A spatio-temporal agent-based approach for modeling the spread of zoonotic cutaneous leishmaniasis in northeast Iran

Abstract

Background Zoonotic cutaneous leishmaniasis (ZCL) is a neglected tropical disease worldwide, especially the Middle East. Although previous works attempt to model the ZCL spread using various environmental factors, the interactions between vectors (Phlebotomus papatasi), reservoir hosts, humans, and the environment can affect its spread. Considering all of these aspects is not a trivial task. Methods An agent-based model (ABM) is a relatively new approach that provides a framework for analyzing the heterogeneity of the interactions, along with biological and environmental factors in such complex systems. The objective of this research is to design and develop an ABM that uses Geospatial Information System (GIS) capabilities, biological behaviors of vectors and reservoir hosts, and an improved Susceptible-Exposed-Infected-Recovered (SEIR) epidemic model to explore the spread of ZCL. Various scenarios were implemented to analyze the future ZCL spreads in different parts of Maraveh Tappeh County, in the northeast region of Golestan Province in northeastern Iran, with alternative socio-ecological conditions. Results The results confirmed that the spread of the disease arises principally in the desert, low altitude areas, and riverside population centers. The outcomes also showed that the restricting movement of humans reduces the severity of the transmission. Moreover, the spread of ZCL has a particular temporal pattern, since the most prevalent cases occurred in the fall. The evaluation test also showed the similarity between the results and the reported spatiotemporal trends. Conclusions This study demonstrates the capability and efficiency of ABM to model and predict the spread of ZCL. The results of the presented approach can be considered as a guide for public health management and controlling the vector population.

Epidemiological Aspects of Zoonotic Cutaneous Leishmaniasis (ZCL) in Iran, the Middle East, and Worldwide: A Comprehensive Systematic Review

“…It is crucial to note the risk of the disease spreading from rural to urban areas, resulting in the anthropization of cutaneous leishmaniasis. Efficiently controlling the-indoor population of sandfly vectors is crucial to reducing the incide-nce of leishmaniases, a pre-ssing need that require-s immediate attention (38,24,11,72).…”

Spatiotemporal distribution of cutaneous leishmaniasis in Sri Lanka and future case burden estimates

“…The predicted case number in 2020 is 5,379 (95% CI [4739, 6017]), which is about a 30% increase from 2019 observations (4,064 cases) (Fig 7). If the current trend holds, the predicted case number will be 9,115 (95% CI [8,157,10,070]) by year 2022 and close to 20,000 by year 2025 (19,212 cases, 95% CI [17,389,21,31]).…”

“…Previous studies have also explored the spatiotemporal patterns of leishmaniasis infections [13][14][15][16], but most of them, if not all, lack predictive power, with the output limited to distribution patterns. Other studies used agent-based modeling to predict the distribution of leishmaniasis [17,18]. These models were built based on the deeper understanding of the biology of sand fly, parasite, and host [19] and therefore, useful for the understanding of the interactions among vectors, parasites, and hosts, and how environmental (include climatic) and socioeconomic (include demographic) factors influence the distribution of leishmaniasis infections [17][18][19].…”

“…However, the persistence of local transmission may also lead to a buildup of case numbers. These components remain as gaps from previous work in this field [13][14][15][16][17][18][19][23][24][25].…”

“…Other studies used agent-based modeling to predict the distribution of leishmaniasis [ 17 , 18 ]. These models were built based on the deeper understanding of the biology of sand fly, parasite, and host [ 19 ] and therefore, useful for the understanding of the interactions among vectors, parasites, and hosts, and how environmental (include climatic) and socioeconomic (include demographic) factors influence the distribution of leishmaniasis infections [ 17 – 19 ]. Since some of the variables used in the models such as climatic data is only available in retrospect, these models are good for static risk assessments and suffer similar limitations of lack of predictive power for future distributions.…”