RESUMEN
Objective: To determine the potential effect of environment variables on cutaneous leishmaniasis occurrence using time-series models and compare the predictive ability of seasonal autoregressive integrated moving average (SARIMA) models and Markov switching model (MSM). Methods: This descriptive study employed yearly and monthly data of 49 364 parasitologically-confirmed cases of cutaneous leishmaniasis in Isfahan province, located in the center of Iran from January 2000 to December 2019. The data were provided by the leishmaniasis national surveillance system, the meteorological organization of Isfahan province, and Iranian Space Agency for vegetation information. The SARIMA and MSM models were implemented to examine the environmental factors of cutaneous leishmaniasis epidemics. Results: The minimum relative humidity, maximum relative humidity, minimum wind speed, and maximum wind speed were significantly associated with cutaneous leishmaniasis epidemics in different lags (P<0.05). Comparing SARIMA and MSM, Akaikes information criterion (AIC), and mean absolute percentage error (MAPE) in MSM were much smaller than SARIMA models (MSM: AIC=0.95, MAPE=3.5%; SARIMA: AIC=158.93, MAPE:11.45%). Conclusions: SARIMA and MSM can be a useful tool for predicting cutaneous leishmaniasis in Isfahan province. Since cutaneous leishmaniasis falls into one of two states of epidemic and non-epidemic, the use of MSM (dynamic) is recommended, which can provide more information compared to models that use a single distribution for all observations (Box-Jenkins SARIMA model).
RESUMEN
Objective: To review the prevalence of cryptosporidiosis among animal population of Iran. Methods: Data were systematically gathered from 1 January 2000 to 1 January 2020 in the Islamic Republic of Iran from the following electronic databases: PubMed, Springer, Google Scholar, Science Direct, Scopus, Web of Science, Magiran, and Scientific Information Database (SID). According to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) and inclusion criteria, 88 eligible studies were obtained. Results: The pooled prevalence of cryptosporidiosis using random and fixed effects model according to heterogeneity among animals was as follows: rodents 18.8% (95% CI 12.6%-25.0%), camels 17.1% (95% CI 8.6%-25.7%), cattle 16.8% (95% CI 13.4%-20.1%), goats 14.1% (95% CI 5.2%-23.0%), horses 12.2% (95% CI 8.3%- 16.2%), birds 10.5% (95% CI 7.6%-13.4%), sheep 9.9% (95% CI 2.4%-4.9%), cats 8.8% (95% CI 4.8%-12.8%) and dogs 3.7% (95% CI 7.0%-12.8%). Conclusions: Cryptosporidiosis has been reported and present in a wide range of animals in Iran over the years and has a high prevalence in most of these species.
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Objective: To determine the temporal patterns of cumulative incidence of brucellosis using autoregressive integrated moving average models. Methods: This cross-sectional study employed yearly and monthly data of 1 117 laboratory-confirmed human brucellosis cases from January 2013 to December 2018 using the Yazd brucellosis national surveillance system. The monthly incidences constructed a timeseries model. The trend of cumulative incidence was perceived by tracing a line plot, which displayed a seasonal trend with periodicity. Thus, the ARIMA models were selected. Thereafter, Akaike information criteria (AIC) and Bayesian information criterion (BIC) values among different models indicated a preferable model from models which were expanded by diverse lags [(3, 0, 3), (2, 0, 3), (3, 0, 2), (4, 0, 3) and (3, 0, 4)]. Then, the achieved ARIMA model was applied to the forecasting cumulative incidence of monthly brucellosis incidences. All analyses were performed using Stata, version 11.2. Results: For the ARIMA (3, 0, 4) model, MAPE value was 56.20% with standard error 0.009-0.016, and white noise diagnostic check (Q=19.79, P=0.975) for the residuals of the selected model showed that the data were completely modelled. The monthly incidences that were fitted by the ARIMA (3, 0, 4) model, with AIC (25.7) and BIC (43.35) with a similar pattern of actual cases from 2013 to 2018 and forecasting incidences from January 2019 to December 2019 were, respectively, 0.50, 0.44, 0.45, 0.49, 0.55, 0.58, 0.56, 0.51, 0.46, 0.44, 0.45 and 0.49 per 100 000 people. Conclusions: In summary, the study showed that the ARIMA (3, 0, 4) model can be applied to forecast human brucellosis patterns in Yazd province, supplementing present surveillance systems, and may be better for health policy-makers and planners.
RESUMEN
Objective: To determine the temporal patterns of cumulative incidence of brucellosis using autoregressive integrated moving average models. Methods: This cross-sectional study employed yearly and monthly data of 1 117 laboratory-confirmed human brucellosis cases from January 2013 to December 2018 using the Yazd brucellosis national surveillance system. The monthly incidences constructed a timeseries model. The trend of cumulative incidence was perceived by tracing a line plot, which displayed a seasonal trend with periodicity. Thus, the ARIMA models were selected. Thereafter, Akaike information criteria (AIC) and Bayesian information criterion (BIC) values among different models indicated a preferable model from models which were expanded by diverse lags [(3, 0, 3), (2, 0, 3), (3, 0, 2), (4, 0, 3) and (3, 0, 4)]. Then, the achieved ARIMA model was applied to the forecasting cumulative incidence of monthly brucellosis incidences. All analyses were performed using Stata, version 11.2. Results: For the ARIMA (3, 0, 4) model, MAPE value was 56.20% with standard error 0.009-0.016, and white noise diagnostic check (Q=19.79, P=0.975) for the residuals of the selected model showed that the data were completely modelled. The monthly incidences that were fitted by the ARIMA (3, 0, 4) model, with AIC (25.7) and BIC (43.35) with a similar pattern of actual cases from 2013 to 2018 and forecasting incidences from January 2019 to December 2019 were, respectively, 0.50, 0.44, 0.45, 0.49, 0.55, 0.58, 0.56, 0.51, 0.46, 0.44, 0.45 and 0.49 per 100 000 people. Conclusions: In summary, the study showed that the ARIMA (3, 0, 4) model can be applied to forecast human brucellosis patterns in Yazd province, supplementing present surveillance systems, and may be better for health policy-makers and planners.