Search

Atrazine simazine and propazine, widely used triazine herbicides on food crops and in residential areas, disrupt the neuroendocrine system raising human health concerns. USEPA developed a PBPK model based on triazine common Mode of Action (MOA)-suppression of luteinizing hormone surge in female rats-to generate human regulatory points of departure (POD: mg/kg/day). We compared triazine Human Administered Equivalent Dose (AEDComputational tools were the following: ToxCast/Tox21 in vitro assays; Toxicogenomic databases to assess concordance with ToxCast/Tox21 targets; integrated chemical environment (ICE) models with ToxCast/Tox21 inputs to predict AEDToxCast/Tox21 assays identified critical targets in the triazine common MOA and gene databases; ICE AEDCompTox tools were used to identify assay targets in the MOA and identify potential molecular initiating targets in the adverse outcome pathway for potential use in risk assessment.

Research impact and influence are commonly measured quantitatively by citation count received by research articles. Many institutes also use citation count as one of the factors in faculty performance appraisal and candidate selection of academic positions. Various strategies were recommended to amplify and accelerate research influence, particularly citation counts, by bringing research articles to a wider reach for potential readers. However, no prior empirical study was conducted to examine and valid effects of those strategies on nursing studies. This study examines and verifies the direct effects and mediation effects of some strategies, namely, the use of Twitter, international collaboration, the use of ResearchGate, and open access publishing, for amplifying the citation of research and review articles in nursing studies.Cross-sectional study design.Articles published in top nursing journals in 2016 were identified in PUBMED and the citation metrics for individual articles until 2021 were extracted from Scopus. The primary outcome was the citation count of the article, while the tweet count on Twitter of the article was considered a mediator. The predictors included paper type, the total number of authors, the proportion of authors with a ResearchGate account in the article, funding support, open-accessed article, and the number of different countries stated in the authors' affiliation. A mediation analysis was conducted to examine the predictors' direct and indirect effects (i.e., via tweet count) on the citation count of the article.A total of 2210 articles were included in this study, of which 223 (10.1%) were review articles. The median (IQR) number of Scopus citations, tweets, countries, and percentage of authors with ResearchGate accounts were 12 (6-21), 2 (0-6), 1 (1-1), and 75% (50%-100%) respectively. In the mediation analysis, tweet count, article type, number of countries, percentage of authors with a ResearchGate account, and journal impact factors in 2014 were positively associated with the Scopus citation count. The effects of article type, open access, and journals' impact factors in 2014 on Scopus citation count were mediated by the tweet count.This study provides empirical support for some strategies researchers may employ to amplify the citation count of their research articles. The methodology of our study can be extended to compare research influence between entities (e.g., across countries or institutes).The citation refers to the research work cited by peers and is one of the indicators for research impact. Higher citations implied the research work is read and used by others, therefore, understanding the associated factors with higher citations is critical.

A ChatGPT search engine with chatbot function offers travellers pre-, during and post-travel consultations. Users should critically evaluate information provided and assess source reliability. Integrating user feedback and reviews could enhance information quality. With responsible artificial intelligence (AI) development, leveraging travel medicine specialists’ expertise and AI-based search engines can transform travel medicine.

