Seasonality and mycobacterial infectious diseases in animals and humans: is there a generality of seasonal patterns for mycobacterial infections?

Abstract

Background: Seasonal patterns of mycobacterial infections affecting humans and animals remain a complex and understudied aspect of infectious disease dynamics. These intra-annual patterns are increasingly relevant in the context of global climate change, which may influence the timing and geographic spread of these diseases. A better understanding of such patterns could improve surveillance, prevention, and control strategies.

Methods: We conducted a mixed-methods bibliometric review combining bibliographic searches and scoping analysis to synthesize decades of research on the seasonality of mycobacterial infections in humans and animals. We systematically searched three major scientific databases-Scopus, PubMed-MEDLINE, and Web of Science-for articles published between 1971 and April 2023. From an initial dataset of 1830 unique articles, we identified and analysed 122 studies that met predefined inclusion criteria. We extracted information on pathogen type, statistical methods, geographic location, and host species. In addition, we conducted a co-citation network analysis to identify key methodological influences and research clusters.

Results: The retained studies encompassed tuberculosis, Buruli ulcer, bovine tuberculosis, and other mycobacterial diseases such as leprosy and Johne's disease. Most articles focused on tuberculosis in humans, followed by Buruli ulcer caused by Mycobacterium ulcerans. There was a marked increase in studies on seasonal trends in tuberculosis and Buruli ulcer over time, with notable variation in geographic and methodological coverage. Research was heavily concentrated in the northern hemisphere, especially in China, while southern regions remained underrepresented. Advanced statistical tools, including generalized linear models and time-series analyses, were instrumental in detecting seasonality, particularly for tuberculosis and Buruli ulcer.

Conclusion: Seasonality appears to be a common yet understudied feature of many mycobacterial infections. Greater interdisciplinary collaboration and the use of appropriate analytical tools are essential to better understand these patterns, especially in underrepresented regions. Addressing methodological and geographic gaps will be crucial to improve responses to these diseases in a changing global environment.

Keywords: Animal; Disease transmission; Human; Non-tuberculous mycobacteria; Seasonality; Temporal dynamics; Tuberculous.

Fig. 1

Representation of seasonal cyclicity in mycobacterial infections

Fig. 2

PRISMA flowchart

Fig. 3

Bibliometric demographics on seasonal trends in mycobacterial infections. a Number of publications on mycobacterial infections published in 1971–2023 period. Total number of publications illustrated with black curve; number of publications on TB with red curve, and number of publications on MU/BU with blue curve (For scale reason, inlet corresponds to MU/BU scientific production in time). b Percentile breakdown by categories of mycobacterial infections excluding M. leprae article (n = 1). TB Tuberculosis, MU Mycobacterium ulcerans, BU Buruli ulcer

Fig. 4

Distribution maps, seasonal trends and seasonal peak shapes for the different publications retained in our mixed-methods review. Upper panel: distribution map of research studies for a TB; and b other mycobacteria (except TB), including bTB and MU/BU. Blue dots: MU/BU, red dots bTB, and black dots others. Base map source: Country borders are based on the CIA World DataBank II. Middle panel: heatmap of the relationship between latitude (in degree) of a given study and month-s for which it is observed an epidemic peak for c TB studies; and d other mycobacterial diseases. Colors from white to red indicate publication frequency with white corresponding to no publication. Lower panel: relationship between statistical-mathematical methodology used (see Table 1) and curve shape of epidemic peak for e TB studies (n = 100, F = 6.9, P < 0.01); and f other mycobacteria studies (n = 25, F = 3.5, P = 0.07). The red curve illustrates the LOWESS tendency, with tension equals 0.5. TB Tuberculosis, MU Mycobacterium ulcerans, BU Buruli ulcer, bTB Bovine tuberculosis, LOWESS Locally Weighted Scatterplot Smoothing

Fig. 5

Co-citation networks of scientific research production on mycobacterial seasonality. a for Chinese research on TB, and b for MU/BU internationally. Co-citation network of the included articles. Node size is proportional to the weighted degree of each item (the sum of co-citations with all other items). Edge thickness is proportional to the frequency of co-citations between two items. The structure reflects the conceptual clustering of the literature. Items located near the center, exhibit higher centrality and lower farness, suggesting that they play a key role in connecting different clusters. TB Tuberculosis, MU Mycobacterium ulcerans, BU Buruli ulcer