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Forecasting national and regional influenza-like illness for the USA


Infectious pathogens with short generation times pose public health challenges because they generate substantial near-term uncertainty in the risk of disease. This uncertainty is most acute and shared globally during the initial stages of emergence of novel human pathogens such as SARS, pandemic influenza, or the Zika virus. However, at national and sub-national levels, uncertainty arises frequently for epidemic pathogens such as seasonal influenza, dengue, RSV and rotavirus; causing problems both for health planners and at-risk individuals who may consider changing their behavior to mitigate their risk during peak periods.

Seasonal influenza affects populations in all global regions and is forecast annually in temperate populations, either implicitly or explicitly. Peak demand for both outpatient and inpatient care is driven by peak incidence of influenza in many years. Therefore, the efficient provision of elective procedures and other non-seasonal health care can be improved by accurate forecasts of seasonal influenza. Implicitly, most temperate health systems use knowledge of historical scenarios with which to plan for their influenza season. The current situation is then assessed against the deviation from the historical averages and worst-cases as observed in their own surveillance system.

The United States Centers for Disease Control and Prevention (CDC) has sought to formalize regional and national forecasts by introducing an annual competition. Each week, participating teams submit weekly estimates of incidence for the next four weeks, season onset, and timing and intensity of the peak. Methods used by teams include purely statistical models, [7–9] mechanistic models [10, 11] machine learning and hybrid approaches [12–14]. Expert-opinion surveys have also been used and performed well. Some teams augment their forecasts of the official ILI data with the use of potentially faster or less-noisy datasets such as Google Flu Trends.

Here we describe our mechanistic-model-supported participation in the 2016-17 CDC influenza forecasting challenge, as an example of a disease forecasting process. We emphasize a subjective human component of this process and also describe a retrospective evaluation of the models for the previous six seasons. All the models described are implemented in the R package Dynamics of Interacting Community Epidemics (DICE,


The CDC Outpatient Influenza-like illness Surveillance Network (ILINet) Health and Human Services (HHS) region and national data were downloaded from the CDC-hosted web application FluView and used to create a historic database of ILI cases. S1 Fig shows which states are grouped into each HHS region, along with the population of each region. Because we require an absolute number of cases per week, the CDC ILINet data is converted from percent ILI cases per patient to ILI cases. We estimate the absolute number of weekly ILI cases by dividing the weighted percent of ILI cases in the CDC data by 100 and multiplying it by the total weekly number of patients. We assume two outpatient visits per person per year so that the total weekly number of patients is estimated as: (total regional population)x(2 outpatient visits per person per year)x(1 year/52 weeks).

The estimate of two outpatient visits per year is based on two studies. In 2006 Schappert and Burt studied the National Ambulatory Medical Care Surveys (NAMCS) and the National Hospital Ambulatory Medical Care Surveys (NHAMCS) and calculated an ambulatory rate of 3.8 visits per capita-year. The 2011 NAMCS and NHAMCS surveys estimated ambulatory visit rates of 3.32 visits per capita per year for physician’s offices, 0.43 for hospital emergency departments and 0.33 hospital outpatient departments. We sum these rates to get an outpatient visit per capita-year of 4.08. We further estimate from the surveys that only half of these outpatient clinics are sites that report to ILINet, and hence we rounded to our two outpatient visits per year estimate.

Specific humidity (SH) is measured in units of kg per kg and is defined as the ratio of water vapor mass to total moist air mass. Two other measurements of humidity are absolute humidity and relative humidity. SH is included in DICE as a potential modifier of transmissibility for this time period and uses Phase-2 of the North American Land Data Assimilation System (NLDAS-2) database provided by NASA [20–22]. The NLDAS-2 database provides hourly specific humidity (measured 2-meters above the ground) for the continental US at a spatial grid of 0.125° which we average to daily and weekly values. The weekly data is then spatially-averaged for the states and CDC regions.

