Dengue fever is a mosquito-borne viral disease prevalent in more than one hundred tropical and subtropical countries. Annually, an estimated 390 million infections occur worldwide. It is transmitted by the bite of an Aedes mosquito infected with the virus. It has become a major public health challenge in recent years for many countries, including Sri Lanka. It is known that climate factors such as rainfall, temperature, and relative humidity influence the generation of mosquito offspring, thus increasing dengue incidences. Identifying the climate factors that affect the spread of dengue fever would be helpful in order for the relevant authorities to take necessary actions. The objective of this study is to build a model for predicting the likelihood of having high dengue incidences based on climate factors. A logistic regression approach was utilized for model formulation. This study found a significant association between high numbers of dengue incidences and rainfall. Furthermore, it was observed that the influence of rainfall on dengue incidences was expected to be visible after some lag period.

Analysis of Variance (ANOVA) is an important topic in introductory statistics. Many students struggle to understand the ANOVA concepts. Statistical concepts are important in engineering education. In this paper, we describe how to use simulation with Excel Data Tables and standard functions to perform one-way ANOVA. We calculate different values of the F-statistic by resampling from the original sample and compute the pvalue of the test. Using this approach, students will be able to get a better feel about the p-value concept. Our preliminary assessment shows that student learning is enhanced by incorporating this approach in the classroom.

Dengue fever is a mosquito-borne disease caused by the dengue virus. Transmission of the virus depends on the presence of Aedes mosquito. Dengue has become a global problem and is common in more than a hundred countries. It is most prevalent in tropical and subtropical regions. It has been a major public health challenge in Sri Lanka in recent years. Mosquito generation and the spread of dengue are known to be influenced by the climate. Identifying the climate factors that affect dengue outbreaks would be helpful to take necessary actions to prevent the spread of dengue. In this paper, we study the climate factors affecting the spread of dengue in the city of Colombo, Sri Lanka from the period of 2010 to 2018. The Poisson and negative binomial regression models were employed to analyze the data. These models fit the monthly dengue incidences against the temperature, rainfall, and relative humidity. This study showed a significant association between monthly dengue incidence and the amount of rainfall. The negative binomial model fits the data more accurately due to the nature of overdispersed data. This study provides useful information in predicting dengue incidences and developing a future warning system.