Open access peer-reviewed chapter - ONLINE FIRST
Abstract
Understanding and adopting various methods for monitoring viral outbreaks is required for pathogen surveillance. Accurate diagnosis can play a significant role in the safe and effective treatment prescribed. Africa remains burdened with a host of infectious diseases, which challenges healthcare systems and the proper management of infectious diseases. Surveillance systems are implemented in some parts of Africa that have the infrastructure and funding to perform routine testing for pathogen screening. However, not all parts of this continent are equipped and have the necessary tools and support. With travel, tourism and goods exchange, infectious diseases can spread across borders rapidly, posing a threat to global health, emphasising the unified need for efforts to prevent, detect, and act on disease management through improved access to vaccinations and treatments. Effective response to disease outbreaks depends on several elements, including laboratory capacity, skilled health staff, and effective surveillance systems to detect and limit the spread of infectious illnesses rapidly. Traditional molecular methods such as genotyping and polymerase chain reaction (PCR)-based detection systems are now being complemented with tools like next-generation sequencing and clustered regularly interspaced short palindromic repeats (CRISPR). This book chapter aims to summarise the current methods and tools required for viral pathogen surveillance and broadly describes the issue of climate change and its impact on viral outbreaks.
Keywords
- surveillance
- molecular diagnostics
- viruses
- outbreaks
- pathogen
1. Introduction
Disease outbreaks are driven by infectious pathogens that contribute to large-scale pandemics. Over the past two decades, there have been notable viral outbreaks globally such as SARS-CoV-2, Dengue, Zika, Ebola, Chikungunya, Swine Flu and Oropouche [1]. The cause of the emerging and re-emerging viruses has been due to climate change, urbanisation, increased travel, ageing with decreased immunity and land use change. This has changed the dynamics in certain areas between human and animal interactions [2]. These outbreaks cause severe threats to global health and impact the economy and livelihoods of all affected individuals [1]. Therefore, one of the main lessons learnt from the SARS-CoV-2 pandemic was the importance of being prepared in terms of surveillance strategies, lab diagnostic tests, upskilling of personnel and the ability to translate the results to relevant health authorities rapidly.
Over 7 million deaths were attributed to the COVID-19 pandemic, driven by the re-emergence of a type of severe acute respiratory coronavirus (SARS-CoV) [3]. Viral outbreaks threaten global public health, often leading to widespread illness, death, and economic disruption. Rapidly identifying and tracking emerging viruses are essential in mitigating the impact of such outbreaks. Molecular diagnostics, particularly techniques that analyse viral nucleic acids (DNA or RNA), have revolutionised the detection, surveillance, and management of viral outbreaks. These techniques offer unparalleled sensitivity, specificity, and speed, enabling public health authorities to respond effectively to emerging infectious diseases. The use of molecular tools, such as polymerase chain reaction (PCR), Next-Generation Sequencing (NGS), and clustered regularly interspaced short palindromic repeats (CRISPR)-based technologies, has become critical for not only diagnosing infections but also tracing viral evolution, detecting mutations, and understanding viral transmission dynamics [4].
Molecular diagnostics allows for the identification of pathogens in asymptomatic individuals and is helpful for indicating previous exposure when assessing a specific virus, aiding scientists and healthcare authorities in controlling transmission [5]. Clinical and public health laboratories play a significant role in the diagnosis and treatment of microbial pathogens, which is important for surveillance and the response to outbreaks of viral pathogens [6]. During the COVID-19 pandemic, PCR-based diagnostics enabled the detection of SARS-CoV-2 around the globe, allowing for early intervention and widespread testing to curb the virus’ spread. In addition, molecular surveillance tools report on valuable data such as viral genetic mutations, facilitating the monitoring of viral evolution and the emergence of variants that may evade immunity or become more transmissible [7]. Since the COVID-19 pandemic, next-generation sequencing has emerged as a crucial tool for genomic surveillance. Its reduced cost has further aided ongoing efforts to enhance the monitoring of infectious pathogens in both clinical and environmental samples, although in low-resource settings, costs can be a challenge [8].
The integration of molecular diagnostics into outbreak surveillance systems is not without challenges. These include the high cost of advanced technologies, the need for specialised infrastructure, and issues surrounding the availability of testing and trained personnel in resource-limited settings [9]. Despite these challenges, the potential of molecular diagnostics to transform outbreak surveillance, offering real-time data and guiding public health decisions, remains immense. This book chapter explores the critical role of molecular diagnostics in viral outbreak surveillance, examining the key technologies, their applications in viral surveillance, and the ongoing efforts to overcome challenges associated with their deployment.
