Disease Ecology of Rickettsial Species: A Data Science Approach

A roadmap towards predicting species interaction networks (across space and time)

Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species-and to describe the structure, variation, and change of the ecological networks they form-we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.

Diversity of rickettsiae in domestic, synanthropic, and sylvatic mammals and their ectoparasites in a spotted fever-epidemic region at the western US-Mexico border

Over one hundred cases of human rickettsiosis, many fatal, are reported annually across the US-Mexico transboundary region, representing a likely undercount. Although cases are often attributed to Rickettsia rickettsii, the agent of Rocky Mountain spotted fever, multiple other Rickettsia pathogens are present in North America. We conducted multiple-host surveillance of domestic, synanthropic, and sylvatic mammals and their ectoparasites to investigate the ecology of Rickettsia species in this region. A total of 499 mammals, including 83 dogs, 23 wild carnivores, five lagomorphs, and 388 rodents were sampled, and 413 fleas and 447 ticks belonging to 15 and 4 species, respectively, were collected during 2017 and 2018. We detected Rickettsia spp. DNA in one blood sample of coyote (Canis latrans), 11 ear tissues of rodents (10.6%), and 79 ectoparasites (9.5%). Of the 64 Rickettsia-positive fleas, 54 were Echidnophaga gallinacea and 10 were Pulex simulans, while of the 15 ticks, 11 were Rhipicephalus sanguineus s.l. and four Ixodes pacificus. The DNA sequence alignment of gltA and ompB regions revealed one and ten genetic variants of Rickettsia spp., respectively. These variants were clustered in clades of zoonotic species (R. felis, R. massiliae, R. parkeri, R. rickettsii, and R. typhi) and organisms of unknown pathogenic significance (R. asembonensis and Candidatus Rickettsia tarasevichiae). The finding of a coyote infected with R. rickettsii and the multiple zoonotic SFG rickettsial agents in the study area suggest that: 1) wild canids could serve as an amplifying host for RMSF, an alternate host for Rh. sanguineus s.l. ticks, and a means to spread infection and ticks over large areas; and 2) at least some of the human rickettsiosis cases attributed to R. rickettsii could be caused by other Rickettsia species. This study strongly supports the importance of multiple-host and vector eco-epidemiological studies and the One Health approach to better understand disease in a RMSF-epidemic region.

Serological evidence indicates widespread distribution of rickettsioses in Myanmar

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Diagnosis of rickettsial infections is difficult in low-resource settings; this leads to delays in receiving appropriate treatment.

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Before this study, the distribution of rickettsioses in Myanmar was not known.

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This serosurvey shows that rickettsioses are widespread in Myanmar.

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Particularly high prevalence of scrub typhus was found in central and northern regions.

Background

Little research has been published on the prevalence of rickettsial infections in Myanmar. This study determined the seroprevalence of immunoglobulin G (IgG) antibodies to rickettsial species in different regions of Myanmar.

Methods

Seven hundred leftover blood samples from patients of all ages in primary care clinics and hospitals in seven regions of Myanmar were collected. Samples were screened for scrub typhus group (STG), typhus group (TG) and spotted fever group (SFG) IgG antibodies using enzyme-linked immunosorbent assays (ELISA). Immunofluorescence assays were performed for the same rickettsial groups to confirm seropositivity if ELISA optical density ≥0.5.

Results

Overall IgG seroprevalence was 19% [95% confidence interval (CI) 16–22%] for STG, 5% (95% CI 3–7%) for TG and 3% (95% CI: 2–5%) for SFG. The seroprevalence of STG was particularly high in northern and central Myanmar (59% and 19–33%, respectively). Increasing age was associated with higher odds of STG and TG seropositivity [per 10-year increase, adjusted odds ratio estimate 1.68 (p < 0.01) and 1.24 (p = 0.03), respectively].

Conclusion

Rickettsial infections are widespread in Myanmar, with particularly high seroprevalence of STG IgG antibodies in central and northern regions. Healthcare workers should consider rickettsial infections as common causes of fever in Myanmar.