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The COVID-19 pandemic has highlighted the need to upgrade systems for infectious disease surveillance and forecasting and modeling of the spread of infection, both of which inform evidence-based public health guidance and policies. Here, we discuss requirements for an effective surveillance system to support decision making during a pandemic, drawing on the lessons of COVID-19 in the U.S., while looking to jurisdictions in the U.S. and beyond to learn lessons about the value of specific data types. In this report, we define the range of decisions for which surveillance data are required, the data elements needed to inform these decisions and to calibrate inputs and outputs of transmission-dynamic models, and the types of data needed to inform decisions by state, territorial, local, and tribal health authorities. We define actions needed to ensure that such data will be available and consider the contribution of such efforts to improving health equity.

Diagnostics are widely considered crucial in the fight against antimicrobial resistance (AMR), which is expected to kill 10 million people annually by 2030. Nevertheless, there remains a substantial gap between the need for AMR diagnostics versus their development and implementation. To help address this problem, target product profiles (TPP) have been developed to focus developers’ attention on the key aspects of AMR diagnostic tests. However, during discussion between a multisectoral working group of 51 international experts from industry, academia and healthcare, it was noted that specific AMR-related TPPs could be extended by incorporating the interdependencies between the key characteristics associated with the development of such TPPs. Subsequently, the working group identified 46 characteristics associated with six main categories (ie, Intended Use, Diagnostic Question, Test Description, Assay Protocol, Performance and Commercial). The interdependencies of these characteristics were then identified and mapped against each other to generate new insights for use by stakeholders. Specifically, it may not be possible for diagnostics developers to achieve all of the recommendations in every category of a TPP and this publication indicates how prioritising specific TPP characteristics during diagnostics development may influence (or not) a range of other TPP characteristics associated with the diagnostic. The use of such guidance, in conjunction with specific TPPs, could lead to more efficient AMR diagnostics development.

Abstract Introduction Multi-parameter diagnostic devices can simplify cardiometabolic disease diagnosis. However, existing devices may not be suitable for use in low-resource settings, where the burden of non-communicable diseases is high. Here we describe the development of a target product profile (TPP) for a point-of-care multi-parameter device for detection of biomarkers for cardiovascular disease and metabolic disorders, including diabetes, in primary care settings in low- and middle-income countries (LMICs). Methods A draft TPP developed by an expert group was reviewed through an online survey and semi-structured expert interviews to identify device characteristics requiring refinement. The draft TPP included 41 characteristics with minimal and optimal requirements; characteristics with an agreement level for either requirement of ≤ 85% in either the survey or among interviewees were further discussed by the expert group and amended as appropriate. Results Twenty people responded to the online survey and 18 experts participated in the interviews. Twenty-two characteristics had an agreement level of ≤ 85% in either the online survey or interviews. The final TPP defines the device as intended to be used for basic diagnosis and management of cardiometabolic disorders (lipids, glucose, HbA1c, and creatinine) as minimal requirement, and offering an expanded test menu for wider cardiometabolic disease management as optimal requirement. To be suitable, the device should be intended for level 1 healthcare settings or lower, used by minimally trained healthcare workers and allow testing using self-contained cartridges or strips without the need for additional reagents. Throughput should be one sample at a time in a single or multi-analyte cartridge, or optimally enable testing of several samples and analytes in parallel with random access. Conclusion This TPP will inform developers of cardiometabolic multi-parameter devices for LMIC settings, and will support decision makers in the evaluation of existing and future devices.

