Foodborne outbreaks affecting millions of people worldwide are a significant and growing global health threat, exacerbated by the emergence of new and increasingly virulent foodborne pathogens. Traditional methods of detecting these outbreaks, including culture-based techniques, serotyping and molecular methods such as real-time PCR, are still widely used. However, these approaches often lack the precision and resolution required to definitively trace the source of an outbreak and distinguish between closely related strains of pathogens. Whole genome sequencing (WGS) has emerged as a revolutionary tool in outbreak investigations, providing high-resolution, comprehensive genetic data that allows accurate species identification and strain differentiation. WGS also facilitates the detection of virulence and antimicrobial resistance (AMR) genes, providing critical insight into the potential pathogenicity, treatment/control options and risks of spreading foodborne pathogens. This capability enhances outbreak surveillance, source tracing and risk assessment, making WGS an increasingly integrated component of public health surveillance systems. Despite its advantages, the widespread implementation of WGS faces several pressing challenges, including high sequencing costs, the need for specialized bioinformatics expertise, limited computational infrastructure in resource-constrained settings, and the standardization of data-sharing frameworks across regulatory and public health agencies. Addressing these barriers is crucial to maximizing the impact of WGS on foodborne disease surveillance. Even so, WGS is emerging as a vital tool in food safety and public health, and its potential to become the gold standard in outbreak detection has been recognized by public health authorities in the USA, the European Union, Australia and China, for example. This review highlights the role of WGS in foodborne outbreak investigations, its implementation challenges, and its impact on public health surveillance.

The fast and reliable characterization of bacterial and fungal pathogens plays an important role in infectious disease control and tracking of outbreak agents. DNA based methods are the gold standard for epidemiological investigations, but they are still comparatively expensive and time-consuming. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a fast, reliable and cost-effective technique now routinely used to identify clinically relevant human pathogens. It has been used for subspecies differentiation and typing, but its use for epidemiological tasks, e. g. for outbreak investigations, is often hampered by the complexity of data analysis. We have analysed publicly available MALDI-TOF mass spectra from a large outbreak of Shiga-Toxigenic Escherichia coli in northern Germany using a general purpose software tool for the analysis of complex biological data. The software was challenged with depauperate spectra and reduced learning group sizes to mimic poor spectrum quality and scarcity of reference spectra at the onset of an outbreak. With high quality formic acid extraction spectra, the software’s built in classifier accurately identified outbreak related strains using as few as 10 reference spectra (99.8% sensitivity, 98.0% specificity). Selective variation of processing parameters showed impaired marker peak detection and reduced classification accuracy in samples with high background noise or artificially reduced peak counts. However, the software consistently identified mass signals suitable for a highly reliable marker peak based classification approach (100% sensitivity, 99.5% specificity) even from low quality direct deposition spectra. The study demonstrates that general purpose data analysis tools can effectively be used for the analysis of bacterial mass spectra.

Analysis of ‘COVID-19 Deaths Over Time’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/55529260-9d00-464d-ad56-187b2ca7cd15 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Note: On January 22, 2022, system updates to improve the timeliness and accuracy of San Francisco COVID-19 cases and deaths data were implemented. You might see some fluctuations in historic data as a result of this change.

A. SUMMARY This dataset represents San Francisco COVID-19 related deaths by day. Deaths are included on the date the individual died.

Data is lagged by five days, meaning the most date included is 5 days prior to today. All data update daily as more information becomes available.

B. HOW THE DATASET IS CREATED COVID-19 deaths are suspected to be associated with COVID-19. This means COVID-19 is listed as a cause of death or significant condition on the death certificate.

Deaths may be reported by:

• Healthcare providers
• The California Department of Public Health Vital Records
• Case investigation or outbreak investigation
• The Office of the Chief Medical Examiner

It takes time to process this data. Because of this, data is lagged by 5 days and death totals for previous days may increase or decrease. More recent data is less reliable.

Data are continually updated to maximize completeness of information and reporting on San Francisco COVID-19 deaths.

C. UPDATE PROCESS Updates automatically at 05:00 AM Pacific Time each day. Redundant runs are scheduled at 07:00 AM and 09:00 AM in case of pipeline failure.

Dataset will not update on the business day following any federal holiday.

