Dengue is a vector-borne disease transmitted to humans by vectors of genus Aedes causing a global threat to health, social, and economic sectors in many of the tropical countries including Sri Lanka. In Sri Lanka, the tropical climate, marked by seasonal weather primarily influenced by monsoons, fosters optimal conditions for the virus to spread efficiently. This heightened transmission results in increased per-capita vector density. In this work, we investigate the dynamic influence of environmental conditions on dengue emergence in Colombo district - the geographical region with the highest recorded dengue threat in Sri Lanka. An iterative approach is employed to dynamically estimate dengue cases leveraging the Markov chain Monte Carlo simulations, utilizing the dynamics of four seasons per year influenced by monsoon weather patterns governing in the region. The developed algorithm allows to estimate the risk of dengue outbreaks in 2017 and 2019 with high precision, facilitating accurate forecasts of upcoming disease emergence patterns for better preparedness. The uncertainty quantification not only validated the accuracy of outbreak estimates but also showcased the model's capacity to capture extreme cases and revealed undisclosed external factors such as human mobility and environmental pollution that might affect dengue transmission in the Colombo district of Sri Lanka.

Background: For decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous lives, yet no cure has been discovered. Genetic traits are often challenging to quantify and particularly susceptible to environmental fluctuations. Assessing genetic traits such as the vector competence of Aedes mosquitoes, which enable transmitting dengue among humans, presents obstacles and is notably sensitive to climate variations, especially considering their probability to flourish in tropical regions of the world. Methods: In this study, we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes using an existing mathematical model, originally developed for malaria, in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of populations are threatened with dengue. Results: Our research successfully deduced vector competence values for each identified season within a year. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model for dengue disease. Conclusion: Our research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease

For decades, dengue has posed a significant threat as a viral infectious disease, affecting numerous human lives globally, particularly in tropical regions, yet no cure has been discovered. The genetic trait of vector competence in Aedes mosquitoes, which facilitates dengue transmission, is difficult to measure and highly sensitive to environmental changes. In this study we attempt, for the first time in a non-laboratory setting, to quantify the vector competence of Aedes mosquitoes assuming its homogeneity across both species; aegypti and albopictus and across the four Dengue serotypes. Estimating vector competence in relation to varying rainfall patterns was focused in this study to showcase the changes in this vector trait with respect to environmental variables. We quantify it using an existing mathematical model originally developed for malaria in a Bayesian inferencing setup. We conducted this study in the Colombo district of Sri Lanka where the highest number of human populations are threatened with dengue. Colombo district experiences continuous favorable temperature and humidity levels throughout the year creating ideal conditions for Aedes mosquitoes to thrive and transmit the Dengue disease. Therefore we only used the highly variable and seasonal rainfall as the primary environmental variable as it significantly influences the number of breeding sites and thereby impacting the population dynamics of Aedes. Our research successfully deduced vector competence values for the four identified seasons based on Monsoon rainfalls experienced in Colombo within a year. We used dengue data from 2009 - 2022 to infer the estimates. These estimated values have been corroborated through experimental studies documented in the literature, thereby validating the malaria model to estimate vector competence for dengue disease. Our research findings conclude that environmental conditions can amplify vector competence within specific seasons, categorized by their environmental attributes. Additionally, the deduced vector competence offers compelling evidence that it impacts disease transmission, irrespective of geographical location, climate, or environmental factors.

Prevailing dengue-control strategies in many developing countries yield only limited benefits due to non-optimality of those strategies. In this paper, we demonstrate how the same strategies could be altered using the same amount of resources in order to yield more fruitful results. Accordingly, we develop a binary integer programming model, aimed at minimising the total number of susceptible individuals with high-risk of being infected with dengue, by identifying the most influential dengue-infected individuals who could undergo an epidemiological isolation, subject to the conditions imposed by the topological properties of the epidemiological network and budgetary constraints. Further, we analyse the proposed epidemiological isolation to examine its adequacy in a real-world implementation.

Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative ( ≈difference of total confirmed cases) and the second derivative ( ≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here.