School vacation schedules were collected for the 2014-15 and 2015-16 academic years for every state. For each state, a school district was identified to represent each of the three largest cities. Vacation schedules were then collected directly from the district websites. These three school vacation schedules were first processed to a weekly schedule with a value of 0 indicating class was in session all five weekdays and a 1 indicating five vacation days. Next, the representative state schedule was produced by averaging the three weekly district schedules. Region schedules are obtained by a population-weighted average of the state schedules. Similarly, the national schedule is generated by a population-weighted average of the regions.

For the 2016-17 season we determine start and end times as well as spring and fall breaks from the previous year’s schedules. Thanksgiving and winter vacation timing was taken from the calendar where the winter break is assumed to be the last two calendar weeks of the year. Based on the proportion of schools closed and number of days closed, p(t) is assigned a value in the range [0, 1]. For example in week ti, if all schools are closed for the entire week then we define the proportion of open schools p(ti) = 1. However, if all schools have Monday and Tuesday off (missing 2 of 5 days), then p(ti) = 0.4. Similarly, if 3 of 10 schools have spring break (entire week off), but the other 7 schools have a full week of class then p(ti) = 0.3. If all schools have a full week of class then p(ti) = 0.

Basic model

The DICE package has been designed to implement meta-population epidemic modeling on an arbitrary spatial scale with or without coupling between the regions. Our model for coupling between spatial regions follows ref. We assume a system of coupled S-I-R equations (susceptible-infectious-recovered) for each spatial region. In this scenario, the rate at which a susceptible person in region j becomes infectious (that is transitions to the I compartment in region j) depends on: (1) the risk of infection from those in the same region j, (2) the risk of infection from infected people from region i who traveled to region j, and (3) the risk of infection encountered when traveling from region j to region i. To account for the three mechanisms of transmission, ref defined the force of infection, or the average rate that susceptible individuals in region i become infected per time step as:


where D is the total number of regions. In our case, unlike reference, the transmissibility is not the same for all regions and it is allowed to depend on time: βj(t).

Given this force of infection we can write the coupled S-I-R equations for each region as:




Eqs (2–4) are the coupled version of the familiar S-I-R equations, where Tg is the recovery rate (assumed to be 2-3 days in the case of influenza). The mobility matrix, mij, of Eq 1 describes the mixing between regions. Thus, element i, j is the probability for an individual from region i, given that the individual made a contact, that that contact was with an individual from region j. As shown below, the sum over each row in the mobility matrix is one and in the limit of no mobility between regions the mobility matrix mij is the identity matrix so that λi(t)=βi(t)IiNi and we recover the familiar (uncoupled) S-I-R equations:




The level of interaction between spatial regions is determined by the mobility matrix and its interaction kernel, κ(rij):


This kernel is expected to depend on the geographic distance between the regions (rij), and following Mills and Riley we use a variation of the off-set power function for it:


where sd is a saturation distance in km and the power γ determines the amount of mixing between the regions: as γ decreases there is more mixing while as γ increases, mixing is reduced. In the limit that γ → ∞ there is no mixing between regions and we recover the uncoupled SIR Eqs (5–7). The DICE package is designed to allow the estimation of these two parameters (γ and sd), but they can also be set to fixed values.

The S-I-R equations model the total population, but the data are the number of weekly observed cases or incidence rate for each spatial region (IjR). The weekly incidence rate is calculated from the continuous S-I-R model by discretizing the rate-of-infection term λj(t)Sj (or βj(t)SjIjNj in the uncoupled case):


scaling by percent clinical pjC, and adding a baseline Bj. The term pjC is the proportion of infectious individuals that present themselves to a clinic with ILI symptoms and Bj is a constant that estimates non-S-I-R or false-ILI cases. The integral runs over one week determining the number of model cases for week ti. Δt approximates the time delay from when an individual becomes infectious to when they visit a sentinel provider for ILI symptoms and is set to 0.5 weeks based on prior calibration [24, 25]. Eq 10 describes how DICE relates its internal, continuous S-I-R model to the discrete ILI data. In the next section we describe the procedure used for fitting this property (by optimizing the parameters: βj, sd, γ, Bj, and PjC) to an ILI profile.

