scholarly journals A comparison of five epidemiological models for transmission of SARS-CoV-2 in India

2020 ◽  
Author(s):  
Soumik Purkayastha ◽  
Rupam Bhattacharyya ◽  
Ritwik Bhaduri ◽  
Ritoban Kundu ◽  
Xuelin Gu ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Prediction Error ◽  
Pearson Correlation ◽  
False Negative ◽  
False Negative Rate ◽  
Correlation Coefficients ◽  
Epidemiological Models ◽  
Baseline Model ◽  
Seir Model ◽  
Active Case

Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures and other non-pharmaceutical interventions. We compare five epidemiological models for forecasting and assessing the course of the pandemic. We compare how the models analyze case-recovery-death count data in India, the country with second highest reported case-counts in a world where a large proportion of infections remain undetected. A baseline curve-fitting model is introduced, in addition to three compartmental models: an extended SIR (eSIR) model, an expanded SEIR model developed to account for infectiousness of asymptomatic and pre-symptomatic cases (SAPHIRE), another SEIR model to handle high false negative rate and symptom-based administration of tests (SEIR-fansy). A semi-mechanistic Bayesian hierarchical model developed at the Imperial College London (ICM) is also examined. Using COVID-19 data for India from March 15 to June 18 to train the models, we generate predictions from each of the five models from June 19 to July 18. To compare prediction accuracy with respect to reported cumulative and active case counts and cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) and mean squared relative prediction error (MSRPE) for each of the five models. For active case counts, SEIR-fansy yields an SMAPE value of 0.72, and the eSIR model yields a value of 33.83. For cumulative case counts, SMAPE values are 1.76 for baseline model, 23.10 for eSIR, 2.07 for SAPHIRE and 3.20 for SEIR-fansy. For cumulative death counts, the SEIR-fansy model performs the best, with an SMAPE of 7.13, as compared to 26.30 for the eSIR model. Using Pearson correlation coefficient and Lin concordance correlation coefficient, for cumulative case counts, the baseline model exhibits highest correlation (both Pearson as well as Lin coefficients), while for cumulative death counts, projections from SEIR-fansy exhibit the best performance: For cumulative cases, correlation coefficients computed for the baseline model are 1 (Pearson) and 0.991 (Lin). For eSIR, those values are 0.985 (Pearson) and 0.316 (Lin). For SAPHIRE, we compute 1 (Pearson) and 0.975 (Lin). Finally, for SEIR-fansy we have those values at 1 (Pearson) and 0.965 (Lin). Similarly, for cumulative deaths, correlation coefficients computed for eSIR is 0.978 (Pearson) and 0.206 (Lin), and for SEIR-fansy we have those values at 0.999 (Pearson) and 0.742 (Lin). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) counts as well. We compute underreporting factors on two specific dates (June 30 and July 10) and note that on both dates, the SEIR-fansy model reports the highest underreporting factor for active cases (June 30: 6.10 and July 10: 6.24) and cumulative deaths (June 30: 3.62 and July 10: 3.99) for both dates, while the SAPHIRE model reports the highest underreporting factor for cumulative cases (June 30: 27.79 and July 10: 26.74).

scholarly journals A Comparison of Five Epidemiological Models for Transmission of SARS-CoV-2 in India

2021 ◽  
Author(s):  
Soumik Purkayastha ◽  
Rupam Bhattacharyya ◽  
Ritwik Bhaduri ◽  
Ritoban Kundu ◽  
Xuelin Gu ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Correlation Coefficients ◽  
Simulation Studies ◽  
Epidemiological Models ◽  
Baseline Model ◽  
Bayesian Hierarchical ◽  
Active Case ◽  
Pandemic Planning ◽  
Appreciable Variation ◽  
Transmission Models

Abstract BackgroundMany popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). MethodsUsing COVID-19 data for India from March 15 to June 18 to train the models, we generate predictions from each of the five models from June 19 to July 18. To compare prediction accuracy with respect to reported cumulative and active case counts and cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. ResultsFor active case counts, SMAPE values are 0.72 (SEIR-fansy) and 33.83 (eSIR). For cumulative case counts, SMAPE values are 1.76 (baseline) 23. (eSIR), 2.07 (SAPHIRE) and 3.20 (SEIR-fansy). For cumulative death counts, the SMAPE values are 7.13 (SEIR-fansy) and 26.30 (eSIR). For cumulative cases and deaths, we compute Pearson’s and Lin’s correlation coefficients to investigate how well the projected and observed reported COVID-counts agree. Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) counts as well. We compute underreporting factors as of June 30 and note that the SEIR-fansy model reports the highest underreporting factor for active cases (6.10) and cumulative deaths (3.62), while the SAPHIRE model reports the highest underreporting factor for cumulative cases (27.79).ConclusionsIn this comparative paper we describe five different models used to study full disease transmission of the SARS-Cov-2 disease transmission in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. Prediction of daily active number of cases does show appreciable variation across models. The largest variability across models is observed in predicting the “total” number of infections including reported and unreported cases. The degree of under-reporting has been a major concern in India.

