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

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

Campylobactériose humaine et variations climatiques au Québec : a nalyse de séries temporelles selon les modèles SARIMA et SARIMAX

Au niveau mondial, les indicateurs climatiques sont de plus en plus décrits comme des facteurs associés aux zoonoses. L’objectif principal de cette étude a été de vérifier la relation entre les variations de la température ambiante maximale mensuelle et du niveau de précipitations totales mensuelles (incluant la neige et la pluie) et l’incidence cumulée mensuelle de la campylobactériose humaine dans trois régions sociosanitaires du Québec ayant des conditions climatiques différentes : Montréal, Mauricie-et-Centre-du-Québec et Capitale-Nationale. Une double analyse de séries temporelles selon les modèles SARIMA (Seasonal Autoregressive integrated Moving Average) et SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous variables) a été réalisée sur la période de 2000 à 2015. Pour les trois régions sociosanitaires, l’incidence cumulée mensuelle de la campylobactériose suivait une tendance saisonnière avec un pic entre juillet et août. Les modèles SARIMAX étaient les meilleurs modèles permettant d’apprécier l’association entre l’incidence cumulée mensuelle de la campylobactériose humaine et les deux variables climatiques. Une corrélation positive entre la température maximale mensuelle et l’incidence cumulée mensuelle de la campylobactériose humaine était observée pour les régions de Montréal (p < 0,0001) et de Mauricie-et-Centre-du-Québec (p = 0,0079). Pour les précipitations, la corrélation était négative et significative seulement pour la région de Montréal (p = 0,0004). Il reste à explorer la corrélation avec plusieurs autres variables climatiques telles que les précipitations pluviales (sans la neige), l’humidité relative et la durée de la saison de croissance des végétaux ainsi qu’à élaborer des modèles SARIMAX de prédiction de l’incidence de la campylobactériose.At the global level, climate indicators are increasingly described as factors related to zoonosis. The main objective of this study was to verify the relationship between changes in monthly maximum ambient temperature and monthly total precipitation level (including snow and rain) and the cumulative monthly incidence of human campylobacteriosis in three health regions of Quebec with different climatic conditions: Montréal, Mauricie-et-Centre-du-Québec and Capitale-Nationale. A double time series analysis, SARIMA (Seasonal Autoregressive Integrated Moving Average) and SARIMAX (Seasonal Autoregressive Integrated Moving Average with Exogenous variables) was carried out over the period 2000-2015. For the three health regions, the campylobacteriosis cumulative monthly incidence followed a seasonal pattern with a peak between July and August. The SARIMAX models were the best models for assessing the association between the cumulative monthly incidence of human campylobacteriosis and the two climatic variables. A positive correlation between monthly maximum temperature and cumulative monthly incidence of human campylobacteriosis was observed for Montréal (p < 0.0001) and Mauricie-et-Centre-du-Québec (p = 0.0079) health regions. On the other hand, for total precipitation, the correlation was negative and significant only for Montréal (p = 0.0004). It remains to explore the link with several other climatic variables, such as rainfall, relative humidity and the length of the plant-growing season, as well as to develop SARIMAX models for campylobacteriosis incidence prediction

Climate change and childhood diarrhoea in Kathmandu, Nepal: a health risk assessment and exploration of surveillance capacity

