Challenges in modeling the emergence of novel pathogens

Estimating the diagnostic performance of serological assays for emerging pathogens using a Bayesian approach: Myxoma virus in the Iberian hare (Lepus granatensis)

Validated diagnostic tools are essential when conducting serological surveys. However, reliable tests are scarce and hard to attain for emerging pathogens due to the lack of reference tests or samples. Recently, a recombinant myxoma virus (MYXV), named ha-MYXV, raised alarm in the Iberian Peninsula for its impact on Iberian hare (Lepus granatensis) populations and its detection in wild (Oryctolagus cuniculus) and domestic rabbits. Here, we follow a Bayesian approach to evaluate two serological tools, an indirect ELISA (iELISA) and a competitive ELISA (cELISA), used to monitor this emerging pathogen in Iberian hare populations. We modelled serological data from 227 hares conveniently selected retrospectively for their apparent healthy status. First, we applied finite mixture models to adjust the cut-off thresholds of both tests, which improved the agreement between both tests (initial kappa = 0.42, after threshold adjustment = 0.78). Then, we employed Bayesian latent class models (BLCM) to estimate the assays' specificity (Sp) and sensitivity (Se). The BLCM estimated median Sp of 94.0 % (95 % posterior probability interval (PPI): 85.9-99.4) and 96.1 % (PPI: 87.2-100.0), and Se of 77.7 % (PPI: 61.5-89.5) and 91.7 % (PPI: 78.1-99.9), for the iELISA and the cELISA, respectively. The true seroprevalence estimations show higher values in south-central Spain (ranging from 13.1 % to 70.4 %) and lower in the north (Navarra: 5.5 %). A Bayesian approach allowed to evaluate diagnostic tools for ha-MYXV, an emerging wildlife pathogen, in the absence of reference tests or samples. Future epidemiological studies of myxomatosis in Iberian hares should calculate true seroprevalence based on our estimations.

Modelling with SPEED: a Stochastic Predictor of Early Epidemic Detection

The frequency of emerging infectious disease outbreaks continues to rise, necessitating predictive frameworks for public health decision-making. This study introduces the Stochastic Predictor of Early Epidemic Detection (SPEED) model, an adaptation of the classic Susceptible-Infected-Recovered model, employing a Gillespie-like algorithm to simulate early-stage stochastic disease transmission. SPEED incorporates individual-level detection probabilities based on the infection time and the lag from GP consultation to lab confirmation. The model dynamically adjusts to public health responses by enhancing testing and reducing detection times once a single case has been identified. SPEED serves two key functionalities. First, as a statistical inference tool refining reproduction number estimates following the detection of a small number of cases. SPEED inference uses specified prior distributions for the reproduction number to provide reliable posterior estimates. Second, to simulate epidemic scenarios under specified values of the reproduction number in order to construct a distribution of the time to subsequent detections. The model is used to evaluate how second case timings can rule out higher values of the reproduction number. Comparisons with simulations under heightened surveillance scenarios demonstrate the model's utility in assessing response efficacy on the initial outbreak spread. Our results demonstrate SPEED applied to a single case of influenza A(H1N2)v, detected through routine flu surveillance on the 23rd November 2023.

Reducing transmission in multiple settings is required to eliminate the risk of major Ebola outbreaks: a mathematical modelling study

The Ebola virus (EV) persists in animal populations, with zoonotic transmission to humans occurring every few months or years. When zoonotic transmission arises, it is important to understand which interventions are most effective at preventing a major outbreak driven by human-to-human transmission. Here, we analyse a mathematical model of EV transmission and calculate the probability of a major outbreak starting from a single introduced case. We consider community, funeral and healthcare facility transmission and conduct sensitivity analyses to explore the effects of non-pharmaceutical interventions (NPIs) that influence these transmission routes. We find that, if the index case is treated in the community, then the elimination of transmission at funerals reduces the probability of a major outbreak substantially (from 0.410 to 0.066 under our baseline model parametrization). However, eliminating the risk of major outbreaks entirely requires combinations of measures that limit transmission in different settings, such as community engagement to promote safe burial practices and implementation of barrier nursing in healthcare facilities. In addition to generating insights into the drivers of Ebola outbreaks, this research provides a modelling framework for assessing the effectiveness of interventions at mitigating outbreaks of other infectious diseases with transmission in multiple settings.

