Surveillance Dataset Research Articles

Characteristics of Early Death in Patients With Localized Nasopharyngeal Cancer: A Population-Based SEER Analysis

Localized nasopharyngeal cancer (NPC) is a highly curable disease, but the prognosis of certain cases is still poor. Distinguishing patients with a poor outcome is necessary when developing therapeutic strategies. The aim of this study was to investigate the characteristics of early death (ED) among patients with localized NPC, and to identify independent predictors of ED. Patients diagnosed with localized NPC were included from the Surveillance, Epidemiology, and End Results dataset, and univariate and multivariate logistic regression analyses were performed to identify ED predictors. A total of 752 patients with localized NPC were enrolled, including 198 cases of ED and 480 long-term survivors. Older age, unmarried status, and white race were risk factors for ED, whereas diagnosis in the recent period and undifferentiated non-keratinizing histology type were protective factors. In addition, for older patients, women and those without radiation treatment, there was less ED for married patients than unmarried patients. In conclusion, this population-based study provides an overview of the characteristics of ED patients with localized NPC. Age, race, marital status, year of diagnosis and histology type are risk factors for ED. Moreover, married patients are at a significantly lower risk of ED. This protective effect is especially pronounced in older people, women and those without radiation treatment.

Chronic and non-communicable disease mortality and trends in Chinese elderly, 2004-2018

Objective: To analyze the mortality level and trend of chronic and non-communicable diseases (NCDs) among elderly residents aged 65 and over in China from 2004 to 2018, and predict the age-standardized mortality rate of NCDs from 2019 to 2023. Methods: Data on resident death was collected from the National Mortality Surveillance data set and used to analyze the unstandardized mortality rates, age-standardized mortality rates, composition ratios and changing trends of NCDs among different genders, urban and rural areas, and geographical regions in China during 2004 to 2018. The age-standardized mortality rates were calculated based on the Year 2010 Population Census of China. The Joinpoint Regression Models were fitted by the weighted least squares method. The average annual percent change (AAPC) and its 95% confidence interval for the entire time period were calculated. Log-linear models were used to predict age-standardized mortality rates. Results: From 2004 to 2018, the age-standardized mortality rates of NCDs decreased from 4 697.05 per 100 000 to 3 555.35 per 100 000, with an average annual decline of 2.0% (95%CI: -2.7%- -1.3%). The age-standardized mortality rates among different genders, urban and rural areas, and regions showed a downward trend. The age-standardized mortality rates of eastern region (AAPC = -2.1%, 95%CI: -2.8%- -1.3%) and central region (AAPC = -2.8%, 95%CI: -3.4%- -2.1%) fell faster than that of western region (AAPC = -0.8%, 95%CI: -1.8%-0.2%). The proportion of deaths caused by NCDs increased from 89.82% to 91.41%, with an average annual increase of 0.1% (95%CI: 0.1%-0.2%). Expected to 2023, the age-standardized mortality rates for male (3 906.23 per 100 000) will be significantly higher than female's (2 708.43 per 100 000); and that in rural areas (3 283.20 per 100 000) will be approximately equal to that in urban areas (3 250.01 per 100 000); the gap of age-standardized mortality rate between the western (3 782.48 per 100 000), eastern (3 037.01 per 100 000), and central region (3 249.24 per 100 000) will be further increased. Conclusion: From 2004 to 2018, age-standardized mortality rates of NCDs of the elderly residents in China showed a downward trend, and the proportion of deaths of NCDs showed an upward trend. Male and the western region elderly residents should be the key population for prevention and control of chronic diseases in the future.

Predictions of mortality related to four major cancers in China, 2020 to 2030.

