Artificial neural networks for prediction of COVID-19 in India by using backpropagation.

The COVID-19 pandemic has affected thousands of people around the world. In this study, we used artificial neural network (ANN) models to forecast the COVID-19 outbreak for policymakers based on 1st January to 31st October 2021 of positive cases in India. In the confirmed cases of COVID-19 in India, it's critical to use an estimating model with a high degree of accuracy to get a clear understanding of the situation. Two explicit mathematical prediction models were used in this work to anticipate the COVID-19 epidemic in India. A Boltzmann Function-based model and Beesham's prediction model are among these methods and also estimated using the advanced ANN-BP models. The COVID-19 information was partitioned into two sections: training and testing. The former was utilized for training the ANN-BP models, and the latter was used to test them. The information examination uncovers critical day-by-day affirmed case changes, yet additionally unmistakable scopes of absolute affirmed cases revealed across the time span considered. The ANN-BP model that takes into consideration the preceding 14-days outperforms the others based on the archived results. In forecasting the COVID-19 pandemic, this comparison provides the maximum incubation period, in India. Mean square error, and mean absolute percent error have been treated as the forecast model performs more accurately and gets good results. In view of the findings, the ANN-BP model that considers the past 14-days for the forecast is proposed to predict everyday affirmed cases, especially in India that have encountered the main pinnacle of the COVID-19 outbreak. This work has not just demonstrated the relevance of the ANN-BP techniques for the expectation of the COVID-19 outbreak yet additionally showed that considering the incubation time of COVID-19 in forecast models might produce more accurate assessments.

A Survey of the Various Methods and Models Used to Detect Long COVID

Long-COVID or post-COVID is a phenomenon where people who have recovered from the COVID-19, suffer persisting symptoms for more than 4 weeks after the confirmed case of COVID-19 and they can last for months. Approximately 20% of the people affected by this Coronavirus disease (COVID-19) are suffering from mid and long term effects known as the Long COVID and it can affect multiple organs in the body and this can lead to death. To date, different studies and researches have been undertaken to understand about the Long COVID and make robust estimates on the predicting factors, symptoms and also to assess the various long term effects on the patients affected by it. Based on the available research articles and the papers published in mainstream journals on Long COVID, this survey paper aims at analyzing various methods and Machine learning models used to detect and predict Long COVID, to help clinicians and researchers working on early diagnosis of Long COVID. © 2022 IEEE.

Implementation of the infection control estimate: A case study on the use of a newly developed digital tool for outbreak management in the acute setting.

Aim: An Infection Control Estimate (ICE) Tool was developed based on a previously published concept of applying military planning techniques to Infection Prevention and Control (IPC) management strategies in the acute healthcare setting. Methods: Initial testing of the outbreak management tool was undertaken in a large acute hospital in the North-West of England during a localised outbreak of COVID-19. The tool, developed using Microsoft Excel, was completed by trained IPC practitioners in real-time to log outbreak details, assign and manage meeting actions and to generate surveillance data. Results: The ICE tool was utilised across five outbreak control meetings to identify and allocate tasks to members of the outbreak control team and to monitor progress. Within the meetings, the tool was used primarily by the trained IPC Specialist Nurses who were guided by and entered data into the relevant sections. Feedback indicated that the tool was easy to use and useful as the sole repository of outbreak information and data. Suggested improvements following the testing period were made and additional functionality was added. Conclusion: Utilisation of the ICE tool has the potential to improve our understanding of the efficacy of currently employed outbreak management interventions and provides a cognitive support and targeted education for teams responsible for the management of outbreaks. It is hoped that by guiding teams through an outbreak with prompts and guidance, as well as facilitating collection and presentation of surveillance data, outbreaks will be resolved sooner and risks to patients will be reduced.

Lessons learned: A look back at the performance of nine COVID-19 serologic assays and their proposed utility.

