scholarly journals A Bayesian approach for estimating typhoid fever incidence from large‐scale facility‐based passive surveillance data

2021 ◽  
Author(s):  
Maile T. Phillips ◽  
James E. Meiring ◽  
Merryn Voysey ◽  
Joshua L. Warren ◽  
Stephen Baker ◽  
...  
Keyword(s):  
Typhoid Fever ◽  
Bayesian Approach ◽  
Large Scale ◽  
Surveillance Data ◽  
Passive Surveillance

scholarly journals A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data

2020 ◽  
Author(s):  
Maile T. Phillips ◽  
James E. Meiring ◽  
Merryn Voysey ◽  
Joshua L. Warren ◽  
Stephen Baker ◽  
...  
Keyword(s):  
Blood Culture ◽  
Typhoid Fever ◽  
Prevention And Control ◽  
Large Scale ◽  
Incidence Rates ◽  
Passive Surveillance ◽  
Sample Collection ◽  
True Incidence ◽  
And Control ◽  
Adjusted Incidence

AbstractBackgroundDecisions about typhoid fever prevention and control are based on current estimates of typhoid incidence and their uncertainty, which can be difficult to measure. Limits of using facility-based estimates alone—the lack of specific clinical diagnostic criteria, poorly sensitive and specific diagnostic tests, and scarcity of accurate and complete datasets—contribute to difficulties in calculating population-level incidence of typhoid.MethodsUsing data from the Strategic Alliance across Africa & Asia (STRATAA) programme, we integrated information from demographic censuses, healthcare utilization surveys, facility-based passive surveillance, and serological surveillance from sites in Malawi, Nepal, and Bangladesh in order to adjust crude incidence estimates to account for under-detection. We developed an approach using a Bayesian framework that adjusts the count of reported blood-culture-positive cases of typhoid for each of the following phases: healthcare seeking, blood culture collection, and blood culture detection. We estimated the proportion of “true” typhoid cases occurring in the population under surveillance captured at each phase by combining information from the observed cases from the STRATAA datasets and estimates from prior published studies. We confirmed that the model was correctly formulated by comparing to simulated data.ResultsThe ratio between the observed and adjusted incidence rates was 8.2 (95% CI: 6.4-13.3) in Malawi, 13.8 (95% CI: 8.8-23.0) in Nepal, and 7.0 (95% CI: 5.5-9.1) in Bangladesh, and varied by age across the three sites. The probability of having blood drawn for culture led to the largest adjustment in Malawi, while the probability of seeking healthcare contributed the most to adjustment factors in Nepal and Bangladesh. Adjusted incidence rates were mostly within the limits of the seroincidence rate of typhoid infection determined by serological data.ConclusionPassive surveillance of blood culture-confirmed typhoid fever without adjustment for case ascertainment, sample collection and diagnostic sensitivity results in considerable underestimation of the true incidence of typhoid in the population. Our approach allows each phase of the typhoid reporting process to be synthesized to estimate the adjusted incidence of typhoid fever while correctly characterizing uncertainty in this estimate, which can inform decision-making for typhoid prevention and control.

scholarly journals Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Meysam Goodarzi ◽  
Darko Cvetkovski ◽  
Nebojsa Maletic ◽  
Jesús Gutiérrez ◽  
Eckhard Grass
Keyword(s):  
Bayesian Approach ◽  
Large Scale ◽  
Hybrid Approach ◽  
Position Estimation ◽  
5G Networks ◽  
Clock Skew ◽  
Depth Analysis ◽  
The Impact ◽  
Mean Square Errors ◽  
Clock Offset

AbstractClock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.

scholarly journals Risk of Fatal Adverse Events after H1N1 Influenza Vaccine: Limitations of Passive Surveillance Data

2010 ◽  
Vol 51 (7) ◽  
pp. 871-872 ◽  
Author(s):  
Michael M. McNeil ◽  
Karen R. Broder ◽  
Claudia Vellozzi ◽  
Frank DeStefano
Keyword(s):  
Adverse Events ◽  
Influenza Vaccine ◽  
H1n1 Influenza ◽  
Surveillance Data ◽  
Passive Surveillance ◽  
Fatal Adverse Events

scholarly journals Epidemiology of measles in Blantyre, Malawi: analyses of passive surveillance data from 1996 to 1998

