Differentiation of COVID-19 from other emergency infectious disease presentations using whole blood transcriptomics then rapid qPCR: a case-control and observational cohort study (preprint)

Background. The overlapping clinical presentations of patients with acute respiratory disease can complicate disease diagnosis. Whilst PCR diagnostic methods to identify SARS-CoV-2 are highly sensitive, they have their shortcomings including false-positive risk and slow turnaround times. Changes in host gene expression can be used to distinguish between disease groups of interest, providing a viable alternative to infectious disease diagnosis. Methods. We interrogated the whole blood gene expression profiles of patients with COVID-19 (n=87), bacterial infections (n=88), viral infections (n=36), and not-infected controls (n=27) to identify a sparse diagnostic signature for distinguishing COVID-19 from other clinically similar infectious and non-infectious conditions. The sparse diagnostic signature underwent validation in a new cohort using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and then underwent further external validation in an independent in silico RNA-seq cohort. Findings. We identified a 10-gene signature (OASL, UBP1, IL1RN, ZNF684, ENTPD7, NFKBIE, CDKN1C, CD44, OTOF, MSR1) that distinguished COVID-19 from other infectious and non-infectious diseases with an AUC of 87.1% (95% CI: 82.6%-91.7%) in the discovery cohort and 88.7% and 93.6% when evaluated in the RT-qPCR validation, and in silico cohorts respectively. Interpretation. Using well-phenotyped samples collected from patients admitted acutely with a spectrum of infectious and non-infectious syndromes, we provide a detailed catalogue of blood gene expression at the time of hospital admission. The findings result in the identification of a 10-gene host diagnostic signature to accurately distinguish COVID-19 from other infection syndromes presenting to hospital. This could be developed into a rapid point-of-care diagnostic test, providing a valuable syndromic diagnostic tool for future early pandemic use.

A human primary airway microphysiological system infected with SARS-CoV-2 distinguishes the treatment efficacy between nirmatrelvir and repurposed compounds fluvoxamine and amodiaquine (preprint)

The COVID-19 pandemic necessitated a rapid mobilization of resources toward the development of safe and efficacious vaccines and therapeutics. Finding effective treatments to stem the wave of infected individuals needing hospitalization and reduce the risk of adverse events was paramount. For scientists and healthcare professionals addressing this challenge, the need to rapidly identify medical countermeasures became urgent, and many compounds in clinical use for other indications were repurposed for COVID-19 clinical trials after preliminary preclinical data demonstrated antiviral activity against SARS-CoV-2. Two repurposed compounds, fluvoxamine and amodiaquine, showed efficacy in reducing SARS-CoV-2 viral loads in preclinical experiments, but ultimately failed in clinical trials, highlighting the need for improved predictive preclinical tools that can be rapidly deployed for events such as pandemic emerging infectious diseases. The PREDICT96-ALI platform is a high-throughput, high-fidelity microphysiological system (MPS) that recapitulates primary human tracheobronchial tissue and supports highly robust and reproducible viral titers of SARS-CoV-2 variants Delta and Omicron. When amodiaquine and fluvoxamine were tested in PREDICT96-ALI, neither compound demonstrated an antiviral response, consistent with clinical outcomes and in contrast with prior reports assessing the efficacy of these compounds in other human cell-based in vitro platforms. These results highlight the unique prognostic capability of the PREDICT96-ALI proximal airway MPS to assess the potential antiviral response of lead compounds.

Strengthening Veterinary Workforce in Cambodia for Infectious Disease Detection and Response (preprint)

An efficient veterinary workforce is paramount for global health security as most emerging infectious diseases are zoonotic. Being a hotspot of disease outbreaks there is a need to strengthen the veterinary field epidemiology capacity in Cambodia. The COVID-19 pandemic has highlighted the need for a strong health security workforce in the Asia-Pacific. This study was conducted with an aim to understand veterinary epidemiology training gaps in Cambodia.A mixed method study using a concurrent triangulation design was conducted targeting the veterinary workforce. Univariable and multivariable regression and an inductive, thematic analysis was used.  Survey responses from 108 veterinarians indicated that most (70%) respondents did not have any training, while only 6.0% had been to a Field Epidemiology Training Program for Veterinarians (FETPV). Lack of formal training in epidemiology was associated with non-participation in outbreak response (P< 0.05).  The key informants suggested system level factors, limited staff, and perceived disconnect between the central and community level as likely barriers to efficient outbreak response. The need for epidemiology training of veterinarians targeting knowledge consolidation and skill development through experiential learning was emphasized. Our assessment recommends that, a multifaceted approach targeting pedagogical and structural aspects of veterinary field epidemiology in Cambodia is required.

