Models for Studying the Distribution of Ticks and Tick-Borne Diseases in Animals: A Systematic Review and a Meta-Analysis with a Focus on Africa

Abstract

Ticks and tick-borne diseases (TTBD) are constraints to the development of livestock and induce potential human health problems. The worldwide distribution of ticks is not homogenous. Some places are ecologically suitable for ticks but they are not introduced in these areas yet. The absence or low density of hosts is a factor affecting the dissemination of the parasite. To understand the process of introduction and spread of TTBD in different areas, and forecast their presence, scientists developed different models (e.g., predictive models and explicative models). This study aimed to identify models developed by researchers to analyze the TTBD distribution and to assess the performance of these various models with a meta-analysis. A literature search was implemented with PRISMA protocol in two online databases (Scopus and PubMed). The selected articles were classified according to country, type of models and the objective of the modeling. Sensitivity, specificity and accuracy available data of these models were used to evaluate their performance using a meta-analysis. One hundred studies were identified in which seven tick genera were modeled, with Ixodes the most frequently modeled. Additionally, 13 genera of tick-borne pathogens were also modeled, with Borrelia the most frequently modeled. Twenty-three different models were identified and the most frequently used are the generalized linear model representing 26.67% and the maximum entropy model representing 24.17%. A focus on TTBD modeling in Africa showed that, respectively, genus Rhipicephalus and Theileria parva were the most modeled. A meta-analysis on the quality of 20 models revealed that maximum entropy, linear discriminant analysis, and the ecological niche factor analysis models had, respectively, the highest sensitivity, specificity, and area under the curve effect size among all the selected models. Modeling TTBD is highly relevant for predicting their distribution and preventing their adverse effect on animal and human health and the economy. Related results of such analyses are useful to build prevention and/or control programs by veterinary and public health authorities.

Bovine anaplasmosis in Zimbabwe: spatio-temporal distribution and environmental drivers

“… 2018 ; Zannou et al. 2021 ; Tagwireyi et al. 2022 ) and are characteristic of the areas found suitable for the disease.…”

First Expert Elicitation of Knowledge on Possible Drivers of Observed Increasing Human Cases of Tick-Borne Encephalitis in Europe

“…However, more studies are needed to accurately predict the distribution of ticks and TBE. Tick-borne disease risk models need to be developed at a fine enough spatial resolution to capture the focal distribution of TBEV within the range of its widespread Ixodes vector and need to integrate climate and landscape drivers and the dynamics of wildlife and domestic hosts and people, aligning with a One Health perspective, and capitalize on advances in digital epidemiology and artificial intelligence (e.g., [ 67 , 68 , 69 ]) and citizen science approaches using, e.g., apps to collect data [ 70 ].…”

Tick-borne zoonotic flaviviruses and Borrelia infections in wildlife hosts: What have field studies contributed?

“…Recent reviews on tick-borne Borrelia spp. and TBFV have focused on getting information on the biology of infection in vertebrate hosts [ 13 , 14 ], reporting prevalence and clinical cases in human [ 15 , 16 ] and discussing diagnostic methodologies [ 17 , 18 ], the role of non-vector transmissions for TBFV [ 19 , 20 ], or on the importance of modelling TBP regarding climate change [ 21 , 22 ]. Some reviews have focused on wildlife hosts but only at a national or continental scale [ 23 , 24 ], or on reservoirs associated with flaviviruses in general, with little focus on TBFV [ 25 ].…”