2. Molecular diagnostic techniques
2.1 Overview
Disease diagnostics plays a significant role in the public and healthcare sector, with agility, speed and flexibility being the three targets when selecting a method for accurate and effective diagnosis. Molecular diagnostics helps in the management of these diseases by detecting the genetic material of pathogens in clinical samples. Earlier techniques such as culture- and PCR-based molecular methods have been the standard procedure for viral outbreak surveillance and pathogen identification [10]. However, some of these methods result in longer turnaround times, quality assurance issues, and accurate decisions [11]. However, recent advancements in next-generation sequencing (NGS), metagenomics, and CRISPR-based diagnostics have revolutionised the field, offering more sensitive, rapid, and comprehensive methods for monitoring and controlling viral outbreaks [12]. These newer methods have become relatively affordable and sustainable in laboratory implementation for surveillance purposes and are slowly becoming the preferred method globally.
2.2 Significance
Africa is undeniably burdened with the highest rate of infectious diseases, coupled with the weakest public health infrastructure globally. Moreover, establishing effective public health systems may take years or even decades. Emerging infectious diseases must be prioritised. Challenges include integrating surveillance with epidemic preparedness and response efforts for priority diseases. This task is challenging due to the limited infrastructure and support for surveillance, research, and training on emerging diseases in Africa. Implementing laboratory-based surveillance and conducting focused research surveys to identify common infection sources in various communities could provide a unified approach to tackling this vast challenge. The most crucial step towards reducing Africa’s infectious disease burden is a substantial increase in the number of qualified personnel, including physicians and scientists [13]. Emerging infectious diseases in sub-Saharan Africa include cholera, meningitis, Ebola, measles, yellow fever, monkeypox, Zika, Rift Valley fever, and COVID-19. Several factors contribute to their rise, such as microorganisms adapting to climate and environmental changes, shifting ecosystems, and increased human vulnerability due to immunosuppression, malnutrition, and inadequate immunisation [14]. Enabling low-resource countries to be prepared for emerging and re-emerging viral outbreaks will help manage the constraints faced by the healthcare systems and government [11].
2.3 Established techniques
2.3.1 PCR-based methods
Polymerase chain reaction (PCR) has been one of the primary methods used to identify viruses [15]. PCR was reported as the most common method used for diagnosing epidemic-prone diseases, followed by ELISA in a survey conducted by the Africa CDC and the European Centre for Disease Prevention and Control (ECDC) on all 55 African Union states [16]. For this method, RNA is extracted and reverse-transcribed to cDNA in the case of RNA viruses, followed by specific primers that span and amplify a region of interest. One such example commonly used during the COVID-19 pandemic was the use of tiling primers, which are primers designed to generate short amplicons that overlap each other and cover the whole region or genome of interest [17]. However, a limiting factor in this approach was the need to regularly update the primers to ensure its sensitivity to current viral variants [18]. Quantitative real-time polymerase chain reaction (qRT-PCR) enables the quantitative detection of amplified genetic material using a fluorescent marker, making it the gold standard for monitoring infection progression and therapy response [19]. Automation and high-throughput advancements in data analysis have further solidified qRT-PCR’s dominance. SYBR green and TaqMan assays are examples of qPCR technologies that can help identify and quantify amplification products [12]. Older methods like transcription-mediated amplification and loop-mediated isothermal amplification have gained popularity [20]. Digital PCR (dPCR) is another PCR method that offers absolute quantification without external calibration curves, making it useful for drug-resistant variants and patient management [21]. Although PCR has been widely used to detect the presence and absence of pathogens in a sample, prior knowledge of the potential pathogens is required, and this may not be an ideal approach for outbreaks caused by novel or unknown pathogens [22].
2.3.2 Loop-mediated isothermal amplification (LAMP)
Loop-mediated isothermal amplification (LAMP) assays were established for the Zika virus, H5N1 avian influenza virus [23, 24, 25] and SARS-CoV-2. Loop-mediated isothermal amplification (LAMP) has been widely used for the detection of pathogens. It uses three primer pairs for selective binding and a DNA polymerase with strong strand displacement capabilities [20]. LAMP has advantages over PCR whereby it eliminates the need for multiple temperature cycles and long reaction times, which can lead to PCR errors [20]. This approach aims to generate a large number of DNA amplification products with an alternating, repetitive structure and compatible sequence [12]. Previously, its use was challenged by non-specific binding due to the formation of primer dimers. Furthermore, the use of multiple primers increased the risk of primer-primer hybridisations, resulting in false-positive results [26]. More recently, this approach has been modified to accurately identify dengue virus, Human Immunodeficiency Virus (HIV)-1, and Zika virus (ZIKV) in clinical samples [27].