BackgroundCancer has become a global health problem, and assessments of cancer mortality are important for effective public health policy‐making and adequate resource allocation. In this study, we aimed to predict the mortality rates and numbers of deaths related to four common cancers (lung, liver, stomach, and esophagus) in China from 2020 to 2030 and to estimate the corresponding cancer burden caused by population aging and tobacco smoking.MethodsCancer mortality data (2004–2017) were extracted from China's death surveillance datasets, and China's population figures (2020–2030) were obtained from the United Nations population projections. Smoking prevalence data were retrieved from a World Health Organization global report, and relative risks of smoking and cancers were derived from large‐scale Asian studies. We predicted the deaths related to the four major cancers and age‐standardized mortality rates using joinpoint regression and linear regression models. The tobacco smoking‐related burden of these four major cancers was estimated using the population attributable fraction.ResultsUnlike lung cancer mortality which was predicted to continue to increase, the age‐standardized mortality rates for digestive cancers (liver, stomach, and esophageal cancers) are predicted to decline over the next decade. The number of deaths caused by the four major cancers is predicted to increase from 1,490,304 in 2020 to 1,823,960 in 2030. The age‐specific mortality rates of the four major cancers are predicted to increase with age after 40–45 years, peaking in the age groups of 80–84 and ≥85 years. In 2030, the combined number of deaths from the four examined cancers among adults aged ≥65 years is predicted to be 1,167,153, accounting for 64% of all deaths from these cancers. Tobacco smoking is predicted to contribute to nearly 29% of deaths from these cancers, corresponding to 527,577 deaths.ConclusionsThe overall trend in the combined total mortality from four major cancers is predicted to decline over the next decade; however, the corresponding death toll is expected to surge, in the context of China's population aging and high smoking prevalence. These estimates provide data‐driven evidence for China to implement effective cancer control measures in the future.

The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage

We propose a reversible face de-identification method for video surveillance data, where landmark-based techniques cannot be reliably used. Our solution generates a photorealistic de-identified stream that meets the data protection regulations and can be publicly released under minimal privacy concerns. Notably, such stream still encapsulates the information required to later reconstruct the original scene, which is useful for scenarios, such as crime investigation, where subjects identification is of most importance. Our learning process jointly optimizes two main components: 1) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">public</i> module, that receives the raw data and generates the de-identified stream; and 2) a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">private</i> module, designed for security authorities, that receives the public stream and reconstructs the original data, disclosing the actual IDs of the subjects in a scene. The proposed solution is landmarks-free and uses a conditional generative adversarial network to obtain synthetic faces that preserve pose, lighting, background information and even facial expressions. Also, we keep full control over the set of soft facial attributes to be preserved/changed between the raw/de-identified data, which extends the range of applications for the proposed solution. Our experiments were conducted in three visual surveillance datasets (BIODI, MARS and P-DESTRE) plus one video face data set (YouTube Faces), showing highly encouraging results. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hugomcp/uu-net</uri> .

Comparing Decision Tree-Based Ensemble Machine Learning Models for COVID-19 Death Probability Profiling

Background: Age group, sex and underlying comorbidity or disease have been identified as major risk factors in COVID-19 severity and death risk. Aim: We compare the performance of major decision tree-based ensemble machine learning models on the task of COVID-19 death probability prediction, conditional on three risk factors: age group, sex and underlying comorbidity or disease, using the US Centers for Disease Control and Prevention (CDC)’s COVID-19 case surveillance dataset. Method: To evaluate the impact of the three risk factors on COVID-19 death probability, we extract and analyze the conditional probability profile produced by the best performing model. Result: The results show the presence of an exponential rise in death probability from COVID-19 with the age group, with males exhibiting a higher exponential growth rate than females, an effect that is stronger when an underlying comorbidity or disease is present, which also acts as an accelerator of COVID-19 death probability rise for both male and female subjects. These results are discussed in connection to healthcare and epidemiological concerns and in the degree to which they reinforce findings coming from other studies on COVID-19.

Spatial and temporal village-level prevalence of Plasmodium infection and associated risk factors in two districts of Meghalaya, India

