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Radarsat observations and forecasting of oil slick trajectory movements

Marghany M

J Environ Sci (China), 2004;16(1):44-8.
PMID: 14971450

RADARSAT data have a potential role for coastal pollution monitoring. This study presents a new approach to detect and forecast oil slick trajectory movements. The oil slick trajectory movements is based on the tidal current effects and Fay's algorithm for oil slick spreading mechanisms. The oil spill trajectory model contains the integration between Doppler frequency shift model and Lagrangian model. Doppler frequency shift model implemented to simulate tidal current pattern from RADARSAT data while the Lagrangian model used to predict oil spill spreading pattern. The classical Fay's algorithm was implemented with the two models to simulate the oil spill trajectory movements. The study shows that the slick lengths are effected by tidal current V component with maximum velocity of 1.4 m/s. This indicates that oil slick trajectory path is moved towards the north direction. The oil slick parcels are accumulated along the coastline after 48 h. The analysis indicated that tidal current V components were the dominant forcing for oil slick spreading.

Matched MeSH terms: Forecasting
Statistical prediction of terrestrial gamma radiation dose rate based on geological features and soil types in Kota Tinggi district, Malaysia

Ramli AT, Rahman AT, Lee MH

Appl Radiat Isot, 2003 Nov-Dec;59(5-6):393-405.
PMID: 14622942

A statistical prediction of terrestrial gamma radiation dose rate has been performed, covering the Kota Tinggi district of Peninsular Malaysia. The prediction has been based on geological features and soil types. The purpose of this study is to provide a methodology to statistically predict the gamma radiation dose rate with minimum surveying in an area. Results of statistical predictions using the hypothesis test were compared with the actual dose rate obtained by measurements.

Matched MeSH terms: Forecasting
Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model

Dikshit A, Pradhan B, Alamri AM

Sci Total Environ, 2021 Feb 10;755(Pt 2):142638.
PMID: 33049536 DOI: 10.1016/j.scitotenv.2020.142638

Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R2) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks.

Matched MeSH terms: Forecasting
Fulltext Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction

Ghazvinian H, Mousavi SF, Karami H, Farzin S, Ehteram M, Hossain MS, et al.

PLoS One, 2019;14(5):e0217634.
PMID: 31150467 DOI: 10.1371/journal.pone.0217634

Solar energy is a major type of renewable energy, and its estimation is important for decision-makers. This study introduces a new prediction model for solar radiation based on support vector regression (SVR) and the improved particle swarm optimization (IPSO) algorithm. The new version of algorithm attempts to enhance the global search ability for the PSO. In practice, the SVR method has a few parameters that should be determined through a trial-and-error procedure while developing the prediction model. This procedure usually leads to non-optimal choices for these parameters and, hence, poor prediction accuracy. Therefore, there is a need to integrate the SVR model with an optimization algorithm to achieve optimal choices for these parameters. Thus, the IPSO algorithm, as an optimizer is integrated with SVR to obtain optimal values for the SVR parameters. To examine the proposed model, two solar radiation stations, Adana, Antakya and Konya, in Turkey, are considered for this study. In addition, different models have been tested for this prediction, namely, the M5 tree model (M5T), genetic programming (GP), SVR integrated with four different optimization algorithms SVR-PSO, SVR-IPSO, Genetic Algorithm (SVR-GA), FireFly Algorithm (SVR-FFA) and the multivariate adaptive regression (MARS) model. The sensitivity analysis is performed to achieve the highest accuracy level of the prediction by choosing different input parameters. Several performance measuring indices have been considered to examine the efficiency of all the prediction methods. The results show that SVR-IPSO outperformed M5T and MARS.

