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.

Antigenic drift and subtype interference shape A(H3N2) epidemic dynamics in the United States.

Influenza viruses continually evolve new antigenic variants, through mutations in epitopes of their major surface proteins, hemagglutinin (HA) and neuraminidase (NA). Antigenic drift potentiates the reinfection of previously infected individuals, but the contribution of this process to variability in annual epidemics is not well understood. Here, we link influenza A(H3N2) virus evolution to regional epidemic dynamics in the United States during 1997-2019. We integrate phenotypic measures of HA antigenic drift and sequence-based measures of HA and NA fitness to infer antigenic and genetic distances between viruses circulating in successive seasons. We estimate the magnitude, severity, timing, transmission rate, age-specific patterns, and subtype dominance of each regional outbreak and find that genetic distance based on broad sets of epitope sites is the strongest evolutionary predictor of A(H3N2) virus epidemiology. Increased HA and NA epitope distance between seasons correlates with larger, more intense epidemics, higher transmission, greater A(H3N2) subtype dominance, and a greater proportion of cases in adults relative to children, consistent with increased population susceptibility. Based on random forest models, A(H1N1) incidence impacts A(H3N2) epidemics to a greater extent than viral evolution, suggesting that subtype interference is a major driver of influenza A virus infection ynamics, presumably via heterosubtypic cross-immunity.

Seasonal influenza (flu) viruses cause outbreaks every winter. People infected with influenza typically develop mild respiratory symptoms. But flu infections can cause serious illness in young children, older adults and people with chronic medical conditions. Infected or vaccinated individuals develop some immunity, but the viruses evolve quickly to evade these defenses in a process called antigenic drift. As the viruses change, they can re-infect previously immune people. Scientists update the flu vaccine yearly to keep up with this antigenic drift. The immune system fights flu infections by recognizing two proteins, known as antigens, on the virus's surface, called hemagglutinin (HA) and neuraminidase (NA). However, mutations in the genes encoding these proteins can make them unrecognizable, letting the virus slip past the immune system. Scientists would like to know how these changes affect the size, severity and timing of annual influenza outbreaks. Perofsky et al. show that tracking genetic changes in HA and NA may help improve flu season predictions. The experiments compared the severity of 22 flu seasons caused by the A(H3N2) subtype in the United States with how much HA and NA had evolved since the previous year. The A(H3N2) subtype experiences the fastest rates of antigenic drift and causes more cases and deaths than other seasonal flu viruses. Genetic changes in HA and NA were a better predictor of A(H3N2) outbreak severity than the blood tests for protective antibodies that epidemiologists traditionally use to track flu evolution. However, the prevalence of another subtype of influenza A circulating in the population, called A(H1N1), was an even better predictor of how severe A(H3N2) outbreaks would be. Perofsky et al. are the first to show that genetic changes in NA contribute to the severity of flu seasons. Previous studies suggested a link between genetic changes in HA and flu season severity, and flu vaccines include the HA protein to help the body recognize new influenza strains. The results suggest that adding the NA protein to flu vaccines may improve their effectiveness. In the future, flu forecasters may want to analyze genetic changes in both NA and HA to make their outbreak predictions. Tracking how much of the A(H1N1) subtype is circulating may also be useful for predicting the severity of A(H3N2) outbreaks.

Disentangling the relationship between cancer mortality and COVID-19 in the US.

Cancer is considered a risk factor for COVID-19 mortality, yet several countries have reported that deaths with a primary code of cancer remained within historic levels during the COVID-19 pandemic. Here, we further elucidate the relationship between cancer mortality and COVID-19 on a population level in the US. We compared pandemic-related mortality patterns from underlying and multiple cause (MC) death data for six types of cancer, diabetes, and Alzheimer's. Any pandemic-related changes in coding practices should be eliminated by study of MC data. Nationally in 2020, MC cancer mortality rose by only 3% over a pre-pandemic baseline, corresponding to ~13,600 excess deaths. Mortality elevation was measurably higher for less deadly cancers (breast, colorectal, and hematological, 2-7%) than cancers with a poor survival rate (lung and pancreatic, 0-1%). In comparison, there was substantial elevation in MC deaths from diabetes (37%) and Alzheimer's (19%). To understand these differences, we simulated the expected excess mortality for each condition using COVID-19 attack rates, life expectancy, population size, and mean age of individuals living with each condition. We find that the observed mortality differences are primarily explained by differences in life expectancy, with the risk of death from deadly cancers outcompeting the risk of death from COVID-19.

