Identifying factors that may improve mechanistic forecasting models for influenza

Abstract

Influenza causes substantial morbidity and mortality and places strain on healthcare systems, some of which could be mitigated by accurate forecasting. Specific humidity and school vacations have both been shown independently to affect the transmission dynamics of influenza at large spatial scales. Here, we compare the ability of five compartmental transmission models, which include these two processes, to explain influenza-like-illness (ILI) incidence data for five United States counties for which school vacations and specific humidity data were available over a span of four seasons. We used the models in two different ways. First we fitted all available data at the same time and assessed model performance using standard measures of parsimony and goodness-of-fit. Then we conducted a retrospective forecasting study in which we attempted to predict incidence beyond a given week by fitting to data available up to that week. In general, when fitting the data using the whole season, we found that either specific humidity, school closures, or a combined model incorporating both effects captured the variability in incidence better than a fully constrained SIR-like model. Moreover, where these factors play a role, the timing of the variations suggests a causal relationship. When school vacations and specific humidity were important, the model-estimated parameters were broadly consistent. Retrospective forecasting simulations were consistent with the explanatory use of the models, with both specific humidity and school vacations giving more accurate forecasts than a simple SIR-like model in some populations and for some seasons. Our results suggest that influenza forecast models should test for the importance of different factors such as school vacations and specific humidity on a population-by-population and year-by-year basis. Author summaryUnderstanding the underlying factors that contribute to the transmission of influenza is crucial for developing models with predictive capabilities. In this study, we address two key effects: humidity and school vacations. We show that both can play an important role, depending on the location of the population as well as the timing of the school vacations. We then demonstrate how such mechanistic models can be used to forecast an influenza season as it unfolds, including estimates of the uncertainty of the predictions at each week of the forecast. To make better influenza forecasts, models need to test whether factors such as school vacations and specific humidity are important for a given season and population.

Forecasting national and regional influenza-like illness for the USA

“…Models that included humidity forcing performed better on average in our analysis of all historical data than equivalent models that did not include those terms, especially for the forecasting of ILI 1-to 4-weeks ahead [34]. However, we did not see similar support for the inclusion of school vacation terms improving accuracy, which has been suggested in a retrospective forecasting study at smaller spatial scales (by this group) [35]. The lack of support for school vacations in the present study could indicate that the prior work was under-powered or that averaging of school vacation effects across large spatial scales-both in the data and the model-degrades its contribution to forecast accuracy.…”

COVID-19 Deaths: Which Explanatory Variables Matter the Most?

“…Individual-based spatial models, for example, can explicitly account for the population-weighted density of the population as well as (at least parametrically) the mobility of people (Flaxman and others , 2020 b ). The significance of climate (particularly temperature, precipitation, and/or humidity) can readily be incorporated as well (Riley and others , 2017; Ben-Nun and others , 2019; Turtle and others , 2019).…”

“…Our objective in this study is to assess which of these explanatory (independent) variables are clearly important for predicting COVID-19 deaths, primarily to aid in the development of mechanistic model refinements (Riley and others , 2013, 2015, 2017; Ben-Nun and others , 2019; Turtle and others , 2019). Mechanistic models, as opposed to statistical or machine-learning models are typically developed by incorporating the minimum number of underlying processes necessary to describe the observations sufficiently well to answer specific questions.…”

Accounting for Healthcare-Seeking Behaviours and Testing Practices in Real-Time Influenza Forecasts

“…Seasonal influenza is an archetypal example, which routinely presents a substantial challenge to healthcare systems in temperate climates, and whose clinical severity profile can differ substantially from one year to the next, and from setting to setting (e.g., city to city). Infectious disease forecasting is now beginning to establish itself as a useful decision-support tool for public health preparedness and response activities, particularly for seasonal influenza [2,3,4,5,6,7,8,9,10,14,15]. One of the many challenges for this field is to account for how behaviour changes affect surveillance data, and how these data should be interpreted [7,8,9,10].…”

“…For example, there is a well-established need to “understand the processes that determine how persons are identified by surveillance systems in order to appropriately adjust for the biases that may be present” [1]. The importance of understanding how people are identified by a surveillance system is particularly problematic in the context of near-real-time influenza forecasting [2,3,4,5,6,7,8,9,10], because healthcare-seeking behaviours and clinical decision-making are dynamic [11,12] and are subject to acute influences (e.g., media coverage [13]). The challenge that this poses is further compounded by delays in data collection and reporting, which reduce forecast performance [9,14,15].…”