Early Detection Of Outbreaks Research Articles

Advancing Pandemic Preparedness in Healthcare 5.0: A Survey of Federated Learning Applications

The intersection of Federated Learning (FL) and Healthcare 5.0 promises a transformative shift towards a more resilient future, particularly concerning pandemic preparedness. Within this context, Healthcare 5.0 signifies a holistic approach to healthcare delivery, where interconnected technologies enable data-driven decision-making, patient-centric care, and enhanced efficiency. This paper provides an in-depth exploration of FL’s role within the framework of Healthcare 5.0 and its implications for the pandemic response. Specifically, FL offers the potential to revolutionize pandemic preparedness within Healthcare 5.0 in several vital ways: it enables collaborative learning from distributed data sources without compromising individual data privacy, facilitates decentralized decision-making by empowering local healthcare institutions to contribute to a collective knowledge pool, and enhances real-time surveillance, enabling early detection of outbreaks and informed responses. We start by laying out the concepts of FL and Healthcare 5.0, followed by an analysis of current pandemic preparedness and response mechanisms. We delve into FL’s applications and case studies in healthcare, highlighting its potential benefits, including privacy protection, decentralized decision-making, and implementation challenges. By articulating how FL fits into Healthcare 5.0, we envisage future applications in a technologically integrated health system. By examining current applications and case studies of FL in healthcare, we highlight its potential benefits, including enhanced privacy protection and more effective decision support systems. Our findings demonstrate that FL can significantly improve pandemic response times and accuracy. Moreover, we speculate on the potential scenarios where FL could enhance pandemic preparedness and make healthcare more resilient. Finally, we recommend that policymakers, technologists, and educators address potential challenges and maximize the benefits of FL in Healthcare 5.0. This paper aims to contribute to the discourse on next-generation healthcare technologies, emphasizing FL’s potential to shape a more resilient healthcare future.

Mhealth tools for community-based infectious disease surveillance in Africa: a scoping review

Abstract Background Community-based surveillance may encourage early detection of outbreaks and other health threats. Mobile digital tools for health (mHealth) can potentially support the role of community health workers (CHW) in disease surveillance. However, limited information is available on mhealth use by CHW for infectious disease surveillance in Africa. The purpose of this review was to provide an overview of available mhealth tools used by CHW for community-based infectious disease surveillance in Africa. Methods The scoping review protocol was registered in OSF registries (doi: 10.17605/OSF.IO/W7MTY). We followed a mixed-methods approach in line with the framework of Arksey and O'Malley amended by Levac et al. and the Joanna Briggs Institute. We searched PubMed, Scopus, Web of Science, CINAHL, Google Scholar, Google and websites of relevant organizations for peer-reviewed and grey literature. Search terms included a range of synonyms for the three concepts of (1) mhealth, (2) CHW and (3) Africa. Reports were included if they reported on mHealth tools used by CHW for infectious disease surveillance in Africa. Results We identified 2,496 titles during our search but only 7 met our inclusion criteria. Studies were conducted in Tanzania, Zambia, Nigeria, Malawi, Kenya and Ghana, and focused on one or more diseases or symptoms: Acute Flaccid Paralysis, Lymphatic Filariasis (LF), COVID-19 and cough with fast respiratory rate. All interventions relied on CHW sending text messages to supervisors via the mhealth tool. Users’ perspective of mhealth tools included: improved surveillance, better linkage with the community and greater efficiency. Conclusions We found a small number of mhealth tools used by CHW for infectious disease surveillance in Africa. Tools focused on epidemic-prone and neglected tropical diseases, and linked CHW with their supervisors. Results indicate that mhealth holds potential for strengthening community-based surveillance in Africa.

