Abstract
Influenza incidence forecasting is used to facilitate better health system planning and could potentially be used to allow at-risk individuals to modify their behavior during a severe seasonal influenza epidemic or a novel respiratory pandemic. For example, the US Centers for Disease Control and Prevention (CDC) runs an annual competition to forecast influenza-like illness (ILI) at the regional and national levels in the US, based on a standard discretized incidence scale. Here, we use a suite of forecasting models to analyze type-specific incidence at the smaller spatial scale of clusters of nearby counties. We used data from point-of-care (POC) diagnostic machines over three seasons, in 10 clusters, capturing: 57 counties; 1,061,891 total specimens; and 173,909 specimens positive for Influenza A. Total specimens were closely correlated with comparable CDC ILI data. Mechanistic models were substantially more accurate when forecasting influenza A positive POC data than total specimen POC data, especially at longer lead times. Also, models that fit subpopulations of the cluster (individual counties) separately were better able to forecast clusters than were models that directly fit to aggregated cluster data. Public health authorities may wish to consider developing forecasting pipelines for type-specific POC data in addition to ILI data. Simple mechanistic models will likely improve forecast accuracy when applied at small spatial scales to pathogen-specific data before being scaled to larger geographical units and broader syndromic data. Highly local forecasts may enable new public health messaging to encourage at-risk individuals to temporarily reduce their social mixing during seasonal peaks and guide public health intervention policy during potentially severe novel influenza pandemics.
Highlights
Influenza infections cause substantial morbidity and mortality across all geographical areas and sociodemographic groups [1]
Forecasting influenza is a public health priority because it allows better planning by both policy makers and healthcare facilities
Total specimens (TS) and specimens positive for influenza A (PA) both showed broadly similar dynamics, there were some intriguing differences between them (Fig 2)
Summary
Influenza infections cause substantial morbidity and mortality across all geographical areas and sociodemographic groups [1]. Influenza forecasters use a variety of methods that can be categorized by their underlying model: mechanistic, statistical, and crowd-sourced. Mechanistic models use epidemiological first principles to approximate disease transmission dynamics In practice this often takes the form of compartmental differential equations describing a metapopulation [10,11,12,13,14]. Statistical models [15,16,17,18,19] use patterns and tendencies in the data-stream history to predict future data values. This includes many forms of implementation including: various types of regression, machine-learning, and time-series methods. Complete model descriptions are much more nuanced with many mechanistic models being housed in a probabilistic framework [10,11,12,13,14], crowd-sourced model selection by machine learning [22], combination mechanistic and statistic models [23], ensembles of automated models [24, 25], multi-team ensembles [2], and models that incorporate social media metrics [19, 26] to name a few
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