BackgroundDespite declining incidence over the past decade, malaria remains an important health burden in India. This study aimed to assess the village-level temporal patterns of Plasmodium infection in two districts of the north-eastern state of Meghalaya and evaluate risk factors that might explain these patterns.MethodsPrimary Health Centre passive malaria case data from 2014 to 2018 were analysed to characterize village-specific annual incidence and temporal trends. Active malaria case detection was undertaken in 2018 and 2019 to detect Plasmodium infections using PCR. A questionnaire collected socio-demographic, environmental, and behavioural data, and households were spatially mapped via GPS. Adult mosquitoes were sampled at a subset of subjects' houses, and Anopheles were identified by PCR and sequencing. Risk factors for Plasmodium infection were evaluated using bivariate and multivariate logistic regression analysis, and spatial cluster analysis was undertaken.ResultsThe annual malaria incidence from PHC-based passive surveillance datasets in 2014–2018 was heterogenous but declining across villages in both districts. Active surveillance in 2018 enrolled 1468 individuals from 468 households (West Jaintia Hills) and 1274 individuals from 359 households (West Khasi Hills). Plasmodium falciparum prevalence per 100 people varied from 0 to 4.1% in the nine villages of West Jaintia Hills, and from 0 to 10.6% in the 12 villages of West Khasi Hills. Significant clustering of P. falciparum infections [observed = 11, expected = 2.15, Relative Risk (RR) = 12.65; p < 0.001] was observed in West Khasi Hills. A total of 13 Anopheles species were found at 53 houses in five villages, with Anopheles jeyporiensis being the most abundant. Risk of infection increased with presence of mosquitoes and electricity in the households [Odds Ratio (OR) = 1.19 and 1.11], respectively. Households with reported animals had reduced infection risk (OR = 0.91).ConclusionMalaria incidence during 2014–2018 declined in all study villages covered by the passive surveillance data, a period that includes the first widespread insecticide-treated net campaign. The survey data from 2018 revealed a significant association between Plasmodium infection and certain household characteristics. Since species of Plasmodium-competent mosquito vectors continue to be abundant, malaria resurgence remains a threat, and control efforts should continue.

GAN based efficient foreground extraction and HGWOSA based optimization for video synopsis generation

Video Synopsis is a smart and efficient solution to summarize a long duration of surveillance video into short. Most of the video synopsis techniques are not suitable to address complex situations like changes in illumination, dynamic background, camera jitter, etc. These techniques firmly depend on the preprocessing results of foreground extraction and multiple objects tracking. Further, the optimization process is a vital phase for the decrement of collision rate among moving objects, where the widely used Simulated Annealing (SA) usually suffers from the issue of slow convergence rate with a high computational overhead. Taking these aforementioned facts into account for feature extraction, we formulate a foreground extraction scheme exploring the concept of multi-frame and multi-scale in Generative Adversarial Network (mFS-GANs). Further, an optimization algorithm is proposed through the hybridization of SA and Grey Wolf Optimizer (GWO), named as, HGWOSA to ensure global optimal result with a low computing overhead. The performance of the proposed scheme is evaluated through extensive simulations and compared with that of the benchmark schemes. The experiments are carried out using some standard surveillance video dataset (ChangeDetection.Net, MIT Surveillance Dataset, and UMN Dataset) and one self-generated surveillance video at IIIT Bhubaneswar. Overall analysis and experimental evaluations demonstrate that our proposed scheme outperforms the other competing schemes in terms of both the quantitative and qualitative measures. Finally, the proposed model can be substantially employed in the generation of off-line video synopsis, which is potentially applicable to video surveillance applications for smart cities.

Role of Ethnicity and Geographic Location on Glioblastoma IDH1/IDH2 Mutations

Previous studies have demonstrated possible differences in glioblastoma (GBM) survival attributable to ethnicity. The goal of this study was to quantify oncogenic differences and evaluate the overall survival (OS) and progression-free survival (PFS) differences in GBM patients across race/ethnicity using both population-based surveillance and institutional data sets from the United States (US) and Mexico. Retrospective cohort study comprising the Texas Cancer Registry (TCR, n = 4134) and referral institutions located in US (n = 254) and Mexico (n = 47) were evaluated. Primary outcomes include OS and PFS. Oncogenic differences attributable to ethnicity were assessed. IDH1/IDH2 status was evaluated by sequencing in US and Mexico samples. Kaplan-Meier and Cox proportional hazards regression for survival analysis. A total of 4134 GBM patients were identified from the TCR data set, ethnicity comparison demonstrated that Hispanic patients were diagnosed at a significantly younger age compared to non-Hispanic white patients (NHW) (median: 58 vs. 62, P < 0.001) and had improved OS (hazard ratio: 0.82, P < 0.001). In the oncogenic analysis, we observed a significant enrichment of IDH1/IDH2 mutations in Mexican Hispanic patients compared to US Hispanic patients (29.8% vs. 7.9%, P = 0.012); IDH2 mutations drove this difference. Post-progression survival was significantly shorter in patients from Mexico than US (3.0 vs. 11.4 months; P < 0.001), while OS remained similar. IDH2 mutations are more prevalent in Mexican Hispanic individuals compared to US individuals and may be a crucial contributor to the previously reported survival benefit of Hispanic individuals in large population databases. These findings are critical for both screening of IDH2 mutations and targeted interventions in GBM.

