Long-term Sudan virus Ebola survivors maintain multiple antiviral defense mechanisms.

BACKGROUND: The critical issues of sustained memory immunity following ebolavirus disease among long-term survivors (EVD) are still unclear. METHODS: Here, we examine virus-specific immune and inflammatory responses in 12 Sudan virus (SUDV) long-term survivors from Uganda's 2000-1 Gulu outbreak, 15 years after recovery following in vitro challenge. Total RNA from isolated SUDV-stimulated and unstimulated PBMCs was extracted and analyzed. Matched serum samples were also collected to determine SUDV IgG levels and functionality. RESULTS: We detected persistent humoral (58%, 7 of 12) and cellular (33%, 4 of 12) immune responses in SUDV long-term survivors and identified critical molecular mechanisms of innate and adaptive immunity. Gene expression in immune pathways, the IFN signaling system, antiviral defense response, and activation and regulation of T- and B-cell responses were observed. SUDV long-term survivors also maintained robust virus-specific IgG antibodies capable of polyfunctional responses, including neutralizing and innate Fc effector functions. CONCLUSIONS: Data integration identified significant correlations among humoral and cellular immune responses and pinpointed a specific innate and adaptive gene expression signature associated with long-lasting immunity. This could help identify natural and vaccine correlates of protection against ebolavirus disease.

CRISPR-assisted test for Schistosoma haematobium.

Schistosomiasis is a major neglected tropical disease targeted for elimination as a public health issue by 2030, however there is an urgent need for more sensitive and specific diagnostic tests suitable to resource-limited settings. Here we developed CATSH, a CRISPR-assisted diagnostic test for Schistosoma haematobium, utilising recombinase polymerase amplification, Cas12a-targeted cleavage and portable real-time fluorescence detection. CATSH showed high analytical sensitivity, consistent detection of a single parasitic egg and specificity for urogenital Schistosoma species. Thanks to a novel CRISPR-compatible sample preparation developed using simulated urine samples containing parasitic eggs, CATSH had a sample-to-result within 2 h. The components of CATSH can be lyophilised, reducing cold chain dependence and widening access to lower and middle-income countries. This work presents a new application of CRISPR diagnostics for highly sensitive and specific detection of parasitic pathogens in remote areas and could have a significant impact on the elimination of neglected tropical diseases.

Thermodynamic analysis of an entropically driven, high-affinity nanobody-HIV p24 interaction.

Protein-protein interactions are fundamental to life processes. Complementary computational, structural, and biophysical studies of these interactions enable the forces behind their specificity and strength to be understood. Antibody fragments such as single-chain antibodies have the specificity and affinity of full antibodies but a fraction of their size, expediting whole molecule studies and distal effects without exceeding the computational capacity of modeling systems. We previously reported the crystal structure of a high-affinity nanobody 59H10 bound to HIV-1 capsid protein p24 and deduced key interactions using all-atom molecular dynamics simulations. We studied the properties of closely related medium (37E7) and low (48G11) affinity nanobodies, to understand how changes of three (37E7) or one (48G11) amino acids impacted these interactions; however, the contributions of enthalpy and entropy were not quantified. Here, we report the use of qualitative and quantitative experimental and in silico approaches to separate the contributions of enthalpy and entropy. We used complementary circular dichroism spectroscopy and molecular dynamics simulations to qualitatively delineate changes between nanobodies in isolation and complexed with p24. Using quantitative techniques such as isothermal titration calorimetry alongside WaterMap and Free Energy Perturbation protocols, we found the difference between high (59H10) and medium (37E7) affinity nanobodies on binding to HIV-1 p24 is entropically driven, accounted for by the release of unstable waters from the hydrophobic surface of 59H10. Our results provide an exemplar of the utility of parallel in vitro and in silico studies and highlight that differences in entropic interactions between amino acids and water molecules are sufficient to drive orders of magnitude differences in affinity.

Validity of Self-testing at Home With Rapid Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Detection by Lateral Flow Immunoassay.

