COVID-19 and India: what next?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramesh Behl ◽  
Manit Mishra
Keyword(s):  
Predictive Modeling ◽  
Tamil Nadu ◽  
Data Science ◽  
Reproduction Number ◽  
Objective Assessment ◽  
Case Fatality ◽  
Susceptible Population ◽  
Content Type ◽  
Effective Contact ◽  
Growth Method

Purpose The study aims to carry out predictive modeling based on publicly available COVID-19 data for the duration April 01, 2020 to June 20, 2020 pertaining to India and five of its most infected states: Maharashtra, Tamil Nadu, Delhi, Gujarat and Rajasthan. Design/methodology/approach The study leverages the susceptible, infected, recovered and dead (SIRD) epidemiological framework for predictive modeling. The basic reproduction number R0 is derived by an exponential growth method using RStudio package R0. The differential equations reflecting the SIRD model have been solved using Python 3.7.4 on the Jupyter Notebook platform. For visualization, Python Matplotlib 3.2.1 package is used. Findings The study offers insights on peak-date, peak number of COVID-19 infections and end-date pertaining to India and five of its states. Practical implications The results subtly indicate toward the amount of effort required to completely eliminate the infection. It could be leveraged by the political leadership and industry doyens for economic policy planning and execution. Originality/value The emergence of a clear picture about COVID-19 lifecycle is impossible without integrating data science algorithms and epidemiology theoretical framework. This study amalgamates these two disciplines to undertake predictive modeling based on COVID-19 data from India and five of its states. Population-specific granular and objective assessment of key parameters such as reproduction number (R0), susceptible population (S), effective contact rate (ß) and case-fatality rate (s) have been used to generate a visualization of COVID-19 lifecycle pattern for a critically affected population.

scholarly journals A comprehensive estimation of country-level basic reproduction numbers R0 for COVID-19: Regime regression can automatically estimate the end of the exponential phase in epidemic data

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254145
Author(s):  
John L. Spouge
Keyword(s):  
Exponential Growth ◽  
Age Groups ◽  
Infectious Agent ◽  
Exponential Phase ◽  
Exploratory Analysis ◽  
Susceptible Population ◽  
Country Level ◽  
Effective Contact ◽  
Reproduction Numbers ◽  
Growth Method

In a compartmental epidemic model, the initial exponential phase reflects a fixed interaction between an infectious agent and a susceptible population in steady state, so it determines the basic reproduction number R0 on its own. After the exponential phase, dynamic complexities like societal responses muddy the practical interpretation of many estimated parameters. The computer program ARRP, already available from sequence alignment applications, automatically estimated the end of the exponential phase in COVID-19 and extracted the exponential growth rate r for 160 countries. By positing a gamma-distributed generation time, the exponential growth method then yielded R0 estimates for COVID-19 in 160 countries. The use of ARRP ensured that the R0 estimates were largely freed from any dependency outside the exponential phase. The Prem matrices quantify rates of effective contact for infectious disease. Without using any age-stratified COVID-19 data, but under strong assumptions about the homogeneity of susceptibility, infectiousness, etc., across different age-groups, the Prem contact matrices also yielded theoretical R0 estimates for COVID-19 in 152 countries, generally in quantitative conflict with the R0 estimates derived from the exponential growth method. An exploratory analysis manipulating only the Prem contact matrices reduced the conflict, suggesting that age-groups under 20 years did not promote the initial exponential growth of COVID-19 as much as other age-groups. The analysis therefore supports tentatively and tardily, but independently of age-stratified COVID-19 data, the low priority given to vaccinating younger age groups. It also supports the judicious reopening of schools. The exploratory analysis also supports the possibility of suspecting differences in epidemic spread among different age-groups, even before substantial amounts of age-stratified data become available.

