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INTRODUCTION: As timeliness metrics gain traction to assess and optimise outbreak detection and response performance, implementation and scale-up require insight into the perspectives of stakeholders adopting these tools. This study sought to characterise the feasibility and utility of tracking One Health outbreak milestones across relevant human, animal, plant, and environmental sectors to systematically quantify timeliness metrics in Uganda, a country prone to outbreaks of WHO priority diseases. METHODS: A database of outbreak events occurring in Uganda between 2018 and 2022 was compiled. Outbreak reports meeting our inclusion criteria were reviewed to quantify the frequency of milestone reporting. Key informant interviews were conducted with expert stakeholders to explore the feasibility and utility of tracking metrics using a framework analysis. Quantitative and qualitative data were collected and analysed concurrently. RESULTS: Of the 282 public health emergencies occurring between 2018 and 2022, 129 events met our inclusion criteria, and complete data were available for 82 outbreaks. For our qualitative portion, 10 informants were interviewed from 7 institutions, representing the human, animal and environmental sectors. Informants agreed most One Health milestones are feasible to track, which was supported by the frequency of milestone reporting; however, there was a demonstrated need for increased reporting of after-action reviews, as well as outbreak start and end dates. Predictive alerts signalling potential outbreaks and preventive responses to alerts are seen as challenging to routinely capture, reflecting the lack of public health action for these domains. CONCLUSION: Despite consensus among stakeholders that timeliness metrics are a beneficial tool to assess outbreak performance, not all One Health metrics are being tracked consistently, thereby missing opportunities to optimise epidemic intelligence, preparedness and prevention. The feasibility of tracking these metrics depends on the integration of reporting channels, enhanced documentation of milestones and development of guidance for early adopters, recognising country-specific on-the-ground realities and challenges to national scaling efforts.

Candida parapsilosis is known to cause severe and persistent outbreaks in clinical settings. Patients infected with multidrug-resistant C. parapsilosis (MDR Cp) isolates were identified in a large Turkish hospital from 2017-2020. We subsequently identified three additional patients infected with MDR Cp isolates in 2022 from the same hospital and two echinocandin-resistant (ECR) isolates from a single patient in another hospital. The increasing number of MDR and ECR isolates contradicts the general principle that the severe fitness cost associated with these phenotypes could prevent their dominance in clinical settings. Here, we employed a multidimensional approach to systematically assess the fitness costs of MDR and ECR C. parapsilosis isolates. Whole-genome sequencing revealed a novel MDR genotype infecting two patients in 2022. Despite severe in vitro defects, the levels and tolerances of the biofilms of our ECR and MDR isolates were generally comparable to those of susceptible wild-type isolates. Surprisingly, the MDR and ECR isolates showed major alterations in their cell wall components, and some of the MDR isolates consistently displayed increased tolerance to the fungicidal activities of primary human neutrophils and were more immunoevasive during exposure to primary human macrophages. Our systemic infection mouse model showed that MDR and ECR C. parapsilosis isolates had comparable fungal burden in most organs relative to susceptible isolates. Overall, we observed a notable increase in the genotypic diversity and frequency of MDR isolates and identified MDR and ECR isolates potentially capable of causing persistent outbreaks in the future.

BackgroundDetection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown.MethodsWe conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods.ResultsReal-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92).ConclusionsAutomated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).

BACKGROUND: Following the first locally transmitted case in Sukhbaatar soum, Selenge Province, we aimed to investigate the ultimate scale of the epidemic in the scenario of uninterrupted transmission. METHODS: This was a prospective case study following the locally modified WHO FFX cases generic protocol. A rapid response team collected data from November 14 to 29, 2020. We created a stochastic process to draw many transmission chains from this greater distribution to better understand and make inferences regarding the outbreak under investigation. RESULTS: The majority of the cases involved household transmissions (35, 52.2%), work transmissions (20, 29.9%), index (5, 7.5%), same apartment transmissions (2, 3.0%), school transmissions (2, 3.0%), and random contacts between individuals transmissions (1, 1.5%). The posterior means of the basic reproduction number of both the asymptomatic cases R 0 Asy $$ {R}_0^{Asy} $$ and the presymptomatic cases R 0 Pre $$ {R}_0^{Pre} $$ (1.35 [95% CrI 0.88-1.86] and 1.29 [95% CrI 0.67-2.10], respectively) were lower than that of the symptomatic cases (2.00 [95% Crl 1.38-2.76]). CONCLUSION: Our study highlights the heterogeneity of COVID-19 transmission across different symptom statuses and underscores the importance of early identification and isolation of symptomatic cases in disease control. Our approach, which combines detailed contact tracing data with advanced statistical methods, can be applied to other infectious diseases, facilitating a more nuanced understanding of disease transmission dynamics.

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (Syndromic Logger) in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospitals electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, p-value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, p = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our Syndromic Logger platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.

Difference-in-differences is undoubtedly one of the most widely used methods for evaluating the causal effect of an intervention in observational (i.e., nonrandomized) settings. The approach is typically used when pre- and postexposure outcome measurements are available, and one can reasonably assume that the association of the unobserved confounder with the outcome has the same absolute magnitude in the two exposure arms and is constant over time; a so-called parallel trends assumption. The parallel trends assumption may not be credible in many practical settings, for example, if the outcome is binary, a count, or polytomous, as well as when an uncontrolled confounder exhibits nonadditive effects on the distribution of the outcome, even if such effects are constant over time. We introduce an alternative approach that replaces the parallel trends assumption with an odds ratio equi-confounding assumption under which an association between treatment and the potential outcome under no treatment is identified with a well-specified generalized linear model relating the pre-exposure outcome and the exposure. Because the proposed method identifies any causal effect that is conceivably identified in the absence of confounding bias, including nonlinear effects such as quantile treatment effects, the approach is aptly called universal difference-in-differences. We describe and illustrate both fully parametric and more robust semiparametric universal difference-in-differences estimators in a real-world application concerning the causal effects of a Zika virus outbreak on birth rate in Brazil. A supplementary digital video is available at: http://links.lww.com/EDE/C90.

Formal infectious disease surveillance in Ukraine has been disrupted by Russia’s 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021–February 23, 2022) and during (February 24–July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control. [ABSTRACT FROM AUTHOR]

Neglected tropical diseases continue to cause a significant burden worldwide, with Africa accounting for more than one-third of the global burden. Over the past decade, progress has been made in eliminating, controlling, and eradicating these diseases in Africa. By December 2022, 47 out of 54 African countries had eliminated at least one neglected tropical disease, and more countries were close to achieving this milestone. Between 2020 and 2021, there was an 80 million reduction in people requiring intervention. However, continued efforts are needed to manage neglected tropical diseases and address their social and economic burden, as they deepen marginalisation and stigmatisation. Wastewater-based epidemiology involves analyzing wastewater to detect and quantify biomarkers of disease-causing pathogens. This approach can complement current disease surveillance systems in Africa and provide an additional layer of information for monitoring disease spread and detecting outbreaks. This is particularly important in Africa due to limited traditional surveillance methods. Wastewater-based epidemiology also provides a tsunami-like warning system for neglected tropical disease outbreaks and can facilitate timely intervention and optimised resource allocation, providing an unbiased reflection of the community's health compared to traditional surveillance systems. In this review, we highlight the potential of wastewater-based epidemiology as an innovative approach for monitoring neglected tropical disease transmission within African communities and improving existing surveillance systems. Our analysis shows that wastewater-based epidemiology can enhance surveillance of neglected tropical diseases in Africa, improving early detection and management of Buruli ulcers, hookworm infections, ascariasis, schistosomiasis, dengue, chikungunya, echinococcosis, rabies, and cysticercosis for better disease control. [ABSTRACT FROM AUTHOR]

