COVID-19 Pandemic Technologies for the Future

Numerous testing advances have benefitted the study of infectious illnesses, beginning with the first light microscope in 1716, followed by Koch’s Postulates in 1890, and, most recently, the discovery of polymerase chain reaction (PCR) in 1983. Many of these breakthroughs have centered on identifying and comprehending the microbial world, with subsequent discoveries aiming at enhancing the speed, efficiency, and mobility of pathogen detection systems. Infectious illness testing has progressed well beyond conventional laboratory-based microscopy, microbiological culture, and polymerase chain reaction (PCR). Rapid immunoassays (e.g., direct pathogen detection, serology), mass spectrometry (MS), and a wide range of molecular methods (e.g., sequencing, point-of-care [POC] PCR) have become ubiquitous and have revolutionized infectious disease treatment (Fig. 1)

Point-of-care (POC) testing at or near the site of patient care. Fig. 2 common POC testing formats. Traditionally, formats included handheld, portable, transportable, and benchtop devices. Since early the 1980s, POC testing has observed dramatic changes related to infectious disease testing over the last ten years including the development of bedside rapid molecular tests, the proliferation of wearable health monitoring devices, and the growth direct to consumer (DTC) and over the counter (OTC) testing.

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This notion is underscored by the COVID-19 pandemic where POC reverse transcription (RT) real-time PCR and isothermal nucleic amplification methods have received applications methods have received United States (US) Food and Drug Administration (FDA) emergency use authorization (EUA) for both home and hospital testing, with some platforms having sensitivity and specificity comparable to laboratory-based methods. Clustered regularly interspaced short palindromic repeats (CRISPR) gene-editing technology has also created new testing opportunities for COVID-19.

This molecular technology enables low-cost rapid amplification-free detection of SARS-CoV-2 that shows comparable performance to RT-PCR. Future CRISPR-based infectious testing may have other applications including integration with flow-based assays to provide low-cost high throughput testing at the point of care.


The COVID-19 pandemic prompted the activation of the FDA EUA pathway to review and approve new in vitro diagnostic tests. Although waived infectious diseases testing is not new, OTC and DTC tests represent a significant paradigm shift. Effectively, under the EUA, OTC COVID-19 tests can now be performed without prescription. It will be interesting to see how the regulatory landscape will evolve post-COVID-19 pandemic and if these EUA technologies catalyze other OTC and DTC infectious disease testing applications in the future where patients become more directly involved in the selection and operation of testing.


Wearable POC devices (e.g., pulse oximetry, continuous glucose monitoring systems) have existed over the last 20 years. Today, a new generation of wearable devices include smart watches and rings that measure parameters such as oxygen saturation, one-lead electrocardiogram (ECG), and heart rate. However, the use of wearable devices for infectious disease testing is relatively new. During the COVID-19 pandemic, FDA EUA was conferred to the Tiger Tech COVID Plus Monitor. This optical detection device is not intended for the diagnosis or exclusion of SARS-CoV-2 infection, but is instead, used for monitoring COVID-19 in an asymptomatic population as part of an infection control plan.

In brief, the device uses two embedded photoplethysmography sensors worn as an armband around an individual’s left arm. Measurements are taken over 3–5 min evaluating pulsatile signals that are then fed into an ML model. Positive percent agreement and negative percent agreement are reported to be 98.6% and 94.5% respectively when compared to PCR.


High-resolution imaging is no longer the unique domain of clinical laboratories. The imaging capabilities of smart devices now rival the performance of consumer-grade cameras, and when coupled with magnifiers, could be used as a low-cost alternative for microscopy. These innovations can be further enhanced by AI/ML which could aid in the detection of pathogens from biological specimens. Machine learning, which will be discussed in greater detail later in this article, has been proposed to analyze microscopic images captured by point-of-care smartphone-based applications.


Mass spectrometry (MS) entered the domain of clinical microbiology around 2014. Matrix-assisted laser desorption ionization (MALDI) – time of flight (TOF) – MS is now used at many institutions to accelerate the detection of bacteria and fungi direct from microbiological culture. More recent innovations in this space have included the use of MALDI-TOF-MS to rapidly detect antimicrobial resistance. Beta-lactamase activity has been observed by MALDI-TOF-MS, with protocols developed for evaluating ertapenem resistance in Bacteroides fragilis strains.