Zoonoses and global epidemics.

PURPOSE OF REVIEW: The purpose of the review is to summarize recent advances in understanding the origins, drivers and clinical context of zoonotic disease epidemics and pandemics. In addition, we aimed to highlight the role of clinicians in identifying sentinel cases of zoonotic disease outbreaks. RECENT FINDINGS: The majority of emerging infectious disease events over recent decades, including the COVID-19 pandemic, have been caused by zoonotic viruses and bacteria. In particular, coronaviruses, haemorrhagic fever viruses, arboviruses and influenza A viruses have caused significant epidemics globally. There have been recent advances in understanding the origins and drivers of zoonotic epidemics, yet there are gaps in diagnostic capacity and clinical training about zoonoses. SUMMARY: Identifying the origins of zoonotic pathogens, understanding factors influencing disease transmission and improving the diagnostic capacity of clinicians will be crucial to early detection and prevention of further epidemics of zoonoses.

Digital Disease Surveillance for Emerging Infectious Diseases: An Early Warning System Using the Internet and Social Media Data for COVID-19 Forecasting in Canada.

BACKGROUND: Emerging Infectious Diseases (EID) are a significant threat to population health globally. We aimed to examine the relationship between internet search engine queries and social media data on COVID-19 and determine if they can predict COVID-19 cases in Canada. METHODS: We analyzed Google Trends (GT) and Twitter data from 1/1/2020 to 3/31/2020 in Canada and used various signal-processing techniques to remove noise from the data. Data on COVID-19 cases was obtained from the COVID-19 Canada Open Data Working Group. We conducted time-lagged cross-correlation analyses and developed the long short-term memory model for forecasting daily COVID-19 cases. RESULTS: Among symptom keywords, "cough," "runny nose," and "anosmia" were strong signals with high cross-correlation coefficients >0.8 ( rCough = 0.825, t - 9; rRunnyNose = 0.816, t - 11; rAnosmia = 0.812, t - 3 ), showing that searching for "cough," "runny nose," and "anosmia" on GT correlated with the incidence of COVID-19 and peaked 9, 11, and 3 days earlier than the incidence peak, respectively. For symptoms- and COVID-related Tweet counts, the cross-correlations of Tweet signals and daily cases were rTweetSymptoms = 0.868, t - 11 and tTweetCOVID = 0.840, t - 10, respectively. The LSTM forecasting model achieved the best performance (MSE = 124.78, R2 = 0.88, adjusted R2 = 0.87) using GT signals with cross-correlation coefficients >0.75. Combining GT and Tweet signals did not improve the model performance. CONCLUSION: Internet search engine queries and social media data can be used as early warning signals for creating a real-time surveillance system for COVID-19 forecasting, but challenges remain in modelling.

Monitoring and responding to emerging infectious diseases in a university setting: A case study using COVID-19.

