Actual Incidence Data Research Articles

Using the Baidu index to predict trends in the incidence of tuberculosis in Jiangsu Province, China.

To analyze the time series in the correlation between search terms related to tuberculosis (TB) and actual incidence data in China. To screen out the "leading" terms and construct a timely and efficient TB prediction model that can predict the next wave of TB epidemic trend in advance. Monthly incidence data of tuberculosis in Jiangsu Province, China, were collected from January 2011 to December 2020. A scoping approach was used to identify TB search terms around common TB terms, prevention, symptoms and treatment. Search terms for Jiangsu Province, China, from January 2011 to December 2020 were collected from the Baidu index database. Correlation coefficients between search terms and actual incidence were calculated using Python 3.6 software. The multiple linear regression model was constructed using SPSS 26.0 software, which also calculated the goodness of fit and prediction error of the model predictions. A total of 16 keywords with correlation coefficients greater than 0.6 were screened, of which 11 were the leading terms. The R2 of the prediction model was 0.67 and the MAPE was 10.23%. The TB prediction model based on Baidu Index data was able to predict the next wave of TB epidemic trends and intensity 2 months in advance. This forecasting model is currently only available for Jiangsu Province.

Identifying shoplifting behaviors and inferring behavior intention based on human action detection and sequence analysis

Identification of abnormal behaviors affecting public safety (e.g., shoplifting, robbery, and stealing) is essential for preventing human casualties and property damage. Many studies have attempted to automatically identify abnormal behaviors by detecting relevant human actions by developing intelligent video surveillance systems. However, these studies have focused on catching predefined actions associated explicitly with the target abnormal behavior, which can lead to errors in judgment when such actions are undetected or inaccurately detected. To better identify abnormal behaviors, it is essential to understand a series of performed actions to capture behaviors’ pre- and post-indications (e.g., repeatably looking around and spotting CCTVs) and infer the intentions underlying such behaviors. Thus, in the present study, we propose a framework to identify abnormal behaviors through deep-learning-based detection of non-semantic-level human action components segmented with a window size of several seconds (e.g., walking, standing, and watching) and performing sequence analyses of the detected action components to infer behavior intentions. Then, we tested the applicability of the framework to the specific scenario of shoplifting, one of the most common crimes. Analysis of actual incident data confirmed that shoplifting intentions could be effectively gauged based on distinct action sequence features, and the intention inference results are continuously updated with the accumulated series of detected actions during the course of the input video stream. The results of this study can help enhance the ability of intelligent surveillance systems by providing a new means for monitoring abnormal behaviors and deeply understanding the underlying intentions.

Estimating the Transmissibility of Mumps: A Modelling Study in Wuhan City, China.

Despite the adoption of a national immunization program in China, the incidence of mumps remains high. This study aimed to describe the epidemiological characteristics, including the time, region, occupation, and age, of mumps in Wuhan from 2005 to 2018 and to evaluate its transmissibility. In this study, the susceptible–exposed–infectious–asymptomatic–recovered (SEIAR) model fitted the actual incidence data of mumps. The effective reproduction number (Rt) was used to evaluate and compare the transmission capacity in different areas. From 2005 to 2018, there were 36,415 cases. The incidence of mumps was highest among people aged 5–10 years (460.02 per 100,000). The SEIAR model fitted the reported mumps data well (P < 0.01). The median transmissibility (Rt) was 1.04 (range = 0–2.50). There were two peak spreads every year (from March to May and from October to December). The Rt peak always appeared in the first 2 months of the peak incidence rate. The peak time of the epidemic spread of mumps was 1–2 months earlier than the peak incidence rate. The prevention and control measures of vaccination for children aged 5–10 years should be taken before the peak transmission capacity each year, 2 months before the peak of the outbreak, to reduce the spread of mumps.

Future Incidence of Malignant Mesothelioma in South Korea: Updated Projection to 2038.

Malignant mesothelioma (MM) is a cancer that is largely caused by exposure to asbestos. Although asbestos is no longer used in South Korea, the incidence of MM continues to increase due to its long latent period. We aimed to update the previous prediction of MM incidence until 2038. We predicted the incidence of MM over the next 20 years (2019–2038) in South Korea using Møller’s age–period–cohort (APC) model and a Poisson regression model based on asbestos consumption. The APC model predicted that the crude incidence rate would increase sharply in men and slowly in women. Despite the sex discrepancy in the rate of increase, the incidence rate for both sexes is expected to continue increasing until 2038. In the Poisson model, the crude incidence rate was predicted to increase continuously until 2038, and far more cases of MM were predicted to occur compared with the results of the APC model. When compared with actual incidence data, the APC model was deemed more suitable than the Poisson model. The APC model predicted a continuous increase over the next 20 years with no peak, suggesting that the incidence of MM will continue to rise far into the future.

