Murray Valley encephalitis virus surveillance and control initiatives in Australia.

Mechanisms for monitoring Murray Valley encephalitis (MVE) virus activity include surveillance of human cases, surveillance for activity in sentinel animals, monitoring of mosquito vectors and monitoring of weather conditions. The monitoring of human cases is only one possible trigger for public health action and the additional surveillance systems are used in concert to signal the risk of human disease, often before the appearance of human cases. Mosquito vector surveillance includes mosquito trapping for speciation and enumeration of mosquitoes to monitor population sizes and relative composition. Virus isolation from mosquitoes can also be undertaken. Monitoring of weather conditions and vector surveillance determines whether there is a potential for MVE activity to occur. Virus isolation from trapped mosquitoes is necessary to define whether MVE is actually present, but is difficult to deliver in a timely fashion in some jurisdictions. Monitoring of sentinel animals indicates whether MVE transmission to vertebrates is actually occurring. Meteorological surveillance can assist in the prediction of potential MVE virus activity by signalling conditions that have been associated with outbreaks of Murray Valley encephalitis in humans in the past. Predictive models of MVE virus activity for south-eastern Australia have been developed, but due to the infrequency of outbreaks, are yet to be demonstrated as useful for the forecasting of major outbreaks. Surveillance mechanisms vary across the jurisdictions. Surveillance of human disease occurs in all States and Territories by reporting of cases to health authorities. Sentinel flocks of chickens are maintained in 4 jurisdictions (Western Australia, the Northern Territory, Victoria and New South Wales) with collaborations between Western Australia and the Northern Territory. Mosquito monitoring complements the surveillance of sentinel animals in these jurisdictions. In addition, other mosquito monitoring programs exist in other States (including South Australia and Queensland). Public health control measures may include advice to the general public and mosquito management programs to reduce the numbers of both mosquito larvae and adult vectors. Strategic plans for public health action in the event of MVE virus activity are currently developed or being developed in New South Wales, the Northern Territory, South Australia, Western Australia and Victoria. A southern tri-State agreement exists between health departments of New South Wales, Victoria and South Australia and the Commonwealth Department of Health and Aged Care. All partners have agreed to co-operate and provide assistance in predicting and combatting outbreaks of mosquito-borne disease in south-eastern Australia. The newly formed National Arbovirus Advisory Committee is a working party providing advice to the Communicable Diseases Network Australia on arbovirus surveillance and control. Recommendations for further enhancement of national surveillance for Murray Valley encephalitis are described

Environmental Pollution and Chronic Disease Management – A Prognostics Approach

No abstract available

Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore

Background: Rainfall patterns are one of the main drivers of dengue transmission as mosquitoes require standing water to reproduce. However, excess rainfall can be disruptive to the Aedes reproductive cycle by “flushing out” aquatic stages from breeding sites. We developed models to predict the occurrence of such “flushing” events from rainfall data and to evaluate the effect of flushing on dengue outbreak risk in Singapore between 2000 and 2016. Methods: We used machine learning and regression models to predict days with “flushing” in the dataset based on entomological and corresponding rainfall observations collected in Singapore. We used a distributed lag nonlinear logistic regression model to estimate the association between the number of flushing events per week and the risk of a dengue outbreak. Results: Days with flushing were identified through the developed logistic regression model based on entomological data (test set accuracy = 92%). Predictions were based upon the aggregate number of thresholds indicating unusually rainy conditions over multiple weeks. We observed a statistically significant reduction in dengue outbreak risk one to six weeks after flushing events occurred. For weeks with five or more flushing events, compared with weeks with no flushing events, the risk of a dengue outbreak in the subsequent weeks was reduced by 16% to 70%. Conclusions: We have developed a high accuracy predictive model associating temporal rainfall patterns with flushing conditions. Using predicted flushing events, we have demonstrated a statistically significant reduction in dengue outbreak risk following flushing, with the time lag well aligned with time of mosquito development from larvae and infection transmission. Vector control programs should consider the effects of hydrological conditions in endemic areas on dengue transmission.Charles Stark Draper Laborator

