title: Forecasting Influenza Outbreaks in Boroughs and Neighborhoods of New York City.
abstract: The ideal spatial scale, or granularity, at which infectious disease incidence should be monitored and forecast has been little explored. By identifying the optimal granularity for a given disease and host population, and matching surveillance and prediction efforts to this scale, response to emergent and recurrent outbreaks can be improved. Here we explore how granularity and representation of spatial structure affect influenza forecast accuracy within New York City. We develop network models at the borough and neighborhood levels, and use them in conjunction with surveillance data and a data assimilation method to forecast influenza activity. These forecasts are compared to an alternate system that predicts influenza for each borough or neighborhood in isolation. At the borough scale, influenza epidemics are highly synchronous despite substantial differences in intensity, and inclusion of network connectivity among boroughs generally improves forecast accuracy. At the neighborhood scale, we observe much greater spatial heterogeneity among influenza outbreaks including substantial differences in local outbreak timing and structure; however, inclusion of the network model structure generally degrades forecast accuracy. One notable exception is that local outbreak onset, particularly when signal is modest, is better predicted with the network model. These findings suggest that observation and forecast at sub-municipal scales within New York City provides richer, more discriminant information on influenza incidence, particularly at the neighborhood scale where greater heterogeneity exists, and that the spatial spread of influenza among localities can be forecast.
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
authors.forename: Wan
authors.identifier: http://orcid.org/0000-0002-7555-9728
authors.initials: W
authors.lastname: Yang
authors.affiliation: New York City Department of Health and Mental Hygiene, New York, New York, United States of America.
authors.forename: Donald R
authors.identifier: http://orcid.org/0000-0002-5980-229X
authors.initials: DR
authors.lastname: Olson
authors.affiliation: Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
authors.forename: Jeffrey
authors.identifier: http://orcid.org/0000-0002-7216-7809
authors.initials: J
authors.lastname: Shaman
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004196:Disease Outbreaks / Q000706:statistics & numerical data*; D005260:Female; D005544:Forecasting / Q000379:methods*; D006801:Humans; D015994:Incidence; D007251:Influenza, Human / Q000453:epidemiology*; D008297:Male; D015233:Models, Statistical; D009519:New York City / Q000453:epidemiology; D011159:Population Surveillance / Q000379:methods*; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012111:Residence Characteristics / Q000706:statistics & numerical data; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D062211:Spatio-Temporal Analysis; D014505:Urban Population / Q000706:statistics & numerical data*