Final Paper Draft

A Statistical Method to Estimate of Contagion Effects in Human Disease And Health Networks

Annotated Bibliography

References Althomsons, S. P., Hill, A. N., Harrist, A. V., France, A. M., Powell, K. M., Posey, J. E., . . . Navin, T. R. (2018). Statistical Method to Detect Tuberculosis Outbreaks among Endemic Clusters in a Low-Incidence Setting. Emerg Infect Dis. In this journal author reported use of genotype surveillance data to predict outbreaks among incident tuberculosis clusters. Authors propose a method to detect possible outbreaks among endemic tuberculosis clusters. Authors detected 15 possible outbreaks, of which 10 had epidemiologic data or whole-genome sequencing results. Eight outbreaks were corroborated. In this article, authors postulate that a statistically driven method can determine the beginning of a TB outbreak in endemic clusters, referred to here as prevalent clusters. Our method searches for instances of excessive unexpected cluster growth above a background rate. authors validated our approach by using a combination of epidemiologic data acquired during field investigations and whole-genome sequencing (WGS), which provides higher resolution of the bacterial genome than current genotyping methods Chong, K. C., Zee, B. C., & Wang, M. H. (2017). A statistical method utilizing information of imported cases to estimate the transmissibility for an influenza pandemic. BMC Medical Research Methodology. In this journal, author developed a likelihood-based method using arrival times of infected cases in different countries to estimate R 0 for influenza pandemics. A simulation was conducted to assess the performance of the proposed method. Authors further applied the method to the outbreak of the influenza pandemic A/H1N1 in Mexico. In the early stage of a pandemic, stochastic effects usually induce spatial variations. Although their estimate (posterior median R 0  = 1.4) was similar to us, time to event data combining the mobility and pattern of epidemic invasion is usually preferred to the count data. This approach as well as the estimate is potential to assist officials in planning control and prevention measures. Improved coordination to streamline or even centralize surveillance of imported cases among countries will thus be beneficial to public health. Hong, H. G., & Li, Y. (2020). Estimation of time-varying reproduction numbers underlying epidemiological processes: A new statistical tool for the COVID-19 pandemic. PLOS ONE. In this journal, authors discuses about coronavirus pandemic has rapidly evolved into an unprecedented crisis. The susceptible-infectious-removed (SIR) model and its variants have been used for modeling the pandemic. However, time-independent parameters in the classical models may not capture the dynamic transmission and removal processes, governed by virus containment strategies taken at various phases of the epidemic. Moreover, few models account for possible inaccuracies of the reported cases. Authors propose a Poisson model with time-dependent transmission and removal rates to account for possible random errors in reporting and estimate a time-dependent disease reproduction number, which may reflect the effectiveness of virus control strategies. Author apply our method to study the pandemic in several severely impacted countries, and analyze and forecast the evolving spread of the coronavirus. Authors have developed an interactive web application to facilitate readers’ use of our method. Ishida , T., Tokuda , K., Hisaka , A., Honma , M., Kijima , S., Takatoku , H., . . . Initiative, T. A. (2018). A Novel Method to Estimate Long‐Term Chronological Changes From Fragmented Observations in Disease Progression. Clinical Pharmacology & Therapeutics . This journal is based on Clinical observations of patients with chronic diseases are often restricted in terms of duration. Therefore, obtaining a quantitative and comprehensive understanding of the chronology of chronic diseases is challenging, because of the inability to precisely estimate the patient's disease stage at the time point of observation. Authors developed a novel method to reconstitute long‐term disease progression from temporally fragmented data by extending the nonlinear mixed‐effects model to incorporate the estimation of “disease time” of each subject. Application of this method to sporadic Alzheimer's disease successfully depicted disease progression over 20 years. The results of a clinical trial of anti‐Aβ antibodies, which are being developed as potential disease‐modifying drugs for AD, suggested that intervention at an earlier stage of the disease would be preferable. Kroncke, B. M., Smith, D. K., Zuo, Y., Glazer, A. M., Roden, D. M., & Blume, J. D. (2020). A Bayesian method to estimate variant-induced disease penetrance. PLOS Genetics. To evaluate this method the clinical implications for genetic variants, even definitively pathogenic variants, can vary strikingly across individuals. Lack of evidence to estimate the probability of disease from identified genetic variants, especially rare variants, presents a major barrier to integrating genotype information into clinical care. Here author advance an approach to estimate the