The application of fungicides for commercial purposes has grown in response to increased food demand and shifting disease patterns. However, there is a scarcity of studies demonstrating their proper degradation. Due to the expanding global population, there is a heightened need for more agriculture to fulfill the growing demand for food, and this necessitates safeguarding crops from pests and diseases. Disease outbreaks in crops can result in the loss of food and the depletion of valuable resources. According to the European Union, organic fungicides constitute 60% of all fungicide sales, while synthetic fungicides make up the remaining 40% of pesticide sales. Fungicide usage is indispensable but needs to be considered within the context of potential environmental risks. Nonetheless, there is a pressing need to break down fungicides to preserve the natural environment. Our research is centered on hybrid enzymes derived from S. marcescens, which exhibit the capability to degrade fungicides. We employed computational techniques to assess the enzymes' effectiveness in breaking down fungicides. S. marcescens was isolated from soil and identified through 16s rRNA analysis. Chitanase and lipase, enzymes identified in S. marcescens, can individually break down fungicides, as confirmed by auto-dock vina docking simulations. Notably, the combined action of these two enzymes surpasses the performance of individual docking, a result also corroborated by auto-dock vina. To further establish the efficacy and degradability of this approach, computational techniques were employed. In the future, it may be possible to create an in vitro antifungal agent, which could be categorized as a bioactive agent. Hybrid enzymes derived from Serratia marcescens demonstrate superior fungicide degradation compared to individual enzymes. Computational simulations reveal enhanced effectiveness of combined lipase and chitinase enzymes. This study supports the potential development of in vitro antifungal bioactive agents. [ABSTRACT FROM AUTHOR]

This study employs the multivariate trend asymmetric GARCH-MIDAS (TAGM) model, an extension of the GARCH-MIDAS model, to explore the potential asymmetric impact of uncertainty shocks, including oil and infectious disease shocks, on the long-term volatility of China's listed tourism firms. Furthermore, we test the out-of-sample forecasting accuracy of uncertainty shocks to China's listed tourism firms' risk, which is measured by the volatility of tourism stocks after the outbreak of coronavirus disease 2019 (COVID-19). The results show that uncertainty shocks have a significant asymmetric effect on the long-run volatility of tourism stocks. The included uncertainty shocks improved accuracy in forecasting China's listed tourism firms' risk after the pandemic outbreak. The empirical results have important implications for tourism investment strategies in unstable environments. [ABSTRACT FROM AUTHOR]

We collect information about more than 5000 Prussian politicians, digitize administrative data on the provision of health-promoting public goods, and gather local-level information on workers' movements to study why elites in industrializing countries implement policies that improve the health of the poor. Using county-level variation in elite structure, we present OLS and IV estimates, suggesting that elites improve access to health services due to pressure exerted by workers' movements. By contrast, policies that prevent disease outbreaks are implemented without such pressure. Analyses of roll- call votes substantiate the findings of the county-level analysis. [ABSTRACT FROM AUTHOR]

In recent years, the world has witnessed the emergence and rapid spread of socially dangerous diseases, highlighting the critical need for robust ethical and legal frameworks within national biosecurity systems. This article discusses the ethical considerations and legal aspects surrounding the prevention and control of these diseases in order to devise effective strategies for their containment and mitigation. Drawing on a comprehensive review of literature, this study explores how societal norms, individual rights, and public health imperatives intersect within the context of biosecurity measures. It underscores the significance of ethical considerations to ensure fair and equitable access to preventive measures, diagnostics, treatments, and vaccines. Additionally, legal frameworks play a pivotal role in enabling the implementation of necessary interventions during disease outbreaks, such as quarantine measures, contact tracing, and the establishment of efficient surveillance systems. This study analyzes the national biological security measures in Azerbaijan and compares them to other countries to identify strategies for preserving and controlling the transmission of public health threats. The research aims to provide insights into the effectiveness of Azerbaijan's current biological security policies and highlight areas for improvement. The study includes a comprehensive review of relevant literature on biosecurity measures of Azerbaijan. The findings of this study will contribute to the global understanding of biological security practices and inform recommendations for enhancing Azerbaijan's preparedness in managing public health threats. Finally, the article emphasizes the need for international cooperation and harmonization of ethical and legal standards in addressing the global spread of socially dangerous diseases. It highlights the significance of sharing best practices and lessons learned to strengthen preparedness and response capacities across nations, facilitating a more coordinated and effective approach to prevent and control disease outbreaks. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusion Processes of self‐reflection and the learning they allow are crucial before, during, and after acute emergencies, including infectious disease outbreaks. Tools—such as Action Reviews—offer World Health Organization (WHO) member states a platform to enhance learning. We sought to better understand the value of these tools and how they may be further refined and better used.We searched the publicly available WHO Strategic Partnership for Health Security website for paired reports of Action Reviews, that is, reports with a comparable follow‐up report. We complemented the paired action reviews, with a literature search, including the gray literature. The paired action reviews were analyzed using the “Learning Health Systems” framework.We identified three paired action reviews: Lassa Fever After Action Reviews (AARs) in Nigeria (2017 and 2018), COVID‐19 Intra‐Action Reviews (IARs) in Botswana (2020 and 2021), and COVID‐19 IARs in South Sudan (2020 and 2021). Action Reviews allowed for surfacing relevant knowledge and, by engaging the right (in different contexts) actors, asking “are we doing things right?” (single loop learning) was evident in all the reports. Single loop learning is often embedded within examples of double loop learning (“are we doing the right things?”), providing a more transformative basis for policy change. Triple loop learning (“are we learning right”?) was evident in AARs, and less in IARs. The range of participants involved, the level of concentrated focus on specific issues, the duration available for follow through, and the pressures on the health system to respond influenced the type (i.e., loop) and the effectiveness of learning.Action Reviews, by design, surface knowledge. With favorable contextual conditions, this knowledge can then be applied and lead to corrective and innovative actions to improve health system performance, and in exceptional cases, continuous learning. [ABSTRACT FROM AUTHOR]

Plant disease outbreaks continuously challenge food security and sustainability. Traditional chemical methods used to treat diseases have environmental and health concerns, raising the need to enhance inherent plant disease resistance mechanisms. Traits, including disease resistance, can be linked to specific loci in the genome and identifying these markers facilitates targeted breeding approaches. Several methods, including genome‐wide association studies and genomic selection, have been used to identify important markers and select varieties with desirable traits. However, these traditional approaches may not fully capture the non‐linear characteristics of the effect of genomic variation on traits. Machine learning, known for its data‐mining abilities, offers an opportunity to enhance the accuracy of the existing trait association approaches. It has found applications in predicting various agronomic traits across several species. However, its use in disease resistance prediction remains limited. This review highlights the potential of machine learning as a complementary tool for predicting the genetic loci contributing to pathogen resistance. We provide an overview of traditional trait prediction methods, summarize machine‐learning applications, and address the challenges and opportunities associated with machine learning‐based crop disease resistance prediction. [ABSTRACT FROM AUTHOR]

Objective: Digital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks. Results: OBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs. [ABSTRACT FROM AUTHOR]