BACKGROUND: We explore severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow immunoassay (LFIA) performance under field conditions compared to laboratory-based electrochemiluminescence immunoassay (ECLIA) and live virus neutralization. METHODS: In July 2021, 3758 participants performed, at home, a self-administered Fortress LFIA on finger-prick blood, reported and submitted a photograph of the result, and provided a self-collected capillary blood sample for assessment of immunoglobulin G (IgG) antibodies using the Roche Elecsys® Anti-SARS-CoV-2 ECLIA. We compared the self-reported LFIA result to the quantitative ECLIA and checked the reading of the LFIA result with an automated image analysis (ALFA). In a subsample of 250 participants, we compared the results to live virus neutralization. RESULTS: Almost all participants (3593/3758, 95.6%) had been vaccinated or reported prior infection. Overall, 2777/3758 (73.9%) were positive on self-reported LFIA, 2811/3457 (81.3%) positive by LFIA when ALFA-reported, and 3622/3758 (96.4%) positive on ECLIA (using the manufacturer reference standard threshold for positivity of 0.8 U mL-1). Live virus neutralization was detected in 169 of 250 randomly selected samples (67.6%); 133/169 were positive with self-reported LFIA (sensitivity 78.7%; 95% confidence interval [CI]: 71.8, 84.6), 142/155 (91.6%; 95% CI: 86.1, 95.5) with ALFA, and 169 (100%; 95% CI: 97.8, 100.0) with ECLIA. There were 81 samples with no detectable virus neutralization; 47/81 were negative with self-reported LFIA (specificity 58.0%; 95% CI: 46.5, 68.9), 34/75 (45.3%; 95% CI: 33.8, 57.3) with ALFA, and 0/81 (0%; 95% CI: 0, 4.5) with ECLIA. CONCLUSIONS: Self-administered LFIA is less sensitive than a quantitative antibody test, but the positivity in LFIA correlates better than the quantitative ECLIA with virus neutralization.

Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies.

Background: Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home, but rely on subjective interpretation of a test line by eye, risking false positives and false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity. Methods: Our computational pipeline uses machine learning, computer vision techniques and signal processing algorithms to analyse images of the Fortress LFIA SARS-CoV-2 antibody self-test, and subsequently classify results as invalid, IgG negative and IgG positive. A large image library of 595,339 participant-submitted test photographs was created as part of the REACT-2 community SARS-CoV-2 antibody prevalence study in England, UK. Alongside ALFA, we developed an analysis toolkit which could also detect device blood leakage issues. Results: Automated analysis showed substantial agreement with human experts (Cohen's kappa 0.90-0.97) and performed consistently better than study participants, particularly for weak positive IgG results. Specificity (98.7-99.4%) and sensitivity (90.1-97.1%) were high compared with visual interpretation by human experts (ranges due to the varying prevalence of weak positive IgG tests in datasets). Conclusions: Given the potential for LFIAs to be used at scale in the COVID-19 response (for both antibody and antigen testing), even a small improvement in the accuracy of the algorithms could impact the lives of millions of people by reducing the risk of false-positive and false-negative result read-outs by members of the public. Our findings support the use of machine learning-enabled automated reading of at-home antibody lateral flow tests as a tool for improved accuracy for population-level community surveillance.

Clinical Validation of a Rapid Variant-Proof RT-RPA Assay for the Detection of SARS-CoV-2.

The COVID-19 pandemic has unveiled a pressing need to expand the diagnostic landscape to permit high-volume testing in peak demand. Rapid nucleic acid testing based on isothermal amplification is a viable alternative to real-time reverse transcription polymerase chain reaction (RT-PCR) and can help close this gap. With the emergence of SARS-CoV-2 variants of concern, clinical validation of rapid molecular tests needs to demonstrate their ability to detect known variants, an essential requirement for a robust pan-SARS-CoV-2 assay. To date, there has been no clinical validation of reverse transcription recombinase polymerase amplification (RT-RPA) assays for SARS-CoV-2 variants. We performed a clinical validation of a one-pot multi-gene RT-RPA assay with the E and RdRP genes of SARS-CoV-2 as targets. The assay was validated with 91 nasopharyngeal samples, with a full range of viral loads, collected at University College London Hospitals. Moreover, the assay was tested with previously sequenced clinical samples, including eleven lineages of SARS-CoV-2. The rapid (20 min) RT-RPA assay showed high sensitivity and specificity, equal to 96% and 97%, respectively, compared to gold standard real-time RT-PCR. The assay did not show cross-reactivity with the panel of respiratory pathogens tested. We also report on a semi-quantitative analysis of the RT-RPA results with correlation to viral load equivalents. Furthermore, the assay could detect all eleven SARS-CoV-2 lineages tested, including four variants of concern (Alpha, Beta, Delta, and Omicron). This variant-proof SARS-CoV-2 assay offers a significantly faster and simpler alternative to RT-PCR, delivering sensitive and specific results with clinical samples.