An Updated Review on COVID-19

2020 ◽  
Vol 20 ◽  
Author(s):  
Mahmoud Ahmed Ebada ◽  
Ahmed Wadaa Allah ◽  
Eshak Bahbah ◽  
Ahmed Negida
Keyword(s):  
Screening Tool ◽  
Distress Syndrome ◽  
Reproduction Number ◽  
Case Fatality ◽  
Current Data ◽  
Future Research ◽  
Rt Pcr ◽  
Viral Structure ◽  
Discharge Criteria ◽  
Serology Testing

: Coronavirus Disease (COVID-19) pandemic has affected more than seven million individuals in 213 countries worldwide with a basic reproduction number ranging from 1.5 to 3.5 and an estimated case fatality rate ranging from 2% to 7%. A substantial proportion of COVID-19 patients are asymptomatic; however, symptomatic cases might present with fever, cough, and dyspnoea or severe symptoms up to acute respiratory distress syndrome. Currently, RNA RT-PCR is the screening tool, while bilateral chest CT is the confirmatory clinical diagnostic test. Several drugs have been repurposed to treat COVID-19, including chloroquine or hydroxychloroquine with or without azithromycin, lopinavir/ritonavir combination, remdesivir, favipiravir, tocilizumab, and EIDD-1931. Recently, Remdesivir gained FDA emergency approval based on promising early findings from the interim analysis of 1063 patients. The recently developed serology testing for SARSCoV-2 antibodies opened the door to evaluate the actual burden of the disease and to determine the rate of the population who have been previously infected (or developed immunity). This review article summarizes current data on the COVID-19 pandemic starting from the early outbreak, viral structure and origin, pathogenesis, diagnosis, treatment, discharge criteria, and future research.

scholarly journals COVID-19 in comparison with other emerging viral diseases: risk of geographic spread via travel

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
A. Wilder-Smith
Keyword(s):  
Reproduction Number ◽  
Emerging Infectious Diseases ◽  
Case Fatality ◽  
The Elderly ◽  
Air Travel ◽  
Viral Diseases ◽  
Fatality Rate ◽  
The Past ◽  
Geographic Spread ◽  
Emerging Viral Diseases

Abstract Purpose of review The COVID-19 pandemic poses a major global health threat. The rapid spread was facilitated by air travel although rigorous travel bans and lockdowns were able to slow down the spread. How does COVID-19 compare with other emerging viral diseases of the past two decades? Recent findings Viral outbreaks differ in many ways, such as the individuals most at risk e.g. pregnant women for Zika and the elderly for COVID-19, their vectors of transmission, their fatality rate, and their transmissibility often measured as basic reproduction number. The risk of geographic spread via air travel differs significantly between emerging infectious diseases. Summary COVID-19 is not associated with the highest case fatality rate compared with other emerging viral diseases such as SARS and Ebola, but the combination of a high reproduction number, superspreading events and a globally immunologically naïve population has led to the highest global number of deaths in the past 20 decade compared to any other pandemic.

Malaysian Enhanced Movement Control Order (EMCO): a unique and impactful approach to combating pandemic COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohd Rohaizat Hassan ◽  
Mohd Nizam Subahir ◽  
Linayanti Rosli ◽  
Shaharom Nor Azian Che Mat Din ◽  
Nor Zaher Ismail ◽  
...  
Keyword(s):  
Total Population ◽  
Case Fatality ◽  
Movement Control ◽  
Essential Elements ◽  
Effective Measure ◽  
Content Type ◽  
Wedding Ceremony ◽  
Control Order ◽  
Targeted Approach ◽  
Close Contacts

PurposeThe paper highlights the process-handling during the Enhanced Movement Control Order (EMCO) in combating pandemic COVID-19 in Malaysia.Design/methodology/approachMalaysia first issued an EMCO following a cluster that involved a religious gathering. The EMCO was issued to lockdown the area, undertake screening, treat positive cases and quarantine their close contacts. Active case detection and mass sampling were the main activities involving the population in both zones.FindingsOne hundred ninety-three confirmed COVID-19 cases were identified from the total population of 2,599. Of these cases, 99.5% were Malaysians, 31.7% were aged >60 years and all four deaths (Case Fatality Rate, 2.1%) were elderly people with comorbidities. One hundred and one cases (52.3%) were asymptomatic, of which 77 (77%) were detected during mass sampling. The risk factors contributing to the outbreak were contacts that had attended the religious gathering, regular mosque congregants, wedding ceremony attendees and close household contacts. Malaysia implemented an effective measure in the form of the EMCO to contain the COVID-19 outbreak, where the last cases were reported 16 days before the EMCO was lifted.Originality/valueThe residents’ compliance and inter-agency cooperation were essential elements to the success of the EMCO. A targeted approach using an EMCO should be implemented in a future pandemic.

