Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

The Next Generation of Dashboards: a Spatial Online Analytical Processing (SOLAP) Platform for COVID-19.

The health and societal impacts of COVID-19 have created tremendous interest in the scientific community, resulting in interdisciplinary research teams that combine their expertise to provide new insights into the epidemic. However, spatial computation, exploratory data analysis, and spatial data exploration tools have yet to be integrated into these dashboards. Despite the availability of these tools, many of the existing COVID-19 dashboards have provided a limited set of data (i.e., last week's total cases), which limits the user's ability to interact with or customize the data visualization. We present a Spatial Online Analytical Platform that integrates spatial analysis tools that enable users to explore and learn more about spatial patterns of COVID-19. We present three interaction classes designed to support users' needs for knowledge about COVID-19 data trends. Our first interaction class allows users to apply user-defined data classifications (i.e., quantile, equal interval, user-defined) and map color choices. The second interaction class applies a risk index across the time series, informing users of the recent temporal trends. The third interaction class allows users to hypothesize about the presence of spatial clusters and receive results on demand. Our SOLAP platform supports the data analysis and exploration needs of big spatial-temporal data.

Infection prevention and control: Qualitative study of the preparedness and response of Christian health Association of Ghana to Marburg virus disease in Ghana.

Objective: Recent disease outbreaks underscore the importance of robust disease surveillance and infection prevention and control (IPC) programmes to bolster Africa's public health response system. Yet, available evidence shows extensive gaps in the emergency response capacity of faith-based healthcare providers on the continent. Accordingly, this study examines the IPC and surveillance strategies adopted by a faith-based healthcare provider and the challenges encountered during Marburg Virus Disease outbreak (MVD) in Ghana. Method: We collected data from 15 clinical and nonclinical health workers from the Christian Health Association of Ghana (CHAG) and the Ghana Health Service (GHS). Data was collected through online interviews to examine two pillars of the WHO COVID-19 SPRP-AFR (2021) framework. We analyzed the data using Braun and Clarke's thematic analysis. Findings: The facility performed creditably well with contact tracing and other quarantine protocols during MVD outbreak in Ghana. However, it also encountered several challenges in the enforcement of IPC protocols, including human resource constraints, the lack of decontamination equipment, and limited infrastructure, among others. Given these limitations, we assessed that the facility cannot handle major outbreaks. Conclusion: Due to numerous infectious disease outbreaks in Sub-Saharan Africa in recent years, the government of Ghana and faith-based healthcare providers must resource their facilities with the relevant equipment and qualified human resources against future disease outbreaks.

COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application-Based Prospective Observational Cohort Study.

BACKGROUND: The COVID-19 pandemic posed significant challenges to global health systems. Efficient public health responses required a rapid and secure collection of health data to improve the understanding of SARS-CoV-2 and examine the vaccine effectiveness (VE) and drug safety of the novel COVID-19 vaccines. OBJECTIVE: This study (COVID-19 study on vaccinated and unvaccinated subjects over 16 years; eCOV study) aims to (1) evaluate the real-world effectiveness of COVID-19 vaccines through a digital participatory surveillance tool and (2) assess the potential of self-reported data for monitoring key parameters of the COVID-19 pandemic in Germany. METHODS: Using a digital study web application, we collected self-reported data between May 1, 2021, and August 1, 2022, to assess VE, test positivity rates, COVID-19 incidence rates, and adverse events after COVID-19 vaccination. Our primary outcome measure was the VE of SARS-CoV-2 vaccines against laboratory-confirmed SARS-CoV-2 infection. The secondary outcome measures included VE against hospitalization and across different SARS-CoV-2 variants, adverse events after vaccination, and symptoms during infection. Logistic regression models adjusted for confounders were used to estimate VE 4 to 48 weeks after the primary vaccination series and after third-dose vaccination. Unvaccinated participants were compared with age- and gender-matched participants who had received 2 doses of BNT162b2 (Pfizer-BioNTech) and those who had received 3 doses of BNT162b2 and were not infected before the last vaccination. To assess the potential of self-reported digital data, the data were compared with official data from public health authorities. RESULTS: We enrolled 10,077 participants (aged ≥16 y) who contributed 44,786 tests and 5530 symptoms. In this young, primarily female, and digital-literate cohort, VE against infections of any severity waned from 91.2% (95% CI 70.4%-97.4%) at week 4 to 37.2% (95% CI 23.5%-48.5%) at week 48 after the second dose of BNT162b2. A third dose of BNT162b2 increased VE to 67.6% (95% CI 50.3%-78.8%) after 4 weeks. The low number of reported hospitalizations limited our ability to calculate VE against hospitalization. Adverse events after vaccination were consistent with previously published research. Seven-day incidences and test positivity rates reflected the course of the pandemic in Germany when compared with official numbers from the national infectious disease surveillance system. CONCLUSIONS: Our data indicate that COVID-19 vaccinations are safe and effective, and third-dose vaccinations partially restore protection against SARS-CoV-2 infection. The study showcased the successful use of a digital study web application for COVID-19 surveillance and continuous monitoring of VE in Germany, highlighting its potential to accelerate public health decision-making. Addressing biases in digital data collection is vital to ensure the accuracy and reliability of digital solutions as public health tools.