Establishing the true death toll of a pandemic like COVID-19 is difficult, as laboratory testing is generally too limited to directly count the number of deaths that can be attributed to a particular pathogen. To overcome this, researchers analyse excess mortality ­ that is, they compare the observed number of deaths with the expected level based on trends in prior years. These techniques have been used for over 100 years to estimate the burden of pandemic influenza and became a popular way to estimate deaths due to the COVID-19 pandemic. Excess mortality can also reveal the impact of COVID-19 on sub-populations with chronic conditions. For example, previous studies showed that deaths with diabetes, heart disease and Alzheimer's disease listed as the primary cause of death increased during waves of COVID-19. Cancer deaths did not show such a pattern, however, despite some epidemiological studies identifying cancer as a risk factor for COVID-19 mortality. To understand why this may be the case, Hansen et al. reviewed death certificates from different states in the United States during the first year of the pandemic. Their analyses of multiple-cause death records (listing cancer anywhere on the death certificate, not just as the primary cause of death) showed that death certificate coding practices during the pandemic did not explain the absence of excess cancer mortality. While a low level of excess mortality was detectable for cancers with longer life expectancy (breast cancer, for example), no elevation was observed for cancers with lower life expectancy, such as pancreatic cancer. The analyses demonstrate that the lack of excess mortality for especially deadly cancers can be explained through competing risks ­ in other words, the high risk of dying from the cancer itself vastly outweighs the additional risk posed by COVID-19. These findings shed light on how competing mortality risks might mask the true impact of COVID-19 on cancer mortality and explain the apparent discrepancy between cohort studies and excess mortality studies. To fully comprehend the impact of COVID-19 on patients living with cancers, future research should look at the possibility of longer-term increases in cancer mortality due to late diagnosis during pandemic lockdowns, and an elevated risk of severe illness.

SARS-CoV-2 correlates of protection from infection against variants of concern.

Serum neutralizing antibodies (nAbs) induced by vaccination have been linked to protection against symptomatic and severe coronavirus disease 2019. However, much less is known about the efficacy of nAbs in preventing the acquisition of infection, especially in the context of natural immunity and against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immune-escape variants. Here we conducted mediation analysis to assess serum nAbs induced by prior SARS-CoV-2 infections as potential correlates of protection against Delta and Omicron infections, in rural and urban household cohorts in South Africa. We find that, in the Delta wave, D614G nAbs mediate 37% (95% confidence interval: 34-40%) of the total protection against infection conferred by prior exposure to SARS-CoV-2, and that protection decreases with waning immunity. In contrast, Omicron BA.1 nAbs mediate 11% (95% confidence interval: 9-12%) of the total protection against Omicron BA.1 or BA.2 infections, due to Omicron's neutralization escape. These findings underscore that correlates of protection mediated through nAbs are variant specific, and that boosting of nAbs against circulating variants might restore or confer immune protection lost due to nAb waning and/or immune escape. However, the majority of immune protection against SARS-CoV-2 conferred by natural infection cannot be fully explained by serum nAbs alone. Measuring these and other immune markers including T cell responses, both in the serum and in other compartments such as the nasal mucosa, may be required to comprehensively understand and predict immune protection against SARS-CoV-2.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

Fine-scale spatial and social patterns of SARS-CoV-2 transmission from identical pathogen sequences.

