Survival Analysis: Techniques for Censored and Truncated Data

Insects are able to see ultraviolet (UV) radiation. Nocturnal insects are often attracted to light sources that emit large amounts of UV radiation, and devices that exploit this behavior, such as light traps for forecasting pest outbreaks, and electric insect killers, have been developed. Some diurnal species are attracted to yellow; yellow pan traps are used for conducting surveys for pest outbreaks and yellow sticky plates are used for pest control. Lamps that give off yellow illumination have been used effectively to control the activity of nocturnal moths and thus reduce damage to fruit, vegetables, and flowers. Covering cultivation facilities with film that filters out near-UV radiation reduces the invasion of pests such as whiteflies and thrips into the facilities, thus reducing damage. Reflective material placed on cultivated land can control the approach of flying insects such as aphids. Future development and use of new light sources such as light-emitting diodes is anticipated for promoting integrated pest management.

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Insect reactions to light and its applications to pest management

Modelling Survival Data in Medical Research 3rd Edition Dec 04, 2014 · Modelling Survival Data in Medical Research describes the modelling approach to the analysis of survival data using a wide range of examples from biomedical research. Well known for its nontechnical style, this third edition contains new chapters on frailty models and their applications, competing risks, non-proportional hazards, and dependent censoring. It also describes techniques for

Modelling Survival Data in Medical Research

Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. These pathogens are transmitted, directly or indirectly, and can lead to epidemics or even pandemics. The resulting infection may lead to mild-to-severe symptoms such as life-threatening fever or diarrhea. Infectious diseases may be asymptomatic in some individuals but may lead to disastrous effects in others. Despite the advances in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries. With the advent of mathematical tools, scientists are now able to better predict epidemics, understand the specificity of each pathogen, and identify potential targets for drug development. Artificial intelligence and its components have been widely publicized for their ability to better diagnose certain types of cancer from imaging data. This chapter aims at identifying potential applications of machine learning in the field of infectious diseases. We are deliberately focusing on key aspects of infection: diagnosis, transmission, response to treatment, and resistance. We are proposing the use of extreme values as an avenue of interest for future developments in the field of infectious diseases. This chapter covers a series of applications selectively chosen to showcase how artificial intelligence is moving the field of infectious disease further and how it helps institutions to better tackles them, especially in low-income countries.

Use of artificial intelligence in infectious diseases

Objective To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines. Design A scoping review. Data sources MEDLINE, EMBASE, CINAHL, ProQuest, Scopus, Web of Science, Cochrane Library, INSPEC and ACM Digital Library were searched on 18 July 2018. Eligibility criteria We included English articles published between 1980 and 2018 that used machine learning to predict population-health-related outcomes. We excluded studies that only used logistic regression or were restricted to a clinical context. Data extraction and synthesis We summarised findings extracted from published reports, which included general study characteristics, aspects of model development, reporting of results and model discussion items. Results Of 22 618 articles found by our search, 231 were included in the review. The USA (n=71, 30.74%) and China (n=40, 17.32%) produced the most studies. Cardiovascular disease (n=22, 9.52%) was the most studied outcome. The median number of observations was 5414 (IQR=16 543.5) and the median number of features was 17 (IQR=31). Health records (n=126, 54.5%) and investigator-generated data (n=86, 37.2%) were the most common data sources. Many studies did not incorporate recommended guidelines on machine learning and predictive modelling. Predictive discrimination was commonly assessed using area under the receiver operator curve (n=98, 42.42%) and calibration was rarely assessed (n=22, 9.52%). Conclusions Machine learning applications in population health have concentrated on regions and diseases well represented in traditional data sources, infrequently using big data. Important aspects of model development were under-reported. Greater use of big data and reporting guidelines for predictive modelling could improve machine learning applications in population health. Registration number Registered on the Open Science Framework on 17 July 2018 (available at https://osf.io/rnqe6/).

https://bmjopen.bmj.com/content/bmjopen/10/10/e037860.full.pdf

Predicting population health with machine learning: a scoping review.

