Predictive Modeling for Public Health

Most recently emerged pneumonia of unknown cause named as Covid-19 has a devastating impact on public health and economy surpassing its counterparts in morbidity and mortality. Asymptomatic spread appears to be prevalent in China from where it is originated lacking clear and precise understanding of the transmission dynamics. Precautionary approach on certain ethnic food from mammalian sources like bats and its possible transmission source has been presented. Biosecurity measures should also be considered. Application of accurate predictive modeling in alleviation of communicable diseases has also been discussed. Covid-19 outbreak seems to be an alarming lesson to global community to start preparing for an open transparent coordinated action by all relevant stakeholders.

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Leveraging Data Mining Strategies for Predictive Modeling of COVID-19 Outbreak and its Socio-economic Impacts: A Simulation for Public Health in Iran

10.1109/tensymp52854.2021.9550819 ◽

2021 ◽

Author(s):

Areeba Jamil ◽

Abu ul Hassan S. Rana ◽

Muhammad Fazal Ijaz ◽

Areej Shahid

Keyword(s):

Public Health ◽

Data Mining ◽

Predictive Modeling ◽

Economic Impacts

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Evolution of Diagnostic Methods and Prevalence Detection of COVID-19: A Review

10.5772/intechopen.99241 ◽

2021 ◽

Author(s):

Hemant Bherwani

Keyword(s):

Public Health ◽

Information System ◽

Geographic Information System ◽

Clinical Research ◽

Predictive Modeling ◽

Diagnostic Techniques ◽

Geographic Information ◽

Diagnostic Methods ◽

Or Management ◽

Affected Area

In clinical, research, and public health laboratories, many diagnostic methods are used to detect the coronavirus. Some tests directly detect infection by detecting viral RNA, while others detect the disease indirectly by detecting host antibodies. Several studies on SARS-CoV-2 diagnostic methods have found varying throughput, batching capacity, infrastructure requirements, analytical efficiency, and turnaround times ranging from minutes to hours. Serosurvey studies have been conducted for antibodies to understand, model, and forecast the prevalence of the disease in an area. While on the research and predictive modeling side, sampling and analysis of sewage have been conducted to determine the number of RNA copies and hence the prevalence. Certain studies indicate usefulness of GIS (Geographic Information System) for understanding the pervasiveness of COVID-19 in an area as well. The current chapter deals with the evolution of diagnostic techniques for COVID-19 and discusses use of specific techniques and appropriateness in certain specified conditions. It also focuses on understanding the methods used for assessing the prevalence of COVID-19 in a particular region to extract mitigative strategies from it, either by prediction or management of the affected area.

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COVID-19 from Food Safety and Biosecurity Perspective

The Open Food Science Journal ◽

10.2174/1874256402012010001 ◽

2020 ◽

Vol 12 (1) ◽

pp. 1-2 ◽

Cited By ~ 1

Author(s):

Samara Bin Salem ◽

Premanandh Jagadeesan

Keyword(s):

Public Health ◽

Food Safety ◽

Infectious Diseases ◽

Predictive Modeling ◽

Transmission Dynamics ◽

Morbidity And Mortality ◽

Global Community ◽

Precautionary Approach ◽

Precise Understanding ◽

Ethnic Food

Most recently emerged pneumonia of unknown cause named COVID-19 has a devastating impact on public health and economy surpassing its counterparts in morbidity and mortality. Asymptomatic spread appears to be prevalent in China from where it is originated, lacking a clear and precise understanding of the transmission dynamics. Precautionary approach on certain ethnic food from mammalian sources like bats and its possible transmission source has been presented. Biosecurity measures should also be considered. The application of accurate predictive modeling in the alleviation of infectious diseases has also been discussed. The COVID-19 outbreak seems to be an alarming lesson to the global community to start preparing for an open, transparent, and coordinated action by all relevant stakeholders.

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TEST SEIRCRT |ˈsəːkrɪt |: For the Health of Our Communities

Journal of Black Studies ◽

10.1177/0021934720964579 ◽

2020 ◽

pp. 002193472096457

Author(s):

Jennifer Mills

Keyword(s):

Public Health ◽

Health Disparities ◽

Critical Race Theory ◽

Predictive Modeling ◽

Early Stage ◽

Race Theory ◽

Seir Model ◽

Key Characteristics ◽

African Canadian ◽

Insight Into

The 2019 coronavirus disease (COVID-19) cases that are being confirmed in Canada provide an opportunity to expand the epidemic model for the simulation of disease infection spread: Susceptible- Exposed-Infectious-Recovered (SEIR). This paper develops a SEIRCRT |ˈsəːkrɪt | model that integrates the Institute for Disease Modeling’s SEIR model and Critical Race Theory (CRT) to answer the question: What is in a SEIRCRT model? SEIRCRT provides a basic modeling structure from a CRT lens to simulate, predict and forecast COVID-19 cases, comorbidities affecting African Canadians, and deaths through predictive modeling. Knowledge of SEIRCRT is critical to characterize the severity of COVID-19 in this early stage. To this end, the purpose of this paper is to describe SEIRCRT’s model and summarize its key characteristics. SEIRCRT as a public health framework provides insight into the conclusions drawn about race and COVID-19, and expands our thinking about what health disparities mean for African Canadian communities.

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Evaluating Short-term Forecast among Different Epidemiological Models under a Bayesian Framework

10.1101/2020.10.11.20210971 ◽

2020 ◽

Author(s):

Qiwei Li ◽

Tejasv Bedi ◽

Guanghua Xiao ◽

Yang Xie

Keyword(s):

Public Health ◽

Predictive Modeling ◽

Growth Models ◽

Sir Model ◽

Global Scale ◽

Bayesian Framework ◽

Epidemiological Models ◽

Short Term ◽

Predictive Capacity ◽

Term Forecast

AbstractForecasting of COVID-19 daily confirmed cases has been one of the several challenges posed on the governments and health sectors on a global scale. To facilitate informed public health decisions, the concerned parties rely on short-term daily projections generated via predictive modeling. We calibrate stochastic variants of growth models and the standard SIR model into one Bayesian framework to evaluate their short-term forecasts. In summary, it was noted that none of the models proved to be golden standards across all the regions in their entirety, while all outperformed ARIMA in a predictive capacity as well as in terms of interpretability.

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