1.C. Workshop: COVID-19 and strategies to improve pandemic preparedness in Africa: Results of 4 systematic reviews

Abstract Background The COVID-19 pandemic has had a massive impact on mortality and health service utilisation globally. However, its impact in low- and lower middle-income countries (LLMIC), in particular in sub-Saharan Africa, remains poorly understood. The German West-African Centre (G-WAC) for Global Health and Pandemic Prevention aims to contribute to a better understanding of the pandemic and to strengthen pandemic preparedness in the region. Objectives The workshop aims to provide an overview of available research on four aspects that are key for understanding the COVID-19 pandemic in sub-Saharan Africa and to support improved pandemic preparedness in the region: (1) the impact of the pandemic on excess mortality, (2) the changes in health services utilisation, (3) the different strategies that support community engagement, and (4) the role of mHealth tools for community-based disease surveillance. Together with workshop participants, we intend to discuss challenges and opportunities for research on the COVID-19 pandemic in sub-Saharan Africa and the potential for research to contribute to better pandemic preparedness. We also wish to receive input for the further research agenda of G-WAC. Added value The workshop will bring together the findings of four systematic reviews that provide complementary evidence contributing to a better understanding of the COVID-19 pandemic in sub-Saharan Africa. It will provide a platform for researchers engaged in related work in Africa or Europe to share their experiences and to contribute to shaping the future research agenda of G-WAC. Coherence of workshop Following a brief introduction explaining the underlying research concept that links the four systematic reviews, results will be presented for: (1) a systematic review and meta-analysis of excess mortality, reporting pooled excess mortality estimates; (2) a scoping review illustrating the range of approaches and settings where research has been conducted on health service utilisation during the pandemic; (3) a scoping review of the various strategies that have been used to foster community engagement to strengthen infection control activities; and (4) a scoping review of different mobile phone-based tools that support Community Health Workers engaged in infectious disease surveillance. Format This is a regular workshop consisting of four brief (7 mins) presentations. Each presentation will be followed by 5 mins discussions, giving room to the audience for questions and comments that relate findings to previous research experiences. Another 10 mins are reserved at the end to discuss further research needs. Several polling rounds will engage the audience and collect input on (a) research experiences in sub-Saharan Africa, (b) reflections about the relevance of presented results, and (c) suggestions for further research. Key messages • The workshop will summarise existing evidence on the impact of the COVID-19 pandemic on mortality and health service utilisation in sub-Saharan Africa. • Community participation may support improved pandemic preparedness and response by contributing to early detection of outbreaks and the successful implementation of infection control activities.

Data-driven adaptive testing resource allocation strategies for real-time monitoring of infectious diseases

Since 2002, with the SARS outbreak, infectious diseases, including the ongoing COVID-19 pandemic, have continued to be a major global public health threat. It is critical to develop effective data science methods to quickly detect disease outbreaks and contain their rapid globalized spread. However, in practice, limited testing availability, and thus insufficient testing data poses significant challenges in effective analysis and real-time monitoring of infectious diseases, especially during early stages of a novel disease outbreak. To tackle these challenges, this article proposes adaptive testing resource allocation strategies integrated with a physics-informed model to dynamically allocate limited testing resources across different communities. The physics-informed model accounts for transmission dynamics and health disparities, enabling effective health risk assessment despite limited data. By integrating nonstationary Multi-Armed Bandit techniques that strike superior balance between exploring the communities with high uncertain risks and exploiting those with high risk levels, the proposed methodology facilitates test allocation to collect high-quality testing data for early outbreak detection. Theoretical analysis is carried out to evaluate the performance of the proposed allocation strategies, ensuring either sublinear or linear dynamic pseudo-regret under regularity assumptions. A comprehensive simulation study is conducted under three transmission scenarios to thoroughly evaluate the proposed methodology.

Early detection of a possible multidrug-resistant Acinetobacter baumannii outbreak in the local hospital setting by using random amplified polymorphism DNA-polymerase chain reaction (RAPD-PCR), oxacillinase gene profiles, and antibiograms.