Evaluating the power of the causal impact method in observational studies of HCV treatment as prevention.

The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem. We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.

Efficient activity recognition using lightweight CNN and DS-GRU network for surveillance applications

Recognizing human activities has become a trend in smart surveillance that contains several challenges, such as performing effective analyses of huge video data streams, while maintaining low computational complexity, and performing this task in real-time. Current activity recognition techniques are using convolutional neural network (CNN) models with computationally complex classifiers, creating hurdles in obtaining quick responses for abnormal activities. To address these challenges in real-time surveillance, this paper proposes a lightweight deep learning-assisted framework for activity recognition. First, we detect a human in the surveillance stream using an effective CNN model, which is trained on two surveillance datasets. The detected individual is tracked throughout the video stream via an ultra-fast object tracker called the ‘minimum output sum of squared error’ (MOSSE). Next, for each tracked individual, pyramidal convolutional features are extracted from two consecutive frames using the efficient LiteFlowNet CNN. Finally, a novel deep skip connection gated recurrent unit (DS-GRU) is trained to learn the temporal changes in the sequence of frames for activity recognition. Experiments are conducted over five benchmark activity recognition datasets, and the results indicate the efficiency of the proposed technique for real-time surveillance applications compared to the state-of-the-art.

Human-Machine Cooperative Video Anomaly Detection

It is still a challenge to detect anomalous events in video sequences in the field of computer vision due to heavy object occlusions, varying crowded densities and complex situations. To address this, we propose a novel human-machine cooperative approach which uses human feedback on anomaly confirmation to inform and enhance video anomaly detection. Specifically, we analyze the spatio-temporal characteristics of sequential frames of a video from the appearance and motion perspective from which spatial and temporal features are identified and extracted. We then develop a convolutional autoencoder neural network to compute an abnormal score based on reconstruction errors. In this process, a group of experts will provide human feedback to a certain proportion of classified frames to be incorporated into the model, and also the final judgment for the event anomalies for training and classification. The proposed approach is evaluated on 3 publicly available surveillance datasets, showing improved accuracy and competitive performance (93.7% AUC) with respect to the best performance (90.6% AUC) of the state-of-the-art approaches. The approach has not been previously seen to the best of our knowledge.

Assessment of Mortality and Hospital Admissions Associated with Confirmed Infection with SARS-CoV-2 Variant of Concern VOC-202012/01 (B.1.1.7) a Matched Cohort and Time-to-Event Analysis

Background: The emergence of VOC202012/01 in England, known as B.1.1.7 or informally as the ‘UK variant’, has coincided with rapid increases in the number of PCR-confirmed positive cases in areas where the variant has been concentrated. Methods: To assess whether infection with SARS-CoV-2 variant VOC202012/01 is associated with more severe clinical outcomes compared to wild-type infection, genomically sequenced and confirmed variant and wild-type cases were linked to routine healthcare and surveillance datasets. Two statistical analyses were conducted to compare the risk of hospital admission and death within 28 days of test between variant and wild-type cases: a case-control study and an adjusted Cox proportional hazards model. Differences in severity of disease were assessed by comparing hospital admission and mortality, including length of hospitalisation and time to death.Results: Of 63,609 genomically sequenced COVID-19 cases tested in England between October and December 2020 6,038 were variant cases. In the matched cohort analysis 2,821 variant cases were matched to 2,821 to wild-type cases. In the time to event analysis we observed a 34% increased risk in hospitalisation associated with the variant compared to wild-type cases, however, no significant difference in the risk of mortality was observed. Conclusion: We found evidence of increased risk of hospitalisation after adjusting for key confounders, suggesting increase infection severity associated with this variant. Follow-up studies are needed to assess potential longer-term differences in the clinical outcomes of people infected with the VOC-202012/01 variant.