D. HOW TO USE THIS DATASET This dataset shows new deaths and cumulative deaths by date of death. New deaths are the count of deaths on that specific date. Cumulative deaths are the running total of all San Francisco COVID-19 deaths up to the date listed.

Use the Deaths by Population Characteristics dataset to see deaths by different subgroups including race/ethnicity, age, gender, and homelessness.

--- Original source retains full ownership of the source dataset ---

In order to facilitate foodborne outbreak investigations there is a need to improve the methods for identifying the food products that should be sampled for laboratory analysis. The aim of this study was to examine the applicability of a likelihood ratio approach previously developed on simulated data, to real outbreak data. We used human case and food product distribution data from the Norwegian enterohaemorrhagic Escherichia coli outbreak in 2006. The approach was adjusted to include time, space smoothing and to handle missing or misclassified information. The performance of the adjusted likelihood ratio approach on the data originating from the HUS outbreak and control data indicates that the adjusted approach is promising and indicates that the adjusted approach could be a useful tool to assist and facilitate the investigation of food borne outbreaks in the future if good traceability are available and implemented in the distribution chain. However, the approach needs to be further validated on other outbreak data and also including other food products than meat products in order to make a more general conclusion of the applicability of the developed approach.

This dataset contains the estimated timing of the emergence and spread of the Omicron variant among 121 countries from September 1, 2021, to January 1, 2021, based on the frequency of Omicron genome samples collected. The Omicron variant genome sequences were collected worldwide and submitted to the Global Initiative on Sharing All Influenza Data (GISAID, https://gisaid.org/) by data contributors of GISAID. The metadata of these genome samples was obtained from gisaid.org and used to calculate the frequency of Omicron samples collected in the selected countries. A Bayesian online change point detection algorithm (BOCP) was then used to identify significant increases in this frequency in each country. The detected change points were validated by comparing them with temporal trends in COVID-19 incidence and the earliest introduction of Omicron to each country as estimated by phylogenetic analysis. This dataset can be used to evaluate the accuracy and timeliness of algorithms for early outbreak detection, supporting research and pandemic preparedness.

Wildlife disease surveillance programs and research studies track infection and identify risk factors for wild populations, humans, and agriculture. Often, several types of samples are collected from individuals to provide more complete information about an animal's infection history. Methods that jointly analyze multiple data streams to study disease emergence and drivers of infection via epidemiological process models remain underdeveloped. Joint-analysis methods can more thoroughly analyze all available data, more precisely quantifying epidemic processes, outbreak status, and risks. We contribute a paired data modeling approach that analyzes multiple samples from individuals. We use "characterization maps" to link paired data to epidemiological processes through a hierarchical statistical observation model. Our approach can provide both Bayesian and frequentist estimates of epidemiological parameters and states. Our approach can also incorporate test sensitivity and specificity, and we propose model-fit diagnostics. We motivate our approach through the need to use paired pathogen and antibody detection tests to estimate parameters and infection trajectories for the widely applicable susceptible, infectious, recovered (SIR) model. We contribute general formulas to link characterization maps to arbitrary process models and datasets and an extended SIR model that better accommodates paired data. We find via simulation that paired data can more efficiently estimate SIR parameters than unpaired data, requiring samples from 5-10 times fewer individuals. We use our method to study SARS-CoV-2 in wild White-tailed deer (Odocoileus virginianus) from three counties in the United States. Estimates for average infectious times corroborate captive animal studies. The estimated average cumulative proportion of infected deer across the three counties is 73%, and the basic reproductive number (R0) is 1.88. Wildlife disease surveillance programs and research studies can use our methods to jointly analyze paired data to estimate epidemiological process parameters and track outbreaks. Paired data analyses can improve precision and accuracy when sampling is limited. Our methods use general statistical theory to let applications extend beyond the SIR model we consider, and to more complicated examples of paired data. The methods can also be embedded in larger hierarchical models to provide landscape-scale risk assessment and identify drivers of infection.

*** The County of Santa Clara Public Health Department discontinued updates to the COVID-19 data tables effective June 30, 2025. The COVID-19 data tables will be removed from the Open Data Portal on December 30, 2025. For current information on COVID-19 in Santa Clara County, please visit the Respiratory Virus Dashboard [sccphd.org/respiratoryvirusdata]. For any questions, please contact phinternet@phd.sccgov.org ***

This dataset includes information on worksite outbreaks reported to the Santa Clara County Public Health Department. Data are updated as the County receives information that is more complete or accurate, therefore the number of outbreaks and information about each outbreak may change through this process. Please note that there is a lag in the data due to the time required for outbreaks to be investigated, closed, and the data processed.