Matched MeSH terms: Forecasting
Incentive spirometry inspiratory capacity changes and predictors after open heart surgery: a 5-day prospective study

Narayanan LT, Hamid SRGS

Med J Malaysia, 2020 05;75(3):226-234.
PMID: 32467537

INTRODUCTION: Incentive spirometry (IS) is commonly used for increasing postoperative IS inspiratory capacity (ISIC) after open heart surgery (OHS). However, little is known about the serial changes in ISIC and their predictive factors.
OBJECTIVE: The aim of this study is to identify the postoperative ISIC changes relative to preoperative ISIC after OHS, and determine their predictors, including patient characteristics factors and IS performance parameters such as inspiration volumes (ISv) and frequencies (ISf).
METHODS: This is a prospective study with blinding procedures involving 95 OHS patients, aged 52.8±11.5 years, whose ISIC was measured preoperatively (PreopISIC) until fifth postoperative day (POD), while ISv and ISf monitored with an electronic device from POD1-POD4. Regression models were used to identify predictors of POD1 ISIC, POD2- POD5 ISIC increments, and the odds of attaining PreopISIC by POD5.
RESULTS: The ISIC reduced to 41% on POD1, increasing thereafter to 57%, 75%, 91%, and 106% from POD2-POD5 respectively. Higher PreopISIC (B=-0.01) significantly predicted lower POD1 ISIC, and, together with hyperlipedemia (B=11.52), which significantly predicted higher POD1 ISIC, explained 13% of variance. ISv at relative percentages of PreopISIC from POD1-POD4 (BPOD1=0.60, BPOD2=0.56, BPOD3=0.49, BPOD4=0.50) significantly predicted ISIC of subsequent PODs with variances at 23%, 24%, 17% and 25% respectively, but no association was elicited for ISf. IS performance findings facilitated proposal of a postoperative IS therapy target guideline. Higher ISv (B=0.05) also increased odds of patients recovering to preoperative ISIC on POD5 while higher PreopISIC (B=- 0.002), pain (B=-0.72) and being of Indian race (B=-1.73) decreased its odds.
CONCLUSION: ISv appears integral to IS therapy efficacy after OHS and the proposed therapy targets need further verification through randomized controlled trials.

Matched MeSH terms: Forecasting
Adult palliative care 2004-2030 population study: estimates and projections in Malaysia

Yang SL, Woon YL, Teoh CCO, Leong CT, Lim RBL

BMJ Support Palliat Care, 2020 Aug 21.
PMID: 32826260 DOI: 10.1136/bmjspcare-2020-002283

OBJECTIVES: To estimate past trends and future projection of adult palliative care needs in Malaysia.
METHODS: This is a population-based secondary data analysis using the national mortality registry from 2004 to 2014. Past trend estimation was conducted using Murtagh's minimum and maximum methods and Gómez-Batiste's method. The estimated palliative care needs were stratified by age groups, gender and administrative states in Malaysia. With this, the projection of palliative care needs up to 2030 was conducted under the assumption that annual change remains constant.
RESULTS: The palliative care needs in Malaysia followed an apparent upward trend over the years regardless of the estimation methods. Murtagh's minimum estimation method showed that palliative care needs grew 40% from 71 675 cases in 2004 to 100 034 cases in 2014. The proportion of palliative care needs in relation to deaths hovered at 71% in the observed years. In 2030, Malaysia should anticipate the population needs to be at least 239 713 cases (240% growth from 2014), with the highest needs among age group ≥80-year-old in both genders. Sarawak, Perak, Johor, Selangor and Kedah will become the top five Malaysian states with the highest number of needs in 2030.
CONCLUSION: The need for palliative care in Malaysia will continue to rise and surpass its service provision. This trend demands a stepped-up provision from the national health system with advanced integration of palliative care services to narrow the gap between needs and supply.