Precognition of Known And Unknown Biothreats: A Risk-Based Approach

Data mining and artificial intelligence algorithms can estimate the probability of future occurrences with defined precision. Yet, the prediction of infectious disease outbreaks remains a complex and difficult task. This is demonstrated by the limited accuracy and sensitivity of current models in predicting the emergence of previously unknown pathogens such as Zika, Chikungunya, and SARS-CoV-2, and the resurgence of Mpox, along with their impacts on global health, trade, and security. Comprehensive analysis of infectious disease risk profiles, vulnerabilities, and mitigation capacities, along with their spatiotemporal dynamics at the international level, is essential for preventing their transnational propagation. However, annual indexes about the impact of infectious diseases provide a low level of granularity to allow stakeholders to craft better mitigation strategies. A quantitative risk assessment by analytical platforms requires billions of near real-time data points from heterogeneous sources, integrating and analyzing univariable or multivariable data with different levels of complexity and latency that, in most cases, overwhelm human cognitive capabilities. Autonomous biosurveillance can open the possibility for near real-time, risk- and evidence-based policymaking and operational decision support.

Exploring the genetic progression of MDR1 in Plasmodium falciparum: A decade of multi-regional genetic analysis (2014-2024)

Background

The genetic progression of the MDR1 gene in Plasmodium falciparum, a key factor in drug resistance, presents significant challenges for malaria control. This study aims to elucidate the genetic diversity and evolutionary dynamics of P. falciparum, particularly focusing on the MDR1 gene across multi-regional populations. To analyze the genetic diversity of P. falciparum MDR1 gene across various multi-regional populations between 2014 and 2024, assessing allelic richness, genetic distances, and evolutionary patterns.

Methods

We conducted an extensive genetic analysis using methods such as Analysis of Molecular Variance (AMOVA), pairwise population matrices of Nei unbiased genetic distance and identity, PhiPT and Phi'PT values, and Principal Coordinates Analysis (PCoA). The study covered diverse P. falciparum populations from India, Nigeria, Ethiopia, Honduras, China, and Cameroon.

Findings

Our findings reveal significant genetic heterogeneity in the MDR1 gene. Populations like India: Odisha (2014) exhibited high allelic richness, indicating diverse drug resistance profiles. Notable genetic divergence was observed, especially between India (2016) and Nigeria (2020), suggesting different evolutionary trajectories in drug resistance. The PCoA analysis highlighted the multi-dimensional genetic variation, reflecting the complex interplay of factors influencing drug resistance in P. falciparum.

Interpretation

The comprehensive analysis of P. falciparum's MDR1 gene provides crucial insights into the multi-regional patterns of drug resistance. This knowledge is essential for developing effective malaria control measures and adapting treatment strategies to the evolving genetic diversity of the parasite.

Role of Piezoelectricity in Disease Diagnosis and Treatment: A Review

Because of their unique electromechanical coupling response, piezoelectric smart biomaterials demonstrated distinctive capability toward effective, efficient, and quick diagnosis and treatment of a wide range of diseases. Such materials have potentiality to be utilized as wireless therapeutic methods with ultrasonic stimulation, which can be used as self-powered biomedical devices. An emerging advancement in the realm of personalized healthcare involves the utilization of piezoelectric biosensors for a range of therapeutic diagnosis such as diverse physiological signals in the human body, viruses, pathogens, and diseases like neurodegenerative ones, cancer, etc. The combination of piezoelectric nanoparticles with ultrasound has been established as a promising approach in sonodynamic therapy and piezocatalytic therapeutics and provides appealing alternatives for noninvasive treatments for cancer, chronic wounds, neurological diseases, etc. Innovations in implantable medical devices (IMDs), such as implantable piezoelectric energy generator (iPEG), offer significant advantages in improving physiological functioning and ability to power a cardiac pacemaker and restore the heart function. This comprehensive review critically evaluates the role of piezoelectricity in disease diagnosis and treatment, highlighting the implication of piezoelectric smart biomaterials for biomedical devices. It also discusses the potential of piezoelectric materials in healthcare monitoring, tissue engineering, and other medical applications while emphasizing future trends and challenges in the field.

Pathogen-Agnostic Advanced Molecular Diagnostic Testing for Difficult-to-Diagnose Clinical Syndromes-Results of an Emerging Infections Network Survey of Frontline US Infectious Disease Clinicians, May 2023

