Changes in climate factors such as temperature, rainfall, humidity, and wind speed are natural processes that could significantly impact the incidence of infectious diseases. Dengue is a widespread disease that has often been documented when it comes to the impact of climate change. It has become a significant concern, especially for the Malaysian health authorities, due to its rapid spread and serious effects, leading to loss of life. Several statistical models were performed to identify climatic factors associated with infectious diseases. However, because of the complex and nonlinear interactions between climate variables and disease components, modelling their relationships have become the main challenge in climate-health studies. Hence, this study proposed a Generalized Linear Model (GLM) via Poisson and Negative Binomial to examine the effects of the climate factors on dengue incidence by considering the collinearity between variables. This study focuses on the dengue hot spots in Malaysia for the year 2014. Since there exists collinearity between climate factors, the analysis was done separately using three different models. The study revealed that rainfall, temperature, humidity, and wind speed were statistically significant with dengue incidence, and most of them shown a negative effect. Of all variables, wind speed has the most significant impact on dengue incidence. Having this kind of relationships, policymakers should formulate better plans such that precautionary steps can be taken to reduce the spread of dengue diseases.

 In spatial analysis, it is important to identify the nature of the relationship that exists between variables. Normally, it is done by estimating parameters with observations which taken from different spatial units that across a study area where parameters are assumed to be constant across space. However, this is not so as the spatial non-stationarity is a condition in which a simple model cannot explain the relationship between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. Non-stationarity means that the relationship between variables under study varies from one location to another depending on physical factors of the environment that are spatially autocorrelated. Geographically Weighted Regression (GWR) is a technique in which it applied to capture the variation by calibrating a multiple regression model, which allows different relationships to exist at different points in space. A robust algorithm has been successfully used in spatial analysis. GWR can theoretically integrate geographical location, altitude, and other factors for spatial analysis estimations, and reflects the non-stationary spatial relationship between these variables. The main goal of this study is to review the potential of the GWR in modelling the spatial relationship between variables either dependent or independent and its used as the spatial prediction models. Based on the application of GWR such as house property indicates that GWR is the best model in estimating the parameters. Hence, from the GWR model, the significance of the variation can also be tested

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Dengue has been a global epidemic since World War II, with millions of individuals being infected every year. Repetitive dengue epidemic is one of the main health problems that, due to its rapid spread and geographically widespread, has become a major concern for the government authorities in dealing with this disease. In Malaysia, cases of dengue are reported annually. To keep cases under control, it is important to examine the possible factors that help the growth of the virus. Climatological factors such as rainfall, temperature, wind speed, and humidity are expected to have high potential to increase the growth of the virus in this study, and their spatial variation is associated with cases of dengue. The result revealed that Ordinary Least Square was not an effective method for modelling the relationships between dengue cases and climate variables, as climate variables in different spatial regions act differently. During the analysis, there could be some issues of non-stationarity since the geographical aspect and spatial data were involved. Hence, the Geographically Weighted Regression (GWR) is implemented due to its capability to identify the spatial non-stationarity behavior of influencing factors on dengue incidence and integrate the geographical location and altitude for the spatial analysis. GWR analysis found that the influenced factors exhibited a significant relationship with dengue incidence. GWR also shows a significant improvement in Akaike Information Criteria (AIC) values with the lowest value and the highest adjusted R square. It is expected that the developed model can help the local hygienic authorities design better strategies for preventing and controlling this epidemic in Malaysia.

Hand, foot, and mouth disease (HFMD) has become an endemic childhood disease in Asia, including Malaysia, over the last few decades. This infectious disease caused by the Entero and Coxsackie viruses has been a major public health threat in Malaysia since 1997. Climate change has been considered an influential factor in HFMD cases and has been explored in other countries using various statistical analyses. The most popular is the Generalized Linear Model (GLM). However, GLM often fails to capture the non-linearity effect of the variables. The study, therefore, proposes to use the Generalized Additive Model (GAM) to analyse the non-linear effects of temperature, humidity, rainfall, and wind speed at varying time lags of HFMD in Selangor. In summary, the result indicates that the weekly temperature, humidity, and rainfall were significantly associated with HFMD cases in Selangor and clarified with two weeks of lag time. This disease’s risk increased in the subsequent two weeks with a temperature range of 27°C to 30°C, 70% to 85% of humidity, and 5mm to 20mm of rainfall. Besides, this study also found that the seasonal distribution of HFMD in Selangor has a large peak during the Southwest monsoon. A small peak was observed at the end of the year during the Northeast monsoon. The findings of this study could be a practical guide for HFMD intervention strategies, especially in Malaysia.