Epidemiological Applications Research Articles

Nucleic acid-based electrochemical biosensor for detection of influenza B by gold nanoparticles.

The influenza virus is a pervasive pathogen that exhibits increased prevalence during colder seasons, resulting in a significant annual occurrence of infections. Notably, pharmaceutical interventions effective against influenza A strains often exhibit limited efficacy against influenza B variants. Against this backdrop, the need for innovative approaches to accurately and swiftly differentiate and detect influenza B becomes evident. Biosensors play a pivotal role in this detection process, offering rapid, specific, and sensitive identification of the virus, facilitating timely intervention and containment efforts. Oligonucleotide sequences targeting the conserved B/Victoria/2/87 influenza virus NP region were designed. Nasopharyngeal swabs were collected from patients suspected of influenza virus infection, and viral RNA was extracted. RNA quality was assessed through one-step PCR. cDNA synthesis was performed using random hexamers, and real-time PCR quantified the influenza genome. Gold nanoparticles were immobilized on a surface to immobilize the specific DNA probe, and electrochemical hybridization was electrochemically followed. The biosensor exhibited high selectivity and effective distinction of complementary sequences from mismatches and influenza virus cDNA genome. The biosensor successfully detected the influenza B virus genome in real samples. Non-influenza samples yielded no significant hybridization signals. The comparison between the results obtained from the biosensor and real-time PCR revealed full agreement of these methods. The biosensor utilized electrochemical detection of hybridization and proved effective in detecting the influenza B virus genome with high specificity, sensitivity, and selectivity. Comparative analysis with real-time PCR underscored the accuracy and potential applicability of the biosensor in rapid and specific virus detection. This innovative approach holds promise for future diagnostic and epidemiological applications in detecting influenza B virus and other pathogens.

Estimation of Norovirus infections in Japan: An application of wastewater-based epidemiology for enteric disease assessment

Noroviruses of genogroup I (NoV GI) and NoV GII are the primary causes of acute gastroenteritis (AGE) in developed countries. However, asymptomatic and untested NoV infections lead to an underestimation of AGE cases, and the lack of mandatory viral identification in clinical cases hinders precise estimation of NoV infections. Back estimation of NoV infections in the community using a wastewater-based epidemiology (WBE) approach can provide valuable insights into the disease's extent, progression, and epidemiology, aiding in developing effective control strategies. This study employed a one-step reverse transcription-quantitative PCR to quantify NoVs GI and GII in wastewater samples (n = 83) collected twice a week from June 2022 to March 2023 in Japan. All samples from the Winter-Spring (n = 27) tested positive for NoV GI and GII RNA, while 73 % and 88 % of samples from the Summer-Autumn (n = 56) were positive for NoV GI and NoV GII RNA, respectively. Significantly higher concentrations of NoV GI/GII RNA were found in the Winter-Spring season compared to the Summer-Autumn season. NoV RNA was consistently detected in wastewater throughout the year, demonstrating the persistence of AGE cases in the catchment, suggesting an endemic NoV infection. Estimates of NoV infection incorporated viral RNA concentrations, wastewater parameters, and signal persistence in a mass balance equation using Monte Carlo Simulation. The median estimated NoV GI infections per 100,000 population for Summer-Autumn was 133 and for the Winter-Spring season, it was 881. Estimated NoV GII infections were 1357 for Summer-Autumn and 11,997 for the Winter-Spring season per 100,000 population. The estimated NoV infections exceeded by 3.2 and 23.9 folds than the reported AGE cases in Summer-Autumn and Winter-Spring seasons, respectively. The seasonal trend of estimated NoV infections closely matched that of AGE cases, highlighting the utility of WBE in understanding the epidemiology of enteric infections.