Detecting the source of a potential outbreak of multidrug resistant (MDR) Acinetobacter baumannii is necessary to be investigated. This study aimed to detect the possibility of A. baumannii outbreak in a hospital setting using a combination of random amplified polymorphism DNA-polymerase chain reaction (RAPD-PCR), antibiograms, and the presence of oxacillinase genes. The antibiogram of 31 clinical isolates and six environmental isolates of A. baumannii were determined by Vitek® 2 Compact. Oxacillinase genes (OXA-23, -24, -51, and -58) were detected by PCR, and RAPD-PCR was conducted using DAF-4 and ERIC-2 primers. The Similarity Index and dendrogram were generated using GelJ v2.3 software. The antibiograms showed that all MDR A. baumannii isolates has very limited susceptibility to cephalosporins, but mostly susceptible to tigecycline. All isolates were positive for bla OXA-51-like gene, thirty-two of 37 total isolates (86.5%) were positive for bla OXA-23-like gene, and none were positive for bla OXA-24-like and bla OXA-58-like genes. RAPD-PCR showed that the DAF-4 primer on average had more band visualization and lower Similarity Index's variation compared to the ERIC-2. The discriminatory power of DAF-4 was 0.906. There was a significant correlation between the DAF-4 dendrogram pattern with the antibiogram (r=0.494, p<0.001) and the presence of bla OXA-23-like gene (r=0.634, p<0.001) from all ICU A isolates. Six out of fourteen ICU A isolates belonged to the same cluster with >95% Similarity Index, while one clinical isolate having an identical dendrogram and antibiogram pattern with an environmental isolate within this cluster. There is a high probability of MDR A. baumannii outbreak within ICU A detected by multiple analysis of RAPD-PCR, antibiogram and the bla OXA-23-like gene profiles. This combinatorial approach is conceivable to mitigate possible outbreak situations of A. baumannii in the local hospital without sophisticated microbiology laboratory.

Comparison of two novel one-tube nested real-time qPCR assays to detect human-infecting Cyclospora spp.

Human-infecting Cyclospora spp. currently include three coccidian parasites that cause the gastrointestinal disease cyclosporiasis in humans. They are often spread through contaminated produce, including leafy greens and berries. The increased availability of sensitive molecular tests for the diagnosis of cyclosporiasis is an important advancement, allowing public health agencies to better understand the scope and source of cyclosporiasis outbreaks. To improve the diagnosis of infected patients, rapidly detect outbreaks, and keep the food supply safe, it is important to continue to develop sensitive, reliable, and inexpensive tests to detect human-infecting Cyclospora spp. In this report, we describe the development and evaluation of two novel one-tube nested qPCR assays for the detection of human-infecting Cyclospora spp. in clinical stool samples, one targeting cytb and the other targeting coxI. Of these, the assay targeting the cytb mitochondrial locus possessed strong performance characteristics compared to a routinely used 18S assay, including a markedly improved (approximately 10-fold lower) relative detection limit of 0.613 oocysts per gram of feces. This is compared to coxI that has a relative detection limit equal to that of the 18S assay. Given the strong performance characteristics of the cytb assay, we propose that it may be useful to diagnostic laboratories wishing to screen clinical fecal specimens suspected of containing human-infecting Cyclospora spp.IMPORTANCEHuman-infecting Cyclospora spp. cause gastrointestinal distress among healthy individuals contributing to morbidity and putting stress on the economics of countries and companies in the form of produce recalls. Accessible and easy-to-use diagnostic tools available to a wide variety of laboratories would aid in the early detection of possible outbreaks of cyclosporiasis. This, in turn, will assist in the timely traceback investigation to the suspected source of an outbreak by informing the smallest possible recall and protecting consumers from contaminated produce. This manuscript describes two novel detection methods with improved performance for the causative agents of cyclosporiasis when compared to the currently used 18S assay.

Community Outbreak of Pseudomonas aeruginosa Infections Associated with Contaminated Piercing Aftercare Solution, Australia, 2021.

In April 2021, the South Eastern Sydney Local Health District Public Health Unit (Sydney, New South Wales, Australia) was notified of 3 patients with Pseudomonas aeruginosa infections secondary to skin piercings performed at the same salon. Active case finding through laboratories, clinician alerts, and monitoring hospital visits for piercing-related infections identified additional cases across New South Wales, and consumers were alerted. We identified 13 confirmed and 40 probable case-patients and linked clinical isolates by genomic sequencing. Ten confirmed case-patients had used the same brand and batch of aftercare solution. We isolated P. aeruginosa from opened and unopened bottles of this solution batch that matched the outbreak strain identified by genomic sequencing. Piercing-related infections returned to baseline levels after this solution batch was recalled. Early outbreak detection and source attribution via genomic sequencing are crucial for controlling outbreaks linked to contaminated products. Manufacturing standards for nonsterile cosmetic products and guidance for piercing aftercare warrant review.

What awaits US in the next bird migration in South America: Prospects for the detection and incidence of HPAIV H5N1 in commercial poultry and wild birds. Proceeding of The First International Avian Influenza Summit, University of Arkansas- October 16-17, 2023".