Abstract A key component of any health system is the capacity to accurately diagnose individuals. One of the six building blocks of a health system as defined by the World Health Organization (WHO) includes diagnostic tools. The WHO’s Noncommunicable Disease Global Action Plan includes addressing the lack of diagnostics for noncommunicable diseases, through multi-stakeholder collaborations to develop new technologies that are affordable, safe, effective and quality controlled, and improving laboratory and diagnostic capacity and human resources. Many challenges exist beyond price and availability for the current tools included in the Package of Essential Noncommunicable Disease Interventions (PEN) for cardiovascular disease, diabetes and chronic respiratory diseases. These include temperature stability, adaptability to various settings (e.g. at high altitude), need for training in order to perform and interpret the test, the need for maintenance and calibration, and for Blood Glucose Meters non-compatible meters and test strips. To date the issues surrounding access to diagnostic and monitoring tools for noncommunicable diseases have not been addressed in much detail. The aim of this Commentary is to present the current landscape and challenges with regards to guidance from the WHO on diagnostic tools using the WHO REASSURED criteria, which define a set of key characteristics for diagnostic tests and tools. These criteria have been used for communicable diseases, but so far have not been used for noncommunicable diseases. Diagnostic tools have played an important role in addressing many communicable diseases, such as HIV, TB and neglected tropical diseases. Clearly more attention with regards to diagnostics for noncommunicable diseases as a key component of the health system is needed.

Background: HIV self-testing (HIVST) is recommended by the WHO as an innovative strategy to reach UNAIDS targets to end HIV by 2030. HIVST with digital supports is defined as the use of digital interventions (e.g., website-based, social media, mobile HIVST applications (apps), text messaging (SMS), digital vending machines (digital VMs)) to improve the efficiency and impact of HIVST. HIVST deployment and integration in health services is an emerging priority. We conducted a systematic review aiming to close the gap in evidence that summarizes the impact of digitally supported HIVST and to inform policy recommendations. Methods: We searched PubMed and Embase for articles and abstracts on HIVST with digital supports published during the period February 1st, 2010 to June 15th, 2021, following Cochrane guidelines and PRISMA methodology. We assessed feasibility, acceptability, preference, and impact outcomes across all populations and study designs. Metrics reported were willingness to use HIVST, preferences for HIVST delivery, proportion of first-time testers, HIVST uptake, HIVST kit return rate, and linkage to care. Heterogeneity of the interventions and reported metrics precluded us from conducting a meta-analysis. Findings: 46 studies were narratively synthesized, of which 72% were observational and 28% were RCTs. Half of all studies (54%, 25/46) assessed web-based innovations (e.g., study websites, videos, chatbots), followed by social media (26%, 12/46), HIVST-specific apps (7%, 3/46), SMS (9%, 4/46), and digital VMs (4%, 2/46). Web-based innovations were found to be acceptable (77–97%), preferred over in-person and hybrid options by more first-time testers (47–48%), highly feasible (93–95%), and were overall effective in supporting linkage to care (53–100%). Social media and app-based innovations also had high acceptability (87–95%) and linkage to care proportions (80–100%). SMS innovations increased kit return rates (54–94%) and HIVST uptake among hard-to-reach groups. Finally, digital VMs were highly acceptable (54–93%), and HIVST uptake was six times greater when using digital VMs compared to distribution by community workers. Interpretation: HIVST with digital supports was deemed feasible, acceptable, preferable, and was shown to increase uptake, engage first-time testers and hard-to-reach populations, and successfully link participants to treatment. Findings pave the way for greater use of HIVST interventions with digital supports globally. Funding: This work was funded by the Foundation for Innovative New Diagnostics. The agency had no role in the decision to submit it for publication, however the funders contributing to the writing of the manuscript. NPP also acknowledges support from the Fonds de recherche du Quebec Sante (Senior scientist scholar award), The Canadian Institutes of Health Research (PJT 153149 and HBR 422155), Grand Challenges Canada (Transition to Scale award, 071005), the India-Canada centre for Innovative Multidisciplinary Partnerships to Accelerate Community Transformation and Sustainability (IC-IMPACTS), and the MUHC Foundation.

