ArticleEcological applications : a publication of the Ecological Society of America2011
Data-model fusion to better understand emerging pathogens and improve infectious disease forecasting.
Article in Ecological applications : a publication of the Ecological Society of America, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- Surveillance of dengue fever virus: a review of epidemiological models and early warning systems.PLoS neglected tropical diseases · 2012Pooled it
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- The potential of remote sensing for improved infectious disease ecology research and practice.Proceedings. Biological sciences · 2024Review
- Vector Surveillance, Host Species Richness, and Demographic Factors as West Nile Disease Risk Indicators.Viruses · 2021Article
- The Role of Vector Trait Variation in Vector-Borne Disease Dynamics.Frontiers in ecology and evolution · 2020Article
- Data-driven modelling and spatial complexity supports heterogeneity-based integrative management for eliminating Simulium neavei-transmitted river blindness.Scientific reports · 2020Article
- Forecasting Zoonotic Infectious Disease Response to Climate Change: Mosquito Vectors and a Changing Environment.Veterinary sciences · 2019Review
- Individual and temporal variation in pathogen load predicts long-term impacts of an emerging infectious disease.Ecology · 2019Article
- Demographic stochasticity drives epidemiological patterns in wildlife with implications for diseases and population management.Scientific reports · 2018Article
- Quantifying the value of surveillance data for improving model predictions of lymphatic filariasis elimination.PLoS neglected tropical diseases · 2018Article
- Role of monkeys in the sylvatic cycle of chikungunya virus in Senegal.Nature communications · 2018Article
- Continental-scale, data-driven predictive assessment of eliminating the vector-borne disease, lymphatic filariasis, in sub-Saharan Africa by 2020.BMC medicine · 2017Article
- Coinfection by Ixodes Tick-Borne Pathogens: Ecological, Epidemiological, and Clinical Consequences.Trends in parasitology · 2016Review
- Projection of Climate Change Influences on U.S. West Nile Virus Vectors.Earth interactions · 2015Article
- Bayesian calibration of simulation models for supporting management of the elimination of the macroparasitic disease, Lymphatic Filariasis.Parasites & vectors · 2015Article
- Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2015Review
- Climate change and Ixodes tick-borne diseases of humans.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2015Review
- Improving the modeling of disease data from the government surveillance system: a case study on malaria in the Brazilian Amazon.PLoS computational biology · 2013Article
- Sequential modelling of the effects of mass drug treatments on anopheline-mediated lymphatic filariasis infection in Papua New Guinea.PloS one · 2013Article
- Remote sensing of climatic anomalies and West Nile virus incidence in the northern Great Plains of the United States.PloS one · 2012Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ecologists worldwide are challenged to contribute solutions to urgent and pressing environmental problems by forecasting how populations, communities, and ecosystems will respond to global change. Rising to this challenge requires organizing ecological information derived from diverse sources and formally assimilating data with models of ecological processes. The study of infectious disease has depended on strategies for integrating patterns of observed disease incidence with mechanistic process models since John Snow first mapped cholera cases around a London water pump in 1854. Still, zoonotic and vector-borne diseases increasingly affect human populations, and methods used to successfully characterize directly transmitted diseases are often insufficient. We use four case studies to demonstrate that advances in disease forecasting require better understanding of zoonotic host and vector populations, as well of the dynamics that facilitate pathogen amplification and disease spillover into humans. In each case study, this goal is complicated by limited data, spatiotemporal variability in pathogen transmission and impact, and often, insufficient biological understanding. We present a conceptual framework for data-model fusion in infectious disease research that addresses these fundamental challenges using a hierarchical state-space structure to (1) integrate multiple data sources and spatial scales to inform latent parameters, (2) partition uncertainty in process and observation models, and (3) explicitly build upon existing ecological and epidemiological understanding. Given the constraints inherent in the study of infectious disease and the urgent need for progress, fusion of data and expertise via this type of conceptual framework should prove an indispensable tool.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.