Epidemiology and Health

COVID-19: Health Statistics

Estimation of the reproduction number and early prediction of the COVID-19 outbreak in India using a statistical computing approach: Karthick Kanagarathinam, Kavaskar Sekar; Epidemiol Health. 2020;42:e2020028. Published online May 9, 2020; DOI: https://doi.org/10.4178/epih.e2020028

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Abstract

Coronavirus disease 2019 (COVID-19), which causes severe respiratory illness, has become a pandemic. The World Health Organization has declared it a public health crisis of international concern. We developed a susceptible, exposed, infected, recovered (SEIR) model for COVID-19 to show the importance of estimating the reproduction number (R0). This work is focused on predicting the COVID-19 outbreak in its early stage in India based on an estimation of R0. The developed model will help policymakers to take active measures prior to the further spread of COVID-19. Data on daily newly infected cases in India from March 2, 2020 to April 2, 2020 were to estimate R0 using the earlyR package. The maximum-likelihood approach was used to analyze the distribution of R0 values, and the bootstrap strategy was applied for resampling to identify the most likely R0 value. We estimated the median value of R0 to be 1.471 (95% confidence interval [CI], 1.351 to 1.592) and predicted that the new case count may reach 39,382 (95% CI, 34,300 to 47,351) in 30 days.

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REDACS: Regional emergency-driven adaptive cluster sampling for effective COVID-19 management
M. Stehlík, J. Kiseľák, A. Dinamarca, E. Alvarado, F. Plaza, F.A. Medina, S. Stehlíková, J. Marek, B. Venegas, A. Gajdoš, Y. Li, S. Katuščák, A. Bražinová, E. Zeintl, Y. Lu
Stochastic Analysis and Applications.2023; 41(3): 474. CrossRef
Application of Mathematical Modeling in Prediction of COVID-19 Transmission Dynamics
Ali AlArjani, Md Taufiq Nasseef, Sanaa M. Kamal, B. V. Subba Rao, Mufti Mahmud, Md Sharif Uddin
Arabian Journal for Science and Engineering.2022; 47(8): 10163. CrossRef
Interval type-2 fuzzy computational model for real time Kalman filtering and forecasting of the dynamic spreading behavior of novel Coronavirus 2019
Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
ISA Transactions.2022; 124: 57. CrossRef
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
K. Karthick, S. K. Aruna, Ravi Samikannu, Ramya Kuppusamy, Yuvaraja Teekaraman, Amruth Ramesh Thelkar, Deepika Koundal
Computational and Mathematical Methods in Medicine.2022; 2022: 1. CrossRef
Using logistic regression to develop a diagnostic model for COVID-19: A single-center study
Raoof Nopour, Mostafa Shanbehzadeh, Hadi Kazemi-Arpanahi
Journal of Education and Health Promotion.2022; 11(1): 153. CrossRef
Comparison of the Effective Reproduction Number (Rt) Estimation Methods of COVID-19 Using Simulation Data Based on Available Data from Iran, USA, UK, India, and Brazil
Ali Karamoozian, Abbas Bahrampour
Journal of Research in Health Sciences.2022; 22(3): e00559. CrossRef
Computational Approach For Real-Time Interval Type-2 Fuzzy Kalman Filtering and Forecasting via Unobservable Spectral Components of Experimental Data
Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
Journal of Control, Automation and Electrical Systems.2021; 32(2): 326. CrossRef
Determination of the Most Important Diagnostic Criteria for COVID-19: A Step forward to Design an Intelligent Clinical Decision Support System
Mostafa Shanbehzadeh, Raoof Nopour, Hadi kazemi-arpanahi
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Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation
Daiana Caroline dos Santos Gomes, Ginalber Luiz de Oliveira Serra
IEEE Journal of Biomedical and Health Informatics.2021; 25(3): 615. CrossRef
Comprehensive Study, Design and Economic Feasibility Analysis of Solar PV Powered Water Pumping System
K. Karthick, K. Jaiganesh, S. Kavaskar
Energy Engineering.2021; 118(6): 1887. CrossRef
Transmission Dynamics of the COVID-19 Epidemic at the District Level in India: Prospective Observational Study
Suman Saurabh, Mahendra Kumar Verma, Vaishali Gautam, Nitesh Kumar, Akhil Dhanesh Goel, Manoj Kumar Gupta, Pankaj Bhardwaj, Sanjeev Misra
JMIR Public Health and Surveillance.2020; 6(4): e22678. CrossRef

Original Articles

Forecasting and prediction of scorpion sting cases in Biskra province, Algeria, using a seasonal autoregressive integrated moving average model: Schehrazad Selmane, Mohamed L’Hadj; Epidemiol Health. 2016;38:e2016044. Published online October 14, 2016; DOI: https://doi.org/10.4178/epih.e2016044

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Abstract

OBJECTIVES
The aims of this study were to highlight some epidemiological aspects of scorpion envenomations, to analyse and interpret the available data for Biskra province, Algeria, and to develop a forecasting model for scorpion sting cases in Biskra province, which records the highest number of scorpion stings in Algeria.

