden of active infection and anti-SARS-CoV-2 IgG antibodies in the general population: Results from a statewide sentinel-based population survey in Karnataka, India.

OBJECTIVE: To estimate the burden of active infection and anti-SARS-CoV-2 IgG antibodies in Karnataka, India, and to assess variation across geographical regions and risk groups. METHODS: A cross-sectional survey of 16,416 people covering three risk groups was conducted between 3-16 September 2020 using the state of Karnataka's infrastructure of 290 healthcare facilities across all 30 districts. Participants were further classified into risk subgroups and sampled using stratified sampling. All participants were subjected to simultaneous detection of SARS-CoV-2 IgG using a commercial ELISA kit, SARS-CoV-2 antigen using a rapid antigen detection test (RAT) and reverse transcription-polymerase chain reaction (RT-PCR) for RNA detection. Maximum-likelihood estimation was used for joint estimation of the adjusted IgG, active and total prevalence (either IgG or active or both), while multinomial regression identified predictors. RESULTS: The overall adjusted total prevalence of COVID-19 in Karnataka was 27.7% (95% CI 26.1-29.3), IgG 16.8% (15.5-18.1) and active infection fraction 12.6% (11.5-13.8). The case-to-infection ratio was 1:40 and the infection fatality rate was 0.05%. Influenza-like symptoms or contact with a COVID-19-positive patient were good predictors of active infection. RAT kits had higher sensitivity (68%) in symptomatic people compared with 47% in asymptomatic people. CONCLUSION: This sentinel-based population survey was the first comprehensive survey in India to provide accurate estimates of the COVID-19 burden. The findings provide a reasonable approximation of the population immunity threshold levels. Using existing surveillance platforms coupled with a syndromic approach and sampling framework enabled this model to be replicable.

The burden of active infection and anti-SARS-CoV-2 IgG antibodies in the general population: Results from a statewide survey in Karnataka, India (preprint)

BackgroundGlobally, the routinely used case-based reporting and IgG serosurveys underestimate the actual prevalence of COVID-19. Simultaneous estimation of IgG antibodies and active SARS-CoV-2 markers can provide a more accurate estimation. MethodsA cross-sectional survey of 16416 people covering all risk groups was done between 3-16 September 2020 using the state of Karnatakas infrastructure of 290 hospitals across all 30 districts. All participants were subjected to simultaneous detection of SARS-CoV-2 IgG using a commercial ELISA kit, SARS-CoV-2 antigen using a rapid antigen detection test (RAT), and reverse transcription-polymerase chain reaction (RT-PCR) for RNA detection. Maximum-likelihood estimation was used for joint estimation of the adjusted IgG, active, and total prevalence, while multinomial regression identified predictors. FindingsThe overall adjusted prevalence of COVID-19 in Karnataka was 27 {middle dot}3% (95% CI: 25 {middle dot}7-28 {middle dot}9), including IgG 16 {middle dot}4% (95% CI: 15 {middle dot}1 - 17 {middle dot}7) and active infection 12 {middle dot}7% (95% CI: 11 {middle dot}5-13 {middle dot}9). The case-to-infection ratio was 1:40, and the infection fatality rate was 0 {middle dot}05%. Influenza-like symptoms or contact with a COVID-19 positive patient are good predictors of active infection. The RAT kits had higher sensitivity (68%) in symptomatic participants compared to 47% in asymptomatic. InterpretationThis is the first comprehensive survey providing accurate estimates of the COVID-19 burden anywhere in the world. Further, our findings provide a reasonable approximation of population immunity threshold levels. Using the RAT kits and following the syndromic approach can be useful in screening and monitoring COVID-19. Leveraging existing surveillance platforms, coupled with appropriate methods and sampling framework, renders our model replicable in other settings.