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.

Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms. We integrated the monthly municipal human incidence with 27 potential predictors, including the observed prevalence of Salmonella in food. We applied the tree regression, random forest and gradient boosting algorithms considering different scenarios and evaluated their predictivity in terms of the mean absolute percentage error (MAPE) and R2. Using a similar dataset from the year 2019, spatiotemporal predictions and their relative sensitivities and specificities were obtained. Random forest and gradient boosting (R2 = 0.55, MAPE = 7.5%) outperformed the tree regression algorithm (R2 = 0.42, MAPE = 8.8%). Salmonella prevalence in food; spatial features; and monitoring efforts in ready-to-eat milk, fruits and vegetables, and pig meat products contributed the most to the models' predictivity, reducing the variance by 90.5%. Conversely, the number of positive samples obtained for specific food matrices minimally influenced the predictions (2.9%). Spatiotemporal predictions for 2019 showed sensitivity and specificity levels of 46.5% (due to the lack of some infection hotspots) and 78.5%, respectively. This study demonstrates the added value of integrating data from human and veterinary health services to develop predictive models of human salmonellosis occurrence, providing early warnings useful for mitigating foodborne disease impacts on public health.

Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic.

Background: The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods: We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results: Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)-third quartile (Q3) = 94-108%) of typical usage to 10% (Q1-Q3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(ß) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (ß = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion: Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges.

Assessing avian influenza surveillance intensity in wild birds using a One Health lens.

Wildlife disease surveillance, particularly for pathogens with zoonotic potential such as Highly Pathogenic Avian Influenza Virus (HPAIV), is critical to facilitate situational awareness, inform risk, and guide communication and response efforts within a One Health framework. This study evaluates the intensity of avian influenza virus (AIV) surveillance in Ontario's wild bird population following the 2021 H5N1 incursion into Canada. Analyzing 2562 samples collected between November 1, 2021, and October 31, 2022, in Ontario, Canada, we identify spatial variations in surveillance intensity relative to human population density, poultry facility density, and wild mallard abundance. Using the spatial scan statistic, we pinpoint areas where public engagement, collaborations with Indigenous and non-Indigenous hunter/harvesters, and working with poultry producers, could augment Ontario's AIV wild bird surveillance program. Enhanced surveillance at these human-domestic animal-wildlife interfaces is a crucial element of a One Health approach to AIV surveillance. Ongoing assessment of our wild bird surveillance programs is essential for strategic planning and will allow us to refine approaches and generate results that continue to support the program's overarching objective of safeguarding the health of people, animals, and ecosystems.

Defining and Risk-Stratifying Immunosuppression (the DESTINIES Study): Protocol for an Electronic Delphi Study.