Background: Tuberculosis (TB) is a deadly infectious disease caused by Mycobacteria tuberculosis. Tuberculosis as a chronic and highly infectious disease is prevalent in almost every part of the globe. More than 95% of TB mortality occurs in low/middle income countries. In 2014, approximately 10 million people were diagnosed with active TB and two million died from the disease. In this study, our aim is to compare the predictive powers of the seasonal autoregressive integrated moving average (SARIMA) and neural network auto-regression (SARIMA-NNAR) models of TB incidence and analyse its seasonality in South Africa. Methods: TB incidence cases data from January 2010 to December 2015 were extracted from the Eastern Cape Health facility report of the electronic Tuberculosis Register (ERT.Net). A SARIMA model and a combined model of SARIMA model and a neural network auto-regression (SARIMA-NNAR) model were used in analysing and predicting the TB data from 2010 to 2015. Simulation performance parameters of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), mean percent error (MPE), mean absolute scaled error (MASE) and mean absolute percentage error (MAPE) were applied to assess the better performance of prediction between the models. Results: Though practically, both models could predict TB incidence, the combined model displayed better performance. For the combined model, the Akaike information criterion (AIC), second-order AIC (AICc) and Bayesian information criterion (BIC) are 288.56, 308.31 and 299.09 respectively, which were lower than the SARIMA model with corresponding values of 329.02, 327.20 and 341.99, respectively. The seasonality trend of TB incidence was forecast to have a slightly increased seasonal TB incidence trend from the SARIMA-NNAR model compared to the single model. Conclusions: The combined model indicated a better TB incidence forecasting with a lower AICc. The model also indicates the need for resolute intervention to reduce infectious disease transmission with co-infection with HIV and other concomitant diseases, and also at festival peak periods.

https://www.mdpi.com/1660-4601/13/8/757/pdf

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model

Students of science disciplines at Fort Hare University must take a course in statistics along with other courses offered in their own disciplines. The goal of this study is to evaluate the general...

A Statistical Analysis of Students’ Attitudes Towards Statistics: A Case Study of Undergraduate Bachelor of Science Students at the University of Fort Hare

Background: Tuberculosis (TB) with human immunodeficiency virus (HIV) coinfection is the highest clinical epidemiology and public health issue. Despite many programs established to tackle the epidemic, TB target controls have not been reached. One of the many factors attributed to the failure in TB treatment is HIV coinfection. The aim of this study is to assess the survival rate of HIV infection among TB patients and the risk factors of death among the TB patients with HIV coinfection during the retro of directly observed treatment, short-course (DOTS) program. Methods: This study is a retrospective cohort conducted to compare the survivorship between TB/HIV patients for 8 months DOTS. Death among TB patients was considered as failures and those defaulted or survived were censored. The Cox proportional-hazards regression and log-linear model were used to establish the hazard ratio (HR) of death for each variable at baseline and estimate the risk factors effect among TB patients. Results: The findings revealed that 50% of death from TB/HIV patients were from HIV coinfection (advanced HR = 2.01, 95% confidence interval = 1.13–3.17). The risk of death was significantly higher in HIV-positive TB patients (P = 0.000) during the extension care phase. TB/HIV-positive patients on antiretroviral therapy have decreased survival rate (log-rank test = 14.88, df = 2, P = 0.0001). The probability of TB patients surviving is significantly decreased in HIV positive with some factors such as age, weight, smoking, and alcohol found significant. Conclusion: The probability of survival in HIV-positive TB patients was significantly lower during the TB treatment. Weight loss, age, alcohol, smoking, and pregnancy were showed to affect the survival probability of TB/HIV patients' coinfection significantly.

Survival model analysis of tuberculosis treatment among patients with human immunodeficiency virus coinfection.

The paper identified the available local resources for extension education and the constraints in the use of these local resources in Nkonkobe local Municipality. The survey was conducted in the peri-urban areas of Fort Beaufort, Alice, Seymour, Balfour, Hogsback and Middledrift from 7th to 29th September 2010 by interviewing 58 farmers on the identification of local resources and their perception of constraints in the use of local resources. The study revealed that there are local resources embedded in the area for use in Extension teaching and learning. The perception of constraint (inadequate access to local resources) increased significantly with age (P = 0.04) and farm experience (P = 0.045). The fundamental strategies for a successful Extension work should be to develop a process which not only creates co-operative platforms for the use of local resources for rural improvement, but also reinforces farmer’s ingenuity and inspires them to learn and accept innovation.The available local resources in Nkonkobe Local Municipality are well distributed in the community and are important for the achievement of educational goals in Extension teaching and learning.

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Analysis of the Use of Local Resources in Extension Education Programme in Nkonkobe Local Municipality of Eastern Cape

The Cox proportional hazards (PH) model with time-dependent covariates has been widely used in medical and health related studies to examine the impacts of time-varying risk factors on survival. Th...

Akinwumi Odeyemi

Papers

Seasonality and Trend Forecasting of Tuberculosis Prevalence Data in Eastern Cape, South Africa, Using a Hybrid Model

A Statistical Analysis of Students’ Attitudes Towards Statistics: A Case Study of Undergraduate Bachelor of Science Students at the University of Fort Hare

Survival model analysis of tuberculosis treatment among patients with human immunodeficiency virus coinfection.

Analysis of the Use of Local Resources in Extension Education Programme in Nkonkobe Local Municipality of Eastern Cape

Statistical Assessment of Malaria Risk Factors Using Cox Proportional Hazard Approach