2.3.3 Culture-based methods
Although some researchers may view the traditional cultural method of viral discovery as outdated, costly, and impractical, it remains in use for several key reasons. Growing a virus in culture provides an enriched template for molecular analysis and serves as an inoculum for animal models, serological tests, and neutralisation assays, which support research into the aetiology of infectious diseases, surveillance and mitigation of viral outbreaks. Avenues such as vaccine testing and drug evaluations are some areas that benefit from stock cultures. However, identifying appropriate cell lines may be challenging and innate immune responses can obstruct these processes, but they can be mitigated using antibodies or molecules that target specific pathways [28]. The culture method was previously thought to be the gold standard for identifying microorganisms. However, molecular methods display the advantage of quickly providing results and revealing unknown microorganisms undetected by culture [29].
2.3.4 Microarrays
Microarrays are a technology designed to conduct complex, parallel tests based on ligand-binding, such as oligonucleotides, which are positioned on a solid support with high packing density to identify a complex mixture of target sequences. For biological applications, the ligands on the array can include DNA, RNA, proteins, polysaccharides, lipids, small organic compounds, or even whole cells. DNA microarrays are the most widely known and popular among these various ligands [30]. This technology is applied in genotyping, chromatin immunoprecipitation, gene expression and transcription factor binding assays [31]. DNA microarray technology significantly influences how molecular biologists view genes and is driving a shift towards a post-genomic era, focusing on structural and functional genome analysis [30]. A study conducted by Yao et al. [32] showed how affordable, sensitive and specific a multi-pathogen microarray was for the detection of Ebola virus and haemorrhagic fever and confirmed its use in the prevention, treatment and surveillance of Ebola.
2.4 Emerging techniques
2.4.1 Next-generation sequencing
Significant developments in sequencing technologies have been made in the past 10 years; these systems provide high-throughput sequencing features and fast assembly of genomes from challenging metagenomic samples in line with an associated cost reduction [33]. With the parallel sequencing of millions to billions of DNA fragments made possible by modern NGS platforms such as Illumina, Pacific Biosciences, and Oxford Nanopore, the field of genomics has been revolutionised [34]. Next-generation sequencing during outbreaks can be used to detect new viral pathogens, study interactions between the host and virus and perform metagenomic analysis of the human virome [15].
2.4.2 Metagenomics
Molecular assays are increasingly used in clinical diagnostics due to speed, sensitivity, and accuracy improvements. The need to create and synthesise PCR primers and probes is eliminated by metagenomics [35]. This saves time, which is important when viral illness outbreaks occur. One of the applications of next-generation sequencing is metagenomics. Metagenomics could be targeted or untargeted. The untargeted approach is better known as shotgun sequencing and allows for sequencing every possible pathogen in a sample [36]. Early viral sequencing from an outbreak aims to identify and locate the reservoir host while characterising the responsible virus [22]. Metagenomics allows for the identification of any pathogen present in a sample, and the clinical application was demonstrated by the use of metagenomics to identify SARS-CoV-2 within the first 4 weeks of the first case [37]. This approach has its challenges mainly due to the high proportion of host DNA in comparison to the pathogen of interest, therefore requiring careful nucleic acid extraction procedures and removal of host DNA [36].
Furthermore, deep sequencing is required to ensure sufficient reads are acquired following removal of background noise and host sequences, and this could increase the costs of sequencing considerably. Complicated sample preparation methods greatly reduce repeatability due to several procedures and protocol variances, which raises error rates [35]. This is a significant obstacle to the clinical use of metagenomics. Targeted approaches consist of specific primers designed based on conserved regions in bacteria (16 metagenomics) and fungi (ITS metagenomics). Viral metagenomics can be slightly challenging due to the low abundance of viral material compared to host material.