BACKGROUND: Globally, there are marked inconsistencies in how immunosuppression is characterized and subdivided into clinical risk groups. This is detrimental to the precision and comparability of disease surveillance efforts-which has negative implications for the care of those who are immunosuppressed and their health outcomes. This was particularly apparent during the COVID-19 pandemic; despite collective motivation to protect these patients, conflicting clinical definitions created international rifts in how those who were immunosuppressed were monitored and managed during this period. We propose that international clinical consensus be built around the conditions that lead to immunosuppression and their gradations of severity concerning COVID-19. Such information can then be formalized into a digital phenotype to enhance disease surveillance and provide much-needed intelligence on risk-prioritizing these patients. OBJECTIVE: We aim to demonstrate how electronic Delphi objectives, methodology, and statistical approaches will help address this lack of consensus internationally and deliver a COVID-19 risk-stratified phenotype for "adult immunosuppression." METHODS: Leveraging existing evidence for heterogeneous COVID-19 outcomes in adults who are immunosuppressed, this work will recruit over 50 world-leading clinical, research, or policy experts in the area of immunology or clinical risk prioritization. After 2 rounds of clinical consensus building and 1 round of concluding debate, these panelists will confirm the medical conditions that should be classed as immunosuppressed and their differential vulnerability to COVID-19. Consensus statements on the time and dose dependencies of these risks will also be presented. This work will be conducted iteratively, with opportunities for panelists to ask clarifying questions between rounds and provide ongoing feedback to improve questionnaire items. Statistical analysis will focus on levels of agreement between responses. RESULTS: This protocol outlines a robust method for improving consensus on the definition and meaningful subdivision of adult immunosuppression concerning COVID-19. Panelist recruitment took place between April and May of 2024; the target set for over 50 panelists was achieved. The study launched at the end of May and data collection is projected to end in July 2024. CONCLUSIONS: This protocol, if fully implemented, will deliver a universally acceptable, clinically relevant, and electronic health record-compatible phenotype for adult immunosuppression. As well as having immediate value for COVID-19 resource prioritization, this exercise and its output hold prospective value for clinical decision-making across all diseases that disproportionately affect those who are immunosuppressed. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/56271.

Strengthening disease surveillance capacity at national level across five countries: a qualitative study.

OBJECTIVES: Disease surveillance is an essential component of public health and a core function of National Public Health Institutes (NPHIs), including to better prepare and respond to infectious diseases outbreaks. Strengthening NPHIs in their efforts to establish and maintain efficient surveillance systems is an opportunity to ensure future outbreak preparedness and response; yet, guidance on how to increase and prioritise capacity building efforts is limited. This study sought to investigate approaches to capacity building and training for disease surveillance at national level and understand the potential role of NPHIs. STUDY DESIGN: Qualitative study. METHODS: This is a qualitative study, based on a literature review and interviews undertaken between June and November 2022. Fifty seven in-depth interviews were conducted in five countries: Côte d'Ivoire, Ecuador, Madagascar, Namibia, and the Kingdom of Saudi Arabia. Participants included a range of professionals from government, NPHIs, academic institutions and the private sector. Interviews were thematically analysed. RESULTS: Selected countries varied in terms of their disease surveillance capacities, as well as in the structure of their surveillance systems and decision-making. Research identified shared priority areas for action at national level, identifying common challenges and opportunities: 1) capacity building, here specifically the need for a training agenda at national level to ensure sustainability and guide donor funded training offers; 2) data tools and technology-to help decision-makers select the best software tool to address countries' identified need; 3) data sharing-the need for clear data sharing standards and norms for national to international data sharing; and 4) genomic sequencing-the need for national genomic surveillance strategies and reporting guidelines. CONCLUSION: Addressing challenges and using opportunities to strengthen disease surveillance at national level is an important step to build capacity in this area and to help prevent future epidemic and pandemics globally. The findings of this study help decision-makers to identify priority areas for capacity building and understand the potential role and significance of NPHIs.

The impact of living conditions and health interventions on tuberculosis, Denmark, 1876 to 2022.

BackgroundDenmark possesses an exceptional historical data collection on tuberculosis (TB) from 1876 to the present, providing a unique opportunity to assess TB epidemiology over 147 years in Denmark.AimOur aim was to describe the TB disease burden in Denmark in relation to historical events, living conditions and health interventions during the past 147 years.MethodsWe performed a nationwide register-based ecological study including all persons with TB in Denmark from 1876 through 2022, correlating the TB incidence to social, economic and health indicators.ResultsIn Denmark, the overall TB incidence and mortality declined markedly over the past 147 years, only marginally influenced by specific TB interventions such as sanatoria, Bacillus Calmette-Guèrin (BCG) vaccination, mass screenings and antibiotics. Parallel to this decline, the country experienced improved living conditions, as illustrated by decreased infant mortality and increased life expectancy and wealth. In 1978, Denmark became a low-incidence country for TB with risk groups predominantly affected, and with a continuous change in demographics towards fewer Danish-born cases and relatively more migrant cases.ConclusionsThe decline over time in TB incidence and mortality in Denmark preceded specific TB interventions and can, first of all, be attributed to improved living conditions. TB has now become a rare disease in Denmark, predominantly occurring in particular risk groups. Future elimination of TB will require a combination of specific health interventions in these risk groups combined with a continued focus on improving socioeconomic status and living conditions.

