Spatial identification of component-based relative risks

This article aims at identifying the high risk provinces in Iraq using a finite Poisson mixture. Through this methodology, the levels of relative risk is determined through identifying the number of components. In this article we do not investigate spatial correlation among regions and assume that the levels of risk observed in different regions are independent each other. The estimation of the model parameters and the model selection are performed using the Bayesian approach which allow to allocate each province to an identified risk level. We consider the data of the Coronavirus disease (COVID-19) infections in 18 provinces in Iraq and determining the levels of relative risks of this pandemic. The results are spatially shown in map which illustrates that the best Bayesian model fitted the data is 3 components model (high, medium and low risk).

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Relative Risk Estimation for Human Leptospirosis Disease in Malaysia Based on Existing Models and Discrete Space-Time Stochastic Sir Model

Pertanika Journal of Science and Technology ◽

10.47836/pjst.29.2.20 ◽

2021 ◽

Vol 29 (2) ◽

Author(s):

Sufi Hafawati Ideris ◽

Muhammad Rozi Malim ◽

Norshahida Shaadan

Keyword(s):

Relative Risk ◽

High Risk ◽

Risk Estimation ◽

Information Criteria ◽

Control Measures ◽

Transmission Model ◽

Surveillance Systems ◽

Relative Risks ◽

Risk Estimates ◽

Risk Areas

The disease leptospirosis is known to be endemic in Malaysia, and it significantly impacts human wellbeing and the national economy. Current surveillance systems are based on morbidity and mortality leptospirosis national data from the Ministry of Health and remain inadequate due to the number of unreported and misdiagnosed cases. A robust surveillance system is needed to monitor temporal and spatial changes which yield improvements in terms of identifying high-risk areas and disease behaviour. The objective of this study is to identify high-risk areas by estimating relative risk using existing models which are the Standardized Morbidity Ratio (SMR), Poisson-gamma, log-normal, Besag, York and Mollié (BYM) and mixture models. An alternative model is also proposed which involves transmission systems and stochastic elements, namely the stochastic Susceptible-Infected-Removed (SIR) transmission model. This estimation of risk is expected to assist in the early detection of high-risk areas which can be applied as a strategy for preventive and control measures. The methodology in this paper applies relative risk estimates to determine the infection risk for all states in Malaysia based on monthly data from 2011 to 2018 using WinBUGS 1.4 software. The results of relative risks are discussed and presented in tables and graphs for each model to disclose high-risk areas across the country. Based on the risk estimates, different models used have different risk interpretations and drawbacks which make each model different in its use depending on the objectives of the study. As a result, the deviance information criteria (DIC) values obtained do not differ greatly from each expected risk which was estimated

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Bayesian Model Selection in Fisheries Management and Ecology

Journal of Fish and Wildlife Management ◽

10.3996/042019-jfwm-024 ◽

2019 ◽

Vol 10 (2) ◽

pp. 691-707

Author(s):

Jason C. Doll ◽

Stephen J. Jacquemin

Keyword(s):

Bayesian Inference ◽

Model Selection ◽

Fisheries Management ◽

Bayesian Model ◽

Bayes Factor ◽

Evaluation Process ◽

Information Criterion ◽

Bayesian Model Selection ◽

Model Parameters ◽

Inference Methods

Abstract Researchers often test ecological hypotheses relating to a myriad of questions ranging from assemblage structure, population dynamics, demography, abundance, growth rate, and more using mathematical models that explain trends in data. To aid in the evaluation process when faced with competing hypotheses, we employ statistical methods to evaluate the validity of these multiple hypotheses with the goal of deriving the most robust conclusions possible. In fisheries management and ecology, frequentist methodologies have largely dominated this approach. However, in recent years, researchers have increasingly used Bayesian inference methods to estimate model parameters. Our aim with this perspective is to provide the practicing fisheries ecologist with an accessible introduction to Bayesian model selection. Here we discuss Bayesian inference methods for model selection in the context of fisheries management and ecology with empirical examples to guide researchers in the use of these methods. In this perspective we discuss three methods for selecting among competing models. For comparing two models we discuss Bayes factor and for more complex models we discuss Watanabe–Akaike information criterion and leave-one-out cross-validation. We also describe what kinds of information to report when conducting Bayesian inference. We conclude this review with a discussion of final thoughts about these model selection techniques.