BACKGROUND: Serologic assays for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been proposed to assist with the acute diagnosis of infection, support epidemiological studies, identify convalescent plasma donors, and evaluate vaccine response. METHODS: We report an evaluation of nine serologic assays: Abbott (AB) and Epitope (EP) IgG and IgM, EUROIMMUN (EU) IgG and IgA, Roche anti-N (RN TOT) and anti-S (RS TOT) total antibody, and DiaSorin (DS) IgG. We evaluated 291 negative controls (NEG CTRL), 91 PCR positive (PCR POS) patients (179 samples), 126 convalescent plasma donors (CPD), 27 healthy vaccinated donors (VD), and 20 allogeneic hematopoietic stem cell transplant (HSCT) recipients (45 samples). RESULTS: We observed good agreement with the method performance claims for specificity (93-100%) in NEG CTRL but only 85% for EU IgA. The sensitivity claims in the first 2 weeks of symptom onset was lower (26-61%) than performance claims based on > 2 weeks since PCR positivity. We observed high sensitivities (94-100%) in CPD except for AB IgM (77%), EP IgM (0%). Significantly higher RS TOT was observed for Moderna vaccine recipients then Pfizer (p-values < 0.0001). A sustained RS TOT response was observed for the five months following vaccination. HSCT recipients demonstrated significantly lower RS TOT than healthy VD (p < 0.0001) at dose 2 and 4 weeks after. CONCLUSIONS: Our data suggests against the use of anti-SARS-CoV-2 assays to aid in acute diagnosis. RN TOT and RS TOT can readily identify past-resolved infection and vaccine response in the absence of native infection. We provide an estimate of expected antibody response in healthy VD over the time course of vaccination for which to compare antibody responses in immunosuppressed patients.

The Deep Learning-Based Framework for Automated Predicting COVID-19 Severity Score.

With the COVID-19 pandemic sweeping the globe, an increasing number of people are working on pandemic research, but there is less effort on predicting its severity. Diagnostic chest imaging is thought to be a quick and reliable way to identify the severity of COVID-19. We describe a deep learning method to automatically predict the severity score of patients by analyzing chest X-rays, with the goal of collaborating with doctors to create corresponding treatment measures for patients and can also be used to track disease change. Our model consists of a feature extraction phase and an outcome prediction phase. The feature extraction phase uses a DenseNet backbone network to extract 18 features related to lung diseases from CXRs; the outcome prediction phase, which employs the MLP regression model, selects several important features for prediction from the features extracted in the previous phase and demonstrates the effectiveness of our model by comparing it with several commonly used regression models. On a dataset of 2373 CXRs, our model predicts the geographic extent score with 1.02 MAE and the lung opacity score with 0.85 MAE.

Vast ecosystem disturbance in a warming climate may jeopardize our climate goal of reducing CO2: a case study for megafires in the Australian 'black summer'.

A warming climate is one of the most important driving forces of intensified wildfires globally. The unprecedented wildfires broke out in the Australian 'Black Summer' (November 2019-February 2020), which released massive heat, gases, and particles into the atmosphere. The total carbon dioxide (CO2) emissions from wildfires were estimated at ∼963 million tons by using a top-down approach based on direct satellite measurements of CO2 and fire radiative power. The fire emissions have led to an approximately 50-80 folds increase in total CO2 emission in Australia compared with the similar seasons of 2014-2019. The excess CO2 from wildfires has offset almost half of the global anthropogenic CO2 emission reductions due to the Corona Virus Disease 2019 in 2020. When the wildfires were intense in December 2019, they caused a 1.48 watts per square meter additional positive radiative forcing above the monthly average in Australia and the vicinity. Our findings demonstrate that vast ecosystem disturbance in a warming climate can strongly influence the global carbon cycle and hamper our climate goal of reducing CO2.

Estimation of COVID-19 Period Prevalence and the Undiagnosed Population in Canadian Provinces: Model-Based Analysis.