Introduction: Climate change is contributing to increase the frequency and severity of climate disasters in Mozambique, leading, since 2019, to extensive damage to infrastructure and displacement 1.3 million people. Aim of this study is to evaluate baseline preparedness to vector-borne and water-borne infections among households and internally displaced people exposed to climate disasters in Mozambique. Methods: This was a cross-sectional, community-based survey assessing the preparedness to infectious diseases outbreaks among people exposed to climate disasters in six districts in Mozambique. Structured form was delivered via face-to-face between October 15th and November 7th, 2022. Study outcome was defined as a seven-point score of preparedness to infectious disease outbreaks. Multivariable analysis of the score was conducted using Conway-Maxwell-Poisson regression. Results: This study included 2,140 households and 11,239 people, with IDPs accounting for 30% of them. Overall, 1,186 (55.4%) households were overcrowded. Median score of preparedness was 3 points (IQR 2–4). At multivariable analyses, districts with low preparedness were Montepuez and Mueda. Higher preparedness was associated with family planning (p < 0.0001), access to primary education for all children living in the household (p < 0.001) and possession of a birth certificate for all children aged < 5y (p < 0.0001), while preparedness was heterogeneous among the districts (p < 0.05). Households composed by IDPs were not associated with a lower preparedness score. Conclusions: In climate-vulnerable communities in Mozambique, households practicing family planning, providing access to primary education and birth certificate for all children were less vulnerable to water-borne and vector-borne infectious disease outbreaks. Being family planning and childcare primarily performed by women, our findings can inform policymakers and practitioners on the importance of addressing women to mitigate the impact of climate disasters and reduce the risk of infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

Background: Infectious disease outbreaks are an ongoing public health concern, requiring extensive resources to prevent and manage. Invasive Meningococcal Disease (IMD) is a severe outcome of infection with Neisseria meningitidis bacteria, which can be carried and transmitted asymptomatically. IMD is not completely vaccine-preventable, presenting an ongoing risk of outbreak development. This review provides a retrospective assessment of public health management of IMD outbreaks. Methods: A systematic search was performed in PubMed and EMBASE. English-language studies reporting on IMD outbreaks and associated public health response were considered eligible. Reporting on key characteristics including outbreak size, duration, location, and public health response were assessed against Strengthening the Reporting of Observational studies in Epidemiology guidelines. A summary of lessons learned and author recommendations for each article were also discussed. Results: 39 eligible studies were identified, describing 35 outbreaks in seven regions. Responses to outbreaks were mostly reactive, involving whole communities over prioritising those at highest risk of transmission. Recent responses identified a need for more proactive and targeted controls. Reporting was inconsistent, with key characteristics such as outbreak size, duration, or response absent or incompletely described. Conclusion: There is a need for clear, comprehensive reporting on IMD outbreaks and their public health response to inform policy and practice for subsequent outbreaks of IMD and other infectious diseases. [ABSTRACT FROM AUTHOR]

Background: Rifampicin resistant tuberculosis (RR-TB) poses a growing threat to individuals and communities. This study utilized a seasonal autoregressive integrated moving average (SARIMA) model to quantitatively predict the monthly incidence of RR-TB in Yunnan Province which could guide government health administration departments and the centers for disease control and prevention (CDC) in preventing and controlling the RR-TB epidemic. Methods: The study utilized routine surveillance reporting data from the infectious Disease Network Surveillance and Reporting System. Monthly incidence rates of RR-TB were collected from January 2019 to December 2022. A time series SARIMA model was used to predict the number of monthly RR-TB cases in Yunnan Province in 2023, and the model was validated using time series plots, seasonal and non-seasonal differencing, autocorrelation and partial autocorrelation analysis, and white noise tests. Results: From 2019 to 2022, the incidence of RR-TB decreases as the incidence of all TB decreases (P < 0.05). There was no significant change in the proportion of RR-TB among all TB cases, which remained within 2.5% (P>0.05). The time series decomposition shows that it presented obvious seasonality, periodicity and randomness after being decomposed. Time series analysis was performed on the original series after 1 non-seasonal difference and 1 seasonal difference, the ADF test showed P < 0.05. According to ACF and PACF, the SARIMA (1, 1, 1) (1, 1, 0)12 model was chosen and statistically significant model parameter estimates (P < 0.05). The predicted seasonal trend of RR-TB incidence in 2019 to 2023 was similar to the actual data. The percentage accuracy in the prediction excesses 80% in 2019 to 2022 and is all within 95% CI. However there was a certain gap between the actual incidence and the predicted value in 2023, and the acutual incidence had increased by 12.4% compared to 2022. The percentage of accuracy in the prediction was only 70% in 2023. Conclusions: We found the incidence of RR-TB was based on that of all TB in Yunnan. The SARIMA model successfully predicted the seasonal incidence trend of RR-TB in Yunnan Province in 2019 to 2023, but the prediction precision could be influenced by factors such as new infectious disease outbreaks or pandemics, social issues, environmental challenges or other unknown risks. Hence CDCs should pay special attention to the post epidemic effects of new infectious disease outbreaks or pandemics, carry out monitoring and early warning, and better optimize disease prediction models. [ABSTRACT FROM AUTHOR]

Climate change is creating a new era of infectious disease crises, further exacerbated by extreme weather. However, the relationship between extreme weather and infectious disease remain unclear. Here, we provide a new quantitative study on the impact of cold wave on COVID‐19 as an example. We found that during cold waves, extreme cold temperatures coupled with rapid aerosol transport accelerated COVID‐19 outbreaks. It directly increased the number of COVID‐19 cases in Beijing by 28.1% in the winter of year 2022. More urgently, cold temperatures led to a higher risk of death during infectious disease outbreaks, with a 7.07% increase in confirmed deaths and a 16.61% increase in excess mortality. Our findings emphasize the urgent need to promote a synergistic policy for responding to infectious diseases during cold wave disasters in order to minimize the risk of death among the elderly and those with underlying diseases. Plain Language Summary: The sudden drop in temperature and rapid spread of aerosols during cold waves resulted in a higher prevalence of infectious diseases. At the same time, low temperatures resulted in a higher risk of death due to infectious diseases and a variety of other factors during the epidemic. Key Points: The rapid dispersion of aerosols during cold waves hastens the transmission of infectious diseasesLow temperatures contribute to higher risk of death during infectious disease outbreaks [ABSTRACT FROM AUTHOR]

Background Outbreaks of vaccine-preventable diseases (VPDs) in healthcare workers (HCWs) can result in morbidity and mortality and cause significant disruptions to healthcare services, patients, and visitors as well as an added burden on the healthcare system. This scoping review aimed to describe the epidemiology of VPD outbreaks in HCWs caused by diseases that are prevented by the 10 vaccines recommended by the World Health Organization for HCWs. Methods In April 2022, CINAHL, MEDLINE, Global Health, and EMBASE were searched for all articles reporting on VPD outbreaks in HCWs since the year 2000. Articles were included regardless of language and study type. Clinical and epidemiological characteristics of VPD outbreaks were described. Results Our search found 9363 articles, of which 216 met the inclusion criteria. Studies describing 6 of the 10 VPDs were found: influenza, measles, varicella, tuberculosis, pertussis, and rubella. Most articles (93%) were from high- and upper-middle-income countries. While most outbreaks occurred in hospitals, several influenza outbreaks were reported in long-term-care facilities. Based on available data, vaccination rates among HCWs were rarely reported. Conclusions We describe several VPD outbreaks in HCWs from 2000 to April 2022. The review emphasizes the need to understand the factors influencing outbreaks in HCWs and highlights the importance of vaccination among HCWs. [ABSTRACT FROM AUTHOR]

