Infection-induced increases to population size during cycles in a discrete-time epidemic model.

One-dimensional discrete-time population models, such as those that involve Logistic or Ricker growth, can exhibit periodic and chaotic dynamics. Expanding the system by one dimension to incorporate epidemiological interactions causes an interesting complexity of new behaviors. Here, we examine a discrete-time two-dimensional susceptible-infectious (SI) model with Ricker growth and show that the introduction of infection can not only produce a distinctly different bifurcation structure than that of the underlying disease-free system but also lead to counter-intuitive increases in population size. We use numerical bifurcation analysis to determine the influence of infection on the location and types of bifurcations. In addition, we examine the appearance and extent of a phenomenon known as the 'hydra effect,' i.e., increases in total population size when factors, such as mortality, that act negatively on a population, are increased. Previous work, primarily focused on dynamics at fixed points, showed that the introduction of infection that reduces fecundity to the SI model can lead to a so-called 'infection-induced hydra effect.' Our work shows that even in such a simple two-dimensional SI model, the introduction of infection that alters fecundity or mortality can produce dynamics can lead to the appearance of a hydra effect, particularly when the disease-free population is at a cycle.

Coordination and modularization: the experience of the joint prevention and control mechanism to COVID emergencies in China.

Background: The integration of disparate emergency resources and the improvement of emergency response teamwork are the underlying trends and shared requirements for building resilience in an era of multiple global public health crises. Objective: This study investigated the emergency response with emergency collaboration networks of each functional module and the overall Joint Epidemic Prevention and Control Mechanism (JPCM) network in China's COVID outbreak prevention and control. Methods: The study employed a scholarly framework of "the integration of JPCM coordination and emergency collaborative modularization" to explore the attributes of JPCM using social network analysis. The data were obtained from administrative records from JPCM's official website, spanning January 2020 to December 2022. Results: The study examined the JPCM coordination and found several functional working modules of JPCM, such as Interrupt Spread, Manage Supply, Medical Rescue, Restore Work and Production, and Implement Responsibility modules. The network structure indicators showed that the Manage Supply module had the most extensive network connectivity, the shortest communication distance, and the most consistent collaboration. The E-I index of the overall JPCM network and the Manage Supply network were - 0.192 and - 0.452, respectively (at p < 0.001 and p < 0.05), indicating more internal relationships than external relationships. The E-I index of the Medical Rescue and Implement Responsibility collaboration networks were 0.122 and 0.147, respectively (at p < 0.001 and p < 0.05), indicating more external relationships than internal relationships. The QAP regression analysis showed that the most vital driver on the overall JPCM network was the Interrupt Spread module, followed by the Implement Responsibility and Medical Rescue modules. Discussion: The Interrupt Spread module initiated emergency coordination with most departments and agencies. The Manage Supply module ensured the flow of medical supplies and survival essentials, while the Medical Rescue module addressed the core aspects of the health emergency response. The Restore Work and Production module repaired the halt in production and livelihoods caused by the outbreak, strengthening and developing emergency coordination and roles across emergency organizations. The Implement Responsibility module provided more heterogeneous emergency response resources for the overall JPCM coordination, complementing the COVID cross-organizational emergency response coordination. Conclusion: The study on the JPCM case in China improves public health emergency management and aids informed decision-making.

Effect of supplementation with yeast polysaccharides on intestinal function in piglets infected with porcine epidemic diarrhea virus.