BACKGROUND: The development of a successful COVID-19 control strategy requires a thorough understanding of the trends in geographic and demographic distributions of disease burden. In terms of the estimation of the population prevalence, this includes the crucial process of unravelling the number of patients who remain undiagnosed. OBJECTIVE: This study estimates the period prevalence of COVID-19 between March 1, 2020, and November 30, 2020, and the proportion of the infected population that remained undiagnosed in the Canadian provinces of Quebec, Ontario, Alberta, and British Columbia. METHODS: A model-based mathematical framework based on a disease progression and transmission model was developed to estimate the historical prevalence of COVID-19 using provincial-level statistics reporting seroprevalence, diagnoses, and deaths resulting from COVID-19. The framework was applied to three different age cohorts (< 30; 30-69; and ≥70 years) in each of the provinces studied. RESULTS: The estimates of COVID-19 period prevalence between March 1, 2020, and November 30, 2020, were 4.73% (95% CI 4.42%-4.99%) for Quebec, 2.88% (95% CI 2.75%-3.02%) for Ontario, 3.27% (95% CI 2.72%-3.70%) for Alberta, and 2.95% (95% CI 2.77%-3.15%) for British Columbia. Among the cohorts considered in this study, the estimated total number of infections ranged from 2-fold the number of diagnoses (among Quebecers, aged ≥70 years: 26,476/53,549, 49.44%) to 6-fold the number of diagnoses (among British Columbians aged ≥70 years: 3108/18,147, 17.12%). CONCLUSIONS: Our estimates indicate that a high proportion of the population infected between March 1 and November 30, 2020, remained undiagnosed. Knowledge of COVID-19 period prevalence and the undiagnosed population can provide vital evidence that policy makers can consider when planning COVID-19 control interventions and vaccination programs.

Estimation of SIRM Model's Nonstationary Parameters of COVID-19 in Czech Rebuplic

This paper is a comprehensive study dedicated to practical solution of estimation problems in models of the spread of infectious diseases. Mentioned algorithms of parameters' estimation make possible to build mathematical models of the spread of infectious diseases based on observations. The results of analysis of the approach to mathematical modeling of the spread of infectious diseases are given in this paper, in particular simulation models and estimation methods in models of population dynamics. Computer simulation for analysis of COVID-19 pandemic in Czech Republic demonstrates efficiency o f t he mentioned algorithm. © 2022 IEEE.

A study on the factors influencing the transfer of COVID-19 severe illness patients out of the ICU based on generalized linear mixed effect model.

The clinical data of 76 severe illness patients with novel coronavirus SARS-CoV-2 from July to August, 2020 admitted to the ICU Intensive Care Unit ward in a hospital in Urumqi were collected in the paper. By using the Laplace approximation parameter estimation method based on maximum likelihood estimation, the generalized linear mixed effect model (GLMM) was established to analyze the characteristics of clinical indicators in critical patients, and to screen the main influencing factors of COVID-19 critical patients' inability to be transferred out of the ICU in a short time: age, C-reactive protein, serum creatinine and lactate dehydrogenase.

Estimating vaccine effectiveness against SARS-CoV-2 infection, hospitalization and death from ecologic data in Costa Rica.

BACKGROUND: Clinical trials and individual-level observational data in Israel demonstrated approximately 95% effectiveness of mRNA-based vaccines against symptomatic SARS-CoV-2 infection. Individual-level data are not available in many countries, particularly low- and middle- income countries. Using a novel Poisson regression model, we analyzed ecologic data in Costa Rica to estimate vaccine effectiveness and assess the usefulness of this approach. METHODS: We used national data from December 1, 2020 to May 13, 2021 to ascertain incidence, hospitalizations and deaths within ecologic units defined by 14 age groups, gender, 105 geographic areas, and day of the epidemic. Within each unit we used the proportions of the population with one and with two vaccinations, primarily tozinameran. Using a non-standard Poisson regression model that included an ecologic-unit-specific rate factor to describe rates without vaccination and a factor that depended on vaccine effectiveness parameters and proportions vaccinated, we estimated vaccine effectiveness. RESULTS: In 3.621 million persons aged 20 or older, there were 125,031 incident cases, 7716 hospitalizations, and 1929 deaths following SARS-CoV-2 diagnosis; 73% of those aged ≥ 75 years received two doses. For one dose, estimated effectiveness was 59% (95% confidence interval 53% to 64%) for SARS-CoV-2 incidence, 76% (68% to 85%) for hospitalizations, and 63% (47% to 80%) for deaths. For two doses, the respective estimates of effectiveness were 93% (90% to 96%), 100% (97% to 100%), and 100% (97% to 100%). CONCLUSIONS: These effectiveness estimates agree well with findings from clinical trials and individual-level observational studies and indicate high effectiveness in the general population of Costa Rica. This novel statistical approach is promising for countries where ecologic, but not individual-level, data are available. The method could also be adapted to monitor vaccine effectiveness over calendar time.

