Drivers of Emerging Infectious Disease Events as a Framework for Digital Detection.
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ABSTRACT:
SUBMITTER: Olson SH
PROVIDER: S-EPMC4517741 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The increasing number of emerging infectious disease events that have spread internationally, such as severe acute respiratory syndrome (SARS) and the 2009 pandemic A/H1N1, highlight the need for improvements in global outbreak surveillance. It is expected that the proliferation of Internet-based reports has resulted in greater communication and improved surveillance and reporting frameworks, especially with the revision of the World Health Organization's (WHO) International Health Regulations (IHR 2005), which went into force in 2007. However, there has been no global quantitative assessment of whether and how outbreak detection and communication processes have actually changed over time. In this study, we analyzed the entire WHO public record of Disease Outbreak News reports from 1996 to 2009 to characterize spatial-temporal trends in the timeliness of outbreak discovery and public communication about the outbreak relative to the estimated outbreak start date. Cox proportional hazards regression analyses show that overall, the timeliness of outbreak discovery improved by 7.3% [hazard ratio (HR) = 1.073, 95% CI (1.038; 1.110)] per year, and public communication improved by 6.2% [HR = 1.062, 95% CI (1.028; 1.096)] per year. However, the degree of improvement varied by geographic region; the only WHO region with statistically significant (? = 0.05) improvement in outbreak discovery was the Western Pacific region [HR = 1.102 per year, 95% CI (1.008; 1.205)], whereas the Eastern Mediterranean [HR = 1.201 per year, 95% CI (1.066; 1.353)] and Western Pacific regions [HR = 1.119 per year, 95% CI (1.025; 1.221)] showed improvement in public communication. These findings provide quantitative historical assessment of timeliness in infectious disease detection and public reporting of outbreaks.
| S-EPMC3003006 | biostudies-literature
Project description:BackgroundType 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system.ObjectiveThe study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information.MethodsWe retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly).ResultsOur analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively.ConclusionsWe presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.
| S-EPMC7450374 | biostudies-literature
Project description:Tularemia is a bacterial disease of humans, wild, and domestic animals. Francisella tularensis, which is a Gram-negative coccobacillus-shaped bacterium, is the causative agent of tularemia. Recently, an increase in the number of human tularemia cases has been noticed in several countries around the world. It has been reported mostly from North America, several Scandinavian countries, and certain Asian countries. The disease spreads through vectors such as mosquitoes, horseflies, deer flies, and ticks. Humans can acquire the disease through direct contact of sick animals, consumption of infected animals, drinking or direct contact of contaminated water, and inhalation of bacteria-loaded aerosols. Low infectious dose, aerosol route of infection, and its ability to induce fatal disease make it a potential agent of biological warfare. Tularemia leads to several clinical forms, such as glandular, ulceroglandular, oculoglandular, oropharyngeal, respiratory, and typhoidal forms. The disease is diagnosed through the use of culture, serology, or molecular methods. Quinolones, tetracyclines, or aminoglycosides are frequently used in the treatment of tularemia. No licensed vaccine is available in the prophylaxis of tularemia and this is need of the time and high-priority research area. This review mostly focuses on general features, importance, current status, and preventive measures of this disease.
| S-EPMC7521936 | biostudies-literature
Project description:The disease chytridiomycosis, caused by the fungus Batrachochytrium dendrobatidis (Bd), has caused dramatic amphibian population declines and extinctions in Australia, Central and North America, and Europe. Bd is associated with >200 species extinctions of amphibians, but not all species that become infected are susceptible to the disease. Specifically, Bd has rapidly emerged in some areas of the world, such as in Australia, USA, and throughout Central and South America, causing population and species collapse. The mechanism behind the rapid global emergence of the disease is poorly understood, in part due to an incomplete picture of the global distribution of Bd. At present, there is a considerable amount of geographic bias in survey effort for Bd, with Asia being the most neglected continent. To date, Bd surveys have been published for few Asian countries, and infected amphibians have been reported only from Indonesia, South Korea, China and Japan. Thus far, there have been no substantiated reports of enigmatic or suspected disease-caused population declines of the kind that has been attributed to Bd in other areas. In order to gain a more detailed picture of the distribution of Bd in Asia, we undertook a widespread, opportunistic survey of over 3,000 amphibians for Bd throughout Asia and adjoining Papua New Guinea. Survey sites spanned 15 countries, approximately 36° latitude, 111° longitude, and over 2000 m in elevation. Bd prevalence was very low throughout our survey area (2.35% overall) and infected animals were not clumped as would be expected in epizootic events. This suggests that Bd is either newly emerging in Asia, endemic at low prevalence, or that some other ecological factor is preventing Bd from fully invading Asian amphibians. The current observed pattern in Asia differs from that in many other parts of the world.