BACKGROUND Predicting early outbreaks and epidemics with artificial intelligence (AI) needs a standardized approach where human trust is explicitly characterized to be maximized. Data science or analytical methods were found useful in predicting health threats. Still, there lacks a systemic thinking of integrating data science and analytical methods to be a transparent and explainable design that human trust is characterized. OBJECTIVE We aimed to formulate a standardized input-analytics-output (i.e., three components) approach to predicting health threats like COVID-19. The approach should be built on a theoretical framework that humans can trust cognitively and emotionally. At the same time, each component was designed to maximize the information load, performance, transparency, and human trust. We aimed to illustrate the standardized approach to predicting Hong Kong’s early outbreaks and the later epidemic of COVID-19 as an example. METHODS We adopted the socio-ecological system (SES) as the theoretical framework to map the accumulated number and time-series data of outbreaks in residential buildings and their influencing factors into a hierarchical structure. Multiple sources of non-health data were analyzed and integrated into the SES hierarchies from the individual, household, to community levels. Analytics included the modeling based on deep neural networks (DNN) of the SES-informed structure, the cross-validations for flagging buildings of more infection risks, and the prospective validations for forecasting 3-, 7- and 14-day daily case increases of all studied buildings. RESULTS We found that the role of sociodemographics and residential environments in the early COVID-19 outbreaks (before the epidemic: 2020-01-23 to 2021-12-23) differed from those in the epidemic (2021-12-24 to 2022-05-21), after studying 345 residential buildings from three adjacent districts. Our DNN accurately classified the high-risk cluster in the epidemic (AUCs=0.99, 0.98, and 0.95). Its extracted Shapley values enabled accurate building-level outbreak forecasting between 2022-05-22 and 2022-07-23 (average AUC>0.95 in 7-day forecasting horizon). Residential environment factors played more significant roles than sociodemographics in the COVID-19 epidemic. Based on the identified influencing factors (e.g., work hours, monthly household income, number of households, number of non-working population and children, and floor plan, number of floors, flats, and corridors), implications on monitoring the early outbreaks and preventing its evolvement into epidemics were discussed. CONCLUSIONS We proposed a human-trust-driven AI design for predicting health threats and demonstrated its use and capability in flagging high-risk subjects with cross-sectional data and forecasting future outbreaks daily at the subject level. The standardized approach provided a systemic logic to design new AI and verify existing AI designs in predicting and understanding public-health threats like COVID-19. It also has excellent extensibility to integrate more data sources, flexibility to accommodate more complicated or uncomplicated modeling with low maintenance costs, and excellent generalizability to other medical research problems using SES or other theoretical frameworks.

Background In addition to high vaccination levels, COVID-19 control requires uptake and continued adherence to personal hygiene and social distancing behaviors. It is unclear whether residents of a city with successive experience in worldwide pandemics such as SARS, would quickly adopt and maintain preventive behaviors. Methods A population-based, longitudinal telephone survey was conducted between in first local wave of the COVID-19 pandemic (April 2020) and third local wave (December 2020) (n = 403). The study examined factors associated with personal hygiene and social distancing behavior fatigue, as measured by reduced adherence. Results Over 9 months, face mask use increased (96.5–100%, p Conclusion Even in regions with past pandemic experience, maintaining social distancing behaviors during a protracted pandemic remains a major public health challenge.

Pneumococcal infection is a leading cause of morbidity and mortality. We aimed to evaluate the cost-effectiveness of 23-valent polysaccharide vaccine (PPV23) together with influenza vaccination or pneumococcal vaccination alone in adults starting from 50 years vs. 65 years in Hong Kong.A hypothetical population of 100,000 older adults was included in a Markov model with age ranging from 50 to 85 years to calculate the cost and quality-adjusted life-years (QALYs) gained for vaccination strategies, including: (1) annual influenza vaccine and PPV23 at 50 and 65 years; (2) annual influenza vaccine and PPV23 at 65 years (similar with the current vaccination programme); (3) PPV23 at 50 and 65 years; (4) PPV23 at 65 years; and (5) no vaccination. We evaluated the incremental cost-effectiveness ratio (ICER) and used Monte Carlo simulation for probabilistic sensitivity analysis. The cost-effectiveness threshold was extracted from previous literature.In comparison with no vaccination, all strategies were cost-effective with ICERs less than the threshold (US$24,302 per QALY gained). When compared with no vaccination, strategies 1-4 saved US$ 49.5, US$ 94.9, US$ 584.3, and US$ 1114.2 to gain one QALY respectively. In comparison with strategy 2, strategy 1 spent US$ 195.3 to gain one QALY, whilst strategies 3 and 4 showed less effectiveness with increased costs.All vaccination strategies were cost-effective, among which the strategy of PPV23 at 50/65 years with annual influenza vaccine was cost-effective even in comparison with current vaccination programme. These findings could help inform the design and implementation of vaccination strategies.