Porcine epidemic diarrhea virus (PEDV) has caused huge economic losses to the pig industry. Yeast polysaccharides (YP) has been used as a feed additive in recent years and poses good anti-inflammatory and antiviral effects. The present study aimed to explore the protective effect of YP on intestinal damage in PEDV-infected piglets. Eighteen 7-day-old piglets with similar body weights were randomly divided into three groups: Control group (basal diet), PEDV group (basal diet), and PEDV+YP group (basal diet +20 mg/kg BW YP), six replicates per group and one pig per replicate. Piglets in PEDV group and PEDV+YP group were orally given PEDV (dose: 1 × 106 TCID50) at 19:30 PM on the 8th day of the experiment. The control group received the same volume of PBS solution. Weight was taken on an empty stomach in the morning of the 11th day, blood was collected and then anesthetic was administered with pentobarbital sodium (50 mg/kg·BW) by intramuscular injection, and samples were slaughtered after the anesthetic was complete. The results showed that YP could alleviate the destruction of intestinal villus morphology of piglets caused by PEDV. Meanwhile, PEDV infection can reduce the activity of glutathione peroxidase, superoxide dismutase and catalase, and increase the content of malondialdehyde. YP can improve the antioxidative capacity in the serum and small intestine of PEDV-infected piglets. In addition, YP inhibited the replication of PEDV in the jejunum ileum and colon. Moreover, YP can regulate the mRNA levels of inflammatory genes (IL-1ß and iNOS) and lipid metabolic genes (APOA4 and APOC3) in the small intestine. In summary, YP could inhibit virus replicates, improve intestinal morphology, enhance antioxidant capacity, relieve inflammation and regulate the metabolism of the intestine in PEDV-infected piglets.

Fentanyl and its derivatives: Pain-killers or man-killers?

Fentanyl is a synthetic µ-opioid receptor agonist approved to treat severe to moderate pain with faster onset of action and about 100 times more potent than morphine. Over last two decades, abuse of fentanyl and its derivatives has an increased trend, globally. Currently, the United States (US) faces the most serious situation related to fentanyl overdose, commonly referred to as the opioid epidemic. Nowadays, fentanyl is considered as the number one cause of death for adults aged 18-45 in the US. Synthesis and derivatization of fentanyl is inexpensive to manufacture and easily achievable. Indeed, more than 1400 fentanyl derivatives have been described in the scientific literature and patents. In addition, accessibility and efficacy of fentanyl and its derivatives can play a potential role in misuse of these compounds as a chemical weapon. In this review, the properties, general pharmacology, and overdose death cases associated with fentanyl and selected derivatives are presented. Moreover, current opioid epidemic in the US, Moscow theatre hostage crisis, and potential misuse of fentanyl and its derivatives as a chemical weapon are disclosed.

On the SARS-CoV-2 Variants.

The evolutionary dynamics of viruses, particularly exemplified by SARS-CoV-2 during the ongoing COVID-19 pandemic, underscore the intricate interplay between genetics, host adaptation, and viral spread. This paper delves into the genetic evolution of SARS-CoV-2, emphasizing the implications of viral variants on global health. Initially emerging from the Wuhan-Hu-1 lineage, SARS-CoV-2 rapidly diversified into numerous variants, each characterized by distinct mutations in the spike protein and other genomic regions. Notable variants such as B.1.1.7 (α), B.1.351 (ß), P.1 (γ), B.1.617.2 (δ), and the Omicron variant have garnered significant attention due to their heightened transmissibility and immune evasion capabilities. In particular, the Omicron variant has presented a myriad of subvariants, raising concerns about its potential impact on public health. Despite the emergence of numerous variants, the vast majority have exhibited limited expansion capabilities and have not posed significant threats akin to early pandemic strains. Continued genomic surveillance is imperative to identify emerging variants of concern promptly. While genetic adaptation is intrinsic to viral evolution, effective public health responses must be grounded in empirical evidence to navigate the evolving landscape of the pandemic with resilience and precision.

MPSTAN: Metapopulation-Based Spatio-Temporal Attention Network for Epidemic Forecasting.

Accurate epidemic forecasting plays a vital role for governments to develop effective prevention measures for suppressing epidemics. Most of the present spatio-temporal models cannot provide a general framework for stable and accurate forecasting of epidemics with diverse evolutionary trends. Incorporating epidemiological domain knowledge ranging from single-patch to multi-patch into neural networks is expected to improve forecasting accuracy. However, relying solely on single-patch knowledge neglects inter-patch interactions, while constructing multi-patch knowledge is challenging without population mobility data. To address the aforementioned problems, we propose a novel hybrid model called metapopulation-based spatio-temporal attention network (MPSTAN). This model aims to improve the accuracy of epidemic forecasting by incorporating multi-patch epidemiological knowledge into a spatio-temporal model and adaptively defining inter-patch interactions. Moreover, we incorporate inter-patch epidemiological knowledge into both model construction and the loss function to help the model learn epidemic transmission dynamics. Extensive experiments conducted on two representative datasets with different epidemiological evolution trends demonstrate that our proposed model outperforms the baselines and provides more accurate and stable short- and long-term forecasting. We confirm the effectiveness of domain knowledge in the learning model and investigate the impact of different ways of integrating domain knowledge on forecasting. We observe that using domain knowledge in both model construction and the loss function leads to more efficient forecasting, and selecting appropriate domain knowledge can improve accuracy further.