Particle filtering in compartmental projection models

Simulation models are important tools for real-time forecasting of pandemics. Models help health decision makers examine interventions and secure strong guidance when anticipating outbreak evolution. However, models usually diverge from the real observations. Stochastics involved in pandemic systems, such as changes in human contact patterns play a substantial role in disease transmissions and are not usually captured in traditional dynamic models. In addition, models of emerging diseases face the challenge of limited epidemiological knowledge about the natural history of disease. Even when the information about natural history is available -- for example for endemic seasonal diseases -- transmission models are often simplified and are involved with omissions. Availability of data streams can provide a view of early days of a pandemic, but fail to predict how the pandemic will evolve. Recent developments of computational statistics algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo, provide the possibility of creating models based on historical data as well as re-grounding models based on ongoing data observations. The objective of this thesis is to combine particle filtering -- a Sequential Monte Carlo algorithm -- with system dynamics models of pandemics. We developed particle filtering models that can recurrently be re-grounded as new observations become available. To this end, we also examined the effectiveness of this arrangement which is subject to specifics of the configuration (e.g., frequency of data sampling). While clinically-diagnosed cases are valuable incoming data stream during an outbreak, new generation of geo-spatially specific data sources, such as search volumes can work as a complementary data resource to clinical data. As another contribution, we used particle filtering in a model which can be re-grounded based on both clinical and search volume data. Our results indicate that the particle filtering in combination with compartmental models provides accurate projection systems for the estimation of model states and also model parameters (particularly compared to traditional calibration methodologies and in the context of emerging communicable diseases). The results also suggest that more frequent sampling from clinical data improves predictive accuracy outstandingly. The results also present that assumptions to make regarding the parameters associated with the particle filtering itself and changes in contact rate were robust across adequacy of empirical data since the beginning of the outbreak and inter-observation interval. The results also support the use of data from Google search API along with clinical data

Spatio-temporal modeling of arthropod-borne zoonotic diseases: a proposed methodology to enhance macro-scale analyses

Zoonotic diseases are infectious diseases that can be transmitted from or through animals to humans, and arthropods often act as vectors for transmission. Emerging infectious diseases have been increasing both in prevalence and geographic range at alarming rates the last 30 years, and the majority of these diseases are zoonotic in nature. Many zoonotic diseases are considered notifiable by the Centers for Disease Control and Prevention (CDC). However, though state regulations or contractual obligations may require the reporting of certain diseases, significant underreporting is known to exist. Because of the rich volume of information captured in health insurance plan databases, administrative medical claims data could supplement the current reporting systems and allow for more comprehensive spatio-temporal analyses of zoonotic infections. The purpose of this dissertation is to introduce the use of electronic administrative medical claims data as a potential new source that could be leveraged in ecological field studies in the surveillance of arthropod-borne zoonotic diseases. If using medical claims data to study zoonoses is a viable approach, it could be used to improve both the temporal and spatial scale of study through the use of long-term longitudinal data covering large geographic expansions and more geographically refined ZIP code scales. Additionally, claims data could supplement the current reporting of notifiable diseases to the CDC. This effort may help bridge the disease incidence gap created by health care providers\u27 underreporting and thus allow for more effective tracking and monitoring of infectious zoonotic diseases across time and space. I specifically examined 5 tick-borne (Lyme disease [LD], babesiosis, ehrlichiosis, Rocky Mountain spotted fever [RMSF], and tularemia) and 2 mosquito-borne (West Nile virus, La Crosse viral encephalitis) diseases known to occur in the southeastern US. I first compared disease incidence rates from cases reported to the Tennessee Department of Health (TDH) state registry system with medically diagnosed cases captured in a southeastern managed care organization (MCO) claims data warehouse. I determined that LD and RMSF are significantly underreported in Tennessee. Three (3) cases of babesiosis were discovered in the claims data, a significant finding as this disease has never been reported in Tennessee. Next, I used a cluster scan statistic to statistically validate when (temporal) and where (spatial) these data sources differ. Findings highlight how the data sources do not overlap in their significant cluster results, supporting the need to integrate administrative and state registry data sources in order to provide a more comprehensive set of case information. Once the usefulness of administrative data was demonstrated, I focused on how these data could improve spatio-temporal macro-scale analyses by examining information at the ZIP code level as opposed to traditional county level assessments. I expanded on the current literature related to spatially explicit modeling by employing more advanced data mining modeling techniques. Four separate modeling techniques were compared (stepwise logistic regression, classification and regression tree, gradient boosted tree, and neural network) to describe the occurrence of tick-borne diseases as they relate to socio-demographic, geographic, and habitat characteristics. Covariates most useful in explaining LD and RMSF were similar and included co-occurrences of RMSF and LD, respectively, amount of forested and non-forested wetlands, pasture/grasslands, and urbanized/developed lands, population counts, and median income levels. Finally, I conclude with a ZIP code level spatio-temporal modeling exercise to determine areas and time periods in Tennessee where significant clusters of the studied diseases occurred. ZIP code level clusters were compared to the previously defined county-level clusters to discuss the importance of spatial scale. The findings suggest that focused disease/vector prevention efforts in non-endemic areas are warranted. Very little work exists using administrative claims data in the study of zoonotic diseases. This body of work thus adds to an area void of much knowledge. Administrative medical claims data are relatively easy to access given the appropriate permissions, have relatively no cost once access is granted, and provides the researcher with a volume rich dataset from which to study. This data source should be properly considered in the wildlife and biological sciences fields of research

