Multiscale influenza forecasting

Competition between transmission lineages mediated by human mobility shapes seasonal influenza epidemics in the US

Due to its climatic variability, complex mobility networks and geographic expanse, the United States represents a compelling setting to explore the transmission processes that lead to heterogeneous yearly seasonal influenza epidemics. By analyzing genomic and epidemiological data collected in the US from 2014 to 2023, we show that epidemics consisted of multiple co-circulating transmission lineages that could emerge from all regions and often rapidly expanded. Lineage spread was characterized by strong spatiotemporal hierarchies and lineage size correlated with timing of establishment in the US. Mechanistic epidemic simulations, supported by phylogeographic analyses, suggest that competition between lineages on a network of human mobility consistent with commuting flows drove lineage dynamics. Our results suggest that the processes that disseminate viruses nationwide are highly structured, but variability in the short-term processes that determine the locations, timing, and explosiveness of initial epidemic sparks limits predictability of regional and national epidemics.

Does spatial information improve forecasting of influenza-like illness?

Seasonal influenza forecasting is critical for public health and individual decision making. We investigate whether the inclusion of data about influenza activity in neighboring states can improve point predictions and distribution forecasting of influenza-like illness (ILI) in each US state using statistical regression models. Using CDC FluView ILI data from 2010-2019, we forecast weekly ILI in each US state with quantile, linear, and Poisson autoregressive models fit using different combinations of ILI data from the target state, neighboring states, and the US population-weighted average. Scoring with root mean squared error and weighted interval score indicated that the covariate sets including neighbors and/or the US weighted average ILI showed slightly higher accuracy than models fit only using lagged ILI in the target state, on average. Additionally, the improvement in performance when including neighbors was similar to the improvement when including the US average instead, suggesting the proximity of the neighboring states is not the driver of the slight increase in accuracy. There is also clear within-season and between-season variability in the effect of spatial information on prediction accuracy.

Flusion: Integrating multiple data sources for accurate influenza predictions

Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC's National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this target signal. To produce forecasts in the presence of limited data for the target surveillance system, we augmented these data with two signals that have a longer historical record: 1) ILI+, which estimates the proportion of outpatient doctor visits where the patient has influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a selected set of healthcare facilities. Our model, Flusion, is an ensemble model that combines two machine learning models using gradient boosting for quantile regression based on different feature sets with a Bayesian autoregressive model. The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only data for the target surveillance signal, NHSN admissions; all three models were trained jointly on data for multiple locations. In each week of the influenza season, these models produced quantiles of a predictive distribution of influenza hospital admissions in each state for the current week and the following three weeks; the ensemble prediction was computed by averaging these quantile predictions. Flusion emerged as the top-performing model in the CDC's influenza prediction challenge for the 2023/24 season. In this article we investigate the factors contributing to Flusion's success, and we find that its strong performance was primarily driven by the use of a gradient boosting model that was trained jointly on data from multiple surveillance signals and multiple locations. These results indicate the value of sharing information across multiple locations and surveillance signals, especially when doing so adds to the pool of available training data.

An adaptive weight ensemble approach to forecast influenza activity in an irregular seasonality context

Forecasting influenza activity in tropical and subtropical regions, such as Hong Kong, is challenging due to irregular seasonality and high variability. We develop a diverse set of statistical, machine learning, and deep learning approaches to forecast influenza activity in Hong Kong 0 to 8 weeks ahead, leveraging a unique multi-year surveillance record spanning 32 epidemics from 1998 to 2019. We consider a simple average ensemble (SAE) of the top two individual models, and develop an adaptive weight blending ensemble (AWBE) that dynamically updates model contribution. All models outperform the baseline constant incidence model, reducing the root mean square error (RMSE) by 23%-29% and weighted interval score (WIS) by 25%-31% for 8-week ahead forecasts. The SAE model performed similarly to individual models, while the AWBE model reduces RMSE by 52% and WIS by 53%, outperforming individual models for forecasts in different epidemic trends (growth, plateau, decline) and during both winter and summer seasons. Using the post-COVID data (2023-2024) as another test period, the AWBE model still reduces RMSE by 39% and WIS by 45%. Our framework contributes to comparing and benchmarking models in ensemble forecasts, enhancing evidence for synthesizing multiple models in disease forecasting for geographies with irregular influenza seasonality.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BACKGROUND

Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.

METHODS

This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.

RESULTS

When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9).