Monitoring of over-the-counter (OTC) and COVID-19 treatment drugs complement wastewater surveillance of SARS-CoV-2

BackgroundThe application of wastewater-based epidemiology to track the outbreak and prevalence of coronavirus disease (COVID-19) in communities has been tested and validated by several researchers across the globe. However, the RNA-based surveillance has its inherent limitations and uncertainties.ObjectiveThis study aims to complement the ongoing wastewater surveillance efforts by analyzing other chemical biomarkers in wastewater to help assess community response (hospitalization and treatment) during the pandemic (2020–2021).MethodsWastewater samples (n = 183) were collected from the largest wastewater treatment facility in Suffolk County, NY, USA and analyzed for COVID-19 treatment drugs (remdesivir, chloroquine, and hydroxychloroquine (HCQ)) and their human metabolites. We additionally monitored 26 pharmaceuticals including common over-the-counter (OTC) drugs. Lastly, we developed a Bayesian model that uses viral RNA, COVID-19 treatment drugs, and pharmaceuticals data to predict the confirmed COVID-19 cases within the catchment area.ResultsThe viral RNA levels in wastewater tracked the actual COVID-19 case numbers well as expected. COVID-19 treatment drugs were detected with varying frequency (9–100%) partly due to their instability in wastewater. We observed a significant correlation (R = 0.30, p < 0.01) between the SARS-CoV-2 genes and desethylhydroxychloroquine (DHCQ, metabolite of HCQ). Remdesivir levels peaked immediately after the Emergency Use Authorization approved by the FDA. Although, 13 out of 26 pharmaceuticals assessed were consistently detected (DF = 100%, n = 111), only acetaminophen was significantly correlated with viral loads, especially when the Omicron variant was dominant. The Bayesian models were capable of reproducing the temporal trend of the confirmed cases.ImpactIn this study, for the first time, we measured COVID-19 treatment and pharmaceutical drugs and their metabolites in wastewater to complement ongoing COVID-19 viral RNA surveillance efforts. Our results highlighted that, although the COVID-19 treatment drugs were not very stable in wastewater, their detection matched with usage trends in the community. Acetaminophen, an OTC drug, was significantly correlated with viral loads and confirmed cases, especially when the Omicron variant was dominant. A Bayesian model was developed which could predict COVID-19 cases more accurately when incorporating other drugs data along with viral RNA levels in wastewater.

Clinical and biochemical associations of urinary metabolites: quantitative epidemiological approach on renal-cardiometabolic biomarkers.

Urinary metabolomics has demonstrated considerable potential to assess kidney function and its metabolic corollaries in health and disease. However, applications in epidemiology remain sparse due to technical challenges. We added 17 metabolites to an open-access urinary nuclear magnetic resonance metabolomics platform, extending the panel to 61 metabolites (n = 994). We also introduced automated quantification for 11 metabolites, extending the panel to 12 metabolites (+creatinine). Epidemiological associations between these 12 metabolites and 49 clinical measures were studied in three independent cohorts (up to 5989 participants). Detailed regression analyses with various confounding factors are presented for body mass index (BMI) and smoking. Sex-specific population reference concentrations and distributions are provided for 61 urinary metabolites (419 men and 575 women), together with methodological intra-assay metabolite variations as well as the biological intra-individual and epidemiological population variations. For the 12 metabolites, 362 associations were found. These are mostly novel and reflect potential molecular proxies to estimate kidney function, as the associations cannot be simply explained by estimated glomerular filtration rate. Unspecific renal excretion results in leakage of amino acids (and glucose) to urine in all individuals. Seven urinary metabolites associated with smoking, providing questionnaire-independent proxy measures of smoking status in epidemiological studies. Common confounders did not affect metabolite associations with smoking, but insulin had a clear effect on most associations with BMI, including strong effects on 2-hydroxyisobutyrate, valine, alanine, trigonelline and hippurate. Urinary metabolomics provides new insight on kidney function and related biomarkers on the renal-cardiometabolic system, supporting large-scale applications in epidemiology.