2002 ◽  
Vol 129 (2) ◽  
pp. 361-369 ◽  
Author(s):  
S. YAMAGUCHI ◽  
A. DUNGA ◽  
R. L. BROADHEAD ◽  
B. J. BRABIN
Keyword(s):  
Vaccine Efficacy ◽  
Screening Method ◽  
Age Distribution ◽  
Surveillance Data ◽  
Passive Surveillance ◽  
District Health Office ◽  
District Health ◽  
Negatively Associated ◽  
Passive Surveillance System ◽  
Vaccination History

Measles surveillance data in Blantyre, Malawi were reviewed for 1996–8 to describe the epidemiology of infection and to estimate vaccine efficacy (VE) by the screening method. A total of 674 measles cases were reported to the Blantyre District Health Office during this period. Age distribution showed that 108 (16.1%) of the cases were aged less than 1 year. The median age was 5 years. Eighty percent of the cases between 1 and 19 years had been previously vaccinated. VE was 68.6% (95% CI, 52.7–79.2) for children 12–23 months of age and 67.3% (95% CI, 48.3–79.3) for infants 9–11 months of age. Reasons for this low vaccine efficacy are discussed. Previous vaccination history was negatively associated with the risk for developing cough during measles infection (odds ratio (OR), 0.30; 95% CI, 0.09–0.91), diarrhoea (OR, 0.64; CI, 0.44–0.95) and pneumonia (OR, 0.40; CI, 0.25–0.62). Logistic regression analysis showed that pneumonia in adults was negatively associated with vaccination history. The passive surveillance system for measles in Malawi was useful to describe the epidemiology of measles.

Mining ICDDR, B Hospital Surveillance Data and Exhibiting Strategies for Balancing Large Unbalanced Datasets

2015 ◽  
Vol 10 (1) ◽  
pp. 39-66 ◽  
Author(s):  
Adnan Firoze ◽  
Rashedur M. Rahman
Keyword(s):  
Large Scale ◽  
Multinomial Logistic Regression ◽  
Short Interval ◽  
Classification Performance ◽  
Computer Applications ◽  
Surveillance Data ◽  
Critical Conditions ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Fitting Problem

This research uses a number of classifier models on Hospital Surveillance data to classify admitted patients according to their critical conditions. Three class labels were used to distinguish the criticality of the admitted patients. Furthermore, set forth are two distinct approaches to address the over-fitting problem in the unbalanced dataset since the frequency of instances of the class ‘low' is significantly higher than the other two classes. Apart from trimming the dataset to balance the classes, this work has dealt with the over-fitting problem by introducing the ‘Synthetic Minority Over-sampling Technique' (SMOTE) algorithm coupled with Locally Linear Embedding (LLE). It has constructed three models that applied the neural, and multinomial logistic regression classifications and finally compared the performance of the work's models with the models developed by Rahman and Hasan (2011) where they used several decision tree models to classify the same dataset using tenfold cross validation. Additionally, for a comprehensive comparative analysis, this work has compared the classification performance of the authors' novel third model using support vector machine (SVM). After comparison, the work shows that one of the authors' models surpasses all prior models in terms of classification performance, taking into account the performance time trade-off, giving them an efficient model that handles large scale unbalanced datasets efficiently with standard classification performance. The models developed in this research can become imperative tools to doctors when large numbers of patients arrive in a short interval especially during epidemics. Since, intervention of machines become a necessity when doctors are scarce, computer applications powered by these models are helpful to diagnose and measure the criticality of the newly arrived patients with the help of the historical data kept in the surveillance database.

scholarly journals Bayesian approach for three-dimensional aquifer characterization at the Hanford 300 Area