Many mechanisms contribute to the variation in the incidence of influenza disease, such as strain evolution, the waning of immunity and changes in social mixing. Although machine learning methods have been developed for forecasting, these methods are used less commonly in influenza forecasts than statistical and mechanistic models. In this study, we applied a relatively new machine learning method, Extreme Gradient Boosting (XGBoost), to ordinal country-level influenza disease data. We developed a machine learning forecasting framework by adopting the XGBoost algorithm and training it with surveillance data for over 30 countries between 2010 and 2018 from the World Health Organisation’s FluID platform. We then used the model to predict incidence 1- to 4-week ahead. We evaluated the performance of XGBoost forecast models by comparing them with a null model and a historical average model using mean-zero error (MZE) and macro-averaged mean absolute error (mMAE). The XGBoost models were consistently more accurate than the null and historical models for all forecast time horizons. For 1-week ahead predictions across test sets, the mMAE of the XGBoost model with an extending training window was reduced by 78% on average compared to the null model. Although the mMAE increased with longer prediction horizons, XGBoost models showed a 62% reduction in mMAE compared to the null model for 4-week ahead predictions. Our results highlight the potential utility of machine learning methods in forecasting infectious disease incidence when that incidence is defined as an ordinal variable. In particular, the XGBoost model can be easily extended to include more features, thus capturing complex patterns and improving forecast accuracy. Given that many natural extreme phenomena, such as floods and earthquakes, are often described on an ordinal scale when informing planning and response, these results motivate further investigation of using similar scales for communicating risk from infectious diseases.Author SummaryAccurate and timely influenza forecasting is essential to help policymakers improve influenza preparedness and responses to potential outbreaks and allocate medical resources effectively. Here, we present a machine learning framework based on Extreme Gradient Boosting (XBoost) for forecast influenza activity. We used publicly available weekly influenza-like illness (ILI) incidence data in 32 countries. The predictive performance of the machine learning framework was evaluated using several accuracy metrics and compared with baseline models. XGBoost model was shown to be the most accurate prediction approach, and its accuracy remained stable with increasing prediction time horizons. Our results suggest that the machine learning framework for forecasting ILI has the potential to be adopted as a valuable public health tool globally in the future.

Background:Over a life course, human adaptive immunity to antigenically mutable pathogens exhibits competitive and facilitative interactions. We hypothesize that such interactions may lead to cyclic dynamics in immune responses over a lifetime.Methods:To investigate the cyclic behavior, we analyzed hemagglutination inhibition titers against 21 historical influenza A(H3N2) strains spanning 47 years from a cohort in Guangzhou, China, and applied Fourier spectrum analysis. To investigate possible biological mechanisms, we simulated individual antibody profiles encompassing known feedbacks and interactions due to generally recognized immunological mechanisms.Results:We demonstrated a long-term periodicity (about 24 years) in individual antibody responses. The reported cycles were robust to analytic and sampling approaches. Simulations suggested that individual-level cross-reaction between antigenically similar strains likely explains the reported cycle. We showed that the reported cycles are predictable at both individual and birth cohort level and that cohorts show a diversity of phases of these cycles. Phase of cycle was associated with the risk of seroconversion to circulating strains, after accounting for age and pre-existing titers of the circulating strains.Conclusions:Our findings reveal the existence of long-term periodicities in individual antibody responses to A(H3N2). We hypothesize that these cycles are driven by preexisting antibody responses blunting responses to antigenically similar pathogens (by preventing infection and/or robust antibody responses upon infection), leading to reductions in antigen-specific responses over time until individual’s increasing risk leads to an infection with an antigenically distant enough virus to generate a robust immune response. These findings could help disentangle cohort effects from individual-level exposure histories, improve our understanding of observed heterogeneous antibody responses to immunizations, and inform targeted vaccine strategy.Funding:This study was supported by grants from the NIH R56AG048075 (DATC, JL), NIH R01AI114703 (DATC, BY), the Wellcome Trust 200861/Z/16/Z (SR), and 200187/Z/15/Z (SR). This work was also supported by research grants from Guangdong Government HZQB-KCZYZ-2021014 and 2019B121205009 (YG and HZ). DATC, JMR and SR acknowledge support from the National Institutes of Health Fogarty Institute (R01TW0008246). JMR acknowledges support from the Medical Research Council (MR/S004793/1) and the Engineering and Physical Sciences Research Council (EP/N014499/1). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Background and objective Few head-to-head evaluations of immune responses to different vaccines have been reported. Methods Surrogate virus neutralization test (sVNT) antibody levels of adults receiving either two doses of BNT162b2 (n = 366) or CoronaVac (n = 360) vaccines in Hong Kong were determined. An age-matched subgroup (BNT162b2 [n = 49] vs. CoronaVac [n = 49]) was tested for plaque reduction neutralization (PRNT) and spike-binding antibody and T-cell reactivity in peripheral blood mononuclear cells. Results One month after the second dose of vaccine, BNT162b2 elicited significantly higher PRNT50 , PRNT90 , sVNT, spike receptor binding, spike N-terminal domain binding, spike S2 domain binding, spike FcR binding and antibody avidity levels than CoronaVac. The geometric mean PRNT50 titres in those vaccinated with BNT162b2 and CoronaVac vaccines were 251.6 and 69.45, while PRNT90 titres were 98.91 and 16.57, respectively. All of those vaccinated with BNT162b2 and 45 (91.8%) of 49 vaccinated with CoronaVac achieved the 50% protection threshold for PRNT90. Allowing for an expected seven-fold waning of antibody titres over 6 months for those receiving CoronaVac, only 16.3% would meet the 50% protection threshold versus 79.6% of BNT162b2 vaccinees. Age was negatively correlated with PRNT90 antibody titres. Both vaccines induced SARS-CoV-2-specific CD4+ and CD8+ T-cell responses at 1 month post-vaccination but CoronaVac elicited significantly higher structural protein-specific CD4+ and CD8+ T-cell responses. Conclusion Vaccination with BNT162b2 induces stronger humoral responses than CoronaVac. CoronaVac induces higher CD4+ and CD8+ T-cell responses to the structural protein than BNT162b2.