There is substantial evidence that the onset and transmission of infectious diseases, particularly vector-borne diseases and diarrheal diseases, are influenced by many factors including climate change. Improving the understanding of the impacts of climate change on infectious diseases is important to inform policy decision making on disease control and prevention, as well as predicting the trends in the infectious diseases burden. Epidemiological analysis of long-term surveillance data on infectious diseases and meteorological factors are instrumental in establishing the association between infectious disease incidence and climate change. Advanced epidemiological techniques are now available to precisely estimate the nature of association (linear, non-linear) as well as the delayed effect: this means that it is possible to plan and design climate-based early warning systems to predict conditions that are likely to be favourable for an outbreak of climate-sensitive infectious disease. However, the association between infectious diseases and climate change varies, depending upon the pathogens responsible for infection. Similarly, the ability of infectious disease surveillance systems or disease control divisions to generate this evidence and utilise the knowledge to cope or adapt to the impacts of climate change is contingent upon the social, economic, political and other contextual problems. In the Nepalese context, the impacts of climate change on infectious diseases, in particular diarrheal disease, remains unknown: similarly, there has been no exploration of the contextual factors associated with the integration of climate change-related risk in Nepalese infectious diseases surveillance systems. Given this background, the first aim of this PhD thesis is to characterize the association between diarrhoea among children below five years of age and climate variables in Kathmandu, Nepal and then project the future burden of diarrhoea due to climate change. The second aim is to understand the association between rotavirus infection among children below five years of age and temperature variability in Kathmandu and compute the fraction of rotavirus infection that is attributable to temperature. The third aim is to explore the extant research on climate change and infectious diseases in Nepal and to identify the reasons behind sparse evidence on the topic. The final aim is to explore social, economic and cultural factors associated with infectious diseases surveillance in Nepal in the context of climate change. A mixed method study design was employed to achieve the goals of this project. There are four analytical chapters in this thesis: two quantitative studies; a study that reviews evidence of the impacts of climate change on infectious disease and policy documents related to infectious disease control and prevention in Nepal; and a qualitative study. Two quantitative studies were carried out to estimate the association between climate variability and childhood diarrhoea, and childhood rotavirus infection in Kathmandu. Study 1 and study 2 utilised time series design involving Poisson regression equations fitted with distributed lag models to characterise exposure-response and possible lagged association between climate variables and diarrhoea, and rotavirus infection. A qualitative research study was undertaken to explore the social, economic, cultural and political factors associated with infectious diseases surveillance in the context of climate change in Nepal. In study 4, semi-structured interviews were conducted with key informants and stakeholders from the Department of Health Services Nepal, World Health Organization Nepal, the Department of Hydrology and Meteorology Nepal and infectious disease experts working in both public and private sectors in Nepal. The interviews and subsequent thematic analysis of data were conducted from a critical realist perspective. Study 1 established a significant positive association between childhood diarrhoea and temperature, and rainfall. A 1°C increase in maximum temperature above the monthly average was found to be associated with 8.1% (RR: 1.081; 95% CI: 1.02-1.14) increase in the monthly count of diarrhoea among children below five years of age living in Kathmandu, Nepal. Similarly, a 10mm increase in monthly cumulative rainfall above the mean value was associated with 0.09% (RR: 1.009; 95% CI: 1.004-1.015) increase in childhood diarrhoea. It was further projected that 1357 (UI: 410–2274) additional cases of childhood diarrhoea could be experienced by 2050 given the projected change in climate under low-risk scenario (0.9°C increase in maximum temperature). Study 2 established a nonlinear negative association between temperature (maximum, mean and minimum) and weekly rotavirus infection cases among children below five years of age in Kathmandu. Compared to the median value of mean temperature, an increased risk (RR: 1.52; 95% CI: 1.08–2.15) of rotavirus infection was detected at the lower quantile (10th percentile) and a decreased risk (RR: 0.64; 95% CI: 0.43–0.95) was detected at the higher quantile (75th percentile). Similarly, an increased risk [(RR: 1.93; 95% CI: 1.40–2.65) and (RR: 1.42; 95% CI: 1.04–1.95)] of infection was detected for both maximum and minimum temperature at their lower quantile (10th percentile). It was further estimated that 47.01% of the rotavirus infection cases reported between 2013 and 2016 in Kathmandu could be attributed to minimum temperature. Study 3 identified that there was little evidence describing the impacts of climate change on infectious diseases and no evidence describing the projected burden under climate change scenarios. I explored the reasons behind paucity in the evidence and challenges faced by epidemiologists in Nepal. The challenges identified included poor quality infectious disease datasets, shortage of trained human resources, inadequate funding and political instability. As such, it was recommended that an integrated digital network of interdisciplinary experts be established and increased collaboration among different stakeholders be promoted to advance the evidence base on the impacts of climate change on infectious diseases in Nepal. The fourth and final study outlined that climate change and its impacts on infectious disease surveillance is treated as a less serious issue than other more ‘salient’ public health risks in the context of Nepal. The study further illustrates how climate change is variably constructed as a contingent risk for infectious diseases transmission and public health systems. The analysis exposes a weaker alliance among different stakeholders, particularly policymakers and evidence generators that leads to the continuation of traditional practices of infectious diseases surveillance without consideration of the impacts of climate change. In summary, this thesis brings to prominence important progress in understanding the link between climate change and infectious diseases, in particular childhood diarrhoea, in a subtropical highland climate from a low and middle income South Asian country. So far, we have not found any other study that explores the contextual factors (social, economic, cultural and political) that impede the integration of climate change-related risk in the disease surveillance systems. Therefore, this thesis illustrates a novel facet of infectious disease surveillance and climate change. This thesis makes an important contribution to address the gap on information related to climate change and infectious diseases in Nepal and can have significant implications towards building a climate-resilient public health system in Nepal.Thesis (Ph.D.) -- University of Adelaide, School of Public Health, 202