To allow for different models for the force of infection/contact rate, we write this term in the most general way as a product of a basic force of infection, Rj0, multiplied by three time dependent terms:


The first time dependent term, F1(t), allows for a dependence of the transmission rate on specific-humidity, the second (F2(t)) on the school vacation schedule, and the third (F3(t)) allows the user to model an arbitrary behavior modification that can drive the transmission rate up or down for a limited period of time. For the purpose of the CDC challenge we only considered models involving either F1(t), F2(t), both, or none (i.e., the contact rate does not depend on time), and the functional form of these terms is discussed in S1 and S2 Texts.

Fitting the model

The DICE fitting procedure determines the joint posterior distribution for the model parameters using a Metropolis-Hastings Markov Chain Monte Carlo (MCMC) procedure. We describe the procedure starting with the simpler uncoupled case. In the uncoupled scenario transmission can only happen within each HHS region, since there is no interaction between different spatial regions. The uncoupled regions are run sequentially and posterior distributions for the model parameters and forecasts are obtained. For each region, we simulated three MCMC chains each with 107 steps and a burn time of 2 × 106 steps. The smallest effective sample size that we report for any parameter is greater than 100. After sampling from the individual posterior densities of each region, we calculate our national forecast as the weighted sum of the regional profiles with the weights given by the relative populations of the regions. The national curve was also fitted directly (without any regional information) using all the models and priors, but these direct results were only used at the end of the season when estimating the performance of each of our procedures.

In the coupled scenario, the MCMC procedure uses Eqs (2–4) along with Eq (10) to simultaneously generate candidate profiles for the coupled ten HHS regions. The log-likelihood of the ten regional profiles is calculated and combined with the proper relative weights to generate a national log-likelihood which is minimized. It is important to note that in the coupled scenario we only optimize the national log-likelihood, and not the individual region-level likelihoods, but the parameters we optimize are still mostly region specific (only sd and γ are not). We also tried fitting the coupled model to the regional log-likelihoods, however the results of the fits were not as accurate as the ones obtained when the national likelihood is optimized (see Discussion).

Both the coupled and uncoupled scenarios begin with the entire population of each region, minus the initial seed of infections which were fitted, in the susceptible state. We also fitted the onset time of each local epidemic and the proportion of infections that were cases. We also considered fitting an initially removed fraction R(0), but found that pc and R(0) were very strongly correlated if both were fitted parameters.

Informative priors

In the previous section we described a traditional MCMC procedure which uses a log uniform distribution for the parameters, which we term an uninformative prior (UP). Early in the flu season, before the ILI curve takes off, this fitting can result in peak intensities that are significantly larger/lower than expected (based on historic values) and/or peak weeks that are inconsistent with past values. One way to constrain the predictions, which has been used by others [27, 28], is to use an informed prior (IP).

To generate informative priors, we used each of the models supported by DICE to fit all previous seasons (starting from 2004) at both the national and regional levels. Using the history of the MCMC chain we then built a posterior distribution for each parameter and fitted it to a 1-D Gaussian. Although it would have been possible to use the posteriors directly from prior years, and thus preserve correlation structure in those distributions, we chose not to do this for two reasons. First, for the parameters we were informing, there was little correlation. Second, by fitting to a 1-D Gaussian, we were able to state precisely the prior assumptions for our actual forecast and quickly assess the degree to which they are informative. This would have been more difficult had we used stochastic draws from multivariate posteriors generated for previous years.

By repeating this procedure for each season and each model, we create a database of prior knowledge which can be used to inform the MCMC procedure for the current season. Specifically, at each week during the CDC challenge, and for each of the ten HHS regions and the national, we find the past season that is most similar to the current data (based on the value of the Pearson correlation, calculated using all weeks of available data and typically found to have a Pearson correletion value that is > 0.9) and use the posterior distributions of that season as an informed prior for the current season. Each region has its own informed prior which is allowed to change from one epidemic week to the other. The Gaussian informed prior provides a simple way to penalize the likelihood when the parameter values sampled by the MCMC procedure are far from what was observed in a past similar season. To allow for an informed prior that is less restrictive, we also use a heated informed prior, where the Gaussian temperature is increased by an order of magnitude (which is equivalent to increasing the variance by a factor of ten). In the Results section we refer to the fitting procedures that use a prior as IP and HIP for informed prior and heated informed prior, respectively. Informed priors were used only with the uncoupled SIR Eqs. In a future study we plan to explore how they would extend to the coupled MCMC procedure.