During routine clinical practice, infectious disease physicians encounter patients with difficult-to-diagnose clinical syndromes and may order advanced molecular testing to detect pathogens. These tests may identify potential infectious causes for illness and allow clinicians to adapt treatments or stop unnecessary antimicrobials. Cases of pathogen-agnostic disease testing also provide an important window into known, emerging, and reemerging pathogens and may be leveraged as part of national sentinel surveillance. A survey of Emerging Infections Network members, a group of infectious disease providers in North America, was conducted in May 2023. The objective of the survey was to gain insight into how and when infectious disease physicians use advanced molecular testing for patients with difficult-to-diagnose infectious diseases, as well as to explore the usefulness of advanced molecular testing and barriers to use. Overall, 643 providers answered at least some of the survey questions; 478 (74%) of those who completed the survey had ordered advanced molecular testing in the last two years, and formed the basis for this study. Respondents indicated that they most often ordered broad-range 16S rRNA gene sequencing, followed by metagenomic next-generation sequencing and whole genome sequencing; and commented that in clinical practice, some, but not all tests were useful. Many physicians also noted several barriers to use, including a lack of national guidelines and cost, while others commented that whole genome sequencing had potential for use in outbreak surveillance. Improving frontline physician access, availability, affordability, and developing clear national guidelines for interpretation and use of advanced molecular testing could potentially support clinical practice and public health surveillance.

Emergence failure of early epidemics: A mathematical modeling approach

Epidemic or pathogen emergence is the phenomenon by which a poorly transmissible pathogen finds its evolutionary pathway to become a mutant that can cause an epidemic. Many mathematical models of pathogen emergence rely on branching processes. Here, we discuss pathogen emergence using Markov chains, for a more tractable analysis, generalizing previous work by Kendall and Bartlett about disease invasion. We discuss the probability of emergence failure for early epidemics, when the number of infected individuals is small and the number of the susceptible individuals is virtually unlimited. Our formalism addresses both directly transmitted and vector-borne diseases, in the cases where the original pathogen is 1) one step-mutation away from the epidemic strain, and 2) undergoing a long chain of neutral mutations that do not change the epidemiology. We obtain analytic results for the probabilities of emergence failure and two features transcending the transmission mechanism. First, the reproduction number of the original pathogen is determinant for the probability of pathogen emergence, more important than the mutation rate or the transmissibility of the emerged pathogen. Second, the probability of mutation within infected individuals must be sufficiently high for the pathogen undergoing neutral mutations to start an epidemic, the mutation threshold depending again on the basic reproduction number of the original pathogen. Finally, we discuss the parameterization of models of pathogen emergence, using SARS-CoV1 as an example of zoonotic emergence and HIV as an example for the emergence of drug resistance. We also discuss assumptions of our models and implications for epidemiology.

Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics

The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression.

A Framework for Assessing the Impact of Outbreak Response Immunization Programs

The impact of outbreak response immunization (ORI) can be estimated by comparing observed outcomes to modelled counterfactual scenarios without ORI, but the most appropriate metrics depend on stakeholder needs and data availability. This study developed a framework for using mathematical models to assess the impact of ORI for vaccine-preventable diseases. Framework development involved (1) the assessment of impact metrics based on stakeholder interviews and literature reviews determining data availability and capacity to capture as model outcomes; (2) mapping investment in ORI elements to model parameters to define scenarios; (3) developing a system for engaging stakeholders and formulating model questions, performing analyses, and interpreting results; and (4) example applications for different settings and pathogens. The metrics identified as most useful were health impacts, economic impacts, and the risk of severe outbreaks. Scenario categories included investment in the response scale, response speed, and vaccine targeting. The framework defines four phases: (1) problem framing and data sourcing (identification of stakeholder needs, metrics, and scenarios); (2) model choice; (3) model implementation; and (4) interpretation and communication. The use of the framework is demonstrated by application to two outbreaks, measles in Papua New Guinea and Ebola in the Democratic Republic of the Congo. The framework is a systematic way to engage with stakeholders and ensure that an analysis is fit for purpose, makes the best use of available data, and uses suitable modelling methodology.

Model-based estimates of chikungunya epidemiological parameters and outbreak risk from varied data types

Assessing the factors responsible for differences in outbreak severity for the same pathogen is a challenging task, since outbreak data are often incomplete and may vary in type across outbreaks (e.g., daily case counts, serology, cases per household). We propose that outbreaks described with varied data types can be directly compared by using those data to estimate a common set of epidemiological parameters. To demonstrate this for chikungunya virus (CHIKV), we developed a realistic model of CHIKV transmission, along with a Bayesian inference method that accommodates any type of outbreak data that can be simulated. The inference method makes use of the fact that all data types arise from the same transmission process, which is simulated by the model. We applied these tools to data from three real-world outbreaks of CHIKV in Italy, Cambodia, and Bangladesh to estimate nine model parameters. We found that these populations differed in several parameters, including pre-existing immunity and house-to-house differences in mosquito activity. These differences resulted in posterior predictions of local CHIKV transmission risk that varied nearly fourfold: 16% in Italy, 28% in Cambodia, and 62% in Bangladesh. Our inference method and model can be applied to improve understanding of the epidemiology of CHIKV and other pathogens for which outbreaks are described with varied data types.

