Epidemic outbreak prediction using machine learning models

In today's world,the risk of emerging and re-emerging epidemics have increased.The recent advancement in healthcare technology has made it possible to predict an epidemic outbreak in a region.Early prediction of an epidemic outbreak greatly helps the authorities to be prepared with the necessary medications and logistics required to keep things in control. In this article, we try to predict the epidemic outbreak (influenza, hepatitis and malaria) for the state of New York, USA using machine and deep learning algorithms, and a portal has been created for the same which can alert the authorities and health care organizations of the region in case of an outbreak. The algorithm takes historical data to predict the possible number of cases for 5 weeks into the future. Non-clinical factors like google search trends,social media data and weather data have also been used to predict the probability of an outbreak.Comment: 16 pages, 5 tables, 4 figure

Gaussian Processes for Spatiotemporal Modelling

A statistical framework for spatiotemporal modelling should ideally be able to assimilate different types of data from different of sources. Gaussian processes are commonly used tool for interpolating values across time and space domains. In this thesis we work on extending the Gaussian processes framework to deal with diverse noise model assumptions. We present a model based on a hybrid approach that combines some of the features of the discriminative and generative perspectives, allowing continuous dimensionality reduction of hybrid discrete-continuous data, discriminative classification with missing inputs and manifold learning informed by class labels. We present an application of malaria density modelling across Uganda using administrative records. This disease represents a threat for approximately 3.3 billion people around the globe. The analysis of malaria based on the records available faces two main complications: noise induced by a highly variable rate of reporting health facilities; and lack of comparability across time, due to changes in districts delimitation. We define a Gaussian process model able to assimilate this features in the data and provide an insight on the generating process behind the records. Finally, a method to monitor malaria case-counts is proposed. We use vector-valued covariance kernels to analyze the time series components individually. The short term variations of the infection are divided into four cyclical phases. The graphical tool provided can help quick response planning and resources allocation

Space-time statistical analysis of malaria morbidity incidence cases in Ghana: A geostatistical modelling approach

Malaria is one of the most prevalent and devastating health problems worldwide. It is a highly endemic disease in Ghana, which poses a major challenge to both the public health and socio-economic development of the country. Major factors accounting for this situation include variability in environmental conditions and lack of prevention services coupled with host of other socio-economic factors. Ghana’s National Malaria Control Programme (NMCP) risk assessment measures have been largely based on household surveys which provided inadequate data for accurate prediction of new incidence cases coupled with frequent incomplete monthly case reports. These raise concerns about annual estimates on the disease burden and also pose serious threats to efficient public health planning including the country’s quest of reducing malaria morbidity and mortality cases by 75% by 2015. In this thesis, both geostatistical space-time models and time series seasonal autoregressive integrated moving average (SARIMA) predictive models have been studied and applied to the monthly malaria morbidity cases from both district and regional health facilities in Ghana. The study sought to explore the spatio-temporal distributions of the malaria morbidity incidence and to account for the potential influence of climate variability, with particular focus on producing monthly spatial maps, delimiting areas with high risk of morbidity. This was achieved by modelling the morbidity cases as incidence rates, being the number of new reported cases per 100,000 residents, which together with the climatic covariates were considered as realisations of random processes occurring in space and/or time. The SARIMA models indicated an upward trend of morbidity incidence in the regions with strong seasonal variation which can be explained primarily by the effects of rainfall, temperature and relative humidity in the month preceding incidence of the disease as well as the morbidity incidence in the previous months. The various spacetime ordinary kriging (STOK) models showed varied spatial and temporal distributions of the morbidity incidence rates, which have increased and expanded across the country over the years. The space-time semivariogram models characterising the spatio-temporal continuity of the incidence rates indicated that the occurrence of the malaria morbidity was spatially and temporally correlated within spatial and temporal ranges varying between 30 and 250 km and 6 and 100 months, respectively. The predicted incidence rates were found to be heterogeneous with highly elevated risk at locations near the borders with neighbouring countries in the north and west as well as the central parts towards the east. The spatial maps showed transition of high risk areas from the north-west to the north-east parts with climatic variables contributing to the variations in the number of morbidity cases across the country. The morbidity incidence estimates were found to be higher during the wet season when temperatures were relatively low whilst low incidence rates were observed in the warm weather period during the dry seasons. In conclusion, the study quantified the malaria morbidity burden in Ghana to produce evidence-based monthly morbidity maps, illustrating the risk patterns of the morbidity of the disease. Increased morbidity risk, delimiting the highest risk areas was also established. This statistical-based modelling approach is important as it allows shortterm prediction of the malaria morbidity incidence in specific regions and districts and also helps support efficient public health planning in the country