Using discounted historical data for not-yet-seen future time points

In addition to informative priors, we also used data augmentation to make maximum use of prior data within a mechanistic framework. For each week during the challenge, our data augmentation was a form of extrapolation in which future unobserved time points were assumed to take either a historical average or values equal to those in the most similar prior season. However, these historically augmented time points were not counted within the likelihood with the same weight as actual observations. The weighting was equal to the value of the Pearson correlation between the observed data in the current year and the historical data for the same period from the year used for augmentation. We shifted from the historic data to the most similar data at epidemic week 6 (EW06) when we subjectively determined that the current season is very different from the historic average. The augmented data was also y-shifted so that it matched the last data point for the current season. The augmented data procedure was used for both the coupled and uncoupled fits and also using a heated augmented procedure (where the log-likelihood is again heated by a factor of ten). In what follows, we refer to the fitting procedures that use data augmentation and heated data augmentation as DA and HDA, respectively.

From model predictions to forecasts

During each of the CDC weeks DICE was used to fit both the regional and the national most recent incidence data using the combinations of coupling, priors and models described in the previous subsections. The uncoupled procedure (and direct fitting of the National %ILI profile) were used throughout the season with all five priors and with the four models for the force of infection leading to: 5 × 4 = 20 forecasts. For the coupled procedure we fitted with the following combinations of priors and data augmentation: uninformed prior with no data augmentation, uninformed prior with data augmentation and uninformed prior with heated data augmentation (UP, DA and HDA, respectively), and with all four models for the force of infection, leading to: 3 × 4 = 12 forecasts.

This total of 32 model-runs were used to make forecasts of incidence at both the national and regional levels. For each region, we simulate three MCMC chains each with 107 steps and a burn time of 2 × 106 steps. The smallest effective sample size that we report for any parameter was greater than 100. After sampling from the individual posterior densities of each region, we calculated our national forecast as the weighted sum of the regional profiles with the weights given by the relative populations of the regions. The national curve was also fitted directly (without any regional information) using all the models and priors, but these direct results were only used at the end of the season when estimating the performance of each of our procedures.

Early in the season we were experimenting with the coupled procedure and we began to use it as described in the manuscript with the DA and HDA priors only on EW 50 and with the UP prior only on EW 9. Hence, some of the coupled results reported in this section were not available in real-time and were generated at the end of the season (but using only the %ILI data that was available in real-time at each forecast week).

Each week a single forecast was selected from these results for each of the ten HHS regions and the national. At the regional level we selected a single forecast from one of the (32) uncoupled or coupled procedures enumerated previously. We first reviewed the results for each region and made a selection which took into account: (1) the historic profile for the region, (2) the quality of the fit (both mean and width), (3) the extent of data adjustments in the past weeks, (4) the lab strain % positive data, and when relevant (5) the impact of upcoming school vacations (particularly the winter break). For the national level we selected either one of the 12 coupled results or the aggregated result obtained as the weighted average of our ten regional selections (with the weights given by the relative population of each region). The selection was made by a single group member (and author here) and then reviewed and modified as needed by three group members (also authors here). In most cases (> 90%) there was an agreement on the initial selection. When there was a disagreement, or general uncertainty amongst the group, we discussed the selection. All the model results for all seven seasons used here and subsequent seasons are displayed on our website at:

When we evaluate the accuracy of the forecasts we use: (1) The mean absolute error (MAE) of the forecasts (calculated using all 28 weeks of the challenge), (2) The MAE relative to the MAE of a historic NULL model, and (3) The CDC logarithmic score: given a forecast with a set of probabilities for p, with pi being the probability for an observed outcome pi, the logarithmic score is:


For onset and peak week the score is calculated using the probability assigned to the correct bin plus those of the preceding and proceeding bins (the bin size is one epidemic week). For peak intensity and the 1 − 4 week forward forecast, the score is calculated using the probability assigned to the correct bin plus those of the five preceding and proceeding bins (the bin size is 0.1%).