Exposure to domoic acid is an ecological driver of cardiac disease in southern sea otters✰

Harmful algal blooms produce toxins that bioaccumulate in the food web and adversely affect humans, animals, and entire marine ecosystems. Blooms of the diatom Pseudo-nitzschia can produce domoic acid (DA), a toxin that most commonly causes neurological disease in endothermic animals, with cardiovascular effects that were first recognized in southern sea otters. Over the last 20 years, DA toxicosis has caused significant morbidity and mortality in marine mammals and seabirds along the west coast of the USA. Identifying DA exposure has been limited to toxin detection in biological fluids using biochemical assays, yet measurement of systemic toxin levels is an unreliable indicator of exposure dose or timing. Furthermore, there is little information regarding repeated DA exposure in marine wildlife. Here, the association between long-term environmental DA exposure and fatal cardiac disease was investigated in a longitudinal study of 186 free-ranging sea otters in California from 2001 – 2017, highlighting the chronic health effects of a marine toxin. A novel Bayesian spatiotemporal approach was used to characterize environmental DA exposure by combining several DA surveillance datasets and integrating this with life history data from radio-tagged otters in a time-dependent survival model. In this study, a sea otter with high DA exposure had a 1.7-fold increased hazard of fatal cardiomyopathy compared to an otter with low exposure. Otters that consumed a high proportion of crab and clam had a 2.5- and 1.2-times greater hazard of death due to cardiomyopathy than otters that consumed low proportions. Increasing age is a well-established predictor of cardiac disease, but this study is the first to identify that DA exposure affects the risk of cardiomyopathy more substantially in prime-age adults than aged adults. A 4-year-old otter with high DA exposure had 2.3 times greater risk of fatal cardiomyopathy than an otter with low exposure, while a 10-year old otter with high DA exposure had just 1.2 times greater risk. High Toxoplasma gondii titers also increased the hazard of death due to heart disease 2.4-fold. Domoic acid exposure was most detrimental for prime-age adults, whose survival and reproduction are vital for population growth, suggesting that persistent DA exposure will likely impact long-term viability of this threatened species. These results offer insight into the pervasiveness of DA in the food web and raise awareness of under-recognized chronic health effects of DA for wildlife at a time when toxic blooms are on the rise.

A Machine Learning-Aided Framework to Predict Outcomes of Anti-PD-1 Therapy for Patients With Gynecological Cancer on Incomplete Post-Marketing Surveillance Dataset

Post-marketing surveillance of antineoplastic agents is performed to evaluate the efficacy and safety in patients aiming at expanding drug indications and discovering potential adverse events. The real-world data is fraught with missing values. Literature addressing different strategies for dealing with missing data in such a situation is scarce. Using machine learning (ML) algorithms for predicting therapeutic outcomes of PD-1/PD-L1 Inhibitors has attracted attention. However, training a predictive model usually requires imaging or biomarker information, which is rarely available in the post-marketing surveillance data. To address these challenges, we propose an ML-aided framework to predict the outcomes of Anti-PD-1 therapy for gynecological malignancy on a dataset with 117 patient samples, treated by Camrelizumab (with 50 patient samples), Sintilimab (44), and Toripalimab (23). Four therapeutic outcomes, including Response Evaluation Criteria in Solid Tumours (RECIST), organ adverse effect (AE), general AE, and death, are predicted. The proposed framework feeds the dataset into a learning pipeline consisting of imputation, feature engineering, model training, ensemble learning, and model selection to generate the final predictive model. We conduct experiments to justify several critical design choices, such as the specific feature engineering strategies and the SMOTE over-sampling technique. The final model for each learning task is selected from a large pool of model candidates based on a joint consideration of accuracy and F1. Moreover, we conduct thorough and visualized model analysis and gain a deeper understanding of model behavior and feature importance. The results, analysis, and findings demonstrate the superiority of the proposed learning-aided framework.