Background: The Portfolio-To-Impact version 2 (P2I v.2) financial forecasting tool estimates funding requirements for development of portfolios of candidate health products (drugs, biologics, vaccines or diagnostics). The assumptions and archetypes relating to diagnostics in P2I v.2 are based on limited data and may not accurately describe research and development costs, timelines and probability of success. This study aimed to revise the P2I v.2 tool by modifying the diagnostic assumptions to improve accuracy of predictions for diagnostic portfolios. Methods: Data from expert interviews and historical information on development of 26 existing diagnostics were used to determine approximate research and development costs, timelines and probability of success for development of diagnostics, and to revise diagnostic archetypes and development phases. To compare the revised tool with P2I v.2, data on 27 candidates from the Foundation for Innovative New Diagnostics (FIND) tuberculosis and pandemic preparedness portfolios were input into both versions. Results: The number of diagnostic archetypes increased from two in P2I v.2 to three in the revised tool. Total estimated costs to move the 27 candidates along the pipeline to launch were US$641.62 million with P2I v.2 and US$274.00 million with the revised model. The number of expected launches was 21.65 over five years with P2I v.2 and 11.48 over eight years with the revised model. Development timelines were extended and probability of success was lower with the revised model compared with P2I v.2. Conclusions: Outputs from the revised tool were in line with expert experience, suggesting that the proposed revisions improve the accuracy of the tool for estimating research and development costs, timelines and probability of success relating to diagnostic portfolios. Additional improvements to the tool could include further refinement of archetypes, incorporation of a measure of potential public health impact, and addition of a commercialization phase for diagnostics.

We describe the first reported case of acute respiratory distress syndrome (ARDS) attributed to Neosartorya udagawae infection. This mold grew rapidly in cultures of multiple respiratory specimens from a previously healthy 43-year-old woman. Neosartorya spp. are a recently recognized cause of invasive disease in immunocompromised patients that can be mistaken for their sexual teleomorph, Aspergillus fumigatus. Because the cultures were sterile, phenotypic identification was not possible. DNA sequencing of ITS, calmodulin and β-tubulin genes supported identification of Neosartorya udagawae. Our case is the first report of ARDS associated with Neosartorya sp. infection and defines a new clinical entity.

Initial antimicrobial treatment of patients with deep seated or invasive infections is typically empiric. Usually, cultures of specimens obtained from the suspected source of infection are performed to identify pathogens and guide continued antimicrobial treatment. When patients present with signs and symptoms of infection, but sterile body fluid or tissue specimens cannot be obtained in a timely fashion, growth of bacterial pathogens in culture may be inhibited following initiation of empiric antibiotic treatment. To address this clinical dilemma, we performed a prospective evaluation of conventional culture vs. PCR coupled to electrospray ionization mass spectrometry (PCR/ESI-MS) on sterile body fluids and tissues submitted to the diagnostic microbiology lab following initiation of empiric antibiotic treatment for patients with suspected infection. In this series of surgical samples, PCR/ESI-MS identified bacterial pathogen(s) in 56% (49/87) of patients with non-diagnostic cultures. Examination of patients stratified by antibiotic treatment duration demonstrated that PCR/ESI-MS sustains high rates of bacterial DNA detection over time by generalized estimating equation models (p