Modelling Infectious Disease Dynamics: A Robust Computational Approach for Stochastic SIRS with Partial Immunity and an Incidence Rate

For decades, understanding the dynamics of infectious diseases and halting their spread has been a major focus of mathematical modelling and epidemiology. The stochastic SIRS (susceptible–infectious–recovered–susceptible) reaction–diffusion model is a complicated but crucial computational scheme due to the combination of partial immunity and an incidence rate. Considering the randomness of individual interactions and the spread of illnesses via space, this model is a powerful instrument for studying the spread and evolution of infectious diseases in populations with different immunity levels. A stochastic explicit finite difference scheme is proposed for solving stochastic partial differential equations. The scheme is comprised of predictor–corrector stages. The stability and consistency in the mean square sense are also provided. The scheme is applied to diffusive epidemic models with incidence rates and partial immunity. The proposed scheme with space’s second-order central difference formula solves deterministic and stochastic models. The effect of transmission rate and coefficient of partial immunity on susceptible, infected, and recovered people are also deliberated. The deterministic model is also solved by the existing Euler and non-standard finite difference methods, and it is found that the proposed scheme forms better than the existing non-standard finite difference method. Providing insights into disease dynamics, control tactics, and the influence of immunity, the computational framework for the stochastic SIRS reaction–diffusion model with partial immunity and an incidence rate has broad applications in epidemiology. Public health and disease control ultimately benefit from its application to the study and management of infectious illnesses in various settings.

Distributed genotyping and clustering of Neisseria strains reveal continual emergence of epidemic meningococcus over a century

Core genome multilocus sequence typing (cgMLST) is commonly used to classify bacterial strains into different types, for taxonomical and epidemiological applications. However, cgMLST schemes require central databases for the nomenclature of new alleles and sequence types, which must be synchronized worldwide and involve increasingly intensive calculation and storage demands. Here, we describe a distributed cgMLST (dcgMLST) scheme that does not require a central database of allelic sequences and apply it to study evolutionary patterns of epidemic and endemic strains of the genus Neisseria. We classify 69,994 worldwide Neisseria strains into multi-level clusters that assign species, lineages, and local disease outbreaks. We divide Neisseria meningitidis into 168 endemic lineages and three epidemic lineages responsible for at least 9 epidemics in the past century. According to our analyses, the epidemic and endemic lineages experienced very different population dynamics in the past 100 years. Epidemic lineages repetitively emerged from endemic lineages, disseminated worldwide, and apparently disappeared rapidly afterward. We propose a stepwise model for the evolutionary trajectory of epidemic lineages in Neisseria, and expect that the development of similar dcgMLST schemes will facilitate epidemiological studies of other bacterial pathogens.

Multiple change point analysis of hepatitis B reports in Xinjiang, China from 2006 to 2021.

Hepatitis B (HB) is a major global challenge, but there has been a lack of epidemiological studies on HB incidence in Xinjiang from a change-point perspective. This study aims to bridge this gap by identifying significant change points and trends. The datasets were obtained from the Xinjiang Information System for Disease Control and Prevention. Change points were identified using binary segmentation for full datasets and a segmented regression model for five age groups. The results showed four change points for the quarterly HB time series, with the period between the first change point (March 2007) and the second change point (March 2010) having the highest mean number of HB reports. In the subsequent segments, there was a clear downward trend in reported cases. The segmented regression model showed different numbers of change points for each age group, with the 30-50, 51-80, and 15-29 age groups having higher growth rates. Change point analysis has valuable applications in epidemiology. These findings provide important information for future epidemiological studies and early warning systems for HB.