2.4.3 Target enrichment sequencing
Hybridisation probe-based capture sequencing is a targeted method that sequences specific regions of interest using short biotin-labelled oligonucleotide probes that complement and hybridise to the selected viral genome [35]. This approach allows for the detection of low-abundance viruses, and it reduces sequencing costs by focusing on relevant genomic regions [38]. Targeted sequence capture is a validated method for enriching specific nucleic acid sequences, providing an alternative approach for the selective isolation of pathogen-derived nucleic acids in metagenomic samples [36, 39]. Commercial assays such as the Illumina Viral surveillance panel, VircapSeq VERT, IDT xGen Custom Hybridisation Panels and Twist Comprehensive Viral Panel are available for this approach. Next-generation sequencing has proven particularly valuable when standard tests fail to provide a diagnosis. A study by Zhou et al. [40] analysed 121 viral clinical samples using the Luminex xTAG RVP assay and NGS random sequencing. The samples were pre-screened for cytopathic effects and initially tested negative for influenza. This study compared the Luminex xTAG Respiratory Viral Panel FAST test and Roche 454 GS FLX Titanium pyrosequencing to identify pathogens not typically monitored in hospitals. NGS confirmed all viruses detected by Luminex and identified additional respiratory viruses, including Dengue, which is not typically a respiratory pathogen. Bacteria such as
Due to insufficient data on its relative efficacy, hybridisation probe-based capture sequencing has not acquired much traction yet, and its price per sample is still high [35]. Furthermore, the viral genomes are unlikely to undergo enrichment if they deviate from probe sequences by more than 40% [35]. Therefore, approaches whereby the cDNA synthesis and PCR using degenerate primers may work better, although the output may contain more “noise” data that can be eliminated during processing [39, 41].
2.4.4 Clustered regularly interspaced short palindromic repeats (CRISPR)-based methods
First identified as an immune protection system in prokaryotes, the CRISPR system has recently gained widespread attraction for its potential in gene regulation and editing and has thus proved to be a competitive technology in disease diagnostics [42]. CRISPR consists of a Cas protein and a guide RNA (gRNA) which directs Cas to the target site [43]. Researchers can target any gene of interest by changing the gRNA sequence to identify a specific site of interest when configuring the Cas protein to target that specific sequence. This system was recently used to diagnose and treat diseases [44, 45]. Initially used for developing anti-virals, the CRISPR-Cas system later became a useful application in gene-editing and gene-detection ability which revolutionised the diagnostics field. Pathogens in clinical samples can accurately and rapidly be identified with CRISPR point-of-care diagnostics and have also been tested for the treatment of HIV [46]. COVID-19 has proved that newer infectious disease detection, prevention and management models were required for early diagnosis. Applications such as the CRISPR-Cas systems offer advancements in molecular diagnosis and treatment which can be easily applied and have shorter turnaround times. Viral diseases such as HIV, Tuberculosis, Dengue, Hepatitis B and Zika have successfully been diagnosed and treated with the CRISPR-Cas system. Essentially, this system has been documented to offer great potential for diagnosing disease, detecting RNA viruses, and identifying certain bacteria [47]. With CRISPR diagnostics specific viral sequences are targeted, ensuring high sensitivity and specificity, thereby reducing false positives or negatives commonly seen with traditional methods like PCR [48]. Its accessibility function enables it to be used in remote or resource-poor settings, empowering viral outbreak surveillance in these areas. This is especially important in controlling outbreaks of emerging infectious diseases, and in low-resource settings [49]. Because CRISPR diagnostics target specific viral sequences, they have excellent sensitivity and specificity, which helps reduce false positives or negatives frequently observed with conventional techniques like PCR [50]. The portability of CRISPR allows it to be used in remote or resource-poor settings, enabling local health authorities to monitor and respond to viral threats without the need for expensive and complicated laboratory infrastructure [51]. Additionally, CRISPR can be used for surveillance in areas with limited access to advanced lab infrastructure.
3. Applications in virus/infectious disease detection
Genotyping is a method used to identify genetic variants within individuals and has been a cost-effective means for obtaining genetic information in many individuals [52]. Genotyping data can support healthcare-associated transmission of SARS-CoV-2, for example, fast-track mitigation, management and prevention strategies [53]. Early identification of the organisms and genotypes causing a specific outbreak can quickly improve viral monitoring efforts [54]. By improving outbreak detection and investigation, tracking transmission pathways and networks, monitoring genetic variations affecting pathogenicity, diagnostics, treatments, and vaccines, and assessing the effectiveness of policies and interventions, pathogen genomics can transform public health surveillance completely [55]. Qiu and colleagues [56] developed a fast genotyping method for point-of-care diagnostics using a digital genotyping model and a one-step fluorescent lateral flow immunoassay (LFIA) strip coated with genotype-specific monoclonal antibodies (mAbs). Simple hepatitis B virus (HBV) genotyping results can be acquired in 20 minutes at a very low cost using the recently established technology by Qiu and colleagues, which uses a digital classifier and a simple one-step fluorescent LFIA strip. Because of the streamlined signal detection and analysis techniques and the simplicity to display test findings, the classifier instrument was specifically built using parts that cost less than $100 for POC diagnostics.