Sub-picomolar lateral flow antigen detection with two-wavelength imaging of composite nanoparticles.

Lateral flow tests, commonly based on metal plasmonic nanoparticles, are rapid, robust, and low-cost. However, improvements in analytical sensitivity are required to allow detection of low-abundance biomarkers, for example detection of low antigen concentrations for earlier or asymptomatic diagnosis of infectious diseases. Efforts to improve sensitivity often require changes to the assay. Here, we developed optical methods to improve the sensitivity of absorption-based lateral flow tests, requiring no assay modifications to existing tests. We experimentally compared five different lock-in and subtraction-based methods, exploiting the narrow plasmonic peak of gold nanoparticles for background removal by imaging at different light wavelengths. A statistical framework and three fitting models were used to compare limits of detection, giving a 2.0-5.4-fold improvement. We then demonstrated the broad applicability of the method to an ultrasensitive assay, designing 530 nm composite nanoparticles to increase the particle volume, and therefore light absorption per particle, whilst retaining the plasmonic peak to allow background removal and without adding any assay steps. This multifaceted, modular approach gave a combined 58-fold improvement in the fundamental limit of detection using a biotin-avidin model over 50 nm gold nanoparticles with single-wavelength imaging. Applying to a sandwich assay for the detection of HIV capsid protein gave a limit of detection of 170 fM. Additionally, we developed an open-source software tool for performing the detection limit analysis used in this work.

A Rapid Drug Resistance Genotyping Workflow for Mycobacterium tuberculosis, Using Targeted Isothermal Amplification and Nanopore Sequencing.

Phenotypic drug susceptibility testing (DST) for tuberculosis (TB) requires weeks to yield results. Although molecular tests rapidly detect drug resistance-associated mutations (DRMs), they are not scalable to cover the full genome and the many DRMs that can predict resistance. Whole-genome sequencing (WGS) methods are scalable, but if conducted directly on sputum, typically require a target enrichment step, such as nucleic acid amplification. We developed a targeted isothermal amplification-nanopore sequencing workflow for rapid prediction of drug resistance of TB isolates. We used recombinase polymerase amplification (RPA) to perform targeted isothermal amplification (37°C for 90 min) of three regions within the Mycobacterium tuberculosis genome, followed by nanopore sequencing on the MinION. We tested 29 mycobacterial genomic DNA extracts from patients with drug-resistant (DR) TB and compared our results to those of WGS by Illumina and phenotypic DST to evaluate the accuracy of prediction of resistance to rifampin and isoniazid. Amplification by RPA showed fidelity equivalent to that of high-fidelity PCR (100% concordance). Nanopore sequencing generated DRM predictions identical to those of WGS, with considerably faster sequencing run times of minutes rather than days. The sensitivity and specificity of rifampin resistance prediction for our workflow were 96.3% (95% confidence interval [CI], 81.0 to 99.9%) and 100.0% (95% CI, 15.8 to 100.0%), respectively. For isoniazid resistance prediction, the sensitivity and specificity were 100.0% (95% CI, 86.3 to 100.0%) and 100.0% (95% CI, 39.8 to 100.0%), respectively. The workflow consumable costs per sample are less than £100. Our rapid and low-cost drug resistance genotyping workflow provides accurate prediction of rifampin and isoniazid resistance, making it appropriate for use in resource-limited settings. IMPORTANCE Current methods for diagnosing drug-resistant tuberculosis are time consuming, resulting in delays in patients receiving treatment and in transmission onwards. They also require a high level of laboratory infrastructure, which is often only available at centralized facilities, resulting in further delays to diagnosis and additional barriers to deployment in resource-limited settings. This article describes a new workflow that can diagnose drug-resistant TB in a shorter time, with less equipment, and for a lower price than current methods. The amount of TB DNA is first increased without the need for bulky and costly thermocycling equipment. The DNA is then read using a portable sequencer called a MinION, which indicates whether there are tell-tale changes in the DNA that indicate whether the TB strain is drug resistant. Our workflow could play an important role in the future in the fight against the public health challenge that is TB drug resistance.