Statistical methodology for ribosomal frameshift detection

During normal protein synthesis, the ribosome shifts along the messenger RNA (mRNA) by exactly three nucleotides for each amino acid added to the protein being translated. However, in special cases, the sequence of the mRNA somehow induces the ribosome to slip, which shifts the "reading frame"in which the mRNA is translated, and gives rise to an otherwise unexpected protein. Such "programmed frameshifts"are well-known in viruses, including coronavirus, and a few cases of programmed frameshifting are also known in cellular genes. However, there is no good way, either experimental or informatic, to identify novel cases of programmed frameshifting. Thus it is possible that substantial numbers of cellular proteins generated by programmed frameshifting in human and other organisms remain unknown. Here, we build on prior works observing that data from ribosome profiling can be analyzed for anomalies in mRNA reading frame periodicity to identify putative programmed frameshifts. We develop a statistical framework to identify all likely (even for very low frameshifting rates) frameshift positions in a genome. We also develop a frameshift simulator for ribosome profiling data to verify our algorithm. We show high sensitivity of prediction on the simulated data, retrieving 97.4% of the simulated frameshifts. Furthermore, our method found all three of the known yeast genes with programmed frameshifts. Our results suggest there could be a large number of un-Annotated alternative proteins in the yeast genome, generated by programmed frameshifting. This motivates further study and parallel investigations in the human genome. © 2022 ACM.

Association between short-term exposure to air pollution and COVID-19 mortality in all German districts: the importance of confounders.

Background: The focus of many studies is to estimate the effect of risk factors on outcomes, yet results may be dependent on the choice of other risk factors or potential confounders to include in a statistical model. For complex and unexplored systems, such as the COVID-19 spreading process, where a priori knowledge of potential confounders is lacking, data-driven empirical variable selection methods may be primarily utilized. Published studies often lack a sensitivity analysis as to how results depend on the choice of confounders in the model. This study showed variability in associations of short-term air pollution with COVID-19 mortality in Germany under multiple approaches accounting for confounders in statistical models. Methods: Associations between air pollution variables PM2.5, PM10, CO, NO, NO2, and O3 and cumulative COVID-19 deaths in 400 German districts were assessed via negative binomial models for two time periods, March 2020-February 2021 and March 2021-February 2022. Prevalent methods for adjustment of confounders were identified after a literature search, including change-in-estimate and information criteria approaches. The methods were compared to assess the impact on the association estimates of air pollution and COVID-19 mortality considering 37 potential confounders. Results: Univariate analyses showed significant negative associations with COVID-19 mortality for CO, NO, and NO2, and positive associations, at least for the first time period, for O3 and PM2.5. However, these associations became non-significant when other risk factors were accounted for in the model, in particular after adjustment for mobility, political orientation, and age. Model estimates from most selection methods were similar to models including all risk factors. Conclusion: Results highlight the importance of adequately accounting for high-impact confounders when analyzing associations of air pollution with COVID-19 and show that it can be of help to compare multiple selection approaches. This study showed how model selection processes can be performed using different methods in the context of high-dimensional and correlated covariates, when important confounders are not known a priori. Apparent associations between air pollution and COVID-19 mortality failed to reach significance when leading selection methods were used. Supplementary Information: The online version contains supplementary material available at 10.1186/s12302-022-00657-5.

COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation.

We provide a methodology by which an epidemiologist may arrive at an optimal design for a survey whose goal is to estimate the disease burden in a population. For serosurveys with a given budget of C rupees, a specified set of tests with costs, sensitivities, and specificities, we show the existence of optimal designs in four different contexts, including the well known c-optimal design. Usefulness of the results are illustrated via numerical examples. Our results are applicable to a wide range of epidemiological surveys under the assumptions that the estimate's Fisher-information matrix satisfies a uniform positive definite criterion.