Estimation and variable selection for high-dimensional spatial dynamic panel data models

Spatiotemporal modeling of networks is of great practical importance, with modern applications in epidemiology and social network analysis. Despite rapid methodological advances, how to effectively and efficiently estimate the parameters of spatial dynamic panel models remains a challenging problem. To tackle this problem, we construct consistent complex least-squares estimators by the eigendecomposition of a spatial weight matrix method originally proposed for undirected networks. We no longer require all eigenvalues and eigenvectors to be real, which is a remarkable achievement as it implies that the proposed method is now applicable to spatiotemporal data modeling of directed networks. Under mild, interpretable conditions, we show that the proposed parameter estimators are consistent and asymptotically normally distributed. We also present a complex orthogonal greedy algorithm for variable selection and rigorously investigate its convergence properties. Moreover, we incorporate fixed effects into the spatial dynamic panel models and provide a model transformation so that the proposed method can also be applied to the transformed model. Extensive simulation studies and data examples demonstrate the effectiveness of the proposed method.

Refining PRRSV-2 genetic classification based on global ORF5 sequences and investigation of their geographic distributions and temporal changes.

Porcine reproductive and respiratory syndrome virus (PRRSV) is an important swine pathogen affecting the global swine industry. The first open reading frame 5 (ORF5)-based genetic lineage classification system describing global PRRSV-2 genetic diversity was introduced over a decade ago. Although refinements have been proposed for the predominant lineage in the USA (lineage 1), PRRSV-2 phylogenetic classification system at international levels has not been thoroughly evaluated and updated since 2010. In this study, based on analysis of 82,237 global ORF5 sequences reported during 1989-2021, we classified PRRSV-2 into 11 genetic lineages (L1‒L11) and 21 sublineages (L1A‒L1F, L1H‒L1J, L5A‒L5B, L8A‒L8E, and L9A‒L9E). The proposed classification system is flexible for growth if additional lineages, sublineages, or more granular classifications are needed. For example, for more fine-scale epidemiological investigation, L1C was further divided into five groups (L1C.1‒L1C.5), with L1C.5 corresponding to the recently emerged L1C variant. Comparison between restriction fragment length polymorphism (RFLP) typing and phylogenetic classification revealed the inaccuracy of using RFLP to determine PRRSV-2 genetic relatedness in most scenarios. Genetic homology of six commercial PRRSV-2 vaccines to each lineage/sublineage and detection frequency of vaccine-like viruses were determined. Global geographic distribution of each lineage/sublineage was presented. Temporal dynamic changes of PRRSV-2 in the USA during 1989-2021 were investigated by analyzing 73,092 ORF5 sequences. In summary, this study refined PRRSV-2 ORF5-based phylogenetic classification and investigated the geographic distribution and temporal changes of PRRSV-2 at the lineage/sublineage levels. The refined classification system and molecular epidemiology data in this study will be invaluable for future characterization of PRRSV-2.IMPORTANCEIn this study, comprehensive analysis of 82,237 global porcine reproductive and respiratory syndrome virus type 2 (PRRSV-2) open reading frame 5 sequences spanning from 1989 to 2021 refined PRRSV-2 genetic classification system, which defines 11 lineages and 21 sublineages and provides flexibility for growth if additional lineages, sublineages, or more granular classifications are needed in the future. Geographic distribution and temporal changes of PRRSV-2 were investigated in detail. This is a thorough study describing the molecular epidemiology of global PRRSV-2. In addition, the reference sequences based on the refined genetic classification system are made available to the public for future epidemiological and diagnostic applications worldwide. The data from this study will facilitate global standardization and application of PRRSV-2 genetic classification.

Laboratory evaluation of ELISA and indirect immunofluorescence assay in response to emergence of Japanese encephalitis virus genotype IV in Australia.

The unexpected recent emergence of Japanese encephalitis virus (JEV) genotype IV in multiple southern states of Australia necessitated an evaluation of JEV serological tests suitable for diagnosing acute infection and for seroprevalence studies. This study examined the analytical and clinical performance of two high-throughput JEV assays, Euroimmun immunofluorescence assay (IFA) and Euroimmun enzyme-linked immunosorbent assay (ELISA), across four cohorts; (1) surveillance of piggery workers in outbreak areas, (2) surveillance of residents in outbreak areas, (3) acute JEV infection and (4) post-JEV vaccination. ELISA and IFA IgM demonstrated minimal cross-reactivity (0-1.8%) with other endemic flaviviruses, with high sensitivity (100%) for acute JEV infection in this low endemicity setting. Differences in IgG serodynamics between the two assays suggest convalescent and paired testing with IgM are critical in diagnosing acute infection. High assay concordance was observed between ELISA and IFA when used in serosurveillance (97.4% agreement, Cohen' κ 0.74 [95% CI 0.614-0.860]) and vaccination cohorts (91.1% agreement, Cohen's κ 0.806 [95% CI 0.672-0.941]). In conclusion, this study highlights the clinical & epidemiological applications and limitations of these two commercial JEV assays.

Optimal vaccination strategies for a heterogeneous population using multiple objectives: The case of [formula omitted] and [formula omitted]formulations

The choice of the objective functional in optimization problems coming from biomedical and epidemiological applications plays a key role in optimal control outcomes. In this study, we investigate the role of the objective functional on the structure of the optimal control solution for an epidemic model for sexually transmitted infections that includes a core group with higher sexual activity levels than the rest of the population. An optimal control problem is formulated to find a targeted vaccination program able to control the spread of the infection with minimum vaccine deployment. Both L1− and L2−objectives are considered as an attempt to explore the trade-offs between control dynamics and the functional form characterizing optimality. The results show that the optimal vaccination policies for both the L1− and the L2−formulation share one important qualitative property, that is, immunization of the core group should be prioritized by policymakers to achieve a fast reduction of the epidemic. However, quantitative aspects of this result can be significantly affected depending on the choice of the control weights between formulations. Overall, the results suggest that with appropriate weight constants, the optimal control outcomes are reasonably robust with respect to the L1− or L2−formulation. This is particularly true when the monetary cost of the control policy is substantially lower than the cost associated with the disease burden. Under these conditions, even if the L1−formulation is more realistic from a modeling perspective, the L2−formulation can be used as an approximation and yield qualitatively comparable outcomes.

Epidemiological application of the cycle threshold value of RT-PCR for estimating infection period in cases of SARS-CoV-2

Epidemiological control of coronavirus disease 2019 (COVID-19) is needed to estimate the infection period of confirmed cases and identify potential cases. The present study, targeting confirmed cases for which the time of COVID-19 symptom onset was disclosed, aimed to investigate the relationship between intervals (day) from symptom onset to testing the cycle threshold (CT) values of real-time reverse transcription-polymerase chain reaction. Of the COVID-19 confirmed cases, those for which the date of suspected symptom onset in the epidemiological investigation was specifically disclosed were included in this study. Interval was defined as the number of days from symptom onset (as disclosed by the patient) to specimen collection for testing. A locally weighted regression smoothing (LOWESS) curve was applied, with intervals as explanatory variables and CT values (CTR for RdRp gene and CTE for E gene) as outcome variables. After finding its non-linear relationship, a polynomial regression model was applied to estimate the 95% confidence interval values of CTR and CTE by interval. The application of LOWESS in 331 patients identified a U-shaped curve relationship between the CTR and CTE values according to the number of interval days, and both CTR and CTE satisfied the quadratic model for interval days. Active application of these results to epidemiological investigations would minimize the chance of failing to identify individuals who are in contact with COVID-19 confirmed cases, thereby reducing the potential transmission of the virus to local communities.

Quasi-neutral dynamics in a coinfection system with N strains and asymmetries along multiple traits.