Assessing the Severity of Acute Pancreatitis with CT Severity Index and Other Conventional Methods in a Tertiary Care Hospital in Tamil Nadu, India – A Comparative Cross-Sectional Study

2021 ◽  
Vol 8 (41) ◽  
pp. 3541-3546
Author(s):  
Jayaprakash Subramani ◽  
Rajesh Prabhu ◽  
Jagadeesapandian Palpandi
Keyword(s):  
Computed Tomography ◽  
Acute Pancreatitis ◽  
Predictive Value ◽  
Tamil Nadu ◽  
Objective Assessment ◽  
Tertiary Care ◽  
Severity Index ◽  
Apache Ii ◽  
Care Hospital ◽  
Male Predominance

BACKGROUND Acute pancreatitis is not uncommon in surgical practice with variable clinical presentation. Because of its potential notable catastrophic complications, it is mandatory to assess the severity at the earliest. In recent times, the decision making in the management is quite difficult due to its complications and outcome. So, an objective assessment of severity based on clinical and laboratory scoring verses computed tomography (CT) severity is still debate, hence the need for study. The purpose of this study was to compare the efficiency of CT severity index verses APACHE II and Ranson criteria in predicting the severity of acute pancreatitis. METHODS A total number of 36 consecutive cases of acute pancreatitis who were admitted between January 2013 and December 2014 in Apollo Specialty Hospitals – Madurai were included in the study. Written informed consent was obtained from all study participants. RESULTS In our study, out of 36 patients, 30 (83.33 %) were males and 6 (16.66 %) were females. The sex distribution shows a clear male predominance. Most of the patients in the present study belonged to the middle age group. Alcohol was the most common cause accounting for 41.7 % of the cases followed by the billiary pathology. CT severity index was the superior tool for prediction of the prognosis and early complications. CONCLUSIONS When using contrast enhanced computed tomography, it was found that there was a significant correlation between the development of organ failure and severity of pancreatitis. The specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of Ranson and acute physiology and chronic health evaluation – II (APACHE II) at 48 hours of admission with acute pancreatitis does not correlate in determining the severity of acute pancreatitis. KEYWORDS Acute Pancreatitis, Severity Markers, CT Severity Index

A bibliometric review of International Marketing Review (IMR): past, present, and future

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Naveen Donthu ◽  
Satish Kumar ◽  
Debidutta Pattnaik ◽  
Neeraj Pandey
Keyword(s):  
Export Performance ◽  
Objective Assessment ◽  
International Marketing ◽  
Business Environment ◽  
Primary Objective ◽  
Global Business ◽  
Content Type ◽  
Bibliographic Data ◽  
Bibliometric Review

PurposeThe primary objective of this endeavour is to form a retrospective overview of the International Marketing Review (IMR) and map its way forward.Design/methodology/approachA range of bibliometric techniques has been employed to analyse the performance of IMR and its stakeholders, map the evolution of its thematic and intellectual structures and analyse the factors driving IMR's academic influence and impactFindingsIMR's academic contributions, influence and impact have grown progressively. The thematic structure of the journal has evolved into six clusters. Simultaneously, its research fronts have submerged to six bibliographic clusters, noted as marketing channels, cross-cultural impact on emerging markets, export performance, country of origin (COO), online consumers and global business environment. Among these, the first four are still evolving, suggesting scope for future submissions.Research limitations/implicationsThe limitation of this endeavour largely arises from its selection of bibliographic data being confined to Scopus.Originality/valueTo the best of the authors’ knowledge, this is the first objective assessment of the journal, useful to its authors, readers, reviewers and editorial board.

scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

scholarly journals A Review of AI and Data Science Support for Cancer Management

2020 ◽  
Author(s):  
E. Parimbelli ◽  
S. Wilk ◽  
R. Cornet ◽  
P. Sniatala ◽  
K. Sniatala ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Decision Support ◽  
Best Practices ◽  
Cancer Patients ◽  
Predictive Modeling ◽  
Data Science ◽  
Cancer Management ◽  
Research Challenges ◽  
Open Research

