Temporal Dynamics of Phlebotomine Sand Flies Population in Response to Ambient Temperature Variation, Bam, Kerman Province of Iran

Geographical and climatic risk factors of cutaneous leishmaniasis in the hyper-endemic focus of Bam County in southeast Iran

Introduction

Cutaneous leishmaniasis (CL) is a prevalent debilitating disease in many countries, particularly in Iran, the Middle East, North Africa, and South America. Bam County is the most important highly endemic focus of anthropometric CL in Iran and has been under consideration by WHO. This study investigated the environmental and geographic factors affecting the occurrence and distribution of CL in this focus.

Methods

Demographic data and the home addresses of CL patients diagnosed from 2015 to 2020 were retrieved from the Leishmaniasis Center of Bam in southeast Iran. The effects of mean annual rainfall (MAR), mean annual humidity (MAH), mean annual temperature (MAT), maximum annual temperature (MaxMAT), minimum annual temperature (MinMAT), mean annual humidity (MAH), mean annual evaporation (MAE), mean annual frosty days (MAFD), mean annual snowy hours (MASH), elevation, and land cover on the distribution of CL were analyzed using geographical information systems (GIS) and univariate and multivariate regression models.

Results

Of 847 patients studied, 50.9% (n = 431) were female and 49.1% (n = 416) were male. The age classes 0-10 (n = 246) and 11-20 (n = 145) showed the highest frequency of patients, respectively. Leishmaniasis patients were reported from 66 villages/cities (11.8%) out of 561 residential areas in Bam County. Univariate analysis showed that urban settings (OR = 21.66), agriculture (OR = 5.73), orchards (OR = 5), salty land (OR = 1.05), and temperatures (OR = 2.37, 2.79 and 3.47) had positive effects on CL occurrence (p < 0.05), while altitude, precipitation, humidity, evaporation, and the number of frozen days had negative effects. Multivariate analysis identified urban settings (OR = 13.6), orchards (OR = 6.29), agriculture (OR = 5.82), and minimum temperature (OR = 2.38) as the most significant determinants of CL occurrence in this region.

Conclusion

Environmental and ecological factors play an important role in the distribution of CL in Bam County. The high-risk zones for CL are cities/large villages, agricultural and orchard areas in lower altitudes and with warmer climates and lower rainfall and humidity. This model can guide researchers and health managers to properly conduct CL control programs and allocate budgets.

A systematic review and global analysis of the seasonal activity of Phlebotomus (Paraphlebotomus) sergenti, the primary vectors of L. tropica

BACKGROUND

Phlebotomus (Paraphlebotomus) sergenti is a widespread proven vector of Leishmania pathogens causing anthroponotic cutaneous leishmaniasis (ACL), due to L. tropica, in the old world. The activity of P. (Par.) sergenti is seasonal and sensitive to general variations in climate. Phenological data sets can thus provide a baseline for continuing investigations on P. (Par.) sergenti population dynamics that may impact future leishmaniasis transmission and control scenarios.

METHODS/PRINCIPAL FINDING

A systematic review of the seasonality of P. (Par.) sergenti was undertaken globally. Six hundred eight scientific papers were identified, and data were extracted from 35 ones, with informative data on sand fly seasonal dynamics on trapping performed from 1992 to December 2021 on 63 sites from 12 countries. Morocco, Saudi Arabia, Iraq, Iran, Pakistan, Palestine, Turkey, Spain, Portugal, Italy, Cyprus, and Georgia. The data extracted from the literature survey were further normalized. Our analysis recorded that the highest P.(Par.) sergenti activity occurs during the hot and dry seasons, primarily in July and August, whatever the location studied. We noticed a relationship between the latitude of sites and sand fly presence (from early April to June) and the type of density trend, varying from a single peak to multiple peaks. On a geographical scale, P. (Par.) sergenti concentrates between 32-37° in latitude in a large interval following the longitude and the highest number of sites with high P. (Par.) sergenti activity is located at the latitude 32°. We also quoted a similar seasonal dynamic and geographic distribution with Phlebotomus (Phlebotomus) papatasi, a proven vector of L. major that causes cutaneous infection. No apparent risk for ACL occurred from December to March, at least in the years and geographic areas considered in this survey. Altogether, knowing that high P. (Par.) sergenti activity would be linked with an increased risk of leishmaniasis transmission, and our study provides information that can be used for control programs on ACL transmission.

CONCLUSIONS

Despite variations, we found a relatively homogeneous pattern of P. (Par.) sergenti potential behavior in sites whose data are published. A higher risk for L. tropica transmission was identified in the June-October period. Still, such risk was not equally distributed throughout the area since density waves of adults occurred earlier and were more frequent in some territories, like Saudi Arabia.