Several factors significantly contribute to the onset of infectious diseases, including direct and indirect transmissions and their respective impacts on incubation periods. The intricate interplay of these factors within social networks remains a puzzle yet to be unraveled. In this study, we conduct a stability analysis within a network-organized SIR model incorporating dual delays to explore the influence of direct and indirect incubation periods on disease spread. Additionally, we investigate how compound networks affect the critical incubation period. Our findings reveal several vital insights. First, by examining crossing curves and the dispersion equation, we establish the conditions for Turing instability and delineate the stable regions associated with dual delays. Second, we ascertain that the critical incubation value exhibits an inverse relationship with a network's eigenvalues, indicating that the Laplacian matrix does not solely dictate periodic behavior in the context of delays. Furthermore, our study elucidates the impact of delays and networks on pattern formation, revealing distinct pattern types across different regions. Specifically, our observations suggest that effectively curtailing the spread of infectious diseases during an outbreak is more achievable when the incubation period for indirect contact is shorter and for direct contact is longer. Namely, our network framework enables regulation of the optimal combination of (τ 1 , τ 2) to mitigate the risk of infectious diseases. In summary, our results offer valuable theoretical insights that can inform strategies for preventing and managing infectious diseases. [ABSTRACT FROM AUTHOR]

Background: Ghana adopted the Integrated Disease Surveillance and Response (IDSR) system, which is an integration of the various programs in the surveillance system and can contain disease outbreaks and pandemics. Implementation of the IDSR is influenced by several factors which can affect its functionality and ability to contain disease outbreaks. This study assessed the factors influencing the IDSR system in selected districts in the Eastern Region of Ghana. Method: A cross-sectional study was conducted between February-March, 2022 in Fanteakwa North, Abuakwa South and New Juaben South districts in the Eastern Region of Ghana among health care workers who are involved in IDRS activities. Both primary and secondary data were collected and analyzed using descriptive statistics and regression analysis at 0.05 significant level with 95% confidence interval. Results: Three hundred and forty-seven (347) health care workers participated in the study with 56.2% (195/347) indicating that rumor registers were available at the health facilities. Most of the respondents (64.8%, 225/347) had means of transport for disease surveillance activities while majority (61.9%, 215/347) had case-based forms for case investigation. About half (51.9%, 180/347) of the participants revealed that they did not receive any feedback from the next higher level in the past year. Availability of transport for IDSR activities was almost 3.4 times more likely to contribute positively to IDSR system compared to facilities without transport (AOR = 3.36; 95% CI = 1.44–7.83; p = 0.005). Respondents who have the capacity to apply case definition are 2 times more likely to contribute to an effective IDSR system compared to health workers who cannot apply case definition (AOR = 1.94; 95% CI = 1.17–3.21; p = 0.013). Respondents who did not receive feedback from the next higher level were 52% less likely to have an effective IDSR system compared to respondents who received feedback from the next higher level (AOR = 0.48; 95% CI = 0.23–1.00; p = 0.05). Conclusion: Effective operation of IDSR is affected by the application of case definition and means of transport at health facilities. In addition, the capacity of health care workers to provide feedback can influence the smooth operation of the IDSR in the studied area in Ghana. [ABSTRACT FROM AUTHOR]

The past decade has seen the global reemergence and rapid spread of enterovirus D68 (EV-D68), a respiratory pathogen that causes severe respiratory illness and paralysis in children. EV-D68 was first isolated in 1962 from children with pneumonia. Sporadic cases and small outbreaks have been reported since then with a major respiratory disease outbreak in 2014 associated with an increased number of children diagnosed with polio-like paralysis. From 2014-2018, major outbreaks were reported every other year in a biennial pattern with > 90% of the cases occurring in children under the age of 16. With the outbreak of SARS-CoV-2 and the subsequent COVID-19 pandemic, there was a significant decrease in the prevalence EV-D68 cases along with other respiratory diseases. However, since the relaxation of pandemic social distancing protocols and masking mandates the number of EV-D68 cases have begun to rise againculminating in another outbreak in 2022. Here we review the virology, pathogenesis, and the immune response to EV-D68, and discuss the epidemiology of EV-D68 infections and the divergence of contemporary strains from historical strains. Finally, we highlight some of the key challenges in the field that remain to be addressed. [ABSTRACT FROM AUTHOR]

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies. Author summary: This article investigates at multi-level the roles played by spatial heterogeneities in epidemic dynamics. For this purpose we propose a novel stochastic-statistical particle model implemented on a two-dimensional lattice field, where various individual-varying spatially inhomogeneous human behavioral factors are incorporated into an agent-based epidemic modeling framework. With extensive simulations of our model, we are able to carry out thorough investigations on the impacts of heterogeneities at individual and spatial levels on epidemics trajectories during the early stage of an outbreak. It has been observed that spatial heterogeneity is consistently shown to be connected with suppressed and delayed epidemic peaks. These findings add valuable knowledge on the mechanism of the form and distribution of population clusters affecting an epidemic wave. Moreover, we examine the interplay between spatial/individual heterogeneities and public-health policies in an intensive and systematic way across various parameter regimes, whose conclusions we believe are going to complement beneficially to both the theoretical and practical understanding of epidemic control. [ABSTRACT FROM AUTHOR]

The biofilm-forming pathogens with acquired antibiotic resistance and associated disease outbreaks are increasing worldwide, especially in the seafood industry. This study hypothesised that the bioengineered iron nanoparticles using the myrobalan (Terminalia chebula) extract (M-FeNPs) and its resin coating have an effective antibiofilm properties. 12 seafood waste-based biofilm-forming strains (SSS) were isolated from SS-316 metal coupon and screened for their antibiotic-resistant profile as per CLSI (2016) standards. M-FeNPs were characterised by UV, FTIR, etc. Over 50 % of SSS were resistant to Ciprofloxacin, Cefalexin and Penicillin-G. The antibiofilm activity of the M-FeNPs showed an excellent inhibition zone (16–24 mm), and the combination of M-FeNPs + Methicillin also showed better activity. in vitro antibiofilm study shows that upon adding M-FeNPs, biofilm formation was reduced from 1.425 g to 0.83 g at the end of the eighth day. The CLSM and SEM images indicated that the M-FeNPs are effective antibiofilm agents against biofilm strains. [ABSTRACT FROM AUTHOR]

While the world recovers from the COVID-19 pandemic, another outbreak of contagious disease remains the most likely future risk to public safety. Now is therefore the time to equip health authorities with effective tools to ensure they are operationally prepared for future events. We propose a direct approach to obtain reliable nearly instantaneous time-varying reproduction numbers for contagious diseases, using only the number of infected individuals as input and utilising the dynamics of the susceptible–infected–recovered (SIR) model. Our approach is based on a multivariate nonlinear regression model simultaneously assessing parameters describing the transmission and recovery rate as a function of the SIR model. Shortly after start of a pandemic, our approach enables estimation of daily reproduction numbers. It avoids numerous sources of additional variation and provides a generic tool for monitoring the instantaneous reproduction numbers. We use Norwegian COVID-19 data as case study and demonstrate that our results are well aligned with changes in the number of infected individuals and the change points following policy interventions. Our estimated reproduction numbers are notably less volatile, provide more credible short-time predictions for the number of infected individuals, and are thus clearly favorable compared with the results obtained by two other popular approaches used for monitoring a pandemic. The proposed approach contributes to increased preparedness to future pandemics of contagious diseases, as it can be used as a simple yet powerful tool to monitor the pandemics, provide short-term predictions, and thus support decision making regarding timely and targeted control measures. [ABSTRACT FROM AUTHOR]