Matched MeSH terms: Forecasting
Fulltext Projected Streamflow in the Kurau River Basin of Western Malaysia under Future Climate Scenarios

Adib MNM, Rowshon MK, Mojid MA, Habibu I

Sci Rep, 2020 05 20;10(1):8336.
PMID: 32433561 DOI: 10.1038/s41598-020-65114-w

Climate change-induced spatial and temporal variability of stremflow has significant implications for hydrological processes and water supplies at basin scale. This study investigated the impacts of climate change on streamflow of the Kurau River Basin in Malaysia using a Climate-Smart Decision Support System (CSDSS) to predict future climate sequences. For this, we used 25 reliazations consisting from 10 Global Climate Models (GCMs) and three IPCC Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). The generated climate sequences were used as input to Soil and Water Assessment Tool (SWAT) to simulate projected changes in hydrological processes in the basin over the period 2021-2080. The model performed fairly well for the Kurau River Basin, with coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) of 0.65, 0.65 and -3.0, respectively for calibration period (1981-1998) and 0.60, 0.59 and -4.6, respectively for validation period (1996-2005). Future projections over 2021-2080 period show an increase in rainfall during August to January (relatively wet season, called the main irrigation season) but a decrease in rainfall during February to July (relatively dry season, called the off season). Temperature projections show increase in both the maximum and minimum temperatures under the three RCP scenarios, with a maximum increase of 2.5 °C by 2021-2080 relative to baseline period of 1976-2005 under RCP8.5 scenario. The model predicted reduced streamflow under all RCP scenarios compared to the baseline period. Compared to 2021-2050 period, the projected streamflow will be higher during 2051-2080 period by 1.5 m3/s except in February for RCP8.5. The highest streamflow is predicted during August to December for both future periods under RCP8.5. The seasonal changes in streamflow range between -2.8% and -4.3% during the off season, and between 0% (nil) and -3.8% during the main season. The assessment of the impacts of climatic variabilities on the available water resources is necessary to identify adaptation strategies. It is supposed that such assessment on the Kurau River Basin under changing climate would improve operation policy for the Bukit Merah reservoir located at downstream of the basin. Thus, the predicted streamflow of the basin would be of importance to quantify potential impacts of climate change on the Bukit Merah reservoir and to determine the best possible operational strategies for irrigation release.

Matched MeSH terms: Forecasting
Predictors of acute neurological complication following tetralogy of Fallot operation in Serdang Hospital, Malaysia

Yap MT, Yubbu P, Yong SW, Hing WV, Ong YS, Devaraj NK, et al.

Med J Malaysia, 2020 09;75(5):494-501.
PMID: 32918416

BACKGROUND: The long waiting time for Tetralogy of Fallot (TOF) operation may potentially increase the risk of hypoxic insult. Therefore, the objective of this study is to determine the frequency of acute neurological complications following primary TOF repair and to identify the peri-operative risk factors and predictors for the neurological sequelae.
METHODS: A retrospective review of the medical and surgical notes of 68 patients who underwent TOF repair in Hospital Serdang, from January 2013 to December 2017 was done. Univariate and multivariate analyses of demographics and perioperative clinical data were performed to determine the risk for the development of acute neurological complications (ANC) among these patients.
RESULTS: ANC was reported in 13 cases (19.1%) with delirium being the most common manifestation (10/68, 14.7%), followed by seizures in 4 (5.9%) and abnormal movements in two patients (2.9%). Univariate analyses showed that the presence of right ventricular (RV) dysfunction, prolonged duration of inotropic support (≥7 days), prolonged duration of mechanical ventilation (≥7 days), longer length of ICU stays (≥7 days), and longer length of hospital stay (≥14 days), were significantly associated with the presence of ANCs (p<0.05). However, multivariate analyses did not show any significant association between these variables and the development of ANC (p>0.05). The predictors for the development of postoperative delirium were pre-operative oxygen saturation less than 75% (Odds Ratio, OR=16.90, 95% Confidence Interval, 95%CI:1.36, 209.71) and duration of ventilation of more than 7 days (OR=13.20, 95%CI: 1.20, 144.98).
CONCLUSION: ANC following TOF repair were significantly higher in patients with RV dysfunction, in those who required a longer duration of inotropic support, mechanical ventilation, ICU and hospital stay. Low pre-operative oxygen saturation and prolonged mechanical ventilation requirement were predictors for delirium which was the commonest neurological complications observed in this study. Hence, routine screening for delirium using an objective assessment tool should be performed on these high-risk patients to enable accurate diagnosis and early intervention to improve the overall outcome of TOF surgery in this country.

