Simulation of COVID-19 Epidemic from Potential Viral Loads in Saudi Arabian Wastewater Treatment Plants
medrxiv(2023)
摘要
SARS-CoV-2 is a contagious respiratory virus that has been discovered in sewage, human waste, and wastewater treatment facilities. Wastewater surveillance has been considered one of the lowest-cost means of testing for tracking the COVID-19 outbreak in communities. This paper highlights the dynamics of the virus’s infection, persistence, and occurrence in wastewater treatment plants. Our aim is to develop and implement a mathematical model to infer the epidemic dynamics from the possible density of SARS-CoV-2 viral load in wastewater. We present a log-normal model and fractional order of susceptible-exposed-infected-recovery (SEIR) epidemic model for predicting the spread of the COVID-19 disease from the wastewater data. We study the dynamic properties of the fractional order SEIR model with respect to the fractional ordered values. The model is used to comprehend how the coronavirus spreads through wastewater treatment plants in Saudi Arabia. Our modeling approach can help with wastewater surveillance for early prediction and cost-effective monitoring of the epidemic outbreak in a situation of low testing capacity. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research work through project number ISP22-6. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The data are available on the website of Ministry of Health, Saudi Arabia. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All the data used in this paper are available on the website of the Ministry of Health, Saudi Arabia (, accessed on 11 July 2023), and on the World Health Organization (WHO) website (, accessed on 11 July 2023).
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要