Introduction: Health system disruptions, caused by unexpected emergencies such as disease outbreaks, natural disasters, and cybercrimes, impact the delivery of routine preventative care. As comprehensive care providers, family physicians (FPs) devote significant time to prevention. However, without emergency and pandemic plans in place in primary care, FPs face added barriers to prioritizing and sustaining preventative care when health systems are strained, which was evident during the COVID-19 pandemic. This study aims to describe FPs' experiences providing preventative care during the COVID-19 pandemic and their perceptions of the impacts of disrupted preventative care in primary care settings.Methods: Using a qualitative descriptive approach, we conducted semistructured interviews with FPs across 4 provinces in Canada (i.e. Newfoundland and Labrador, Nova Scotia, Ontario, British Columbia) between October 2020 and June 2021 as part of a larger multiple case study. These interviews broadly explored the roles and responsibilities of FPs during the COVID-19 pandemic. Interviews were coded thematically and codes from the larger study were analysed further using an iterative, phased process of thematic analysis.Results: Interviews averaged 58 min in length (range 17-97 min) and FPs had a mean of 16.9 years of experience. We identified 4 major themes from interviews with FPs (n = 68): (i) lack of capacity and coordination across health systems, (ii) patient fear, (iii) impacts on patient care, and (iv) negative impacts on FPs. Physicians voiced concerns with managing patients' prevention needs when testing availability and coordination of services was limited. Early in the pandemic, patients were also missing or postponing their own primary care appointments. Change in the provision and coordination of routine preventative care had negative impacts on both patients and physicians, affecting disease incidence/progression, physician workload, and psychological wellbeing.Conclusion: During the COVID-19 pandemic, upstream care efforts were impacted, and FPs were forced to reduce their provision of preventative care. FPs contribute direct insight to primary care delivery that can support pandemic planning to ensure preventative care is sustained during future emergencies. [ABSTRACT FROM AUTHOR]

We propose an alternative decision-making methodology based on adopting a mixed risk-averse and risk-taking behavior, improving the objectivity of decision-making. We demonstrate the methodology by prioritizing Iranian tourism centers' activity under pandemic conditions, providing insights to policymakers on those to keep active or reduce the activity of – hence, those worth developing ahead of future disease outbreaks. This research follows a three-step methodology. First, criteria for evaluation are identified and categorized into tourist attractions, infrastructure, and healthcare dimensions. Second, criterion weights are calculated based on expert opinions, collected using a best-worst method-based questionnaire. Third, tourism centers are evaluated by employing risk-averse and risk-taking best-worst methods. We identify popular attractions, general services, and drugstore accessibility as the primary indicators of tourist attractions, infrastructure, and healthcare, respectively. By clustering tourism centers using K-means algorithm, we find that, in order, the cities of Semnan, Kerman and Zahedan are the tourism centers most suited to staying active during disease outbreaks. For multi-criteria decision-making problems that rely on experts' evaluations, the proposed methodology can improve the reliability of decision-making. The methodology and framework presented can be used to support various types of decision-making, including evaluation, ranking, selection or sorting. [ABSTRACT FROM AUTHOR]

AbstractFrequent viral infections leading to infectious disease outbreaks have become a significant global health concern. Fully elucidating the molecular mechanisms of the immune response against viral infections is crucial for epidemic prevention and control. The innate immune response, the host’s primary defense against viral infection, plays a pivotal role and has become a breakthrough in research mechanisms. A component of the innate immune system, damage-associated molecular patterns (DAMPs) are involved in inducing inflammatory responses to viral infections. Numerous DAMPs are released from virally infected cells, activating downstream signaling pathways via internal and external receptors on immune cells. This activation triggers immune responses and helps regulate viral host invasion. This review examines the immune regulatory mechanisms of various DAMPs, such as the S100 protein family, high mobility group box 1 (HMGB1), and heat shock proteins, in various viral infections to provide a theoretical basis for designing novel antiviral drugs. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the effectiveness of synbiotic microcapsules through feed on growth performance and immune response of Pacific white shrimp (Litopenaeus vannamei) in field testing during IMNV disease outbreaks and fluctuating weather. Four groups of shrimps (4.11 ± 0.05 g) with four replicates were fed with different doses of synbiotic microcapsules SIN 0 (0%), SIN 0.5 (0.5%), SIN 1 (1%), and SIN 1.5 (1.5%) for 60 days with a feeding frequency of 4 times a day (07.00, 11.00, 15.00, and 19.00) based on a feeding rate from 4% (w/w) for average body weight < 4 g, 3% (w/w) for average body weight 4–10 g, and reduced until 2% (w/w) for average body weight 10–25 g, depending on the feed index adapting to operational standards procedures for Pacific white shrimp cultivation production from PT Steladus. The synbiotic microcapsules consisted of prebiotic mannan oligosaccharide/MOS, probiotic Pseudoalteromonas piscicida (1UB), and Bacillus cereus (BR2). This study focused on parameters of growth performance (survival rate/SR, specific growth rate/SGR, feed conversion ratio/FCR), immune response (total hemocyte count/THC, differential hemocyte count/DHC, respiratory burst/RB, phenoloxidase/PO activity, phagocytic activity/PA), microbiology (total bacterial count/TBC, 1UB + BR2 TetR count, total Vibrio count/TVC), and histopathology. The results showed significant results that synbiotic microcapsules increased SR, SGR also reduced FCR with the best results in SIN 1.5 (SR: 93.50 ± 1.30%, SGR: 4.83 ± 0.01% days−1, and FCR: 1.16 ± 0.02). Immune response of THC, RB, PO, and PA in the synbiotic microcapsule treatment showed higher values with the best results in the SIN 1.5 treatment, while DHC had no significant difference. TBC, 1UB, and BR2 TetR count values increased until the 60th day, with the highest value in the SIN 1.5, while TVC showed the lowest value in both intestinal and hepatopancreas organs. Histopathological results showed that hepatopancreas and intestinal organs experienced tissue damage in all treatments, with synbiotic microcapsules showing less damage when compared to SIN 0. In conclusion, supplementation of dual-species synbiotic microcapsules through feed can improve growth performance and immune response and suppress Vibrio during the outbreak with the best dose of 1.5% synbiotic microcapsules. [ABSTRACT FROM AUTHOR]

A health crisis can impact GVCs adversely by raising bilateral trade costs and via supply‐ and demand‐side shocks in the exporting and importing countries. Focusing on trade in select GVC‐intensive sectors, we disentangle the effects of these different channels in the context of SARS and MERS in a structural gravity framework. The estimated effects are found to be small in magnitude and show significant heterogeneity by sector, channel and disease outbreak. SARS‐induced rise in bilateral trade costs is found to reduce the export value and number of products traded of both intermediate and final goods, while similar adverse effects from MERS are only observed on intermediate goods export value. There is more evidence for the adverse effects of supply‐shocks from both SARS and MERS in our results, while the expected negative effects of the demand‐shock are only observed for MERS. The SARS effects are found to diminish over time, pointing to resilience of the associated value‐chains. We also find suggestive evidence for SARS in particular being associated with geographical diversification and widening of value‐chains. [ABSTRACT FROM AUTHOR]