Dengue is a one of the diseases in the world which has no exact treatment. It is rapidly spreading throughout the world by causing large number of deaths. In Sri Lanka, there is an increase of reported dengue cases over recent years. The majority of dengue cases reported in the Colombo district within the Sri Lanka. Effective dengue management strategies should be implemented to reduce the deaths from the disease. Modelling and predicting the distribution of the dengue will be useful in detecting outbreaks of the dengue and to execute controlling actions beforehand. The objective of this study is to develop an appropriate modelling technique to predict dengue cases. To accomplish this objective, we have chosen our study area as Colombo, Sri Lanka. Seven modelling techniques, namely, Naïve, Seasonal Naïve, Random Walk with Drift, Mean Forecasting, Autoregressive Integrated Moving Average, Exponential Smoothing and TBATS were chosen in this study to model dengue data. For model development process, monthly reported dengue cases in Colombo from January 2010 to December 2018 were used and validated using the data from January to December in 2019. Mean error, root mean squared error and mean absolute percentage error measurements were used to select the most parsimonious model to predict dengue cases in Colombo. Both Exponential and TBATS models were competed in predicting dengue cases by reporting minimum error measures. Therefore, results disclosed that among the selected methods either Exponential Smoothing model or TBATS model can be used to predict dengue cases in Colombo, Sri Lanka.

In a critical area like health sector centralized computer system helps to improve the efficiency of the health system. In particular, controlling an epidemic is usually difficult in developing countries. In this study we introduce a multi-platform, centralized pro-active management system to manage dengue controlling activities in Sri Lanka. The system make common platform (ProDMS) for all sectors who contribute their services for mitigating dengue. We mainly focused to the special feature of the system which enhance the centralized property. Cross platform environment was developed under this feature as a bridge to connect researches and general public. ProDMS is a internet base web application and researches can plug their dengue forecasting models to the system and publish their outputs as graphs through the web system. The ProDMS web application, which consisting of plug and play system architecture concepts, fully support for any statistical or mathematical model to publish its results online. In this work we use one of the univariate time series modelling approaches; namely exponential smoothing to plug with the system. This research helps to enhance efficiency of Dengue controlling process and support to generalize centralization.

The medical background enables CCPs to act as technical experts and medical administrators in public health services. Key functions of CCPs outlined in the job description (2) include public h e a l t h p r o g r a m m e m a n a g e m e n t , p o l i c y analysis/development, strategic planning, advocacy, raising awareness on health, surveillance monitoring, evaluation, research, quality assurance, training, capacity building and fund mobilization. CCPs who function as medical administrators too, implement above functions as per relevance to the intuitions, directorates or programmes. Although public health has a well-agreed definition (3), there seems to be many definitions for CM and conflicts about roles and professional identity of CCPs. It is a timely requirement to explore evidence to illustrate a model of service delivery for CCPs. A thorough literature search and a narrative review based on the content revealed the following four main themes related to CM:

Dengue is among the most prevalent arboviral disease in humans worldwide. Infection with dengue can cause a wide spectrum of disease indications, from clinically inapparent infection to life threatening severe disease. Despite the increasing burden of dengue, development of an effective vaccine has rather remained elusive. One reason is the complex immunopathogenesis involved during an infection. To understand the viral dynamics and immune responses during dengue, we develop a mathematical model which describes the dynamics of healthy cells, infected cells and pathogens in the presence of innate and adaptive immune responses. A detailed analysis of the model was done and the most important model parameters were identified. The analytical findings were demonstrated by numerical simulations. It was observed that the model has five equilibria, namely, the disease free equilibrium, immunity induced viral clearance, no immune equilibrium, virus persistence in the absence of adaptive immune response and no antibody equilibrium. A detailed stability analysis of each equilibrium was implemented. It was observed that no antibody immune equilibrium is unstable for some parameter values. Thus it can be inferred from the detailed analysis, that the introduction of immune response strongly affects the stability of the system and antibodies plays a very important role in shaping dengue dynamics.

This chapter deals with various time series approaches which can be applied in modeling the transmission of dengue disease. It is one of the fastest spreading diseases in the world, reporting a high number of mortalities. Innovative, intentional methods must be organized for productive dengue management. The availability of effective prediction models will be helpful in controlling dengue disease. Particularly, time series analysis is one of the prominent approaches available in the area of statistics. Some of the powerful techniques accessible through time series analysis can be applied to model dengue effectively and efficiently. The chapter discusses underlying theories, advantages and disadvantages of common techniques available in time series analysis such as Autoregressive Integrated Moving Average (ARIMA), exponential smoothing, decomposition, Alpha-Sutte modeling, Autoregressive Integrated Moving Average with Explanatory variables (ARIMAX) and exponential smoothing with explanatory variables. Then, the chapter illustrates applications of the methods through cases studies of reported dengue cases from Colombo in Sri Lanka and Jakarta in Indonesia. The chapter continues to discuss theories of combining time series forecast approaches. The concluding section of the chapter emphasizes the importance and effectiveness of the combined approaches as a current trend of modeling over the classical approaches mentioned.

