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Emerging Challenges and Opportunities in Infectious Disease Epidemiology.
Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners
Machine learning in drug supply chain management during disease outbreaks: a systematic review
The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks
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
When Infodemic Meets Epidemic: a Systematic Literature Review
Epidemics and outbreaks present arduous challenges requiring both individual
and communal efforts. Social media offer significant amounts of data that can
be leveraged for bio-surveillance. They also provide a platform to quickly and
efficiently reach a sizeable percentage of the population, hence their
potential impact on various aspects of epidemic mitigation. The general
objective of this systematic literature review is to provide a methodical
overview of the integration of social media in different epidemic-related
contexts. Three research questions were conceptualized for this review,
resulting in over 10000 publications collected in the first PRISMA stage, 129
of which were selected for inclusion. A thematic method-oriented synthesis was
undertaken and identified 5 main themes related to social media enabled
epidemic surveillance, misinformation management, and mental health. Findings
uncover a need for more robust applications of the lessons learned from
epidemic post-mortem documentation. A vast gap exists between retrospective
analysis of epidemic management and result integration in prospective studies.
Harnessing the full potential of social media in epidemic related tasks
requires streamlining the results of epidemic forecasting, public opinion
understanding and misinformation propagation, all while keeping abreast of
potential mental health implications. Pro-active prevention has thus become
vital for epidemic curtailment and containment
The problem of scale in the prediction and management of pathogen spillover
Disease emergence events, epidemics and pandemics all underscore the need to predict zoonotic pathogen spillover. Because cross-species transmission is inherently hierarchical, involving processes that occur at varying levels of biological organization, such predictive efforts can be complicated by the many scales and vastness of data potentially required for forecasting. A wide range of approaches are currently used to forecast spillover risk (e.g. macroecology, pathogen discovery, surveillance of human populations, among others), each of which is bound within particular phylogenetic, spatial and temporal scales of prediction. Here, we contextualize these diverse approaches within their forecasting goals and resulting scales of prediction to illustrate critical areas of conceptual and pragmatic overlap. Specifically, we focus on an ecological perspective to envision a research pipeline that connects these different scales of data and predictions from the aims of discovery to intervention. Pathogen discovery and predictions focused at the phylogenetic scale can first provide coarse and pattern-based guidance for which reservoirs, vectors and pathogens are likely to be involved in spillover, thereby narrowing surveillance targets and where such efforts should be conducted. Next, these predictions can be followed with ecologically driven spatio-temporal studies of reservoirs and vectors to quantify spatio-temporal fluctuations in infection and to mechanistically understand how pathogens circulate and are transmitted to humans. This approach can also help identify general regions and periods for which spillover is most likely. We illustrate this point by highlighting several case studies where long-term, ecologically focused studies (e.g. Lyme disease in the northeast USA, Hendra virus in eastern Australia, Plasmodium knowlesi in Southeast Asia) have facilitated predicting spillover in space and time and facilitated the design of possible intervention strategies. Such studies can in turn help narrow human surveillance efforts and help refine and improve future large-scale, phylogenetic predictions. We conclude by discussing how greater integration and exchange between data and predictions generated across these varying scales could ultimately help generate more actionable forecasts and interventions
Determinants in the emergence of viral agents: the SARS-CoV-2
Abstract. The objective of this paper is to clarify, whenever possible, the determinants in the emergence of biological agents to improve aspects connected with public health and biosecurity. Case study of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is investigated to assess the likely emergence from a wildlife spillover and/or scientific research in labs with unexpected accident. Using a meta-analysis, results suggest that a natural spillover of SARS-CoV-2 that has generated more than 5.2 million of deaths, in analogy with natural disaster, seems to have a remote probability, instead a lab accident in the process of scientific research has a probability of occurrence of about 15-30%. These results here are important to support decision making of policymakers for global biosecurity strategies with appropriate responses to prevent the future diffusion of vital agents similar to SARS-CoV-2 in environment and society.Keywords. Viral agent; Biological agent; SARS-CoV-2; Novel coronavirus; Zoonoses; Natural disaster; Lab accident; Laboratory biosafety; Biosecurity risks..JEL. C52; L25; M14
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