Matched MeSH terms: Forecasting
Fulltext Monitoring of three-phase variations in the mortality of COVID-19 pandemic using control charts: where does Pakistan stand?

Mahmood Y, Ishtiaq S, Khoo MBC, Teh SY, Khan H

Int J Qual Health Care, 2021 Apr 16;33(2).
PMID: 33822932 DOI: 10.1093/intqhc/mzab062

BACKGROUND: At the end of December 2019, the world in general and Wuhan, the industrial hub of China, in particular, experienced the COVID-19 pandemic. Massive increment of cases and deaths occurred in China and 209 countries in Europe, America, Australia, Asia and Pakistan. Pakistan was first hit by COVID-19 when a case was reported in Karachi on 26 February 2020. Several methods were presented to model the death rate due to the COVID-19 pandemic and to forecast the pinnacle of reported deaths. Still, these methods were not used in identifying the first day when Pakistan enters or exits the early exponential growth phase.
OBJECTIVE: The present study intends to monitor variations in deaths and identify the growth phases such as pre-growth, growth, and post-growth phases in Pakistan due to the COVID-19 pandemic.
METHODS: New approaches are needed that display the death patterns and signal an alarming situation so that corrective actions can be taken before the condition worsens. To meet this purpose, secondary data on daily reported deaths due to the COVID-19 pandemic have been considered, and the $c$ and exponentially weighted moving average (EWMA) control charts are used To meet this purpose, secondary data on daily reported deaths in Pakistan due to the COVID-19 pandemic have been considered. The $ c$ and exponentially weighted moving average (EWMA) control charts have been used for monitoring variations.
RESULTS: The chart shows that Pakistan switches from the pre-growth to the growth phase on 31 March 2020. The EWMA chart demonstrates that Pakistan remains in the growth phase from 31 March 2020 to 17 August 2020, with some indications signaling a decrease in deaths. It is found that Pakistan moved to a post-growth phase for a brief period from 27 July 2020 to 28 July 2020. Pakistan switches to re-growth phase with an alarm on 31/7/2020, right after the short-term post-growth phase. The number of deaths starts decreasing in August in that Pakistan may approach the post-growth phase shortly.
CONCLUSION: This amalgamation of control charts illustrates a systematic implementation of the charts for government leaders and forefront medical teams to facilitate the rapid detection of daily reported deaths due to COVID-19. Besides government and public health officials, it is also the public's responsibility to follow the enforced standard operating procedures as a temporary remedy of this pandemic in ensuring public safety while awaiting a suitable vaccine to be discovered.

Matched MeSH terms: Forecasting
An ensemble model for forecasting infectious diseases in India

Shashvat K, Basu R, Bhondekar PA, Kaur A

Trop Biomed, 2019 Dec 01;36(4):822-832.
PMID: 33597454

Time series modelling and forecasting plays an important role in various domains. The objective of this paper is to construct a simple average ensemble method to forecast the number of cases for infectious diseases like dengue and typhoid and compare it by applying models for forecasting. In this paper we have also evaluated the correlation between the number of typhoid and dengue cases with the ecological variables. The monthly data of dengue and typhoid cases from 2014 to 2017 were taken from integrated diseases surveillance programme, Government of India. This data was analysed by three models namely support vector regression, neural network and linear regression. The proposed simple average ensemble model was constructed by ensemble of three applied regression models i.e. SVR, NN and LR. We combine the regression models based upon the error metrics such as Mean Square Error, Root Mean Square Error and Mean Absolute Error. It was found that proposed ensemble method performed better in terms of forecast measures. The finding demonstrates that the proposed model outperforms as compared to already available applied models on the basis of forecast accuracy.