| S-EPMC3156717 | biostudies-literature
Project description:Emerging infectious diseases (EIDs), including the latest COVID-19 pandemic, have emerged and raised global public health crises in recent decades. Without existing protective immunity, an EID may spread rapidly and cause mass casualties in a very short time. Therefore, it is imperative to identify cases with risk of disease progression for the optimized allocation of medical resources in case medical facilities are overwhelmed with a flood of patients. This study has aimed to cope with this challenge from the aspect of preventive medicine by exploiting machine learning technologies. The study has been based on 83,227 hospital admissions with influenza-like illness and we analysed the risk effects of 19 comorbidities along with age and gender for severe illness or mortality risk. The experimental results revealed that the decision rules derived from the machine learning based prediction models can provide valuable guidelines for the healthcare policy makers to develop an effective vaccination strategy. Furthermore, in case the healthcare facilities are overwhelmed by patients with EID, which frequently occurred in the recent COVID-19 pandemic, the frontline physicians can incorporate the proposed prediction models to triage patients suffering minor symptoms without laboratory tests, which may become scarce during an EID disaster. In conclusion, our study has demonstrated an effective approach to exploit machine learning technologies to cope with the challenges faced during the outbreak of an EID.
| S-EPMC8748708 | biostudies-literature
Project description:The emergence and global impact of the novel influenza A(H1N1)v highlights the continuous threat to public health posed by a steady stream of new and unexpected infectious disease outbreaks in animals and humans. Once an emerging epidemic is detected, public health authorities will attempt to mitigate the epidemic by, among other measures, reducing further spread as much as possible. Scarce and/or costly control measures such as vaccines, anti-infective drugs, and social distancing must be allocated while epidemiological characteristics of the disease remain uncertain. Here we present first principles for allocating scarce resources with limited data. We show that under a broad class of assumptions, the simple rule of targeting intervention measures at the group with the highest risk of infection per individual will achieve the largest reduction in the transmission potential of a novel infection. For vaccination of susceptible persons, the appropriate risk measure is force of infection; for social distancing, the appropriate risk measure is incidence of infection. Unlike existing methods that rely on detailed knowledge of group-specific transmission rates, the method described here can be implemented using only data that are readily available during an epidemic, and allows ready adaptation as the epidemic progresses. The need to observe risk of infection helps to focus the ongoing planning and design of new infectious disease surveillance programs; from the presented first principles for allocating scarce resources, we can adjust the prioritization of groups for intervention when new observations on an emerging epidemic become available.
| S-EPMC2818907 | biostudies-literature
Project description:Hyperpredation refers to an enhanced predation pressure on a secondary prey due to either an increase in the abundance of a predator population or a sudden drop in the abundance of the main prey. This scarcely documented mechanism has been previously studied in scenarios in which the introduction of a feral prey caused overexploitation of native prey. Here we provide evidence of a previously unreported link between Emergent Infectious Diseases (EIDs) and hyperpredation on a predator-prey community. We show how a viral outbreak caused the population collapse of a host prey at a large spatial scale, which subsequently promoted higher-than-normal predation intensity on a second prey from shared predators. Thus, the disease left a population dynamic fingerprint both in the primary host prey, through direct mortality from the disease, and indirectly in the secondary prey, through hyperpredation. This resulted in synchronized prey population dynamics at a large spatio-temporal scale. We therefore provide evidence for a novel mechanism by which EIDs can disrupt a predator-prey interaction from the individual behavior to the population dynamics. This mechanism can pose a further threat to biodiversity through the human-aided disruption of ecological interactions at large spatial and temporal scales.
| S-EPMC2390756 | biostudies-literature
Project description:Biosurveillance, a relatively young field, has recently increased in importance because of increasing emphasis on global health. Databases and tools describing particular subsets of disease are becoming increasingly common in the field. Here, we present an infectious disease database that includes diseases of biosurveillance relevance and an extensible framework for the easy expansion of the database.
| S-EPMC5547458 | biostudies-other
Project description: Not available
| S-EPMC11017537 | biostudies-literature
Project description:ObjectivesThroughout the coronavirus disease 2019 pandemic, susceptible-infectious-recovered (SIR) modeling has been the preeminent modeling method to inform policy making worldwide. Nevertheless, the usefulness of such models has been subject to controversy. An evolution in the epidemiological modeling field is urgently needed, beginning with an agreed-upon set of modeling standards for policy recommendations. The objective of this article is to propose a set of modeling standards to support policy decision making.MethodsWe identify and describe 5 broad standards: transparency, heterogeneity, calibration and validation, cost-benefit analysis, and model obsolescence and recalibration. We give methodological recommendations and provide examples in the literature that employ these standards well. We also develop and demonstrate a modeling practices checklist using existing coronavirus disease 2019 literature that can be employed by readers, authors, and reviewers to evaluate and compare policy modeling literature along our formulated standards.ResultsWe graded 16 articles using our checklist. On average, the articles met 6.81 of our 19 categories (36.7%). No articles contained any cost-benefit analyses and few were adequately transparent.ConclusionsThere is significant room for improvement in modeling pandemic policy. Issues often arise from a lack of transparency, poor modeling assumptions, lack of a system-wide perspective in modeling, and lack of flexibility in the academic system to rapidly iterate modeling as new information becomes available. In anticipation of future challenges, we encourage the modeling community at large to contribute toward the refinement and consensus of a shared set of standards for infectious disease policy modeling.
| S-EPMC8110035 | biostudies-literature