Misdiagnosis prevents accurate monitoring of transmission and burden for sub-critical pathogens: a case study of Plasmodium knowlesi malaria

Maintaining surveillance of emerging infectious diseases presents challenges for monitoring their transmission and burden. Incomplete observation of infections and imperfect diagnosis reduce the observed sizes of transmission chains relative to their true sizes. Previous studies have examined the effect of incomplete observation on estimates of pathogen transmission and burden. However, each study assumed that, if observed, each infection was correctly diagnosed. Here, I leveraged principles from branching process theory to examine how misdiagnosis could contribute to bias in estimates of transmission and burden for emerging infectious diseases. Using the zoonotic Plasmodium knowlesi malaria as a case study, I found that, even when assuming complete observation of infections, the number of misdiagnosed cases within a transmission chain for every correctly diagnosed case could range from 0 (0 - 4) when R0 was 0.1 to 86 (0 - 837) when R0 was 0.9. Data on transmission chain sizes obtained using an imperfect diagnostic could consistently lead to underestimates of R0, the basic reproduction number, and simulations revealed that such data on up to 1,000 observed transmission chains was not powered to detect changes in transmission. My results demonstrate that misdiagnosis may hinder effective monitoring of emerging infectious diseases and that sensitivity of diagnostics should be considered in evaluations of surveillance systems.

Source identification of infectious diseases in networks via label ranking

Background Outbreaks of infectious diseases would cause great losses to the human society. Source identification in networks has drawn considerable interest in order to understand and control the infectious disease propagation processes. Unsatisfactory accuracy and high time complexity are major obstacles to practical applications under various real-world situations for existing source identification algorithms. Methods This study attempts to measure the possibility for nodes to become the infection source through label ranking. A unified Label Ranking framework for source identification with complete observation and snapshot is proposed. Firstly, a basic label ranking algorithm with complete observation of the network considering both infected and uninfected nodes is designed. Our inferred infection source node with the highest label ranking tends to have more infected nodes surrounding it, which makes it likely to be in the center of infection subgraph and far from the uninfected frontier. A two-stage algorithm for source identification via semi-supervised learning and label ranking is further proposed to address the source identification issue with snapshot. Results Extensive experiments are conducted on both synthetic and real-world network datasets. It turns out that the proposed label ranking algorithms are capable of identifying the propagation source under different situations fairly accurately with acceptable computational complexity without knowing the underlying model of infection propagation. Conclusions The effectiveness and efficiency of the label ranking algorithms proposed in this study make them be of practical value for infection source identification.

Reproducibility challenges for biomarker detection with uncertain but informative experimental data

Recent studies have revealed that circulating microRNAs are promising biomarkers for detecting toxicity or disease. Quantitative real-time polymerase chain reaction (qPCR) is often used to measure the levels of microRNAs. Besides complete and certain data, investigators inevitably have observed technically incomplete or uncertain qPCR data. Investigators usually set incomplete observations equal to the maximum quality number of qPCR cycles, apply the complete-observation method, or choose not to analyze targets with incomplete observations. Using biostatistical knowledge and published studies, we show that three commonly applied methods tend to cause biased inference and decrease reproducibility in biomarker detection. More efforts are needed to address the challenges to identify and detect reliable, novel circulating biomarkers in liquid biopsies.

The effect of complete versus partial observations on service evaluations

Purpose The purpose of this paper is to investigate the effect of complete versus partial observations of service failure and recovery. This study also aims at investigating the effect of observing customers’ need for cognitive closure and types of compensation that a service provider offers. Design/methodology/approach Two experiments are conducted to test the research hypotheses. The authors use scenarios describing failure and recovery encounters that occur to a target customer at restaurant settings, and through manipulation of complete versus partial observations, they investigate observers’ attitudes and behavioral intentions. Findings The results suggest that customers with a partial observation are less forgiving than those with a complete observation. In particular, the former sympathized more with a target customer, blamed a service provider more and a target customer less and had lower repurchase intentions than the latter. The authors find that the need for cognitive closure heightens this tendency following a partial observation of service failure. They also find that following a complete (versus partial) observation, observers reacted more favorably to service recovery when it included (versus did not include) monetary compensation. Research limitations/implications This research studies the effect of locus of causality following a partial versus complete observation. Future research could further examine the effect of stability and controllability. Also, the authors examined the effect of the need for cognitive closure on evaluations of service failure following a partial versus complete observation. Future research could examine the effect of some other individual difference variables. Practical implications The results offer some measures to be taken by practitioners. In particular, practitioners are advised to not offer monetary compensation when majority of observers have had a partial observation. Moreover, they are advised to offer some explanation in a timely and effective manner to ensure observers who are under the negative impact of a partial observation have some information so that they revisit their service evaluations. Originality/value The literature assumes that in failure and recovery incidents, all observing customers would know the entire story. This research challenges this assumption and highlights the key role of observation type (partial versus complete observation). Further, this research examines the effect of the need for cognitive closure on service evaluations following a partial versus complete observation. The current research finds that supposedly favorable measures by a firm (i.e. monetary compensation) may in fact backfire when a partial observation is at play.