COVID-19 Prediction using LSTM

As COVID-19 has transformed into a pandemic, the pollution, disasters, and ramifications for the economy have turned out to be indisputable. Sensible systems ought to be used to evaluate the money related impact of future disease guides to restrict fear and dubiousness about COVID-19 pandemic's monetary impact. Gotten from Epidemics already (like influenza) and monetary examples, this assessment gathered a plague affliction evaluation framework and a money related circumstance estimate model. Using this methodology, the author moreover guesses the monetary aftereffects of future COVID-19 spread. The disclosures of the audit are according to the accompanying. In any case, the significant learning-based monetary effect assumption model was attempted with really look at data to ensure that it actually expected development rates by percent. Second, that used a significant learning-based compelling disease money related impact estimate model, the makers present the COVID- 19 example and future financial effect assumption results for the looming year. At the present time, a large portion of COVID- 19 assessment is on method for managing drug spread using quantifiable mathematical estimations. This work will be used as a definite reference for compelling and preventive bearing by expecting the spread of diseases and monetary issues related with COVID-19 using significant learning advancement and credible overpowering ailment data. © 2022 IEEE.

Mathematical modeling on the transmission of COVID-19 and its reproduction numbers in SAARC countries

In the middle of December 2019, a virus known as coronavirus (COVID-19) generated by severe acute respiratory syndrome corona virus 2 (SARC-CoV-2) was first detected in Wuhan, Hubei Province, China. As of the 9th of March, 2022, spread to over 212 countries, causing 429 million confirmed cases and 6 million people to lose their lives worldwide. In developing countries like the South Asian area, alarming dynamic variations in the pattern of confirmed cases and death tolls were displayed. During epidemics, accurate assessment of the characteristics that characterize infectious disease transmission is critical for optimizing control actions, planning, and adapting public health interventions. The reproductive number, or the typical number of secondary cases caused by an infected individual, can be employed to determine transmissibility. Several statistical and mathematical techniques have been presented to calculate across the duration of an epidemic. A technique is provided for calculating epidemic reproduction numbers. It is a MATLAB version of the EpiEstim package's R function estimate R, version 2.2-3. in the South Asian Association for Regional Cooperation (SAARC) countries. The three methodologies supported are 'parametric SI,' 'non-parametric SI,' and 'uncertain SI.' The present study indicated that the highest reproduction number was 12.123 and 11.861 on 5th and 14th March 2020 in India and Sri_Lanka, whereas the lowest reproduction number was the lowest was 0.300 and 0.315 in Sri_Lanka and India. The Maximum and minimum reproductive number of Bangladesh was 3.752 and 0.725. In this study, we have tried to point out the worst, best and current situation of SAARC countries.

Iteratively regularized Gauss-Newton type methods for approximating quasi-solutions of irregular nonlinear operator equations in Hilbert space with an application to COVID-19 epidemic dynamics.

We investigate a class of iteratively regularized methods for finding a quasi-solution of a noisy nonlinear irregular operator equation in Hilbert space. The iteration uses an a priori stopping rule involving the error level in input data. In assumptions that the Frechet derivative of the problem operator at the desired quasi-solution has a closed range, and that the quasi-solution fulfills the standard source condition, we establish for the obtained approximation an accuracy estimate linear with respect to the error level. The proposed iterative process is applied to the parameter identification problem for a SEIR-like model of the COVID-19 pandemic.

Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model.

BACKGROUND: Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission. METHOD: We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization. RESULTS: In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53-15.80], incubation period (OR 0.83; 95% CI 0.68-0.99), number of comorbidities (OR 1.76; 95% CI 1.03-3.05), D-dimer (OR 7.05; 95% CI, 1.35-45.7), C-reactive protein (OR 1.06; 95% CI 1.02-1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27-1.82). The model showed good fitting (Hosmer-Lemeshow goodness, X2(8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949-0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUCPR = 0.934). We prepared a nomogram and a freely available online prediction platform ( https://deterioration-risk-model-of-covid-19.shinyapps.io/DRMapp/ ). CONCLUSION: We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources.