Fireworks: Requisite for Forensics Evolution from Conventional to Green Crackers in India - A Perspective

Social life in India is enriched with festivals, traditions and cultural events, often, associated with bursting crackers during celebration. Firework exhibition and bursting crackers are a symbol of jubilation. The hazardous effects of fireworks are often obscured by the cloud of smoke and amusement ultimately breeding root cause to severe accidental injuries. Fire accidents occur due to surpassing supervisory measures while handling fireworks [1-3]. The illicit production, sale, illegal trade of crackers is another major direct challenge for criminal judiciary system and regulatory authorities [4-6]. Television, print and social media are crammed with evidences apart from actual incident data where crackers played a major role in crime. The chemistry of conventional fireworks has not much transmuted since archaic times. Common constituents in fireworks are carbon centered fuel, a reducing agent and an amalgamation of metal salts functioning as oxidant and colourants. The lightening of fireworks commences with an exothermic process engendering smoke as a by-product holding gases like carbon monoxide, carbon dioxide, sulphur dioxide and nitrogen dioxide, as well as suspended particulate matter between PM2.5-PM10. Conventional fireworks also integrate hazardous chemicals like nitrates of strontium, sodium and barium, carbonates of strontium, calcium, barium and copper, sulphates of strontium and calcium, chlorides of calcium and barium, aluminium and magnesium salts, sodium oxalate, copper oxide, iron dust powder and potassium perchlorate, which act as stabilizers, oxidizers and colorants [7]. Both metals and radicals determine the presence of explosive material. All these residues are hazardous in nature

Epidemic Situation of Brucellosis in Jinzhou City of China and Prediction Using the ARIMA Model

Objective This study aimed to investigate the specific epidemiological characteristics and epidemic situation of brucellosis in Jinzhou City of China so as to establish a suitable prediction model potentially applied as a decision-supportive tool for reasonably assigning health interventions and health delivery. Methods Monthly morbidity data from 2004 to 2013 were selected to construct the autoregressive integrated moving average (ARIMA) model using SPSS 13.0 software. Moreover, stability analysis and sequence tranquilization, model recognition, parameter test, and model diagnostic were also carried out. Finally, the fitting and prediction accuracy of the ARIMA model were evaluated using the monthly morbidity data in 2014. Results A total of 3078 cases affected by brucellosis were reported from January 1998 to December 2015 in Jinzhou City. The incidence of brucellosis had shown a fluctuating growth gradually. Moreover, the ARIMA(1,1,1)(0,1,1)12 model was finally selected among quite a few plausible ARIMA models based upon the parameter test, correlation analysis, and Box–Ljung test. Notably, the incidence from 2005 to 2014 forecasted using this ARIMA model fitted well with the actual incidence data. Notably, the actual morbidity in 2014 fell within the scope of 95% confidence limit of values predicted by the ARIMA(1,1,1)(0,1,1)12 model, with the absolute error between the predicted and the actual values in 2014 ranging from 0.02 to 0.74. Meanwhile, the MAPE was 19.83%. Conclusion It is suitable to predict the incidence of brucellosis in Jinzhou City of China using the ARIMA(1,1,1)(0,1,1)12 model.

Effects of climate change on Plasmodium vivax malaria transmission dynamics: A mathematical modeling approach

Malaria is a major public health concern and an economic burden around the world. Currently, malaria has been declared as endemic in 91 countries. Every year more than 200 million cases of malaria are reported and more than US$2 billion has been invested in malaria control. Recent climate change caused by global warming has affected the pattern of malaria outbreak and expanded the endemic regions. In this study, we develop a malaria transmission model associated with climate-dependent parameters. We analyze the impact of climate change on malaria transmission using climate data based on Representative Concentration Pathways (RCP) scenarios. We investigate the potential risk of malaria outbreaks through the computation of seasonal reproduction number and vectorial capacity under RCP scenarios and perform a sensitivity analysis of the model parameters. Our region of focus is the northern part of South Korea where Plasmodium vivax malaria is currently endemic. Our results show that climate change will increase the risk of massive malaria outbreaks in the area. Furthermore, based on the actual incidence data, we estimate the parameter values that reflect the level of controls implemented recently in Korea. We found that the current level of controls is not sufficient to mitigate the effect of climate change on malaria outbreaks and a combination of stronger control measures such as at least a 10% increase of control for humans combined with a 5% increase of control for adult mosquitoes should be implemented to reduce the risk of malaria outbreaks.