scholarly journals A comparison of five epidemiological models for transmission of SARS-CoV-2 in India

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Soumik Purkayastha ◽  
Rupam Bhattacharyya ◽  
Ritwik Bhaduri ◽  
Ritoban Kundu ◽  
Xuelin Gu ◽  
...  
Keyword(s):  
Disease Transmission ◽  
Correlation Coefficients ◽  
Credible Interval ◽  
R Package ◽  
Good Choice ◽  
Epidemiological Models ◽  
Baseline Model ◽  
Validation Data ◽  
Active Case ◽  
Credible Intervals

Abstract Background Many popular disease transmission models have helped nations respond to the COVID-19 pandemic by informing decisions about pandemic planning, resource allocation, implementation of social distancing measures, lockdowns, and other non-pharmaceutical interventions. We study how five epidemiological models forecast and assess the course of the pandemic in India: a baseline curve-fitting model, an extended SIR (eSIR) model, two extended SEIR (SAPHIRE and SEIR-fansy) models, and a semi-mechanistic Bayesian hierarchical model (ICM). Methods Using COVID-19 case-recovery-death count data reported in India from March 15 to October 15 to train the models, we generate predictions from each of the five models from October 16 to December 31. To compare prediction accuracy with respect to reported cumulative and active case counts and reported cumulative death counts, we compute the symmetric mean absolute prediction error (SMAPE) for each of the five models. For reported cumulative cases and deaths, we compute Pearson’s and Lin’s correlation coefficients to investigate how well the projected and observed reported counts agree. We also present underreporting factors when available, and comment on uncertainty of projections from each model. Results For active case counts, SMAPE values are 35.14% (SEIR-fansy) and 37.96% (eSIR). For cumulative case counts, SMAPE values are 6.89% (baseline), 6.59% (eSIR), 2.25% (SAPHIRE) and 2.29% (SEIR-fansy). For cumulative death counts, the SMAPE values are 4.74% (SEIR-fansy), 8.94% (eSIR) and 0.77% (ICM). Three models (SAPHIRE, SEIR-fansy and ICM) return total (sum of reported and unreported) cumulative case counts as well. We compute underreporting factors as of October 31 and note that for cumulative cases, the SEIR-fansy model yields an underreporting factor of 7.25 and ICM model yields 4.54 for the same quantity. For total (sum of reported and unreported) cumulative deaths the SEIR-fansy model reports an underreporting factor of 2.97. On October 31, we observe 8.18 million cumulative reported cases, while the projections (in millions) from the baseline model are 8.71 (95% credible interval: 8.63–8.80), while eSIR yields 8.35 (7.19–9.60), SAPHIRE returns 8.17 (7.90–8.52) and SEIR-fansy projects 8.51 (8.18–8.85) million cases. Cumulative case projections from the eSIR model have the highest uncertainty in terms of width of 95% credible intervals, followed by those from SAPHIRE, the baseline model and finally SEIR-fansy. Conclusions In this comparative paper, we describe five different models used to study the transmission dynamics of the SARS-Cov-2 virus in India. While simulation studies are the only gold standard way to compare the accuracy of the models, here we were uniquely poised to compare the projected case-counts against observed data on a test period. The largest variability across models is observed in predicting the “total” number of infections including reported and unreported cases (on which we have no validation data). The degree of under-reporting has been a major concern in India and is characterized in this report. Overall, the SEIR-fansy model appeared to be a good choice with publicly available R-package and desired flexibility plus accuracy.

scholarly journals Incorporating false negative tests in epidemiological models for SARS-CoV-2 transmission and reconciling with seroprevalence estimates