Bloodstream infection (BSI) and sepsis are rising in incidence throughout the developed world. The spread of multi-drug resistant organisms presents increasing challenges to treatment. Surviving BSI is dependent on rapid and accurate identification of causal organisms, and timely application of appropriate antibiotics. Current culture-based methods used to detect and identify agents of BSI are often too slow to impact early therapy and may fail to detect relevant organisms in many positive cases. Existing methods for direct molecular detection of microbial DNA in blood are limited in either sensitivity (likely the result of small sample volumes) or in breadth of coverage, often because the PCR primers and probes used target only a few specific pathogens. There is a clear unmet need for a sensitive molecular assay capable of identifying the diverse bacteria and yeast associated with BSI directly from uncultured whole blood samples. We have developed a method of extracting DNA from larger volumes of whole blood (5 ml per sample), amplifying multiple widely conserved bacterial and fungal genes using a mismatch- and background-tolerant PCR chemistry, and identifying hundreds of diverse organisms from the amplified fragments on the basis of species-specific genetic signatures using electrospray ionization mass spectrometry (PCR/ESI-MS). We describe the analytical characteristics of the IRIDICA BAC BSI Assay and compare its pre-clinical performance to current standard-of-care methods in a collection of prospectively collected blood specimens from patients with symptoms of sepsis. The assay generated matching results in 80% of culture-positive cases (86% when common contaminants were excluded from the analysis), and twice the total number of positive detections. The described method is capable of providing organism identifications directly from uncultured blood in less than 8 hours.The IRIDICA BAC BSI Assay is not available in the United States.

We describe a new approach for infectious disease surveillance that facilitates rapid identification of known and emerging pathogens. The process uses broad-range polymerase chain reaction (PCR) to amplify nucleic acid targets from large groupings of organisms, electrospray ionization mass spectrometry for accurate mass measurements of PCR products, and base composition signature analysis to identify organisms in a sample. We demonstrate this principle by using 14 isolates of 9 diverse Coronavirus spp., including the severe acute respiratory syndrome–associated coronavirus (SARS-CoV). We show that this method could identify and distinguish between SARS and other known CoV, including the human CoV 229E and OC43, individually and in a mixture of all 3 human viruses. The sensitivity of detection, measured by using titered SARS-CoV spiked into human serum, was ≈1 PFU/mL. This approach, applicable to the surveillance of bacterial, viral, fungal, or protozoal pathogens, is capable of automated analysis of >900 PCR reactions per day.

PCR coupled with electrospray ionization mass spectrometry (ESI-MS) is a diagnostic approach that has demonstrated the capacity to detect pathogenic organisms from culture negative clinical samples after antibiotic treatment has been initiated. [1] We describe the application of PCR/ESI-MS for detection of bacteria in original patient specimens that were obtained after administration of antibiotic treatment in an open investigation analysis.We prospectively identified cases of suspected bacterial infection in which cultures were not obtained until after the initiation of antimicrobial treatment. PCR/ESI-MS was performed on 76 clinical specimens that were submitted for conventional microbiology testing from 47 patients receiving antimicrobial treatment.In our series, 72% (55/76) of cultures obtained following initiation of antimicrobial treatment were non-diagnostic (45 negative cultures; and 10 respiratory specimens with normal flora (5), yeast (4), or coagulase-negative staphylococcus (1)). PCR/ESR-MS detected organisms in 83% (39/47) of cases and 76% (58/76) of the specimens. Bacterial pathogens were detected by PCR/ESI-MS in 60% (27/45) of the specimens in which cultures were negative. Notably, in two cases of relapse of prosthetic knee infections in patients on chronic suppressive antibiotics, the previous organism was not recovered in tissue cultures taken during extraction of the infected knee prostheses, but was detected by PCR/ESI-MS.Molecular methods that rely on nucleic acid amplification may offer a unique advantage in the detection of pathogens collected after initiation of antimicrobial treatment and may provide an opportunity to target antimicrobial therapy and "salvage" both individual treatment regimens as well as, in select cases, institutional antimicrobial stewardship efforts.