VectorMap-GR: A local scale operational management tool for entomological monitoring, to support vector control activities in Greece and the Mediterranean Basin

Over the past decade, Greece and other Mediterranean countries have witnessed the emergence and resurgence of several vector-borne diseases (VBDs), posing important public health challenges and threatening the tourist industry. An essential prerequisite for the design and execution of efficient and sustainable context-specific VBD control programmes is the establishment of integrative entomological and epidemiological surveillance systems. However, the monitoring and management of surveillance datasets (often chronologically fragmented, scattered in regional health district offices and partially accessible upon requisition), as well as their transformation into actionable information, is a complex undertaking. In light of aiding and optimizing vector control efforts in the Mediterranean Basin, we developed VectorMap-GR, an online, open access, operational management tool for entomological and complementary epidemiological monitoring data. The toolʼs key components are a set of controlled vocabularies (ontologies) running throughout the system, the systemʼs database and a map interface for data querying and display. The tool supports transformation of raw data into operationally relevant information (i.e. customized maps, charts, tables and reports) in a highly interactive fashion achieved through query filters and the ArcGIS technology embedded in the system. End-users may search for and obtain information on (i) the mosquito fauna composition, abundance and spatiotemporal dynamics; (ii) the mosquito insecticide resistance status and underlying resistance mechanisms; (iii) the occurrence of VBD pathogens and infections in vectors, animals and humans; and (iv) operationally relevant physical feature georeferenced datasets (e.g. mosquito breeding sites). VectorMap-GR was pilot implemented during 2018–2020 in a mosquito control programme in the Region of Crete (southern Greece). The programmeʼs control efforts coupled with VectorMap-GR pilot implementation phase, very likely contributed to the reduction of vector population numbers and the prevention of human VBD occurrences, recorded in this period.

Intermediate fused network with multiple timescales for anomaly detection

This paper proposes an intermediate fused network with multiple timescales to predict future video segments for video anomaly detection. Video prediction technique for anomaly detection requires to derive an anomaly-distinguishable future frame from normal distributions provided by training data. Then by measuring the difference between generated frames and reference frames, the model can tell which frames represent anomalous events in certain video sequence. In order to synthesize more distinctive future frames, we propose to boost image predictions by integrating several input video segments in multiple timescales. Specifically, the diversity in timescales means the diversity in sampling frequencies of video frames. In addition, we introduce a novel intermediate fusion strategy by concatenating feature maps from intermediate layers to better preserve different characteristics from different timescales. We evaluate the proposed method on some public video surveillance datasets and achieve competitive results with respect to the state-of-the-art approaches.

A method for estimating the transmissibility of influenza using serial cross-sectional seroepidemiological data

BackgroundSeroepidemiological surveillance data has been demonstrated to be useful for estimating the cumulative incidence of influenza, and measures the difference between pre- and post-epidemic seropositive fractions. Despite this, such studies relied on a chosen cut-off value for seropositivity. The aim of the present study is to develop a method to analyze distributions of serial cross-sectional seroepidemiological surveillance datasets using an epidemiological model so that the transmission potential can be estimated without imposing a cut-off value. MethodsA mathematical model of influenza transmission with a discrete antibody titer level was constructed. The final size equation for pre- and post-epidemic titer levels was derived. Subsequently, using the estimated distribution of the dilution increase caused by infection and the measurement error distribution, the model parameters were optimized using the maximum likelihood method. A bootstrap-based confidence interval calculation and sensitivity analysis were also performed. ResultsWithout imposing a cut-off value, the cumulative incidence was quantified, thereby yielding an estimate of the basic reproduction number. For the purpose of exposition, the proposed method was applied to influenza A/Victoria/3/75(H3N2) data, and serological data between 1975 and 1976 were compared. The estimated reproduction number was greater than that using the cut-off value of the hemagglutination inhibition level with titer level 20 (dilution 1:20) or above to define positives. ConclusionThe proposed method without a cut-off value offers an unbiased approach to estimating the cumulative incidence along with the reproduction number. If a cut-off value is required, the results imply that titer level 20 or above may better represent a reasonable cut-off value for calculating the incidence, but it could underestimate the basic reproduction number.

Predictors of virologic failure among people living with HIV in Oman: a national study from the Middle East.