Individuals often underestimate disaster risk and overestimate their own preparedness, even for disasters they have experienced. Considering the varied effects of prior disaster exposure on preparedness, the psychological trauma literature can inform models of preparedness among hazard survivors. Posttraumatic growth (PTG), or positive changes after a stressor, could explain how some, but not all, disaster survivors come to value and engage in preparedness. University students and employees (N=1,113) in a tornado-prone region of the United States were surveyed from 2015 to 2016 about preparedness for tornadoes, structural fires, and large-scale disease outbreaks. For each hazard type, they reported perceived likelihood, self-efficacy responding to the hazard, how much they valued preparedness, and their prior exposure to the hazard. Tornado survivors (N=661) were further asked how many tornadoes they had experienced, the severity of the impact (e.g., injured, lost electricity), and their perceived PTG from tornadoes. Comparing each variable by hazard type, perceived likelihood, and prior exposure were greater for tornadoes compared with disease and fire. For tornado survivors, PTG mediated the effect of tornado severity on both preparedness and valuing preparedness. In conclusion, PTG is a process by which disaster experiences result in increased disaster preparedness. Simply experiencing a disaster, even a severe one, does not necessarily instill disaster preparedness as a value; however, positive change in recovery from a severe disaster involves caring more about preparedness and becoming increasingly prepared. Enhanced disaster preparedness is not an inevitable outcome of disaster exposure, but it is a potential outcome of psychological recovery from disaster trauma. Integrating the present findings, the preparedness literature, and the psychological trauma literature, we propose an integrated individual-level disaster resilience cycle model. [ABSTRACT FROM AUTHOR]

Global amphibian declines are compounded by deadly disease outbreaks caused by the chytrid fungus, Batrachochytrium dendrobatidis (Bd). Much has been learned about the roles of amphibian skin-produced antimicrobial components and microbiomes in controlling Bd, yet almost nothing is known about the roles of skin-resident immune cells in anti-Bd defenses. Mammalian mast cells reside within and serve as key immune sentinels in barrier tissues like skin. Accordingly, we investigated the roles of Xenopus laevis frog mast cells during Bd infections. Our findings indicate that enrichment of X. laevis skin mast cells confers anti-Bd protection and ameliorates the inflammation-associated skin damage caused by Bd infection. This includes a significant reduction in infiltration of Bd-infected skin by neutrophils, promoting mucin content within cutaneous mucus glands, and preventing Bd-mediated changes to skin microbiomes. Mammalian mast cells are known for their production of the pleiotropic interleukin-4 (IL4) cytokine and our findings suggest that the X. laevis IL4 plays a key role in manifesting the effects seen following cutaneous mast cell enrichment. Together, this work underscores the importance of amphibian skin-resident immune cells in anti-Bd defenses and illuminates a novel avenue for investigating amphibian host-chytrid pathogen interactions. [ABSTRACT FROM AUTHOR]

Aquaculture plays a critical role in Zambia's food and nutrition security. However, aquaculture is highly vulnerable to the effects of a changing climate which can lead to economic losses, and food and nutrition insecurity. In this study, we investigated the perceived effects of climate change on aquaculture production in Zambia, vulnerability factors, and adaptation strategies. We used semi-structured and structured questionnaires to collect data from aquaculture producers in all provinces across the country. Results revealed high (over 80%) awareness of climate change, primarily attributed to literacy levels and access to media. Producers perceived occurrence of climate-related changes such as rising temperature, altered rainfall patterns, cyclones frequency, and disease outbreaks, impacting production costs. Generally, the results indicate a high to medium perception of rising temperature and increased frequency of flooding and droughts across the country. Although rising temperature did not appear to affect aquaculture production cost, the frequency of droughts, floods, cyclones, and disease outbreaks showed a significant association with production costs. Identified vulnerability factors include reliance on a limited range of fish species, absence of insurance coverage, and low adoption of adaptation practices. Vulnerability levels vary across regions, with provinces in agroecological zone I (Eastern, Lusaka, Southern, and Western) showing higher vulnerability. Despite this, many producers aren't implementing adaptation measures due to financial limitations, species diversity constraints, and insufficient knowledge of alternative economic pursuits. Main adaptation strategies involve livelihood diversification and adjustments in fish cultivation periods and infrastructure. To foster aquaculture sustainability amid climate change, critical interventions such as farminsurance, research diversification in aquaculture species, and enhancing producer resilience are necessary. [ABSTRACT FROM AUTHOR]

Networks are widely used in understanding the spread of infectious disease. Universities and colleges are characterized by frequent social gatherings and extensive interpersonal communication, making them ideal applications of complex networks. In this paper, a compound model consisting of a homogeneous (small world) network and a heterogeneous (scale free) network is established, the spreading characteristics of epidemics are analyzed by using the mean‐field (MF) and the heterogeneous mean‐field (HMF) approaches, and the effects of various factors such as the total node number (N), the randomly reconnection probability (p), the initial proportion of infected node (p0), the average degree (k), and the shared nodes (s) are simulated by numerical calculations. The simulation results are in consistent with theoretical predictions, indicating that the randomness effect decreases with increasing k and N and is small when k (k = 8) and N (N = 200) are relatively large; the randomness effect is more significant in the scale free (SF) network than in the small world (SW) network, and the disease spread faster in the SF network; shared nodes can weakly increase the disease spread, but when the shared nodes are involved in an infected and an uninfected network, it can induce the disease outbreak in the uninfected network. [ABSTRACT FROM AUTHOR]

The accelerated warming of the planet caused by anthropogenic climate change is very concerning, as its impacts have the potential to be broad and its effects widespread. One climate change impact with significant interest from scientists, politicians, the media and the general public, is the proposed changes to infectious disease dynamics. Climate change has the potential to alter disease transmission through expansion to naive populations or by worsening risk factors. However, limitations exist in our ability to forecast the climate and disease, including how we incorporate changes in human behaviour and how we attribute climate/weather events solely to an infectious disease outcome. Broad statements about the impact of the climate on infectious disease may not be helpful, as these relationships are highly complex and likely lead to an oversimplification. The interdisciplinary field of climate-health research has the attention of those outside of science, and it is the responsibility of those involved to communicate attribution on an evidence basis, for better scientific communication and public spending. The uncertainty around the impacts of climate change is a call for action, to prevent pushing the Earth's systems into the unknown. [ABSTRACT FROM AUTHOR]

This article explores the lessons learned from the Covid-19 pandemic and the significance of being prepared for future pandemics. It focuses on the advancements in vaccine development that have resulted in the creation of safe and effective vaccines for Covid-19. The article emphasizes the importance of collaboration between private, government, and academic sectors to address public health challenges. It also mentions Project NextGen, an ongoing initiative aimed at developing next-generation vaccines for Covid-19 and other infectious diseases. The article concludes by discussing the establishment of a correlate of protection (CoP) to assess vaccine effectiveness and the duration of immunity. Additionally, the text delves into the role of cellular immunity in protecting against severe disease during a pandemic, suggesting that memory T cells may be less affected by virus mutations and can recognize more conserved epitopes. It highlights the need for improved assays to measure T-cell-mediated immune responses and the challenges faced by vaccine manufacturers in maintaining surge capacity and supply chains. The article stresses the importance of expanding our understanding of vaccine immunology and building trust in vaccines. Lastly, it includes a personal anecdote about a medical student's experience with a recurring nodule on his wrist. [Extracted from the article]