Some Properties and Estimation of Parameters for the Five Parameter Type I Generalized Half Logistic Distribution under Complete Observation

This paper is a further study of the five parameter type I generalized half logistic distribution. We derived some properties of the distribution. Estimation of the parameters of the distribution under complete observation was studied using the maximum likelihood method. To assess the flexibility of the distribution, it was applied to a real lifetime data and the results when compared to the sub-models showed that the five parameter type I generalized half logistic distribution performed best.

Stochastic filtering and optimal control of pure jump Markov processes with noise-free partial observation

We consider an infinite horizon optimal control problem for a pure jump Markov process X, taking values in a complete and separable metric space I, with noise-free partial observation. The observation process is defined as Yt = h(Xt), t ≥ 0, where h is a given map defined on I. The observation is noise-free in the sense that the only source of randomness is the process X itself. The aim is to minimize a discounted cost functional. In the first part of the paper we write down an explicit filtering equation and characterize the filtering process as a Piecewise Deterministic Process. In the second part, after transforming the original control problem with partial observation into one with complete observation (the separated problem) using filtering equations, we prove the equivalence of the original and separated problems through an explicit formula linking their respective value functions. The value function of the separated problem is also characterized as the unique fixed point of a suitably defined contraction mapping.

Decentralized Multi-Robot Motion Planning Applicable to Dynamic Environment

Multi-robots navigation in dynamic environment is a promising topic in intelligent robotics with motion planning being one of the fundamental problems. However, in practicel, multi-robots motion planning is challenging with traditional centralized approach since computational demand makes it less practical and robust for the motion planning of a large number of robots. In this paper, a decentralized distribute robots motion planning framework (DDRMPF) is discussed which addresses the specific issue. DDRMPF directly maps raw sensor data to steering command to generate optimal paths for each constituent robot. Unlike centralized method which needs a complete observation along with a center agent which processes heavy data collected from all the robots, DDRMPF allows each agent to generate an optimal local path needing only partial observation, thus rendering motion planning involving large numbers of robots more practical and robust. DDRMPF trains the policy for each robot in the complex and dynamic environment simultaneously based on the reinforcement algorithm.

Wishful Extrapolation from Research to Patient Care

This chapter critiques how evidence from randomized trials has been used to inform medical decision making. Trials have long enjoyed a favored status within medical research on treatment response and are often called the “gold standard” for such research. The U.S. Food and Drug Administration (FDA) ordinarily considers only trial data when making decisions on drug approval. The well-known appeal of trials is that, given sufficient sample size and complete observation of outcomes, they deliver credible findings about treatment response within the study population. However, it is also well-known that extrapolation of findings from trials to clinical practice can be difficult. Researchers and guideline developers often use untenable assumptions to extrapolate. The chapter refers to this practice as wishful extrapolation. It discusses multiple reasons why extrapolation of research findings to clinical practice may be suspect.

STRUKTUR DAN DIKSI TEKS LAPORAN HASIL OBSERVASI KARYA SISWA KELAS VII SMP NEGERI 24 PADANG

ABSTRACT This study aims to (1) describe the structure of the report text of the observations of the work of class VII students of SMP Negeri 24 PAdang. (2) describe the diction of the report text of the observations by the VII grade students of SMP Negeri 24 Padang. The results of this study are (1) in writing the text of the report on the observation of the seventh grade students of Padang Public Middle School 24 using the three text structures of the observation report. The third structure of the report's text structure is the general definition, part description and description of benefits. This is evident from the 30 text reports on the results of the observations that have been analyzed, there are 28 complete observation report texts using general definitions, part descriptions, and description of benefits. (2) when viewed from diction usage with 94% accuracy of diction usage and 6% inaccurate use of diction in observation report text by class VII students of Padang Middle School 24. Kata Kunci: stuktur teks, diksi, dan teks laporan hasil observasi