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rupam Bhattacharyya ◽  
Ritoban Kundu ◽  
Ritwik Bhaduri ◽  
Debashree Ray ◽  
Lauren J. Beesley ◽  
...  
Keyword(s):  
False Negative ◽  
Population Based ◽  
Training Data ◽  
Epidemiological Models ◽  
Rt Pcr ◽  
Antibody Prevalence ◽  
Igg Antibody ◽  
False Negatives ◽  
Seir Model ◽  
Study Population

AbstractSusceptible-Exposed-Infected-Removed (SEIR)-type epidemiologic models, modeling unascertained infections latently, can predict unreported cases and deaths assuming perfect testing. We apply a method we developed to account for the high false negative rates of diagnostic RT-PCR tests for detecting an active SARS-CoV-2 infection in a classic SEIR model. The number of unascertained cases and false negatives being unobservable in a real study, population-based serosurveys can help validate model projections. Applying our method to training data from Delhi, India, during March 15–June 30, 2020, we estimate the underreporting factor for cases at 34–53 (deaths: 8–13) on July 10, 2020, largely consistent with the findings of the first round of serosurveys for Delhi (done during June 27–July 10, 2020) with an estimated 22.86% IgG antibody prevalence, yielding estimated underreporting factors of 30–42 for cases. Together, these imply approximately 96–98% cases in Delhi remained unreported (July 10, 2020). Updated calculations using training data during March 15-December 31, 2020 yield estimated underreporting factor for cases at 13–22 (deaths: 3–7) on January 23, 2021, which are again consistent with the latest (fifth) round of serosurveys for Delhi (done during January 15–23, 2021) with an estimated 56.13% IgG antibody prevalence, yielding an estimated range for the underreporting factor for cases at 17–21. Together, these updated estimates imply approximately 92–96% cases in Delhi remained unreported (January 23, 2021). Such model-based estimates, updated with latest data, provide a viable alternative to repeated resource-intensive serosurveys for tracking unreported cases and deaths and gauging the true extent of the pandemic.

scholarly journals A Quantitative Comparison of Multispectral Refraction Topography and Autorefractometer in Young Adults

2021 ◽  
Vol 8 ◽  
Author(s):  
Yunru Liao ◽  
Zhenlan Yang ◽  
Zijing Li ◽  
Rui Zeng ◽  
Jing Wang ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Cross Sectional Study ◽  
Cross Sectional ◽  
High Agreement ◽  
Intraclass Correlation Coefficients ◽  
Repeatability And Reproducibility ◽  
The Mean

Purpose: Purpose of this study is to evaluate the measuring consistency of central refraction between multispectral refraction topography (MRT) and autorefractometry.Methods: This was a descriptive cross-sectional study including subjects in Sun Yat-sen Memorial Hospital from September 1, 2020, to December 31, 2020, ages 20 to 35 years with a best corrected visual acuity of 20/20 or better. All patients underwent cycloplegia, and the refractive status was estimated with autorefractometer, experienced optometrist and MRT. We analyzed the central refraction of the autorefractometer and MRT. The repeatability and reproducibility of values measured using both devices were evaluated using intraclass correlation coefficients (ICCs).Results: A total of 145 subjects ages 20 to 35 (290 eyes) were enrolled. The mean central refraction of the autorefractometer was −4.69 ± 2.64 diopters (D) (range −9.50 to +4.75 D), while the mean central refraction of MRT was −4.49 ± 2.61 diopters (D) (range −8.79 to +5.02 D). Pearson correlation analysis revealed a high correlation between the two devices. The intraclass correlation coefficient (ICC) also showed high agreement. The intrarater and interrater ICC values of central refraction were more than 0.90 in both devices and conditions. At the same time, the mean central refraction of experienced optometrist was −4.74 ± 2.66 diopters (D) (range −9.50 to +4.75D). The intra-class correlation coefficient of central refraction measured by MRT and subjective refraction was 0.939.Conclusions: Results revealed that autorefractometry, experienced optometrist and MRT show high agreement in measuring central refraction. MRT could provide a potential objective method to assess peripheral refraction.