Deep learning of HIV field-based rapid tests.

Although deep learning algorithms show increasing promise for disease diagnosis, their use with rapid diagnostic tests performed in the field has not been extensively tested. Here we use deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa. Using newly developed image capture protocols with the Samsung SM-P585 tablet, 60 fieldworkers routinely collected images of HIV lateral flow tests. From a library of 11,374 images, deep learning algorithms were trained to classify tests as positive or negative. A pilot field study of the algorithms deployed as a mobile application demonstrated high levels of sensitivity (97.8%) and specificity (100%) compared with traditional visual interpretation by humans-experienced nurses and newly trained community health worker staff-and reduced the number of false positives and false negatives. Our findings lay the foundations for a new paradigm of deep learning-enabled diagnostics in low- and middle-income countries, termed REASSURED diagnostics1, an acronym for real-time connectivity, ease of specimen collection, affordable, sensitive, specific, user-friendly, rapid, equipment-free and deliverable. Such diagnostics have the potential to provide a platform for workforce training, quality assurance, decision support and mobile connectivity to inform disease control strategies, strengthen healthcare system efficiency and improve patient outcomes and outbreak management in emerging infections.

Risk factors, symptom reporting, healthcare-seeking behaviour and adherence to public health guidance: protocol for Virus Watch, a prospective community cohort study.

INTRODUCTION: The coronavirus (COVID-19) pandemic has caused significant global mortality and impacted lives around the world. Virus Watch aims to provide evidence on which public health approaches are most likely to be effective in reducing transmission and impact of the virus, and will investigate community incidence, symptom profiles and transmission of COVID-19 in relation to population movement and behaviours. METHODS AND ANALYSIS: Virus Watch is a household community cohort study of acute respiratory infections in England and Wales and will run from June 2020 to August 2021. The study aims to recruit 50 000 people, including 12 500 from minority ethnic backgrounds, for an online survey cohort and monthly antibody testing using home fingerprick test kits. Nested within this larger study will be a subcohort of 10 000 individuals, including 3000 people from minority ethnic backgrounds. This cohort of 10 000 people will have full blood serology taken between October 2020 and January 2021 and repeat serology between May 2021 and August 2021. Participants will also post self-administered nasal swabs for PCR assays of SARS-CoV-2 and will follow one of three different PCR testing schedules based on symptoms. ETHICS AND DISSEMINATION: This study has been approved by the Hampstead National Health Service (NHS) Health Research Authority Ethics Committee (ethics approval number 20/HRA/2320). We are monitoring participant queries and using these to refine methodology where necessary, and are providing summaries and policy briefings of our preliminary findings to inform public health action by working through our partnerships with our study advisory group, Public Health England, NHS and government scientific advisory panels.

Automated phenotyping of mosquito larvae enables high-throughput screening for novel larvicides and offers potential for smartphone-based detection of larval insecticide resistance.

Pyrethroid-impregnated nets have contributed significantly to halving the burden of malaria but resistance threatens their future efficacy and the pipeline of new insecticides is short. Here we report that an invertebrate automated phenotyping platform (INVAPP), combined with the algorithm Paragon, provides a robust system for measuring larval motility in Anopheles gambiae (and An. coluzzi) as well as Aedes aegypti with the capacity for high-throughput screening for new larvicides. By this means, we reliably quantified both time- and concentration-dependent actions of chemical insecticides faster than using the WHO standard larval assay. We illustrate the effectiveness of the system using an established larvicide (temephos) and demonstrate its capacity for library-scale chemical screening using the Medicines for Malaria Venture (MMV) Pathogen Box library. As a proof-of-principle, this library screen identified a compound, subsequently confirmed to be tolfenpyrad, as an effective larvicide. We have also used the INVAPP / Paragon system to compare responses in larvae derived from WHO classified deltamethrin resistant and sensitive mosquitoes. We show how this approach to monitoring larval response to insecticides can be adapted for use with a smartphone camera application and therefore has potential for further development as a simple portable field-assay with associated real-time, geo-located information to identify hotspots.

Harnessing recombinase polymerase amplification for rapid multi-gene detection of SARS-CoV-2 in resource-limited settings.

