Seasonal behavior and forecasting trends of tuberculosis incidence in Holy Kerbala, Iraq

Mohammed, Suhad Hadi; Ahmed, Mohanad Mohsin; Al-Mousawi, Ali; Azeez, Adeboye

doi:10.4103/ijmy.ijmy_109_18

Cited by 21 publications

(17 citation statements)

References 2 publications

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“…30 But inconsistent with whose in Spain (which peaked June), 31 Japan (which showed a semi-annual highrisk season peaking in June and October, respectively), 32 and Iraq (which peaked in spring and winter). 33 To date, there is in the absence of exact causes that can be used to account for the seasonal patterns of TB incidence in China, yet given that there is an average incubation period of 4 to 8 weeks from TB infection to onset of symptoms and it still is required for an about 2-month delay from symptom appearance to clinical diagnosis. 34 The following causes seem to be of special concern.…”

Section: Discussionmentioning

confidence: 99%

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

Wang

Ren

et al. 2020

IDR

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Tuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China. Methods: We collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects. Results: In the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead forecasting. The descriptive results to the data revealed that TB incidence showed notable seasonal characteristics with predominant peaks of spring and early summer and began to be plunging at on average 3.722% per year since 2008. However, this rate reduced to 2.613% per year since 2015 and furthermore such a trend would be predicted to continue in years ahead. Conclusion: The ETS framework has the ability to conduct long-term forecasting for TB incidence, which may be beneficial for the long-term planning of the TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, more particular strategies should be formulated to dramatically accelerate progress towards the goals of the End TB Strategy.

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Section: Discussionmentioning

confidence: 99%

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

Wang

Ren

et al. 2020

IDR

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“…In Iraq and many other countries, several studies showed increasing frequency of MDR Esch. coli in different infections like UTI (49,50,51,52,53), bacteremia (49) and children diarrhea (54) . These studies showed high resistance rate to a wide range of antibiotics including ampicillin, cephalosporin and nalidixic acid.…”

Section: Discussionmentioning

confidence: 99%

“…In this context, Al-Aaboda and Al-Notazy (2018)(51) demonstrated high sensitivity rate of Esch.coli to norfloxacin and chloramphenicol in patients with UTI in adults. Similarly,Mohammed et al (2014)(49) showed high sensitivity rate of Esch.coli isolates to chloramphenicol (81%) and imipenem (100%). In addition, Polse et al (2016)(56) also demonstrated 100 % sensitivity rate to imipenem and meropenem.…”

mentioning

confidence: 98%

Isolation and Diagnosis of Bacterial Infections Among Children in the West Bank of Mosul City

AL-Hammadi¹

2020

J.Edu.Sci.

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Infections caused by bacteria are common in infants and neonates. Some of these infections are severe and usually carry high risk of fatal complications such as septicemia and / meningitis if left untreated. Very often, the diagnosis of these infections is clinical as well as laboratory diagnosis to determine the type of bacteria in body fluids such as urine, stool, blood and CSF. The present study aims to isolate and identify the bacteria responsible for some types of severe infections in neonates such as urinary tract infections (UTI), gastrointestinal tract (GIT) infections and bacteremia in West bank of Mosul city and test their susceptibility to different antibiotics. Twenty-seven (27) samples from blood, 28 from urine and 6 from stool were collected from neonates admitted to "Mosul General Hospital" in West Bank of Mosul city in the period between 1/7/2018 to 1/9/2018. Our results indicated that Staphylococcus aureus was the most common bacterial isolate in blood (81%), whereas both Staphylococcus aureus and Esch. coli were common in urine (48% and 43% respectively) followed by Proteus and Pseudomonas aeruginosa (4.5 % each). Both Staphylococcus aureus and Esch. coli were highly sensitive to amoxiclav (83% for and 100% respectively), levofloxacin (88% for Staphylococcus aureus and 100 % for Esch.coli) and meropenem (100% for both Staphylococcus aureus and Esch .coli). However, both of them were highly resistant to ampicillins (100%) and 3rd generation cephalosporins. Although meropenem is effective, it's use should be selected and restricted to highly resistant cases to avoid the emergence of early antibiotic resistance.

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“…Hence, ARIMA models have been widely used for epidemic time series forecasting including hemorrhagic fever, dengue fever, and tuberculosis. For the later and other infectious diseases, seasonality is an important aspect (Mohammed et al, 2018). Using seasonal ARIMA (SAR-IMA) and neural network auto-regression (SARIMA-NNAR) tuberculosis incidence and seasonality was analyzed in South Africa.…”

Section: Epidemiology and Transmissionmentioning

confidence: 99%

Use of artificial intelligence in infectious diseases

Agrebi

Larbi

2020

Artificial Intelligence in Precision Health

140

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Seasonal behavior and forecasting trends of tuberculosis incidence in Holy Kerbala, Iraq

Cited by 21 publications

References 2 publications

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

<p>Secular Seasonality and Trend Forecasting of Tuberculosis Incidence Rate in China Using the Advanced Error-Trend-Seasonal Framework</p>

Isolation and Diagnosis of Bacterial Infections Among Children in the West Bank of Mosul City

Use of artificial intelligence in infectious diseases

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