Chemical characterization and source apportionment of PM2.5 in a Northeastern China city during the epidemic period.

To investigate the influence of COVID-19 lockdown measures on PM2.5 and its chemical components in Shenyang, PM2.5 samples were continuously collected from January 1 to May 31, 2020. The samples were then analyzed for water-soluble inorganic ions, metal elements, organic carbon, and elemental carbon. The findings indicated a significant decrease in PM2.5 and its various chemical components during the lockdown period, compared to pre-lockdown levels (p < 0.05), suggesting a substantial improvement in air quality. Water-soluble inorganic ions (WSIIs) were identified as the primary contributors to PM2.5, accounting for 47% before the lockdown, 46% during the lockdown, and 37% after the lockdown. Ionic balance analysis revealed that PM2.5 exhibited neutral, weakly alkaline, and alkaline characteristics before, during, and after the lockdown, respectively. NH4+ was identified as the main balancing cation and was predominantly present in the form of NH4NO3 in the absence of complete neutralization of SO42- and NO3-. Moreover, the higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), along with the significant increase in PM2.5/EC, suggested intense secondary transformation during the lockdown period. The elevated OC/EC ratio during the lockdown period implied higher secondary organic carbon (SOC), and the notable increase in SOC/EC ratio indicated a significant secondary transformation of total carbon. The enrichment factor (EF) results revealed that during the lockdown, 9 metal elements (As, Sn, Pb, Zn, Cu, Sb, Ag, Cd, and Se) were substantially impacted by anthropogenic emissions. Source analysis of PMF was employed to identify the sources of PM2.5 in Shenyang during the study period, and the analysis identified six factors: secondary sulfate and vehicle emissions, catering fume sources, secondary nitrate and coal combustion emissions, dust sources, biomass combustion, and industrial emissions, with secondary sulfate and vehicle emissions and catering fume sources contributing the most to PM2.5.

Sources of negative emotions and tactics of self-emotion regulation among college students during COVID-19 school closure in China.

Background: This study investigated the level of anxiety and depression in Chinese college students since the COVID-19 pandemic and explored the sources of their negative emotions and students' self-emotion regulation strategies. Methods: A stratified cluster sampling questionnaire was used to survey college students during the pandemic via the Anxiety Depression, Self-made Negative Emotion Source, and Negative Emotion Regulation Strategy Scales. Results: The prevalence of anxiety and depression was 23.3 and 20.1%, respectively. These levels were higher in women than in men. Furthermore, senior students reported higher levels than freshmen. Anxiety and depression mainly came from the pressure to grow and the narrowed scope of social activities. Proper relaxation via entertainment and communication with family and friends were popular ways of regulating their negative emotions. Conclusion: College students should confront their negative emotions and understand their source, use psychological methods to regulate their anxiety and depression or seek professional help, improve their psychological resilience, and adopt positive coping measures.

Porcine epidemic diarrhea virus E protein induces formation of stress granules and attenuates protein translation through activation of the PERK/eIF2α signaling pathway.

Porcine epidemic diarrhea virus (PEDV) envelope protein (E) has been characterized as an important structural protein that plays critical roles in the interplay with its host to affect the virus life cycle. Stress granules (SGs) are host translationally silent ribonucleoproteins, which are mainly induced by the phosphorylation of eIF2α in the PERK/eIF2α signaling pathway. Our previous study found that PEDV E protein caused endoplasmic reticulum stress response (ERS)-mediated suppression of antiviral proteins' translation. However, the link and the underlying mechanism by which PEDV induces SGs formation and suppresses host translation remain elusive. In this study, our results showed that PEDV E protein significantly elevated the expression of GRP78, CANX, and phosphorylation of PERK and eIF2α, indicating that the PERK/eIF2α branch of ERS was activated. PEDV E protein localized to the ER and aggregated into puncta to reconstruct ER structure, and further induced SGs formation, which has been caused through upregulating the G3BP1 expression level. In addition, a significant global translational stall and endogenous protein translation attenuation were detected in the presence of E protein overexpression, but the global mRNA transcriptional level remained unchanged, suggesting that the shutoff of protein translation was associated with the translation, not with the transcription process. Collectively, this study demonstrates that PERK/eIF2α activation is required for SGs formation and protein translation stall. This study is beneficial for us to better understand the mechanism by which PEDV E suppresses host protein synthesis, and provides us a new insight into the host translation regulation during virus infection.

SIR or MADAM? The impact of privilege on careers in epidemic modelling.

As we emerge from what may be the largest global public health crises of our lives, our community of epidemic modellers is naturally reflecting. What role can modelling play in supporting decision making during epidemics? How could we more effectively interact with policy makers? How should we design future disease surveillance systems? All crucial questions. But who is going to be addressing them in 10 years' time? With high burnout and poor attrition rates in academia, both magnified in our field by our unprecedented efforts during the pandemic, and with low wages coinciding with inflation at its highest for decades, how do we retain talent? This is a multifaceted challenge, that I argue is underpinned by privilege. In this perspective, I introduce the notion of privilege and highlight how various aspects of privilege (namely gender, ethnicity, sexual orientation, language and caring responsibilities) may affect the ability of individuals to access to and progress within academic modelling careers. I propose actions that members of the epidemic modelling research community may take to mitigate these issues and ensure we have a more diverse and equitable workforce going forward.

Modeling and analysis of a tourism supply chain considering service level and advertising in the context of regular epidemic prevention and control under COVID-19.

In the context of regular epidemic prevention and control, this paper considers a two-stage tourism supply chain consisting of a scenic spot that attracts tourists through advertising and a travel agency that invests in service improvement and epidemic prevention. By establishing theoretical game models of a tourism supply chain, we investigate how the service level and advertising level can affect the retail price, product service level, and profits of the supply chain. The results show that the service level of travel agencies could improve consumers' preferences, expand the market demand for tourism products, and improve the efficiency of the supply chain to achieve a win-win situation and increase the profits of the scenic spot and the travel agency. The retailer price, service level, promotion level, and supply chain profit all increase as the service coefficient and advertising coefficient increase, and the speed of the increase is higher for the centralized model than for other models. Some valuable information could be provided for supply chain enterprises to develop collaborative strategies and promote tourism supply chain management practices.

Comprehensive analysis of a stochastic wireless sensor network motivated by Black-Karasinski process.

Wireless sensor networks (WSNs) encounter a significant challenge in ensuring network security due to their operational constraints. This challenge stems from the potential infiltration of malware into WSNs, where a single infected node can rapidly propagate worms to neighboring nodes. To address this issue, this research introduces a stochastic S E I R S model to characterize worm spread in WSNs. Initially, we established that our model possesses a globally positive solution. Subsequently, we determine a threshold value for our stochastic system and derive a set of sufficient conditions that dictate the persistence or extinction of worm spread in WSNs based on the mean behavior. Our study reveals that environmental randomness can impede the spread of malware in WSNs. Moreover, by utilizing various parameter sets, we obtain approximate solutions that showcase these precise findings and validate the effectiveness of the proposed S E I R S model, which surpasses existing models in mitigating worm transmission in WSNs.

Multiple introductions of monkeypox virus to Ireland during the international mpox outbreak, May 2022 to October 2023.