Cancer in Australia: Actual incidence data from 1982 to 2013 and mortality data from 1982 to 2014 with projections to 2017.

Cancer is a major cause of illness in Australia and therefore ongoing monitoring of cancer in Australia is important. This study sources data from the Australian Institute of Health and Welfare (AIHW) and reports actual number of cases diagnosed and deaths, for all cancers combined and selected cancers, from 1982 to 2013 for incidence and from 1982 to 2014 for mortality, with projections to 2017 for both. It is estimated that 134174 new cases of cancer (excluding basal and squamous cell carcinomas of the skin) will be diagnosed in 2017. The number of new cancer cases more than doubled from 47414 in 1982. Prostate cancer is estimated to be the most commonly diagnosed cancer in males in 2017, whereas breast cancer is estimated to be the most commonly diagnosed cancer in females. It is estimated that there will be 47753 deaths from cancer in Australia in 2017. Although the mortality rate decreased, the number of people who died in Australia from cancer almost doubled from 24922 in 1982. Lung cancer was the most common cause of cancer death in both males and females. In 2011, cancer was the leading cause of disease burden in Australia.

Application of ARIMA model on prediction of malaria incidence

To predict the incidence of local malaria of Hubei Province applying the Autoregressive Integrated Moving Average model (ARIMA). SPSS 13.0 software was applied to construct the ARIMA model based on the monthly local malaria incidence in Hubei Province from 2004 to 2009. The local malaria incidence data of 2010 were used for model validation and evaluation. The model of ARIMA (1, 1, 1) (1, 1, 0)12 was tested as relatively the best optimal with the AIC of 76.085 and SBC of 84.395. All the actual incidence data were in the range of 95% CI of predicted value of the model. The prediction effect of the model was acceptable. The ARIMA model could effectively fit and predict the incidence of local malaria of Hubei Province.

Cancer in Australia 2014: actual incidence data from 1982 to 2011 and mortality data from 1982 to 2012 with projections to 2014.

The Australian Institute of Health and Welfare (AIHW) is a major national agency that provides authoritative information and statistics on Australia's health and welfare. The AIHW and the Australasian Association of Cancer Registries collaborate every year to provide updated information on cancer occurrences and trends in Australia. The actual number of cases diagnosed and deaths, for all cancers combined and selected cancers, are presented from 1982 to 2011 for incidence and from 1982 to 2012 for mortality, with projections to 2014. Data on key population groups, survival, prevalence, hospitalizations and national population screening programs are also provided. In 2014, it was estimated that 123 920 new cases of cancer (excluding basal and squamous cell carcinoma of the skin) were diagnosed. Prostate cancer was the most commonly diagnosed cancer in males whereas breast cancer was the most commonly diagnosed cancer in females. It was estimated that 45 780 deaths from cancer occurred in Australia in 2014. Lung cancer was the most common cause of cancer death in both males and females. From 1982 to 2014, it was estimated that the number of new cancer cases diagnosed in Australia more than doubled from 47 417 to 123 920. More males (55%) than females (45%) were diagnosed with cancer in 2014. From 1982 to 2014, the number of people who died in Australia from cancer almost doubled from 24 922 to 45 780. In 2014, more males (57%) than females (43%) were estimated to have died from cancer.

What is the Most Suitable Time Period to Assess the Time Trends in Cancer Incidence Rates to Make Valid Predictions--an Empirical Approach.

Projections of cancer cases are particularly useful in developing countries to plan and prioritize both diagnostic and treatment facilities. In the prediction of cancer cases for the future period say after 5 years or after 10 years, it is imperative to use the knowledge of past time trends in incidence rates as well as in population at risk. In most of the recently published studies the duration for which the time trend was assessed was more than 10 years while in few studies the duration was between 5-7 years. This raises the question as to what is the optimum time period which should be used for assessment of time trends and projections. Thus, the present paper explores the suitability of different time periods to predict the future rates so that the valid projections of cancer burden can be done for India. The cancer incidence data of selected cancer sites of Bangalore, Bhopal, Chennai, Delhi and Mumbai PBCR for the period of 1991-2009 was utilized. The three time periods were selected namely 1991-2005; 1996-2005, 1999-2005 to assess the time trends and projections. For the five selected sites, each for males and females and for each registry, the time trend was assessed and the linear regression equation was obtained to give prediction for the years 2006, 2007, 2008 and 2009. These predictions were compared with actual incidence data. The time period giving the least error in prediction was adjudged as the best. The result of the current analysis suggested that for projections of cancer cases, the 10 years duration data are most appropriate as compared to 7 year or 15 year incidence data.

