Various species of Ebolavirus have caused, and are still causing, zoonotic outbreaks and public health crises in Africa. Bats have long been hypothesized to be important reservoir populations for a series of viruses such as Hendra or Marburg viruses, the severe acute respiratory syndrome coronavirus (SARS-CoV, SARS-CoV-2) as well as Ebolaviruses [2, 3]. However the ecology of disease dynamics, disease transmission, and coevolution with their natural hosts of these viruses is still poorly understood, despite their importance for predicting novel outbreaks in human or livestock populations. The evidence that bats function as sylvatic reservoirs for Ebola viruses is yet only partial. Indeed, only few serological studies demonstrated the presence of Ebolavirus antibodies in young bats [4], albeit without providing positive controls of viral detection or identifying the viral species (via PCR). There is thus an unexplained discrepancy between serological data and viral detection [2, 4]. 

In this article, Pleydell et al. [1] use a modeling approach as well as published serological and age-structure (of the bat population) data to calibrate the model simulations. The study starts with the development of an age-structured epidemiological model which includes seasonal birth pulses and waning immunity, both generating pulses of Ebolavirus transmission within a colony of African straw-coloured fruit bats (Eidolon helvum). The epidemiological dynamics of such system of ordinary differential equations can generate annual outbreaks, skipped years or multi-annual cycles up to chaotic dynamics. Therefore, the calibration of the parameters, and the definition of biologically relevant priors, are key. To this aim, the serological data are obtained from a previous study in Cameroon [5], and the age structured of the bat population (birth and mortality) from a population study in Ghana [6]. These data are integrated into the Bayesian analysis and statistical framework to fit the model and generate predictions. In a nutshell, the authors show an overlap between the data and credibility intervals generated by the calibrated model, which thus explains well the seasonality of age-structure, namely changes in pup presence, number of lactating females, or proportion of juveniles in May. The authors can estimate that 76% of adults and 39% of young bats do survive each year, and infections are expected to last one and a half weeks. The epidemiological model predicts that annual birth pulses likely generate annual disease outbreaks, so that weeks 30 to 31 of each year, are predicted to be the best period to isolate the circulating Ebolavirus in this bat population. From the model predictions, the authors estimate the probability to have missed an infectious bat among all the samples tested by PCR being approximately of one per two thousands. The disease dynamics pattern observed in the serology data, and replicated by the model, is likely driven by seasonal pulses of young susceptible bats entering the population. This seasonal birth event increases the viral transmission, resulting in the observed peak of viral prevalence. With the inclusion of immunity waning and antibody persistence, the model results illuminate therefore why previous studies have detected only few positive cases by PCR tests, in contrast to the evidence from serological data. 

 This study provides a first proof of principle that epidemiological modeling, despite its many simplifying assumptions, can be applied to wild species reservoirs of zoonotic diseases in order to optimize the design of field studies to detect viruses. Furthermore, such models can contribute to assess the probability and timing of zoonotic outbreaks in human or livestock populations. This article illustrates one of the manifold applications of mathematical theory of disease epidemiology to optimize sampling of pathogens/parasites or vaccine development and release [7, 8]. The further coupling of such models with population genetics theory and statistical inference methods (using parasite genome data) increasingly provide insights into the adaptation and evolution of parasites to human, crops and livestock populations [9, 10].

References

[1] Pleydell D.R.J., Ndong Bass I., Mba Djondzo F.A., Djomsi D.M., Kouanfack C., Peeters M., and J. Cappelle. 2023. A Bayesian analysis of birth pulse effects on the probability of detecting Ebola virus in fruit bats. bioRxiv, ver. 3 peer reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2023.08.10.552777

[2] Caron A., Bourgarel M., Cappelle J., Liégeois F., De Nys H.M., and F. Roger. 2018. Ebola virus maintenance: if not (only) bats, what else? Viruses 10, 549. https://doi.org/10.3390/v10100549

[3] Letko M., Seifert S.N., Olival K.J., Plowright R.K., and V.J. Munster. 2020. Bat-borne virus diversity, spillover and emergence. Nature Reviews Microbiology 18, 461–471. https://doi.org/10.1038/s41579-020-0394-z

[4] Leroy E.M., Kumulungui B., Pourrut X., Rouquet P., Hassanin A., Yaba P., Délicat A., Paweska J.T., Gonzalez J.P., and R. Swanepoel. 2005. Fruit bats as reservoirs of Ebola virus. Nature 438, 575–576. https://doi.org/10.1038/438575a

