Internet-based Surveillance Systems and Infectious Diseases Prediction: An Updated Review of the Last 10 Years and Lessons from the COVID-19 Pandemic.

The last decade has seen major advances and growth in internet-based surveillance for infectious diseases through advanced computational capacity, growing adoption of smart devices, increased availability of Artificial Intelligence (AI), alongside environmental pressures including climate and land use change contributing to increased threat and spread of pandemics and emerging infectious diseases. With the increasing burden of infectious diseases and the COVID-19 pandemic, the need for developing novel technologies and integrating internet-based data approaches to improving infectious disease surveillance is greater than ever. In this systematic review, we searched the scientific literature for research on internet-based or digital surveillance for influenza, dengue fever and COVID-19 from 2013 to 2023. We have provided an overview of recent internet-based surveillance research for emerging infectious diseases (EID), describing changes in the digital landscape, with recommendations for future research directed at public health policymakers, healthcare providers, and government health departments to enhance traditional surveillance for detecting, monitoring, reporting, and responding to influenza, dengue, and COVID-19.

Developing a Research Network of Early Warning Systems for Infectious Diseases Transmission Between China and Australia.

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

Immune imprinting in early life shapes cross-reactivity to influenza B virus haemagglutinin.

Influenza exposures early in life are believed to shape future susceptibility to influenza infections by imprinting immunological biases that affect cross-reactivity to future influenza viruses. However, direct serological evidence linked to susceptibility is limited. Here we analysed haemagglutination-inhibition titres in 1,451 cross-sectional samples collected between 1992 and 2020, from individuals born between 1917 and 2008, against influenza B virus (IBV) isolates from 1940 to 2021. We included testing of 'future' isolates that circulated after sample collection. We show that immunological biases are conferred by early life IBV infection and result in lineage-specific cross-reactivity of a birth cohort towards future IBV isolates. This translates into differential estimates of susceptibility between birth cohorts towards the B/Yamagata and B/Victoria lineages, predicting lineage-specific birth-cohort distributions of observed medically attended IBV infections. Our data suggest that immunological measurements of imprinting could be important in modelling and predicting virus epidemiology.

Seasonal antigenic prediction of influenza A H3N2 using machine learning.

Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine updates as well as for pandemic preparedness. Performing antigenic characterization of IAV on a global scale is confronted with high costs, animal availability, and other practical challenges. Here we present a machine learning model that accurately predicts (normalized) outputs of HI assays involving circulating human IAV H3N2 viruses, using their hemagglutinin subunit 1 (HA1) sequences and associated metadata. Each season, the model learns an updated nonlinear mapping of genetic to antigenic changes using data from past seasons only. The model accurately distinguishes antigenic variants from non-variants and adaptively characterizes seasonal dynamics of HA1 sites having the strongest influence on antigenic change. Antigenic predictions produced by the model can aid influenza surveillance, public health management, and vaccine strain selection activities.

Assessment of the antigenic evolution of a clade 6B.1 human H1N1pdm influenza virus revealed differences between ferret and human convalescent sera.

BACKGROUND: Influenza viruses continually acquire mutations in the antigenic epitopes of their major viral antigen, the surface glycoprotein haemagglutinin (HA), allowing evasion from immunity in humans induced upon prior influenza virus infections or vaccinations. Consequently, the influenza strains used for vaccine production must be updated frequently. METHODS: To better understand the antigenic evolution of influenza viruses, we introduced random mutations into the HA head region (where the immunodominant epitopes are located) of a pandemic H1N1 (H1N1pdm) virus from 2015 and incubated it with various human sera collected in 2015-2016. Mutants not neutralized by the human sera were sequenced and further characterized for their haemagglutination inhibition (HI) titers with human sera and with ferret sera raised to H1N1pdm viruses from 2009 to 2015. FINDINGS: The largest antigenic changes were conferred by mutations at HA amino acid position 187; interestingly, these antigenic changes were recognized by human, but not by ferret serum. H1N1pdm viruses with amino acid changes at position 187 were very rare until the end of 2018, but have become more frequent since; in fact, the D187A amino acid change is one of the defining changes of clade 6B.1A.5a.1 viruses, which emerged in 2019. INTERPRETATION: Our findings indicate that amino acid substitutions in H1N1pdm epitopes may be recognized by human sera, but not by homologous ferret sera. FUNDING: This project was supported by funding from the NIAID-funded Center for Research on Influenza Pathogenesis (CRIP, HHSN272201400008C).

