Data Integration and Predictive Analysis System for Disease Prophylaxis: Incorporating Dengue Fever Forecasts

The goal of the Data Integration and Predictive Analysis System (IPAS) is to enable prediction, analysis, and response management for incidents of infectious diseases. IPAS collects and integrates comprehensive datasets of previous disease incidents and potential influencing factors to facilitate multivariate, predictive analysis of disease patterns, intensity, and timing. We have used the IPAS technology to generate successful forecasts for Influenza Like Illness (ILI). In this study, IPAS was expanded to forecast Dengue fever in the cities of San Juan, Puerto Rico and Iquitos, Peru. Data provided by the National Oceanic and Atmospheric Administration (NOAA) was processed and used to generate prediction models. Predictions were developed with modern machine learning algorithms, identifying the one-week and four-week forecast of Dengue incidences in each city. Prediction model results are presented along with the features of the IPAS system

Effects of Extreme Precipitation to the Distribution of Infectious Diseases in Taiwan, 1994–2008

The incidence of extreme precipitation has increased with the exacerbation of worldwide climate disruption. We hypothesize an association between precipitation and the distribution patterns that would affect the endemic burden of 8 infectious diseases in Taiwan, including water- and vector-borne infectious diseases. A database integrating daily precipitation and temperature, along with the infectious disease case registry for all 352 townships in the main island of Taiwan was analysed for the period from 1994 to 2008. Four precipitation levels, <130 mm, 130–200 mm, 200–350 mm and >350 mm, were categorized to represent quantitative differences, and their associations with each specific disease was investigated using the Generalized Additive Mixed Model and afterwards mapped on to the Geographical Information System. Daily precipitation levels were significantly correlated with all 8 mandatory-notified infectious diseases in Taiwan. For water-borne infections, extreme torrential precipitation (>350 mm/day) was found to result in the highest relative risk for bacillary dysentery and enterovirus infections when compared to ordinary rain (<130 mm/day). Yet, for vector-borne diseases, the relative risk of dengue fever and Japanese encephalitis increased with greater precipitation only up to 350 mm. Differential lag effects following precipitation were statistically associated with increased risk for contracting individual infectious diseases. This study’s findings can help health resource sector management better allocate medical resources and be better prepared to deal with infectious disease outbreaks following future extreme precipitation events

A DECISION SUPPORT SYSTEM FRAMEWORK FOR SEASONAL ZOONOSIS PREDICTION

The arising number of zoonosis epidemics and the potential threat to human highlight the need to apply stringent system to contend zoonosis outbreak. Zoonosis is any infectious disease that is able to be transmitted from other animals, both wild and domestic, to humans. The increasing number of zoonotic diseases coupled with the frequency of occurrences, especially lately, has made the need to study and develop a framework to predict future number of zoonosis incidence. Unfortunately, study of literatures showed most prediction models are case-specific and often based on a single forecasting technique. This research analyses and presents the application of a decision support system (DSS) that applied multi forecasting methods to support and provide prediction on the number of zoonosis human incidence. The focus of this research is to identify and to design a DSS framework on zoonosis that is able to handle two seasonal time series type, namely additive seasonal model and multiplicative seasonal model. The first dataset describes the seasonal data pattern that exhibited the constant variation, while the second dataset showed the upward/downward trend. Two case studies were selected to evaluate the proposed framework: Salmonellosis and Tuberculosis for additive time series and Tuberculosis for multiplicative time series. Data was collected from the number of human Salmonellosis and Tuberculosis incidence in the United States published by Centers for Disease Control and Prevention (CDC). These data were selected based on availability and completeness. The proposed framework consists of three components: database management subsystem, model management subsystem, and dialog generation and management subsystem. A set of 168 monthly data (1993–2006) of Salmonellosis and Tuberculosis was used for developing the database management subsystem. Six forecasting methods, including five statistical methods and one soft computing method, were applied in the model management subsystem. They were regression analysis, moving average, decomposition, Holt-Winter’s, ARIMA, and neural network. The results of each method were compared using ANOVA, while Duncan Multiple Range Test was employed to identify the compatibility of each method to the time series. Coefficient of Variation (CV) was used to determine the most appropriate method among them. In the user interface subsystem, “What If” (sensitivity) analysis was chosen to construct this component. This analysis provided the fluctuation of forecasting results which was influenced by the changes in data. The sensitivity analysis was able to determine method with the highest fluctuation based on data update. Observation of the result showed that regression analysis was the fittest method for Salmonellosis and neural network was the fittest method of Tuberculosis. Thus, it could be concluded that results difference of both cases was affected by the available data series. Finally, the design of Graphical User Interface (GUI) was presented to show the connectivity flow between all DSS components. The research resulted in the development of a DSS theoretical framework for a zoonosis prediction system. The results are also expected to serve as a guide for further research and development of DSS for other zoonosis, not only for seasonal zoonosis but also for nonseasonal zoonosis

Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review

Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models

Potential for using climate forecasts in spatio-temporal prediction of dengue fever incidence in Malaysia

Dengue fever is a viral infection transmitted by the bite of female \textit{Aedes aegypti} mosquitoes. It is estimated that nearly 40\% of the world's population is now at risk from Dengue in over 100 endemic countries including Malaysia. Several studies in various countries in recent years have identified statistically significant links between Dengue incidence and climatic factors. There has been relatively little work on this issue in Malaysia, particularly on a national scale. This study attempts to fill that gap. The primary research question is `to what extent can climate variables be used to assist predictions of dengue fever incidence in Malaysia?'. The study proposes a potential framework of modelling spatio-temporal variation in dengue risk on a national scale in Malaysia using both climate and non-climate information. Early chapters set the scene by discussing Malaysia and Climate in Malaysia and reviewing previous work on dengue fever and dengue fever in Malaysia. Subsequent chapters focus on the analysis and modelling of annual dengue incidence rate (DIR) for the twelve states of Peninsular Malaysia for the period 1991 to 2009 and monthly DIR for the same states in the period 2001 to 2009. Exploratory analyses are presented which suggest possible relationships between annual and monthly DIR and climate and other factors. The variables that were considered included annual trend, in year seasonal effects, population, population density and lagged dengue incidence rate as well as climate factors such as average rainfall and temperature, number of rainy days, ENSO and lagged values of these climate variables. Findings include evidence of an increasing annual trend in DIR in all states of Malaysia and a strong in-year seasonal cycle in DIR with possible differences in this cycle in different geographical regions of Malaysia. High population density is found to be positively related to monthly DIR as is the DIR in the immediately preceding months. Relationships between monthly DIR and climate variables are generally quite weak, nevertheless some relationships may be able to be usefully incorporated into predictive models. These include average temperature and rainfall, number of rainy days and ENSO. However lagged values of these variables need to be considered for up to 6 months in the case of ENSO and from 1-3 months in the case of other variables. These exploratory findings are then more formally investigated using a framework where dengue counts are modelled using a negative binomial generalised linear model (GLM) with a population offset. This is subsequently extended to a negative binomial generalised additive model (GAM) which is able to deal more flexibly with non-linear relationships between the response and certain of the explanatory variables. The model successfully accounts for the large amount of overdispersion found in the observed dengue counts. Results indicated that there are statistically significant relationships with both climate and non-climate covariates using this modelling framework. More specifically, smooth functions of year and month differentiated by geographical areas of the country are significant in the model to allow for seasonality and annual trend. Other significant covariates included were mean rainfall at lag zero month and lag 3 months, mean temperature at lag zero month and lag 1 month, number of rainy days at lag zero month and lag 3 months, sea surface temperature at lag 6 months, interaction between mean temperature at lag 1 month and sea surface temperature at lag 6 months, dengue incidence rate at lag 3 months and population density. Three final competing models were selected as potential candidates upon which an early warning system for dengue in Malaysia might be able to be developed. The model fits for the whole data set were compared using simulation experiments to allow for both parameter and negative binomial model uncertainty and a single model preferred from the three models was identified. The `out of sample' predictive performance of this model was then compared and contrasted for different lead times by fitting the model to the first 7 years of the 9 years monthly data set covering 2001-2009 and then analysing predictions for the subsequent 2 years for lead time of 3, 6 12 and 24 months. Again simulation experiments were conducted to allow for both parameter and model uncertainty. Results were mixed. There does seem to be predictive potential for lead times of up to six months from the model in areas outside of the highly urbanised South Western states of Kuala Lumpur and Selangor and such a model may therefore possibly be useful as a basis for developing early warning systems for those areas. However, none of the models developed work well for Kuala Lumpur and Selangor where there are clearly more complex localised influences involved which need further study. This study is one of the first to look at potential climatic influences on dengue incidence on a nationwide scale in Malaysia. It is also one of the few studies worldwide to explore the use of generalised additive models in the spatio-temporal modelling of dengue incidence. Although, the results of the study show a mixed picture, hopefully the framework developed will be able to be used as a starting point to investigate further if climate information can valuably be incorporated in an early warning system for dengue in Malaysia.Ministry of Education Malaysia (MOE