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Modelling Bluetongue Risk in Kazakhstan

10.21203/rs.3.rs-76231/v1 ◽

2020 ◽

Author(s):

Sarsenbay Abdrakmanov ◽

Beisembayev Kanatzhan ◽

Akmetzhan Sultanov ◽

Yersyn Mukhanbetkaliyev ◽

Ablaikhan Kadyrov ◽

...

Keyword(s):

Relative Risk ◽

Climatic Factors ◽

Model Parameters ◽

Biting Midges ◽

Density Estimates ◽

Relative Risks ◽

Livestock Density ◽

Low Levels ◽

Serious Disease ◽

And Control

Abstract Background: Bluetongue is a serious disease of ruminants transmitted by biting midges (Culicoides spp). Serological evidence from livestock and the presence of at least one vector competent spp of Culicoides suggests that transmission of bluetongue is possible and may have occurred in Kazakhstan. Methods: We estimated the relative risk of transmission using a mathematical model of the reproduction number R0 for bluetongue. This model depends on livestock density and climatic factors which affect vector density. Data on climate and livestock numbers from the 2778 local communities were used. This together with previously published model parameters was used to estimate R0 for each month of the year, which was rescaled to give a relative risk of transmission. These relative risks were mapped using kernal density estimates using R statistical software and mapping tools.Results: The results suggest that transmission of bluetongue in Kazakhstan is not possible in the winter from November to March. Assuming there are vector competent species of Culicoides endemic in Kazakhstan, then low levels of risk first appear in the south of Kazakhstan in April before spreading north and intensifying reaching maximum levels in northern Kazakhstan in August. The risk decline in September with only a low risk of transmission in October.Conclusion: These results should aid in surveillance efforts for the detection and control of bluetongue in Kazakhstan by indicating where and when outbreaks of bluetongue are most likely to occur.

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Modelling Bluetongue Risk in Kazakhstan

10.21203/rs.3.rs-97709/v1 ◽

2020 ◽

Author(s):

Sarsenbay Abdrakhmanov ◽

Beisembayev Kanatzhan ◽

Akmetzhan Sultanov ◽

Yersyn Mukhanbetkaliyev ◽

Ablaikhan Kadyrov ◽

...

Keyword(s):

Relative Risk ◽

Climatic Factors ◽

Model Parameters ◽

Biting Midges ◽

Density Estimates ◽

Relative Risks ◽

Livestock Density ◽

Low Levels ◽

Serious Disease ◽

And Control

Abstract Background: Bluetongue is a serious disease of ruminants transmitted by biting midges (Culicoides spp). Serological evidence from livestock and the presence of at least one vector competent spp of Culicoides suggests that transmission of bluetongue is possible and may have occurred in Kazakhstan. Methods: We estimated the relative risk of transmission using a mathematical model of the reproduction number R0 for bluetongue. This model depends on livestock density and climatic factors which affect vector density. Data on climate and livestock numbers from the 2466 local communities were used. This together with previously published model parameters was used to estimate R0 for each month of the year, which was rescaled to give a relative risk of transmission. These relative risks were mapped using kernal density estimates using R statistical software and mapping tools.Results: The results suggest that transmission of bluetongue in Kazakhstan is not possible in the winter from November to March. Assuming there are vector competent species of Culicoides endemic in Kazakhstan, then low levels of risk first appear in the south of Kazakhstan in April before spreading north and intensifying reaching maximum levels in northern Kazakhstan in August. The risk decline in September with only a low risk of transmission in October.Conclusion: These results should aid in surveillance efforts for the detection and control of bluetongue in Kazakhstan by indicating where and when outbreaks of bluetongue are most likely to occur.

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Adapting Gravity Model for Predicting the Risk of COVID -19 in Nigeria

Coronaviruses ◽

10.2174/2666796701999200915150039 ◽

2020 ◽

Vol 01 ◽

Author(s):

Olanrewaju Samson Olaitan ◽

Olowoporoku Oluwaseun

Keyword(s):

