Nor Azah Samat

Vector-borne infectious disease mapping with stochastic difference equations: an analysis of dengue disease in Malaysia

Few publications consider the estimation of relative risk for vector-borne infectious diseases. Most of these articles involve exploratory analysis that includes the study of covariates and their effects on disease distribution and the study of geographic information systems to integrate patient-related information. The aim of this paper is to introduce an alternative method of relative risk estimation based on discrete time–space stochastic SIR-SI models (susceptible–infective–recovered for human populations; susceptible–infective for vector populations) for the transmission of vector-borne infectious diseases, particularly dengue disease. First, we describe deterministic compartmental SIR-SI models that are suitable for dengue disease transmission. We then adapt these to develop corresponding discrete time–space stochastic SIR-SI models. Finally, we develop an alternative method of estimating the relative risk for dengue disease mapping based on these models and apply them to analyse dengue data from Malaysia. This new approach offers a better model for estimating the relative risk for dengue disease mapping compared with the other common approaches, because it takes into account the transmission process of the disease while allowing for covariates and spatial correlation between risks in adjacent regions.

Dengue disease mapping in Malaysia based on stochastic SIR models in human populations

Relative risk estimation is one of the most important issues in the study of geographical distributions of disease occurrence or disease mapping. For the case of dengue, there are only a few studies that use statistical methods to estimate the relative risk for disease mapping. Therefore this research will introduce an alternative method to estimate the relative risk of dengue occurrence based initially on discrete-time, discrete-space stochastic SIR models (Susceptible-Infective-Removed) in human populations for dengue disease transmission, to overcome the drawbacks of relative risk estimation in disease mapping using a classical method based on standardized morbidity ratio (SMR), and the earliest example of Bayesian mapping which involves a Poisson-Gamma model. The estimation of relative risk is applied to dengue data in Malaysia which will then be displayed in a map to represent the high and low risk areas of dengue occurrence.

Risk Estimation for Lung Cancer in Libya: Analysis Based on Standardized Morbidity Ratio, Poisson-Gamma Model, BYM Model and Mixture Model

Cancer is the most rapidly spreading disease in the world, especially in developing countries, including Libya. Cancer represents a significant burden on patients, families, and their societies. This disease can be controlled if detected early. Therefore, disease mapping has recently become an important method in the fields of public health research and disease epidemiology. The correct choice of statistical model is a very important step to producing a good map of a disease. Libya was selected to perform this work and to examine its geographical variation in the incidence of lung cancer. The objective of this paper is to estimate the relative risk for lung cancer. Four statistical models to estimate the relative risk for lung cancer and population censuses of the study area for the time period 2006 to 2011 were used in this work. They are initially known as Standardized Morbidity Ratio, which is the most popular statistic, which used in the field of disease mapping, Poisson-gamma model, which is one of the earliest applications of Bayesian methodology, Besag, York and Mollie (BYM) model and Mixture model. As an initial step, this study begins by providing a review of all proposed models, which we then apply to lung cancer data in Libya. Maps, tables and graph, goodness-of-fit (GOF) were used to compare and present the preliminary results. This GOF is common in statistical modelling to compare fitted models. The main general results presented in this study show that the Poisson-gamma model, BYM model, and Mixture model can overcome the problem of the first model (SMR) when there is no observed lung cancer case in certain districts. Results show that the Mixture model is most robust and provides better relative risk estimates across a range of models.

Predictions of Relative Risks for Dengue Disease Mapping in Malaysia Based on Stochastic SIR-SI Vector-Borne Infectious Disease Transmission Model

The main aim of this paper is to propose a procedure for predicting relative risk in order to suggest approximate future dengue disease progression patterns for Malaysia. Firstly, we discuss the posterior predictive distributions that are used to generate forecasts of relative risk. This description includes explanations of the simulating process that is required to generate posterior predictive distributions. Then, we apply this procedure to generate predictions for our case studies relating to dengue disease in Malaysia for discrete time-space data. Finally, we present the findings of our predictive analysis, comparing and displaying the forecasts of relative risk in graph, table and map. Results of the numerical analysis that we implemented to generate predictions of relative risk based on discrete time-space stochastic SIR-SI vector-borne infectious disease transmission models using dengue data of Malaysia suggest that the future forecasts of posterior predictive relative risk have strikingly similar patterns as the fitted posterior expected relative risks. This similarity shows the stationary process between the observed and the predicted values under our model assumptions. In practical application, we recommend that shorter-term forecasts should be used. These can then be updated continually as more incidence data become available.

Bladder cancer mapping in Libya based on standardized morbidity ratio and log-normal model

Disease mapping contains a set of statistical techniques that detail maps of rates based on estimated mortality, morbidity, and prevalence. A traditional approach to measure the relative risk of the disease is called Standardized Morbidity Ratio (SMR). It is the ratio of an observed and expected number of accounts in an area, which has the greatest uncertainty if the disease is rare or if geographical area is small. Therefore, Bayesian models or statistical smoothing based on Log-normal model are introduced which might solve SMR problem. This study estimates the relative risk for bladder cancer incidence in Libya from 2006 to 2007 based on the SMR and log-normal model, which were fitted to data using WinBUGS software. This study starts with a brief review of these models, starting with the SMR method and followed by the log-normal model, which is then applied to bladder cancer incidence in Libya. All results are compared using maps and tables. The study concludes that the log-normal model gives better rel...

Disease Mapping for Stomach Cancer in Libya Based on Besag– York– Mollié (BYM) Model

Globally, Cancer is the ever-increasing health problem and most common cause of medical deaths. In Libya, it is an important health concern, especially in the setting of an aging population and limited healthcare facilities. Therefore, the goal of this research is to map of the county’ cancer incidence rate using the Bayesian method and identify the high-risk regions (for the first time in a decade). In the field of disease mapping, very little has been done to address the issue of analyzing sparse cancer diseases in Libya. Standardized Morbidity Ratio or SMR is known as a traditional approach to measure the relative risk of the disease, which is the ratio of observed and expected number of accounts in a region that has the greatest uncertainty if the disease is rare or small geographical region. Therefore, to solve some of SMR’s problems, we used statistical smoothing or Bayesian models to estimate the relative risk for stomach cancer incidence in Libya in 2007 based on the BYM model. This research begins with a short offer of the SMR and Bayesian model with BYM model, which we applied to stomach cancer incidence in Libya. We compared all of the results using maps and tables. We found that BYM model is potentially beneficial, because it gives better relative risk estimates compared to SMR method. As well as, it has can overcome the classical method problem when there is no observed stomach cancer in a region.

Effect of (social) media on the political figure fever model: Jokowi-fever model

In recent years, political figures begin to utilize social media as one of alternative to engage in communication with their supporters. Publics referred to Jokowi, one of the candidates in Indonesia presidential election in 2014, as the first politician in Indonesia to truly understand the power of social media. Social media is very important in shaping public opinion. In this paper, effect of social media on the Jokowi-fever model in a closed population will be discussed. Supporter population is divided into three class sub-population, i.e susceptible supporters, Jokowi infected supporters, and recovered supporters. For case no positive media, there are two equilibrium points; the Jokowi-fever free equilibrium point in which it locally stable if basic reproductive ratio less than one and the Jokowi-fever endemic equilibrium point in which it locally stable if basic reproductive ratio greater than one. For case no negative media, there is only the Jokowi-fever endemic equilibrium point in which it locall...