During an infectious disease outbreak, public health policy makers are tasked with strategically implementing interventions whilst balancing competing objectives. To provide a quantitative framework that can be used to guide these decisions, it is helpful to devise a clear and specific objective function that can be evaluated to determine the optimal outbreak response. In this study, we have developed a mathematical modelling framework representing outbreaks of a novel emerging pathogen for which non-pharmaceutical interventions (NPIs) are imposed or removed based on thresholds for hospital occupancy. These thresholds are set at different levels to define four unique strategies for disease control. We illustrate that the optimal intervention strategy is contingent on the choice of objective function. Specifically, the optimal strategy depends on the extent to which policy makers prioritise reducing health costs due to infection over the costs associated with maintaining interventions. Motivated by the scenario early in the COVID-19 pandemic, we incorporate the development of a vaccine into our modelling framework and demonstrate that a policy maker's belief about when a vaccine will become available in future, and its eventual coverage (and/or effectiveness), affects the optimal strategy to adopt early in the outbreak. Furthermore, we show how uncertainty in these quantities can be accounted for when deciding which interventions to introduce. This research highlights the benefits of policy makers being explicit about the precise objectives of introducing interventions. Author summary: While often necessary to contain an infectious disease outbreak, extensive interventions result in education, economic and societal harms. For the optimal response to be determined, the trade-off between the costs and benefits of disease control requires policy makers to define and communicate their objectives for managing the outbreak. We use a mathematical model to simulate outbreaks where social distancing is implemented when disease prevalence within hospital settings is high. We consider four distinct strategies for managing the outbreak based on different hospital prevalence thresholds for switching between levels of social distancing. The optimal strategy to implement depends on how a policy maker balances the importance of reducing the number of hospitalisations and the costs of maintaining interventions. The optimal strategy to implement at the beginning of the outbreak is further impacted by beliefs regarding the future availability of a vaccine. We develop a quantitative decision making framework which explicitly considers the objectives of policy makers and allows robust strategies for future disease outbreaks to be designed. [ABSTRACT FROM AUTHOR]

Objective To explore the epidemiological characteristics of foodborne disease events in a rural banquet in Sichuan Province from 2004 to 2023, and to provide a scientific basis for the prevention and control of foodborne disease in the rural banquet. Methods The data of foodborne disease events in a rural banquet in Sichuan Province from 2004 to 2023 were collected from the National Foodborne Disease Outbreaks Surveillance System and National Management Information System of Public Health Emergencies and analyzed by descriptive statistics. Results From 2004 to 2023, a total of 189 foodborne disease events in rural banquet were reported in Sichuan Province with 5 971 cases and 22 deaths, and the case fatality rate was 0.37%. The third quarterly was the high incidence period, accounting for 43.39% of the total events. The top three regions with the highest number of reported events were Chengdu (25 events), Luzhou (19 events) and Yibin (18 events). Microbial pathogens were the main pathogenic factors, accounting for 66.67% of the total events. Chemicals were the main causes of death, with 15 and 5 deaths caused by methanol and nitrite, respectively. Meat and meat products were the main causes of foodborne diseases. Improper processing and storage were the main inducing factors. Conclusion The targeted health education, supervision, monitoring, and emergency handling should be carried out to effectively prevent and control foodborne disease events in rural banquets. [ABSTRACT FROM AUTHOR]

Background: Keeping the health workforce and the public informed about the latest evolving health information during a health emergency is critical to preventing, detecting and responding to infectious disease outbreaks or other health emergencies. Having a well-informed, ready, willing, and skilled workforce and an informed public can help save lives, reduce diseases and suffering, and minimize socio-economic loss in affected communities and countries. Providing "just in time" support and opportunities for learning in health emergencies is much needed for capacity building. In this paper, 'learning intervention' refers to the provision of ad-hoc, focused, or personalized training sessions with the goal of preparing the health workers for emergencies or filling specific knowledge or skill gaps. We refer to 'training methods' as instructional design strategies used to teach someone the necessary knowledge and skills to perform a task. Methods: We conducted a scoping review to map and better understand what learning interventions and training methods have been used in different types of health emergencies and by whom. Studies were identified using six databases (Pubmed/Medline, Embase, Hinari, WorldCat, CABI and Web of Science) and by consulting with experts. Characteristics of studies were mapped and displayed and major topic areas were identified. Results: Of the 319 records that were included, contexts most frequently covered were COVID-19, disasters in general, Ebola and wars. Four prominent topic areas were identified: 1) Knowledge acquisition, 2) Emergency plans, 3) Impact of the learning intervention, and 4) Training methods. Much of the evidence was based on observational methods with few trials, which likely reflects the unique context of each health emergency. Evolution of methods was apparent, particularly in virtual learning. Learning during health emergencies appeared to improve knowledge, general management of the situation, quality of life of both trainers and affected population, satisfaction and clinical outcomes. Conclusion: This is the first scoping review to map the evidence, which serves as a first step in developing urgently needed global guidance to further improve the quality and reach of learning interventions and training methods in this context. [ABSTRACT FROM AUTHOR]

Syndromic surveillance is a type of public health surveillance that utilizes nonspecific indicators or symptoms associated with a particular disease or condition to detect and track disease outbreaks early. However, data completeness has been a significant challenge for syndromic surveillance systems in many countries. Incomplete data may make it difficult to accurately identify anomalies or trends in surveillance data. In this study, a new disease mapping method based on a high‐accuracy, low‐rank tensor completion (HaLRTC) algorithm is proposed to estimate the quarterly positivity rate of the human influenza virus (IFV) based on highly insufficient 2010–2015 respiratory syndromic surveillance data from the subtropical monsoon region of China. The HaLRTC algorithm is a spatiotemporal interpolation method applied to fill in missing or incomplete data using a low‐rank tensor structure. The results show that the accuracy (R2 = 0.880, RMSE = 0.037) of the proposed method is much higher than that of three traditional disease mapping methods: Cokriging, hierarchical Bayesian, and sandwich estimation methods. This study provides a new disease mapping approach to improve the quality and completeness of data in syndrome surveillance or other familiar systems with a large proportion of missing data. [ABSTRACT FROM AUTHOR]

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Foodborne illnesses caused by consuming contaminated fresh produce not only pose serious public health risks but also lead to huge economic losses. Rockmelons (cantaloupes) have emerged as a recurrent source of disease outbreaks caused by foodborne pathogens, including Listeria monocytogenes, Salmonella, and Escherichia coli. The most common factor of the outbreaks was the microbial contamination of rockmelons at the farm, and subsequently, the pathogenic bacteria were transferred to the flesh during cutting and processing. One of the deadliest outbreaks occurred in the USA due to L. monocytogenes contamination of rockmelons which caused 33 deaths in 2011. Since then, several guidelines and recommendations have been developed for food safety management to reduce the microbial contamination of melons on farms and post-harvest operations. This article explicitly provides an updated overview of microbiological contamination, disease outbreaks, pathogens prevalence, and mitigation strategies to reduce public health risks due to the consumption of rockmelons. [ABSTRACT FROM AUTHOR]