Matched MeSH terms: Forecasting
Reproduction research and health part II--Fetal health

Sinnathuray TA

Med J Malaysia, 1979 Dec;34(2):176-80.
PMID: 548724
Matched MeSH terms: Forecasting
Winged bean (Psophocarpus tetragonolobus (L.) DC.) for food and nutritional security: synthesis of past research and future direction

Tanzi AS, Eagleton GE, Ho WK, Wong QN, Mayes S, Massawe F

Planta, 2019 Sep;250(3):911-931.
PMID: 30911885 DOI: 10.1007/s00425-019-03141-2

MAIN CONCLUSION: Winged bean is popularly known as "One Species Supermarket" for its nutrient-dense green pods, immature seeds, tubers, leaves, and mature seeds. This underutilised crop has potential beneficial traits related to its biological nitrogen-fixation to support low-input farming. Drawing from past knowledge, and based on current technologies, we propose a roadmap for research and development of winged bean for sustainable food systems. Reliance on a handful of "major" crops has led to decreased diversity in crop species, agricultural systems and human diets. To reverse this trend, we need to encourage the greater use of minor, "orphan", underutilised species. These could contribute to an increase in crop diversity within agricultural systems, to improve human diets, and to support more sustainable and resilient food production systems. Among these underutilised species, winged bean (Psophocarpus tetragonolobus) has long been proposed as a crop for expanded use particularly in the humid tropics. It is an herbaceous perennial legume of equatorial environments and has been identified as a rich source of protein, with most parts of the plant being edible when appropriately prepared. However, to date, limited progress in structured improvement programmes has restricted the expansion of winged bean beyond its traditional confines. In this paper, we discuss the reasons for this and recommend approaches for better use of its genetic resources and related Psophocarpus species in developing improved varieties. We review studies on the growth, phenology, nodulation and nitrogen-fixation activity, breeding programmes, and molecular analyses. We then discuss prospects for the crop based on the greater understanding that these studies have provided and considering modern plant-breeding technologies and approaches. We propose a more targeted and structured research approach to fulfil the potential of winged bean to contribute to food security.

Matched MeSH terms: Forecasting
Analysing the accuracy of machine learning techniques to develop an integrated influent time series model: case study of a sewage treatment plant, Malaysia

Ansari M, Othman F, Abunama T, El-Shafie A

Environ Sci Pollut Res Int, 2018 Apr;25(12):12139-12149.
PMID: 29455350 DOI: 10.1007/s11356-018-1438-z

The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R2) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models. An integrated model was developed based on the individual models' prediction ability for low, average and peak flow. An initial assessment of the results showed that the ARIMA model was the least accurate and the NAR model was the most accurate. The RE results also prove that the SVM model's frequency of errors above 10% or below - 10% was greater than the NAR model's. The influent was also forecasted up to 44 weeks ahead by both models. The graphical results indicate that the NAR model made better predictions than the SVM model. The final evaluation of NAR and SVM demonstrated that SVM made better predictions at peak flow and NAR fit well for low and average inflow ranges. The integrated model developed includes the NAR model for low and average influent and the SVM model for peak inflow.

Matched MeSH terms: Forecasting
Review on applications of artificial intelligence methods for dam and reservoir-hydro-environment models

Allawi MF, Jaafar O, Mohamad Hamzah F, Abdullah SMS, El-Shafie A

Environ Sci Pollut Res Int, 2018 May;25(14):13446-13469.
PMID: 29616480 DOI: 10.1007/s11356-018-1867-8

Efficacious operation for dam and reservoir system could guarantee not only a defenselessness policy against natural hazard but also identify rule to meet the water demand. Successful operation of dam and reservoir systems to ensure optimal use of water resources could be unattainable without accurate and reliable simulation models. According to the highly stochastic nature of hydrologic parameters, developing accurate predictive model that efficiently mimic such a complex pattern is an increasing domain of research. During the last two decades, artificial intelligence (AI) techniques have been significantly utilized for attaining a robust modeling to handle different stochastic hydrological parameters. AI techniques have also shown considerable progress in finding optimal rules for reservoir operation. This review research explores the history of developing AI in reservoir inflow forecasting and prediction of evaporation from a reservoir as the major components of the reservoir simulation. In addition, critical assessment of the advantages and disadvantages of integrated AI simulation methods with optimization methods has been reported. Future research on the potential of utilizing new innovative methods based AI techniques for reservoir simulation and optimization models have also been discussed. Finally, proposal for the new mathematical procedure to accomplish the realistic evaluation of the whole optimization model performance (reliability, resilience, and vulnerability indices) has been recommended.