AbstractIntroductionThanks to improvement of care, cancer has become a chronic condition. But due to the toxicity of treatment, the importance of supporting the quality of life (QoL) of cancer patients increases. Monitoring and managing QoL relies on data collected by the patient in his/her home environment, its integration, and its analysis, which supports personalization of cancer management recommendations. We review the state-of-the-art of computerized systems that employ AI and Data Science methods to monitor the health status and provide support to cancer patients managed at home.ObjectiveOur main objective is to analyze the literature to identify open research challenges that a novel decision support system for cancer patients and clinicians will need to address, point to potential solutions, and provide a list of established best-practices to adopt.MethodsWe designed a review study, in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzing studies retrieved from PubMed related to monitoring cancer patients in their home environments via sensors and self-reporting: what data is collected, what are the techniques used to collect data, semantically integrate it, infer the patient’s state from it and deliver coaching/behavior change interventions.ResultsStarting from an initial corpus of 819 unique articles, a total of 180 papers were considered in the full-text analysis and 109 were finally included in the review. Our findings are organized and presented in four main sub-topics consisting of data collection, data integration, predictive modeling and patient coaching.ConclusionDevelopment of modern decision support systems for cancer needs to utilize best practices like the use of validated electronic questionnaires for quality-of-life assessment, adoption of appropriate information modeling standards supplemented by terminologies/ontologies, adherence to FAIR data principles, external validation, stratification of patients in subgroups for better predictive modeling, and adoption of formal behavior change theories. Open research challenges include supporting emotional and social dimensions of well-being, including PROs in predictive modeling, and providing better customization of behavioral interventions for the specific population of cancer patients.

scholarly journals Estimating upper-extremity function from kinematics in stroke patients following goal-oriented computer-based training

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Belén Rubio Ballester ◽  
Fabrizio Antenucci ◽  
Martina Maier ◽  
Anthony C. C. Coolen ◽  
Paul F. M. J. Verschure
Keyword(s):  
Data Science ◽  
Objective Assessment ◽  
Noise Model ◽  
Alternative Methods ◽  
Reaching Movements ◽  
Stroke Patients ◽  
Kinematic Data ◽  
Work Area ◽  
Wide Range ◽  
Computer Based

Abstract Introduction After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients’ hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model’s performance to estimate FM-UE scores reaches an accuracy of $$R^2$$ R 2 : 0.38 with an error ($$\sigma$$ σ : 12.8). Next, we evaluate its reliability ($$r=0.89$$ r = 0.89 for test-retest), longitudinal external validity ($$95\%$$ 95 % true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements ($$R^2$$ R 2 : 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory ($$R^2$$ R 2 : 0.40) and Barthel Index ($$R^2$$ R 2 : 0.35). Conclusions Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.

scholarly journals A study on the outbreak of dengue fever in a tertiary care children’s hospital in southern Tamil Nadu, India

2019 ◽  
Vol 6 (3) ◽  
pp. 1194
Author(s):  
Belgin Premkumar ◽  
Baburaj S. ◽  
Margaret Hepzibah N. ◽  
Misha K. P. ◽  
Binu Abraham
Keyword(s):  
Dengue Fever ◽  
Tamil Nadu ◽  
Tertiary Care ◽  
Case Fatality ◽  
Dengue Shock Syndrome ◽  
Viral Disease ◽  
Severe Dengue ◽  
Care Hospital ◽  
Clinical Criteria ◽  
Public Health Challenge

Background: Dengue fever is the most rapidly spreading mosquito-borne viral disease in the world.Incidence has increased 230-fold with increasing geographic expansion with potential for further spread. The rapidly expanding global footprint of dengue is a public health challenge with an economic burden. This study’s objective is to assess the outbreak of epidemic of dengue fever in a tertiary care children hospital and to describe their socio-demographic, clinical outcome and serological profile.Methods: It is an observational descriptive study conducted for a period of 1 year in less than 12 years old children in a tertiary care hospital at Southern Tamil Nadu.Results: Among the 360 children admitted with dengue fever, there were 198 boys (55%) and 162 (45%) were girls. Maximum incidence of dengue incidence was seen in infants less than 1 year (25%). The highest number of cases were admitted during September and October. The most common affected age group was less than 3 years with 179 (49%). Among the cases, 297 (82%) were of severe dengue which constitute dengue haemorrhagic fever-183(38%) and Dengue shock syndrome 114 (62%). Serological analysis showed NS1 Ag was positive in 144 children (40%), Dengue IgM was positive in 54 children (15%), both IgM and IgG positive in 126 children (35%) and IgG was positive in 36 children (10%). Out of the total children admitted with dengue fever, the case fatality was 0.5% (2 children).Conclusions: This study highlights the importance of WHO clinical criteria for early diagnosis of severe dengue. Moreover, the early and intensive management reduces the mortality significantly.

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