Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review

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EFFECTS OF EXTREME PRECIPITATION ON GASTROINTESITNAL RELATED HOSPITAL ADMISSIONS IN TEXAS

Extreme precipitation has been implicated in more than 51% of the waterborne disease outbreaks in the United States between 1948-1994. With increased incidence of extreme precipitation projected to be more likely due to ongoing climate change, the burden of waterborne disease is expected to rise even in the United States where drinking water is considered to be one of the safest in the world. In this study we aim to quantify the risk of extreme precipitation on gastrointestinal (GI) related hospital admissions by using meteorological and emergency hospital data from twelve major metropolitan statistical areas (MSA) of Texas from year 2004 to 2013. We used distributed lag non-linear model with quasi Poisson regression to estimate the relative risk of GI-related hospital admission occurring at the certain value of precipitation (90th, 95th and 99th percentile) following 15-day period to the probability of the event occurring at the reference value of no precipitation (0 mm). The results showed that the cumulative risk of GI-related hospital admission following days with extreme precipitation was consistently elevated in overall as well as age stratified population in most of the MSAs. The relative risks were significantly higher in children under 6 years and elderly above 65 years compared to adults between 6 to 65 years. The largest effect was observed in Corpus Christi with an estimated relative risk of 2.19 [95% CI: 1.35,3.54] among children under 6 years and 1.65 [95% CI: 1.33, 2.05] among elderly population above 65 years.The results from casue specific analysis showed diarrheal specific causes were responsible for most of the risks observed compared to pathogen or other/ill-defined causes. The findings underscore the need for development of policies and infrastructures to address the effects of extreme precipitation on global/local disease burden given the projected increase in such inclement weather events in the future

Spatial Analysis of Mosquito-Borne Diseases in Europe: A Scoping Review

Mosquito-borne infections are increasing in endemic areas and previously unaffected regions. In 2020, the notification rate for Dengue was 0.5 cases per 100,000 population, and for Chikungunya <0.1/100,000. In 2019, the rate for Malaria was 1.3/100,000, and for West Nile Virus, 0.1/100,000. Spatial analysis is increasingly used in surveillance and epidemiological investigation, but reviews about their use in this research topic are scarce. We identify and describe the methodological approaches used to investigate the distribution and ecological determinants of mosquito-borne infections in Europe. Relevant literature was extracted from PubMed, Scopus, and Web of Science from inception until October 2021 and analysed according to PRISMA-ScR protocol. We identified 110 studies. Most used geographical correlation analysis (n = 50), mainly applying generalised linear models, and the remaining used spatial cluster detection (n = 30) and disease mapping (n = 30), mainly conducted using frequentist approaches. The most studied infections were Dengue (n = 32), Malaria (n = 26), Chikungunya (n = 26), and West Nile Virus (n = 24), and the most studied ecological determinants were temperature (n = 39), precipitation (n = 24), water bodies (n = 14), and vegetation (n = 11). Results from this review may support public health programs for mosquito-borne disease prevention and may help guide future research, as we recommended various good practices for spatial epidemiological studies.info:eu-repo/semantics/publishedVersio

Use of Mapping and Spatial and Space-Time Modeling Approaches in Operational Control of Aedes aegypti and Dengue

The aims of this review paper are to 1) provide an overview of how mapping and spatial and space-time modeling approaches have been used to date to visualize and analyze mosquito vector and epidemiologic data for dengue; and 2) discuss the potential for these approaches to be included as routine activities in operational vector and dengue control programs. Geographical information system (GIS) software are becoming more user-friendly and now are complemented by free mapping software that provide access to satellite imagery and basic feature-making tools and have the capacity to generate static maps as well as dynamic time-series maps. Our challenge is now to move beyond the research arena by transferring mapping and GIS technologies and spatial statistical analysis techniques in user-friendly packages to operational vector and dengue control programs. This will enable control programs to, for example, generate risk maps for exposure to dengue virus, develop Priority Area Classifications for vector control, and explore socioeconomic associations with dengue risk

Evaluation by Geospatial and Spatiotemporal Distribution of Tularemia Cases in Arkansas

Tularemia is a vector-borne disease of global concern with diverse regional foci. Arkansas is an endemic state with differences in case distribution and land suitability supporting host and vector sustainment. The aim of this study was to conduct a geospatial and spatiotemporal assessment of factors associated with case distribution and timeliness and completeness of public reporting. Guided with direction from spatial epidemiology and nidality, referring to the association of ecology, climate, and proximity of disease, analysis included secondary data collected from the Arkansas Department of Health between 1995 and 2018. Using Poisson-based software, 2 clusters were found: a high-risk cluster encompassing 23% of the total population within 24 counties spanning an 8-year period (RR = 4.98, p \u3c 0.05), and a low risk cluster that included 25% of the population within 28 counties during a 12-year period (RR 0.14, p \u3c 0.05). Analysis of ecological data revealed associations between annual precipitation within the high-risk cluster and total number of cases (AUC = 0.716 and AUC = 0.726, respectively) with trends toward higher incidence rates in suitable land cover and moderate to high elevation using maximum entropy software. Analysis of timeliness and completeness revealed gaps for clinical form and transmission mode determination (p \u3c 0.05), while increases in probable cases followed decreases in confirmed cases revealing gaps in laboratory diagnostics. Positive social change necessitates multidisciplinary collaboration between climatologists, clinicians, and epidemiologists to reach high-risk populations and promote educational awareness. The potential for social change includes predictive modeling optimizing funding while representing underserved populations

