Literature DB >> 34006346

Mathematical Modeling and Covid-19 Forecast in Texas, USA: a prediction model analysis and the probability of disease outbreak.

Md Nazmul Hassan1,2, Md Shahriar Mahmud3, Kaniz Fatema Nipa1, Md Kamrujjaman4,5.   

Abstract

BACKGROUND: Response to the unprecedented COVID-19 outbreak needs to be augmented in Texas, USA, where the first 5 cases were reported on March 6, 2020, were rapidly followed by an exponential rise within the next few weeks. This study aimed to determine the ongoing trend and upcoming infection status of COVID-19 in county levels of Texas.
METHODS: Data were extracted from the following sources: published literature, surveillance, unpublished reports, and websites of Texas Department of State Health Services (DSHS), Natality report of Texas and WHO Coronavirus Disease (COVID-19) Dashboard. Four-compartment Susceptible-Exposed-Infectious-Removal (SEIR) mathematical model was used to estimate the current trend and future prediction of basic reproduction number and infection case in Texas. Since the basic reproduction number is not sufficient to predict the outbreak, we applied the Continuous-Time Markov Chain (CTMC) model to calculate the probability of the COVID-19 outbreak.
RESULTS: The estimated mean basic reproduction number of COVID-19 in Texas is predicted 2.65 by January 31, 2021. Our model indicated that the third wave might occur at the beginning of May of 2021, which will peak at the end of June 2021. This prediction may come true if the current spreading situation/level persists, i.e., no clinically effective vaccine is available,or this vaccination program fails for some reason in this area.
CONCLUSION: Our analysis indicates an alarming ongoing and upcoming infection rate of COVID-19 at county levels of Texas, thereby emphasizing promoting more coordinated and disciplined actions by both policymakers and the population to contain its devastating impact.

Entities:  

Keywords:  COVID-19; Continuous-Time Markov Chain (CTMC); Parameters; SEIR model; Texas

Year:  2021        PMID: 34006346     DOI: 10.1017/dmp.2021.151

Source DB:  PubMed          Journal:  Disaster Med Public Health Prep        ISSN: 1935-7893            Impact factor:   1.385


  5 in total

1.  Predicting the Willingness and Purchase of Travel Insurance During the COVID-19 Pandemic.

Authors:  Abdullah Al Mamun; Muhammad Khalilur Rahman; Qing Yang; Taslima Jannat; Anas A Salameh; Syed Ali Fazal
Journal:  Front Public Health       Date:  2022-07-04

2.  Dynamical analysis of coronavirus disease with crowding effect, and vaccination: a study of third strain.

Authors:  Ali Raza; Muhammad Rafiq; Jan Awrejcewicz; Nauman Ahmed; Muhammad Mohsin
Journal:  Nonlinear Dyn       Date:  2022-01-04       Impact factor: 5.741

3.  Sensitivity theorems of a model of multiple imperfect vaccines for COVID-19.

Authors:  Fernando Javier Aguilar-Canto; Ugo Avila-Ponce de León; Eric Avila-Vales
Journal:  Chaos Solitons Fractals       Date:  2022-01-31       Impact factor: 5.944

4.  Vaccine efficacy and SARS-CoV-2 control in California and U.S. during the session 2020-2026: A modeling study.

Authors:  Md Shahriar Mahmud; Md Kamrujjaman; Md Mashih Ibn Yasin Adan; Md Alamgir Hossain; Md Mizanur Rahman; Md Shahidul Islam; Muhammad Mohebujjaman; Md Mamun Molla
Journal:  Infect Dis Model       Date:  2021-11-27

5.  Methodology to estimate natural- and vaccine-induced antibodies to SARS-CoV-2 in a large geographic region.

Authors:  Stacia M DeSantis; Luis G León-Novelo; Michael D Swartz; Ashraf S Yaseen; Melissa A Valerio-Shewmaker; Yashar Talebi; Frances A Brito; Jessica A Ross; Harold W Kohl; Sarah E Messiah; Steve H Kelder; Leqing Wu; Shiming Zhang; Kimberly A Aguillard; Michael O Gonzalez; Onyinye S Omega-Njemnob; David Lakey; Jennifer A Shuford; Stephen Pont; Eric Boerwinkle
Journal:  PLoS One       Date:  2022-09-09       Impact factor: 3.752

  5 in total

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