Australian vaccine preventable disease epidemiological review series: Hepatitis B, 2000-2019.

Introduction: Hepatitis B vaccination was nationally funded for adolescents in 1996, with inclusion of universal infant immunisation under the National Immunisation Program (NIP) in May 2000. This study describes hepatitis B epidemiology in Australia in the two decades since 2000. Methods: This article analyses newly-acquired (within the prior 24 months) and unspecified (all other) hepatitis B notifications (2000-2019) from the National Notifiable Diseases Surveillance System; acute hepatitis B hospitalisations (2001-2019) from the National Hospital Morbidity Database; and acute (2000-2019) and chronic (2006-2019) hepatitis B deaths from the Australian Bureau of Statistics and Australian Coordinating Registry. Rates over the reporting period were described overall, and by age group, sex, and Aboriginal and Torres Strait Islander status (Aboriginal and/or Torres Strait Islander versus other [neither Aboriginal nor Torres Strait Islander, unknown or not stated]). Trend analyses were performed using Poisson or negative binomial regression. Additional analyses were performed for the cohort born after May 2000. Results and discussion: The annual all-age notification rate per 100,000 per year declined (p < 0.001) from 2.13 in 2000 to 0.65 in 2019 for newly-acquired hepatitis B and from 38.3 to 22.3 for unspecified hepatitis B (likely to predominantly represent chronic hepatitis B). Newly-acquired and unspecified hepatitis B notification rates were lowest among children aged < 15 years. The most substantial reductions in notification rates of newly-acquired hepatitis B were among adolescents aged 15-19 years and young adults aged 20-24 and 25-29 years (respectively 17-, 11-, and 7-fold); these age groups also recorded the most substantial reductions in unspecified hepatitis B notifications (respectively 5-, 3.5-, and 2-fold). Newly-acquired hepatitis B notification and acute hepatitis B mortality rates were two- to threefold higher in males than females. The all-age newly-acquired hepatitis B notification rate in Aboriginal and Torres Strait Islander people decreased twofold between 2000 and 2019, but remained threefold higher than in other people. Acute hepatitis B hospitalisations also declined over the study period (p < 0.001) and followed similar patterns. There were no acute or chronic hepatitis B deaths among people born after May 2000; this cohort featured 52 newly-acquired and 887 unspecified hepatitis B notifications. Due to lack of data on country of birth (and hence eligibility for infant vaccination under the NIP or overseas programs), vaccination status and likely transmission routes, we were unable to assess factors contributing to these potentially preventable infections. Conclusion: Adolescent and infant immunisation under the NIP has led to significant reductions in notification rates of newly-acquired hepatitis B, and in acute hepatitis B hospitalisation rates, both overall and in Aboriginal and Torres Strait Islander people. Unspecified hepatitis B notification rates have also greatly decreased in children and young adults, likely largely due to the impact of overseas infant immunisation programs on prevalence in child and adolescent migrants. Work to improve completeness of variables within national datasets is crucial, along with enhanced surveillance of both newly-acquired and unspecified hepatitis B cases to investigate transmission routes, vaccination status and factors contributing to acquisition of hepatitis B, in order to optimise the impact of immunisation programs and ensure linkage with care.

Epidemiological Trends of Dengue in One of the Western Hilly Areas of Himachal Pradesh.

Background: Dengue is one of the neglected tropical diseases, with a wide spectrum of diseases, ranging from acute febrile illness dengue fever to life-threatening dengue hemorrhagic fever or dengue shock syndrome. In recent years, it has become a major public health concern in many nonendemic areas as well. Materials and Methods: A secondary data analysis of records available with district Integrated Disease Surveillance Programme cell was conducted to study distribution (time, place, and person) of dengue from 2017 to 2022 in Kangra, a sub-Himalayan district of Himachal Pradesh (HP). Results: In the evaluated period (2017-2022), a total of 6008 cases suspected of dengue were tested and test positivity of 7% (441) with male gender predominance was found. Mean age of the diagnosed cases was 37.7 ± 16.8 years. A seasonal trend was observed starting from late August to November in all study years. Conclusion: Dengue is still a neglected disease, but it has shown its presence especially in this part of HP, indicating the need for better preparation and sensitization of vector-borne disease control program activities, especially in post-monsoon, to prevent future epidemics.