2010 ◽  
Vol 14 (10) ◽  
pp. 1989-2001 ◽  
Author(s):  
H. Murakami ◽  
X. Chen ◽  
M. S. Hahn ◽  
Y. Liu ◽  
M. L. Rockhold ◽  
...  
Keyword(s):  
Test Data ◽  
Bayesian Approach ◽  
Posterior Distribution ◽  
Large Scale ◽  
Three Dimensional ◽  
Measurement Data ◽  
Data Uncertainty ◽  
Small Scale ◽  
Injection Test ◽  
Conductivity Profile

Abstract. This study presents a stochastic, three-dimensional characterization of a heterogeneous hydraulic conductivity field within the Hanford 300 Area, Washington, USA, by assimilating large-scale, constant-rate injection test data with small-scale, three-dimensional electromagnetic borehole flowmeter (EBF) measurement data. We first inverted the injection test data to estimate the transmissivity field, using zeroth-order temporal moments of pressure buildup curves. We applied a newly developed Bayesian geostatistical inversion framework, the method of anchored distributions (MAD), to obtain a joint posterior distribution of geostatistical parameters and local log-transmissivities at multiple locations. The unique aspects of MAD that make it suitable for this purpose are its ability to integrate multi-scale, multi-type data within a Bayesian framework and to compute a nonparametric posterior distribution. After we combined the distribution of transmissivities with depth-discrete relative-conductivity profile from the EBF data, we inferred the three-dimensional geostatistical parameters of the log-conductivity field, using the Bayesian model-based geostatistics. Such consistent use of the Bayesian approach throughout the procedure enabled us to systematically incorporate data uncertainty into the final posterior distribution. The method was tested in a synthetic study and validated using the actual data that was not part of the estimation. Results showed broader and skewed posterior distributions of geostatistical parameters except for the mean, which suggests the importance of inferring the entire distribution to quantify the parameter uncertainty.

Enhanced Passive Surveillance Methods Using CDC and State Child Blood Lead Surveillance Data

2018 ◽  
Vol 2018 (1) ◽  
Author(s):  
Tim Dignam ◽  
James Hodge ◽  
Stella Chuke ◽  
Wd Flanders
Keyword(s):  
Blood Lead ◽  
Surveillance Data ◽  
Passive Surveillance ◽  
State Child

scholarly journals Continuing Decline in Varicella Incidence After the 2-Dose Vaccination Recommendation—Connecticut, 2009–2014

2015 ◽  
Vol 2 (4) ◽  
Author(s):  
Jocelyn Mullins ◽  
Kathy Kudish ◽  
Lynn Sosa ◽  
Jim Hadler
Keyword(s):  
Advisory Committee ◽  
Surveillance Data ◽  
School Entry ◽  
Age Group ◽  
Passive Surveillance ◽  
Dose Recommendation ◽  
Dose Requirement ◽  
Incidence Trends ◽  
Vaccination Recommendation

Abstract Background.  Varicella is a highly contagious vaccine-preventable illness. In 1996, the Advisory Committee for Immunization Practices recommended 1 dose of vaccine for children, and in 2006 it recommended 2 doses; Connecticut required 1 dose for school entry in 2000 and 2 doses for school entry starting in 2011. Connecticut varicella incidence overall and among persons aged 1–14 years declined during 2005–2008. We analyzed varicella surveillance data for 2009–2014 to characterize overall and age group-specific trends in the setting of the 2-dose requirement. Methods.  Passive surveillance was used to collect data and identify incidence trends and changes in proportions, and these were assessed by χ2 tests for trend and proportion, respectively. Results.  Varicella incidence decreased from 13.8 cases/100 000 persons during 2009 to 5.1 cases/100 000 persons during 2014 (P < .001); significant declines in incidence occurred among children aged 1–4, 5–9, and 10–14 years (P < .01 for each age group). Cases classified as preventable decreased from 44% during 2009 to 25% during 2014 (P < .01); significant declines in percentages of preventable cases occurred only among those aged 5–9 years (P < .05) and 10–14 (P < .01) years. Conclusions.  Varicella incidence continued to decline in Connecticut in the setting of the 2-dose school-entry program. Continued surveillance is needed to assess the full influence of the 2-dose recommendation.

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