This data table was updated for the last time on April 18, 2022

Smallpox, caused by the variola virus, was responsible for millions of deaths worldwide before its eradication was declared by the World Health Assembly in 1980. While modern disease outbreaks are modelled using contemporary data, the potential use of smallpox as a bioterrorist threat remains a concern, continuing to generate debate about the most effective modelling strategies to inform public health preparedness. Understanding how to control an eradicated disease like smallpox relies on the analysis of historical data.

This study focuses on transcribed smallpox outbreak data from Somalia’s last recorded outbreaks (1976–1977), which were not only the final outbreaks in Somalia but also the last naturally occurring smallpox outbreaks in the world. The original handwritten records, obtained from the World Health Organization by a Public Health England (now UK Health Security Agency) study team, were digitized from PDF format into a Microsoft Excel worksheet through manual transcription. To ensure data accuracy and consistency, rigorous validation processes were applied. The resulting line-list dataset comprises 3,255 recorded cases of variola minor smallpox and includes key epidemiological variables: national case series number, age, sex, date of rash onset, date detected, village/locality, district, region, regional outbreak number, and national outbreak number.

The primary goal of this project is to make these historical data globally accessible to epidemiologists and infectious disease modelers, enhancing our understanding of smallpox transmission dynamics in Somalia during the eradication period and informing strategies for potential future outbreaks in naïve populations. This data transcription forms part of a broader initiative, which also includes:

1. A descriptive epidemiological study characterizing smallpox cases and estimating incidence rates during the final outbreaks.
2. A modelling study estimating the basic reproduction number (R) for smallpox using two different disease dynamic models.

By preserving and analyzing these historical data, this project provides valuable insights for infectious disease preparedness and response, bridging past experiences with future public health strategies.

The Ebola virus ELISA kit market, while niche, exhibits significant growth potential driven by the persistent threat of Ebola outbreaks and the increasing need for rapid and accurate diagnostic tools. The market's size in 2025 is estimated at $50 million, reflecting a steady expansion fueled by advancements in ELISA technology, offering improved sensitivity and specificity compared to older diagnostic methods. A Compound Annual Growth Rate (CAGR) of 8% is projected from 2025 to 2033, indicating a considerable increase in market value to approximately $95 million by 2033. Key drivers include the rising prevalence of infectious diseases globally, government initiatives to enhance disease surveillance and control, and growing investments in research and development of novel diagnostic tools. The market is segmented by product type (e.g., IgG, IgM, total antibody), end-user (hospitals, research labs, public health organizations), and geography. The competitive landscape includes a range of established players such as Biomatik, DiaMetra, and Elabscience Biotechnology, each striving to differentiate their offerings based on assay sensitivity, turnaround time, and cost-effectiveness. The market faces constraints including the high cost of assay development and validation, and the need for specialized equipment and trained personnel for proper test execution. However, ongoing technological advancements and the potential for point-of-care diagnostics are expected to mitigate these challenges in the long term. The market's growth trajectory is influenced by several factors, including increased government funding for infectious disease research in regions prone to Ebola outbreaks, improvements in diagnostic accuracy and reduced testing times, and a growing awareness among healthcare professionals about the benefits of rapid and sensitive ELISA tests for effective disease management. Competitive dynamics are also important; manufacturers are continuously working to improve the performance and affordability of their kits, fostering a more competitive and innovative landscape. Though regional data is absent, a reasonable estimation would see a significant market share in Africa, given the historical prevalence of Ebola, followed by North America and Europe, driven by research activities and preparedness measures for potential outbreaks. The forecast period of 2025-2033 provides substantial opportunities for market expansion, indicating significant investments and further innovation in ELISA kit development for faster, more accessible, and more affordable Ebola virus diagnostics.

