OBJECTIVESThe 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.METHODSIn 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.RESULTSThe 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.CONCLUSIONSSARIMA 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.

Scorpionism represents a serious public health problem in Algeria. More than 68% of the national population is at risk of scorpion stings. M'Sila ranks among the endemic provinces of the country and records every year a high incidence of scorpion stings. A survey on epidemiological characteristics of scorpion stings was established. Using the monthly recorded scorpion sting data for the period 2001-2010 for M'Sila province, the linkage between scorpion stings and weather conditions was demonstrated through time series analysis and regression analysis considering the number of scorpion stings as dependent variable and climatic conditions as independent variables. The temperature, precipitation and wind are the retained climate factors, and the temperature has the higher effect. The model predicted the number of scorpion stings in 2011 with a good accuracy. The model could be used by public health makers of the province to anticipate the demand for antivenoms and symptomatic drugs so that they can be distributed in advance. This raises optimism for forecasting scorpion stings provided the availability of appropriate climate information.

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Despite Algeria has been able to join the group of countries with moderate tuberculosis (TB) prevalence since the 1980s, the disease remains one of the major public health issues in the country. Over the past decade, the annual incidence rate has hovered around 55 per 100 000 people. The incidence rate remains, however, very high in some provinces. The aim of this study was to describe the temporal patterns of TB in Médéa province which records the highest incidence rate in the country. In this retrospective study, the monthly pulmonary TB (PTB) and extrapulmonary TB (EPTB) data from 2008 to 2017, extracted from the national surveillance system, were analyzed and seasonal fluctuation was examined. The Box-Jenkins approach to fit seasonal autoregressive integrated moving average (SARIMA) model to the monthly PTB and EPTB notification data from 2008 to 2016 was performed. The models were used to predict the monthly cases of PTB and EPTB for the year 2017. The models were found to be adequate. Our findings indicate that SARIMA models are useful tools for monitoring and for predicting trends of TB incidence in Médea province.

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, 13670 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 100000 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.

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.