Highly pathogenic avian influenza virus (HPAIV) H5N1 poses a significant threat to both commercial poultry operations and wild bird populations worldwide. South America, with its rich avian biodiversity and extensive poultry industry, is susceptible to the introduction and spread of this devastating virus. This study explores the imminent challenges and potential outcomes associated with the upcoming bird migration season in South America, focusing on the detection and incidence of HPAIV H5N1. Employing a multidisciplinary approach is necessary to integrate spatial modeling, genomic surveillance, and epidemiological analyses to forecast the dynamics of HPAIV H5N1 in the region. By examining historical migration patterns, host susceptibility, and environmental factors, it will be possible to elucidate potential pathways for virus dissemination among wild bird populations and spillover into commercial poultry farms. Furthermore, this study evaluates the efficacy of current surveillance and monitoring strategies in detecting HPAIV H5N1, identifying potential gaps, and offering recommendations for enhanced surveillance protocols. Leveraging advanced molecular techniques is necessary to analyze viral genomes to track potential mutations associated with increased virulence or altered host range, thereby providing crucial insights for targeted intervention measures. The outcomes of this research will advance our understanding of the ecological and epidemiological factors influencing the spread of HPAIV H5N1 in South America and inform proactive strategies for early detection, containment, and mitigation of future outbreaks. This comprehensive assessment is a pivotal step towards safeguarding poultry industries and avian biodiversity in the face of emerging infectious diseases.

Role of Wildlife in The Transmission of Zoonotic Diseases in Ghana

Purpose: The aim of the study was to investigate the role of wildlife in the transmission of zoonotic diseases.&#x0D; Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.&#x0D; Findings: This transmission often occurs at the interface between humans, domestic animals, and wildlife. Factors like habitat destruction, increased human-wildlife interactions, and the wildlife trade have heightened the risk of zoonotic disease emergence. Understanding these dynamics is crucial for early detection and prevention of zoonotic outbreaks, as wildlife plays a pivotal role in the maintenance and amplification of these pathogens, posing ongoing challenges to global public health.&#x0D; Unique Contribution to Theory, Practice and Policy: One Health Theory, Ecological Reservoir Theory and Behavioral Ecology Theory Change may be used to anchor future studies on the role of wildlife in the transmission of zoonotic diseases. Strategies enhanced surveillance and monitoring of wildlife populations, especially those in close proximity to human settlements, are crucial. Governments and international organizations should prioritize habitat conservation and the protection of biodiversity.

Reporting delays of chikungunya cases during the 2017 outbreak in Lazio region, Italy.

Emerging arboviral diseases in Europe pose a challenge due to difficulties in detecting and diagnosing cases during the initial circulation of the pathogen. Early outbreak detection enables public health authorities to take effective actions to reduce disease transmission. Quantification of the reporting delays of cases is vital to plan and assess surveillance and control strategies. Here, we provide estimates of reporting delays during an emerging arboviral outbreak and indications on how delays may have impacted onward transmission. Using descriptive statistics and Kaplan-Meyer curves we analyzed case reporting delays (the period between the date of symptom onset and the date of notification to the public health authorities) during the 2017 Italian chikungunya outbreak. We further investigated the effect of outbreak detection on reporting delays by means of a Cox proportional hazard model. We estimated that the overall median reporting delay was 15.5 days, but this was reduced to 8 days after the notification of the first case. Cases with symptom onset after outbreak detection had about a 3.5 times higher reporting rate, however only 3.6% were notified within 24h from symptom onset. Remarkably, we found that 45.9% of identified cases developed symptoms before the detection of the outbreak. These results suggest that efforts should be undertaken to improve the early detection and identification of arboviral cases, as well as the management of vector species to mitigate the impact of long reporting delays.

A Countrywide Seroepidemiological Survey of Rift Valley Fever in Livestock, Uganda, 2017.