Mass spectrometry-based detection of pathogens is not limited to MALDI-TOF-MS for testing pure culture isolates. The use of liquid chromatography (LC) – MS using electron spray ionization (ESI) has also shown promise in this space. This has now expanded to using hybrid MS and molecular techniques. In this approach, molecular methods such as PCR can rapidly amplify pathogen genetic targets. These amplicons are then tested by ESI-MS where the mass spectra are unique for specific microorganisms. This approach is useful for detecting pathogens that are difficult to culture and have also been applied for SARS-CoV-2 testing under EUA.


Alternative testing approaches using MALDI-TOF-MS have been proposed as a low-cost, rapid, and high-throughput solution to alleviate the demand for molecular testing. For COVID-19, anterior nares swab samples could be tested by MALDI-TOF-MS to produce spectra representing ionizable proteins consistent with the host’s response to infection. Due to the complex spectra produced by these patient samples, AI/ML is used to analyze the data and predict COVID-19 status. In recent studies, a neural network approach was used to analyze MALDI-TOF-MS spectra and achieved a sensitivity and specificity of 100% and 96% respectively when compared against PCR, with an area under the ROC curve of 0.99 when using 487 peaks that span 1993.91 to 19,9590.89 m/z.


Artificial intelligence is the field of computer science that strives to develop technologies that can replicate human behavior. Machine learning is a subset of AI that develops systems that can improve performance when trained with new data. Examples of current AI/ML uses include allowing businesses to predict customer needs, autonomous vehicles to replicate human driving behavior, and helping individuals search for information on the internet. In this same fashion, and already shown in this article, there are many applications that can disrupt healthcare, including the field of infectious disease testing.


Borrelia burgdorferi is the causative agent for Lyme disease. AI/ML was proposed to augment performance when combined with a POC sero-diagnostic test that targeted bacterial antigens: OspC, BmpA, P41, ErpD, Crasp1, OspA, DbpB, VlsE, P35 and Mod-C6. The ML algorithm was able to achieve a sensitivity of 90.5% and specificity of 87% with conventional serology.


Meningitis remains a significant healthcare burden with 36,000 hospitalizations reported in the United States annually. Rapid detection of pathogens causing meningitis has been augmented by molecular diagnostics, however, the primary specimen type remains cerebrospinal fluid (CSF). To overcome these limitations, AI/ML has been applied to non-CSF parameters in hopes of predicting meningitis.


Early recognition of severe sepsis is critical to survival and every hour delayed in initiating appropriate therapy significantly increases mortality odds. Being a repository of data, electronic medical records (EMR) are uniquely poised to leverage AI/ML to facilitate early sepsis recognition. However, such an ML model would perform poorly for special sepsis populations such as burns patients who are at high risk for sepsis. This limitation highlights a strength of AI/ML whereby algorithms could be trained for these special populations when new data is available.


Another AI/ML infectious disease application is molecular host response testing. Expanding from traditional indicators of sepsis, a multi-RNA host response profiles augmented by AI/ML has been shown to predict bacterial and viral infections. The study by Ducharme et al. evaluated a 29-host-mRNA 30-minute POC test that utilized AI/ML to identify patients with bacterial or viral infections. This platform utilized a neural network ML algorithm which achieved an area under the ROC curve of 0.92.

The complex and evolving nature of infectious diseases requires constant innovation to stay ahead of current and future pathogens. Advances in automation and miniaturization have fueled the expansion of molecular infectious disease testing into the POC testing space. The COVID-19 pandemic has required innovators to think well outside the box to keep up with the rapidly changing science behind SARS-CoV-2 infections. Disruptive technologies such as wearable devices, novel low-cost molecular approaches (i.e., CRISPR), AI/ML, and MS are poised to transform infectious disease testing. These new technologies can be further enhanced with less invasive sampling techniques (e.g., saliva, breath) via OTC/DTC tests and address challenges that limit patient access to care.


Nam K. Tran, Samer Albahra, Hooman Rashidi, Larissa May, Innovations in infectious disease testing: Leveraging COVID-19 pandemic technologies for the future, Clinical Biochemistry, 2022, ISSN 0009-9120,


Dr Shilpa Subramanian

Dr. Shilpa Subramanian is an MDS, Periodontist and currently manages Global Pharmacovigilance and Medical Affairs Operations at a Healthcare company in Mumbai. She is passionate about staying ahead of the curve in clinical and non-clinical advances in the field of pharma and healthcare.

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