METHODS

In addition to analysing the epidemiological profile of scorpion stings that occurred throughout the year 2013, we used the Box-Jenkins approach to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded scorpion sting cases in Biskra from 2000 to 2012.

RESULTS

The epidemiological analysis revealed that scorpion stings were reported continuously throughout the year, with peaks in the summer months. The most affected age group was 15 to 49 years old, with a male predominance. The most prone human body areas were the upper and lower limbs. The majority of cases (95.9%) were classified as mild envenomations. The time series analysis showed that a (5,1,0)×(0,1,1)12 SARIMA model offered the best fit to the scorpion sting surveillance data. This model was used to predict scorpion sting cases for the year 2013, and the fitted data showed considerable agreement with the actual data.

CONCLUSIONS

SARIMA models are useful for monitoring scorpion sting cases, and provide an estimate of the variability to be expected in future scorpion sting cases. This knowledge is helpful in predicting whether an unusual situation is developing or not, and could therefore assist decision-makers in strengthening the province’s prevention and control measures and in initiating rapid response measures.

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Epidemiological aspects of scorpion stings in Algeria: A monocentric retrospective study
Mohamed Amine Kerdoun
Toxicologie Analytique et Clinique.2022; 34(1): 4. CrossRef
Terrestrial venomous animals, the envenomings they cause, and treatment perspectives in the Middle East and North Africa
Timothy P. Jenkins, Shirin Ahmadi, Matyas A. Bittenbinder, Trenton K. Stewart, Dilber E. Akgun, Melissa Hale, Nafiseh N. Nasrabadi, Darian S. Wolff, Freek J. Vonk, Jeroen Kool, Andreas H. Laustsen, Jean-Philippe Chippaux
PLOS Neglected Tropical Diseases.2021; 15(12): e0009880. CrossRef
Time Series Analysis of Tuberculosis in Medea Province in Algeria
Mohamed L'HADJ, Schehrazad SELMANE
Journal of Engineering Technology and Applied Sciences.2019; 4(2): 85. CrossRef
Predictive determinants of scorpion stings in a tropical zone of south Iran: use of mixed seasonal autoregressive moving average model
Vahid Ebrahimi, Esmael Hamdami, Mohammad Djaefar Moemenbellah-Fard, Shahrokh Ezzatzadegan Jahromi
Journal of Venomous Animals and Toxins including Tropical Diseases.2017;[Epub] CrossRef

Long-term prediction of gastric cancer mortality in Korea.: Jin Gwack, Yunhee Choi, Hai Rim Shin, Yun Chul Hong, Keun Young Yoo; Korean J Epidemiol. 2005;27(1):163-172.

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Abstract PDF: Abstract
PURPOSE
This study was carried out to predict the mortality rate for gastric cancer up to 2020 in Korea with forecasting model.
METHODS
The trends of the age-adjusted mortality rate was calculated from 1983 to 2003 using the mortality data of the past 20 years in Korea, and projected up to the year of 2020 with log-linear models for each gender. The number of deaths from gastric cancer was calculated from the predicted mortality rate.
RESULTS
Age-adjusted mortality rates for gastric cancer per 100,000 persons were 32.13 in 1983, 23.95 in 1990, and 15.99 in 2003 for women, and 70.37, 58.74, 41.04 for men, respectively. The expected age-adjusted mortality rates for gastric cancer were 16.50 in 2005, 14.27 in 2010, and 10.66 in 2020 for women, and 39.14, 33.83, 25.28 for men, respectively. In contrast to this decreasing trend, it is predicted that mortality rates for those aged 75 or over would increase steadily. The predicted number of deaths from gastric cancer was 6,519 for women and 13,743 for men in 2020.
CONCLUSIONS
This study suggests that gastric cancer mortality rate would decrease continuously except for some aged groups. The declining trends in gastric cancer mortality are regarded as a result of lifestyle changes, improvements in screening methods and treatments. Strategies for aged groups should be developed in order to control increasing mortality rates.; Summary

A Forecasting Model for the Epidemic of Nationally Notifiable Communicable Diseases in Korea.: Yonggyu Park, Hyoung Ah Kim, Kyung Hwan Cho, Euichul Shin, Kwang Ho Meng; Korean J Epidemiol. 2000;22(2):108-115.

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Abstract PDF: Abstract
PURPOSE
S: The authors derived two forecasting models which can be used as objective tools for detecting epidemics and predicting the future frequencies of communicable diseases.
METHODS
In this study, regression analysis using trigonometric functions, Box and Jenkins's seasonal ARIMA model were applied to the monthly accumulated data of five nationally notifiable communicable diseases from January 1987 to December 1998 in Korea.
RESULTS
Between two forecasting models, seasonal ARIMA model gives more precise predicted frequencies than regression model in the neighborhood of the current time points and future time, but the regression model is better in overall agreement between the predicted and observed frequencies during 7 years(1992-1998).
CONCLUSIONS
These forecasting models can be usefully applied in deciding and carrying out a national policy in preventing epidemics in the future, and graphic program is much helpful to understand the present status of disease occurrence.; Summary

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