tetano
Editor, Senior Moderator
R Soc Open Sci. 2019 Nov 13;6(11):190883. doi: 10.1098/rsos.190883. eCollection 2019 Nov. [h=1]Testing predictability of disease outbreaks with a simple model of pathogen biogeography.[/h]
Dallas TA[SUP]1,[/SUP][SUP]2[/SUP], Carlson CJ[SUP]3[/SUP], Poisot T[SUP]4[/SUP].
[h=3]Author information[/h] 1 Research Centre for Ecological Change, University of Helsinki, 00840 Helsinki, Finland. 2 Department of Biology, Louisiana State University, Baton Rouge, LA 70803, USA. 3 Department of Biology, Georgetown University, Washington, DC 20057, USA. 4 D?pt de Sciences Biologiques, Univ. de Montr?al, Montr?al, Canada.
[h=3]Abstract[/h] Predicting disease emergence and outbreak events is a critical task for public health professionals and epidemiologists. Advances in global disease surveillance are increasingly generating datasets that are worth more than their component parts for prediction-oriented work. Here, we use a trait-free approach which leverages information on the global community of human infectious diseases to predict the biogeography of pathogens through time. Our approach takes pairwise dissimilarities between countries' pathogen communities and pathogens' geographical distributions and uses these to predict country-pathogen associations. We compare the success rates of our model for predicting pathogen outbreak, emergence and re-emergence potential as a function of time (e.g. number of years between training and prediction), pathogen type (e.g. virus) and transmission mode (e.g. vector-borne). With only these simple predictors, our model successfully predicts basic network structure up to a decade into the future. We find that while outbreak and re-emergence potential are especially well captured by our simple model, prediction of emergence events remains more elusive, and sudden global emergences like an influenza pandemic are beyond the predictive capacity of the model. However, these stochastic pandemic events are unlikely to be predictable from such coarse data. Together, our model is able to use the information on the existing country-pathogen network to predict pathogen outbreaks fairly well, suggesting the importance in considering information on co-occurring pathogens in a more global view even to estimate outbreak events in a single location or for a single pathogen.
? 2019 The Authors.
[h=4]KEYWORDS:[/h] community dissimilarity; community ecology; disease forecasting; emerging infectious disease; pathogen biogeography
PMID: 31827836 PMCID: PMC6894608 DOI: 10.1098/rsos.190883
Dallas TA[SUP]1,[/SUP][SUP]2[/SUP], Carlson CJ[SUP]3[/SUP], Poisot T[SUP]4[/SUP].
[h=3]Author information[/h] 1 Research Centre for Ecological Change, University of Helsinki, 00840 Helsinki, Finland. 2 Department of Biology, Louisiana State University, Baton Rouge, LA 70803, USA. 3 Department of Biology, Georgetown University, Washington, DC 20057, USA. 4 D?pt de Sciences Biologiques, Univ. de Montr?al, Montr?al, Canada.
[h=3]Abstract[/h] Predicting disease emergence and outbreak events is a critical task for public health professionals and epidemiologists. Advances in global disease surveillance are increasingly generating datasets that are worth more than their component parts for prediction-oriented work. Here, we use a trait-free approach which leverages information on the global community of human infectious diseases to predict the biogeography of pathogens through time. Our approach takes pairwise dissimilarities between countries' pathogen communities and pathogens' geographical distributions and uses these to predict country-pathogen associations. We compare the success rates of our model for predicting pathogen outbreak, emergence and re-emergence potential as a function of time (e.g. number of years between training and prediction), pathogen type (e.g. virus) and transmission mode (e.g. vector-borne). With only these simple predictors, our model successfully predicts basic network structure up to a decade into the future. We find that while outbreak and re-emergence potential are especially well captured by our simple model, prediction of emergence events remains more elusive, and sudden global emergences like an influenza pandemic are beyond the predictive capacity of the model. However, these stochastic pandemic events are unlikely to be predictable from such coarse data. Together, our model is able to use the information on the existing country-pathogen network to predict pathogen outbreaks fairly well, suggesting the importance in considering information on co-occurring pathogens in a more global view even to estimate outbreak events in a single location or for a single pathogen.
? 2019 The Authors.
[h=4]KEYWORDS:[/h] community dissimilarity; community ecology; disease forecasting; emerging infectious disease; pathogen biogeography
PMID: 31827836 PMCID: PMC6894608 DOI: 10.1098/rsos.190883