BACKGROUND Pneumococcal infection is a leading cause of disability and death globally. The Hong Kong Government has launched two programmes for pneumococcal vaccination - The Vaccination Subsidy Scheme (VSS) and the Government Vaccination Programme (GVP). This study aimed to examine the enabling factors, obstacles and perception of pneumococcal vaccination, and their association with its uptake in a general Chinese population. METHODS We performed a population-based, telephone survey in the general public aged 65 or above via simple random sampling. A validated survey based on the Health Belief Model (HBM) was used. Their socio-demographic information; history of previous participation in the GVP or VSS; and self-perceived health status were captured. Binary logistic regression models were constructed to examine the factors independently associated with vaccination. RESULTS A total of 1,000 respondents were enrolled in the study, with 402 (40.2%) respondents having intention to join the GVP/VSS in 12 months. Respondents with long-term medical consultant and medication (adjusted odds ratios [aOR] = 1.541, 95% confidence interval (CI): 1.008-2.356, p = 0.046); high levels of perceived susceptibility of infections (aOR = 3.624, 95 %CI: 2.318-5.665, p

As the COVID-19 pandemic continues, the availability of several different new vaccines, their varying supply levels, effectiveness, and immunity duration across different ethnic populations, together with natural infection rates, will have an impact on when each country can reach herd immunity (ranging from 15.3% to 77.1%). Here we estimate the population proportions still required to gain immunity (ranging from 0.01% to 48.8%) to reach an overall herd immunity level to stop the exponential virus spread in 32 selected countries.

