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

One of the greatest achievements of health sciences is the eradication of infectious diseases such as smallpox that in the past imposed a severe burden on humankind, through global vaccination campaigns. Moreover, progress towards the eradication of others such as poliomyelitis, dracunculiasis, and yaws is being made.

In contrast, other infections that were previously contained are reemerging, due to several factors, including lack of access to vaccines due to geopolitical reasons, the rise of anti-vaccine movements, and the constant mobility of infected persons from the endemic sites.

One of such disease is diphtheria, caused by Corynebacterium diphtheriae and a few other related species such as C. ulcerans and C. pseudotuberculosis. Importantly, in France, diphtheria cases reported in 2022 increased 7-fold from the average of previously recorded cases per year in the previous 4 years and the situation in other European countries is similar.

Hence, as reported here, Hennart et al. (2023) developed DIPHTOSCAN, a free access bioinformatics tool with user-friendly interphase, aimed to easily identify, extract and interpret important genomic features such as the sublineage of the strain, the presence of the tox gene (as a string predictor for toxigenic disease) as well as genes coding other virulence factors such as fimbriae, and the presence of know resistant mechanisms towards antibiotics like penicillin and erythromycin currently used in the clinic to treat this infection.

The authors validated the performance of their tool with a large collection of genomes, including those obtained from the isolates of the 2022 outbreak in France, more than 1,200 other genomes isolated from France, Algeria, and Yemen, and more than 500 genomes from several countries from Europe, America, Africa, Asia, and Oceania that are available through the NCBI site.

DIPHTOSCAN will allow the rapid identification and surveillance of potentially dangerous strains such as those being tox-positive isolates and resistant to multiple drugs and/or first-line treatments and a better understanding of the epidemiology and evolution of this important reemerging disease.

Reference

Hennart M., Crestani C., Bridel S., Armatys N., Brémont S., Carmi-Leroy A., Landier A., Passet V., Fonteneau L., Vaux S., Toubiana J., Badell E. and Brisse S. (2023). A global Corynebacterium diphtheriae genomic framework sheds light on current diphtheria reemergence. bioRxiv, 2023.02.20.529124, ver 3 peer-reviewed and recommended by PCI Infections. https://doi.org/10.1101/2023.02.20.529124

Mycobacterium bovis, also called M. tuberculosis var. bovis, is a bacterium belonging to the M. tuberculosis complex (i.e., MTBC) and which can cause through zoonotic transmission another form of human tuberculosis (Tb). It is above all the agent of bovine tuberculosis (i.e., bTb) which affects not only cattle (wild or farmed) but also a large diversity of other wild mammals worldwide. An increasing number of infected animal cases are being discovered in many regions of the world, thus raising the problem of tuberculosis transmission, including to humans, more complex than previously thought. Efforts have been made in terms of vaccination or culling of populations of host carrier species, such as the badger for example, however leading to consequences of greater dispersion of the infectious agent. M. bovis shows a more or less significant capacity to persist outside its hosts, particularly in the environment under certain abiotic and biotic conditions. This bacillus can be transmitted and spread in many ways, including through aerosol, mucus and sputum, urine and feces, by direct contact with infected animals, their dead bodies or rather via their excreta or by inhalation of aerosols, depending on the host species concerned.

In this paper, Calenge and his collaborators (Callenge et al. 2024) benefited from a national surveillance program on M. bovis cases in wild species, set up in 2011 in France, i.e., Sylvatub, for detecting and monitoring M. bovis infection in European badger (Meles meles) populations. Sylvatub is a participatory program involving both national and local stakeholder systems in order to determine changes in bTb infection levels in domestic and wild animal species. This original work had two aims: to describe spatial disease dynamics in the three clusters under scrutiny using a complex Bayesian model; and to develop indicators for the monitoring of the M. bovis infection by stakeholders and decision-makers of the program. This paper is timely and very comprehensive.

