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Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness.

ABSTRACT: Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal can be disaggregated into distinct viral components. We generate separate predictions for six contributing pathogens (influenza A/H1, A/H3, B, respiratory syncytial virus, and human parainfluenza virus types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions. The relative contribution of each pathogen to the total ILI signal is estimated using a Markov Chain Monte Carlo (MCMC) method upon forecast aggregation. We find highly variable overall contributions from influenza type A viruses across seasons, but relatively stable contributions for the other pathogens. Using historical data from 1997 to 2014 at US national and regional levels, the proposed forecasting system generates improved predictions of both seasonal and near-term targets relative to a baseline method that simulates ILI as a single pathogen. The hierarchical forecasting system can generate predictions for each viral component, as well as infer and predict their contributions to ILI, which may additionally help physicians determine the etiological causes of ILI in clinical settings.

SUBMITTER: Pei S

PROVIDER: S-EPMC7608986 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness.

Pei Sen S Shaman Jeffrey J

PLoS computational biology 20201022 10

Influenza-like illness (ILI) is a commonly measured syndromic signal representative of a range of acute respiratory infections. Reliable forecasts of ILI can support better preparation for patient surges in healthcare systems. Although ILI is an amalgamation of multiple pathogens with variable seasonal phasing and attack rates, most existing process-based forecasting systems treat ILI as a single infectious agent. Here, using ILI records and virologic surveillance data, we show that ILI signal c ...[more]

PMID: 33090997

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Project description:In recent years, nidoviruses have emerged as important respiratory pathogens of reptiles, affecting captive python populations. In pythons, nidovirus (recently reclassified as serpentovirus) infection induces an inflammation of the upper respiratory and alimentary tract which can develop into a severe, often fatal proliferative pneumonia. We observed pyogranulomatous and fibrinonecrotic lesions in organ systems other than the respiratory tract during full postmortem examinations on 30 serpentovirus reverse transcription-PCR (RT-PCR)-positive pythons of varying species originating from Switzerland and Spain. The observations prompted us to study whether this not yet reported wider distribution of lesions is associated with previously unknown serpentoviruses or changes in the serpentovirus genome. RT-PCR and inoculation of Morelia viridis cell cultures served to recruit the cases and obtain virus isolates. Immunohistochemistry and immunofluorescence staining against serpentovirus nucleoprotein demonstrated that the virus infects not only a broad spectrum of epithelia (respiratory and alimentary epithelium, hepatocytes, renal tubules, pancreatic ducts, etc.), but also intravascular monocytes, intralesional macrophages, and endothelial cells. With next-generation sequencing we obtained a full-length genome for a novel serpentovirus species circulating in Switzerland. Analysis of viral genomes recovered from pythons showing serpentovirus infection-associated respiratory or systemic disease did not reveal sequence association to phenotypes; however, functional studies with different strains are needed to confirm this observation. The results indicate that serpentoviruses have a broad cell and tissue tropism, further suggesting that the course of infection could vary and involve lesions in a broad spectrum of tissues and organ systems as a consequence of monocyte-mediated viral systemic spread.IMPORTANCE During the last years, python nidoviruses (now reclassified as serpentoviruses) have become a primary cause of fatal disease in pythons. Serpentoviruses represent a threat to captive snake collections, as they spread rapidly and can be associated with high morbidity and mortality. Our study indicates that, different from previous evidence, the viruses do not only affect the respiratory tract, but can spread in the entire body with blood monocytes, have a broad spectrum of target cells, and can induce a variety of lesions. Nidovirales is an order of animal and human viruses that comprises important zoonotic pathogens such as Middle East respiratory syndrome coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and SARS-CoV-2. Serpentoviruses belong to the same order as the above-mentioned human viruses and show similar characteristics (rapid spread, respiratory and gastrointestinal tropism, etc.). The present study confirms the relevance of natural animal diseases to better understand the complexity of viruses of the order Nidovirales.

Project description:Health planners use forecasts of key metrics associated with influenza-like illness (ILI); near-term weekly incidence, week of season onset, week of peak, and intensity of peak. Here, we describe our participation in a weekly prospective ILI forecasting challenge for the United States for the 2016-17 season and subsequent evaluation of our performance. We implemented a metapopulation model framework with 32 model variants. Variants differed from each other in their assumptions about: the force-of-infection (FOI); use of uninformative priors; the use of discounted historical data for not-yet-observed time points; and the treatment of regions as either independent or coupled. Individual model variants were chosen subjectively as the basis for our weekly forecasts; however, a subset of coupled models were only available part way through the season. Most frequently, during the 2016-17 season, we chose; FOI variants with both school vacations and humidity terms; uninformative priors; the inclusion of discounted historical data for not-yet-observed time points; and coupled regions (when available). Our near-term weekly forecasts substantially over-estimated incidence early in the season when coupled models were not available. However, our forecast accuracy improved in absolute terms and relative to other teams once coupled solutions were available. In retrospective analysis, we found that the 2016-17 season was not typical: on average, coupled models performed better when fit without historically augmented data. Also, we tested a simple ensemble model for the 2016-17 season and found that it underperformed our subjective choice for all forecast targets. In this study, we were able to improve accuracy during a prospective forecasting exercise by coupling dynamics between regions. Although reduction of forecast subjectivity should be a long-term goal, some degree of human intervention is likely to improve forecast accuracy in the medium-term in parallel with the systematic consideration of more sophisticated ensemble approaches.

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Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness.

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Aggregating forecasts of multiple respiratory pathogens supports more accurate forecasting of influenza-like illness.

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