Understanding the interplay of different traits in a co-infection system with multiple strains has many applications in ecology and epidemiology. Because of high dimensionality and complex feedback between traits manifested in infection and co-infection, the study of such systems remains a challenge. In the case where strains are similar (quasi-neutrality assumption), we can model trait variation as perturbations in parameters, which simplifies analysis. Here, we apply singular perturbation theory to many strain parameters simultaneously and advance analytically to obtain their explicit collective dynamics. We consider and study such a quasi-neutral model of susceptible-infected-susceptible (SIS) dynamics among N strains, which vary in 5 fitness dimensions: transmissibility, clearance rate of single- and co-infection, transmission probability from mixed coinfection, and co-colonization vulnerability factors encompassing cooperation and competition. This quasi-neutral system is analyzed with a singular perturbation method through an appropriate slow-fast decomposition. The fast dynamics correspond to the embedded neutral system, while the slow dynamics are governed by an N-dimensional replicator equation, describing the time evolution of strain frequencies. The coefficients of this replicator system are pairwise invasion fitnesses between strains, which, in our model, are an explicit weighted sum of pairwise asymmetries along all trait dimensions. Remarkably these weights depend only on the parameters of the neutral system. Such model reduction highlights the centrality of the neutral system for dynamics at the edge of neutrality and exposes critical features for the maintenance of diversity.

Accurate and fast graph-based pangenome annotation and clustering with ggCaller.

Bacterial genomes differ in both gene content and sequence mutations, which underlie extensive phenotypic diversity, including variation in susceptibility to antimicrobials or vaccine-induced immunity. To identify and quantify important variants, all genes within a population must be predicted, functionally annotated, and clustered, representing the "pangenome." Despite the volume of genome data available, gene prediction and annotation are currently conducted in isolation on individual genomes, which is computationally inefficient and frequently inconsistent across genomes. Here, we introduce the open-source software graph-gene-caller (ggCaller). ggCaller combines gene prediction, functional annotation, and clustering into a single workflow using population-wide de Bruijn graphs, removing redundancy in gene annotation and resulting in more accurate gene predictions and orthologue clustering. We applied ggCaller to simulated and real-world bacterial data sets containing hundreds or thousands of genomes, comparing it to current state-of-the-art tools. ggCaller has considerable speed-ups with equivalent or greater accuracy, particularly with data sets containing complex sources of error, such as assembly contamination or fragmentation. ggCaller is also an important extension to bacterial genome-wide association studies, enabling querying of annotated graphs for functional analyses. We highlight this application by functionally annotating DNA sequences with significant associations to tetracycline and macrolide resistance in Streptococcus pneumoniae, identifying key resistance determinants that were missed when using only a single reference genome. ggCaller is a novel bacterial genome analysis tool with applications in bacterial evolution and epidemiology.

Tracking the effects of the COVID-19 pandemic on viral gastroenteritis through wastewater-based retrospective analyses

The COVID-19 pandemic possibly disrupted the circulation and seasonality of gastroenteritis viruses (e.g., Norovirus (NoV), Sapovirus (SaV), group A rotavirus (ARoV), and Aichivirus (AiV)). Despite the growing application of wastewater-based epidemiology (WBE), there remains a lack of sufficient investigations into the actual impact of the COVID-19 pandemic on the prevalence of gastroenteritis viruses. In this study, we measured NoV GI and GII, SaV, ARoV, and AiV RNA concentrations in 296 influent wastewater samples collected from three wastewater treatment plants (WWTPs) in Sapporo, Japan between October 28, 2018 and January 12, 2023 using the highly sensitive EPISENS™ method. The detection ratios of SaV and ARoV after May 2020 (SaV: 49.8 % (134/269), ARoV: 57.4 % (151/263)) were significantly lower than those before April 2020 (SaV: 93.9 % (31/33), ARoV: 97.0 % (32/33); SaV: p < 3.5×10−7, ARoV: p < 1.5×10−6). Furthermore, despite comparable detection ratios before (88.5 %, 23/26) and during (66.7 %, 80/120) the COVID-19 pandemic (p = 0.032), the concentrations of NoV GII revealed a significant decrease after the onset of the pandemic (p < 1.5×10−7, Cliff's delta = 0.72). NoV GI RNA were sporadically detected (24.7 %, 8/33) before April 2020 and after May 2020 (6.5 %, 17/263), whereas AiV was consistently (100 %, 33/33) detected from wastewater throughout the study period (95.8 %, 252/263). The WBE results demonstrated the significant influence of COVID-19 countermeasures on the circulation of gastroenteritis viruses, with variations observed in the magnitude of their impact across different types of viruses. These epidemiological findings highlight that the hygiene practices implemented to prevent COVID-19 infections may also be effective for controlling the prevalence of gastroenteritis viruses, providing invaluable insights for public health units and the development of effective disease management guidelines.