Predicting the trends and cost impact of COVID-19 OSHA citations on US construction contractors using machine learning and simulation

Purpose Occupational Safety and Health Administration (OSHA) of the U.S. government ensures that all health and safety regulations, protecting the workers, are enforced. OSHA officers conduct inspections and assess fines for non-compliance and regulatory violations. Literature discussion on the economic impact of OSHA inspections with COVID-19 related citations for the construction sector is lacking. This study aims to investigate the relationships between the number of COVID-19 cases, construction employment and OSHA citations and it further evaluates the total and monthly predicted cost impact of OSHA citations associated with COVID-19 violations. Design/methodology/approach An application of multiple regression analysis, a supervised machine learning linear regression model, based on K-fold cross validation sampling and a probabilistic risk-based cost estimate Monte Carlo simulation were utilized to evaluate the data. The data were collected from numerous websites including OSHA, Centers for Disease Control and the World Health Organization. Findings The results show that as the monthly construction employment increased, there was a decrease in OSHA citations. Conversely, the cost impact of OSHA citations had a positive relationship with the number of COVID-19 cases. In addition, the monthly cost impact of OSHA COVID-19 related citations along with the total cost impact of citations were predicted and analyzed. Originality/value The application of the two models on cost analysis provides a thorough comparison of predicted and overall cost impact, which can assist the contractors to better understand the possible cost ramifications. Based on the findings, it is suggested that the contractors include contingency fees within their contracts, hire safety managers to implement specific safety protocols related to COVID-19 and request a safety action plan when qualifying their subcontractors to avoid potential fines and citations.

A New Performance Metric to Estimate the Risk of Exposure to Infection in a Health Care Setting: Descriptive Study.

BACKGROUND: Despite several measures to monitor and improve hand hygiene (HH) in health care settings, health care-acquired infections (HAIs) remain prevalent. The measures used to calculate HH performance are not able to fully benefit from the high-resolution data collected using electronic monitoring systems. OBJECTIVE: This study proposes a novel parameter for quantifying the HAI exposure risk of individual patients by considering temporal and spatial features of health care workers' HH adherence. METHODS: Patient exposure risk is calculated as a function of the number of consecutive missed HH opportunities, the number of unique rooms visited by the health care professional, and the time duration that the health care professional spends inside and outside the patient's room without performing HH. The patient exposure risk is compared to the entrance compliance rate (ECR) defined as the ratio of the number of HH actions performed at a room entrance to the total number of entrances into the room. The compliance rate is conventionally used to measure HH performance. The ECR and the patient exposure risk are analyzed using the data collected from an inpatient nursing unit for 12 weeks. RESULTS: The analysis of data collected from 59 nurses and more than 25,600 records at a musculoskeletal rehabilitation unit at the Toronto Rehabilitation Institute, KITE, showed that there is no strong linear relation between the ECR and patient exposure risk (r=0.7, P<.001). Since the ECR is calculated based on the number of missed HH actions upon room entrance, this parameter is already included in the patient exposure risk. Therefore, there might be scenarios that these 2 parameters are correlated; however, in several cases, the ECR contrasted with the reported patient exposure risk. Generally, the patients in rooms with a significantly high ECR can be potentially exposed to a considerable risk of infection. By contrast, small ECRs do not necessarily result in a high patient exposure risk. The results clearly explained the important role of the factors incorporated in patient exposure risk for quantifying the risk of infection for the patients. CONCLUSIONS: Patient exposure risk might provide a more reliable estimation of the risk of developing HAIs compared to ECR by considering both the temporal and spatial aspects of HH records.

Statistical Inferences: Based on Exponentiated Exponential Model to Assess Novel Corona Virus (COVID-19) Kerala Patient Data

In this article, we use exponentiated exponential distribution as a suitable statistical lifetime model for novel corona virus (covid-19) Kerala patient data. The suitability of the model has been followed by different statistical tools like the value of logarithm of likelihood, Kolmogorov–Smirnov distance, Akaike information criterion, Bayesian information criterion. Moreover, likelihood ratio test and empirical posterior probability analysis are performed to show its suitability. The maximum-likelihood and asymptotic confidence intervals for the parameters are derived from Fisher information matrix. We use the Markov Chain Monte Carlo technique to generate samples from the posterior density function. Based on generated samples, we can compute the Bayes estimates of the unknown parameters and can also construct highest posterior density credible intervals. Further we discuss the Bayesian prediction for future observation based on the observed sample. The Gibbs sampling technique has been used for estimating the posterior predictive density and also for constructing predictive intervals of the order statistics from the future sample.