Adiposity, smoking, and lower socioeconomic position (SEP) increase COVID-19 risk while the association of vitamin D, blood pressure, and glycemic traits in COVID-19 risk were less clear. Whether angiotensin-converting enzyme 2 (ACE2), the key receptor for SARS-CoV-2, mediates these associations has not been investigated. We conducted a Mendelian randomization study to assess the role of these exposures in COVID-19 and mediation by ACE2.We extracted genetic variants strongly related to various exposures (vitamin D, blood pressure, glycemic traits, smoking, adiposity, and educational attainment [SEP proxy]), and ACE2 cis-variants from genome-wide association studies (GWAS, n ranged from 28 204 to 3 037 499) and applied them to GWAS summary statistics of ACE2 (n = 28 204) and COVID-19 (severe, hospitalized, and susceptibility, n ≤ 2 942 817). We used inverse variance weighted as the main analyses, with MR-Egger and weighted median as sensitivity analyses. Mediation analyses were performed based on product of coefficient method.Higher adiposity, lifetime smoking index, and lower educational attainment were consistently associated with higher risk of COVID-19 phenotypes while there was no strong evidence for an association of other exposures in COVID-19 risk. ACE2 partially mediates the detrimental effects of body mass index (ranged from 4.3% to 8.2%), waist-to-hip ratio (ranged from 11.2% to 16.8%), and lower educational attainment (ranged from 4.0% to 7.5%) in COVID-19 phenotypes while ACE2 did not mediate the detrimental effect of smoking.We provided genetic evidence that reducing ACE2 could partly lower COVID-19 risk amongst people who were overweight/obese or of lower SEP.

Genetic mutations can cause life-threatening diseases such as cancers and sickle cell anemia. Gene detection is thus of importance for disease-risk prediction or early diagnosis and treatment. Apart from genetic defects, gene detection techniques can also be applied to gene-related diseases with high risk to human health such as human papillomavirus (HPV) infection. HPV infection has been strongly linked to cervical cancer. To achieve a high-throughput HPV gene detection platform, the flow-through hybridization system appears to be one of the commercialized diagnostic techniques for this purpose. The flow-through hybridization technique is based on a vacuum-guided flow of DNA fragments which is continuously directed toward the oligoprobes that are immobilized on the testing membrane. However, the conventional colorimetric method and signal read-out approach suffers a problem of low sensitivity. On the contrary, fluorescence approaches allow more sensitive detection and broad sensing ranges. In this work, a fluorescent dye HCAP, which possesses aggregation-induced emission (AIE) properties and is responsive to alkaline phosphatase, was developed and applied to the flow-through hybridization platform to achieve HPV genome diagnosis of clinical samples. Also, an automatic membrane reader was constructed based on the AIE-based diagnosis platform which can identify the diagnostic result of patient DNA with a total concordance rate of 100% in the clinical trial.

Four seasonal coronaviruses, including human coronavirus (HCoV)-229E and HCoV-OC43, HCoV-NL63, and HCoV-HKU1 cause approximately 15-30% of common colds in adults. However, the full landscape of the immune trajectory to these viruses that covers the whole childhood period is still not well understood.We evaluated the serological responses against the four seasonal coronaviruses in 1886 children aged under 18 years by using enzyme-linked immunosorbent assay. The optical density values against each HCoV were determined from each sample. Generalized additive models were constructed to determine the relationship between age and seroprevalence throughout the whole childhood period. The specific antibody levels against the four seasonal coronaviruses were also tested from the plasma samples of 485 pairs of postpartum women and their newborn babies.The immunoglobulin (Ig) G levels of the four seasonal coronaviruses in the mother and the newborn babies were highly correlated (229E: r = 0.63; OC43: r = 0.65; NL63: r = 0.69; HKU1: r = 0.63). The seroprevalences in children showed a similar trajectory in that the levels of IgG in the neonates dropped significantly and reached the lowest level after the age of around 1 year (229E: 1.18 years; OC43: 0.97 years; NL63: 1.01 years; HKU1: 1.02 years) and then resurgence in the children who aged older than 1 year. Using the lowest level from the generalized additive models as our cutoff, the seroprevalences for HCoV-229E, HCoV-OC43, HCoV-NL63, and HCoV-HKU1 were 98.11%, 96.23%, 96.23% and 94.34% at the age of 16-18 years.Mothers share HCoV-specific IgGs with their newborn babies and the level of maternal IgGs waned at around 1 year after birth. The resurgence of the HCoV-specific IgGs was found thereafter with the increase in age suggesting repeated infection occurred in children.

