Spatio-temporal history of H9N2 viruses in Iran and neighbor countries by Bayesian analysis and molecular characterization

Objective: To delineate the H9N2 influenza virus circulation within Iran and its neighboring countries, the potential source of the epidemic in these countries, and its date of origin.Methods: We obtained all hemagglutinin(HA) and neuraminidase(NA) nucleotide sequences of influenza H9N2 available up to December 25, 2020 from Iran and its neighboring countries(i.e., Pakistan, Afghanistan, Turkmenistan, Armenia, Azerbaijan, Turkey, and Iraq). We also performed a Bayesian Markov chain Monte Carlo method to infer the evolutionary dynamic and the most recent common ancestor for the HA and NA sequences.Results: H9N2 epidemic may have started in Iran and Pakistan much earlier than the other investigated countries in the region, and an ongoing bidirectional dispersion of the virus between the investigated countries was also observed. The mean time of the most recent common ancestor of H9N2 viruses was 1988 for HA, and 1992 for NA.Conclusions: Strains from investigated countries rooted in Pakistan and Iran. Regular surveillance of H9N2 viruses, especially in the live bird markets, enhancing the biosecurity of poultry industry and screening newly arriving immigrants and tourists from neighboring countries at border should be considered to control spread of the virus. Furthermore, surveillance of viral molecular evolution should be initiated for effective prevention of epidemic and pandemic spreads.

Predicting cutaneous leishmaniasis using SARIMA and Markov switching models in Isfahan, Iran: A time-series study

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 49364 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).