Pathogen genomics can provide insights into disease transmission patterns, but new methods are needed to handle modern large-scale pathogen genome datasets. Genetically proximal viruses indicate epidemiological linkage and are informative about transmission events. Here, we leverage pairs of identical sequences using 114,298 SARS-CoV-2 genomes collected via sentinel surveillance from March 2021 to December 2022 in Washington State, USA, with linked age and residence information to characterize fine-scale transmission. The location of pairs of identical sequences is highly consistent with expectations from mobility and social contact data. Outliers in the relationship between genetic and mobility data can be explained by SARS-CoV-2 transmission between postal codes with male prisons, consistent with transmission between prison facilities. Transmission patterns between age groups vary across spatial scales. Finally, we use the timing of sequence collection to understand the age groups driving transmission. This work improves our ability to characterize transmission from large pathogen genome datasets.

Differential impact of COVID-19 non-pharmaceutical interventions on the epidemiological dynamics of respiratory syncytial virus subtypes A and B.

Nonpharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic have disrupted the dynamics of respiratory syncytial virus (RSV) on a global scale; however, the cycling of RSV subtypes in the pre- and post-pandemic period remains poorly understood. Here, we used a two subtype RSV model supplemented with epidemiological data to study the impact of NPIs on the two circulating subtypes, RSV-A and RSV-B. The model is calibrated to historic RSV subtype data from the United Kingdom and Finland and predicts a tendency for RSV-A dominance over RSV-B immediately following the implementation of NPIs. Using a global genetic dataset, we confirm that RSV-A has prevailed over RSV-B in the post-pandemic period, consistent with a higher R0 for RSV-A. With new RSV infant monoclonals and maternal and elderly vaccines becoming widely available, these results may have important implications for understanding intervention effectiveness in the context of disrupted subtype dynamics.

Assessing population-level target product profiles of universal human influenza A vaccines.

Influenza A has two hemagglutinin groups, with stronger cross-immunity to reinfection within than between groups. Here, we explore the implications of this heterogeneity for proposed cross-protective influenza vaccines that may offer broad, but not universal, protection. While the development goal for the breadth of human influenza A vaccine is to provide cross-group protection, vaccines in current development stages may provide better protection against target groups than non-target groups. To evaluate vaccine formulation and strategies, we propose a novel perspective: a vaccine population-level target product profile (PTPP). Under this perspective, we use dynamical models to quantify the epidemiological impacts of future influenza A vaccines as a function of their properties. Our results show that the interplay of natural and vaccine-induced immunity could strongly affect seasonal subtype dynamics. A broadly protective bivalent vaccine could lower the incidence of both groups and achieve elimination with sufficient vaccination coverage. However, a univalent vaccine at low vaccination rates could permit a resurgence of the non-target group when the vaccine provides weaker immunity than natural infection. Moreover, as a proxy for pandemic simulation, we analyze the invasion of a variant that evades natural immunity. We find that a future vaccine providing sufficiently broad and long-lived cross-group protection at a sufficiently high vaccination rate, could prevent pandemic emergence and lower the pandemic burden. This study highlights that as well as effectiveness, breadth and duration should be considered in epidemiologically informed TPPs for future human influenza A vaccines.

When do we need multiple infectious disease models? Agreement between projection rank and magnitude in a multi-model setting.

Mathematical models are useful for public health planning and response to infectious disease threats. However, different models can provide differing results, which can hamper decision making if not synthesized appropriately. To address this challenge, multi-model hubs convene independent modeling groups to generate ensembles, known to provide more accurate predictions of future outcomes. Yet, these hubs are resource intensive, and how many models are sufficient in a hub is not known. Here, we compare the benefit of predictions from multiple models in different contexts: (1) decision settings that depend on predictions of quantitative outcomes (e.g., hospital capacity planning), where assessments of the benefits of multi-model ensembles have largely focused; and (2) decisions settings that require the ranking of alternative epidemic scenarios (e.g., comparing outcomes under multiple possible interventions and biological uncertainties). We develop a mathematical framework to mimic a multi-model prediction setting, and use this framework to quantify how frequently predictions from different models agree. We further explore multi-model agreement using real-world, empirical data from 14 rounds of U.S. COVID-19 Scenario Modeling Hub projections. Our results suggest that the value of multiple models could be different in different decision contexts, and if only a few models are available, focusing on the rank of alternative epidemic scenarios could be more robust than focusing on quantitative outcomes. Although additional exploration of the sufficient number of models for different contexts is still needed, our results indicate that it may be possible to identify decision contexts where it is robust to rely on fewer models, a finding that can inform the use of modeling resources during future public health crises.

Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years.

Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Local-scale phylodynamics reveal differential community impact of SARS-CoV-2 in a metropolitan US county.

SARS-CoV-2 transmission is largely driven by heterogeneous dynamics at a local scale, leaving local health departments to design interventions with limited information. We analyzed SARS-CoV-2 genomes sampled between February 2020 and March 2022 jointly with epidemiological and cell phone mobility data to investigate fine scale spatiotemporal SARS-CoV-2 transmission dynamics in King County, Washington, a diverse, metropolitan US county. We applied an approximate structured coalescent approach to model transmission within and between North King County and South King County alongside the rate of outside introductions into the county. Our phylodynamic analyses reveal that following stay-at-home orders, the epidemic trajectories of North and South King County began to diverge. We find that South King County consistently had more reported and estimated cases, COVID-19 hospitalizations, and longer persistence of local viral transmission when compared to North King County, where viral importations from outside drove a larger proportion of new cases. Using mobility and demographic data, we also find that South King County experienced a more modest and less sustained reduction in mobility following stay-at-home orders than North King County, while also bearing more socioeconomic inequities that might contribute to a disproportionate burden of SARS-CoV-2 transmission. Overall, our findings suggest a role for local-scale phylodynamics in understanding the heterogeneous transmission landscape.

The association between influenza vaccination uptake and influenza and pneumonia-associated deaths in the United States.

BACKGROUND: The influenza mortality burden has remained substantial in the United States (US) despite relatively high levels of influenza vaccine uptake. This has led to questions regarding the effectiveness of the program against this outcome, particularly in the elderly. The aim of this evaluation was to develop and explore a new approach to estimating the population-level effect of influenza vaccination uptake on pneumonia and influenza (P&I) associated deaths. METHODS: Using publicly available data we examined the association between state-level influenza vaccination and all-age P&I associated deaths in the US from the 2013-2014 influenza season to the 2018-2019 season. In the main model, we evaluated influenza vaccine uptake in all those age 6 months and older. We used a mixed-effects regression analysis with generalised least squares estimation to account for within state correlation in P&I mortality. RESULTS: From 2013-2014 through 2018-2019, the total number of all-age P&I related deaths during the influenza seasons was 480,111. The mean overall cumulative influenza vaccine uptake (age 6 months and older) across the states and years considered was 46.7%, with higher uptake (64.8%) observed in those aged ≥ 65 years. We found that overall influenza vaccine uptake (6 months and older) had a statistically significant protective association with the P&I death rate. This translated to a 0.33 (95% CI: 0.20, 0.47) per 100,000 population reduction in P&I deaths in the influenza season per 1% increase in overall influenza vaccine uptake. DISCUSSION: These results using a population-level statistical approach provide additional support for the overall effectiveness of the US influenza vaccination program. This reassurance is critical given the importance of ensuring confidence in this life saving program. Future research is needed to expand on our approach using more refined data.

Ensemble2: Scenarios ensembling for communication and performance analysis.

Throughout the COVID-19 pandemic, scenario modeling played a crucial role in shaping the decision-making process of public health policies. Unlike forecasts, scenario projections rely on specific assumptions about the future that consider different plausible states-of-the-world that may or may not be realized and that depend on policy interventions, unpredictable changes in the epidemic outlook, etc. As a consequence, long-term scenario projections require different evaluation criteria than the ones used for traditional short-term epidemic forecasts. Here, we propose a novel ensemble procedure for assessing pandemic scenario projections using the results of the Scenario Modeling Hub (SMH) for COVID-19 in the United States (US). By defining a "scenario ensemble" for each model and the ensemble of models, termed "Ensemble2", we provide a synthesis of potential epidemic outcomes, which we use to assess projections' performance, bypassing the identification of the most plausible scenario. We find that overall the Ensemble2 models are well-calibrated and provide better performance than the scenario ensemble of individual models. The ensemble procedure accounts for the full range of plausible outcomes and highlights the importance of scenario design and effective communication. The scenario ensembling approach can be extended to any scenario design strategy, with potential refinements including weighting scenarios and allowing the ensembling process to evolve over time.