4. Genome characterisation and identification
Infections caused by viruses are the most common cause of human diseases [57]. Human Immunodeficiency Virus (HIV) and Hepatitis continue to cause deaths worldwide including re-emerging viruses such as influenza A (H5N1) and Swine influenza (H1N1), SARS-CoV, Zika virus (ZIKV), and Ebola virus and monkeypox (Mpox) [58, 59]. Although these infectious diseases are detectable worldwide, Africa remains the most affected. Therefore, it is becoming more important for the continent to strengthen its internal capacity to identify, track, and manage the spread of these viral pathogens.
5. Rapid detection systems
Speed and accuracy are essential factors for diagnosing and managing infectious diseases during viral outbreaks. Using molecular techniques such as PCR, NGS, and CRISPR helps public health officials make quick decisions to stop the spread of illnesses and prevent pandemics. Importantly, these molecular diagnostic techniques are sensitive in identifying the existing pathogen and characterising its genetic profile, which is essential for diagnosing diseases in the early phases of an outbreak. This information enables the identification of viral genetic material necessary for detecting new or emerging viruses that conventional diagnostic methods may miss [10]. Rapid molecular detection systems are essential for viral outbreak monitoring as they provide early alerts and diagnosis, particularly in asymptomatic individuals. Therefore, molecular diagnostic tests help detect viruses and mutations, thereby aiding in tracking the dissemination of viruses and recognising new variants. Advancements in these diagnostic tests can also allow for portability, enabling on-site testing in remote locations and accelerating the decision-making process [60]. Moreover, molecular data may be incorporated into worldwide surveillance systems, facilitating a unified reaction and allowing for more precise public health measures.
6. Viral load quantification
Viral load is the amount of virus in an infected person’s body and is typically measured in samples like blood or respiratory fluids. It is required for tracking viral outbreak surveillance because it provides data on disease severity, patterns of transmission, and public health responses [61]. A high viral load often correlates with higher infectivity, implying that these individuals are more likely to spread the virus to others, especially during the early stages of infection. By examining viral loads in a population through testing and sequencing, health authorities can locate regions with active transmission, assess the virus’s reproductive number (R0), and detect potential outbreaks before they escalate. During the COVID-19 pandemic, research indicated that SARS-CoV-2-positive individuals with high viral loads were more infectious and contributed to spreading the virus during contact tracing and isolation measures [62]. Furthermore, tracking viral load trends over time could shed light on changes in virulence, as lower loads suggest a reduced risk of spread [63, 64]. On a broader scale, wastewater monitoring uses viral load evaluations to detect the presence of a virus in a population, including among those without symptoms, serving as an early warning system for potential outbreaks. As a result, viral load data enhances surveillance by gauging the virus’s spread and enabling swift, targeted responses [65]. Measuring viral load within virus-infected individuals is an important step for viral outbreak surveillance, specifically for infectious diseases such as HIV, influenza, COVID-19 and hepatitis. Evaluating a patient’s viral load provides important information about prognosis and predictions related to viral outbreaks, transmission trends, and the effectiveness of potential treatments [66].
7. Epidemiological surveillance and monitoring
Epidemiological surveillance and monitoring are crucial for disease management and being ready for a pandemic. Emerging and re-emerging pathogens continue to burden the already pressurised healthcare systems, especially in low and middle-income countries, by challenging pharmaceutical industries and overwhelming medical care. Circulating viral strains may harbour genetic variation due to mutations that arise over time. These genetic variations could compromise the effectiveness of current diagnostic tests [67]. Poor documentation of surveillance data makes it challenging to implement timely and effective interventions [68]. Despite the significance of this data, endemic countries still lack adequate surveillance for high-priority diseases [69, 70]. Such challenges, especially in low and middle-income countries, prohibit the tracking and spread of pathogens in communities, identifying populations at high risk, and determining the impact at a local and global level. Data gaps in this context delay response efforts and the potential for the development of accurate predictive models and early warning systems. Improving global surveillance systems and ensuring that emerging pathogens are closely monitored is essential for improving pandemic preparedness [8]. Early detection and monitoring of disease trends aid in surveillance systems, reducing the impact of transmission and strain on healthcare management. With advancements in data acquisition and analysis, including the integration of machine learning and real-time reporting, public health authorities are enabled to better manage viral outbreaks, minimising their spread and safeguarding public health [71].