In this cogent study, the authors illustrate this point by using epidemiological surveillance to obtain large amounts of data (which is generally lacking in human epidemiology, but more dramatically lacking in animal epidemiology) and a highly sophisticated biostatistical analysis (Callenge et al. 2024). It is in itself a demonstration of the current capabilities of population dynamics applied to infectious disease situations, in this case animal, in the rapidly developing discipline of disease ecology and evolution. One of the aims of the study is to propose statistical models that can be used by the different stakeholders in charge, for instance, of wildlife conservation or the regional or State veterinary services to assess disease risk in the most affected regions.

References

Assel AKHMETOVA​, Jimena GUERRERO​, Paul McADAM, Liliana CM SALVADOR​, Joseph CRISPELL​, John LAVERY​, Eleanor PRESHO​, Rowland R KAO​, Roman BIEK​, Fraser MENZIES​, Nigel TRIMBLE​, Roland HARWOOD​, P Theo PEPLER, Katarina ORAVCOVA​, Jordon GRAHAM​, Robin SKUCE​, Louis DU PLESSIS​, Suzan THOMPSON​, Lorraine WRIGHT​, Andrew W BYRNE​, Adrian R ALLEN. 2023. Genomic epidemiology of Mycobacterium bovis infection in sympatric badger and cattle populations in Northern Ireland. Microbial Genomics 9: mgen001023. https://doi.org/10.1099/mgen.0.001023

Roman BIEK, Anthony O’HARE, David WRIGHT, Tom MALLON, Carl McCORMICK, Richard J ORTON, Stanley McDOWELL, Hannah TREWBY, Robin A SKUCE, Rowland R KAO. 2012. Whole genome sequencing reveals local transmission patterns of Mycobacterium bovis in sympatric cattle and badger populations. PLoS Pathogens 8: e1003008. https://doi.org/10.1371/journal.ppat.1003008

Clément CALENGE, Ariane PAYNE, Edouard REVEILLAUD, Céline RICHOMME, Sébastien GIRARD, Stephanie DESVAUX. 2024. Assessing the dynamics of Mycobacterium bovis infection in three French badger populations. bioRxiv, ver. 3 peer-reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2023.05.31.543041

Marc CHOISY, Pejman ROHANI. 2006. Harvesting can increase severity of wildlife disease epidemics. Proceedings of the Royal Society, London, Ser. B 273: 2025-2034. https://doi.org/10.1098/rspb.2006.3554

Shannon C DUFFY, Sreenidhi SRINIVASAN, Megan A SCHILLING, Tod STUBER, Sarah N DANCHUK, Joy S MICHAEL, Manigandan VENKATESAN, Nitish BANSAL, Sushila MAAN, Naresh JINDAL, Deepika CHAUDHARY, Premanshu DANDAPAT, Robab KATANI, Shubhada CHOTHE, Maroudam VEERASAMI, Suelee ROBBE-AUSTERMAN, Nicholas JULEFF, Vivek KAPUR, Marcel A BEHR. 2020. Reconsidering Mycobacterium bovis as a proxy for zoonotic tuberculosis: a molecular epidemiological surveillance study. Lancet Microbe 1: e66-e73. https://doi.org/10.1016/S2666-5247(20)30038-0

Jean-François GUEGAN. 2019. The nature of ecology of infectious disease. The Lancet Infectious Diseases 19. https://doi.org/10.1016/s1473-3099(19)30529-8

Brandon H HAYES, Timothée VERGNE, Mathieu ANDRAUD, Nicolas ROSE. 2023. Mathematical modeling at the livestock-wildlife interface: scoping review of drivers of disease transmission between species. Frontiers in Veterinary Science 10: 1225446. https://doi.org/10.3389/fvets.2023.1225446

David KING, Tim ROPER, Douglas YOUNG, Mark EJ WOOLHOUSE, Dan COLLINS, Paul WOOD. 2007. Bovine tuberculosis in cattle and badgers. Report to Secretary of State about tuberculosis in cattle and badgers. London, UK.