CONCLUSION

This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Commuting-driven competition between transmission chains shapes seasonal influenza virus epidemics in the United States

Despite intensive study, much remains unknown about the dynamics of seasonal influenza virus epidemic establishment and spread in the United States (US) each season. By reconstructing transmission lineages from seasonal influenza virus genomes collected in the US from 2014 to 2023, we show that most epidemics consisted of multiple distinct transmission lineages. Spread of these lineages exhibited strong spatiotemporal hierarchies and lineage size was correlated with timing of lineage establishment in the US. Mechanistic epidemic simulations suggest that mobility-driven competition between lineages determined the extent of individual lineages' geographical spread. Based on phylogeographic analyses and epidemic simulations, lineage-specific movement patterns were dominated by human commuting behavior. These results suggest that given the locations of early-season epidemic sparks, the topology of inter-state human mobility yields repeatable patterns of which influenza viruses will circulate where, but the importance of short-term processes limits predictability of regional and national epidemics.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging. Consequently, there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals. This paper examines the role of medical data in the early identification of infectious diseases, explores early warning technologies for infectious disease recognition, and assesses monitoring and early warning mechanisms for infectious diseases. We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems, in compliance with national strategies to integrate clinical treatment and disease prevention. Furthermore, hospitals should establish institution-specific, clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

Identify future epidemic threshold and intensity for influenza-like illness in Iraq by using the moving epidemic method

Objectives

Influenza-like illness (ILI) entered the Iraq surveillance system in 2021. The alert threshold was determined using the cumulative sum 2 method, which did not provide other characteristics. This study uses the moving epidemic method (MEM) to describe duration and estimate alert thresholds for ILI in Iraq for 2023-2024.

Methods

MEM default package was used to estimate influenza 2023-2024 epidemic thresholds. Analysis was repeated using optimum parameter of epidemic timing for fixed criteria method, which is 3.3. Arithmetic means and 95% confidence interval upper limit were used to estimate threshold. Geometric mean and 40%, 90%, and 97.3% confidence interval upper limits were used to estimate intensity levels. Aggregated Centers for Disease Control and Prevention surveillance data were used to detect epidemic thresholds, length, sensitivity, and predictive values.

Results

ILI activity starts at week 30 and lasts 7 weeks. Optimized epidemic threshold is 4513 cases, lower than default (4540 cases). Optimized medium-intensity level was higher than default, and high and very high-intensity levels were lower.

Conclusions

MEM is essential to determine an influenza epidemic's threshold and intensity levels. Despite requiring 3-5 years of data, using it on data for 2.5 years has resulted in an epidemic threshold slightly higher than the threshold calculated using the cumulative sum 2 method.

Forecasting influenza hospital admissions within English sub-regions using hierarchical generalised additive models

BACKGROUND

Seasonal influenza places a substantial burden annually on healthcare services. Policies during the COVID-19 pandemic limited the transmission of seasonal influenza, making the timing and magnitude of a potential resurgence difficult to ascertain and its impact important to forecast.

METHODS

We have developed a hierarchical generalised additive model (GAM) for the short-term forecasting of hospital admissions with a positive test for the influenza virus sub-regionally across England. The model incorporates a multi-level structure of spatio-temporal splines, weekly cycles in admissions, and spatial correlation. Using multiple performance metrics including interval score, coverage, bias, and median absolute error, the predictive performance is evaluated for the 2022-2023 seasonal wave. Performance is measured against autoregressive integrated moving average (ARIMA) and Prophet time series models.

RESULTS

Across the epidemic phases the hierarchical GAM shows improved performance, at all geographic scales relative to the ARIMA and Prophet models. Temporally, the hierarchical GAM has overall an improved performance at 7 and 14 day time horizons. The performance of the GAM is most sensitive to the flexibility of the smoothing function that measures the national epidemic trend.

CONCLUSIONS

This study introduces an approach to short-term forecasting of hospital admissions for the influenza virus using hierarchical, spatial, and temporal components. The methodology was designed for the real time forecasting of epidemics. This modelling framework was used across the 2022-2023 winter for healthcare operational planning by the UK Health Security Agency and the National Health Service in England.

Multiplexed on-site sample-in-result-out test through microfluidic real-time PCR (MONITOR) for the detection of multiple pathogens causing influenza-like illness

IMPORTANCE

This study combines quantitative polymerase chain reaction (qPCR) and microfluidics to introduce MONITOR, a portable field detection system for multiple pathogens causing influenza-like illness. MONITOR can be rapidly deployed to enable simultaneous sample-in-result-out detection of eight common influenza-like illness (ILI) pathogens with heightened sensitivity and specificity. It is particularly well suited for communities and regions without centralized laboratories, offering robust technical support for the prompt and accurate monitoring and detection of ILI. It holds the potential to be a potent tool in the early detection and prevention of infectious diseases.

Explaining transmission rate variations and forecasting epidemic spread in multiple regions with a semiparametric mixed effects SIR model

The transmission rate is a central parameter in mathematical models of infectious disease. Its pivotal role in outbreak dynamics makes estimating the current transmission rate and uncovering its dependence on relevant covariates a core challenge in epidemiological research as well as public health policy evaluation. Here, we develop a method for flexibly inferring a time-varying transmission rate parameter, modeled as a function of covariates and a smooth Gaussian process (GP). The transmission rate model is further embedded in a hierarchy to allow information borrowing across parallel streams of regional incidence data. Crucially, the method makes use of optional vaccination data as a first step toward modeling of endemic infectious diseases. Computational techniques borrowed from the Bayesian spatial analysis literature enable fast and reliable posterior computation. Simulation studies reveal that the method recovers true covariate effects at nominal coverage levels. We analyze data from the COVID-19 pandemic and validate forecast intervals on held-out data. User-friendly software is provided to enable practitioners to easily deploy the method in public health research.

Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States

The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policymakers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision-makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.

Predicting influenza with pandemic-awareness via Dynamic Virtual Graph Significance Networks

Every year, influenza spreads worldwide and burdens people's health substantially. We need a reliable model to help hospitals, pharmaceutical companies, and governments better prepare for influenza outbreaks in a timely manner. However, the domain knowledge for such public health events, such as the variable influenza seasonality and occasional pandemics, poses significant challenges in predicting influenza outbreaks. The existing methods use current and historical values in a user-defined time window as input to predict future values but lack considering the situations outside the window. To address these limitations, we proposed Dynamic Virtual Graph Significance Networks (DVGSN). The graph-based algorithm can supervisedly and dynamically learn the implied knowledge from similar "infection situations" in all the historical timepoints without the limitation of time window. Furthermore, representation learning on the dynamic virtual graph can tackle the varied seasonality with pandemic-awareness without requiring domain knowledge input. The extensive experiments on real-world influenza data demonstrate that DVGSN significantly outperforms the state-of-the-art methods. To the best of our knowledge, this is the first attempt to supervisedly learn a dynamic virtual graph for time-series prediction tasks. Moreover, the proposed method has rich interpretabilities, which makes the method more acceptable in the fields of public health, life sciences, and so on. Our source code and dataset are available at https://github.com/aI-area/DVGSN.

A semi-parametric, state-space compartmental model with time-dependent parameters for forecasting COVID-19 cases, hospitalizations and deaths

Short-term forecasts of the dynamics of coronavirus disease 2019 (COVID-19) in the period up to its decline following mass vaccination was a task that received much attention but proved difficult to do with high accuracy. However, the availability of standardized forecasts and versioned datasets from this period allows for continued work in this area. Here, we introduce the Gaussian infection state space with time dependence (GISST) forecasting model. We evaluate its performance in one to four weeks ahead forecasts of COVID-19 cases, hospital admissions and deaths in the state of California made with official reports of COVID-19, Google’s mobility reports and vaccination data available each week. Evaluation of these forecasts with a weighted interval score shows them to consistently outperform a naive baseline forecast and often score closer to or better than a high-performing ensemble forecaster. The GISST model also provides parameter estimates for a compartmental model of COVID-19 dynamics, includes a regression submodel for the transmission rate and allows for parameters to vary over time according to a random walk. GISST provides a novel, balanced combination of computational efficiency, model interpretability and applicability to large multivariate datasets that may prove useful in improving the accuracy of infectious disease forecasts.

Fast and accurate influenza forecasting in the United States with Inferno

Infectious disease forecasting is an emerging field and has the potential to improve public health through anticipatory resource allocation, situational awareness, and mitigation planning. By way of exploring and operationalizing disease forecasting, the U.S. Centers for Disease Control and Prevention (CDC) has hosted FluSight since the 2013/14 flu season, an annual flu forecasting challenge. Since FluSight’s onset, forecasters have developed and improved forecasting models in an effort to provide more timely, reliable, and accurate information about the likely progression of the outbreak. While improving the predictive performance of these forecasting models is often the primary objective, it is also important for a forecasting model to run quickly, facilitating further model development and improvement while providing flexibility when deployed in a real-time setting. In this vein I introduce Inferno, a fast and accurate flu forecasting model inspired by Dante, the top performing model in the 2018/19 FluSight challenge. When pseudoprospectively compared to all models that participated in FluSight 2018/19, Inferno would have placed 2nd in the national and regional challenge as well as the state challenge, behind only Dante. Inferno, however, runs in minutes and is trivially parallelizable, while Dante takes hours to run, representing a significant operational improvement with minimal impact to performance. Forecasting challenges like FluSight should continue to monitor and evaluate how they can be modified and expanded to incentivize the development of forecasting models that benefit public health.

Infectious disease forecasting, if accurate, timely, and reliable, can assist decision makers with resource allocation planning in an attempt to curb the negative impacts of an outbreak. Forecasting challenges, like the U.S. Centers for Disease Control and Prevention’s flu forecasting challenge, FluSight, provide a space for teams to develop and operationalize real-time forecasting models that benefit public health, with weekly forecasts made at the state-level, Health and Human Services region-level, and the United States. The ultimate goal of these models is to produce accurate forecasts within the constraints of the forecasting challenge. Having a forecasting model that runs quickly is also important for future scalability, model development, and operational flexibility. In this paper, I present a fast and accurate flu forecasting model, Inferno. Through retrospective comparisons with FluSight-participating models, Inferno was shown to be a leading forecasting model in the field. Inferno, however, runs in minutes not hours, as other leading forecasting models do. This reduction in runtime constitutes an advancement in flu forecasting, positioning Inferno to scale to more granular geographic units, like counties or health care providers.