Long-range local influenza forecasts via distributed syndromic monitoring: preliminary results

Forecasting influenza primes public health systems to respond, reducing transmission, morbidity and mortality. Most influenza forecasts to date have, by necessity, relied on spatially course-grained data (e.g. state-or country-level incidence), and have operated at time horizons of 12 weeks or less. If influenza outbreaks could be predicted farther in advance and with increased spatial precision, then limited public health resources could be adaptively managed to minimize spread and improve health outcomes. Here, we use real-time syndromic data from a distributed network of thermometers to construct city-specific forecasts of influenza-like illness (ILI) with a horizon of 30 weeks. Daily geolocated ILI data from the network allows for estimates of recurrent city-specific patterns in ILI transmission rates. These “transmission templates” are used to parameterize an ensemble of ILI forecasts that differ randomly in three parameters, representing city- and season-specific rates of susceptible depletion and reporting, as well as differences in influenza season onset timing. For nine cities across the US, the best-in-hindsight model matches the observed data, and the best forecast variants can be identified in the early season. ### Competing Interest Statement Competing Interests SDC, IS, PP, ALD and CAA are/were employees of and shareholders in Kinsa, Inc. IS conceived of and designed Kinsa products to track the spread of infectious disease. BDD has no competing financial interests. All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf Ethics Statement An oversight determination for this research was issued by the Institutional Review Board of Oregon State University (Study Number IRB-2020-0687). The determination was that this research does not involve human subjects under the regulations set forth by Department of Health and Human Services 45 CFR 46. This work is not a clinical trial and is instead a population-level observational study, where all user information is anonymized and aggregated to the scale of United States cities. It therefore does not use individual, private or personally identifiable information. ### Funding Statement BDD is supported by US National Science Foundation award EEID-1911994, by the David and Lucile Packard Foundation, and by a sponsored research agreement with Kinsa, Inc. PP, ALD, CAA, SDC, and IS are/were employees and shareholders of Kinsa, Inc. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: An oversight determination for this research was issued by the Institutional Review Board of Oregon State University (Study Number IRB-2020-0687). The determination was that this research does not involve human subjects under the regulations set forth by Department of Health and Human Services 45 CFR 46. All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Requests for data used in the manuscript can be made to Kinsa Inc.