We conducted a cross-sectional analysis to determine HIV virologic failure and its associated factors among Omani people living with HIV who are on ART for > 6 months. Patients (n = 1427) were identified from a central national HIV surveillance dataset. Two-thirds (67%) of patients were male, and the median age was 39 years (IQR, 32-48 years). Out of 1427 patients, 14.4% had virologic failure (HIV viral load [VL] ≥ 200 copies/ml). The multivariate analysis showed that patients aged 25-49 years (adjusted odds ratio [aOR]: 1.76, 95% CI: 1.01-3.08) were significantly more likely to fail treatment, compared to those aged ≥ 50 years. Besides, having "Other" HIV risk factor (compared to heterosexuals, aOR: 1.82, 95% CI: 1.02-3.24) and receiving HIV care outside the capital Muscat (compared to those cared for in Muscat, aOR: 1.73, 95% CI: 1.11-2.7) were independently associated with virologic failure. HIV viral suppression (85.6%) in Oman is encouraging; however, further strategies, mainly targeting patients who are young (<50 years), those not disclosing their HIV risk factor and those attending HIV treatment centres outside Muscat, are required to enhance HIV treatment outcome in Oman.

Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables.

Oceanic and coastal ecosystems have undergone complex environmental changes in recent years, amid a context of climate change. These changes are also reflected in the dynamics of water-borne diseases as some of the causative agents of these illnesses are ubiquitous in the aquatic environment and their survival rates are impacted by changes in climatic conditions. Previous studies have established strong relationships between essential climate variables and the coastal distribution and seasonal dynamics of the bacteria Vibrio cholerae, pathogenic types of which are responsible for human cholera disease. In this study we provide a novel exploration of the potential of a machine learning approach to forecast environmental cholera risk in coastal India, home to more than 200 million inhabitants, utilising atmospheric, terrestrial and oceanic satellite-derived essential climate variables. A Random Forest classifier model is developed, trained and tested on a cholera outbreak dataset over the period 2010–2018 for districts along coastal India. The random forest classifier model has an Accuracy of 0.99, an F1 Score of 0.942 and a Sensitivity score of 0.895, meaning that 89.5% of outbreaks are correctly identified. Spatio-temporal patterns emerged in terms of the model’s performance based on seasons and coastal locations. Further analysis of the specific contribution of each Essential Climate Variable to the model outputs shows that chlorophyll-a concentration, sea surface salinity and land surface temperature are the strongest predictors of the cholera outbreaks in the dataset used. The study reveals promising potential of the use of random forest classifiers and remotely-sensed essential climate variables for the development of environmental cholera-risk applications. Further exploration of the present random forest model and associated essential climate variables is encouraged on cholera surveillance datasets in other coastal areas affected by the disease to determine the model’s transferability potential and applicative value for cholera forecasting systems.

Code-Sharing in Cost-of-Illness Calculations: An Application to Antibiotic-Resistant Bloodstream Infections.

Background: More data-driven evidence is needed on the cost of antibiotic resistance. Both Japan and England have large surveillance and administrative datasets. Code sharing of costing models enables reduced duplication of effort in research.Objective: To estimate the burden of antibiotic-resistant Staphylococcus aureus bloodstream infections (BSIs) in Japan, utilizing code that was written to estimate the hospital burden of antibiotic-resistant Escherichia coli BSIs in England. Additionally, the process in which the code-sharing and application was performed is detailed, to aid future such use of code-sharing in health economics.Methods: National administrative data sources were linked with voluntary surveillance data within the Japan case study. R software code, which created multistate models to estimate the excess length of stay associated with different exposures of interest, was adapted from previous use and run on this dataset. Unit costs were applied to estimate healthcare system burden in 2017 international dollars (I$).Results: Clear supporting documentation alongside open-access code, licensing, and formal communication channels, helped the re-application of costing code from the English setting within the Japanese setting. From the Japanese healthcare system perspective, it was estimated that there was an excess cost of I$6,392 per S. aureus BSI, whilst oxacillin resistance was associated with an additional I$8,155.Conclusions: S. aureus resistance profiles other than methicillin may substantially impact hospital costs. The sharing of costing models within the field of antibiotic resistance is a feasible way to increase burden evidence efficiently, allowing for decision makers (with appropriate data available) to gain rapid cost-of-illness estimates.