Understanding endemic and new zoonotic impacts from rodent trapping studies

The global impact of new zoonotic pathogen spillovers has been highlighted through the ongoing coronavirus disease 2019 (COVID-19) pandemic caused by the rapid outbreak of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus . In the past, similar effects have been seen during the HIV/AID and Spanish flu pandemics.

Study: Studies on rodent trapping as an overlooked source of information to understand endemic and novel zoonotic spillover​​​​​​​.  Image Credit: Erni/ShutterstockStudy: Studies on rodent trapping as an overlooked source of information to understand endemic and novel zoonotic spillover​​​​​​​. Image Credit: Erni/Shutterstock


Due to climate change, global population increase, urbanization, and agricultural intensification, there have been changes in rodent species assemblages. Unfortunately, these factors have also increased the frequency of zoonotic spillover events, which increase the risk of emergence of new zoonotic pathogens from rodents. In general, endemic zoonoses disproportionately affect those who come from weaker socio-economic backgrounds, live in close contact with animals and have limited access to health care.

Rodents and bats cause the most significant number of zoonotic spillover events. In particular, among the 2,220 extant rodent species, 10.7% are considered a reservoir of zoonotic pathogens. The greatest probabilities of being zoonotic reservoirs are associated with short gestation times and early maturation. Rodents with these traits can thrive in human-dominated spaces, making it essential to describe rodent species assemblages and host-pathogen associations.

In a recent PLoS Neglected tropical diseases journal study, the scientists summarized rodent trapping studies published between 1964 and 2022. The studies were conducted across West Africa, which was identified as a region at high risk for the spread of rodent-borne diseases rodents.

About the studio

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

Third, CLOVER, a consolidated dataset, was used to compare the identified host-pathogen associations. The main purpose of this step was to detect discrepancies in rodent host-pathogen associations and to estimate the fraction of assays positive for the pathogens of interest. The last step involved investigating the spatial extent of current host-pathogen sampling within the collected data. The goal was to detect sparse sampling regions of pathogens within their host ranges.

Rodent trapping sites throughout West Africa. A) The location of trapping sites in West Africa. No sites have been registered from Togo or The Gambia. Heterogeneity is observed in each country’s coverage by night trap (colour) and site location. 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) were performed at each site. A labeled map of the study region is attached in S5 Fig. Basemap shapefile obtained from GADM 4.0.4

Key Findings

In the curated datasets, the majority of rodents analyzed harbored no known zoonotic pathogens. Twenty-five host-pathogen pairs were identified, of which 15 were not included in a consolidated host-pathogen dataset. The number and spatial extent of different species tested for a microorganism were limited, highlighting sampling bias.

Biodiversity data and, more generally, rodent trapping data showed significant spatial biases. Bias was greatest in Guinea, Senegal, Benin and Sierra Leone. Significant research has been conducted on the risk of outbreaks of endemic zoonoses (eg. Lassa mammarenavirus) and the invasion of non-indigenous rodent species (e.g. M. muscular And R. rat).

Additional information on human population density, trapping effort and land use type was incorporated, as well as identifying previous sampling locations of rodents and pathogens. This should help researchers identify places where predictions based on the underlying data might be prone to sampling errors, and also identify undersampled places. The modeling approach developed in this study identified northwestern Nigeria as an immediate priority for sampling of rodents and their pathogens due to its human-dominated landscape and high population density. There is evidence that much of West Africa is still under-championed, especially Burkina Faso, Ivory Coast, Ghana and Nigeria.

Rodent trapping studies provide data on both detection and non-detection of species, which can improve species distribution patterns. This is important to evaluate the effect of climate change and land use on the risk of zoonotic impacts on human populations. However, currently available consolidated datasets such as CLOVER, EID2 and GMPD2 do not include temporal or spatial components. Therefore, current models dependent 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 trapping studies could not be accounted for. It is possible that land use and population densities at trapping sites varied during this time period. Despite this limitation, the finding that capture is oriented towards human-dominated and densely populated landscapes should remain valid.


Understudied regions 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 the emergence of new pathogens. Broader sharing of research and data on the location of trapping sites and trapping efforts should be strongly encouraged. The researchers hypothesized that the curated datasets, integrated with rodent trapping studies, could be instrumental in quantifying the risk of zoonotic spillover events and monitoring the emergence of new pathogens.

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