A DECISION SUPPORT SYSTEM FRAMEWORK FOR SEASONAL ZOONOSIS PREDICTION

The arising number of zoonosis epidemics and the potential threat to human highlight the need to apply stringent system to contend zoonosis outbreak. Zoonosis is any infectious disease that is able to be transmitted from other animals, both wild and domestic, to humans. The increasing number of zoonotic diseases coupled with the frequency of occurrences, especially lately, has made the need to study and develop a framework to predict future number of zoonosis incidence. Unfortunately, study of literatures showed most prediction models are case-specific and often based on a single forecasting technique. This research analyses and presents the application of a decision support system (DSS) that applied multi forecasting methods to support and provide prediction on the number of zoonosis human incidence. The focus of this research is to identify and to design a DSS framework on zoonosis that is able to handle two seasonal time series type, namely additive seasonal model and multiplicative seasonal model. The first dataset describes the seasonal data pattern that exhibited the constant variation, while the second dataset showed the upward/downward trend. Two case studies were selected to evaluate the proposed framework: Salmonellosis and Tuberculosis for additive time series and Tuberculosis for multiplicative time series. Data was collected from the number of human Salmonellosis and Tuberculosis incidence in the United States published by Centers for Disease Control and Prevention (CDC). These data were selected based on availability and completeness. The proposed framework consists of three components: database management subsystem, model management subsystem, and dialog generation and management subsystem. A set of 168 monthly data (1993–2006) of Salmonellosis and Tuberculosis was used for developing the database management subsystem. Six forecasting methods, including five statistical methods and one soft computing method, were applied in the model management subsystem. They were regression analysis, moving average, decomposition, Holt-Winter’s, ARIMA, and neural network. The results of each method were compared using ANOVA, while Duncan Multiple Range Test was employed to identify the compatibility of each method to the time series. Coefficient of Variation (CV) was used to determine the most appropriate method among them. In the user interface subsystem, “What If” (sensitivity) analysis was chosen to construct this component. This analysis provided the fluctuation of forecasting results which was influenced by the changes in data. The sensitivity analysis was able to determine method with the highest fluctuation based on data update. Observation of the result showed that regression analysis was the fittest method for Salmonellosis and neural network was the fittest method of Tuberculosis. Thus, it could be concluded that results difference of both cases was affected by the available data series. Finally, the design of Graphical User Interface (GUI) was presented to show the connectivity flow between all DSS components. The research resulted in the development of a DSS theoretical framework for a zoonosis prediction system. The results are also expected to serve as a guide for further research and development of DSS for other zoonosis, not only for seasonal zoonosis but also for nonseasonal zoonosis

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice