Regional Outbreaks Research Articles

The Spatial Variation of Water Clouds, NH3, and H2O on Jupiter Using Keck Data at 5 Microns

We obtained high-resolution spectra of Jupiter between 4.6 and 5.4 µm using NIRSPEC on the Keck 2 telescope in February 2017. We measured the spatial variation of NH3, H2O, and the pressure level of deep (p > 3 bar) clouds using two geometries. We aligned the slit north–south on Jupiter’s Central Meridian to measure the spatial variation of the gas composition and cloud structure between 66°N and 70°S. With the slit aligned east–west, we also examined the longitudinal variation at two regions of the North Equatorial Belt (NEB) at 18°N and at 8°N near the latitude of the Galileo Probe entry site. We used the integrated line absorption, also known as the equivalent width, of deuterated methane (CH3D) at 4.66 µm to derive the pressure level of deep clouds between 3 and 7 bar. From thermochemical models, these are most likely water clouds. At the location of a deep cloud revealed by HST methane-band imaging, we found spectroscopic evidence for an opaque cloud at the 5 bar level. We also identified regions on Jupiter that lacked deep clouds but exhibited evidence for upper clouds and enhanced NH3. We estimated column-averaged mole fractions of H2O and NH3 above the opaque lower boundary of the deep cloud. The meridional scan exhibited significant belt-zone structure with retrieved NH3 abundances in the 200–400 ppm range above the opaque lower cloud, except for a depletion (down to 90 ppm) in the NEB. Water in Jupiter’s belts varies from a maximum of 7 ppm at 8°S to a minimum of 1.5 ppm at 23°S. We found evidence for water clouds and enhanced NH3 and H2O in the South Equatorial Belt Outbreak region at 13°S. The NEB is a heterogeneous region with significant variation in all of these quantities. The NH3 abundance at 18°N and 8°N varies with the longitude with mole fractions between 120 and 300 ppm. The H2O abundance at these same latitudes varies with the longitude with mole fractions between 3 and 10 ppm. Our volatile mole fractions apply to the 5 to 8 bar pressure range (or to the level of an opaque cloud top where found at shallower pressure); therefore, they imply a deeper gradient continuing to increase toward higher concentrations detected by the Galileo Probe Mass Spectrometer at 11 and 20 bar. Hot Spots in the NEB exhibit minimal cloud opacity; however, they lack prominent anomalies in the concentrations of NH3 or H2O.

Stopping syphilis transmission in Arctic communities through rapid diagnostic testing: The STAR study protocol.

BackgroundIntense transmission of syphilis has emerged in some Canadian Arctic communities despite screening and prevention efforts. The remoteness of most communities and limited diagnostic infrastructure yield long delays (≥14 days) between screening and treatment of cases. These hamper syphilis control efforts and may contribute to sustained transmission. Syphilis rapid diagnostic tests (RDTs) have been developed to make screening more accessible and to inform clinical decision-making within the same clinical encounter. These RDTs have been successfully deployed in several countries, but not yet in Canada.Methods and designWe describe the methodology of the “Stopping Syphilis Transmission in Arctic Communities Through Rapid Diagnostic Testing” (STAR) study, wherein the clinical and epidemiological impact of deploying a dual syphilis RDT in the context of ongoing transmission in Nunavut and Nunavik will be evaluated. In this prospective multisite field evaluation, sexually active individuals aged ≥14 years at risk for syphilis will be offered screening by an RDT at the point-of-care by non-laboratory trained registered nurses. Whole blood and serum specimens will be concurrently collected, when feasible, for rapid testing with an RDT containing both treponemal and non-treponemal components (Chembio DPP® Syphilis Screen & Confirm) and compared to laboratory-based reference testing according to a reverse sequence algorithm. The diagnostic accuracy of the RDT, using both whole blood and centrifuged serum specimens, will be validated under real-world conditions in remote Northern settings, outside of specialized laboratories. Additionally, screening-to-treatment time, case detection rates, and the number of infectious contacts averted by using the RDT relative to reference testing will be estimated. The impact of both diagnostic approaches on syphilis transmission dynamics will also be modeled.DiscussionThis study will provide much needed evidence for strengthening rapid responses to emerging syphilis outbreaks in remote Arctic regions, by supplementing traditional diagnostic strategies with an RDT to rapidly triage patients likely in need of treatment. These results will also inform the development and tailoring of future diagnostic strategies and public health responses to emerging outbreaks in the North.

Different clinical features of children and adults in regional outbreak of Delta COVID-19

BackgroundThis study compared clinical features of the Delta variant of coronavirus disease 2019 (COVID-19) in children and adults.MethodsClinical data included 80 children and 132 adults with the Delta variant of COVID-19, hospitalized in the Affiliated Hospital of Putian College between September and October 2021. The data was analyzed retrospectively.ResultsThe proportion of mild patients in the children group (50%) was higher than that in the adults group (17.9%). Cough (25%, 20/80) and diarrhea (1.3%, 1/80) symptoms in children group were significantly less frequent. Compared with adults, there was no significant difference in the viral load of SARS-CoV-2 in samples collected by nasopharyngeal swabs. In children, lymphocyte count was higher [1.98 (0.25–4.25) vs 1.20 (0.29–4.27) ×109/L], whereas the interleukin-6 level was lower [5.87 (1.50–61.40) vs 15.15 (1.79–166.30) pg/mL] than that in adults group. Additionally, the incidence of liver injury in children group was lower than that in adults group. There was no significant difference in the incidence of proteinuria (22/75 vs 45/112) between the two groups, but the serum creatinine level in children was lower [42.0 (28.0–73.0) vs 57.0 (32.0–94.0) µmol/L].ConclusionCompared with adults, children with the Delta variant of COVID-19 have differences in symptoms, clinical classification, inflammatory indices, and liver/kidney function injury. Children’s illness is relatively mild. Clinicians should pay attention to their differences and use drugs accurately.

A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19.

Collaboration is the key to resilience

Owing to the impact of the Covid pandemic that swept across Europe and the world since early 2020, never before has infectious diseases and their potential as a health and economic risk been so forefront in the minds of the European public, at least not in recent times. Those of us that been involved in medical and veterinary entomology or vector-borne disease related research and surveillance know only too well how quickly new and existing diseases can emerge to cause local and regional outbreaks. The outbreak of Zika in the Americas highlighted the prospect of global transmission of a vector-borne disease with outbreaks concerning the Americas, suddenly a concern for Europe.

Electrochemical Immunosensors Based on Zinc Oxide Nanorods for Detection of Antibodies Against SARS-CoV-2 Spike Protein in Convalescent and Vaccinated Individuals.

Even after over 2 years of the COVID-19 pandemic, research on rapid, inexpensive, and accurate tests remains essential for controlling and avoiding the global spread of SARS-CoV-2 across the planet during a potential reappearance in future global waves or regional outbreaks. Assessment of serological responses for COVID-19 can be beneficial for population-level surveillance purposes, supporting the development of novel vaccines and evaluating the efficacy of different immunization programs. This can be especially relevant for broadly used inactivated whole virus vaccines, such as CoronaVac, which produced lower titers of neutralizing antibodies. and showed lower efficacy for specific groups such as the elderly and immunocompromised. We developed an impedimetric biosensor based on the immobilization of SARS-CoV-2 recombinant trimeric spike protein (S protein) on zinc oxide nanorod (ZnONR)-modified fluorine-doped tin oxide substrates for COVID-19 serology testing. Due to electrostatic interactions, the negatively charged S protein was immobilized via physical adsorption. The electrochemical response of the immunosensor was measured at each modification step and characterized by scanning electron microscopy and electrochemical techniques. We successfully evaluated the applicability of the modified ZnONR electrodes using serum samples from COVID-19 convalescent individuals, CoronaVac-vaccinated with or without positive results for SARS-CoV-2 infection, and pre-pandemic samples from healthy volunteers as controls. ELISA for IgG anti-SARS-CoV-2 spike protein was performed for comparison, and ELISA for IgG anti-RBDs of seasonal coronavirus (HCoVs) was used to test the specificity of immunosensor detection. No cross-reactivity with HCoVs was detected using the ZnONR immunosensor, and more interestingly, the sensor presented higher sensitivity when compared to negative ELISA results. The results demonstrate that the ZnONRs/spike-modified electrode displayed sensitive results for convalescents and vaccinated samples and shows excellent potential as a tool for the population’s assessment and monitoring of seroconversion and seroprevalence.

Availability, scope and quality of monkeypox clinical management guidelines globally: a systematic review

BackgroundMonkeypox (MPX) is an important human Orthopoxvirus infection. There has been an increase in MPX cases and outbreaks in endemic and non-endemic regions in recent decades. We appraised the availability,...

Epidemiological Characteristics of COVID-19 under Government-Mandated Control Measures during January-February 2020 in Inner Mongolia, China.

The coronavirus disease 2019 (COVID-19) pandemic has had severe health impacts worldwide. We aim to provide suggestions to the government for managing serious infectious disease outbreaks in remote regions having relatively poor medical resources. Basic reproduction number (R0), incubation period, time from symptom onset to confirmation, and duration of hospitalization were analyzed. We compared the compositions of imported and local secondary cases and cases with mild/common and severe/critical illnesses according to age, sex, and clinical symptoms. From January 23 to February 19, 2020 (less than one month), 75 local COVID-19 cases were confirmed in Inner Mongolia. Among these, the median age was 45.0 years, and 33 (44.0%) were imported cases. More than 80.0% cases had mild/common illness. The case fatality rate was 1.3%, and R0 was estimated to be 2.3. The median incubation period was 8.5 days. There was a significant difference in the incubation period between imported and local secondary cases (P < 0.001). Early and mandatory control strategies implemented by the government were associated with a rapid reduction in COVID-19 incidence in Inner Mongolia.

After-action review of rabies and anthrax outbreaks multisectoral response in Tanzania, challenges and lessons.

After-action review uses experiences gained from past events to adopt best practices, thereby improving future interventions. In December 2016 and late 2018, the government of Tanzania with support from partners responded to anthrax and rabies outbreaks in Arusha and Morogoro regions respectively. The One Health Coordination Desk (OHCD) of the Prime Minister’s Office (PMO) later coordinated after-action reviews to review the multi-sectoral preparedness and response to the outbreaks. To establish and describe actions undertaken by the multi-sectoral investigation and response teams during planning and deployment, execution of field activities, and outbreak investigation and response, system best practices and deficiencies. These were cross-sectional surveys. Semi-structured, open and closed-ended questionnaire and focus group discussions were administered to collect information from responders at the national and subnational levels. It was found that the surveillance and response systems were weak at community level, lack of enforcement of public health laws including vaccination of livestock and domestic animals and joint preparedness efforts were generally undermined by differential disease surveillance capacities among sectors. Lack of resources in particular funds for supplies, transport and deployment of response teams contributed to many shortfalls. The findings underpin the importance of after-action reviews in identifying critical areas for improvement in multi-sectoral prevention and control of disease outbreaks. Main sectors under the coordination of the OHCD should include after action reviews in their plans and budget it as a tool to continuously assess and improve multi-sectoral preparedness and response to public health emergencies.

Deep similarity analysis and forecasting of actual outbreak of major infectious diseases using Internet-Sourced data

Perhaps no other generation in the span of recorded human history has endured the risks of infectious diseases as has the current generation. The prevalence of infectious diseases is caused mainly by unlimited contact between people in a highly globalized world. Disease control and prevention (CDC) promptly collect and produce disease outbreak statistics, but CDCs rely on a curated, centralized collection system, and requires up to two weeks of lead time. Consequently, the quick release of disease outbreak information has become a great challenge. Infectious disease outbreak information is recorded and spread somewhere on the Internet much faster than CDC announcements, and Internet-sourced data have shown non-substitutable potential to watch and predict infectious disease outbreaks in advance. In this study, we performed a thorough analysis to show the similarity between the Korean Center of Disease Control (KCDC) infectious disease datasets and three Internet-sourced data for nine major infectious diseases in terms of time-series volume. The results show that many of infectious disease outbreak have strongly related to Internet-sourced data. We analyzed several factors that affect the similarity. Our analysis shows that the increase in the number of Internet-sourced data correlates with the increase in the number of infected people and thus, show the positive similarity. We also found that the greater the number of infectious disease outbreaks corresponds to having a wider spread of outbreak regions, in which it also proves to have higher similarity. We presented the prediction result of infectious disease outbreak using various Internet-sourced data and an effective deep learning algorithm. It showed that there are positive correlations between the number of infected people or the number of related web data and the prediction accuracy. We developed and currently operate a web-based system to show the similarity between KCDC and related Internet-sourced data for infectious diseases. This paper helps people to identify what kind of Internet-sourced data they need to use to predict and track a specific infectious disease outbreak. We considered as much as nine major diseases and three kinds of Internet-sourced data together, and we can say that our finding did not depend on specific infectious disease nor specific Internet-sourced data.

Prediction-driven collaborative emergency medical resource allocation with deep learning and optimization

This study addresses two key issues, ie, the “cold-start problem” in transmission prediction of new or rare epidemics and the collaborative allocation of emergency medical resources considering multiple objectives. These two issues have not yet been well addressed in data-driven emergency medical resource allocation systems. A decision support prediction-then-optimization framework combing deep learning and optimization is developed to address these two issues. Two transfer learning based convolutional neural network models are built for epidemic transmission predictions in the initial and the subsequent outbreak regions using transfer learning to deal with the “cold-start problem”. A prediction-driven collaborative emergency medical resource allocation model is built to address the issue of collaborative decisions by simultaneously considering the inter- and intra-echelon resource flows in a multi-echelon system and considering the efficiency and fairness as the objective functions. A case study of the COVID-19 pandemic shows that combining transfer learning and convolutional neural networks can improve the performances of epidemic transmission predictions, and good predictions can improve both the efficiency and fairness of emergency medical resource allocation decisions. Moreover, the computational results show that the prediction errors are asymmetrically amplified in the optimization stage, and the shortage of the resource reserve quantity mediates the asymmetrical amplification effect.

Charting current evidence on the health and non-health benefits and equity impacts of pandemic/epidemic individual-level economic relief programmes: a scoping review protocol

IntroductionThe emergence of a regional or global scale infectious disease outbreak often requires the implementation of economic relief programmes in affected jurisdictions to sustain societal welfare and, presumably, population health....

Epidemiologic Profile of Severe Acute Respiratory Infection in Brazil During the COVID-19 Pandemic: An Epidemiological Study.

BackgroundThe COVID-19 is a significant public health issue, and monitoring confirmed cases and deaths is an essential epidemiologic tool. We evaluated the features in Brazilian hospitalized patients due to severe acute respiratory infection (SARI) during the COVID-19 pandemic in Brazil. We grouped the patients into the following categories: Influenza virus infection (G1), other respiratory viruses' infection (G2), other known etiologic agents (G3), SARS-CoV-2 infection (patients with COVID-19, G4), and undefined etiological agent (G5).MethodsWe performed an epidemiological study using data from DataSUS (https://opendatasus.saude.gov.br/) from December 2019 to October 2021. The dataset included Brazilian hospitalized patients due to SARI. We considered the clinical evolution of the patients with SARI during the COVID-19 pandemic according to the SARI patient groups as the outcome. We performed the multivariate statistical analysis using logistic regression, and we adopted an Alpha error of 0.05.ResultsA total of 2,740,272 patients were hospitalized due to SARI in Brazil, being the São Paulo state responsible for most of the cases [802,367 (29.3%)]. Most of the patients were male (1,495,416; 54.6%), aged between 25 and 60 years (1,269,398; 46.3%), and were White (1,105,123; 49.8%). A total of 1,577,279 (68.3%) patients recovered from SARI, whereas 701,607 (30.4%) died due to SARI, and 30,551 (1.3%) did not have their deaths related to SARI. A major part of the patients was grouped in G4 (1,817,098; 66.3%) and G5 (896,207; 32.7%). The other groups account for <1% of our sample [G1: 3,474 (0.1%), G2: 16,627 (0.6%), and G3: 6,866 (0.3%)]. The deaths related to SARI were more frequent in G4 (574,887; 34.7%); however, the deaths not related to SARI were more frequent among the patients categorized into the G3 (1,339; 21.3%) and G5 (25,829; 4.1%). In the multivariate analysis, the main predictors to classify the patients in the G5 when compared with G4 or G1-G4 were female sex, younger age, Black race, low educational level, rural place of residence, and the use of antiviral to treat the clinical signs. Furthermore, several features predict the risk of death by SARI, such as older age, race (Black, Indigenous, and multiracial background), low educational level, residence in a flu outbreak region, need for intensive care unit, and need for mechanical ventilatory support.ConclusionsThe possible COVID-19 underreporting (G5) might be associated with an enhanced mortality rate, more evident in distinct social groups. In addition, the patients' features are unequal between the patients' groups and can be used to determine the risk of possible COVID-19 underreporting in our population. Patients with a higher risk of death had a different epidemiological profile when compared with patients who recovered from SARI, like older age, Black, Indigenous, and multiracial background races, low educational level, residence in a flu outbreak region, need for intensive care unit and need for mechanical ventilatory support.

Lethal and sublethal effects of chlorantraniliprole on the migratory moths Agrotis ipsilon and A. segetum: New perspectives for pest management strategies.

Agrotis ipsilon and A. segetum are major migratory pests of many crops in China, and frequent regional outbreaks cause severe yield losses. Use of food attractants is one of the most promising control methods against adult lepidoptera, notably through the attract-and-kill strategy. Chlorantraniliprole's acute toxicity and sublethal effects on both moths were evaluated. Chlorantraniliprole showed high activity against both adults of both species, with LC20 and LC50 values of 0.08 and 0.21 mg L-1 (A. ipsilon), and 0.14 and 0.51 mg L-1 (A. segetum). The fecundity, effective oviposition rate, and egg hatching rate of both species in dual-sex exposure treatments were all significantly reduced compared with the control, and the population growth coefficients in the LC50 ♀ × LC50 ♂ treatments were only 0.32% (A. ipsilon) and 3.35% (A. segetum) that of the control. Furthermore, the flight distance was significantly suppressed from 6.67 km (control) to 0.01 km (LC50 ) for A. ipsilon, and from 7.39 km (control) to 0.78 km (LC50 ) for A. segetum. The proportions of robust- and medium-flight individuals of A. ipsilon and A. segetum in exposure treatments were greatly reduced. Low lethal concentration exposures to chlorantraniliprole can drastically reduce the reproduction and flight performance of A. ipsilon and A. segetum, while inhibiting the production of offspring, suggesting chlorantraniliprole would be an excellent compound for use in combination with food attractants. Chlorantraniliprole has good potential for management of the two long-range migratory pests tested using an attract-and-kill strategy. © 2022 Society of Chemical Industry.

Epidemics that End with a Bang

To answer how epidemics end, one must ask two intersecting but separate questions: first, how particular waves of epidemics end, whether of yellow fever, cholera, plague; and second, how epidemic diseases become eradicated-either through scientific intervention, as with smallpox in the 1970s, or simply by disappearing for reasons that remain mysterious, as with the Second Plague Pandemic from ca. 1347. This article challenges two general notions on how epidemics end. First, individual waves of plagues in European municipalities or regional states did not just fade into the sunset. By the late 16th century, their ends were celebrated with artistic displays, musical and poetic performances, ex-voto gifts, and bangs ranging from tambourines to military salutes. Second, the five-century Second Pandemic of plague-that is, the disease-did not end with declarations or scripted performances, but with another sort of bang. Instead of the usual assumption that epidemic diseases decline gradually over time, progressively inflicting lighter loads of virulence and mortality, the last significant plague outbreaks in most regions in Europe returned to heights of mortality not seen since the Black Death of 1347-1351. Finally, this article adds an obvious but rarely mentioned variable for understanding epidemic endings. Before widespread vaccination, the characteristics of these disappearances depended on the type of disease.

Research and Design of An Early Warning System for Emerging Infectious Diseases Based on UWB

Aiming at the problem of cross-infection in hospitals and other areas of infectious diseases, we designed a system based on ultra-wideband and using TDOA positioning method combined with smart wearable devices to realize health warning and contact trajectory tracking of medical staff. In the hospital area with stricter management, we obtain the user’s consent and wear smart bracelets to the users. These bracelets are used for positioning and temperature detection.. First, we model the indoor area based on the measurement data of the indoor area and install the base station, use the ultra-wideband technology combined with the wireless time synchronization TDOA method to achieve high-precision positioning in the indoor area, and use Kalman filter to filter the positioning noise data. Then, it combines the body temperature detection data of the wearable bracelet and the system positioning data to establish a user track tracking service to further record the user’s activity range. In addition, the area is strictly controlled, the trajectory of the infected person is restored and the contact person is quickly located, and timely warning can be provided through information such as electronic fence and body temperature. The system can trace the source of regional outbreaks of infectious diseases, provide health warnings for users, and quickly locate the relevant contacts of virus carriers.

Weather parameters largely regulate the outbreak of rice bacterial blight: an endemic disease poses a threat for food security in Bangladesh

Bacterial blight (BB) caused by Xanthomonas oryzae pv. oryzae is a destructive disease of rice causing significant yield losses. Surveys were conducted to know the influence of weather parameters on epidemic development of BB in the thirty agroecological zones of Bangladesh. The maximum (55.11%) bacterial blight incidence and severity were recorded Mymensingh division (AEZs 8 and 9) while the minimum (3.25%) incidence was observed in Barishal division (AEZ 13) in both rainfed and irrigated seasons. In rainfed conditions, the maximum BB incidence and severity were recorded in hybrid varieties Aloron from Netrokona (AEZ 9) and the minimum incidence was observed in the local variety Sughondi from Jalhokhati (AEZ 13). On the other hand, in irrigated conditions, the highest BB incidence was recorded in the hybrid variety Hera-2 from Netrokona (AEZ 9) and the lowest incidence was observed in the local variety from Hobiganj (AEZ 20, 21, 22). Swarna, Puja, BRRIdhan28, BRRIdhan29, and BRRIdhan49 showed intermediate levels of infection, while hybrid Hera-2, Arize TejGold, Partex, Agrodhan, Aloron, ACI-2, and Dhani Gold showed higher level of infection (susceptibility) to BB. The data analysis on BB incidence with the weather parameters of rice growing seasons from 2015 to 2017 revealed that the rainfall distribution varied greatly within rice growing seasons over the years. The results indicated that weather parameters, viz. temperature (25-34°C), relative humidity (70-84%), and rainfall (&gt;200mm), largely affected the prevalence, incidence and severity of BB. The correlation studies revealed that, among the climate variables, minimum temperature exhibits a negative correlation. However, rainfall, maximum temperature, morning relative humidity and evening relative humidity were found positively correlated, while wind velocity imparted a significant positive correlation with BB infection. The regional outbreak of BB indicated a considerable yield loss of rice and give rise a threat for food security of the country.

Antimicrobial Resistance and Virulence Characteristics of Klebsiella pneumoniae Isolates in Kenya by Whole-Genome Sequencing.

Klebsiella pneumoniae is a globally significant opportunistic pathogen causing healthcare-associated and community-acquired infections. This study examined the epidemiology and the distribution of resistance and virulence genes in clinical K. pneumoniae strains in Kenya. A total of 89 K. pneumoniae isolates were collected over six years from five counties in Kenya and were analyzed using whole-genome sequencing and bioinformatics. These isolates were obtained from community-acquired (62/89) and healthcare-associated infections (21/89), and from the hospital environment (6/89). Genetic analysis revealed the presence of blaNDM-1 and blaOXA-181 carbapenemase genes and the armA and rmtF genes known to confer pan-aminoglycoside resistance. The most abundant extended-spectrum beta-lactamase genes identified were blaCTX-M-15 (36/89), blaTEM (35/89), and blaOXA (18/89). In addition, one isolate had a mobile colistin resistance gene (mcr-8). Fluoroquinolone resistance-conferring mutations in gyrA and parC genes were also observed. The most notable virulence factors were those associated with hyper-virulence (rmpA/A2 and magA), yersiniabactin (ybt), salmochelin (iro), and aerobactin (iuc and iutA). A total of 38 distinct sequence types were identified, including known global lineages ST14, ST15, ST147, and ST307, and a regional clone ST17 implicated in regional outbreaks. In addition, this study genetically characterized two potential hypervirulent isolates and two community-acquired ST147 high-risk clones that contained carbapenemase genes, yersiniabactin, and other multidrug resistance genes. These results demonstrate that the resistome and virulome of Kenyan clinical and hospital environmental K. pneumoniae isolates are diverse. The reservoir of high-risk clones capable of spreading resistance, and virulence factors have the potential to cause unmanageable infection outbreaks with high morbidity and mortality.

Behavioural insights and the evolving COVID-19 pandemic.

Behavioural sciences have complemented medical and epidemiological sciences in the response to the SARS-CoV-2 pandemic. As vaccination uptake continues to increase across the EU/EEA – including booster vaccinations – behavioural science research remains important for both pandemic policy, planning of services and communication. From a behavioural perspective, the following three areas are key as the pandemic progresses: (i) attaining and maintaining high levels of vaccination including booster doses across all groups in society, including socially vulnerable populations, (ii) informing sustainable pandemic policies and ensuring adherence to basic prevention measures to protect the most vulnerable population, and (iii) facilitating population preparedness and willingness to support and adhere to the reimposition of restrictions locally or regionally whenever outbreaks may occur. Based on mixed-methods research, expert consultations, and engagement with communities, behavioural data and interventions can thus be important to prevent and effectively respond to local or regional outbreaks, and to minimise socioeconomic and health disparities. In this Perspective, we briefly outline these topics from a European viewpoint, while recognising the importance of considering the specific context in individual countries.

Drivers of melioidosis endemicity: epidemiological transition, zoonosis, and climate change.

Melioidosis, caused by the soil-dwelling bacterium Burkholderia pseudomallei, is a tropical infection associated with high morbidity and mortality. This review summarizes current insights into melioidosis' endemicity, focusing on epidemiological transitions, zoonosis, and climate change. Estimates of the global burden of melioidosis affirm the significance of hot-spots in Australia and Thailand. However, it also highlights the paucity of systematic data from South Asia, The Americas, and Africa. Globally, the growing incidence of diabetes, chronic renal and (alcoholic) liver diseases further increase the susceptibility of individuals to B. pseudomallei infection. Recent outbreaks in nonendemic regions have further exposed the hazard from the trade of animals and products as potential reservoirs for B. pseudomallei. Lastly, global warming will increase precipitation, severe weather events, soil salinity and anthrosol, all associated with the occurrence of B. pseudomallei. Epidemiological transitions, zoonotic hazards, and climate change are all contributing to the emergence of novel melioidosis-endemic areas. The adoption of the One Health approach involving multidisciplinary collaboration is important in unraveling the real incidence of B. pseudomallei, as well as reducing the spread and associated mortality.