Modélisation de données de surveillance épidémiologique de la faune sauvage en vue de la détection de problèmes sanitaires inhabituels

Recent studies have shown that amongst emerging infectious disease events in humans, about 40% were zoonoses linked to wildlife. Disease surveillance of wildlife should help to improve health protection of these animals and also of domestic animals and humans that are exposed to these pathogenic agents. Our aim was to develop tools capable of detecting unusual disease events in free ranging wildlife, by adopting a syndromic approach, as it is used for human health surveillance, with pathological profiles as early unspecific health indicators. We used the information registered by a national network monitoring causes of death in wildlife in France since 1986, called SAGIR. More than 50.000 cases of mortality in wildlife were recorded up to 2007, representing 244 species of terrestrial mammals and birds, and were attributed to 220 different causes of death. The network was first evaluated for its capacity to detect early unusual events. Syndromic classes were then defined by a statistical typology of the lesions observed on the carcasses. Syndrome time series were analyzed, using two complimentary methods of detection, one robust detection algorithm developed by Farrington and another generalized linear model with periodic terms. Historical trends of occurrence of these syndromes and greater-than-expected counts (signals) were identified. Reporting of unusual mortality events in the network bulletin was used to interpret these signals. The study analyses the relevance of the use of syndromic surveillance on this type of data and gives elements for future improvements.Des études récentes ont montré que parmi les infections émergentes chez l'homme, env. 40% étaient des zoonoses liées à la faune sauvage. La surveillance sanitaire de ces animaux devrait contribuer à améliorer la protection de leur santé et aussi celle des animaux domestiques et des hommes. Notre objectif était de développer des outils de détection de problèmes sanitaires inhabituels dans la faune sauvage, en adoptant une approche syndromique, utilisée en santé humaine, avec des profils pathologiques comme indicateurs de santé non spécifiques. Un réseau national de surveillance des causes de mortalité dans la faune sauvage, appelé SAGIR, a fourni les données. Entre 1986 et 2007, plus de 50.000 cas ont été enregistrés, représentant 244 espèces de mammifères terrestres et d'oiseaux, et attribués à 220 différentes causes de mort. Le réseau a d'abord été évalué pour sa capacité à détecter précocement des événements inhabituels. Des classes syndromiques ont ensuite été définies par une typologie statistique des lésions observées sur les cadavres. Les séries temporelles des syndromes ont été analysées en utilisant deux méthodes complémentaires de détection : un algorithme robuste développé par Farrington et un modèle linéaire généralisé avec des termes périodiques. Les tendances séculaires de ces syndromes et des signaux correspondent a des excès de cas ont été identifiés. Les signalements de problèmes de mortalité inhabituelle dans le bulletin du réseau ont été utilisés pour interpréter ces signaux. L'étude analyse la pertinence de l'utilisation de la surveillance syndromique sur ce type de données et donne des éléments pour des améliorations futures