A practical guide to mathematical methods for estimating infectious disease outbreak risks

Mathematical models are increasingly used throughout infectious disease outbreaks to guide control measures. In this review article, we focus on the initial stages of an outbreak, when a pathogen has just been observed in a new location (e.g., a town, region or country). We provide a beginner's guide to two methods for estimating the risk that introduced cases lead to sustained local transmission (i.e., the probability of a major outbreak), as opposed to the outbreak fading out with only a small number of cases. We discuss how these simple methods can be extended for epidemiological models with any level of complexity, facilitating their wider use, and describe how estimates of the probability of a major outbreak can be used to guide pathogen surveillance and control strategies. We also give an overview of previous applications of these approaches. This guide is intended to help quantitative researchers develop their own epidemiological models and use them to estimate the risks associated with pathogens arriving in new host populations. The development of these models is crucial for future outbreak preparedness. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Managing host-parasite interactions in humans and wildlife in times of global change

Global change in the Anthropocene has modified the environment of almost any species on earth, be it through climate change, habitat modifications, pollution, human intervention in the form of mass drug administration (MDA), or vaccination. This can have far-reaching consequences on all organisational levels of life, including eco-physiological stress at the cell and organism level, individual fitness and behaviour, population viability, species interactions and biodiversity. Host-parasite interactions often require highly adapted strategies by the parasite to survive and reproduce within the host environment and ensure efficient transmission among hosts. Yet, our understanding of the system-level outcomes of the intricate interplay of within host survival and among host parasite spread is in its infancy. We shed light on how global change affects host-parasite interactions at different organisational levels and address challenges and opportunities to work towards better-informed management of parasite control. We argue that global change affects host-parasite interactions in wildlife inhabiting natural environments rather differently than in humans and invasive species that benefit from anthropogenic environments as habitat and more deliberate rather than erratic exposure to therapeutic drugs and other control efforts.

The challenges of data in future pandemics

The use of data has been essential throughout the unfolding COVID-19 pandemic. We have needed it to populate our models, inform our understanding, and shape our responses to the disease. However, data has not always been easy to find and access, it has varied in quality and coverage, been difficult to reuse or repurpose. This paper reviews these and other challenges and recommends steps to develop a data ecosystem better able to deal with future pandemics by better supporting preparedness, prevention, detection and response.

Challenges for modelling interventions for future pandemics

Mathematical modelling and statistical inference provide a framework to evaluate different non-pharmaceutical and pharmaceutical interventions for the control of epidemics that has been widely used during the COVID-19 pandemic. In this paper, lessons learned from this and previous epidemics are used to highlight the challenges for future pandemic control. We consider the availability and use of data, as well as the need for correct parameterisation and calibration for different model frameworks. We discuss challenges that arise in describing and distinguishing between different interventions, within different modelling structures, and allowing both within and between host dynamics. We also highlight challenges in modelling the health economic and political aspects of interventions. Given the diversity of these challenges, a broad variety of interdisciplinary expertise is needed to address them, combining mathematical knowledge with biological and social insights, and including health economics and communication skills. Addressing these challenges for the future requires strong cross-disciplinary collaboration together with close communication between scientists and policy makers.

Wastewater Surveillance for SARS-CoV-2 RNA in Canada

Wastewater surveillance for SARS-CoV-2 RNA is a relatively recent adaptation of long-standing wastewater surveillance for infectious and other harmful agents. Individuals infected with COVID-19 were found to shed SARS-CoV-2 in their faeces. Researchers around the world confirmed that SARS-CoV-2 RNA fragments could be detected and quantified in community wastewater. Canadian academic researchers, largely as volunteer initiatives, reported proof-of-concept by April 2020. National collaboration was initially facilitated by the Canadian Water Network.

Many public health officials were initially skeptical about actionable information being provided by wastewater surveillance even though experience has shown that public health surveillance for a pandemic has no single, perfect approach. Rather, different approaches provide different insights, each with its own strengths and limitations. Public health science must triangulate among different forms of evidence to maximize understanding of what is happening or may be expected. Well-conceived, resourced, and implemented wastewater-based platforms can provide a cost-effective approach to support other conventional lines of evidence. Sustaining wastewater monitoring platforms for future surveillance of other disease targets and health states is a challenge. Canada can benefit from taking lessons learned from the COVID-19 pandemic to develop forward-looking interpretive frameworks and capacity to implement, adapt, and expand such public health surveillance capabilities.