In 2016, an outbreak of Rift Valley fever was reported in the Kabale District in Uganda for the first time in 48 years. Three human cases were confirmed by polymerase chain reaction, and subsequent serological investigations revealed an overall IgG seropositivity of 13% in humans and 13% in animals. In response to this reemergence, we designed a countrywide survey to determine the seropositivity of anti-Rift Valley fever virus (RVFV) IgG antibodies in livestock. Samples were collected from 27 districts and tested for RVFV anti-IgG antibodies. A total of 3,181 livestock samples were tested, of which 54.4% were cattle (1,732 of 3,181), 34.3% were goats (1,091 of 3,181), and 11.3% were sheep (358 of 3,181). Overall RVFV seropositivity was 6.9% (221 of 3,181). Seroprevalence was greater in cattle (10.7%) compared with goats (2.6%) and sheep (2.0%), among females (7.5%) compared with males (5.2%), and among adults (7.6%) compared with juveniles (4.9%) and nurslings (6.4%). Exotic breeds and animals with a history of abortion or stillbirth also had greater odds of RVFV seropositivity. Animals grazed under tethering and paddocking had greater RVFV seropositivity compared with animals that grazed communally, and livestock in the western and eastern regions had the greatest seroprevalence. In a multivariate regression model, animal species (odds ratio [OR], 6.4; 95% CI, 3.5-11.4) and age (OR, 2.3; 95% CI, 1.4-3.6) were associated significantly with RVFV seropositivity. This study could be important in developing risk-based surveillance for early outbreak detection to limit the spread of RVFV in both human and animal populations.

A strategy for entomo-virological surveillance of yellow fever, dengue, zika, and chikungunya viruses in field-collected mosquitoes

Arboviruses transmitted by Culicidae insects are significant threats to human health, presenting dynamic transmission cycles and involving different vectors and hosts. The surveillance and characterization of the vectors involved in these cycles are crucial for understanding and preventing potential outbreaks. Therefore, we propose a strategy that we used for entomological surveillance of urban, rural, and sylvatic mosquitoes and to characterize natural infection by four major arboviruses.•Immature and adult mosquitoes were collected intra, peri and extradomicilie of urban and rural households, using different collection methodologies.•Mosquitoes were pooled or separated in head-thorax and abdomen, according to the species.•A multiplex nested RT-PCR (Reverse transcription polymerase chain reaction) method was used for the simultaneous detection of dengue virus (DENV), zika virus (ZIKV), chikungunya virus (CHIKV), and yellow fever virus (YFV).Overall, this strategy proved helpful for vectors surveillance at different ecosystems, as well as for implementing a low-cost molecular surveillance system that allows the early detection of potential outbreaks, and identify other potential vectors involved in viral transmission.

The Rise and Fall of Omicron BA.1 Variant as Seen in Wastewater Supports Epidemiological Model Predictions.

The COVID-19 pandemic caused by the SARS-CoV-2 virus has inflicted significant mortality and morbidity worldwide. Continuous virus mutations have led to the emergence of new variants. The Omicron BA.1 sub-lineage prevailed as the dominant variant globally at the beginning of 2022 but was subsequently replaced by BA.2 in numerous countries. Wastewater-based epidemiology (WBE) offers an efficient tool for capturing viral shedding from infected individuals, enabling early detection of potential pandemic outbreaks without relying solely on community cooperation and clinical testing resources. This study integrated RT-qPCR assays for detecting general SARS-CoV-2 and its variants levels in wastewater into a modified triple susceptible-infected-recovered-susceptible (SIRS) model. The emergence of the Omicron BA.1 variant was observed, replacing the presence of its predecessor, the Delta variant. Comparative analysis between the wastewater data and the modified SIRS model effectively described the BA.1 and subsequent BA.2 waves, with the decline of the Delta variant aligning with its diminished presence below the detection threshold in wastewater. This study demonstrates the potential of WBE as a valuable tool for future pandemics. Furthermore, by analyzing the sensitivity of different variants to model parameters, we are able to deduce real-life values of cross-variant immunity probabilities, emphasizing the asymmetry in their strength.

Comparison of statistical methods for the early detection of disease outbreaks in small population settings

ObjectivesThis study examines the performance of 6 aberration detection algorithms for the early detection of disease outbreaks in small population settings using syndrome-based early warning surveillance data collected by the Pacific Syndromic Surveillance System (PSSS). Although previous studies have proposed statistical methods for detecting aberrations in larger datasets, there is limited knowledge about how these perform in the presence of small numbers of background cases. MethodsTo address this gap a simulation model was developed to test and compare the performance of the 6 algorithms in detecting outbreaks of different magnitudes, durations, and case distributions. ResultsThe study found that while the Early Aberration Reporting System–C1 algorithm developed by Hutwagner et al. outperformed others, no single approach provided reliable monitoring across all outbreak types. Furthermore, aberration detection approaches could only detect very large and acute outbreaks with any reliability. ConclusionThe findings of this study suggest that algorithm-based approaches to outbreak signal detection perform poorly when applied to settings with small numbers of background cases and should not be relied upon in these contexts. This highlights the need for alternative approaches for accurate and timely outbreak detection in small population settings, particularly those that are resource-constrained.