Models selected for forecasts

We selected different FOI variants during different weeks. At the regional level, although we selected the most flexible humidity and school vacation assumptions (HV) more often (47.9% 134/280) than the alternatives (Fig 1), we did select humidity-only (H, 47/280 16.8%), school-vacations-only (V, 62/280 22.1%) and fixed transmissibility (F, 37/280 13.2%) models on a number of occasions. For the national model, we only used the aggregated forecast of the regional models on 9/28 (32.1%) occasions. (The CDC challenge lasts 28 weeks and there are 10 HHS regions, hence the 28 and 280 in the denominators).

For assumptions about the inferential prior, before the epidemic peaked (EW 06 for the nation) we rarely chose an uninformed prior (UP). Most often we chose the heated data augmentation option (HDA). Early in the season (EWs 43-49), when our options did not include the coupled procedure, we chose the informed prior (IP) or heated informed prior (HIP) most often. Once the season had peaked the uninformed prior was selected often both for the national profile and individual regions, we continued to select the data augmentation (DA) and HDA options for the nation well after the season has peaked (EWs 12-14, 16 and 18).

For assumptions about coupling, once the coupled procedure was available, it was often selected for both the national profile and most regions (1, right panel), with the exception of regions 1 and 8. We found that the coupled procedure used regions 8 (and to a lesser extent 1) as a way to reduce the error to the national fit, at the cost of producing poor fits to these regions, hence their coupled results were rarely selected for submission. The aggregate option for the national selection was only selected at EWs 5 and 6, the weeks prior to the peak and the peak week itself. For these two weeks our errors for both season targets and 1 − 4 week forecasts were large (see Discussion below).

Accuracy of forecasts

At both the national and regional levels, the accuracy of the weekly %-ILI forecasts decreased as the lead time increased. The %ILI 1-4 week forecast and observed data for the national data and the three largest (by population) HHS regions (S1 Fig): 4, 5, and 9 is shown in Fig 2. Early in the season, up to and including EW01, the national curve is nearly identical to the historic national curve, whereas our mechanistic forecasts consistently overestimated incidence. After EW01, our national predictions improve significantly, while the historic curve no longer follows the 2016-17 national profile. Similarly, for the three largest HHS regions, the historic curve is similar to the 2016-17 profile until EW01, at which point they start to deviate and our forecasts become more accurate.

Averaged over the entire season, our selected national forecast does better than the historic NULL model only for the 1-week prediction window (top left panel in Fig 2). However, for the largest HHS region (region 4, top right panel) we perform better for all four prediction time horizons, for region 9 (bottom left panel) for the first three, and for region 5 (bottom right panel) for the first two.

Although our forecasts gave potentially useful information over and above the NULL model for the timing of the peak week (Fig 3) and for the amplitude of peak intensity, the peak week of EW06 was the same as the historical mean. Between EW50 (eight weeks before the season peaks) and EW04 (two weeks before the season peak) our forecast correctly predicted to within ±1 week of the observed peak week (EW06). One week before the season peaks, and at the peak week (EW05 and EW06), our model forecast has an error of two weeks.

Forecasts based on the mechanistic model performed better than the historic NULL model for the peak intensity (Fig 3A/3B/3C/3D). Two weeks before the peak week (and three weeks early in the season) we started predicting the correct peak intensity of 5.1% (to within ±0.5%). The mean and median historic values are significantly lower (4.4% and 4.1% respectively) and outside the ±0.5% range. Our apparent forecast performance for intensity appears to drop off at the end of the season. However, this is an artifact of the forecasting work flow. Once the peak had clearly passed, the final model was selected for reasons other than the peak intensity and the already-observed peak intensity was submitted.