Existing surveillance systems should be leveraged and adapted for the surveillance of pathogens. The COVID-19 pandemic paved the way for viral genomic surveillance, showing its importance for public health. Hill et al. [72] prompted a call for action for global virus surveillance networks to provide data on viral evolution and lineage transmission during and in between outbreaks. The main aim of this endeavour is to provide a streamlined workflow for labs to rapidly sequence different viruses, analyse the data and respond to emerging viruses [72]. Using existing infrastructure for surveillance of new viral pathogens can be challenging, particularly when changes to lab protocols are needed. Most SARS-CoV-2 sequencing labs follow a standardised workflow that includes sample selection, viral amplification, library preparation, sequencing and data analysis. Adapting an existing pipeline for new viruses may require modifications, especially with sample preparation and bioinformatics processing. A typical example is SARS-CoV-2, which relies on a targeted, multiplexed tiling PCR-based approach, whereas sequencing a virus like monkeypox traditionally requires metagenomic sequencing [73]. For many viruses, the rest of the library preparation and sequencing workflow can remain the same by simply using target-specific PCR primer sets. This approach allows researchers to repurpose their existing expertise and infrastructure for surveillance of various viral pathogens [72].
8. Challenges and limitations of molecular diagnostics
Half of the world lacks access to essential healthcare services placing a significant strain on those who cannot afford these services. The World Health Organisation has encouraged policymakers and authorities from developing nations to incorporate eHealth to improve accessibility to healthcare, and quality of services and to achieve the United Nations Sustainable Development Goal 3 to “access to quality essential health-care services” [74]. However, the study by Asah and Kaasbøll made a few remarks on the difficulty of integrating eHealth in low- and middle-income countries (LMICs) as well as developing countries. These include limited funding, poor infrastructure, and poor governance which could be improved with computational systems, in-service training, and advancing academic modules. To solidify this implementation, it would be beneficial to have relevant eHealth policies and guidelines supported by committed leaders, thereby building global healthcare capacity [74]. Molecular diagnostics can be more effectively used in healthcare when combined with eHealth services to advance prevention, management and preparedness strategies in an outbreak.
Sequencing was mostly used as a research tool or not at all in LMICs prior to SARS-CoV-2 [8]. Implementing pathogen genomics remains difficult in many low-resource settings due to a lack of qualified staff, poor laboratory infrastructure, and a restricted ability to use genomic data in public health response [75]. It is crucial to keep in mind that not all African nations may find sequencing feasible, and diagnosing and treating infectious diseases may present significant challenges. Pathogen sequencing in Africa has advanced the diagnostic landscape for accurately detecting, identifying and monitoring disease outbreaks from emerging and re-emerging pathogens and facilitating data exchange. Since the COVID-19 pandemic, African countries have established capacity for molecular testing and genomic surveillance of SARS-CoV-2 variants instead of exporting samples. Decreased sequencing costs have resulted in massive volumes of genomic sequencing data around the world, bringing upon a high demand for skilled bioinformaticians to analyse and interpret data [76].
Gaps in molecular diagnostics were identified during the COVID-19 pandemic, with infections in sub-Saharan Africa going undetected for months because of low-resource healthcare systems [77]. Integrating pathogen genomics into public health initiatives in LMICs requires conquering hurdles, which comprise offering training programmes, improving laboratory capacity and increasing the availability of affordable sequencing platforms. Until these challenges are addressed, the full potential of molecular diagnostics in controlling infectious diseases and improving patient care may remain out of reach for many in these regions. Another factor is the availability of computational resources and bioinformaticians to process and analyse genomic sequencing data [76].
Aside from COVID-19, Ebola and Yellow fever were detected weeks earlier with the help of molecular diagnostics compared to standard methods using on-the-spot PCR testing [11]. Combining culture-based methods and whole genome sequencing enabled the identification of atypical pathogens and antimicrobial resistance profiles in Gambia [78]. The global genomic surveillance strategy for pathogens with pandemic and epidemic potential 2022–2032 was designed by the World Health Organisation (WHO) to support national genomic surveillance efforts in pathogen detection. This strategy also highlighted the need for efficiently collecting and disseminating genomic and antimicrobial resistance (AMR) data to aid decision-making on epidemic response.