Evaluation of Cultural Intelligence in Staff of Industries, Mines and Trades Organizations - Case Study

2017 ◽  
pp. 429-438
Author(s):  
Mohammad Abdolshah ◽  
Baranak Geranfar ◽  
Eisa Akbari ◽  
Jalil Vaziri
Keyword(s):  
Organizational Culture ◽  
Correlation Coefficient ◽  
Cultural Intelligence ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Statistical Population ◽  
Key Competencies ◽  
The Relationship ◽  
Personnel Department

This article examines one of the key competencies of the 21st century known as cultural intelligence (CQ). This study investigates the relationship between CQ, organizational culture, and the effectiveness of staff in the industry, mine, and trade organizations of Semnan province in Iran. Using correlational analysis, the statistical population includes a total of 103 people from 141 employees based on personnel department documents. Three questionnaires were used to measure the variables and descriptive and deductive statistics were applied to evaluate and analyze the data. The Pearson correlation coefficient and multivariate regression were used in deductive statistics to obtain the results. The findings show there is a significant relationship between CQ, organizational culture, and effectiveness. Among four cultural intelligence factors, only the knowledge of CQ can predict the effectiveness. The calculated correlation coefficient indicates that the creativity factors and communication pattern have the highest correlation coefficients.

scholarly journals Prospective Study Comparing Deep Throat Saliva With Other Respiratory Tract Specimens in the Diagnosis of Novel Coronavirus Disease 2019

2020 ◽  
Vol 222 (10) ◽  
pp. 1612-1619 ◽  
Author(s):  
Christopher K C Lai ◽  
Zigui Chen ◽  
Grace Lui ◽  
Lowell Ling ◽  
Timothy Li ◽  
...  
Keyword(s):  
Prospective Study ◽  
Pearson Correlation ◽  
False Negative ◽  
False Negative Rate ◽  
Productive Cough ◽  
Rt Pcr ◽  
Virus Shedding ◽  
Positive Rate ◽  
Novel Coronavirus ◽  
Rna Concentration

Abstract Background Self-collected specimens have been advocated to avoid infectious exposure to healthcare workers. Self-induced sputum in those with a productive cough and saliva in those without a productive cough have been proposed, but sensitivity remains uncertain. Methods We performed a prospective study in 2 regional hospitals in Hong Kong. Results We prospectively examined 563 serial samples collected during the virus shedding periods of 50 patients: 150 deep throat saliva (DTS), 309 pooled-nasopharyngeal (NP) and throat swabs, and 104 sputum. Deep throat saliva had the lowest overall reverse-transcriptase polymerase chain reaction (RT-PCR)-positive rate (68.7% vs 89.4% [sputum] and 80.9% [pooled NP and throat swabs]) and the lowest viral ribonucleic acid (RNA) concentration (mean log copy/mL 3.54 vs 5.03 [sputum] and 4.63 [pooled NP and throat swabs]). Analyses with respect to time from symptom onset and severity also revealed similar results. Virus yields of DTS correlated with that of sputum (Pearson correlation index 0.76; 95% confidence interval, 0.62–0.86). We estimated that the overall false-negative rate of DTS could be as high as 31.3% and increased 2.7 times among patients without sputum. Conclusions Deep throat saliva produced the lowest viral RNA concentration and RT-PCR-positive rate compared with conventional respiratory specimens in all phases of illness. Self-collected sputum should be the choice for patients with sputum.

Assessing the Motor Status Score: A Scale for the Evaluation of Upper Limb Motor Outcomes in Patients after Stroke

2002 ◽  
Vol 16 (3) ◽  
pp. 283-289 ◽  
Author(s):  
Mark Ferraro ◽  
Jennifer Hogan Demaio ◽  
Jennifer Krol ◽  
Chris Trudell ◽  
Keren Rannekleiv ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Upper Limb ◽  
Pearson Correlation ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Pearson Correlation Coefficient ◽  
Maximum Score ◽  
Intraclass Correlation Coefficients ◽  
Upper Extremity Impairment ◽  
Motor Outcomes

The Motor Status Scale (MSS) measures shoulder, elbow (maximum score = 40), wrist, hand, and finger movements (maximum score = 42), and expands the measurement of upper extremity impairment and disability provided by the Fugl-Meyer (FM) score. This work examines the interrater reliability and criterion validity of the MSS performed in patients admitted to a rehabilitation hospital 21 ± 4 days after stroke. Using the MSS and the FM, 7 occupational therapists masked to each other’s judgments, evaluated 12 consecutive patients with stroke. Two therapists evaluated 6 additional patients on consecutive days. Intraclass correlation coefficients were significant for each group of raters for the shoulder/elbow and for the wrist/hand (P < 0.0001); test-retest measures were also significant for the shoulder/elbow (Pearson correlation coefficient r = 0.99, P < 0.004) and for the wrist/hand (Pearson correlation coefficient r = 0.99, P < 0.003). The internal item consistency for the overall MSS was significant (Cronbach alpha = 0.98, P < 0.0001). Finally the correlation between the MSS and the FM (R 2 = 0.964) was significant (P < 0.0001). The MSS affords a reliable and valid assessment of upper limb impairment and disability following stroke.