Four seasonal coronaviruses, including HCoV-NL63 and HCoV-229E, HCoV-OC43 and HCoV-HKU1 cause approximately 15–30% of common colds in adults. However, the frequency and timing of early infection with four seasonal coronaviruses in the infant are still not well studied. Here, we evaluated the serological response to four seasonal coronaviruses in 1886 children under 18-year-old to construct the viral infection rates. The antibody levels were also determined from the plasma samples of 485 pairs postpartum women and their newborn babies. This passive immunity waned at one year after birth and the resurgence of the IgGs were found thereafter with the increase of the age. Taken together, our results show the age-related seroprevalence trajectories of seasonal coronaviruses in children and provide useful information for deciding vaccine strategy for coronaviruses in the future.

Over a life-course, human adaptive immunity to antigenically mutable pathogens exhibits competitive and facilitative interactions. We hypothesize that such interactions may lead to cyclic dynamics in immune responses over a lifetime. Here, we demonstrated a long-term periodicity (about 24 years) in individual antibody responses, by analyzing hemagglutination inhibition titers against 21 historical influenza A(H3N2) strains spanning 47 years from a cohort in Guangzhou, China. The reported cycles were robust to analytic and sampling approaches. Simulations suggested that individual-level cross-reaction between antigenically similar strains likely explain the reported cycle. We showed that the reported cycles are predictable at both individual and birth-cohort level and that cohorts show a diversity of phases of these cycles. Phase of cycle was associated with the risk of response to circulating strains, after accounting for age and pre-existing titers of the circulating strains. Our findings reveal the existence of long-term periodicities in individual antibody responses to A(H3N2). We hypothesize that these cycles are driven by pre-existing antibody responses blunting responses to antigenically similar pathogens (by preventing infection and/or robust antibody responses upon infection), leading to reductions in antigen specific responses over time until individual’s increasing risk leads to an infection with an antigenically distant enough virus to generate a robust immune response. These findings could help disentangle cohort-effects from individual-level exposure histories, improve our understanding of observed heterogeneous antibody responses to immunizations, and inform targeted vaccine strategy.

COVID‐19 has hit the world by surprise, causing substantial mortality and morbidity since 2020. This narrative review aims to provide an overview of the epidemiology, induced impact, viral kinetics and clinical spectrum of COVID‐19 in the Asia‐Pacific Region, focusing on regions previously exposed to outbreaks of coronavirus. COVID‐19 progressed differently by regions, with some (such as China and Taiwan) featured by one to two epidemic waves and some (such as Hong Kong and South Korea) featured by multiple waves. There has been no consensus on the estimates of important epidemiological time intervals or proportions, such that using them for making inferences should be done with caution. Viral loads of patients with COVID‐19 peak in the first week of illness around days 2 to 4 and hence there is very high transmission potential causing community outbreaks. Various strategies such as government‐guided and suppress‐and‐lift strategies, trigger‐based/suppression approaches and alert systems have been employed to guide the adoption and easing of control measures. Asymptomatic and pre‐symptomatic transmission is a hallmark of COVID‐19. Identification and isolation of symptomatic patients alone is not effective in controlling the ongoing outbreaks. However, early, prompt and coordinated enactment predisposed regions to successful disease containment. Mass COVID‐19 vaccinations are likely to be the light at the end of the tunnel. There is a need to review what we have learnt in this pandemic and examine how to transfer and improve existing knowledge for ongoing and future epidemics.

