ABSTRACT
Objective To evaluate the impact of environmental and climatic factors on the distribution of suitable habitats of Haemaphysalis longicornis, and to predict the potential distribution of H. longicornis under different climate patterns in China. Methods Data pertaining to the distribution of H. longicornis were retrieved from public literatures. The effects of 19 climatic factors (annual mean temperature, annual mean temperature difference between day and night, isothermality, standard deviation of seasonal variation of temperature, maximum temperature of the warmest month, minimum temperature of the coldest month, temperature annual range, mean temperature of the wettest season, mean temperature of the driest season, mean temperature of the warmest season, mean temperature of the coldest season, annual mean precipitation, precipitation of the wettest month, precipitation of the driest month, coefficient of variance of precipitation, precipitation of the wettest season, precipitation of the driest season, precipitation of the warmest season and precipitation of the coldest season) and 4 environmental factors (elevation, slope, slope aspect and vegetation coverage) on the potential distribution of H. longicornis were assessed using the maximum entropy (MaxEnt) model based on the H. longicornis distribution data and climatic and environmental data, and the potential distribution of H. longicornis was predicted under the RCP 2.6 and 8.5 emissions scenarios. Results Among the environmental and climatic factors affecting the geographical distribution of H. longicornis in China, the factors contributing more than 10% to the distribution of H. longicornis mainly included the precipitation of the driest month (26.0%), annual mean temperature (11.2%), annual mean precipitation (10.0%) and elevation (24.2%). Under the current climate pattern, the high-, medium- and low-suitable habitats of H. longicornis are 1 231 900, 1 696 200 km2 and 1 854 400 km2 in China, respectively. The distribution of H. longicornis increased by 336 100 km2 and 367 300 km2 in 2050 and 2070 under the RCP 2.6 emissions scenario, and increased by 381 000 km2 and 358 000 km2 in 2050 and 2070 under the RCP 8.5 emissions scenario in China, respectively. Conclusions Climatic and environmental factors, such as precipitation, temperature and elevation, greatly affect the distribution of H. longicornis in China, and the suitable habitats of H. longicornis may expand in China under different climate patterns in future.
ABSTRACT
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).
ABSTRACT
In this paper,the potential climate factors affecting the Pairs polyphylla var. yunnanensis distribution in China at rational scales were selected from related literatures, using the sampling point geographic information from of P. polyphylla var. yunnanensis, combine the maximum entropy model (MaxEnt) with spatial analyst function of ArcGIS software, to study the climate suitability of P. polyphylla var. yunnanensis cultivating region in China and the leading climate factors. The results showed that, average rainfall in August, average rainfall in October, coefficient of variation of seasonal precipitation, the average temperature of the dry season, isothermal characteristic, average temperature in July were the leading climate factors affecting the potential distribution of P. polyphylla var. yunnanensis cultivating region in China, with their cumulative contribution rate reached 97.2% of all candidate climate factors. Existence probability of the region to be predicted of P. polyphylla var. yunnanensis through the constructed model, the climate unsuitable region, low, medium and high region of P. polyphylla var. yunnanensis in China were clarified and the threshold of climatic factors were gave and clarified the climate characteristics of the cultivating region in each climatic suitability division. The results of research can provide reference for production layout and introduction of P. polyphylla var. yunnanensis.
ABSTRACT
Peritonitis is one of the major complication of continuous ambulatory peritoneal dialysis (CAPD) and the most common cause of hospital admission and for termination of peritoneal dialysis. We retrospectively analyzed the incidences and causative organisms of CAPD peritonitis according to season/month of the year under the hypothesis that climate factors, increased temperature and humidity, may changes the incidences and causative organisms of peritonitis. There were a few studies about this issue and in most cases the result was inconclusive because of the limitation in the limited range of climate factors such as temperature and humidity. Wide annual differences of temperature (-3.4-25.4 degrees C) and humidity (61-81%) may affect the rate of peritonitis episode in the area where the current study was performed. Data from 80 patients(49 male, 31 female), with a mean age 48.3+/-14.5 years and mean CAPD period 14.0+/-9.0 months, followed from September 1996 to July 1999, were reviewed. Fifty-three cases of peritonitis were found in 1,123 patient-months, a rate of 0.56 episode/patients- year, and 0.047 episode/patient-month. The months in which the incidence of peritonitis above average was March (5.05%), May(7.96%), July (10.8%), August (6.25%), September (6.06%). The incidence of peritonitis was the lowest in November (1.31%). The incidence in hot season (May-September : average temperature for three years 21.9degrees C, humidity 74%) was 0.065 episodes/patient-month, which was significantly higher than in cold season (October-February : 5.9degrees C, 64.4%)(p<0.05). Average temperature for three years in the study area was 13.2degrees C with maximal temperature of 25.4degrees C (August) and minimal of -3.4 degrees C (January). Average humidity for three years in the study area was 68.4% with maximal humidity of 81% (July) and minimal of 61% (April). The incidence of peritonitis paralleled with temperature and humidity, highest in July (0.080/pt-month) and lowest in November (0.013/pt-month) and were directly correlated with temperature (r=0.53, p<0.05) and humidity (r=0.59, p<0.05). Among 53 episodes of peritonitis, gram positive peritonitis, gram negative peritonitis and culture negative peritonitis were 36.9%, 15.0% and 45.2%, respectively. From March to August, gram positive peritonitis was 50% and culture negative peritonitis was 42.4%. From September to February, culture-negative peritonitis was 52.9% and gram negative peritonitis organisms was 29.4%. In contrast to gram positive organisms which showed increased in hot weather, gram negative organisms showed uniform distribution throughout the year. There were no significant monthly differences in peritoneal fluid WBC count on admission and negative conversion period of that. Our data suggest that high temperature and humidity can adversely affect the incidence of CAPD peritonitis and may change the distribution of causative organisms.