Data-Centric Epidemic Forecasting: A Survey

The COVID-19 pandemic has brought forth the importance of epidemic forecasting for decision makers in multiple domains, ranging from public health to the economy as a whole. While forecasting epidemic progression is frequently conceptualized as being analogous to weather forecasting, however it has some key differences and remains a non-trivial task. The spread of diseases is subject to multiple confounding factors spanning human behavior, pathogen dynamics, weather and environmental conditions. Research interest has been fueled by the increased availability of rich data sources capturing previously unobservable facets and also due to initiatives from government public health and funding agencies. This has resulted, in particular, in a spate of work on 'data-centered' solutions which have shown potential in enhancing our forecasting capabilities by leveraging non-traditional data sources as well as recent innovations in AI and machine learning. This survey delves into various data-driven methodological and practical advancements and introduces a conceptual framework to navigate through them. First, we enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting, capturing various factors like symptomatic online surveys, retail and commerce, mobility, genomics data and more. Next, we discuss methods and modeling paradigms focusing on the recent data-driven statistical and deep-learning based methods as well as on the novel class of hybrid models that combine domain knowledge of mechanistic models with the effectiveness and flexibility of statistical approaches. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems including decision-making informed by forecasts. Finally, we highlight some challenges and open problems found across the forecasting pipeline.Comment: 67 pages, 12 figure

Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The unavailability of specific drugs and ready-to-use vaccines to prevent most of these epidemics makes the situation worse. These force public health officials and policymakers to rely on early warning systems generated by reliable and accurate forecasts of epidemics. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyse a wide variety of epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it EWNet model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with several statistical, machine learning, and deep learning models. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods

Climate information for public health : a curriculum for best practices : putting principles to work

The mission of the IRI is to enhance society’s capability to understand, anticipate and manage the impacts of climate in order to improve human welfare and the environment, especially in developing countries. The IRI conducts this mission through strategic and applied research, education, capacity building, and by providing forecasts and information products with an emphasis on practical and verifiable utility and partnership

Global Interest for Health Professions Education: A Geographic and Temporal Analyses Through Web Search Differences from 2010-2019

Purpose: The purpose of this study was to analyze the spatio-temporal differences in web search trends for dental degrees (DD), medical degrees (MD), and nursing degrees (ND) across 197 countries from 2010 to 2019. Method: A search string was used to initiate a search query using Google Trends. The parameters used were DD, MD, and ND as search terms; worldwide as Location; 2010 to 2019 as time range; health education & medical training as category; and web search as database. Data were downloaded and analyzed. Results: Via one-way ANOVA and post hoc Dunnett test, the searches for DD were found to be significantly lower in 2011 (3.2 ± 0.3, p = .044), 2012 (2.6 ± 0.2, p \u3c .001), 2013 (2.8 ± 0.3, p = .006), 2014 (3.0 ± 0.3, p = .017), and 2015 (2.9 ± 0.3, p = .010) compared to the year 2010 (4.5 ± 0.6); the searches for MD was significantly higher in 2019 (84.5 ± 2.5, p = .002) compared to the year 2010 (73.0 ± 1.7); and the searches for ND were statistically significantly higher in 2015 (28.9 ± 1.1, p = .024) and 2019 (31.7 ± 1.1, p = .001) compared to the year 2010 (24.5.0 ± 1.2). The search trend for MD increased in 31 countries and decreased in 14 countries while searches for ND increased in 40 countries and decreased in 5 countries as determined by a two-way ANOVA with Holm-Sidak’s multiple comparison test. The 12-month forecast for the search interests of these health professions predicted a rise in the third quarter and an abrupt decline at the end of the year. Conclusions: Geographic and time factors affect the search interests for health professions. In a span of a decade, the disparity of interests shown by the low interests for DD and ND compared to MD in some countries underscores the need to promote these professions to fill the future health care workforce

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Characterizing the Information Needs of Rural Healthcare Practitioners with Language Agnostic Automated Text Analysis

Objectives – Previous research has characterized urban healthcare providers\u27 information needs, using various qualitative methods. However, little is known about the needs of rural primary care practitioners in Brazil. Communication exchanged during tele-consultations presents a unique data source for the study of these information needs. In this study, I characterize rural healthcare providers\u27 information needs expressed electronically, using automated methods. Methods – I applied automated methods to categorize messages obtained from the telehealth system from two regions in Brazil. A subset of these messages, annotated with top-level categories in the DeCS terminology (the regional equivalent of MeSH), was used to train text categorization models, which were then applied to a larger, unannotated data set. On account of their more granular nature, I focused on answers provided to the queries sent by rural healthcare providers. I studied these answers, as surrogates for the information needs they met. Message representations were generated using methods of distributional semantics, permitting the application of k-Nearest Neighbor classification for category assignment. The resulting category assignments were analyzed to determine differences across regions, and healthcare providers. Results – Analysis of the assigned categories revealed differences in information needs across regions, corresponding to known differences in the distributions of diseases and tele-consultant expertise across these regions. Furthermore, information needs of rural nurses were observed to be different from those documented in qualitative studies of their urban counterparts, and the distribution of expressed information needs categories differed across types of providers (e.g. nurses vs. physicians). Discussion – The automated analysis of large amounts of digitally-captured tele-consultation data suggests that rural healthcare providers\u27 information needs in Brazil are different than those of their urban counterparts in developed countries. The observed disparities in information needs correspond to known differences in the distribution of illness and expertise in these regions, supporting the applicability of my methods in this context. In addition, these methods have the potential to mediate near real-time monitoring of information needs, without imposing a direct burden upon healthcare providers. Potential applications include automated delivery of needed information at the point of care, needs-based deployment of tele-consultation resources and syndromic surveillance. Conclusion – I used automated text categorization methods to assess the information needs expressed at the point of care in rural Brazil. My findings reveal differences in information needs across regions, and across practitioner types, demonstrating the utility of these methods and data as a means to characterize information needs

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

Emerg Infect Dis

PMC4550154611

Vector-borne diseases: studies in human West Nile Virus and canine Lyme nephritis

Vector-borne diseases are a resurgent focus in public health. As concern about climate change mounts, the close relationship between these diseases and the environment has garnered growing attention. This dissertation examines the relationship between environment and vector-borne disease in both human and veterinary medical contexts and on both a local and national scale. The first study investigated using a novel Internet-based surveillance system for risk mapping of West Nile Virus (WNV) in the contiguous United States from 2007-2014, with meteorological, demographic, and land use variables as predictors. The study found that annual average temperature, minimum temperature, precipitation, and human population density were predictive of WNV reports, but that the novel surveillance data appeared to have systematic gaps that impair the utility of the model. However, the results may help to guide improvements in novel surveillance systems. The second study used the logistic regression model developed in the first study to predict the risk of WNV in the contiguous United States in 2050 and 2070 under four projected climate scenarios. The study found that Southern California is likely to remain the area of greatest risk under all scenarios and that risk would be expected to increase across much of the West under the scenario of uncontrolled carbon dioxide emissions. The results of this study may inform development of more sophisticated models and may help to direct public health resources to areas of greatest impact. The third study investigated the relationship between cases of canine Lyme nephritis and precipitation in the months prior to diagnosis. Precipitation three months prior to diagnosis was found to be associated with the development of Lyme nephritis (hazard ratio for 1 inch/month 1.125, 95% confidence interval 1.009 – 1.254). This finding may improve diagnostic accuracy for dogs with protein-losing nephropathies and may guide studies of additional risk factors