Understanding endemic and emerging zoonotic consequences from rodent trapping studies

The global impact of the spillover of new zoonotic pathogens has been highlighted through the current 2019 coronavirus disease (COVID-19) pandemic caused by the rapid outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). In the past, similar effects were noted during the HIV/AIDS and Spanish flu pandemics.

Study: Rodent trapping studies as a neglected source of information for understanding endemic and emerging zoonotic spread​​​​​​.  Image credit: Erni / ShutterstockStudy: Rodent trapping studies as a neglected source of information for understanding endemic and emerging zoonotic spread​​​​​​. Image credit: Erni / Shutterstock

Background

As a result of climate change, increase in global population, urbanization and intensification of agriculture, there have been changes in groups of rodent species. Unfortunately, these factors have also increased the frequency of zoonotic spillovers, which increases the risk of new zoonotic pathogens emerging in rodents. In general, endemic zoonoses disproportionately affect those who come from weaker socioeconomic backgrounds, live in close contact with animals, and have limited access to health care.

Rodents and bats cause the largest number of zoonotic spills. Namely, among the 2220 existing species of rodents, 10.7% are considered reservoirs of zoonotic pathogens. The increased probability that the reservoirs are zoonotic is associated with a short gestation period and early maturation. Rodents with these traits can thrive in human-dominated environments, making it essential to describe rodent species assemblages and host-pathogen associations.

In a recent PLoS Neglected Tropical Diseases journal study, scientists synthesized rodent trapping studies published between 1964–2022. The studies were conducted across West Africa, which has been identified as a high-risk region for the spread of rodent-borne diseases.

About the study

The current study conducts four main exercises, the first of which is an analysis of geographic sampling biases related to land use classification and population density. Second, to understand the differences in reported geographic distributions of hosts, the results of step 1 were compared with selected host datasets (IUCN and GBIF).

Third, CLOVER, a consolidated dataset, was used to contrast identified host-pathogen associations. The main goal of this step was to detect deviations in the rodent host-pathogen association and to estimate the proportion of positive tests for the pathogens of interest. The final step involved investigating the spatial extent of current host and pathogen sampling within the collected data. The goal was to detect areas of rare pathogen samples within their host ranges.

Rodent trapping sites across West Africa. A) Location of capture site in West Africa. No sites from Togo or Gambia have been recorded. Heterogeneity is observed in the coverage of each country’s trap night (color) and the position of the locations. For example, Senegal, Mali and Sierra Leone generally have good coverage compared to Guinea and Burkina Faso. B) Histogram of trap nights performed at each study site. A median of 248 trap nights (IQR 116–500) was derived at each site. A marked map of the study region is attached in S5 Figure. Base map shape file obtained from GADM 4.0.4

Key findings

In the selected datasets, the majority of tested rodents did not host known zoonotic pathogens. Twenty-five host-pathogen pairs were identified, 15 of which were not included in the consolidated host-pathogen dataset. The number and spatial extent of the different species tested for microorganisms were limited, highlighting sampling bias.

Biodiversity data and, more generally, rodent trapping data showed significant spatial biases. The bias was higher in Guinea, Senegal, Benin and Sierra Leone. Significant research has been carried out on the risk of outbreaks of endemic zoonoses (e.g. Lassa mammarenavirus) and the invasion of non-native rodent species (e.g. M. musculus and R. rattus).

Additional information on human population density, trapping effort, and land use type was included, in addition to identifying previous rodent and pathogen sampling locations. This should help researchers identify locations where predictions based on baseline data may be subject to sampling bias and to identify undersampled locations. The modeling approach developed in this study identified northwestern Nigeria as an immediate priority for sampling rodents and their pathogens due to its human-dominated landscape and high population density. It turns out that much of West Africa is still under-sampled, particularly Burkina Faso, Côte d’Ivoire, Ghana and Nigeria.

Rodent trapping studies provide data on species detection and non-detection, which can improve species distribution models. This is important for assessing the impact of climate change and land use on the risk of zoonotic spillover to human populations. However, currently available consolidated datasets, such as CLOVER, EID2 and GMPD2, do not contain temporal or spatial components. Therefore, current models that depend on these data sources cannot account for spatial heterogeneity in pathogen prevalence.

Due to the paucity of data, temporal change over the six decades of rodent capture research could not be explained. It is possible that land use and population density at capture sites varied over this time period. Despite this limitation, the finding that trapping is biased towards densely populated, human-dominated landscapes is expected.

Findings

Understudied areas should be prioritized in future rodent trapping studies. More information on rodents in West Africa should help model the changing risk of endemic zoonosis and the potential for emerging pathogens. Wider sharing of research and data on trap locations and trapping attempts should be strongly encouraged. The researchers hypothesized that selected data sets, supplemented by rodent trapping studies, could be instrumental in quantifying the risk of zoonotic spillovers and monitoring the emergence of new pathogens.

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