Non-SARS-CoV-2 respiratory viral detection and whole genome sequencing from COVID-19 rapid antigen test devices: a laboratory evaluation study.

BACKGROUND: There has been high uptake of rapid antigen test device use for point-of-care COVID-19 diagnosis. Individuals who are symptomatic but test negative on COVID-19 rapid antigen test devices might have a different respiratory viral infection. We aimed to detect and sequence non-SARS-CoV-2 respiratory viruses from rapid antigen test devices, which could assist in the characterisation and surveillance of circulating respiratory viruses in the community. METHODS: We applied archival clinical nose and throat swabs collected between Jan 1, 2015, and Dec 31, 2022, that previously tested positive for a common respiratory virus (adenovirus, influenza, metapneumovirus, parainfluenza, rhinovirus, respiratory syncytial virus [RSV], or seasonal coronavirus; 132 swabs and 140 viral targets) on PCR to two commercially available COVID-19 rapid antigen test devices, the Panbio COVID-19 Ag Rapid Test Device and Roche SARS-CoV-2 Antigen Self-Test. In addition, we collected 31 COVID-19 rapid antigen test devices used to test patients who were symptomatic at The Royal Melbourne Hospital emergency department in Melbourne, Australia. We extracted total nucleic acid from the device paper test strips and assessed viral recovery using multiplex real-time PCR (rtPCR) and capture-based whole genome sequencing. Sequence and genome data were analysed through custom computational pipelines, including subtyping. FINDINGS: Of the 140 respiratory viral targets from archival samples, 89 (64%) and 88 (63%) were positive on rtPCR for the relevant taxa following extraction from Panbio or Roche rapid antigen test devices, respectively. Recovery was variable across taxa: we detected influenza A in nine of 18 samples from Panbio and seven of 18 from Roche devices; parainfluenza in 11 of 20 samples from Panbio and 12 of 20 from Roche devices; human metapneumovirus in 11 of 16 from Panbio and 14 of 16 from Roche devices; seasonal coronavirus in eight of 19 from Panbio and two of 19 from Roche devices; rhinovirus in 24 of 28 from Panbio and 27 of 28 from Roche devices; influenza B in four of 15 in both devices; and RSV in 16 of 18 in both devices. Of the 31 COVID-19 devices collected from The Royal Melbourne Hospital emergency department, 11 tested positive for a respiratory virus on rtPCR, including one device positive for influenza A virus, one positive for RSV, four positive for rhinovirus, and five positive for SARS-CoV-2. Sequences of target respiratory viruses from archival samples were detected in 55 (98·2%) of 56 samples from Panbio and 48 (85·7%) of 56 from Roche rapid antigen test devices. 98 (87·5%) of 112 viral genomes were completely assembled from these data, enabling subtyping for RSV and influenza viruses. All 11 samples collected from the emergency department had viral sequences detected, with near-complete genomes assembled for influenza A and RSV. INTERPRETATION: Non-SARS-CoV-2 respiratory viruses can be detected and sequenced from COVID-19 rapid antigen devices. Recovery of near full-length viral sequences from these devices provides a valuable opportunity to expand genomic surveillance programmes for public health monitoring of circulating respiratory viruses. FUNDING: Australian Government Medical Research Future Fund and Australian National Health and Medical Research Council.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations.

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.