Use of data mining and artificial intelligence to derive public health evidence from large datasets

This thesis explores the use of data mining and AI-tailored frameworks for extracting public health evidence from large health datasets. The research presented in this thesis demonstrates the potential of these tools for automating and simplifying the data mining process, and for providing valuable insights into various public health issues.In Paper I, we used data mining and natural language processing to analyze the characteristics of genomic research on non-communicable diseases (NCDs) from the GWAS Catalog (2005 to 2022). We found that the majority of research institutions leading the work are often US-based and the majority of first, senior and all authors were male. The vast majority of complex trait GWAS has been performed in European ancestry populations, with cohorts and scientists predominantly located in medium-to-high socioeconomically ranked countries. This lack of diversity in both the data and the authorship of GWAS research has potential implications for the generalizability of genetic discoveries and the development of future interventions.In Paper II, we analyzed data collected through the app-based COVID Symptom Study in Sweden. We then created a symptom-based model to estimate the individual probability of symptomatic COVID-19 and employed this to estimate daily regional COVID-19 prevalence. We also used this data to predict next week COVID-19 hospital admissions and compared it to a model based on case notifications. We found that the symptom-based model had a lower median absolute percentage error during the first wave of the pandemic and that the model was transferable to an English dataset. The findings of this study demonstrate the feasibility of large-scale syndromic surveillance and the potential for population-based participatory surveillance initiatives in future pandemics and epidemics.In Paper III, we used data from over 500,000 participants in the COVID Symptom Study to investigate the impact of obesity and diabetes on the symptoms and duration of long-COVID. Using advanced data mining techniques, we found that individuals with higher BMI and diabetes had a higher burden of symptoms during the initial COVID-19 infection and a prolonged duration of long-COVID symptoms. We also found that vaccination had a protective effect against both COVID-19 symptoms and long-COVID symptoms in these at-risk groups. Our results demonstrate the disproportionate impact of COVID-19 on certain populations and the utility of app-based syndromic surveillance in providing timely and accurate information on the spread and impact of the virus

Development of a Web-enabled Spatial Decision Support System (SDSS) for Prevention of Tick Borne Disease in Kuantan, Malaysia

Ticks are the second most common vectors of human disease after mosquitoes. They are found on many small mammal hosts and also blood-feed on humans with the risk of transmitting diseases. Considering the diseases’ risks, this study has investigated the potential for a web-enabled spatial decision support system (SDSS) to assist government decision-makers in the control, management of resources and prevention of tick borne diseases specifically in the study area of Kuantan, Malaysia