Emerging infection diseases (EIDs) are an increasing threat to global public health, especially when the disease is newly emerging. Institutions of higher education (IHEs) are particularly vulnerable to EIDs because student populations frequently share high-density residences and strongly mix with local and distant populations. In fall 2020, IHEs responded to a novel EID, COVID-19. Here, we describe Quinnipiac University's response to SARS-CoV-2 and evaluate its effectiveness through empirical data and model results. Using an agent-based model to approximate disease dynamics in the student body, the University established a policy of dedensification, universal masking, surveillance testing via a targeted sampling design, and app-based symptom monitoring. After an extended period of low incidence, the infection rate grew through October, likely due to growing incidence rates in the surrounding community. A super-spreader event at the end of October caused a spike in cases in November. Student violations of the University's policies contributed to this event, but lax adherence to state health laws in the community may have also contributed. The model results further suggest that the infection rate was sensitive to the rate of imported infections and was disproportionately impacted by non-residential students, a result supported by the observed data. Collectively, this suggests that campus-community interactions play a major role in campus disease dynamics. Further model results suggest that app-based symptom monitoring may have been an important regulator of the University's incidence, likely because it quarantined infectious students without necessitating test results. Targeted sampling had no substantial advantages over simple random sampling when the model incorporated contact tracing and app-based symptom monitoring but reduced the upper boundary on 90% prediction intervals for cumulative infections when either was removed. Thus, targeted sampling designs for surveillance testing may mitigate worst-case outcomes when other interventions are less effective. The results' implications for future EIDs are discussed.

Interventions to Reduce Risk for Pathogen Spillover and Early Disease Spread to Prevent Outbreaks, Epidemics, and Pandemics.

The pathogens that cause most emerging infectious diseases in humans originate in animals, particularly wildlife, and then spill over into humans. The accelerating frequency with which humans and domestic animals encounter wildlife because of activities such as land-use change, animal husbandry, and markets and trade in live wildlife has created growing opportunities for pathogen spillover. The risk of pathogen spillover and early disease spread among domestic animals and humans, however, can be reduced by stopping the clearing and degradation of tropical and subtropical forests, improving health and economic security of communities living in emerging infectious disease hotspots, enhancing biosecurity in animal husbandry, shutting down or strictly regulating wildlife markets and trade, and expanding pathogen surveillance. We summarize expert opinions on how to implement these goals to prevent outbreaks, epidemics, and pandemics.

SARS-CoV-2 incubation period across variants of concern, individual factors, and circumstances of infection in France: a case series analysis from the ComCor study.

BACKGROUND: The incubation period of SARS-CoV-2 has been estimated for the known variants of concern. However, differences in study designs and settings make comparing variants difficult. We aimed to estimate the incubation period for each variant of concern compared with the historical strain within a unique and large study to identify individual factors and circumstances associated with its duration. METHODS: In this case series analysis, we included participants (aged ≥18 years) of the ComCor case-control study in France who had a SARS-CoV-2 diagnosis between Oct 27, 2020, and Feb 4, 2022. Eligible participants were those who had the historical strain or a variant of concern during a single encounter with a known index case who was symptomatic and for whom the incubation period could be established, those who reported doing a reverse-transcription-PCR (RT-PCR) test, and those who were symptomatic by study completion. Sociodemographic and clinical characteristics, exposure information, circumstances of infection, and COVID-19 vaccination details were obtained via an online questionnaire, and variants were established through variant typing after RT-PCR testing or by matching the time that a positive test was reported with the predominance of a specific variant. We used multivariable linear regression to identify factors associated with the duration of the incubation period (defined as the number of days from contact with the index case to symptom onset). FINDINGS: 20 413 participants were eligible for inclusion in this study. Mean incubation period varied across variants: 4·96 days (95% CI 4·90-5·02) for alpha (B.1.1.7), 5·18 days (4·93-5·43) for beta (B.1.351) and gamma (P.1), 4·43 days (4·36-4·49) for delta (B.1.617.2), and 3·61 days (3·55-3·68) for omicron (B.1.1.529) compared with 4·61 days (4·56-4·66) for the historical strain. Participants with omicron had a shorter incubation period than participants with the historical strain (-0·9 days, 95% CI -1·0 to -0·7). The incubation period increased with age (participants aged ≥70 years had an incubation period 0·4 days [0·2 to 0·6] longer than participants aged 18-29 years), in female participants (by 0·1 days, 0·0 to 0·2), and in those who wore a mask during contact with the index case (by 0·2 days, 0·1 to 0·4), and was reduced in those for whom the index case was symptomatic (-0·1 days, -0·2 to -0·1). These data were robust to sensitivity analyses correcting for an over-reporting of incubation periods of 7 days. INTERPRETATION: SARS-CoV-2 incubation period is notably reduced in omicron cases compared with all other variants of concern, in young people, after transmission from a symptomatic index case, after transmission to a maskless secondary case, and (to a lesser extent) in men. These findings can inform future COVID-19 contact-tracing strategies and modelling. FUNDING: Institut Pasteur, the French National Agency for AIDS Research-Emerging Infectious Diseases, Fondation de France, the INCEPTION project, and the Integrative Biology of Emerging Infectious Diseases project.

Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland.

Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between 28 February and 20 March 2020. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimated the time-varying R0 nationally and in eleven cantons by fitting a stochastic transmission model explicitly simulating within-hospital dynamics. We used individual-level data from more than 1000 hospitalised patients in Switzerland and public daily reports of hospitalisations and deaths. We estimated the national R0 to be 2.8 (95% confidence interval 2.1–3.8) at the beginning of the epidemic. Starting from around 7 March, we found a strong reduction in time-varying R0 with a 86% median decrease (95% quantile range [QR] 79–90%) to a value of 0.40 (95% QR 0.3–0.58) in the period of 29 March to 5 April. At the cantonal level, R0 decreased over the course of the epidemic between 53% and 92%. Reductions in time-varying R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We inferred that most of the reduction of transmission is attributable to behavioural changes as opposed to natural immunity, the latter accounting for only about 4% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of time-varying R0 well below one are promising. However, as of 24 April 2020, at least 96% (95% QR 95.7–96.4%) of the Swiss population remains susceptible to SARS-CoV-2. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.

Spatiotemporal reproduction number with Bayesian model selection for evaluation of emerging infectious disease transmissibility: an application to COVID-19 national surveillance data.

BACKGROUND: To control emerging diseases, governments often have to make decisions based on limited evidence. The effective or temporal reproductive number is used to estimate the expected number of new cases caused by an infectious person in a partially susceptible population. While the temporal dynamic is captured in the temporal reproduction number, the dominant approach is currently based on modeling that implicitly treats people within a population as geographically well mixed. METHODS: In this study we aimed to develop a generic and robust methodology for estimating spatiotemporal dynamic measures that can be instantaneously computed for each location and time within a Bayesian model selection and averaging framework. A simulation study was conducted to demonstrate robustness of the method. A case study was provided of a real-world application to COVID-19 national surveillance data in Thailand. RESULTS: Overall, the proposed method allowed for estimation of different scenarios of reproduction numbers in the simulation study. The model selection chose the true serial interval when included in our study whereas model averaging yielded the weighted outcome which could be less accurate than model selection. In the case study of COVID-19 in Thailand, the best model based on model selection and averaging criteria had a similar trend to real data and was consistent with previously published findings in the country. CONCLUSIONS: The method yielded robust estimation in several simulated scenarios of force of transmission with computing flexibility and practical benefits. Thus, this development can be suitable and practically useful for surveillance applications especially for newly emerging diseases. As new outbreak waves continue to develop and the risk changes on both local and global scales, our work can facilitate policymaking for timely disease control.

Development of animal models for emerging infectious diseases by breaking the barrier of species susceptibility to human pathogens.

Outbreaks of emerging infectious diseases pose a serious threat to public health security, human health and economic development. After an outbreak, an animal model for an emerging infectious disease is urgently needed for studying the etiology, host immune mechanisms and pathology of the disease, evaluating the efficiency of vaccines or drugs against infection, and minimizing the time available for animal model development, which is usually hindered by the nonsusceptibility of common laboratory animals to human pathogens. Thus, we summarize the technologies and methods that induce animal susceptibility to human pathogens, which include viral receptor humanization, pathogen-targeted tissue humanization, immunodeficiency induction and screening for naturally susceptible animal species. Furthermore, the advantages and deficiencies of animal models developed using each method were analyzed, and these will guide the selection of susceptible animals and potentially reduce the time needed to develop animal models during epidemics.

Planning For The Next Pandemic: Lab Systems Need Policy Shift To Speed Emerging Infectious Disease Warning And Tracking.