Two years into the coronavirus 2019 (COVID-19) pandemic, populations with less built-up immunity continued to devise ways to optimize social distancing measures (SDMs) relaxation levels for outbreaks triggered by SARS-CoV-2 and its variants to resume minimal economics activities while avoiding hospital system collapse.An age-stratified compartmental model featuring social mixing patterns was first fitted the incidence data in second wave in Hong Kong. Hypothetical scenario analysis was conducted by varying population mobility and vaccination coverages (VCs) to predict the number of hospital and intensive-care unit admissions in outbreaks initiated by ancestral strain and its variants (Alpha, Beta, Gamma, Delta and Omicron). Scenarios were "unsustainable" if either of admissions was larger than the maximum of its occupancy.At VC of 65%, scenarios of full SDMs relaxation (mean daily social encounters prior to COVID-19 pandemic = 14.1 contacts) for outbreaks triggered by ancestral strain, Alpha and Beta were sustainable. Restricting levels of SDMs was required such that the optimal population mobility had to be reduced to 0.9, 0.65 and 0.37 for Gamma, Delta and Omicron associated outbreaks respectively. VC improvement from 65% to 75% and 95% allowed complete SDMs relaxation in Gamma-, and Delta-driven epidemic respectively. However, this was not supported for Omicron-triggered epidemic.To seek a path to normality, speedy vaccine and booster distribution to the majority across all age groups is the first step. Gradual or complete SDMs lift could be considered if the hybrid immunity could be achieved due to high vaccination coverage and natural infection rate among vaccinated or the COVID-19 case fatality rate could be reduced similar to that for seasonal influenza to secure hospital system sustainability.

International audience; Responses to the early (February–July 2020) COVID-19 pandemic varied widely, globally. Reasons for this are multiple but likely relate to the healthcare and financial resources then available, and the degree of trust in, and economic support provided by, national governments. Cultural factors also affected how different populations reacted to the various pandemic restrictions, like masking, social distancing and self-isolation or self-quarantine. The degree of compliance with these measures depended on how much individuals valued their needs and liberties over those of their society. Thus, several themes may be relevant when comparing pandemic responses across different regions. East and Southeast Asian populations tended to be more collectivist and self-sacrificing, responding quickly to early signs of the pandemic and readily complied with most restrictions to control its spread. Australasian, Eastern European, Scandinavian, some Middle Eastern, African and South American countries also responded promptly by imposing restrictions of varying severity, due to concerns for their wider society, including for some, the fragility of their healthcare systems. Western European and North American countries, with well-resourced healthcare systems, initially reacted more slowly, partly in an effort to maintain their economies but also todelay imposing pandemic restrictions that limited the personal freedoms of their citizens.

Background Influenza infection is often measured by a fourfold antibody titer increase over an influenza season (ie seroconversion). However, this approach may fail when influenza seasons are less distinct as it does not account for transient effects from recent infections. Here, we present a method to determine seroconversion for non‐paired sera, adjusting for changes in individuals’ antibody titers to influenza due to the transient impact of recent exposures, varied sampling times, and laboratory processes. Methods We applied our method using data for five H3N2 strains collected from 942 individuals, aged 2‐90 years, during the first two study visits of the Fluscape cohort study (2009‐2012) in Guangzhou, China. Results After adjustment, apparent seroconversion rates for non‐circulating strains decreased while we observed a 20% increase in seroconversion rates to recently circulating strains. When examining seroconversion to the most recently circulating strain (A/Brisbane/20/2007) in our study, participants aged under 18, and over 64 had the highest seroconversion rates compared to other age groups. Conclusions Our results highlight the need for improved methods when using antibody titers as an endpoint in settings where there is no clear influenza “off” season. Methods, like those presented here, that use titers from circulating and non‐circulating strains may be key.

SUMMARY Super-spreading events in an outbreak can change the nature of an epidemic. Therefore, it is useful for public health teams to determine if an ongoing outbreak has any contribution from such events, which may be amenable to interventions. We estimated the basic reproductive number (R0) and the dispersion factor (k) from empirical data on clusters of epidemiologically-linked COVID-19 cases in Hong Kong, Japan and Singapore. This allowed us to infer the presence or absence of super-spreading events during the early phase of these outbreaks. The relatively large values of k implied that large cluster sizes, compatible with super-spreading, were unlikely.