Road development in the Brazilian Amazon and its ecological implications

Roads are a distinctive feature in any landscape, with many countries giving 1-2% of their land surface over to roads and roadsides (Forman 1998). However, the ecological effects of roads spread beyond the physical footprint of the network and may impact 15-20% of the land or more (Forman & Alexander 1998). The Brazilian Amazon contains approximately one third of the world’s remaining rainforest, covering an area of 4.1 million km2. The region is highly biodiverse with 10-20 percent of the planet’s known species, it is also one of the three most bioculturally diverse areas in the world (Loh & Harmon 2005), and it provides many valuable ecosystem services. However, the Brazilian Amazon is rapidly undergoing extensive development with widespread land-use conversion. Road development is often perceived as the initial stage of development, opening access to remote areas for colonisation, agriculture development, resource extraction, and linked with these; deforestation (Chomitz & Gray 1996, Laurance et al. 2001, Perz et al. 2007, Laurance et al. 2009, Caldas et al. 2010). As such roads are a key spatial determinant of land use conversion in the Amazon region, dictating the spatial pattern of deforestation and biodiversity loss (Fearnside 2005, Kirby et al. 2006, Perz et al. 2008). Given that roads are a key spatial determinant of land use conversion and that they have extensive impacts on rates and patterns of habitat loss, it is important that we know how much, how fast and where road networks are developing in this globally important ecosystem. In this thesis, I aim to construct models of road network development to help better understand and predict the impacts of economic development in the Brazilian Amazon.Open Acces

A stochastic model of malaria transmission

Malaria models have evolved since Ross and Macdonald. By using an agent-based stochastic model we have looked into di erent aspects of disease transmission: 1. Gametocytemia phase transition between epidemic stability and disease elimination, and the potential bene t of combining gametocidal agents and ivermectin. 2. Heterogeneity promotes disease spreading. 3. Disease supression from the combined use of ivermectin and primaquine. 4. Utility of Hurst exponent and Shannon entropy in malaria forecasting. Results and conclusion: Malaria transmission was simulated with a computational agent-based model assuming a small African village. We have con rmed gametocytemia as a critical factor in disease transmission, revealing an abrupt phase transition between epidemic stability and disease elimination [326]. We have also found that synergism between gametocidal agents (primaquine) and ivermectin (a selective Anophelocide drug a ecting parasite maturation after mosquito infection) could e ectively suppress human-to-mosquito disease transmission [326]. We have found that heterogeneity ampli es disease transmission (roughly three times in our model). Different aspects of heterogeneity were analyzed such as human migration, mosquito density, and rainfall [327]. We have con rmed the potential bene t of suppressing heterogeneity-induced disease transmission with the use of gametocidal agents and ivermectin. Hurst exponent has been used in hydrology and in the stock market. No previous evidence of its application to infectious theory has been found. Yet, our data suggests that Hurst exponent and information entropy could be useful in malaria forecasting [328]. Our results support the combined use of gametocidal agents (primaquine or methylene blue) and ivermectin as part of an integrated approach to malaria.Os modelos de malária são úteis desde Ross e Macdonald. Através de um modelo estocástico de agente, foram analisados vários aspectos da transmissão da malária: 1. A existência de uma transição de fase entre estabilidade e eliminação da doença em função da gametocitemia. 2. O uso combinado de fármacos gametocidas e ivermectina na redução da transmissão. 3. O papel da heterogeneidadena propagação da malária. 4. A utilidade do expoente de Hurst e da entropia de Shannon na previão da malária. Resultados e conclusões: Foi utilizado um modelo computacional de agente com simulação da transmissão de malária numa pequena aldeia africana. Confirmámos a gametocitemia como um factor crítico na propagação da malária demonstrando uma transição abrupta de fase entre estabilidade epidémica e eliminação da doença. No nosso modelo foi demonstrado que na presença de heterogeneidade a transmissão de malária pode sofrer uma amplificação significativa, de aproximadamente três vezes. Foram analisados diferentes aspectos da heterogeneidade tais como a migração humana, a densidade vectorial e a precipitação sazonal. Foi confirmado o potencial benefício de supressão da transmissão da malária na presença de heterogeneidade com a utilização de fármacos gametocidas (primaquina) e ivermectina. O expoente de Hurst tem sido aplicado com sucesso nas áreas da hidrologia e do mercado bolsista. Não houve até agora evidência da sua aplicação à área da infecciologia. No entanto, os dados apresentados sugerem a sua utilidade, a par da entropia de Shannon, na previsão da incidência da malária. Foi demonstrado que o uso combinado de agentes gametocidas (primaquina ou azul de metileno) e ivermectina pode constituir uma abordagem eficaz na prevenção da malári