A review by Chidzwondo and Mutapi [79] discusses the burden of disease, co-infections and the challenges in managing specific diseases in Africa, these include Ebola, Plasmodium species and cholera. Debilitated health systems and insufficient response tools worsen the impact of these diseases. Hence, it is important that diagnostics for infectious diseases occur quickly, infected individuals are isolated, and the right treatment is prescribed to contain disease outbreaks by actively working together to develop rapid point-of-care diagnostic tools for effectively monitoring, managing and mediating actionable changes for sustainable healthcare in Africa [79].
To strengthen outbreak management and emergency response, it is imperative that the correct disease is diagnosed and pathogen identified which will assist with the applicable treatment in containing the spread of viral infections. The fear of the unknown was emphasized in relation to understanding and responding to public health emergencies where the situation at hand is unknown [80]. Even though the United States of America (USA) and Mexico had a pandemic influenza plan in place from 2004 to 2006, it was designed for an outbreak that would occur far away rather than anticipating that it would occur nearby. Coherent procedures should also be in place for determining when it is appropriate to proceed to the next level of response, putting clinical procedures and operations into place, and making decisions in the middle of the course. Models should include flexibility and adaptability to assist with the action plan for individual needs, even though many emergency plans can be narrative and administratively focused [80].
The exponential era of data-driven research generates significant digital data in different sectors such as research, industry, policymakers, health officials and data scientists. Governments, international development agencies, and multilateral organisations are requesting digital data to be considered a public good and made publicly available. A 2024 study by Cengiz and colleagues [81] explored the advantages and challenges of sharing health-related research data across borders. The study found that past exploitation and unequal resource allocation in African institutions lack data-sharing agreements, and because of this, African researchers require more robust government structures that place a high priority on accountability, openness, and fair benefit distribution to protect their data, advance their skills, and facilitate efficient sharing procedures. Since the primary owners have no control over users who access, reuse their data, or recover costs related to data collection, preparation, and unauthorised use, making data openly and freely available has not gained traction in the research community. For private research activities, the disparity in rewards and risks has led to data exclusion and/or protection through a variety of mechanisms, including licencing, legal protection, and geographic and climatic restrictions. It also discourages open data sharing and restricts data release. In theory, if there is a chance that researchers would profit from their investment, they will be encouraged to make their data publicly accessible. Since owners have not been able to control access, reuse their data, or recover costs related to data collection, preparation, and unauthorised use, making data openly and freely available has not gained traction in the research community [82].
A wide variety of methods are available for rapidly detecting epidemic signals through both standard and syndromic surveillance. These techniques can generally be categorised based on temporal clusters, with several outbreak detection algorithms falling under each category. However, a key challenge is that health and public health professionals often struggle to understand all the methods available for continuous data monitoring within surveillance systems and to determine when to implement them. Furthermore, with regards to NGS, establishing validation thresholds and creating a standard strategy for NGS is important and must be resolved before implementation in clinical practice [35]. Another limitation is that the algorithms used in viral surveillance are tested with real data, while only a few studies have used fully simulated, reproducible datasets. In contrast, real-world datasets are rarely free, limiting the ability to conduct robust, authentic, and meaningful research worldwide. As a result, comparing algorithms becomes difficult due to the varying results and assessments across different studies [83].
Genomic sequencing helps track how viruses evolve and spread, but to understand its impact on public health the genomic data needs to be combined with data on patient demographics, epidemiology and clinical outcomes. These are necessary for identifying links between specific viral strains and factors such as patient risk profiles, disease severity and transmission rates. By linking genomic data with details such as demographics, clinical outcomes and epidemiological trends, scientists can uncover which viral variants are more dangerous, spread more easily, evade immunity, or respond differently to treatments [84]. Therefore, accessibility and sharing of research data across and within borders is necessary.
Finally, having robust viral diagnostic methods, analysis pipelines and sample metadata during an outbreak is ineffective in controlling the spread of infection during an outbreak if the results are not reported to public health authorities in a timely manner [85]. Effective reporting for viral surveillance requires timely and comprehensive data. However, ensuring this requires technical expertise, reliable data-sharing networks, and standardised reporting protocols to ensure that information is shared efficiently between all relevant individuals [84].