Designing a Wastewater-Based Epidemiology Study at the U.S. Air Force Academy: Using Severe Acute Respiratory Syndrome Coronavirus 2 to Test a Sentinel System forEarly Disease Outbreak Detection.

The presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) in wastewater has been proposed as a sentinel surveillance epidemiological tool for detection of infectious disease at a community level and as a complementary approach to syndromic surveillance of infectious disease outbreaks. We have designed a study to test the presence and quantity of SARS-CoV2, the virus responsible for COVID19, in the wastewater treatment facility (WWTF) of the U.S. Air Force Academy. Wastewater samples were tested in the laboratory to quantify the amount of SARS-CoV2 RNA using reverse transcription-quantitative polymerase chain reaction. Raw SARS-CoV2 viral titer in wastewater was normalized to the viral titer of a fecal marker, pepper mild mottle virus, to correct for dilutions. Temporal and spatial trends of COVID19 were analyzed. Furthermore, we compared wastewater analysis results against clinical data to assist public health decisions. Preliminary data suggest that wastewater analysis can provide temporal and spatial trends of COVID19. The geographically discrete WWTF at the U.S. Air Force suggests that wastewater testing is a useful approach to developing a comprehensive sentinel surveillance system. Together with ongoing syndromic surveillance data, this proof-of-concept study seeks to determine whether early detection of SARS-CoV2 in a closed system WWTF correlates to changes in community and clinically reported COVID19. The well-documented population served by the geographically discrete WWTF at the U.S. Air Force Academy may serve to better elucidate the adjunctive role of wastewater testing in a comprehensive surveillance system. These results may be of particular interest to the DoD and local commanders given the WWTFs under their immediate control and the information that these studies may provide in support of operational readiness through early detection of disease outbreaks.

Pivotal role of CHWs in prevention, early detection and control of outbreaks: neglected key primary players; a call to action

Pivotal role of CHWs in prevention, early detection and control of outbreaks: neglected key primary players; a call to action

Early detection and environmental drivers of sewage fungus outbreaks in rivers

Abstract Sewage effluent is a major ongoing threat to water quality and biodiversity in freshwater environments. It can cause outbreaks of sewage fungus (fungus‐like bacteria which form macroscopic masses) but, until now, these were only qualitatively recorded from visual inspection, ignoring microscopic forms. Here, we used an innovative method that combines machine learning, microscopy and flow cytometry, to rapidly and efficiently quantify the presence and abundance of sewage fungus in rivers. Our study involved 11 rivers with (n = 6) and without (n = 5) sewage input in England over four sampling occasions. We were able to detect and enumerate the filaments before masses became visible to the naked eye and, as expected, we found a higher number of filaments downstream of sites where treated sewage was offloaded into the river. Therefore, our detection method could be used as a ‘canary in the coal mine’ for future outbreaks allowing early intervention. Combining our quantitative data on filaments with data on the physical and chemical parameters of the rivers, we found that high conductivity, sulphate, nitrates and TDS were associated with the presence and proliferation of sewage fungus. This information can be extremely useful for regulatory bodies and water companies to develop mitigating strategies and action to prevent future outbreaks.

Could it be monkeypox? Use of an AI-based epidemic early warning system to monitor rash and fever illness

