» Articles » PMID: 33195884

Predictive Modeling of COVID-19 Death Cases in Pakistan

Overview
Date 2020 Nov 16
PMID 33195884
Citations 5
Authors
Affiliations
Soon will be listed here.
Abstract

Background: The world is presently facing the challenges posed by COVID-19 (2019-nCoV), especially in the public health sector, and these challenges are dangerous to both health and life. The disease results in an acute respiratory infection that may result in pain and death. In Pakistan, the disease curve shows a vertical trend by almost 256K established cases of the diseases and 6035 documented death cases till August 5, 2020.

Objective: The primary purpose of this study is to provide the statistical model to predict the trend of COVID-19 death cases in Pakistan. The age and gender of COVID-19 victims were represented using a descriptive study.

Method: ology: Three regression models, which include Linear, logarithmic, and quadratic, were employed in this study for the modelling of COVID-19 death cases in Pakistan. These three models were compared based on R, Adjusted R, AIC, and BIC criterions. The data utilized for the modelling was obtained from the National Institute of Health of Pakistan from February 26, 2020 to August 5, 2020.

Conclusion: The finding deduced after the prediction modelling is that the rate of mortality would decrease by the end of October. The total number of deaths will reach its maximum point; then, it will gradually decrease. This indicates that the curve of total deaths will continue to be flat, i.e., it will shift to be constant, which is also the upper bound of the underlying function of absolute death.

Citing Articles

Evaluation of clinical and laboratory characteristics of dengue viral infection and risk factors of dengue hemorrhagic fever: a multi-center retrospective analysis.

Riaz M, Harun S, Mallhi T, Khan Y, Butt M, Husain A BMC Infect Dis. 2024; 24(1):500.

PMID: 38760732 PMC: 11102246. DOI: 10.1186/s12879-024-09384-z.


PulmoNet: a novel deep learning based pulmonary diseases detection model.

Abdulahi A, Ogundokun R, Adenike A, Shah M, Ahmed Y BMC Med Imaging. 2024; 24(1):51.

PMID: 38418987 PMC: 10903074. DOI: 10.1186/s12880-024-01227-2.


On the Implementation of the Artificial Neural Network Approach for Forecasting Different Healthcare Events.

Alshanbari H, Iftikhar H, Khan F, Rind M, Ahmad Z, El-Bagoury A Diagnostics (Basel). 2023; 13(7).

PMID: 37046528 PMC: 10093335. DOI: 10.3390/diagnostics13071310.


Panel Associations Between Newly Dead, Healed, Recovered, and Confirmed Cases During COVID-19 Pandemic.

Guan M J Epidemiol Glob Health. 2021; 12(1):40-55.

PMID: 34893956 PMC: 8664669. DOI: 10.1007/s44197-021-00019-z.


Zero-Inflated Time Series Modelling of COVID-19 Deaths in Ghana.

Tawiah K, Iddrisu W, Asosega K J Environ Public Health. 2021; 2021:5543977.

PMID: 34012470 PMC: 8086432. DOI: 10.1155/2021/5543977.

References
1.
Li Q, Guan X, Wu P, Wang X, Zhou L, Tong Y . Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia. N Engl J Med. 2020; 382(13):1199-1207. PMC: 7121484. DOI: 10.1056/NEJMoa2001316. View

2.
Ghanbarzadeh Lak M, Sabour M, Amiri A, Rabbani O . Application of quadratic regression model for Fenton treatment of municipal landfill leachate. Waste Manag. 2012; 32(10):1895-902. DOI: 10.1016/j.wasman.2012.05.020. View

3.
Hou C, Chen J, Zhou Y, Hua L, Yuan J, He S . The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID-19): A well-mixed SEIR model analysis. J Med Virol. 2020; 92(7):841-848. DOI: 10.1002/jmv.25827. View

4.
Ogundokun R, Lukman A, Kibria G, Awotunde J, Aladeitan B . Predictive modelling of COVID-19 confirmed cases in Nigeria. Infect Dis Model. 2020; 5:543-548. PMC: 7428444. DOI: 10.1016/j.idm.2020.08.003. View

5.
Chadsuthi S, Modchang C, Lenbury Y, Iamsirithaworn S, Triampo W . Modeling seasonal leptospirosis transmission and its association with rainfall and temperature in Thailand using time-series and ARIMAX analyses. Asian Pac J Trop Med. 2012; 5(7):539-46. DOI: 10.1016/S1995-7645(12)60095-9. View