Achieving a rapid microbiological diagnosis is crucial for decreasing morbidity and mortality of patients with a bloodstream infection, as it leads to the administration of an appropriate empiric antimicrobial therapy. Molecular methods may offer a rapid alternative to conventional microbiological diagnosis involving blood culture. In this study, the performance of a new technology that uses broad-spectrum PCR coupled with mass spectrometry (PCR/ESI-MS) was evaluated for the detection of microorganisms directly from whole blood. A total of 247 whole blood samples and paired blood cultures were prospectively obtained from 175 patients with a suspicion of sepsis. Both sample types were analyzed using the PCR/ESI-MS technology, and the results were compared with those obtained by conventional identification methods. The overall agreement between conventional methods and PCR/ESI-MS performed in blood culture aliquots was 94.2% with 96.8% sensitivity and 98.5% specificity for the molecular method. When comparing conventional methods with PCR/ESI-MS performed in whole blood specimens, the overall agreement was 77.1% with 50% sensitivity and 93.8% specificity for the molecular method. Interestingly, the PCR/ESI-MS technology led to the additional identification of 13 pathogens that were not found by conventional methods. Using the PCR/ESI-MS technology the microbiological diagnosis of bloodstream infections could be anticipated in about half of the patients in our setting, including a small but significant proportion of patients newly diagnosed. Thus, this promising technology could be very useful for the rapid diagnosis of sepsis in combination with traditional methods.

Person-to-person transmission of influenza viruses occurs by contact (direct and fomites) and non-contact (droplet and small particle aerosol) routes, but the quantitative dynamics and relative contributions of these routes are incompletely understood. The transmissibility of influenza strains estimated from secondary attack rates in closed human populations is confounded by large variations in population susceptibilities. An experimental method to phenotype strains for transmissibility in an animal model could provide relative efficiencies of transmission. We developed an experimental method to detect exhaled viral aerosol transmission between unanesthetized infected and susceptible ferrets, measured aerosol particle size and number, and quantified the viral genomic RNA in the exhaled aerosol. During brief 3-hour exposures to exhaled viral aerosols in airflow-controlled chambers, three strains of pandemic 2009 H1N1 strains were frequently transmitted to susceptible ferrets. In contrast one seasonal H1N1 strain was not transmitted in spite of higher levels of viral RNA in the exhaled aerosol. Among three pandemic strains, the two strains causing weight loss and illness in the intranasally infected 'donor' ferrets were transmitted less efficiently from the donor than the strain causing no detectable illness, suggesting that the mucosal inflammatory response may attenuate viable exhaled virus. Although exhaled viral RNA remained constant, transmission efficiency diminished from day 1 to day 5 after donor infection. Thus, aerosol transmission between ferrets may be dependent on at least four characteristics of virus-host relationships including the level of exhaled virus, infectious particle size, mucosal inflammation, and viral replication efficiency in susceptible mucosa.

Technology for comprehensive identification of biothreats in environmental and clinical specimens is needed to protect citizens in the case of a biological attack. This is a challenge because there are dozens of bacterial and viral species that might be used in a biological attack and many have closely related near-neighbor organisms that are harmless. The biothreat agent, along with its near neighbors, can be thought of as a biothreat cluster or a biocluster for short. The ability to comprehensively detect the important biothreat clusters with resolution sufficient to distinguish the near neighbors with an extremely low false positive rate is required. A technological solution to this problem can be achieved by coupling biothreat group-specific PCR with electrospray ionization mass spectrometry (PCR/ESI-MS). The biothreat assay described here detects ten bacterial and four viral biothreat clusters on the NIAID priority pathogen and HHS/USDA select agent lists. Detection of each of the biothreat clusters was validated by analysis of a broad collection of biothreat organisms and near neighbors prepared by spiking biothreat nucleic acids into nucleic acids extracted from filtered environmental air. Analytical experiments were carried out to determine breadth of coverage, limits of detection, linearity, sensitivity, and specificity. Further, the assay breadth was demonstrated by testing a diverse collection of organisms from each biothreat cluster. The biothreat assay as configured was able to detect all the target organism clusters and did not misidentify any of the near-neighbor organisms as threats. Coupling biothreat cluster-specific PCR to electrospray ionization mass spectrometry simultaneously provides the breadth of coverage, discrimination of near neighbors, and an extremely low false positive rate due to the requirement that an amplicon with a precise base composition of a biothreat agent be detected by mass spectrometry.