BackgroundMpox, caused by monkeypox virus (MPXV), was considered a rare zoonotic disease before May 2022, when a global epidemic of cases in non-endemic countries led to the declaration of a Public Health Emergency of International Concern. Cases of mpox in Ireland, a country without previous mpox reports, could reflect extended local transmission or multiple epidemiological introductions.AimTo elucidate the origins and molecular characteristics of MPXV circulating in Ireland between May 2022 and October 2023.MethodsWhole genome sequencing of MPXV from 75% of all Irish mpox cases (182/242) was performed and compared to sequences retrieved from public databases (n = 3,362). Bayesian approaches were used to infer divergence time between sequences from different subclades and evaluate putative importation events from other countries.ResultsOf 242 detected mpox cases, 99% were males (median age: 35 years; range: 15-60). All 182 analysed genomes were assigned to Clade IIb and, presence of 12 distinguishable subclades suggests multiple introductions into Ireland. Estimation of time to divergence of subclades further supports the hypothesis for multiple importation events from numerous countries, indicative of extended and sustained international spread of mpox. Further analysis of sequences revealed that 92% of nucleotide mutations were from cytosine to thymine (or from guanine to adenine), leading to a high number of non-synonymous mutations across subclades; mutations associated with tecovirimat resistance were not observed.ConclusionWe provide insights into the international transmission dynamics supporting multiple introductions of MPXV into Ireland. Such information supported the implementation of evidence-informed public health control measures.

Analyses of drinking water quality during a protracted cholera epidemic in Malawi - a cross-sectional study of key physicochemical and microbiological parameters.

Anecdotal evidence and available literature indicated that contaminated water played a major role in spreading the prolonged cholera epidemic in Malawi from 2022 to 2023. This study assessed drinking water quality in 17 cholera-affected Malawi districts from February to April 2023. Six hundred and thirty-three records were analysed. The median counts/100 ml for thermotolerant coliform was 98 (interquartile range (IQR): 4-100) and that for Escherichia coli was 0 (IQR: 0-9). The drinking water in all (except one) districts was contaminated by thermotolerant coliform, while six districts had their drinking water sources contaminated by E. coli. The percentage of contaminated drinking water sources was significantly higher in shallow unprotected wells (80.0% for E. coli and 95.0% for thermotolerant coliform) and in households (55.8% for E. coli and 86.0% for thermotolerant coliform). Logistic regression showed that household water has three times more risk of being contaminated by E. coli and two and a half times more risk of being contaminated by thermotolerant coliform compared to other water sources. This study demonstrated widespread contamination of drinking water sources during a cholera epidemic in Malawi, which may be the plausible reason for the protracted nature of the epidemic.

Evaluating the spike in the symptomatic proportion of SARS-CoV-2 in China in 2022 with variolation effects: a modeling analysis.

Despite most COVID-19 infections being asymptomatic, mainland China had a high increase in symptomatic cases at the end of 2022. In this study, we examine China's sudden COVID-19 symptomatic surge using a conceptual SIR-based model. Our model considers the epidemiological characteristics of SARS-CoV-2, particularly variolation, from non-pharmaceutical intervention (facial masking and social distance), demography, and disease mortality in mainland China. The increase in symptomatic proportions in China may be attributable to (1) higher sensitivity and vulnerability during winter and (2) enhanced viral inhalation due to spikes in SARS-CoV-2 infections (high transmissibility). These two reasons could explain China's high symptomatic proportion of COVID-19 in December 2022. Our study, therefore, can serve as a decision-support tool to enhance SARS-CoV-2 prevention and control efforts. Thus, we highlight that facemask-induced variolation could potentially reduces transmissibility rather than severity in infected individuals. However, further investigation is required to understand the variolation effect on disease severity.

COVID-19 surveillance report for Sudan, 2020 to 2021.