Study Design: Retrospective study.Purpose: To investigate the incidence of symptomatic and asymptomatic implant failure in spinal metastasis surgery and identify potential risk factors.Overview of Literature: Surgical stabilization with instrumentation is an established method for the treatment of spinal metastasis. However, very few studies have investigated the incidence and risk factors for implant failure after spinal instrumentation surgery for the treatment of spinal metastasis.Methods: This study recruited 88 patients who received surgical stabilization with instrumentation for the treatment of spinal metastasis. Their medical records and postoperative X-rays were reviewed for evidence of implant failure. Statistical analysis with logistic regression was performed to assess nine potential risk factors for the development of implant failure, including patient’s age at operation, gender, survival, primary tumor, spinal level involved, construct length, decompression levels, fusion material utilization, and radiotherapy application either before or after surgery, to identify potential contributing risk factors.Results: Implant failure was identified in nine out of 88 cases (10.2%) with two cases requiring implant removal: one case included a progressive kyphosis that resulted in nonhealing sore and the other involved a deep-seated wound infection that spread to the implants. Another case required wound debridement due to superficial wound infection. The remaining six cases were asymptomatic, despite postoperative X-rays demonstrating evidence of implant failure. No patient required implant revision. Logistic regression analysis demonstrated that patients who received radiotherapy either before or after surgery were less likely to develop implant failure.Conclusions: The development of radiological implant failure following surgical treatment of spinal metastasis is common. However, symptomatic implant failure leading to revision surgery is uncommon. Our findings suggest that radiotherapy, either before or after spinal surgery, is not associated with the development of implant failure.

KEY POINTS Hong Kong, a special administrative region of the People’s Republic of China, is located in southern China and shares a border with mainland China. It has a population of more than 7.45 million and is a regional travel hub, with the eighth busiest airport in the world and with more than

Background: Transmission of respiratory pathogens such as SARS-CoV-2 depends on patterns of contact and mixing across populations. Understanding this is crucial to predict pathogen spread and the effectiveness of control efforts. Most analyses of contact patterns to date have focused on high-income settings. Methods: Here, we conduct a systematic review and individual-participant meta-analysis of surveys carried out in low- and middle-income countries and compare patterns of contact in these settings to surveys previously carried out in high-income countries. Using individual-level data from 28,503 participants and 413,069 contacts across 27 surveys, we explored how contact characteristics (number, location, duration, and whether physical) vary across income settings. Results: Contact rates declined with age in high- and upper-middle-income settings, but not in low-income settings, where adults aged 65+ made similar numbers of contacts as younger individuals and mixed with all age groups. Across all settings, increasing household size was a key determinant of contact frequency and characteristics, with low-income settings characterised by the largest, most intergenerational households. A higher proportion of contacts were made at home in low-income settings, and work/school contacts were more frequent in high-income strata. We also observed contrasting effects of gender across income strata on the frequency, duration, and type of contacts individuals made. Conclusions: These differences in contact patterns between settings have material consequences for both spread of respiratory pathogens and the effectiveness of different non-pharmaceutical interventions. Funding: This work is primarily being funded by joint Centre funding from the UK Medical Research Council and DFID (MR/R015600/1)., eLife digest Infectious diseases, particularly those caused by airborne pathogens like SARS-CoV-2, spread by social contact, and understanding how people mix is critical in controlling outbreaks. To explore these patterns, researchers typically carry out large contact surveys. Participants are asked for personal information (such as gender, age and occupation), as well as details of recent social contacts, usually those that happened in the last 24 hours. This information includes, the age and gender of the contact, where the interaction happened, how long it lasted, and whether it involved physical touch. These kinds of surveys help scientists to predict how infectious diseases might spread. But there is a problem: most of the data come from high-income countries, and there is evidence to suggest that social contact patterns differ between places. Therefore, data from these countries might not be useful for predicting how infections spread in lower-income regions. Here, Mousa et al. have collected and combined data from 27 contact surveys carried out before the COVID-19 pandemic to see how baseline social interactions vary between high- and lower-income settings. The comparison revealed that, in higher-income countries, the number of daily contacts people made decreased with age. But, in lower-income countries, younger and older individuals made similar numbers of contacts and mixed with all age groups. In higher-income countries, more contacts happened at work or school, while in low-income settings, more interactions happened at home and people were also more likely to live in larger, intergenerational households. Mousa et al. also found that gender affected how long contacts lasted and whether they involved physical contact, both of which are key risk factors for transmitting airborne pathogens. These findings can help researchers to predict how infectious diseases might spread in different settings. They can also be used to assess how effective non-medical restrictions, like shielding of the elderly and workplace closures, will be at reducing transmissions in different parts of the world.