Aptamer-based assay for rapid detection, surveillance, and screening of pathogenic Leptospira in water samples

Leptospirosis is a potentially fatal waterborne infection caused by Leptospira interrogans, impacting both humans and animals in tropical regions. However, current diagnostic methods for detecting pathogenic Leptospira have sensitivity, cost, and time limitations. Therefore, there is a critical need for a rapid, sensitive, and cost-effective detection method. This study presents the development of an aptamer-based assay for pathogenic Leptospira detection. Aptamers targeting Leptospira were generated using the SELEX method and screened for binding affinity with major Leptospiral outer membrane proteins through in silico analysis. The aptamer with the highest binding affinity was selected for further evaluation. To enable visual detection, the aptamer was conjugated to gold nanoparticles (AuNPs), resulting in a colorimetric response in the presence of L. interrogans. The aptamer-AuNP-based colorimetric assay exhibited a detection limit of 57 CFU/mL and demonstrated high specificity and reproducibility in detecting pathogenic Leptospira in water samples. This aptamer-based assay represents a significant advancement in leptospirosis diagnostics, offering a rapid, sensitive, and cost-effective approach for detecting pathogenic Leptospira. Its potential for epidemiological applications, such as outbreak source identification and improved prevention, diagnosis, and treatment, particularly in resource-limited settings, highlights its importance in addressing the challenges associated with leptospirosis.

Wastewater genomic sequencing for SARS-CoV-2 variants surveillance in wastewater-based epidemiology applications

Wastewater-based epidemiology (WBE) has been widely used as a complementary approach to SARS-CoV-2 clinical surveillance. Wastewater genomic sequencing could provide valuable information on the genomic diversity of SARS-CoV-2 in the surveyed population. However, reliable detection and quantification of variants or mutations remain challenging. In this study, we used mock wastewater samples created by spiking SARS-CoV-2 variant standard RNA into wastewater RNA to evaluate the impacts of sequencing throughput on various aspects such as genome coverage, mutation detection, and SARS-CoV-2 variant deconvolution. We found that wastewater datasets with sequencing throughput greater than 0.5 Gb yielded reliable results in genomic analysis. In addition, using in silico mock datasets, we evaluated the performance of the adopted pipeline for variant deconvolution. By sequencing 86 wastewater samples covering more than 6 million people over 7 months, we presented two use cases of wastewater genomic sequencing for surveying COVID-19 in Hong Kong in WBE applications, including the replacement of Delta variants by Omicron variants, and the prevalence and development trends of three Omicron sublineages. Importantly, the wastewater genomic sequencing data were able to reveal the variant trends 16 days before the clinical data did. By investigating mutations of the spike (S) gene of the SARS-CoV-2 virus, we also showed the potential of wastewater genomic sequencing in identifying novel mutations and unique alleles. Overall, our study demonstrated the crucial role of wastewater genomic surveillance in providing valuable insights into the emergence and monitoring of new SARS-CoV-2 variants and laid a solid foundation for the development of genomic analysis methodologies for WBE of other novel emerging viruses in the future.