Correlations of Online Search Engine Trends with Coronavirus Disease (COVID-19) Incidence: Infodemiology Study

Background: The coronavirus disease (COVID-19) is the latest pandemic of the digital age. With the internet harvesting large amounts of data from the general population in real time, public databases such as Google Trends (GT) and the Baidu Index (BI) can be an expedient tool to assist public health efforts. Objective: The aim of this study is to apply digital epidemiology to the current COVID-19 pandemic to determine the utility of providing adjunctive epidemiologic information on outbreaks of this disease and evaluate this methodology in the case of future pandemics. Methods: An epidemiologic time series analysis of online search trends relating to the COVID-19 pandemic was performed from January 9, 2020, to April 6, 2020. BI was used to obtain online search data for China, while GT was used for worldwide data, the countries of Italy and Spain, and the US states of New York and Washington. These data were compared to real-world confirmed cases and deaths of COVID-19. Chronologic patterns were assessed in relation to disease patterns, significant events, and media reports. Results: Worldwide search terms for shortness of breath, anosmia, dysgeusia and ageusia, headache, chest pain, and sneezing had strong correlations (r>0.60, P<.001) to both new daily confirmed cases and deaths from COVID-19. GT COVID-19 (search term) and GT coronavirus (virus) searches predated real-world confirmed cases by 12 days (r=0.85, SD 0.10 and r=0.76, SD 0.09, respectively, P<.001). Searches for symptoms of diarrhea, fever, shortness of breath, cough, nasal obstruction, and rhinorrhea all had a negative lag greater than 1 week compared to new daily cases, while searches for anosmia and dysgeusia peaked worldwide and in China with positive lags of 5 days and 6 weeks, respectively, corresponding with widespread media coverage of these symptoms in COVID-19. Conclusions: This study demonstrates the utility of digital epidemiology in providing helpful surveillance data of disease outbreaks like COVID-19. Although certain online search trends for this disease were influenced by media coverage, many search terms reflected clinical manifestations of the disease and showed strong correlations with real-world cases and deaths

Using internet search queries for infectious disease surveillance: screening diseases for suitability

Background: Internet-based surveillance systems provide a novel approach to monitoring infectious diseases. Surveillance systems built on internet data are economically, logistically and epidemiologically appealing and have shown significant promise. The potential for these systems has increased with increased internet availability and shifts in health-related information seeking behaviour. This approach to monitoring infectious diseases has, however, only been applied to single or small groups of select diseases. This study aims to systematically investigate the potential for developing surveillance and early warning systems using internet search data, for a wide range of infectious diseases. Methods: Official notifications for 64 infectious diseases in Australia were downloaded and correlated with frequencies for 164 internet search terms for the period 2009–13 using Spearman’s rank correlations. Time series cross correlations were performed to assess the potential for search terms to be used in construction of early warning systems. Results: Notifications for 17 infectious diseases (26.6%) were found to be significantly correlated with a selected search term. The use of internet metrics as a means of surveillance has not previously been described for 12 (70.6%) of these diseases. The majority of diseases identified were vaccine-preventable, vector-borne or sexually transmissible; cross correlations, however, indicated that vector-borne and vaccine preventable diseases are best suited for development of early warning systems. Conclusions: The findings of this study suggest that internet-based surveillance systems have broader applicability to monitoring infectious diseases than has previously been recognised. Furthermore, internet-based surveillance systems have a potential role in forecasting emerging infectious disease events, especially for vaccine-preventable and vector-borne diseases. Electronic supplementary material The online version of this article (doi:10.1186/s12879-014-0690-1) contains supplementary material, which is available to authorized users

Characterising Livestock System Zoonoses Hotspots

A systematic review of the published literature was undertaken, to explore the ability of different types of model to help identify the relative importance of different drivers leading to the development of zoonoses hotspots. We estimated that out of 373 papers we included in our review, 108 papers touched upon the objective of 'Assessment of interventions and intervention policies', 75 addressed the objective of 'Analysis of economic aspects of disease outbreaks and interventions', 67 the objective of 'Prediction of future outbreaks', but only 37 broadly addressed the objective of 'Sensitivity analysis to identify criteria leading to enhanced risk'. Most models of zoonotic diseases are currently capturing outbreaks over relatively short time and largely ignoring socio-economic drivers leading to pathogen emergence, spill-over and spread. In order to study long-term changes we need to understand how socio-economic and climatic changes affect structure of livestock production and how these in turn affect disease emergence and spread. Models capable of describing this processes do not appear to exist, although some progress has been made in linking social and economical aspects of livestock production and in linking economics to disease dynamics. Henceforth we conclude that a new modelling framework is required that expands and formalises the 'one world, one health' strategy, enabling its deployment in the re-thinking of prevention and control strategies. Although modelling can only provide means to identify risks associated with socio-economic changes, it can never be a substitute for data collection. Finally, we note that uncertainty analysis and uncertainty communication form a key element of modelling process and yet are rarely addressed

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