A Practice-based Taxonomy for Radiation Treatment Errors

PurposeAn absence of a common language for incident classification limits knowledge sharing within and between organizations in the radiotherapy community. This challenge provided the motivation to develop a clinically relevant taxonomy for radiotherapy errors. Materials and MethodsThis was a multicenter, prospective study that consisted of three phases: (1) an initial version of the taxonomy was developed based on the World Health Organization Conceptual Framework for the International Classification for Patient Safety and taxonomy models from radiotherapy and other industries; (2) the taxonomy was evaluated using actual incident data from a single practitioner and revised; and (3) face validity testing of the taxonomy was performed by two additional practitioners from different radiotherapy centers using simulated incident cases. ResultsThe taxonomy consisted of seven classes: incident nature, impact, incident type, stage of origin, stage of discovery, contributing factors, and preventative strategies. Each class was divided into subcategories containing increasingly detailed information. A total of 191 consecutive incidents were classified in phase 2 to ensure no further revision to the taxonomy was required. In phase 3, low interobserver agreement (<60%) was obtained for most classes of the taxonomy in the first face validity test. After revisions were made to the taxonomy based on practitioners' feedback, a second face validity test yielded a high degree of agreement (70%–93%) for all classes. ConclusionsOur multiphase, iterative approach has yielded a workable and multidimensional set of incident classifiers that can be scaled to accommodate local, regional and discipline-specific requirements. Opportunities exist to implement this taxonomy in institutional and national incident databases to facilitate incident learning within and between institutions.

Cancer in Australia: Actual incidence data from 1991 to 2009 and mortality data from 1991 to 2010 with projections to 2012

The Australian Institute of Health and Welfare (AIHW) is a major national agency established by the Australian government to provide information and statistics on Australia's health and welfare. The AIHW and the Australasian Association of Cancer Registries collaborate every year to provide updated information on cancer occurrences and trends in Australia. Actual number of cases and deaths is presented together with age-standardized rates for all cancers combined and selected cancer sites from 1991 to 2009 for incidence and from 1991 to 2010 for mortality, with projections to 2012. Differences in incidence and mortality rates according to age, Indigenous status and remoteness areas are also provided. In addition, information on hospitalizations due to cancer, palliative care and participation in Australia's national screening programs for breast, cervical and bowel cancer is presented. In 2012, it was estimated that 120 710 new cases of cancer (excluding basal and squamous cell carcinoma of the skin) were diagnosed. Prostate cancer was estimated to be the most commonly diagnosed cancer in males, while breast cancer was estimated to be the most commonly diagnosed cancer in females. In 2010, more than 42 800 deaths from cancer occurred in Australia. Lung cancer was by far the most common cause of cancer death in both males and females. In the last decade, cancer incidence rates increased in males and stabilized in females, while mortality rates decreased steadily. The overall incidence and mortality rates of cancer for Indigenous Australians were higher than for non-Indigenous Australians.

A COMPARATIVE ANALYSIS OF INCIDENT SERVICE TIME ON URBAN FREEWAYS

The occurrence of an incident is one of the main causes of traffic congestion and delay on urban freeways. Currently, the development of an efficient traffic incident management process is an emerging issue in intelligent transport systems. The focus of this study is to develop an incident response model for use in an incident management program designed to reduce incident related traffic congestion and delays. Establishing a fuzzy control system based on a real operation process of incident response would help decrease the workload of operators who are constantly making essential decisions, and would be helpful in developing a more reliable operation procedure. Traffic incident data obtained from traffic detectors and information systems on freeways should be considered in order to determine the best form of operation. In this study, a fuzzy incident response model is formulated in terms of incident, type of vehicle, type of incident vehicle, location of incident vehicle, and incident service time. In order to analyze the reliability of the proposed model, the application of the model is made using the actual incident data collected on the freeway in the Los Angeles area. The application of the model shows that the fuzzy incident response system is very effective in describing the actual judgment of the incident operators in terms of incident service time.