| Autor: |
Ogbunugafor CB; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06511, USA. brandon.ogbunu@yale.edu.; Department of Ecology and Evolutionary Biology, Brown University, Providence, 02912, USA. brandon.ogbunu@yale.edu.; Center for Computational Molecular Biology, Brown University, Providence, 02912, USA. brandon.ogbunu@yale.edu., Miller-Dickson MD; Department of Ecology and Evolutionary Biology, Brown University, Providence, 02912, USA., Meszaros VA; Department of Ecology and Evolutionary Biology, Brown University, Providence, 02912, USA., Gomez LM; Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT, 06511, USA.; Department of Ecology and Evolutionary Biology, Brown University, Providence, 02912, USA., Murillo AL; Department of Pediatrics, Warren Alpert Medical School, Brown University, Providence, 02912, USA.; Center for Statistical Sciences, Brown University School of Public Health, Providence, 02903, USA., Scarpino SV; Network Science Institute, Northeastern University, Boston, 02115, USA.; Roux Institute, Northeastern University, Portland, 04101, USA.; Santa Fe Institute, Santa Fe, 87501, USA. |
| Abstrakt: |
Variation in free-living microparasite survival can have a meaningful impact on the ecological dynamics of established and emerging infectious diseases. Nevertheless, resolving the importance of indirect and environmental transmission in the ecology of epidemics remains a persistent challenge. It requires accurately measuring the free-living survival of pathogens across reservoirs of various kinds and quantifying the extent to which interaction between hosts and reservoirs generates new infections. These questions are especially salient for emerging pathogens, where sparse and noisy data can obfuscate the relative contribution of different infection routes. In this study, we develop a mechanistic, mathematical model that permits both direct (host-to-host) and indirect (environmental) transmission and then fit this model to empirical data from 17 countries affected by an emerging virus (SARS-CoV-2). From an ecological perspective, our model highlights the potential for environmental transmission to drive complex, nonlinear dynamics during infectious disease outbreaks. Summarizing, we propose that fitting alternative models with indirect transmission to real outbreak data from SARS-CoV-2 can be useful, as it highlights that indirect mechanisms may play an underappreciated role in the dynamics of infectious diseases, with implications for public health. |
