A Hybrid Approach Based on Seasonal Autoregressive Integrated Moving Average and Neural Network Autoregressive Models to Predict Scorpion Sting Incidence in El Oued Province, Algeria, From 2005 to 2020.

BACKGROUND: This study was designed to find the best statistical approach to scorpion sting predictions. Study Design: A retrospective study. METHODS: Multiple regression, seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly scorpion sting cases in El Oued province. The root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to quantitatively compare different models. RESULTS: In general, 96909 scorpion stings were recorded in El Oued province from 2005-2020. The incidence rate experienced a gradual decrease until 2012 and since then slight fluctuations have been noted. Scorpion stings occurred throughout the year with peaks in September followed by July and August and troughs in December and January. Sting cases were not evenly distributed across demographic groups; the most affected age group was 15-49 years, and males were more likely to be stung. Of the reported deaths, more than half were in children 15 and younger. Scorpion's activity was conditioned by climate factors, and temperature had the highest effect. The SARIMA(2,0,2)(1,1,1)12, NNAR(1,1,2)12, and SARIMA(2,0,2)(1,1,1)12-NNAR(1,1,2)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were lower than those of the NNAR model in fitting and forecasting; however, the NNAR model could produce better predictive accuracy. CONCLUSION: The NNAR model is preferred for short-term monthly scorpion sting predictions. An in-depth understanding of the epidemiologic triad of scorpionism and the development of predictive models ought to establish enlightened, informed, better-targeted, and more effective policies.

Epidemiology and time series analysis of human brucellosis in Tebessa province, Algeria, from 2000 to 2020.

BACKGROUND: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. STUDY DESIGN: A retrospective study. METHODS: Seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly brucellosis notifications. The prediction performance of these models was compared using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). RESULTS: Overall, 13 670 human brucellosis cases were notified in Tebessa province from 2000-2020 with a male-to-female ratio of 1.3. The most affected age group was 15-44 years (56.2%). The cases were reported throughout the year with manifest seasonality. The annual notification rate ranged from 30.9 (2013) to 246.7 (2005) per 100 000 inhabitants. The disease was not evenly distributed, rather spatial and temporal variability was observed. The SARIMA (2,1,3) (1,1,1)12, NNAR (12,1,6)12, and SARIMA (2,0,2) (1,1,0)12-NNAR (5,1,4)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were by far lower than those of the NNAR model. Moreover, the SARIMA-NNNAR hybrid model achieved a slightly better prediction accuracy for 2020 than the SARIMA model. CONCLUSION: As evidenced by the obtained results, both SARIMA and hybrid SARIMA-NNAR models are suitable to predict human brucellosis cases with high accuracy. Reasonable predictions, along with mapping brucellosis incidence, could be of great help to veterinary and health policymakers in the development of informed, effective, and targeted policies, as well as timely interventions.

Spatiotemporal analysis and seasonality of tuberculosis in Algeria.

Background: This study aimed to describe the spatiotemporal distribution, to build a forecasting model, and to determine the seasonal pattern of tuberculosis (TB) in Algeria. Methods: The Box-Jenkins methodology was used to develop predictive models and GeoDa software was used to perform spatial autocorrelation. Results: Between 1982 and 2019, the notification rate per 100,000 population of smear-positive pulmonary TB (SPPTB) has dropped 62.2%, while that of extrapulmonary TB (EPTB) has risen 91.3%. For the last decade, the mean detection rate of PTB was 82.6%. At around, 2% of PTB cases were yearly reported in children under 15 years old, a peak in notification rate was observed in the elderly aged 65 and over, and the sex ratio was in favor of men. Between 52% and 59% of EPTB cases were lymphadenitis TB and between 15% and 23% were pleural TB. About two-third of EPTB cases were females and around 10% were children under the age of 15. The time series analysis showed that (1,1, 2) × (1, 1, 0)4 (respectively (0, 1, 2) × (1, 1, 0)4, (3, 1, 0) × (1, 1, 0)4) offered the best forecasting model to quarterly TB (respectively EPTB, SPPTB) surveillance data. The most hit part was the Tell followed by high plateaus which accounted for 96.6% of notifications in 2017. Significant hot spots were identified in the central part for EPTB notification rate and in the northwestern part for SPPTB. Conclusions: There is a need to reframe the set objectives in the state strategy to combat TB taking into account seasonality and spatial clustering to ensure improved TB management through targeted and effective interventions.

Epidemiology and clinical characteristics of tuberculosis in leon bernard tuberculosis unit in algeria.