A kinetic framework under the action of an external force field: Analysis and application in epidemiology

The tools of kinetic theory allow to describe the dynamics and evolution of a system composed of stochastically interacting particles. The interaction is modeled by means of two classes of parameters, i.e. interaction rates and transition probabilities. Therefore, a system of nonlinear ordinary differential equations is derived. Nevertheless, in general, this structure does not consider the action of an external environment. This paper aims at providing a new kinetic model where an external action occurs. Specifically, this action over the system is modeled by introducing an external force field. Then, a new kinetic model is derived, and some analytical results towards the solution of the related Cauchy problem are provided, in the conservative case: existence, uniqueness, positivity and boundedness. Finally, an application in the contest of mathematical epidemiology is given; the new kinetic framework is characterized for three classical compartmental models: SIR, SEIIR and SEIIRS. Stability results and numerical simulations, in agreement with classical theory, confirm the adherence to reality of this new model.

Challenges and opportunities of molecular epidemiology: using omics to address complex One Health issues in tropical settings

The molecular biology tools available since the early 1970s have been crucial to the development of molecular epidemiology as an important branch of public health, and are used for the identification of host genetic and environmental factors associated with both communicable (CDs) and non-communicable diseases (NCDs) across human and animal populations. Molecular epidemiology has significantly contributed to the understanding of etiological agents, disease distribution, and how to track outbreaks, as well as to prevention and control measures against tropical infectious diseases. However, there have been significant limitations compromising the successful application of molecular epidemiology in low-to-middle income countries (LMICs) to address complex issues at the animal–human–environment interface. Recent advances in our capacity to generate information by means of high-throughput DNA genomic sequencing, transcriptomics, and metabolomics have allowed these tools to become accessible at ever-lower costs. Furthermore, recently emerged omics fields such as lipidomics are improving our insights into molecular epidemiology by measuring lipid phenotypes that gauge environmental and genetic factors in large epidemiological studies. In parallel, the development of bioinformatic tools has revolutionized the utility of omics, providing novel perspectives to better characterize pools of biological molecules and translate them into the structure, function, and dynamics of organisms. Unfortunately, the use of such powerful tools has not been optimal for a One Health approach to both CDs and NCDs, particularly in low-resource tropical settings. The aim of this review is to present the fundamentals of omics tools and their potential use in molecular epidemiology, and to critically discuss the impact of omics on the evolving One Health dimension applied to tropical diseases. We use Ethiopia and Brazil as model systems to illustrate existing gaps and opportunities, while also addressing global applications. Moreover, we also discuss perspectives on exploring omics based molecular epidemiology in the context of One Health as a crucial approach to preventing and mitigating the burden of CDs and NCDs at the interface of human health, animal health, and the environment. This review shows that building capacity in the tropical regions is crucial to establishing equitable global health.

A Two-Step Single Plex PCR Method for Evaluating Key Colonic Microbiota Markers in Young Mexicans with Autism Spectrum Disorders: Protocol and Pilot Epidemiological Application.

Many neurological disorders have a distinctive colonic microbiome (CM) signature. Particularly, children with autism spectrum disorders (ASD) exhibit a very dissimilar CM when compared to neurotypical (NT) ones, mostly at the species level. Thus far, knowledge on this matter comes from high-throughput (yet very expensive and time-consuming) analytical platforms, such as massive high-throughput sequencing of bacterial 16S rRNA. Here, pure (260/280 nm, ~1.85) stool DNA samples (200 ng.µL-1) from 48 participants [39 ASD, 9 NT; 3-13 y] were used to amplify four candidate differential CM markers [Bacteroides fragilis (BF), Faecalibacterium prausnitzii (FP), Desulfovibrio vulgaris (DV), Akkermansia muciniphila (AM)], using micro-organism-specific oligonucleotide primers [265 bp (BF), 198 bp (FP), 196 bp (DV), 327 bp (AM)] and a standardized two-step [low (step 1: °Tm-5 °C) to high (stage 2: °Tm-0 °C) astringent annealing] PCR protocol (2S-PCR). The method was sensitive enough to differentiate all CM biomarkers in the studied stool donors [↑ abundance: NT (BF, FP, AM), ASD (DV)], and phylogenetic analysis confirmed the primers' specificity.