The US COVID-19 and Influenza Scenario Modeling Hubs: Delivering long-term projections to guide policy.

Between December 2020 and April 2023, the COVID-19 Scenario Modeling Hub (SMH) generated operational multi-month projections of COVID-19 burden in the US to guide pandemic planning and decision-making in the context of high uncertainty. This effort was born out of an attempt to coordinate, synthesize and effectively use the unprecedented amount of predictive modeling that emerged throughout the COVID-19 pandemic. Here we describe the history of this massive collective research effort, the process of convening and maintaining an open modeling hub active over multiple years, and attempt to provide a blueprint for future efforts. We detail the process of generating 17 rounds of scenarios and projections at different stages of the COVID-19 pandemic, and disseminating results to the public health community and lay public. We also highlight how SMH was expanded to generate influenza projections during the 2022-23 season. We identify key impacts of SMH results on public health and draw lessons to improve future collaborative modeling efforts, research on scenario projections, and the interface between models and policy.

Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic.

To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.

Predictors of Severity of Influenza-Related Hospitalizations: Results From the Global Influenza Hospital Surveillance Network (GIHSN).

BACKGROUND: The Global Influenza Hospital Surveillance Network (GIHSN) has since 2012 provided patient-level data on severe influenza-like-illnesses from >100 participating clinical sites worldwide based on a core protocol and consistent case definitions. METHODS: We used multivariable logistic regression to assess the risk of intensive care unit admission, mechanical ventilation, and in-hospital death among hospitalized patients with influenza and explored the role of patient-level covariates and country income level. RESULTS: The data set included 73 121 patients hospitalized with respiratory illness in 22 countries, including 15 660 with laboratory-confirmed influenza. After adjusting for patient-level covariates we found a 7-fold increase in the risk of influenza-related intensive care unit admission in lower middle-income countries (LMICs), compared with high-income countries (P = .01). The risk of mechanical ventilation and in-hospital death also increased by 4-fold in LMICs, though these differences were not statistically significant. We also find that influenza mortality increased significantly with older age and number of comorbid conditions. Across all severity outcomes studied and after controlling for patient characteristics, infection with influenza A/H1N1pdm09 was more severe than with A/H3N2. CONCLUSIONS: Our study provides new information on influenza severity in underresourced populations, particularly those in LMICs.

Modeling the impact of COVID-19 nonpharmaceutical interventions on respiratory syncytial virus transmission in South Africa.

Background: The South African government employed various nonpharmaceutical interventions (NPIs) to reduce the spread of SARS-CoV-2. Surveillance data from South Africa indicates reduced circulation of respiratory syncytial virus (RSV) throughout the 2020-2021 seasons. Here, we use a mechanistic transmission model to project the rebound of RSV in the two subsequent seasons. Methods: We fit an age-structured epidemiological model to hospitalization data from national RSV surveillance in South Africa, allowing for time-varying reduction in RSV transmission during periods of COVID-19 circulation. We apply the model to project the rebound of RSV in the 2022 and 2023 seasons. Results: We projected an early and intense outbreak of RSV in April 2022, with an age shift to older infants (6-23 months old) experiencing a larger portion of severe disease burden than typical. In March 2022, government alerts were issued to prepare the hospital system for this potentially intense outbreak. We then assess the 2022 predictions and project the 2023 season. Model predictions for 2023 indicate that RSV activity has not fully returned to normal, with a projected early and moderately intense wave. We estimate that NPIs reduced RSV transmission between 15% and 50% during periods of COVID-19 circulation. Conclusions: A wide range of NPIs impacted the dynamics of the RSV outbreaks throughout 2020-2023 in regard to timing, magnitude, and age structure, with important implications in a low- and middle-income countries (LMICs) setting where RSV interventions remain limited. More efforts should focus on adapting RSV models to LMIC data to project the impact of upcoming medical interventions for this disease.

Correction: Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

[This corrects the article DOI: 10.1371/journal.pmed.1003793.].