Modeling Precipitation, Acute Gastrointestinal Illness, and Environmental Factors in North Carolina, USA

Increasing intensity and frequency of extreme weather events due to climate change underscores the importance of understanding the influence of hydroclimatic variability on health. Meteorological drivers affect rates of acute gastrointestinal illness (AGI), but the association between precipitation and AGI, the sensitivity to modeling decisions, and the effects of sociodemographic and environmental risk factors are not well characterized. Furthermore, methodological differences may reduce inter-study comparability and can affect model estimates.In this dissertation, we reviewed the methodologies of recent time series AGI-weather studies, including outcome and exposure variables, data sources, spatiotemporal aggregation, and model specification. To investigate the sensitivity of the association between AGI and precipitation to exposure definitions and effect measure modification (EMM), we used AGI emergency department (ED) visit and weather data (2008-2015) from North Carolina (NC) to develop daily, ZIP code-level quasi-Poisson generalized linear models and distributed lag models. We compared multiple precipitation metrics: absolute (total precipitation), extreme (90th, 95th, and 99th percentiles with and without zero-precipitation days), and antecedent (cumulative wet-dry days; 8-week wet-dry periods). We assessed for potential EMM by physiographic region, the density of hogs in concentrated animal feeding operations (CAFOs), and percent of population on private drinking water wells.Depending on exposure definition, we observed an overall cumulative decrease of 1-18% in AGI ED rates following extreme precipitation events (over 0-7 days), with stronger effects associated with heavier rainfall, and a 2% (95% CI: 1.02, 1.03) increase after antecedent (8-week) wet periods. Inverse statewide results following extreme precipitation—dominated by the demographic weight of urban centers in the Piedmont region—were consistent with dilution effects posited by the concentration-dilution hypothesis but obscured dramatic sub-state variation. While EMM by private wells was inconclusive, region and hog density strongly modified the associations observed, with increased AGI ED rates following 95th percentile precipitation in the mountains (18%), coastal plains (19%), and areas exposed to hog CAFOs (7-15%). Our results reveal the vulnerability of mountainous, coastal, and CAFO-impacted areas in NC to rainfall-exacerbated AGI risk. This dissertation highlights the hazards of data aggregation and importance of precipitation exposure definitions and effect measure modification when modeling climate-health relationships.Doctor of Philosoph

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies

Spatio-temporal forecasting of network data

In the digital age, data are collected in unprecedented volumes on a plethora of networks. These data provide opportunities to develop our understanding of network processes by allowing data to drive method, revealing new and often unexpected insights. To date, there has been extensive research into the structure and function of complex networks, but there is scope for improvement in modelling the spatio-temporal evolution of network processes in order to forecast future conditions. This thesis focusses on forecasting using data collected on road networks. Road traffic congestion is a serious and persistent problem in most major cities around the world, and it is the task of researchers and traffic engineers to make use of voluminous traffic data to help alleviate congestion. Recently, spatio-temporal models have been applied to traffic data, showing improvements over time series methods. Although progress has been made, challenges remain. Firstly, most existing methods perform well under typical conditions, but less well under atypical conditions. Secondly, existing spatio-temporal models have been applied to traffic data with high spatial resolution, and there has been little research into how to incorporate spatial information on spatially sparse sensor networks, where the dependency relationships between locations are uncertain. Thirdly, traffic data is characterised by high missing rates, and existing methods are generally poorly equipped to deal with this in a real time setting. In this thesis, a local online kernel ridge regression model is developed that addresses these three issues, with application to forecasting of travel times collected by automatic number plate recognition on London’s road network. The model parameters can vary spatially and temporally, allowing it to better model the time varying characteristics of traffic data, and to deal with abnormal traffic situations. Methods are defined for linking the spatially sparse sensor network to the physical road network, providing an improved representation of the spatial relationship between sensor locations. The incorporation of the spatio-temporal neighbourhood enables the model to forecast effectively under missing data. The proposed model outperforms a range of benchmark models at forecasting under normal conditions, and under various missing data scenarios