High Risk ◽

Gross Domestic Product ◽

Gravity Model ◽

Infection Risk ◽

Risk Level ◽

Ogun State ◽

Factors Influencing ◽

Government Data ◽

Housing Standards ◽

Very High

Background: It is against the background of the emerging incidence of coronavirus pandemic in Nigeria, and the need for its management that this study adapts gravity model for predicting the risk of the disease across states of the country. Methods: The paper relied on published government data on population, and gross domestic product, while the distance of town to the nearest international airport was also obtained. These data were log transformed and further used in the calculation of gravity scores for each state of the federation. Results: The study discovered that with the gravity score ranging from 2.942 to 4.437, all the states of the federation have the risk of being infected with the pandemic. Meanwhile Ogun State (4.837) has a very high risk of being infected with the disease. Other states with high risks are Oyo (4.312), Jigawa (4.235), Niger (4.148) and Katsina (4.083). However, Taraba State has the least infection risk of the pandemic in Nigeria. Factors influencing the risk level of the pandemic are proximity, porous boundary between states, and elitism. Conclusion: The paper advocates border settlement planning, review of housing standards, and advocacy for sanitation in different states. It therefore concludes that adequate urban planning in unison with economic and epidemiology techniques will provide a strong strategy for the management of the disease.

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Decision Aids for Generating Analytical Review Alternatives: The Impact of Goal Framing and Audit-Risk Level

Behavioral Research in Accounting ◽

10.2308/bria.2002.14.1.157 ◽

2002 ◽

Vol 14 (1) ◽

pp. 157-177 ◽

Cited By ~ 20

Author(s):

Jennifer M. Mueller ◽

John C. Anderson

Keyword(s):

High Risk ◽

Cognitive Load ◽

Information Search ◽

Decision Aid ◽

Decision Aids ◽

Risk Level ◽

Audit Risk ◽

Goal Framing ◽

Analytical Review ◽

The Impact

An auditor generating potential explanations for an unusual variance in analytical review may utilize a decision aid, which provides many explanations. However, circumstances of budgetary constraints and limited cognitive load deter an auditor from using a lengthy list of explanations in an information search. A two-way between-subjects design was created to investigate the effects of two complementary approaches to trimming down the lengthy list on the number of remaining explanations carried forward into an information search. These two approaches, which represent the same goal (reducing the list) but framed differently, are found to result in a significantly different number of remaining explanations, in both low- and high-risk audit environments. The results of the study suggest that the extent to which an auditor narrows the lengthy list of explanations is important to the implementation of decision aids in analytical review.

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Risk Factors for Pneumonia and Death in Adult Patients With Seasonal Influenza and Establishment of Prediction Scores: A Population-Based Study

Open Forum Infectious Diseases ◽

10.1093/ofid/ofab068 ◽

2021 ◽

Vol 8 (3) ◽

Author(s):

Koichi Miyashita ◽

Eiji Nakatani ◽

Hironao Hozumi ◽

Yoko Sato ◽

Yoshiki Miyachi ◽

...

Keyword(s):

High Risk ◽

Seasonal Influenza ◽

Prediction Models ◽

Validation Cohort ◽

Risk Groups ◽

Population Based ◽

Limited Data ◽

Population Based Study ◽

Relative Risks ◽

Primary Care Settings

Abstract Background Seasonal influenza remains a global health problem; however, there are limited data on the specific relative risks for pneumonia and death among outpatients considered to be at high risk for influenza complications. This population-based study aimed to develop prediction models for determining the risk of influenza-related pneumonia and death. Methods We included patients diagnosed with laboratory-confirmed influenza between 2016 and 2017 (main cohort, n = 25 659), those diagnosed between 2015 and 2016 (validation cohort 1, n = 16 727), and those diagnosed between 2017 and 2018 (validation cohort 2, n = 34 219). Prediction scores were developed based on the incidence and independent predictors of pneumonia and death identified using multivariate analyses, and patients were categorized into low-, medium-, and high-risk groups based on total scores. Results In the main cohort, age, gender, and certain comorbidities (dementia, congestive heart failure, diabetes, and others) were independent predictors of pneumonia and death. The 28-day pneumonia incidence was 0.5%, 4.1%, and 10.8% in the low-, medium-, and high-risk groups, respectively (c-index, 0.75); the 28-day mortality was 0.05%, 0.7%, and 3.3% in the low-, medium-, and high-risk groups, respectively (c-index, 0.85). In validation cohort 1, c-indices for the models for pneumonia and death were 0.75 and 0.87, respectively. In validation cohort 2, c-indices for the models were 0.74 and 0.87, respectively. Conclusions We successfully developed and validated simple-to-use risk prediction models, which would promptly provide useful information for treatment decisions in primary care settings.

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