scholarly journals Measurement of flow harmonics correlations with mean transverse momentum in lead–lead and proton–lead collisions at $$\sqrt{s_{\mathrm{NN}}} = 5.02~\hbox {TeV}$$ with the ATLAS detector

2019 ◽  
Vol 79 (12) ◽  
Author(s):  
G. Aad ◽  
◽  
B. Abbott ◽  
D. C. Abbott ◽  
A. Abed Abud ◽  
...  
Keyword(s):  
Charged Particle ◽  
Transverse Momentum ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Gluon Plasma ◽  
Pearson Correlation Coefficient ◽  
Order Flow ◽  
Momentum Range ◽  
Ion Collisions

AbstractTo assess the properties of the quark–gluon plasma formed in ultrarelativistic ion collisions, the ATLAS experiment at the LHC measures a correlation between the mean transverse momentum and the flow harmonics. The analysis uses data samples of lead–lead and proton–lead collisions obtained at the centre-of-mass energy per nucleon pair of 5.02 TeV, corresponding to total integrated luminosities of $$22~\upmu \text {b}^{-1}$$22μb-1 and $$28~\text {nb}^{-1}$$28nb-1, respectively. The measurement is performed using a modified Pearson correlation coefficient with the charged-particle tracks on an event-by-event basis. The modified Pearson correlation coefficients for the 2nd-, 3rd-, and 4th-order flow harmonics are measured in the lead–lead collisions as a function of event centrality quantified as the number of charged particles or the number of nucleons participating in the collision. The measurements are performed for several intervals of the charged-particle transverse momentum. The correlation coefficients for all studied harmonics exhibit a strong centrality evolution, which only weakly depends on the charged-particle momentum range. In the proton–lead collisions, the modified Pearson correlation coefficient measured for the 2nd-order flow harmonics shows only weak centrality dependence. The lead-lead data is qualitatively described by the predictions based on the hydrodynamical model.

scholarly journals Machine Learning Assisted Cervical Cancer Detection

2021 ◽  
Vol 9 ◽  
Author(s):  
Mavra Mehmood ◽  
Muhammad Rizwan ◽  
Michal Gregus ml ◽  
Sidra Abbas
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Mean Squared Error ◽  
Medical Center ◽  
Pearson Correlation ◽  
False Negative ◽  
Hybrid Approach ◽  
False Negative Rate ◽  
Machine Learning Algorithms ◽  
Screening Programs

Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named CervDetect that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. CervDetect uses Pearson correlation between input variables as well as with the output variable to pre-process the data. CervDetect uses the random forest (RF) feature selection technique to select significant features. Finally, CervDetect uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that CervDetect accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100%.

scholarly journals Methods of Estimating Correlation Coefficients in the Presence of Influential Outlier(s)

2021 ◽  
Vol 4 (3) ◽  
pp. 157-185
Author(s):  
Etaga H.O. ◽  
Okoro I. ◽  
Aforka K.F. ◽  
Ngonadi L.O.
Keyword(s):  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pearson Correlation Coefficient ◽  
Spearman Correlation ◽  
Correlation Methods ◽  
Linear Relationships ◽  
Median Correlation ◽  
Standing Problem ◽  
Research Analysis

Correlation methods are indispensable in the study of the linear relationship between two variables. However, many researchers often adopt inappropriate correlation methods in the study of linear relationships which usually leads to unreliable results. Recurrently, most researchers ignorantly employ the Pearson method in a dataset that contained outliers, instead of more appropriate correlation methods such as Spearman, Kendall Tau, Median and Quadrant which might be suitable in the calculation of correlation coefficient in the presence of influential outliers. It is noted that the accuracy of estimation of correlation coefficients under outliers has been a long-standing problem for methodological researchers. This is due to low knowledge of correlation methods and their assumptions which have led to inappropriate application of correlation methods in research analysis. Five different methods of estimating correlation coefficients in the presence of influential outlier (contaminated data) were considered: Pearson Correlation Coefficient, Spearman Correlation Coefficient, Kendall Tau Correlation Coefficient, Median Correlation Coefficient and Quadrant Correlation Coefficient.

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