Selected forecasts based on the mechanistic model did not accurately predict onset. Both the mean of onset (EW51) and median (EW50) historic values were within a week of the observed 2016-17 onset week (EW 50). However, our model was unable to properly predict the onset until it happened. As with peak values, once onset had been observed in the data, we used the observed value in our formal submission, which was not reflected in onset values from the chosen model.

Similar models were correlated with each other in their forecasts of 1- to 4-week ahead ILI, but with decreasing strength as forecasts were made for longer time horizons. S5 Fig in the SI shows the Pearson correlation between the 32 models calculated for the 1-, 2-, 3-, and 4- week forward forecast using all 28 weeks of the challenge. The high correlation shows how closely related to each other many of the models are. But this Fig also shows that this correlation decreases when the forecast horizon increases and that there is a spread in the predictions (manifested by negative values of the Pearson correlation).

Retrospective analyses of model forecasting ability

Once the challenge was over, we examined retrospectively the performance of all mechanistic model variants over the course of all seasons in the historical database and separately for the 2016-17 season. To assess the quality of all the near-term forecasts (1 − 4 weeks) from the different models and assumptions about priors, we show in Fig 4 their weekly CDC score (see Methods), for the 4 different forecast lead times (1-, 2-, 3- and 4- weeks ahead), and for the prediction of the National %ILI intensity. The models are arranged based on their CDC score (averaged across all weeks, numbers on the right y-axis) from best (top) to worst (bottom). Coupled models were more accurate than non-coupled models for the 2016-17 season and for historical seasons for all 4 lead times. The model labeled ‘ensemble’ is the average of the 32 model variants. For the 1-, 2-, and 3-weeks ahead forecasts the subjectively selected model does substantially better than the ensemble model and for the 4-weeks ahead forecast they score practically the same.

The performance of mechanistic models was comparable to that of the historical average NULL model at the beginning and end of the season. However, in the middle of the season, when there is greater variation in the historical data, the performance of the best mechanistic model variants was substantially better than that of the historical average model.

For predicting ILI incidence for the 2016-17 season, which followed similar trend to the historical average, coupled models that used data augmentation were more accurate than coupled models that did not use data augmentation. However, on average for historical seasons, coupled models that did not use augmented data were more accurate than those that did. Also, on average for historical seasons, coupled models that included humidity were more accurate than those that did not (see dark banding in upper portion of charts on the right hand side of Fig 4).

We examined the performance of the different model variants for individual regions for the near-term forecasting of %-ILI (S3 and S4 Figs). Again, the coupled models with uninformative prior outperformed other model variants. Although for some regions the improvement in forecast score for the uninformative prior variants over other coupled variants was less pronounced (regions 1 and 7), these models never appeared to be inferior to the other variants.

In a similar way, we examined the forecast accuracy of different mechanistic model variants in forecasting season-level targets: onset, peak time and peak intensity for the 2016-17 season and on average across all seasons (Fig 5). Here too the models are arranged based on their overall performance from best to worse (top to bottom). Again, for all three targets during 2016-17, the coupled uninformative model variant was at least as good as other coupled options and better than the non-coupled variants. For all three season targets, the selected model performed better (and in the case of peak week significantly better) than the ensemble model. We note that in the latter part of the season, after the single observed onset and peak had passed, results from a single season do not contain much information about model performance. However, the performance of the coupled uninformative prior model was on average better than other model variants across the historical data and different epidemic weeks for all three targets, other than one exception. From EW01 onwards for peak intensity, uncoupled heated augmented prior variants performed better than did coupled uninformative prior model variants.

We were able to compare our performance over the course of the season to the performance of the other teams using the public website that supports the challenge ( Averaged across all weeks of forecast and all forecast targets, we were ranked 13 out of 29 teams. For 1-, 2-, 3- and 4-weeks ahead forecasts we were ranked 6, 11, 9 and 16 respectively; again, out of 29 teams. We were ranked 14 for the timing of onset, 5 for the timing of the peak and 14 for intensity of the peak. Probing beyond the overall rankings, our performance was similar to the other better-performing teams in the challenge. Also, our performance improved substantially as measured by both in absolute terms and relative to other teams across the season (S6 and S7 Figs).