ObjectivesThe EPIWATCH artificial intelligence (AI) system scans open-source data using automated technology and can be used to detect early warnings of infectious disease outbreaks. In May 2022, a multicountry outbreak of Mpox in non-endemic countries was confirmed by the World Health Organization. This study aimed to identify signals of fever and rash-like illness using EPIWATCH and, if detected, determine if they represented potential Mpox outbreaks. Study designThe EPIWATCH AI system was used to detect global signals for syndromes of rash and fever that may have represented a missed diagnosis of Mpox from 1 month prior to the initial case confirmation in the United Kingdom (7 May 2022) to 2 months following. MethodsArticles were extracted from EPIWATCH and underwent review. A descriptive epidemiologic analysis was conducted to identify reports pertaining to each rash-like illness, locations of each outbreak and report publication dates for the entries from 2022, with 2021 as a control surveillance period. ResultsReports of rash-like illnesses in 2022 between 1 April and 11 July (n = 656 reports) were higher than in the same period in 2021 (n = 75 reports). The data showed an increase in reports from July 2021 to July 2022, and the Mann–Kendall trend test showed a significant upward trend (P = 0.015). The most frequently reported illness was hand-foot-and-mouth disease, and the country with the most reports was India. ConclusionsVast open-source data can be parsed using AI in systems such as EPIWATCH to assist in the early detection of disease outbreaks and monitor global trends.

Whole-Genome Sequence-Based Characterization of Pre-XDR M. tuberculosis Clinical Isolates Collected in Kazakhstan.

Kazakhstan has a high burden of multidrug-resistant tuberculosis in the Central Asian region. This study aimed to perform genomic characterization of Mycobacterium tuberculosis strains obtained from Kazakhstani patients with pre-extensively drug-resistant tuberculosis diagnosed in Kazakhstan. Whole-genome sequencing was performed on 10 pre-extensively drug-resistant M. tuberculosis strains from different regions of Kazakhstan. All strains had high-confidence resistance mutations according to the resistance grading system previously established by the World Health Organization. The genome analysis was performed using TB-Profiler, Mykrobe, CASTB, and ResFinder. Valuable information for understanding the genetic diversity of tuberculosis in Kazakhstan can also be obtained from whole-genome sequencing. The results from the Phenotypic Drug Susceptibility Testing (DST) of bacterial strains were found to be consistent with the drug resistance information obtained from genomic data that characterized all isolates as pre-XDR. This information can help in developing targeted prevention and control strategies based on the local epidemiology of tuberculosis. Furthermore, the data obtained from whole-genome sequencing can help in tracing the transmission pathways of tuberculosis and facilitating early detection of outbreaks. The results from whole-genome sequencing of tuberculosis clinical samples in Kazakhstan provide important insights into the drug resistance patterns and genetic diversity of tuberculosis in the country. These results can contribute to the improvement of tuberculosis control and management programs in Kazakhstan.

Triplex qPCR assay for Campylobacter jejuni and Campylobacter coli monitoring in wastewater

Campylobacter spp. is one of the most frequent pathogens of bacterial gastroenteritis recorded worldwide. Campylobacter jejuni (C. jejuni) and Campylobacter coli (C. coli) are the two major disease-associated species, accounting for >95 % of infections, and thus have been selected for disease surveillance. Monitoring temporal variations in pathogen concentration and diversity excreted from community wastewater allows the early detection of outbreaks. Multiplex real-time/quantitative PCR (qPCR) enables multi-target quantification of pathogens in various types of samples including wastewater. Also, an internal amplification control (IAC) is required for each sample when adopting PCR-based methods for pathogen detection and quantification in wastewater to exclude the inhibition of the wastewater matrix. To achieve reliable quantification of C. jejuni and C. coli towards wastewater samples, this study developed and optimized a triplex qPCR assay by combining three qPCR primer-probe sets targeting Campylobacter jejuni subsp. jejuni, Campylobacter coli, and Campylobacter sputorum biovar sputorum (C. sputorum), respectively. This triplex qPCR assay not only can directly and simultaneously detect the concentration of C. jejuni and C. coli in wastewater but also can achieve the PCR inhibition control using C. sputorum primer-probe set. This is the first developed triplex qPCR assay with IAC for C. jejuni and C. coli, to be used in the wastewater-based epidemiology (WBE) applications. The optimized triplex qPCR assay enables the detection limit of the assay (ALOD100%) and wastewater (PLOD80%) as 10 gene copy/μL and 2 log10 cells/mL (2 gene copies/μL of extracted DNA), respectively. The application of this triplex qPCR to 52 real raw wastewater samples from 13 wastewater treatment plants demonstrated its potential as a high-throughput and economically viable tool for the long-term monitoring of C. jejuni and C. coli prevalence in communities and the surrounding environments. This study provided an accessible methodology and a solid foundation for WBE-based monitoring of Campylobacter spp. relevant diseases and paved the road for future WBE back-estimation of C. jejuni and C. coli prevalence.