[5] Djomsi D.M. et al. 2022. Dynamics of antibodies to Ebolaviruses in an Eidolon helvum bat colony in Cameroon. Viruses 14, 560. https://doi.org/10.3390/v14030560

[6] Peel A.J. et al. 2016. Bat trait, genetic and pathogen data from large-scale investigations of African fruit bats Eidolon helvum. Scientific data 3, 1–11. https://doi.org/10.1038/sdata.2016.49

[7] Nyandjo Bamen H.L., Ntaganda J.M., Tellier A. and O. Menoukeu Pamen. 2023. Impact of imperfect vaccine, vaccine trade-off and population turnover on infectious disease dynamics. Mathematics, 11(5), p.1240. https://doi.org/10.3390/math11051240

[8] Saadi N., Chi Y.L., Ghosh S., Eggo R.M., McCarthy C.V., Quaife M., Dawa J., Jit M. and A. Vassall. 2021. Models of COVID-19 vaccine prioritisation: a systematic literature search and narrative review. BMC medicine, 19, pp.1-11. https://doi.org/10.1186/s12916-021-02190-3

[9] Maerkle, H., John S., Metzger, L., STOP-HCV Consortium, Ansari, M.A., Pedergnana, V. and Tellier, A., 2023. Inference of host-pathogen interaction matrices from genome-wide polymorphism data. bioRxiv, https://doi.org/10.1101/2023.07.06.547816.

[10] Gandon S., Day T., Metcalf C.J.E. and B.T. Grenfell. 2016. Forecasting epidemiological and evolutionary dynamics of infectious diseases. Trends in ecology & evolution, 31(10), pp.776-788. https://doi.org/10.1016/j.tree.2016.07.010

Worldwide, ticks and tick-borne diseases are a persistent example of problems at the One Health interface between humans, wildlife, and environment (1, 2). The management and prevention of ticks and tick-borne diseases require a better understanding of host, tick and pathogen interactions and thus get a better view of the tick-borne pathosystems.

In this study (3), the tick-borne pathosystem included three component species: first a seabird host, the Yellow-legged gull (YLG - Larus michahellis, Laridae), second a soft nidicolous tick (Ornithodoros maritimus, Argasidae, syn. Alectorobius maritimus) known to infest this host and third a blood parasite (Babesia sp. YLG, Piroplasmidae). In this pathosystem, authors investigated the role of the soft tick, Ornithodoros maritimus, as a potential vector of Babesia sp. YLG. They analyzed the transmission of Babesia sp. YLG by collecting different tick life stages from YLG nests during 4 consecutive years on the islet of Carteau (Gulf of Fos, Camargue, France). Ticks were dissected and organs were analyzed separately to detect the presence of Babesia sp DNA and to evaluate different transmission pathways.

While the authors detected Babesia sp. YLG DNA in the salivary glands of nymphs, females and males, this result reveals a strong suspicion of transmission of the parasite by the soft tick. Babesia sp. YLG DNA was also found in tick ovaries, which could indicate possible transovarial transmission. Finally, the authors detected Babesia sp. YLG DNA in several male testes and in endospermatophores, and notably in a parasite-free female (uninfected ovaries and salivary glands). These last results raise the interesting possibility of sexual transmission from infected males to uninfected females.

As pointed out by both reviewers, this is a nice study, well written and easy to read. All the results are new and allow to better understand the role of the soft tick, Ornithodoros maritimus, as a potential vector of Babesia sp. YLG. They finally question about the degree to which the parasite can be maintained locally by ticks and the epidemiological consequences of infection for both O. maritimus and its avian host. For all these reasons, I chose to recommend this article for Peer Community In Infections.

References

1. Dantas-Torres et al (2012). Ticks and tick-borne diseases: a One Health perspective. Trends Parasitol. 28:437. https://doi.org/10.1016/j.pt.2012.07.003 
2. Johnson N et al (2022). One Health Approach to Tick and Tick-Borne Disease Surveillance in the United Kingdom. Int J Environ Res Public Health. 19:5833. https://doi.org/10.3390/ijerph19105833
3. Bonsergent C, Vittecoq M, Leray C, Jouglin M, Buysse M, McCoy KD, Malandrin L. A soft tick vector of Babesia sp. YLG in Yellow-legged gull (Larus michahellis) nests. bioRxiv, 2023.03.24.534071, ver. 3 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.1101/2023.03.24.534071