Outbreaks of H5N1 High Pathogenicity Avian Influenza in South Africa in 2023 Were Caused by Two Distinct Sub-Genotypes of Clade 2.3.4.4b Viruses.

In 2023, South Africa continued to experience sporadic cases of clade 2.3.4.4b H5N1 high-pathogenicity avian influenza (HPAI) in coastal seabirds and poultry. Active environmental surveillance determined that H5Nx, H7Nx, H9Nx, H11Nx, H6N2, and H12N2, amongst other unidentified subtypes, circulated in wild birds and ostriches in 2023, but that H5Nx was predominant. Genome sequencing and phylogenetic analysis of confirmed H5N1 HPAI cases determined that only two of the fifteen sub-genotypes that circulated in South Africa in 2021-2022 still persisted in 2023. Sub-genotype SA13 remained restricted to coastal seabirds, with accelerated mutations observed in the neuraminidase protein. SA15 caused the chicken outbreaks, but outbreaks in the Paardeberg and George areas, in the Western Cape province, and the Camperdown region of the KwaZulu-Natal province were unrelated to each other, implicating wild birds as the source. All SA15 viruses contained a truncation in the PB1-F2 gene, but in the Western Cape SA15 chicken viruses, PA-X was putatively expressed as a novel isoform with eight additional amino acids. South African clade 2.3.4.4b H5N1 viruses had comparatively fewer markers of virulence and pathogenicity compared to European strains, a possible reason why no spillover to mammals has occurred here yet.

Communicable diseases in Ukraine during the period of 2018-2023: Impact of the COVID-19 pandemic and war.

BACKGROUND: By examining 2018-2023 data, this study explored the intricate impact of the Russian invasion, ongoing COVID-19 pandemic, and environmental disruptions on communicable diseases in Ukraine. This conflict exacerbates challenges in disease surveillance and healthcare, compounding stress among the population. METHODS: Leveraging the Centers for Disease Prevention Control's surveillance system, the study employs active and passive surveillance, utilizing medical records and laboratory reports. Notification rates gauge the incidence of communicable diseases, offering insights into trends during the study period. RESULTS: While salmonellosis, shigellosis, and rotavirus incidence are decreasing overall, there is a surge in viral hepatitis A, chronic hepatitis B, and C. This conflict hampers hepatitis C management, as evidenced by decreased numbers of treatment centers and patient enrollment. The prevalence of cough cases will increase in 2023, emphasizing the importance of sustained vaccination. The incidence of tuberculosis will increase in 2023 despite a general decrease. CONCLUSION: This study underscores the urgent need for sustained efforts and adequate resources, infrastructure, and international support to mitigate public health challenges in conflict-ridden Ukraine. Prioritizing vaccination programmes and enhancing healthcare accessibility in affected regions are crucial.

Monitoring livestock pregnancy loss.

Systematically tracking and analysing reproductive loss in livestock helps with efforts to safeguard the health and productivity of food animals by identifying causes and high-risk areas.

Repeatability and reproducibility of hunter-harvest sampling for avian influenza virus surveillance in Great Britain.

Emerging pathogens can threaten human and animal health, necessitating reliable surveillance schemes to enable preparedness. We evaluated the repeatability and reproducibility of a method developed previously during a single year at one study site. Hunter-harvested ducks and geese were sampled for avian influenza virus at three discrete locations in the UK. H5N1 highly pathogenic avian influenza (HPAIV) was detected in four species (mallard [Anas platyrhynchos], Eurasian teal [Anas crecca], Eurasian wigeon [Mareca penelope] and pink-footed goose [Anser brachyrhynchus]) across all three locations and two non-HPAIV H5N1, influenza A positive detections were made from a mallard and Eurasian wigeon at two locations. Virus was detected within 1-to-4 days of sampling at every location. Application of rapid diagnostic methods to samples collected from hunter-harvested waterfowl offers potential as an early warning system for the surveillance and monitoring of emerging and existing strains of avian influenza A viruses in key avian species.