Matched MeSH terms: Forecasting
MicroRNAs and colorectal cancer: clinical potential and regulatory networks

Osei GY, Adu-Amankwaah J, Koomson S, Beletaa S, Asiamah EA, Smith-Togobo C, et al.

Mol Biol Rep, 2023 Nov;50(11):9575-9585.
PMID: 37776413 DOI: 10.1007/s11033-023-08810-w

Colorectal cancer (CRC) is a serious global health concern, with a high incidence and mortality rate. Although there have been advancements in the early detection and treatment of CRC, therapy resistance is common. MicroRNAs (miRNAs), a type of small non-coding RNA that regulates gene expression, are key players in the initiation and progression of CRC. Recently, there has been growing attention to the complex interplay of miRNAs in cancer development. miRNAs are powerful RNA molecules that regulate gene expression and have been implicated in various physiological and pathological processes, including carcinogenesis. By identifying current challenges and limitations of treatment strategies and suggesting future research directions, this review aims to contribute to ongoing efforts to enhance CRC diagnosis and treatment. It also provides a comprehensive overview of the role miRNAs play in CRC carcinogenesis and explores the potential of miRNA-based therapies as a treatment option. Importantly, this review highlights the exciting potential of targeted modulation of miRNA function as a therapeutic approach for CRC.

Matched MeSH terms: Forecasting
Fulltext Prediction of Epidemic Peak and Infected Cases for COVID-19 Disease in Malaysia, 2020

Alsayed A, Sadir H, Kamil R, Sari H

Int J Environ Res Public Health, 2020 Jun 08;17(11).
PMID: 32521641 DOI: 10.3390/ijerph17114076

The coronavirus COVID-19 has recently started to spread rapidly in Malaysia. The number of total infected cases has increased to 3662 on 05 April 2020, leading to the country being placed under lockdown. As the main public concern is whether the current situation will continue for the next few months, this study aims to predict the epidemic peak using the Susceptible-Exposed-Infectious-Recovered (SEIR) model, with incorporation of the mortality cases. The infection rate was estimated using the Genetic Algorithm (GA), while the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was used to provide short-time forecasting of the number of infected cases. The results show that the estimated infection rate is 0.228 ± 0.013, while the basic reproductive number is 2.28 ± 0.13. The epidemic peak of COVID-19 in Malaysia could be reached on 26 July 2020, with an uncertain period of 30 days (12 July-11 August). Possible interventions by the government to reduce the infection rate by 25% over two or three months would delay the epidemic peak by 30 and 46 days, respectively. The forecasting results using the ANFIS model show a low Normalized Root Mean Square Error (NRMSE) of 0.041; a low Mean Absolute Percentage Error (MAPE) of 2.45%; and a high coefficient of determination (R2) of 0.9964. The results also show that an intervention has a great effect on delaying the epidemic peak and a longer intervention period would reduce the epidemic size at the peak. The study provides important information for public health providers and the government to control the COVID-19 epidemic.

Matched MeSH terms: Forecasting
Fulltext Humans as animal sentinels for forecasting asthma events: helping health services become more responsive

Soyiri IN, Reidpath DD

PLoS One, 2012;7(10):e47823.
PMID: 23118897 DOI: 10.1371/journal.pone.0047823

The concept of forecasting asthma using humans as animal sentinels is uncommon. This study explores the plausibility of predicting future asthma daily admissions using retrospective data in London (2005-2006). Negative binomial regressions were used in modeling; allowing the non-contiguous autoregressive components. Selected lags were based on partial autocorrelation function (PACF) plot with a maximum lag of 7 days. The model was contrasted with naïve historical and seasonal models. All models were cross validated. Mean daily asthma admission in 2005 was 27.9 and in 2006 it was 28.9. The lags 1, 2, 3, 6 and 7 were independently associated with daily asthma admissions based on their PACF plots. The lag model prediction of peak admissions were often slightly out of synchronization with the actual data, but the days of greater admissions were better matched than the days of lower admissions. A further investigation across various populations is necessary.