Background: Following the World Health Organization declaration, COVID-19 was first appearance in Sudan was in March 2020. Cases were reported to the Sudan Federal Ministry of Heath through the surveillance system from different sources. This study used surveillance data from 2020 to 2021 to describe the epidemiologic patterns of COVID-19 occurrence in Sudan and provide insight for better preparedness and response. Methods: Through a retrospective descriptive study, COVID19 cases records obtained from the national surveillance line-list in Surveillance and Information Directorate in Federal Ministry of Health. The analysis of data was done with SPSS version 21. Descriptive analysis done by frequencies and percentages, and further analysis through performing multivariate logistic regression. Results: Out of 48,545 suspected cases tested for COVID-19 using RT-PCR, 27,453 (56.5%) tested positive with case fatality ratio of 6.5%. Higher death rate among elderly (78% > 60-year-old) and males (70.1%). From the reported cases, 53.8% showed no symptoms, while the common symptoms among symptomatic patients were; fever (26.4%), cough (19.1%), shortness of breath (16.8%) with small proportion (4.5%) reported loss of smell and taste. Specific states, Khartoum, Gezira and Red Sea showed highest prevalence. The disease peaked four times during 2020-2021, with a proposed alert threshold of 200-250 cases per week acting as an explosion point nationwide. Conclusions: The high case fatality rate in the country requires further analysis, as well as the high proportion of asymptomatic infection. This will be ensured by improving the quality and completeness of surveillance data. A proposed threshold of 200-250 cases per week should be an alert to augment the measures of controlling the pandemic over the country, including providing enough supplies to decrease mortality.

Predictors of persistent opioid use in bladder cancer patients undergoing radical cystectomy: A SEER-Medicare analysis.

PURPOSE: To evaluate patient and provider characteristics that predict persistent opioid use following radical cystectomy for bladder cancer including non-opioid naïve patients. METHODS: Patients undergoing cystectomy between July 2007 and December 2015 were identified using the SEER-Medicare database. Opioid exposure was identified before and after cystectomy using Medicare Part D data. Multivariable analyses were used to identify predictors of the primary outcomes: persistent opioid use (prescription 3-6 months after surgery) and postoperative opioid prescriptions (within 30 days of surgery). Secondary outcomes included physician prescribing practices and rates of persistent opioid use in their patient cohorts. RESULTS: A total of 1,774 patients were included; 29% had prior opioid exposure. Compared to opioid-naïve patients, non-opioid naïve patients were more frequently younger, Black, and living in less educated communities. The percentage of persistent postoperative use was 10% overall and 24% in non-opioid naïve patients. Adjusting for patient factors, opioid naïve individuals were less likely to develop persistent use (OR 0.23) while a 50-unit increase in oral morphine equivalent per day prescribed following surgery nearly doubled the likelihood of persistent use (OR 1.98). Practice factors such as hospital size, teaching affiliation, and hospital ownership failed to predict persistent use. 29% of patients filled an opioid prescription postoperatively. Opioid naïve patients (OR 0.13) and those cared for at government hospitals (OR 0.59) were less likely to fill an opioid script along with those residing in the Northeast. Variability between physicians was seen in prescribing practices and rates of persistent use. CONCLUSIONS: Non-opioid naïve patients have higher rates of post-operative opioid prescription than opioid-naïve patients. Physician prescribing practices play a role in persistent use, as initial prescription amount predicts persistent use even in non-opioid naïve patients. Significant physician variation in both prescribing practices and rates of persistent use suggest a role for standardizing practices.

Minimizing the Recurrence Rate in Covid Associated Mucormycosis: Our Experience in 500 Patients.

Mucormycosis is an aggressive, fatal fungal infection. The fungal organisms are ubiquitous and easily affect immunocompromised patients. The main aim of this article is to emphasize over the knowledge of different diagnostic methods (diagnostic nasal endoscopy, ct/ contrast mri pns + orbit + brain, Wet KOH mount), the importance of practising an aggressive surgical resection, medical treatments (liposomal amphotericin B, amphotericin gel, tablet posaconazole, nasal douching, retrorbital amphotericin injection), suction cleaning and regular follow up of the patient after surgical management. This can greatly help in minimizing the recurrence of mucormycosis even in immunocompromised patients in a population. The study performed was a prospective study conducted from April 2021 to July 2021 in which we included 500 patients who presented to the OPD & Department of ENT or Emergency Department of Maharaja Yashwantrao Hospital with complaints suggestive of mucormycosis. The patients who were surgically fit were operated. Out of the 500 patients who were diagnosed with Mucormycosis, from April 2021 to July 2021 complete cure was achieved in 456 patients( 91.2%) and only 44 patients (8.8%) have shown recurrence due to various causes (specially those who did not came for regular follow up). Rhinorbital was the most common site to be involved. In the study it was found that most of the patients which showed recurrence were male, post covid and immunocompromised. Diabetes mellitus was found to be most common among immunocompromised patients. The recurrence in patients with mucormycosis can be minimise by educating the masses regarding importance of public and personal hygiene, and multidisciplinary management with regular follow up offers a better approach to treat this life-threatening condition. The study shows the importance of clinical diagnosis, concurrent surgical treatment, medical treatment, post op care, suction cleaning and regular follow up advice. It is necessary us to take the step forward in this regard, so that in the future we will be better prepared for such type of epidemic.