Thanks to the coordinated and sustained efforts of national control programs, the World Health Organization (WHO), bilateral cooperation and nongovernmental organizations, the incidence of Human African Trypanosomiasis (HAT), better known as sleeping sickness, has drastically decreased during the last two decades (WHO, 2023a). Indeed, between 1999 and 2022, the reported number of new cases of the chronic form of sleeping sickness (Trypanosoma brucei gambiense) fell by 97% (from 27 862 to 799), and the number of newly reported cases of the acute form of HAT (Trypanosoma brucei rhodesiense) fell by 94% (from 619 to 38) (WHO, 2023b). These encouraging trends led the WHO to target this debilitating and highly fatal (if untreated) vector-borne parasitic disease for elimination as a public health problem by 2020, and for interruption of transmission (zero case) by 2030 (WHO, 2021, WHO, 2023a). However, the disease is persisting in many foci, and even some cases of resurgence have been documented after unfortunate events such as war or pandemics (Moore et al., 1999; Sah et al., 2023. Simarro et al). Although effective control measures, diagnosis and treatment are complex and require specific skills (WHO, 2023), especially in a context which animal reservoirs, including hidden reservoirs, can contribute to the maintenance/persistence of infection (Welburn and Maudlin, 2012; Camara et al., 2021). Vector control therefore appears as a viable alternative to accelerate sleeping sickness transmission interruption, and WHO has identified some critical actions for HAT elimination, including the coordination of vector control and animal trypanosomiasis management among countries, stakeholders and other sectors (e.g. tourism and wildlife) through multisectoral national bodies to maximize synergies (WHO, 2021).

The paper by Kagbadouno and Collaborators (2024) uses microsatellite markers genotyping and population genetics tools to investigate the impact of 11 years of tiny target-based vector control on the population biology of Glossina palpalis gambiensis in Boffa, one of the three active sleeping sickness foci in Guinea (Kagbadouno et al., 2012). Although vector control significantly reduced the apparent densities of tsetse flies (and therefore the human exposure to the vector) as well as the prevalence and incidence of the disease in the Boffa HAT focus (Courtin et al., 2015), no genetic signature of vector control was observed as no difference in population size, before and after the onset of the control policy, was found. The authors then provided national programs and implementing partners with indications on the actions to be taken to (i) maintain the achievements of vector control (thus avoiding rebound/resurgence as was experienced in the past (Franco et al., 2014), and (ii) accelerate the momentum towards elimination by for example combining these vector control efforts with medical surveys for case detection and treatment, in line with WHO recommendations (WHO, 2021). 

References

Camara M, Soumah AM, Ilboudo H, Travaillé C, Clucas C, Cooper A, Kuispond Swar NR, Camara O, Sadissou I, Calvo Alvarez E, Crouzols A, Bart JM, Jamonneau V, Camara M, MacLeod A, Bucheton B, Rotureau B. Extravascular Dermal Trypanosomes in Suspected and Confirmed Cases of gambiense Human African Trypanosomiasis. Clin Infect Dis. 2021 Jul 1;73(1):12-20. https://doi.org/10.1093/cid/ciaa897

Courtin F, Camara M, Rayaisse JB, Kagbadouno M, Dama E, Camara O, Traore IS, Rouamba J, Peylhard M, Somda MB, Leno M, Lehane MJ, Torr SJ, Solano P, Jamonneau V, Bucheton B (2015) Reducing human-tsetse contact significantly enhances the efficacy of sleeping sickness active screening campaigns: a promising result in the context of elimination. PLoS Neglected Tropical Diseases, 9. https://doi.org/10.1371/journal.pntd.0003727

Franco JR, Simarro PP, Diarra A, Jannin JG. (2014) Epidemiology of human African trypanosomiasis. Clin Epidemiol. 6:257-75. https://doi.org/10.2147/CLEP.S39728

Kagbadouno, M. S., Séré, M., Ségard, A., Camara, A. D., Camara, M., Bucheton, B., ... & Ravel, S. (2023). Population genetics of Glossina palpalis gambiensis in the sleeping sickness focus of Boffa (Guinea) before and after eight years of vector control: no effect of control despite a significant decrease of human exposure to the disease. bioRxiv, ver. 2 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.1101/2023.07.25.550445