Matched MeSH terms: Forecasting
Fulltext Artificial Intelligence Techniques: Analysis, Application, and Outcome in Dentistry-A Systematic Review

Ahmed N, Abbasi MS, Zuberi F, Qamar W, Halim MSB, Maqsood A, et al.

Biomed Res Int, 2021;2021:9751564.
PMID: 34258283 DOI: 10.1155/2021/9751564

OBJECTIVE: The objective of this systematic review was to investigate the quality and outcome of studies into artificial intelligence techniques, analysis, and effect in dentistry.
MATERIALS AND METHODS: Using the MeSH keywords: artificial intelligence (AI), dentistry, AI in dentistry, neural networks and dentistry, machine learning, AI dental imaging, and AI treatment recommendations and dentistry. Two investigators performed an electronic search in 5 databases: PubMed/MEDLINE (National Library of Medicine), Scopus (Elsevier), ScienceDirect databases (Elsevier), Web of Science (Clarivate Analytics), and the Cochrane Collaboration (Wiley). The English language articles reporting on AI in different dental specialties were screened for eligibility. Thirty-two full-text articles were selected and systematically analyzed according to a predefined inclusion criterion. These articles were analyzed as per a specific research question, and the relevant data based on article general characteristics, study and control groups, assessment methods, outcomes, and quality assessment were extracted.
RESULTS: The initial search identified 175 articles related to AI in dentistry based on the title and abstracts. The full text of 38 articles was assessed for eligibility to exclude studies not fulfilling the inclusion criteria. Six articles not related to AI in dentistry were excluded. Thirty-two articles were included in the systematic review. It was revealed that AI provides accurate patient management, dental diagnosis, prediction, and decision making. Artificial intelligence appeared as a reliable modality to enhance future implications in the various fields of dentistry, i.e., diagnostic dentistry, patient management, head and neck cancer, restorative dentistry, prosthetic dental sciences, orthodontics, radiology, and periodontics.
CONCLUSION: The included studies describe that AI is a reliable tool to make dental care smooth, better, time-saving, and economical for practitioners. AI benefits them in fulfilling patient demand and expectations. The dentists can use AI to ensure quality treatment, better oral health care outcome, and achieve precision. AI can help to predict failures in clinical scenarios and depict reliable solutions. However, AI is increasing the scope of state-of-the-art models in dentistry but is still under development. Further studies are required to assess the clinical performance of AI techniques in dentistry.

Matched MeSH terms: Forecasting
A climate distribution model of malaria transmission in Sudan

Musa MI, Shohaimi S, Hashim NR, Krishnarajah I

Geospat Health, 2012 Nov;7(1):27-36.
PMID: 23242678

Malaria remains a major health problem in Sudan. With a population exceeding 39 million, there are around 7.5 million cases and 35,000 deaths every year. The predicted distribution of malaria derived from climate factors such as maximum and minimum temperatures, rainfall and relative humidity was compared with the actual number of malaria cases in Sudan for the period 2004 to 2010. The predictive calculations were done by fuzzy logic suitability (FLS) applied to the numerical distribution of malaria transmission based on the life cycle characteristics of the Anopheles mosquito accounting for the impact of climate factors on malaria transmission. This information is visualized as a series of maps (presented in video format) using a geographical information systems (GIS) approach. The climate factors were found to be suitable for malaria transmission in the period of May to October, whereas the actual case rates of malaria were high from June to November indicating a positive correlation. While comparisons between the prediction model for June and the case rate model for July did not show a high degree of association (18%), the results later in the year were better, reaching the highest level (55%) for October prediction and November case rate.

Matched MeSH terms: Forecasting/methods
Recent and prospective population trends in Malaysia

Jones GW, Tan PC

J Southeast Asian Stud, 1985 Sep;16(2):262-80.
PMID: 12267554
Matched MeSH terms: Forecasting*

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