Mortality Associated with Priority Diseases in Flood-Affected Areas Using District Health Information System (DHIS2) During September-December 2022: Pakistan Experience.

OBJECTIVES: To quantify the burden of communicable diseases and characterize the most reported infections during public health emergency of floods in Pakistan. METHODS: The study's design is a descriptive trend analysis. The study utilized the disease data reported to District Health Information System (DHIS2) for the 12 most frequently reported priority diseases under the Integrated Disease Surveillance and Response (IDSR) system in Pakistan. RESULTS: In total, there were 1,532,963 suspected cases during August to December 2022 in flood-affected districts (n = 75) across Pakistan; Sindh Province reported the highest number of cases (n = 692,673) from 23 districts, followed by Khyber Pakhtunkhwa (KP) (n = 568,682) from 17 districts, Balochistan (n = 167,215) from 32 districts, and Punjab (n = 104,393) from 3 districts. High positivity was reported for malaria (79,622/201,901; 39.4%), followed by acute diarrhea (non-cholera) (23/62; 37.1%), hepatitis A and E (47/252; 18.7%), and dengue (603/3245; 18.6%). The crude mortality rate was 11.9 per 10 000 population (1824/1,532,963 [deaths/cases]). CONCLUSION: The study identified acute respiratory infection, acute diarrhea, malaria, and skin diseases as the most prevalent diseases. This suggests that preparedness efforts and interventions targeting these diseases should be prioritized in future flood response plans. The study highlights the importance of strengthening the IDSR as a Disease Early Warning System through the implementation of the DHIS2.

A Multi-pronged Approach to Addressing Global Poxviruses Vaccine Inequity: A Case of Monkeypox.

Monkeypox has been endemic in Congo and Nigeria for at least five decades. Since early May 2022, there have been numerous unprecedented outbreaks throughout the world in places without any previously reported cases. While a majority of the diagnosed cases have been within Europe and the Americas, several cases have occurred in non-endemic African countries. As of December 2022, 82,999 cases had been reported globally, prompting concern among the World Health Organization (WHO) members. While the WHO has not labeled this epidemic a Global Health Emergency, member states have begun to put forward plans to consolidate their emergency vaccine stockpiles and share the limited number of vaccines made by the single FDA-approved manufacturer, Bavarian Nordic. Many countries are concerned about how vaccines will be shared. Some of the larger donor States are positioned to be the biggest beneficiaries of vaccine sharing, while States from areas that have been suffering from the virus since the 1970s have not been allocated any. This pattern of vaccine distribution echoes that seen during the early part of the COVID-19 pandemic. Due to the similarities between Monkeypox and Smallpox, contact precautions and vaccination seem to be effective strategies to combat its rapid spread. We aim to evaluate how an eradication program model similar to that used for Smallpox can be applied to Monkeypox, and whether it can address vaccine inequity. To do this, we use a multi-pronged approach targeting disease surveillance, vaccine awareness, manufacturing, cost, and distribution strategies.

Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Applicability of selected reaction monitoring for precise screening tests.

INTRODUCTION: The proactive identification of diseases through screening tests has long been endorsed as a means to preempt symptomatic onset. However, such screening endeavors are fraught with complications, such as diagnostic inaccuracies, procedural risks, and patient unease during examinations. These challenges are amplified when screenings for multiple diseases are administered concurrently. Selected Reaction Monitoring (SRM) offers a unique advantage, allowing for the high-throughput quantification of hundreds of analytes with minimal interferences. AREAS COVERED: Our research posits that SRM-based assays, traditionally tailored for single-disease biomarker profiling, can be repurposed for multi-disease screening. This innovative approach has the potential to substantially alleviate time, labor, and cost demands on healthcare systems and patients alike. Nonetheless, there are formidable methodological hurdles to overcome. These include difficulties in detecting low-abundance proteins and the risk of model overfitting due to the multiple functionalities of single proteins across different disease spectrums - issues especially pertinent in blood-based assays where detection sensitivity is constrained. As we move forward, technological strides in sample preparation, online extraction, throughput, and automation are expected to ameliorate these limitations. EXPERT OPINION: The maturation of mass spectrometry's integration into clinical laboratories appears imminent, positioning it as an invaluable asset for delivering highly sensitive, reproducible, and precise diagnostic results.