Seasonal Distribution of Phlebotomus papatasi, Vector of Zoonotic Cutaneous Leishmaniasis

PURPOSE

The activity of Phlebotomus papatasi Scopoli, a vector of cutaneous leishmaniasis, is known to be seasonal, but the information describing these patterns is sparse in time and space. The purpose of his paper is to conduct a meta-analysis to depict the seasonality of P. papatasi at the global scale.

METHODS

The current paper uses data from a collection of 1200 scientific papers, where 300 are about the vector and 36 include monthly data (of 61 sites) of the P. papatasi from 15 countries published from 1994 to 2017.

RESULTS

The activity peaked mainly in dry periods in all explored sites. A map of seasonal distribution depicted a peak activity in August, followed by September. Geographically (geographic preference), August and September at the latitude 33° showed the highest abundance at a global scale. Concerning the latitude P. papatasi distribution, the high number is localized between 27° and 35°. However, the high number of this species according to the longitude is localized between - 4° and 58°, which shows a high interval of activity. Locally, for the habitat preference, the findings show a preference of this vector to indoor than to outdoor.

CONCLUSIONS

All these results can be helpful in identifying the period and sites of a future potential risk of leishmaniasis. This is the first attempt at the update of the vector activity both at global and local scale.

Spatial Dynamics of a Phlebotomine Sand Flies Population in Response to Climatic Conditions in Bushehr Province of Iran

BACKGROUND

Phlebotomine (Ph.) sand flies are the vectors of different types of leishmaniasis and arboviruses to humans. Variations in climate conditions could lead to changes in the dynamics of zoonotic cutaneous leishmaniasis (ZCL) and its agents, such as Ph. sand flies and reservoirs.

OBJECTIVE

The aim of this study was to analyze the spatial relationship among climatic factors and phlebotomine sand flies in abundance in Bushehr Province of Iran.

MATERIALS AND METHODS

In this study, six village sites in Bushehr Province were selected for collecting Ph. sand flies. All-night landing catches of Ph. sand flies occurred between January 15, 2016, and December 15, 2016. Two types of climatic data were used: satellite-based data included daytime and nighttime land surface temperature and the normalized deference vegetation index, and station-based data included annual rainfall and annual mean air temperature and solar radiation (W/M2). Spatial correlation analysis and multivariate ordinary least square regression was used to detect the spatial association between caught Ph. sand flies and climatic factors in a 0.95 confidence level (p = 0.05).

RESULTS

The results of the spatial correlation matrix indicated thermal factors, such as mean and maximum air temperature, instantaneous daytime and nighttime land surface temperatures, and radiation, show a direct and significant spatial correlation with the number of sand flies caught. The annual frequency of sand flies in the region showed the highest direct spatial relationship with the annual maximum air temperature (r = 0.91). Environmental moisture factors, such as precipitation and the vegetation index of the region, have spatial correlations of 0.63 and 0.44 with the frequency of the annual caught sand flies, which are significant at the confidence level of 0.95. The results of the multivariable model for estimating the frequency of the caught sand flies indicate environmental estimators, including mean air temperature, rainfall, radiation, and vegetation index, in a linear estimation model can explain the 0.85 spatial variation of the caught sand flies population.This study aimed to detect spatial correlations between the sand fly population and climatic factors. The results showed climatic factors were the most important controllers of the sand fly population in the interannual scale. If sand fly control programs are conducted in accordance with the climatic conditions of the area, the population of this carrier in Bushehr Province can be controlled and the incidence of disease significantly reduced.

A climate-driven and field data-assimilated population dynamics model of sand flies

Sand flies are responsible for the transmission of leishmaniasis, a neglected tropical disease claiming more than 50,000 lives annually. Leishmaniasis is an emerging health risk in tropical and Mediterranean countries as well as temperate regions in North America and Europe. There is an increasing demand for predicting population dynamics and spreading of sand flies to support management and control, yet phenotypic diversity and complex environmental dependence hamper model development. Here, we present the principles for developing predictive species-specific population dynamics models for important disease vectors. Based on these principles, we developed a sand fly population dynamics model with a generic structure where model parameters are inferred using a surveillance dataset collected from Greece and Cyprus. The model incorporates distinct life stages and explicit dependence on a carefully selected set of environmental variables. The model successfully replicates the observations and demonstrates high predictive capacity on the validation dataset from Turkey. The surveillance datasets inform about biological processes, even in the absence of laboratory experiments. Our findings suggest that the methodology can be applied to other vector species to predict abundance, control dispersion, and help to manage the global burden of vector-borne diseases.