When combating a respiratory disease outbreak, the effectiveness of protective measures hinges on spontaneous shifts in human behavior driven by risk perception and careful cost-benefit analysis. In this study, a novel concept has been introduced, integrating social distancing and mask-wearing strategies into a unified framework that combines evolutionary game theory with an extended classical epidemic model. To yield deeper insights into human decision-making during COVID-19, we integrate both the prevalent dilemma faced at the epidemic's onset regarding mask-wearing and social distancing practices, along with a comprehensive cost-benefit analysis. We explore the often-overlooked aspect of effective mask adoption among undetected infectious individuals to evaluate the significance of source control. Both undetected and detected infectious individuals can significantly reduce the risk of infection for non-masked individuals by wearing effective facemasks. When the economic burden of mask usage becomes unsustainable in the community, promoting affordable and safe social distancing becomes vital in slowing the epidemic's progress, allowing crucial time for public health preparedness. In contrast, as the indirect expenses associated with safe social distancing escalate, affordable and effective facemask usage could be a feasible option. In our analysis, it was observed that during periods of heightened infection risk, there is a noticeable surge in public interest and dedication to complying with social distancing measures. However, its impact diminishes beyond a certain disease transmission threshold, as this strategy cannot completely eliminate the disease burden in the community. Maximum public compliance with social distancing and mask-wearing strategies can be achieved when they are affordable for the community. While implementing both strategies together could ultimately reduce the epidemic's effective reproduction number (Re) to below one, countries still have the flexibility to prioritize either of them, easing strictness on the other based on their socio-economic conditions.

Dataset from Villa G, Dellafiore F, Caruso R, Arrigoni C, Galli E, Moranda D, Prampolini L, Bascape B, Merlo MG, Giannetta N, Manara DF. Experiences of healthcare providers from a working week during the first wave of the COVID-19 outbreak. Acta Biomed. 2021 Oct 5;92(S6):e2021458. doi: 10.23750/abm.v92iS6.12311. PMID: 34739456.

Abstract

Background and aim of the work: The delivery of care to patients with COVID-19 enhanced many psychological issues among healthcare workers (HCWs), exacerbating the risk of burnout and compromising the efficacy and quality of services provided to patients. In this context, the peculiarities regarding professional roles in delivering care to patients with COVID-19 might reflect daily lived experiences that could impact psychological outcomes in specific professional groups. However, daily lived experiences considering different groups of HCWs have been poorly investigated, especially with a longitudinal qualitative study. Accordingly, our study aims firstly to longitudinally explore perceptions and experiences of HCWs about their daily working life during the initial COVID-19 outbreak, highlighting the specific lived experiences of physicians, nurses, radiology technicians, and healthcare assistants.

Methods: A longitudinal qualitative content analysis was conducted to analyse the comments and quotations made on a daily diary lasting seven days by physicians, nurses, radiology technicians, and healthcare assistants during the first wave of the COVID-19 outbreak. According to Elo and Kyngäs recommendation, the data analysis process was developed in three main phases: preparation, organising, and reporting.

Results: Four main generic categories emerged by data analysis: 'Clinical practice in COVID-19 patients'; 'The importance of relationship'; 'Navigating by sight'; and 'Good always pays off'. Several differences emerged from the sentences of the HCWs, which require further investigation.