In this study, we have described our participation in a prospective forecasting challenge. Although we drew on results from a large set of mechanistic models, our single forecast for each metric was made after choosing between available model results for that metric in that week and was therefore somewhat subjective. We performed poorly at the start of the competition when our mechanistic models consistently over-estimated incidence. However, during the middle phase of the season, our models produced less biased estimates and consistently outperformed non-mechanistic models based on the average of historical data. A robust testing of model variants using historical data suggests that spatially coupled models are systematically better than historical NULL models during the middle of the season and are not significantly worse even at the start of the season. We evaluated a simple ensemble and showed that the subjective model choice was better. However, the ranking of individual models suggests that an ensemble of coupled models may outperform our subjective choice. We are considering exactly this experiment for the upcoming season.

This study is slightly different from some prior studies of influenza forecasting in that it describes and assesses a subjective choice between multiple mechanistic models as the basis of a prospective forecast, rather than describing the performance of a single model or single ensemble of models used for an entirely objective forecast. Although this could be viewed as a limitation of our work, because individual subjective decisions cannot be reproduced, we suggest that the explicit description of a partially subjective process is a strength. In weather forecasting, there is a long history of evaluating the accuracy of entirely objective forecasts versus partially subjective forecasts [30, 31]. Broadly, for each different forecast target and each forecast lead-time, there has been a gradual progression over time such that objective forecasts become more accurate than subjective forecasts. We note also that although we describe the subjective process as it was conducted, we also provide a thorough retrospective assessment of the predictive performance of each model variant.

We may refine our ensemble approach for future iterations of the competition. It seems clear that the coupled models produce more accurate forecasts than the uncoupled models for most targets, so we would consider an ensemble only of the coupled model variants. We will also consider weighted ensembles of models and attempt to find optimal weights by studying all prior years. Also, data were often updated after being reported and we did not include an explicit reporting model in our inferential framework (also sometimes referred to as a backfill model). Rather, we used knowledge of past adjustments to data during our discussions and eventual subjective choice of models. We aim to include a formal reporting model in future versions of our framework.

By reflecting on our choices and their performance, we can evaluate the importance of a number of different model features. Our coupled model variants performed much better than uncoupled variants consistently across the 2016-17 season, for different targets and when evaluated using the historical data. This prospective study supports recent retrospective results suggesting that influenza forecasts can be more accurate if they explicitly represent spatial structure [32, 33]. Given that the model structure we used to represent space was relatively coarse, further work is warranted to test how forecast accuracy at finer spatial scales can be improved by models that include iteratively finer spatial resolution.

In submitting forecasts based on uninformed mechanistic priors using an uncoupled model at the start of the season, we failed to learn lessons that have been present in the influenza forecasting literature for some time. Historical variance is low during the start of the season and the growth pattern is not exponential. Therefore, it would be reasonable to forecast early exponential growth only in the most exceptional of circumstance, such as during the early weeks of a pandemic. Model solutions that are anchored to the historical average in some way, such as by the use of augmented data for not-yet-seen time points, are likely to perform better. Also, forecasting competitions may want to weight performance differentially across time, with greater weight given to forecasts during periods where there is a higher variance in incidence.

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. 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). 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. Also, we chose to present our accuracy of predicting peak height and timing relative to actual epidemiological week so that it was consistent with our presentation of accuracy of other forecast targets. However, it may be more appropriate in some circumstances to present accuracy of targets associated with the peak relative to the eventual peak.

We found the experience of participating in a prospective forecasting challenge to be different to that of a retrospective modeling study. The feedback in model accuracy was much faster and the need for statistically robust measures of model likelihood or parsimony less obvious. We encourage the use of forecasting challenges for other infectious disease systems as a focus for better understanding of underlying dynamics in addition to the generation of actionable public health information.