A qualitative exploration of the impacts of COVID-19 in two rural Southwestern Alaska communities.

This manuscript presents a qualitative exploration of the experiences of people in two Southwestern Alaska communities during the emergence of COVID-19 and subsequent pandemic response. The project used principles of community based participatory research and honoured Indigenous ways of knowing throughout the study design, data collection, analysis, and dissemination. Data was collected in 2022 through group and individual conversations with community members, exploring impacts of the COVID-19 pandemic. Participants included Elders, community health workers, Tribal council members, government employees, school personnel, and emergency response personnel. Notes and written responses were coded using thematic qualitative analysis. The most frequently identified themes were 1) feeling disconnected from family, friends, and other relationships, 2) death, 3) the Tribal councils did a good job, and 4) loss of celebrations and ceremonies. While the findings highlighted grief and a loss of social cohesion due to the pandemic, they also included indicators of resilience and thriving, such as appropriate and responsive local governance, revitalisation of traditional medicines, and coming together as a community to survive. This case study was conducted as part of an international collaboration to identify community-driven, evidence-based recommendations to inform pan-Arctic collaboration and decision making in public health during global emergencies.

Epidemiological monitoring and genetic variation analysis of pathogens associated with porcine viral diarrhea in southern China from 2021 to 2023.

Large-scale outbreaks of virus-associated severe diarrhea have occurred in pig populations since 2010. To investigate the prevalence and genetic evolution of the diarrhea-associated viruses responsible for the outbreaks, we tested 1,791 diarrhea samples collected from 213 pig farms in five provinces in southern China between 2021 and 2023. The test results showed that porcine epidemic diarrhea virus (PEDV) was the most frequently detected virus. The prevalence rates ranged from 47.40 to 52.22% in samples and 76.06% (162/213) in pig farms. Porcine rotavirus (PoRV) was the second common virus, with prevalence rates ranging from 25.81 to 50.81% in samples and 72.77%(155/213) in pig farms. Porcine delta coronavirus (PDCoV) was the third common virus, with prevalence rates ranging from 16.33 to 17.48% in samples and 38.50% (82/213) in pig farms. The detection rates of both transmissible gastroenteritis virus (TGEV) and porcine acute diarrheal syndrome coronavirus (SADS-CoV) were very low, less than 1.01% in samples and less than 3.76% in pig farms. In this study, we found SADS-CoV only in piglet diarrhea samples from Jiangxi, Guangdong, and Guangxi provinces in China, with a prevalence rate of 5.16% (11/213) in pig farms. Co-infection with these diarrhea-associated viruses is a common occurrence. The most common co-infections were PEDV and PoRV, with a prevalence rate of 6.64% (119/1,791), followed by PDCoV and PoRV, with a prevalence rate of 4.19% (75/1,791). Phylogenetic analyses showed that PEDV and PEDV variants prevalent in southern China during the past three years clustered into genotype GIIb and recombinant PEDV subtypes. Among the currently endemic PEDV, the most common mutations occurred in the collagenase equivalent (COE) and epitope regions of the spike gene. PoRV strains were mainly dominated by the G9 subtype, followed by the G5, G3 and G4 subtypes. Our results suggest that variant PEDV, PDCoV and PoRV are the main pathogens of swine diarrhea, and singular- or co-infection with pathogenic enteric CoV is common in pig herds in southern China. Therefore, prevention and control of porcine viral diarrhea should be given high attention.