The Plague of Justinian was an outbreak of bubonic plague that ravaged the Mediterranean and its surrounding area, between 541 and 767CE. It was likely the first major outbreak of bubonic plague in Europe, and possibly the earliest pandemic to have been recorded reliably and with relative accuracy. Contemporary scholars described the symptoms and effects of the disease in detail, and these matched descriptions of the Black Death and Third Pandemic, leading most historians to believe that this was bubonic plague. It was also assumed that the plague originated in sub-Saharan Africa, before making its way along the Nile to Egypt, and then across the Mediterranean to Constantinople. In 2013, scientists were able to confirm that Justinian's Plague was in fact Yersinia pestis (the bacteria which causes bubonic plague), and recent theories suggest that the plague originated in the Eurasian Steppes, where the Black Death and Third Pandemic are also thought to have originated from, and that it was brought to Europe by the Hunnic Tribes of the sixth century. Plague of Justinian The pandemic itself takes its name from Emperor Justinian I, who ruled the Byzantine Empire (or Eastern Roman Empire) at the time of the outbreak, and who actually contracted the disease (although he survived). Reports suggest that Constantinople was the hardest hit city during the pandemic, and saw upwards of five thousand deaths per day during the most severe months. There are a multitude of sources with differing estimates for the plague's death toll, with most ranging between 25 and 100 million. Until recently, scholars assumed that the plague killed between one third and 40 percent of the world's population, with populations in infected regions declining by up to 25 percent in early years, and up to 60 percent over two centuries. The plague was felt strongest during the initial outbreak in Constantinople, however it remained in Europe for over two centuries, with the last reported cases in 767. Pre-2019 sources vary in their estimates, with some suggesting that up to half of the world's population died in the pandemic, while others state that it was just a quarter of the Mediterranean or European population; however most of them agree that the death toll was in the tens of millions. Historians have also argued about the plague's role in the fall of the Roman Empire, with opinions ranging from "fundamental" to "coincidental", although new evidence is more aligned with the latter theories. Challenging theories As with the recent studies which propose a different origin for the disease, one study conducted by researchers in Princeton and Jerusalem calls into question the accuracy of the death tolls estimated by historians in the 19th and 20th centuries. In 2019, L. Mordechai and M. Eisenberg published a series of papers suggesting that, although the plague devastated Constantinople, it did not have the same impact as the Black Death. The researchers argue that modern historians have taken a maximalist approach to the death tolls of the pandemic, and have applied the same models of distribution to Justinian's Plague as they believe occurred during the Black Death; however there is little evidence to support this. They examine the content and number of contemporary texts, as well archaeological, agricultural and genetic evidence which shows that the plague did spread across Europe, but did not seem to cause the same societal upheaval as the Black Death. It is likely that there will be further investigation into this outbreak in the following years, which may shed more light on the scale of this pandemic.

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock.

This dataset provides district-level, weekly data for various epidemic outbreaks across India, covering the period from 2009 to 2023. It bridges the gap between climate variability and public health outcomes by integrating epidemiological data with key climatic and environmental parameters. The dataset is curated for applications in predictive climate-health modeling, enabling researchers to analyze the interplay between climate factors and disease outbreaks.

Key Features:

1. Epidemiological Data:

   • Disease Types: Includes diseases such as Acute Diarrhoeal Disease, Malaria, and others.
   • Metrics: Number of cases and deaths per district per week.

2. Temporal Details:

   • Weekly breakdown of outbreak periods (e.g., "1st week", "2nd week").
   • Specific dates (day, month, and year).

3. Geographical Information:

   • District and state/union territory names.
   • Latitude and longitude for precise spatial analysis.

4. Climatic and Environmental Parameters:

   • Precipitation (preci): Daily precipitation in mm.
   • Leaf Area Index (LAI): Proxy for vegetation health.
   • Temperature (Temp): Average temperature in Kelvin.

Structure:

• Total Records: 8,985 rows.
• Columns:
  • week_of_outbreak: Week of disease outbreak.
  • state_ut: Name of the state or union territory.
  • district: Name of the district.
  • Disease: Type of disease reported.
  • Cases: Number of reported cases.
  • Deaths: Number of deaths reported (if available).
  • day, mon, year: Day, month, and year of the record.
  • Latitude, Longitude: Geographical coordinates of the district.
  • preci: Daily precipitation in mm.
  • LAI: Leaf Area Index, indicating vegetation density.
  • Temp: Average temperature in Kelvin.

Public alerts for the Chicago Department of Public Health's (CDPH) Health Alert Network (HAN). The HAN provides CDPH with the capacity for quick, efficient, reliable, and secure web-based communication with CDPH staff, providers of medical care, laboratories, first responders and other local public health agencies and partners. The HAN facilitates CDPH’s day-to-day activities, including outbreak detection, investigation, and emergency response. This dataset is published as a convenience to complement the HAN site, itself. While alerts generally are published promptly, for uses involving risk to health and/or life, please contact the HAN team directly to discuss other methods of receiving alerts. The contents of this dataset, the HAN site, and CDPH's Web site are not intended to be substitutes for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition. Professional medical advice should not be ignored because of something you have read on this data portal or any CDPH site.

This survey was conducted by The University of Chicago Divinity School and The Associated Press-NORC Center for Public Affairs Research and with funding from NORC at the University of Chicago.

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The two organizations have established The AP-NORC Center to conduct, analyze, and distribute social science research in the public interest on newsworthy topics, and to use the power of journalism to tell the stories that research reveals. The founding principles of The AP-NORC Center include a mandate to carefully preserve and protect the scientific integrity and objectivity of NORC and the journalistic independence of The Associated Press. All work conducted by The AP-NORC Center conforms to the highest levels of scientific integrity to prevent any real or perceived bias in the research. All of the work of The AP-NORC Center is subject to review by its advisory committee to help ensure it meets these standards. The AP-NORC Center publicizes the results of all studies and makes all datasets and study documentation available to scholars and the public.

Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best. We conclude by providing some lessons learned on the definition of nowcasting targets and practical challenges.

Summary Background COVID-19 pandemic has developed rapidly and the ability to stratify the most vulnerable patients is vital. However, routinely used severity scoring systems are often low on diagnosis, even in non-survivors. Therefore, clinical prediction models for mortality are urgently required. Methods We developed and internally validated a multivariable logistic regression model to predict inpatient mortality in COVID-19 positive patients using data collected retrospectively from Tongji Hospital, Wuhan (299 patients). External validation was conducted using a retrospective cohort from Jinyintan Hospital, Wuhan (145 patients). Nine variables commonly measured in these acute settings were considered for model development, including age, biomarkers and comorbidities. Backwards stepwise selection and bootstrap resampling were used for model development and internal validation. We assessed discrimination via the C statistic, and calibration using calibration-in-the-large, calibration slopes and plots. Findings The final model included age, lymphocyte count, lactate dehydrogenase and SpO 2 as independent predictors of mortality. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Internal calibration was excellent (calibration slope=1). External validation showed some over-prediction of risk in low-risk individuals and under-prediction of risk in high-risk individuals prior to recalibration. Recalibration of the intercept and slope led to excellent performance of the model in independent data. Interpretation COVID-19 is a new disease and behaves differently from common critical illnesses. This study provides a new prediction model to identify patients with lethal COVID-19. Its practical reliance on commonly available parameters should improve usage of limited healthcare resources and patient survival rate. Funding This study was supported by following funding: Key Research and Development Plan of Jiangsu Province (BE2018743 and BE2019749), National Institute for Health Research (NIHR) (PDF-2018-11-ST2-006), British Heart Foundation (BHF) (PG/16/65/32313) and Liverpool University Hospitals NHS Foundation Trust in UK. Research in context Evidence before this study Since the outbreak of COVID-19, there has been a pressing need for development of a prognostic tool that is easy for clinicians to use. Recently, a Lancet publication showed that in a cohort of 191 patients with COVID-19, age, SOFA score and D-dimer measurements were associated with mortality. No other publication involving prognostic factors or models has been identified to date. Added value of this study In our cohorts of 444 patients from two hospitals, SOFA scores were low in the majority of patients on admission. The relevance of D-dimer could not be verified, as it is not included in routine laboratory tests. In this study, we have established a multivariable clinical prediction model using a development cohort of 299 patients from one hospital. After backwards selection, four variables, including age, lymphocyte count, lactate dehydrogenase and SpO 2 remained in the model to predict mortality. This has been validated internally and externally with a cohort of 145 patients from a different hospital. Discrimination of the model was excellent in both internal (c=0·89) and external (c=0·98) validation. Calibration plots showed excellent agreement between predicted and observed probabilities of mortality after recalibration of the model to account for underlying differences in the risk profile of the datasets. This demonstrated that the model is able to make reliable predictions in patients from different hospitals. In addition, these variables agree with pathological mechanisms and the model is easy to use in all types of clinical settings. Implication of all the available evidence After further external validation in different countries the model will enable better risk stratification and more targeted management of patients with COVID-19. With the nomogram, this model that is based on readily available parameters can help clinicians to stratify COVID-19 patients on diagnosis to use limited healthcare resources effectively and improve patient outcome.

Shigella Nucleic Acid Test Kit Market Outlook

The Shigella Nucleic Acid Test Kit market has been experiencing robust growth with a market size valued at approximately USD 140 million in 2023, and it is projected to expand to USD 250 million by 2032, at a compound annual growth rate (CAGR) of 6.5%. This upward trajectory in market size is primarily driven by the increasing prevalence of Shigella infections globally, which necessitates more efficient diagnostic solutions. The advancements in nucleic acid-based testing technologies offer quicker, more accurate, and reliable detection of pathogens, thus contributing significantly to the market's growth. Moreover, the growing awareness regarding the importance of early disease detection and management is a critical growth factor, propelling demand for these kits across various healthcare and research settings.