Dengue disease is a serious threat for the world. The number of infections increase annually forcing implementation of prompt actions in dengue management. Common practice of modelling is associated with point measurements. However, an interval representation for a point measure provides an additional information for the spread, capture uncertainties associated with variables and useful in making more precise decisions. Further, interval predictions are appropriate in the situations of exact predictions are not essential. Interval-valued analysis in the dengue disease is important as actions taking towards controlling the disease do not depend on the exact number but on the magnitude of the values represented by the interval. In the area of regression analysis, there are techniques to handle interval-valued dependent and independent variables. The present chapter discusses theories of interval regression procedures: centre method, centre and range method, constrained centre and range method, interval regression based on interval least squares algorithm and fuzzy regression techniques. The chapter illustrates applications of these methods using interval-valued data in Colombo, Sri Lanka, and Jakarta, Indonesia. Finally, the chapter emphasizes the importance and effectiveness of the interval regressions over traditional linear regression as well as added advantages of soft computing methods.

Background Understanding the dynamical behavior of dengue transmission is essential in designing control strategies. Mathematical models have become an important tool in describing the dynamics of a vector borne disease. Classical compartmental models are well–known method used to identify the dynamical behavior of spread of a vector borne disease. Due to use of fixed model parameters, the results of classical compartmental models do not match realistic nature. The aim of this study is to introduce time in varying model parameters, modify the classical compartmental model by improving its predictability power. Results In this study, per–capita vector density has been chosen as the time in varying model parameter. The dengue incidences, rainfall and temperature data in urban Colombo are analyzed using Fourier mathematical analysis tool. Further, periodic pattern of the reported dengue incidences and meteorological data and correlation of dengue incidences with meteorological data are identified to determine climate data–driven per–capita vector density parameter function. By considering that the vector dynamics occurs in faster time scale compares to host dynamics, a two dimensional data–driven compartmental model is derived with aid of classical compartmental models. Moreover, a function for per–capita vector density is introduced to capture the seasonal pattern of the disease according to the effect of climate factors in urban Colombo. Conclusions The two dimensional data–driven compartmental model can be used to predict weekly dengue incidences upto 4 weeks. Accuracy of the model is evaluated using relative error function and the model can be used to predict more than 75% accurate data.

Introduction: Awareness and knowledge about cervical smear are lacking among the women in the developing world. The aim of the study was to study the awareness and knowledge regarding cervical smear screening among a cohort of Sri Lankan women. Methods: A cross-sectional study was conducted at a gynaecology clinic in a tertiary care hospital, Sri Lanka from January to August 2018. A consecutively recruited clinic attendees who attended the clinic for the first-time; aged over 35 years were interviewed using a previously piloted questionnaire. Outcome measures were s ocio-demographic and clinical characteristics, knowledge about cervical smear screening and uptake of Pap smear testing. Results: Among the 165 women interviewed, 146 (88.5%) had heard about cervical carcinoma. Median (IQR) age was 46.0 (39.0-54.0) years. Only 75.2% had heard about cervical screening. Knowledge about cervical smear screening was low and 60% of women were not worried about themselves getting cervical carcinoma. Awareness about cervical smear screening was 58.8% and only 78/165 (47.3%) had ever undergone screening. Fear of vaginal examination and lack of awareness was noted in 6.7% and 29.7% of them respectively. Public health midwives were the commonest source of information to them regarding cervical smear. Conclusion: Awareness and knowledge regarding cervical carcinoma is poor in the study sample.

In order to recover the damage to the economy by the ongoing COVID-19 pandemic, many countries consider the transition from strict lockdowns to partial lockdowns through relaxation of preventive measures. In this work, we propose an optimal lockdown relaxation strategy, which is aimed at minimizing the damage to the economy, while confining the COVID-19 incidence to a level endurable by the available healthcare facilities in the country. In order to capture the transmission dynamics, we adopt the compartment models and develop the relevant optimization model, which turns out to be nonlinear. We generate approximate solutions to the problem, whereas our experimentation is based on the data on the COVID-19 outbreak in Sri Lanka.