9. Future directions
Artificial Intelligence (AI) has emerged as a powerful and transformative tool in public health applications such as rapid identification, diagnosis, and prediction of infectious disease outbreaks. Previously, epidemics were discovered and predicted based on manual data retrieval, statistical models and clinical observations. Solutions for viral disease outbreaks have now been advanced by integrating AI for its potential to provide quicker, scalable and accurate results [86]. Techniques using AI algorithms for discovering infectious diseases are improved and can be detected earlier, even prior to clinical presentation in patients. Machine learning methods enable the analyses of vast amounts of clinical data, such as medical images, patient records and genetic sequences, to uncover fine details and patterns indicative of an outbreak [87, 88]. AI models are being trained with data collected from previous outbreaks to identify, characterise and predict the emergence of new and future pathogens, strains or variations, thereby enabling quicker response and actions for mitigation and management [89, 90]. Hence, it is essential to merge clinical virology with advanced technologies such as artificial intelligence (AI), machine learning (ML), and deep learning (DL) because of their competence in handling extensive datasets, their capability to learn pattern recognition, and their potential to deliver precise and prompt answers.
9.1 Predictive outbreaks
AI’s predictive functionality can assist in forecasting the spread of infectious diseases, and simulations from these predictions can uncover how a specific disease or pathogen is likely to spread across communities and populations by using previous outbreak data, demographic information, environmental factors, and travel patterns. Altogether, these models can help predict when and where an outbreak is likely to occur, its potential severity, and the effectiveness of different intervention strategies [91]. An example is using AI algorithms from epidemiological modelling, which incorporate machine learning processes like neural networks, random forests and support vector machines to predict the trajectory of influenza, Ebola, and zika virus [92]. Continuous integration of real-time data in public healthcare records, airports and hospitals can be routinely refined and processed to provide almost immediate predictions that guide and inform decision-making. Moreover, AI strengthens the monitoring of climate change impacts on driving infectious diseases. Estimating future seasonal epidemics can be improved with machine learning algorithms that can examine data from vector populations such as mosquitoes spreading malaria or dengue, temperature and precipitation records [93].
9.2 Enhancing surveillance systems
Surveillance systems have been one area supported by advancements through the automation of large volumes of data collection and analysis. Previously, health systems used manual reporting and analysis, which required time and has now been improved with AI to automate these processes, essentially detecting early signs of a disease outbreak and predicting the nature of an outbreak from certain locations [94]. During the COVID-19 pandemic, major growth in digital surveillance of infectious diseases has been achieved using computational models. Since then, the migration towards integrating cloud-based data approaches has shifted to develop newer and refined approaches in improving surveillance of infectious diseases for pandemic preparedness [95, 96]. Integrating AI and internet-based models for monitoring viral outbreaks will significantly enhance predictions and preparedness and support public health officials in their decision-making for future viral attacks.
A qualitative study by Buchbinder et al. [97] describes the ethical and practical concerns when preparing and implementing Data-2-Care (D2C) programmes for HIV-positive people. Suggestions made by some of the stakeholders who participated in this study include overseeing data collection, usage and exchange, privacy and protection against hackers, and verifying data accuracy and security. Sharing public health and surveillance data strengthens the global community by preventing or mitigating the severity of infectious diseases and outbreaks. Public health surveillance data, when shared, enhance the capacity for detecting and responding to disease, whilst finding the source of an outbreak and diminishing its impact [98].
In summary, AI will be a revolutionary tool in advancing diagnostic application and improving prediction models for viral outbreaks. With AI advancement, its role in pandemic readiness and outbreak management will continue to develop, offering new insight and guidance for protecting global health. Known for its efficiency in analysing large data sets, uncovering needle-in-a-haystack trends, and enabling real-time predictions, it equips public health systems with unprecedented tools to identify, prevent, and respond to emerging diseases [99]. Apart from disease predictions, AI will also benefit humans in understanding the aetiology of viral diseases and support drug discovery and vaccine development in the space of infectious diseases [100].
10. Conclusion
In addition to having diagnostic tools available for viral outbreak surveillance, careful planning and coordination is necessary. Genomic epidemiology relies on prompt access to clinical samples and data, which requires effective engagement with local communities, public health agencies, clinics, and researchers [22]. Ongoing surveys to identify obstacles to viral diagnostics and sampling selection techniques will yield important data for upcoming monitoring programmes in addition to the guidelines issued by the WHO and other international public health agencies [75].
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