Kagbadouno MS, Camara M, Rouamba J, Rayaisse JB, Traoré IS, Camara O, Onikoyamou MF, Courtin F, Ravel S, De Meeûs T, Bucheton B, Jamonneau V, Solano P (2012) Epidemiology of sleeping sickness in boffa (Guinea): where are the trypanosomes? PLoS Neglected Tropical Diseases, 6, e1949. https://doi.org/10.1371/journal.pntd.0001949 

Moore A, Richer M, Enrile M, Losio E, Roberts J, Levy D. Resurgence of sleeping sickness in Tambura County, Sudan. Am J Trop Med Hyg. 1999 Aug;61(2):315-8. https://doi.org/10.4269/ajtmh.1999.61.315

Sah R, Mohanty A, Rohilla R, Padhi BK. A resurgence of Sleeping sickness amidst the COVID-19 pandemic: Correspondence. Int J Surg Open. 2023 Apr;53:100604. https://doi.org/10.1016/j.ijso.2023.100604

Welburn SC, Maudlin I. Priorities for the elimination of sleeping sickness. Adv Parasitol. 2012;79:299-337. https://doi.org/10.1016/B978-0-12-398457-9.00004-4

World Health Organization, 2021. Ending the neglect to attain the Sustainable Development Goals: a road map for neglected tropical diseases 2021–2030. World Health Organization, Geneva, Switzerland. ISBN: 978 92 4 001035 2. 196p. 

World Health Organization, 2023a. Trypanosomiasis, human African (sleeping sickness): key facts. Accessed at https://www.who.int/news-room/fact-sheets/detail/trypanosomiasis-human-african-(sleeping-sickness) on February 19, 2023.

World Health Organization, 2023b. Human African Trypanosomiasis, (sleeping sickness): the global health observatory. Accessed at https://www.who.int/data/gho/data/themes/topics/human-african-trypanosomiasis on February 19, 2023.

The question on the role of different alternative hosts in the life cycle of acanthocephalan parasites has not been fully resolved to date. There is some information on the use of fish hosts in the genus Pomphorhynchus (Perrot-Minnot et al. 2019). It is known that acanthocephalans of the genus Pomphorhynchus can infect a number of different amphipod species (e.g. Bauer et al. 2000; Cornet et al. 2010; Dezfuli et al. 1999) but it is not clear if some host species might be more “advantageous” for the parasite, or if the parasite is more virulent to some host species than to others. Bauer et al. (2024) investigated different well characterized cryptic lineages of Gammarus fossarum (Weiss et al. 2013) for their susceptibility for two Pomphorhynchus sp. The results show that there is a difference in susceptibility to acanthocephalans between different linages of G. fossarum. Additionally, a parasite species specific difference was detected: the difference in susceptibility was more pronounced for P. tereticollis than for P. laevis. P. tereticollis was less virulent and developed slower than P. laevis (in G. fossarum).

Besides the improved understanding of the biology of acanthocephalan parasites, this study clearly points out that we have to be careful with putting the “generalist” label on parasites simply due to the number of alternative host species we find them in. Instead, we should always have in mind that some of these hosts might be less suitable for the parasite than others when comparing quantitative data on the infection success.

I highly appreciate the experimental approach taken that allows more profound conclusions than evaluations of field data. Experiments and analyses have been conducted well. I think this paper is significantly enhancing our knowledge on the specificity for the intermediate host. I find it highly remarkable that this was even found among different host lineages.

References

Bauer, A., Trouve, S., Gregoire, A., Bollache, L., Cezilly, F. (2000) Differential influence of Pomphorhynchus laevis (Acanthocephala) on the behaviour of native and invader gammarid species. International Journal for Parasitology, 30(14), 1453-1457. https://doi.org/10.1016/s0020-7519(00)00138-7

Bauer, A., Develay Nguyen, L., Motreuil, S., Teixeira, M., Debrosse, N., Rigaud, T. (2024) Experimental infections reveal differences in specificity, development time and virulence between the acanthocephalan parasite Pomphorhynchus tereticollis and its sympatric counterpart P. laevis, in two cryptic species of Gammarus fossarum. HAL, Ver. 2, Peer-Reviewed and Recommended by Peer Community in Infections, hal-04455823. https://hal.science/hal-04455823  

Cornet, S., Sorci, G., Moret, Y. (2010) Biological invasion and parasitism: invaders do not suffer from physiological alterations of the acanthocephalan Pomphorhynchus laevis. Parasitology, 137(1), 137-147. https://doi.org/10.1017/S0031182009991077 