Background: There is an increasing disease trend for SARS-COV-2, so need a quick and affordable diagnostic method. It should be highly accurate and save costs compared to other methods. The purpose of this research is to achieve these goals. Methods: This study analyzed 342 samples using TaqMan One-Step RT-qPCR and fast One-Step RT-LAMP (Reverse Transcriptase Loop-Mediated Isothermal Amplification). The One-Step LAMP assay was conducted to assess the sensitivity and specificity. Results: The research reported positive samples using two different methods. In the RT-LAMP method, saliva had 92 positive samples (26.9%) and 250 negative samples (73.09%) and nasopharynx had 94 positive samples (27.4%) and 248 negative samples (72.51%). In the RT-qPCR method, saliva had 86 positive samples (25.1%) and 256 negative samples (74.8%) and nasopharynx had 93 positive samples (27.1%) and 249 negative samples (72.8%). The agreement between the two tests in saliva and nasopharynx samples was 93% and 94% respectively, based on Cohen's kappa coefficient (κ) (P < 0.001). The rate of sensitivity in this technique was reported at a dilution of 1 × 101 and 100% specificity. Conclusions: Based on the results of the study the One-Step LAMP assay has multiple advantages. These include simplicity, cost-effectiveness, high sensitivity, and specificity. The One-Step LAMP assay shows promise as a diagnostic tool. It can help manage disease outbreaks, ensure prompt treatment, and safeguard public health by providing rapid, easy-to-use testing. [ABSTRACT FROM AUTHOR]

AbstractObjectiveParticipantsMethodsResultsConclusionsExamine college students’ behavior intention to reduce viral outbreak transmission in the context of Mpox, and what preventive messaging strategies would be most effective in future transmissible disease outbreaks based on the Reasoned Action Approach (RAA).Undergraduates at a mid-Atlantic U.S. University.An online survey (n = 266) conducted at the height of the recent Mpox outbreak in the U.S., asked about five target behaviors to reduce Mpox transmission, including RAA determinants for each behavior.Highest intention was safe sex practices, lowest were vaccination and sexual abstinence, and sharing dishes and fabrics were in between. RAA determinants differed by target behavior, although attitudes were significantly positively associated with intention for all five behaviors.College students are potentially open to preventative behaviors to reduce viral transmission during an outbreak. However, specific target behavior matters and messaging should focus on differing RAA determinants depending on target behavior. [ABSTRACT FROM AUTHOR]

The implementation of isolation precautions for patients with suspected Coronavirus Disease 2019 (COVID-19) and pending test results is resource intensive. Due to the limited availability of single-bed rooms at our institution, we isolated patients with suspected COVID-19 together with patients without suspected COVID-19 on-site in multiple-bed rooms until SARS-CoV-2-test results were available. We evaluated the likelihood of SARS-CoV-2 transmission to individuals sharing the room with patients isolated on-site. This observational study was performed at the University Hospital Basel, Switzerland, from 03/20 − 11/20. Secondary attack rates were compared between patients hospitalized in multiple-bed rooms and exposed to individuals subjected to on-site isolation precautions (on-site isolation group), and patients exposed to individuals initially not identified as having COVID-19, and not placed under isolation precautions until the diagnosis was suspected (control group). Transmission events were confirmed by whole-genome sequencing. Among 1,218 patients with suspected COVID-19, 67 (5.5%) tested positive for SARS-CoV-2. Of these, 21 were isolated on-site potentially exposing 27 patients sharing the same room. Median contact time was 12 h (interquartile range 7–18 h). SARS-CoV-2 transmission was identified in none of the patients in the on-site isolation group vs. 10/63 (15.9%) in the control group (p = 0.03). Isolation on-site of suspected COVID-19-patients in multiple-bed rooms avoided single-room occupancy and subsequent in-hospital relocation for many patients without confirmed SARS-CoV-2-infection. The absence of secondary transmission among the exposed patients in the on-site isolation group allows for assessment of the risk/benefit ratio of this strategy given the limitation of a small sample size. [ABSTRACT FROM AUTHOR]

Over the last 4 decades, coral disease research has continued to provide reports of diseases, the occurrence and severity of disease outbreaks and associated disease signs. Histology using systematic protocols is a gold standard for the microscopic assessment of diseases in veterinary and medical research, while also providing valuable information on host condition. However, uptake of histological analysis for coral disease remains limited. Increasing disease outbreaks on coral reefs as human impacts intensify highlights a need to understand the use of histology to date in coral disease research. Here, we apply a systematic approach to collating, mapping and reviewing histological methods used to study coral diseases with 'white' signs (i.e., white diseases) in hard coral taxa and map research effort in this field spanning study design, sample processing and analysis in the 33 publications identified between 1984 and 2022. We find that studies to date have not uniformly detailed methodologies, and terminology associated with reporting and disease description is inconsistent between studies. Combined these limitations reduce study repeatability, limiting the capacity for researchers to compare disease reports. A primary outcome of this study is the provision of transparent and repeatable protocols for systematically reviewing literature associated with white diseases of hard coral taxa, and development of recommendations for standardised reporting procedures with the aim of increasing uptake of histology in addition to allowing for ongoing comparative analysis through living systematic reviews for the coral disease field. [ABSTRACT FROM AUTHOR]

Sheep pox and goat pox are infectious viral diseases that affect ovine and caprine animals and are caused by two viruses of the family Poxviridae, genus Capripoxvirus. Sheep pox has been traditionally endemic in Africa, the Middle East, and several Southeast Asian countries, but it is considered a transboundary disease capable of affecting previously free countries epidemically. It is a disease of compulsory immediate notification to the World Organization for Animal Health (WOAH) and the European Union (EU). On 19 September 2022, the disease reemerged in Spain, which had been free of it since 1968, causing a total of 30 outbreaks until 17 May 2023, when the last outbreak of the disease was reported. The control and eradication measures implemented were those laid down in EU legislation, based on the total stamping out of positive herds, zoning and restriction of movement, and strengthening of biosecurity and passive surveillance. This manuscript describes the outbreak, as well as assesses the challenges and lessons learned in relation to its management, with the aim of helping in the effective management of future outbreaks of this disease. [ABSTRACT FROM AUTHOR]

In populations of healthy show horses, the subclinical transmission and circulation of respiratory pathogens can lead to disease outbreaks. Due to recent outbreaks of equine herpesvirus-1 myeloencephalopathy (EHM) in the USA and Europe, many show organizers have instituted various biosecurity protocols such as individual horse testing, monitoring for early clinical disease and increasing hygiene and cleanliness protocols. The aim of this study was to determine the accuracy of detecting EHV-1 in the various environmental samples collected from the stalls of subclinical shedders. Four healthy adult horses were vaccinated intranasally with a modified-live EHV-1 vaccine in order to mimic subclinical shedding. Three additional horses served as non-vaccinated controls. All the horses were stabled in the same barn in individual stalls. Each vaccinated horse had nose-to-nose contact with at least one other horse. Prior to the vaccine administration, and daily thereafter for 10 days, various samples were collected, including a 6" rayon-tipped nasal swab, an environmental sponge, a cloth strip placed above the automatic waterer and an air sample. The various samples were processed for nucleic acid purification and analyzed for the presence of EHV-1 via quantitative PCR (qPCR). EHV-1 in nasal secretions was only detected in the vaccinated horses for 1–2 days post-vaccine administration. The environmental sponges tested EHV-1 qPCR-positive for 2–5 days (median 3.5 days) in the vaccinated horses and 1 day for a single control horse. EHV-1 was detected by qPCR in stall strips from three out of four vaccinated horses and from two out of three controls for only one day. EHV-1 qPCR-positive air samples were only detected in three out of four vaccinated horses for one single day. For the vaccinated horses, a total of 25% of the nasal swabs, 35% of the environmental stall sponges, 7.5% of the strips and 7.5% of the air samples tested qPCR positive for EHV-1 during the 10 study days. When monitoring the subclinical EHV-1 shedders, the collection and testing of the environmental sponges were able to detect EHV-1 in the environment with greater frequency as compared to nasal swabs, stationary strips and air samples. [ABSTRACT FROM AUTHOR]