penetrance, or positive predictive value of the discovery of a genetic variant, in service of advancing the use of genetic information in personalized medicine. A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. Ogburn, E. L. (2018). Challenges to Estimating Contagion Effects from Observational Data. In Complex Spreading Phenomena in Social Systems (pp. 47-64). Springer. This article will focus on the challenges and the open problems and will not weigh in on that dilemma, except to mention here that the most responsible way to use any statistical method, especially when it is well-known that the assumptions on which it rests do not hold, is with a healthy dose of skepticism, with honest acknowledgment and deep understanding of the limitations, and with copious caveats about how to interpret the results. A growing body of literature attempts to learn about contagion using observational (i.e., non-experimental) data collected from a single social network. While the conclusions of these studies may be correct, the methods rely on assumptions that are likely—and sometimes guaranteed to be—false, and therefore the evidence for the conclusions is often weaker than it seems. Developing methods that do not need to rely on implausible assumptions is an incredibly challenging and important open problem in statistics. Petropoulou, M., Nikolakopoulou, A., Veroniki, A.-A., Rios, P., Vafaei, A., Zarin, W., . . . Salanti, G. (2017). Bibliographic study showed improving statistical methodology of network meta-analyses published between 1999 and 2015. Journal of Clinical Epidemiology. To assess the characteristics and core statistical methodology specific to network meta-analyses (NMAs) in clinical research articles. Many NMAs published in the medical literature have significant limitations in both the conduct and reporting of the statistical analysis and numerical results. The situation has, however, improved in recent years, in particular with respect to the evaluation of the underlying assumptions, but considerable room for further improvements remains. Xu, R. (2020). Statistical methods for the estimation of contagion effects in human disease and health networks. Computational and Structural Biotechnology Journal. The study of human disease and health networks. Accurate estimation and identification of contagion effects are important in terms of understanding the spread of human disease and health behavior, and they also have various implications for designing effective public health interventions. In this journal author explain the challenges in estimating contagion effects, and how they can be framed as an omitted variable bias problem. Author then discuss how such challenges have been addressed in randomized experiments and traditional statistical analyses, as well as several state-of-the-art statistical methods. Finally, author conclude by summarizing recent advancements and noting remaining challenges, as well as appropriate next steps. Simulation evidence from Xu showed a substantial upward bias in the estimates of contagion effects when an omitted variable is present in both the behavioral and network selection model and a downward bias in the estimates of contagion effects when the omitted variable only presents in the behavioral model while the network is static. Zhang, J., Zhao, Z., Guo, X., Guo, B., & Wu, B. (2019). Powerful statistical method to detect disease‐associated genes using publicly available genome‐wide association studies summary data. Wiley Periodicals. In this journal, author conducted thorough simulation studies to verify that the proposed method controls type I errors well, and performs favorably compared to single‐marker analysis and other existing methods. Authors demonstrated the utility of our proposed method through analysis of GWAS meta‐analysis results for fasting glucose and lipids from the international MAGIC consortium and Global Lipids Consortium, respectively. The proposed method identified some novel trait associated genes which can improve our understanding of the mechanisms involved in ‐cell function, glucose homeostasis, and lipids traits. Zimmet, P., Alberti, K. G., Magliano, D. J., & Bennett, P. H. (2016). Diabetes mellitus statistics on prevalence and mortality: facts and fallacies. Nature Reviews Endocrinology. This peer reviewed article which inadequate for providing a complete and accurate assessment of the prevalence of diabetes mellitus. International consensus on uniform standards and criteria for reporting national data on diabetes mellitus prevalence as well as for common complications of diabetes mellitus and mortality need to be developed. Diabetes mellitus is one of the most important public health challenges of the twenty-first century. Until the past decade, it has been seriously underrated as a global health threat. Major gaps exist in efforts to comprehend the burden nationally and globally, especially in developing nations, due to a lack of accurate data for monitoring and surveillance. Early attempts to obtain accurate data, discussed in this article, seem to have been cast aside so, at present, these needs remain unmet. Existing international efforts to assemble information fall far short of requirements.