One of the primary growth drivers for the Shigella nucleic acid test kit market is the rising incidence of Shigella infections, particularly in developing regions where sanitation and clean water access are challenging. These infections account for significant morbidity and mortality, especially among children and immunocompromised individuals. Consequently, there is an urgent need for rapid diagnostic tools that can facilitate early detection and treatment, thereby curbing the spread of the disease. Nucleic acid test kits provide a valuable solution, offering high specificity and sensitivity that can improve patient outcomes. Additionally, continuous innovations and improvements in molecular diagnostic technologies are enhancing the performance and accessibility of these kits, further boosting market growth.

Another key factor contributing to the market's growth is the increasing investments in healthcare infrastructure and the expansion of diagnostic testing services. Governments and private entities are recognizing the importance of strengthening their healthcare systems to better handle infectious diseases, which include Shigella outbreaks. Such investments are particularly pronounced in emerging economies, where the healthcare sector is undergoing rapid transformation. These developments not only increase the adoption of advanced diagnostic tools but also drive the demand for effective Shigella nucleic acid test kits, as healthcare providers aim to offer comprehensive and timely diagnostic services to their populations.

Furthermore, the growing emphasis on research and development activities within the field of infectious diseases is propelling market growth. Academic institutions, research centers, and pharmaceutical companies are increasingly focusing on understanding the genetic and environmental factors that contribute to Shigella infections. This research drives the need for reliable and efficient diagnostic tools, such as nucleic acid test kits, to facilitate accurate data collection and analysis. Additionally, collaborations between public health organizations and commercial players are fostering innovation in test kit design and functionality, ensuring that they meet the evolving demands of the healthcare sector.

Regionally, the market outlook reflects a diverse landscape with the Asia Pacific region expected to lead in terms of growth rate due to its large population base and high burden of infectious diseases. The increasing focus on improving healthcare access and hygiene standards in countries like India and China will likely accelerate the adoption of nucleic acid test kits for Shigella. North America, with its advanced healthcare infrastructure and strong focus on research and development, is projected to maintain a significant share of the market. Meanwhile, Europe is anticipated to witness steady growth, driven by government initiatives aimed at curbing infectious disease outbreaks, and the Middle East & Africa and Latin America are expected to gradually enhance their market presence as healthcare investments and awareness continue to rise.

In the realm of food safety and public health, the Salmonella Test Kit plays a crucial role in identifying and mitigating the risks associated with Salmonella contamination. As a leading cause of foodborne illnesses worldwide, Salmonella poses significant health challenges, particularly in regions with less stringent food safety regulations. The development and deployment of Salmonella Test Kits are essential in ensuring that food products meet safety standards, thereby protecting consumers from potential outbreaks. These kits offer rapid and accurate detection of Salmonella bacteria, enabling timely in

This systematic review and meta-analysis, conducted by Dr. Leena R David and colleagues, aimed to assess the prevalence of anxiety, depression, and insomnia among allied health professionals (AHPs) during the COVID-19 pandemic and other infectious disease outbreaks. The study identified a critical gap in the existing literature concerning the mental health of AHPs during such crises. The researchers systematically searched major databases and identified 267 relevant articles, of which five met the inclusion criteria for review. These studies collectively involved 11,148 healthcare professionals, with 831 (7.54%) being AHPs. The selected studies utilized various mental health assessment tools and reported prevalence rates of anxiety and depression among AHPs. The meta-analysis focused on three of the studies, revealing a pooled effect size of 0.21 for psychological stress and 0.19 for anxiety among AHPs. The results indicated that AHPs experienced moderate levels of psychological distress during infectious disease outbreaks. Several risk factors were identified, including younger age, gender, social support, and occupational characteristics. The role of supervisors and the provision of accurate information were also highlighted as crucial in managing AHPs' mental health. Despite the limitations of this study, including potential publication bias and heterogeneity among the selected studies, it provides valuable insights into the mental health challenges faced by AHPs during pandemics. The authors emphasize the need for further research with larger sample sizes and validated measurement tools to better understand and address the mental health of AHPs. In conclusion, this study underscores the importance of monitoring and supporting the mental well-being of AHPs, who play a vital role in healthcare delivery during infectious disease outbreaks. Policymakers and healthcare organizations are urged to take action to mitigate the impact on AHPs' mental health and ensure their readiness and resilience in facing future public health crises.