The COVID-19 pandemic is challenging diagnostic testing capacity worldwide. The mass testing needed to limit the spread of the virus requires new molecular diagnostic tests to dramatically widen access at the point-of-care in resource-limited settings. Isothermal molecular assays have emerged as a promising technology, given the faster turn-around time and minimal equipment compared to gold standard laboratory PCR methods. However, unlike PCR, they do not typically target multiple SARS-CoV-2 genes, risking sensitivity and specificity. Moreover, they often require multiple steps thus adding complexity and delays. Here we develop a multiplexed, 1-2 step, fast (20-30 min) SARS-CoV-2 molecular test using reverse transcription recombinase polymerase amplification to simultaneously detect two conserved targets - the E and RdRP genes. The agile multi-gene platform offers two complementary detection methods: real-time fluorescence or dipstick. The analytical sensitivity of the fluorescence test was 9.5 (95% CI: 7.0-18) RNA copies per reaction for the E gene and 17 (95% CI: 11-93) RNA copies per reaction for the RdRP gene. The analytical sensitivity for the dipstick method was 130 (95% CI: 82-500) RNA copies per reaction. High specificity was found against common seasonal coronaviruses, SARS-CoV and MERS-CoV model samples. The dipstick readout demonstrated potential for point-of-care testing in decentralised settings, with minimal or equipment-free incubation methods and a user-friendly prototype smartphone application. This rapid, simple, ultrasensitive and multiplexed molecular test offers valuable advantages over gold standard tests and in future could be configurated to detect emerging variants of concern.

Genomic characteristics and clinical effect of the emergent SARS-CoV-2 B.1.1.7 lineage in London, UK: a whole-genome sequencing and hospital-based cohort study.

BACKGROUND: Emergence of variants with specific mutations in key epitopes in the spike protein of SARS-CoV-2 raises concerns pertinent to mass vaccination campaigns and use of monoclonal antibodies. We aimed to describe the emergence of the B.1.1.7 variant of concern (VOC), including virological characteristics and clinical severity in contemporaneous patients with and without the variant. METHODS: In this cohort study, samples positive for SARS-CoV-2 on PCR that were collected from Nov 9, 2020, for patients acutely admitted to one of two hospitals on or before Dec 20, 2020, in London, UK, were sequenced and analysed for the presence of VOC-defining mutations. We fitted Poisson regression models to investigate the association between B.1.1.7 infection and severe disease (defined as point 6 or higher on the WHO ordinal scale within 14 days of symptoms or positive test) and death within 28 days of a positive test and did supplementary genomic analyses in a cohort of chronically shedding patients and in a cohort of remdesivir-treated patients. Viral load was compared by proxy, using PCR cycle threshold values and sequencing read depths. FINDINGS: Of 496 patients with samples positive for SARS-CoV-2 on PCR and who met inclusion criteria, 341 had samples that could be sequenced. 198 (58%) of 341 had B.1.1.7 infection and 143 (42%) had non-B.1.1.7 infection. We found no evidence of an association between severe disease and death and lineage (B.1.1.7 vs non-B.1.1.7) in unadjusted analyses (prevalence ratio [PR] 0·97 [95% CI 0·72-1·31]), or in analyses adjusted for hospital, sex, age, comorbidities, and ethnicity (adjusted PR 1·02 [0·76-1·38]). We detected no B.1.1.7 VOC-defining mutations in 123 chronically shedding immunocompromised patients or in 32 remdesivir-treated patients. Viral load by proxy was higher in B.1.1.7 samples than in non-B.1.1.7 samples, as measured by cycle threshold value (mean 28·8 [SD 4·7] vs 32·0 [4·8]; p=0·0085) and genomic read depth (1280 [1004] vs 831 [682]; p=0·0011). INTERPRETATION: Emerging evidence exists of increased transmissibility of B.1.1.7, and we found increased virus load by proxy for B.1.1.7 in our data. We did not identify an association of the variant with severe disease in this hospitalised cohort. FUNDING: University College London Hospitals NHS Trust, University College London/University College London Hospitals NIHR Biomedical Research Centre, Engineering and Physical Sciences Research Council.

Tracking COVID-19 using online search.

Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on associated symptom categories identified by the United Kingdom's National Health Service and Public Health England. We then attempt to minimise an expected bias in these signals caused by public interest-as opposed to infections-using the proportion of news media coverage devoted to COVID-19 as a proxy indicator. Our analysis indicates that models based on online searches precede the reported confirmed cases and deaths by 16.7 (10.2-23.2) and 22.1 (17.4-26.9) days, respectively. We also investigate transfer learning techniques for mapping supervised models from countries where the spread of the disease has progressed extensively to countries that are in earlier phases of their respective epidemic curves. Furthermore, we compare time series of online search activity against confirmed COVID-19 cases or deaths jointly across multiple countries, uncovering interesting querying patterns, including the finding that rarer symptoms are better predictors than common ones. Finally, we show that web searches improve the short-term forecasting accuracy of autoregressive models for COVID-19 deaths. Our work provides evidence that online search data can be used to develop complementary public health surveillance methods to help inform the COVID-19 response in conjunction with more established approaches.

Spin-enhanced nanodiamond biosensing for ultrasensitive diagnostics.

The quantum spin properties of nitrogen-vacancy defects in diamond enable diverse applications in quantum computing and communications1. However, fluorescent nanodiamonds also have attractive properties for in vitro biosensing, including brightness2, low cost3 and selective manipulation of their emission4. Nanoparticle-based biosensors are essential for the early detection of disease, but they often lack the required sensitivity. Here we investigate fluorescent nanodiamonds as an ultrasensitive label for in vitro diagnostics, using a microwave field to modulate emission intensity5 and frequency-domain analysis6 to separate the signal from background autofluorescence7, which typically limits sensitivity. Focusing on the widely used, low-cost lateral flow format as an exemplar, we achieve a detection limit of 8.2 × 10-19 molar for a biotin-avidin model, 105 times more sensitive than that obtained using gold nanoparticles. Single-copy detection of HIV-1 RNA can be achieved with the addition of a 10-minute isothermal amplification step, and is further demonstrated using a clinical plasma sample with an extraction step. This ultrasensitive quantum diagnostics platform is applicable to numerous diagnostic test formats and diseases, and has the potential to transform early diagnosis of disease for the benefit of patients and populations.

Cantilever Sensors for Rapid Optical Antimicrobial Sensitivity Testing.

Growing antimicrobial resistance (AMR) is a serious global threat to human health. Current methods to detect resistance include phenotypic antibiotic sensitivity testing (AST), which measures bacterial growth and is therefore hampered by a slow time to obtain results (∼12-24 h). Therefore, new rapid phenotypic methods for AST are urgently needed. Nanomechanical cantilever sensors have recently shown promise for rapid AST but challenges of bacterial immobilization can lead to variable results. Herein, a novel cantilever-based method is described for detecting phenotypic antibiotic resistance within ∼45 min, capable of detecting single bacteria. This method does not require complex, variable bacterial immobilization and instead uses a laser and detector system to detect single bacterial cells in media as they pass through the laser focus. This provides a simple readout of bacterial antibiotic resistance by detecting growth (resistant) or death (sensitive), much faster than the current methods. The potential of this technique is demonstrated by determining the resistance in both laboratory and clinical strains of Escherichia coli (E. coli), a key species responsible for clinically burdensome urinary tract infections. This work provides the basis for a simple and fast diagnostic tool to detect antibiotic resistance in bacteria, reducing the health and economic burdens of AMR.

Digital technologies in the public-health response to COVID-19.

Digital technologies are being harnessed to support the public-health response to COVID-19 worldwide, including population surveillance, case identification, contact tracing and evaluation of interventions on the basis of mobility data and communication with the public. These rapid responses leverage billions of mobile phones, large online datasets, connected devices, relatively low-cost computing resources and advances in machine learning and natural language processing. This Review aims to capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers. The future of public health is likely to become increasingly digital, and we review the need for the alignment of international strategies for the regulation, evaluation and use of digital technologies to strengthen pandemic management, and future preparedness for COVID-19 and other infectious diseases.

Taking connected mobile-health diagnostics of infectious diseases to the field.

Mobile health, or 'mHealth', is the application of mobile devices, their components and related technologies to healthcare. It is already improving patients' access to treatment and advice. Now, in combination with internet-connected diagnostic devices, it offers novel ways to diagnose, track and control infectious diseases and to improve the efficiency of the health system. Here we examine the promise of these technologies and discuss the challenges in realizing their potential to increase patients' access to testing, aid in their treatment and improve the capability of public health authorities to monitor outbreaks, implement response strategies and assess the impact of interventions across the world.