Dezfuli, B.S., Rossetti, E., Bellettato, C.M., Maynard, B.J. (1999) Pomphorhynchus laevis in its intermediate host Echinogammarus stammeri in the River Brenta, Italy. Journal of Helminthology, 73(2), 95-102. https://doi.org/10.1017/S0022149X00700277 

Perrot-Minnot, M.J., Guyonnet, E., Bollache, L., Lagrue, C. (2019) Differential patterns of definitive host use by two fish acanthocephalans occurring in sympatry: Pomphorhynchus laevis and Pomphorhynchus tereticollis. International Journal for Parasitology: Parasites and Wildlife, 8, 135-144. https://doi.org/10.1016/j.ijppaw.2019.01.007 

Weiss, M., Macher, J.N., Seefeldt, M.A., Leese, F. (2013) Molecular evidence for further overlooked species within the Gammarus fossarum complex (Crustacea: Amphipoda). Hydrobiologia, 721(1), 165-184. https://doi.org/10.1007/s10750-013-1658-7

Understanding the ecological and evolutionary factors underlying the spread of new fungal pathogen populations can inform the development of more effective management strategies. In plant pathology, pathogenicity is generally presented as having two components: ‘virulence’ (qualitative pathogenicity) and aggressiveness (quantitative pathogenicity). Changes in virulence in response to the deployment of new resistant varieties are a major driver of the spread of new populations (called pathotypes, or races) in modern agrosystems, and the genomic (i.e. proximal) and eco-evolutionary (i.e. ultimate) factors underlying these changes are well-documented [1,2,3]. By contrast, the role of changes in aggressiveness in the spread of pathotypes remains little known [4].

The study by Cécilia Fontyn and collaborators [5] set out to characterize changes in aggressiveness for isolates of two pathotypes of the wheat leaf rust (Puccinia triticina) that have been dominant in France during the 2005-2016 period. Isolates were genetically characterized using multilocus microsatellite typing and phenotypically characterized for three components of aggressiveness on wheat varieties: infection efficiency, latency period, and sporulation capacity. Using experiments that represent quite a remarkable amount of work and effort, Fontyn et al. showed that each dominant pathotype consisted of several genotypes, including common genotypes whose frequency changed over time. For each pathotype, the genotypes that were more common initially were replaced by a more aggressive genotype. Together, these results show that changes in the genetic composition of populations of fungal plant pathogens can be associated with, and may be caused by, changes in the quantitative components of pathogenicity. This study also illustrates how extensive, decade-long monitoring of fungal pathogen populations, such as the one conducted for wheat leaf rust in France, represents a very valuable resource for research.

REFERENCES

[1] Brown, J. K. (1994). Chance and selection in the evolution of barley mildew. Trends in Microbiology, 2(12), 470-475. https://doi.org/10.1016/0966-842x(94)90650-5

[2] Daverdin, G., Rouxel, T., Gout, L., Aubertot, J. N., Fudal, I., Meyer, M., Parlange, F., Carpezat, J., & Balesdent, M. H. (2012). Genome structure and reproductive behaviour influence the evolutionary potential of a fungal phytopathogen. PLoS Pathogens, 8(11), e1003020. https://doi.org/10.1371/journal.ppat.1003020

[3] Gladieux, P., Feurtey, A., Hood, M. E., Snirc, A., Clavel, J., Dutech, C., Roy, M., & Giraud, T. (2015). The population biology of fungal invasions.Molecular Ecology, 24(9), 1969-86. https://doi.org/10.1111/mec.13028

[4] Fontyn, C., Zippert, A. C., Delestre, G., Marcel, T. C., Suffert, F., & Goyeau, H. (2022). Is virulence phenotype evolution driven exclusively by Lr gene deployment in French Puccinia triticina populations?. Plant Pathology, 71(7), 1511-1524. https://doi.org/10.1111/ppa.13599

[5] Fontyn, C., Meyer, K. J., Boixel, A. L., Delestre, G., Piaget, E., Picard, C., Suffer, F., Marcel, T.C., & Goyeau, H. (2022). Evolution within a given virulence phenotype (pathotype) is driven by changes in aggressiveness: a case study of French wheat leaf rust populations. bioRxiv, 2022.08.29.505401, ver. 3 peer-reviewed and recommended by Peer Community in Infections.  https://doi.org/10.1101/2022.08.29.505401