Early detection and ongoing monitoring of infectious diseases depends on diagnostic testing. The US has a large, diverse system of public, academic, and private laboratories that develop new diagnostic tests; perform routine testing; and conduct specialized reference testing, such as genomic sequencing. These laboratories operate under a complex mix of laws and regulations at the federal, state, and local levels. The COVID-19 pandemic exposed major weaknesses in the nation's laboratory system, some of which were seen again during the global mpox outbreak in 2022. In this article we review how the US laboratory system has been designed to detect and monitor emerging infections, describe what gaps were revealed during COVID-19, and propose specific steps that policy makers can take both to strengthen the current system and to prepare the US for the next pandemic.

Domestic Animals as Potential Reservoirs of Zoonotic Viral Diseases.

Zoonoses are diseases and infections naturally transmitted between humans and vertebrate animals. Over the years, zoonoses have become increasingly significant threats to global health. They form the dominant group of diseases among the emerging infectious diseases (EID) and currently account for 73% of EID. Approximately 25% of zoonoses originate in domestic animals. The etiological agents of zoonoses include different pathogens, with viruses accounting for approximately 30% of all zoonotic infections. Zoonotic diseases can be transmitted directly or indirectly, by contact, via aerosols, through a vector, or vertically in utero. Zoonotic diseases are found in every continent except Antarctica. Numerous factors associated with the pathogen, human activities, and the environment play significant roles in the transmission and emergence of zoonotic diseases. Effective response and control of zoonotic diseases call for multiple-sector involvement and collaboration according to the One Health concept.

Exploring scenarios for the food system-zoonotic risk interface.

The unprecedented economic and health impacts of the COVID-19 pandemic have shown the global necessity of mitigating the underlying drivers of zoonotic spillover events, which occur at the human-wildlife and domesticated animal interface. Spillover events are associated to varying degrees with high habitat fragmentation, biodiversity loss through land use change, high livestock densities, agricultural inputs, and wildlife hunting-all facets of food systems. As such, the structure and characteristics of food systems can be considered key determinants of modern pandemic risks. This means that emerging infectious diseases should be more explicitly addressed in the discourse of food systems to mitigate the likelihood and impacts of spillover events. Here, we adopt a scenario framework to highlight the many connections among food systems, zoonotic diseases, and sustainability. We identify two overarching dimensions: the extent of land use for food production and the agricultural practices employed that shape four archetypal food systems, each with a distinct risk profile with respect to zoonotic spillovers and differing dimensions of sustainability. Prophylactic measures to curb the emergence of zoonotic diseases are therefore closely linked to diets and food policies. Future research directions should explore more closely how they impact the risk of spillover events.

Epidemic modeling with heterogeneity and social diffusion.

We propose and analyze a family of epidemiological models that extend the classic Susceptible-Infectious-Recovered/Removed (SIR)-like framework to account for dynamic heterogeneity in infection risk. The family of models takes the form of a system of reaction-diffusion equations given populations structured by heterogeneous susceptibility to infection. These models describe the evolution of population-level macroscopic quantities S, I, R as in the classical case coupled with a microscopic variable f, giving the distribution of individual behavior in terms of exposure to contagion in the population of susceptibles. The reaction terms represent the impact of sculpting the distribution of susceptibles by the infection process. The diffusion and drift terms that appear in a Fokker-Planck type equation represent the impact of behavior change both during and in the absence of an epidemic. We first study the mathematical foundations of this system of reaction-diffusion equations and prove a number of its properties. In particular, we show that the system will converge back to the unique equilibrium distribution after an epidemic outbreak. We then derive a simpler system by seeking self-similar solutions to the reaction-diffusion equations in the case of Gaussian profiles. Notably, these self-similar solutions lead to a system of ordinary differential equations including classic SIR-like compartments and a new feature: the average risk level in the remaining susceptible population. We show that the simplified system exhibits a rich dynamical structure during epidemics, including plateaus, shoulders, rebounds and oscillations. Finally, we offer perspectives and caveats on ways that this family of models can help interpret the non-canonical dynamics of emerging infectious diseases, including COVID-19.