Background: This study aimed at highlighting some demographic and clinical features of tuberculosis (TB) at Leon Bernard TB unit, Algeria. Methods: This was a retrospective and descriptive study based on TB data extracted from TB patient records during 2009-2019 at Leon Bernard TB unit. Results: Of the total 1375 TB patients, 602 (43.8%) had extrapulmonary TB (EPTB), 482 (35.1%) had new sputum smear-positive pulmonary TB (PTB), 42 (3.1%) had sputum smear-negative with culture-positive PTB, and 97 (7%) EPTB patients had concomitant pulmonary involvement. The male-to-female ratio was 1.07. A total of 116 (8.4%) TB relapses were reported with predominance among ETPB cases (54.3%). Lymphadenitis TB was the most common manifestation of EPTB with 301 cases (39.6%), followed by pleural TB with 237 cases (31.2%). The number of bacteriologically not confirmed EPTB was 22.6% more than half (53.3%) of whom were pleural TB. Among patients with new sputum smear-positive PTB, 71.2% were males, whereas the reverse was observed among patients with EPTB where 62.3% were female. Two-third of recorded cases were between 15 and 45 years old. Nearly all children had EPTB (64/69). The results revealed that most of the patients who passed away were affected by PTB (15/18), and the most frequent cmorbidities were diabetes (9/18) and high blood pressure (6/18). Conclusion: Specific attention needs to be given to an examination of the risk factors of PTB among male population and of EPTB among female population and children and to diagnosis of pleural TB and primary PTB.

Demographic and epidemiological characteristics of scorpion envenomation and daily forecasting of scorpion sting counts in Touggourt, Algeria.

OBJECTIVES: This study was conducted to provide better insights into the demographic and epidemiological characteristics of scorpion envenomation in an endemic area in Algeria and to identify the model that best predicted daily scorpion sting counts. METHODS: Daily sting data from January 1, 2013 to August 31, 2016 were extracted from questionnaires designed to elicit information on scorpion stings from the two emergency medical service providers in Touggourt, Algeria. Count regression models were applied to the daily sting data. RESULTS: A total of 4,712 scorpion sting cases were documented, of which 70% occurred in people aged between 10 years and 49 years. The male-to-female ratio was 1.3. The upper and lower limbs were the most common locations of scorpion stings (90.4% of cases). Most stings (92.8%) were mild. The percent of people stung inside dwellings was 68.8%. The hourly distribution of stings showed a peak between 10:00 a.m. and 11:00 a.m. The daily number of stings ranged from 0 to 24. The occurrence of stings was highest on Sundays. The incidence of scorpion stings increased sharply in the summer. The mean annual incidence rate was 542 cases per 100,000 inhabitants. The fitted count regression models showed that a negative binomial hurdle model was appropriate for forecasting daily stings in terms of temperature and relative humidity, and the fitted data agreed considerably with the actual data. CONCLUSIONS: This study showed that daily scorpion sting data provided meaningful insights; and the negative binomial Hurdle model was preferable for predicting daily scorpion sting counts.

Forecasting and prediction of scorpion sting cases in Biskra province, Algeria, using a seasonal autoregressive integrated moving average model.

OBJECTIVES: The aims of this study were to highlight some epidemiological aspects of scorpion envenomations, to analyse and interpret the available data for Biskra province, Algeria, and to develop a forecasting model for scorpion sting cases in Biskra province, which records the highest number of scorpion stings in Algeria. METHODS: In addition to analysing the epidemiological profile of scorpion stings that occurred throughout the year 2013, we used the Box-Jenkins approach to fit a seasonal autoregressive integrated moving average (SARIMA) model to the monthly recorded scorpion sting cases in Biskra from 2000 to 2012. RESULTS: The epidemiological analysis revealed that scorpion stings were reported continuously throughout the year, with peaks in the summer months. The most affected age group was 15 to 49 years old, with a male predominance. The most prone human body areas were the upper and lower limbs. The majority of cases (95.9%) were classified as mild envenomations. The time series analysis showed that a (5,1,0)×(0,1,1)12 SARIMA model offered the best fit to the scorpion sting surveillance data. This model was used to predict scorpion sting cases for the year 2013, and the fitted data showed considerable agreement with the actual data. CONCLUSIONS: SARIMA models are useful for monitoring scorpion sting cases, and provide an estimate of the variability to be expected in future scorpion sting cases. This knowledge is helpful in predicting whether an unusual situation is developing or not, and could therefore assist decision-makers in strengthening the province's prevention and control measures and in initiating rapid response measures.

Dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis in Biskra Province in Algeria.

BACKGROUND AND OBJECTIVES: The present study aimed to examine the dynamic relationship between climate factors and the incidence of cutaneous leishmaniasis (CL) in Biskra province, the largest focus of CL in Algeria, recording every year the highest incidence of CL in the country. The goal was to find the relationship between climate factors and CL incidence and identify the best model to estimate the variability among future CL cases. METHODS: We carried out a time series analysis based on the Box-Jenkins method to fit an autoregressive moving average (ARMA) model incorporating climate factors to the monthly recorded CL cases in Biskra province from 2000 to 2014. RESULTS: An ARMA (3,3) model incorporating temperature at a lag of 5 months and relative humidity was appropriate for forecasting the monthly data for CL between 2000 and 2009 in Biskra province. Temperature had a higher effect followed by relative humidity. The model was used for predicting monthly CL cases from January 2010 to December 2014; the predictions matched the recorded data. CONCLUSIONS: ARMA models produce reliable models for prediction of CL cases provided that climate variables are available. The models could assist public health services in preparing for the future. This is an optimistic finding for forecasting CL by means of a surveillance system using climate information.