Robert MM SMITH , Francis DROBNIEWSKI, Andrea GIBSON, John DE MONTAGUE, Margaret N LOGAN, David HUNT, Glyn HEWINSON, Roland L SALMON, Brian O’NEILL. 2004. Mycobacterium bovis Infection, United Kingdom. Emerging Infectious Diseases 10: 539-541. https://doi.org/10.3201/eid1003.020819 

Mosquitoes are first vector of pathogen worldwide and transmit several arbovirus, most of them leading to major outbreaks (1). Chikungunya virus (CHIKV) is a perfect example of the “explosive type” of arbovirus, as observed in La Réunion Island in 2005-2006 (2-6) and also in the outbreak of 2007 in Italy (7), both vectorized by Ae. albopictus. Being able to better understand CHIKV intra-vector dynamics is still of major interest since not all chikungunya strain are explosive ones (8). 

In this study (9), the authors have evaluated the vector competence of a local strain of Aedes albopictus (collected in Lyon, France) for CHIKV. They evaluated infection, dissemination and transmission dynamics of CHIKV using different dose of virus in individual mosquitoes from day 2 to day 20 post exposure, by titration and quantification of CHIKV RNA load in the saliva. As highlighted by both reviewers, the most innovative idea in this study was the use of three different oral doses trying to span human viraemia detected in two published studies (10-11), doses that were estimated through their model of human CHIKV viremia in the blood.  They have found that CHIKV dissemination from the Ae. albopictus midgut depends on the interaction between time post-exposure and virus dose (already highlighted by other international publications).  Then their results were implemented in the agent-based model nosoi to estimate the epidemic potential of CHIKV in a French population of Ae. albopictus, using realistic vectorial capacity parameters.

To conclude, the authors have discussed the importance of other parameters that could influence vector competence as mosquito microbiota and temperature, parameters that need also to be estimated in local mosquito population to improve the risk assessment through modelling.  

As pointed out by both reviewers, this is a nice study, well written and easy to read. These results allow filling in another gap of our understanding of CHIKV intra-vector dynamics and highlight the epidemic potential of CHIKV upon transmission by Aedes albopictus in mainland France. For all these reasons, I chose to recommend this article for Peer Community In Infections.

References

1.       Marine Viglietta, Rachel Bellone, Adrien Albert Blisnick, Anna-Bella Failloux. (2021). Vector Specificity of Arbovirus Transmission. Front Microbiol Dec 9;12:773211. https://doi.org/10.3389/fmicb.2021.773211

2.       Schuffenecker I, Iteman I, Michault A, Murri S, Frangeul L, Vaney M-C, Lavenir R, Pardigon N, Reynes J-M, Pettinelli F, Biscornet L, Diancourt L, Michel S, Duquerroy S, Guigon G, Frenkiel M-P, Bréhin A-C, Cubito N, Desprès P, Kunst F, Rey FA, Zeller H, Brisse S. (2006). Genome Microevolution of Chikungunya viruses Causing the Indian Ocean Outbreak. 2006. PLoS Medicine, 3, e263. https://doi.org/10.1371/journal.pmed.0030263

3.       Bonilauri P, Bellini R, Calzolari M, Angelini R, Venturi L, Fallacara F, Cordioli P, 687 Angelini P, Venturelli C, Merialdi G, Dottori M. (2008). Chikungunya Virus in Aedes albopictus, Italy. Emerging Infectious 689 Diseases, 14, 852–854. https://doi.org/10.3201/eid1405.071144

4.       Pagès F, Peyrefitte CN, Mve MT, Jarjaval F, Brisse S, Iteman I, Gravier P, Tolou H, Nkoghe D, Grandadam M. (2009). Aedes albopictus Mosquito: The Main Vector of the 2007 Chikungunya Outbreak in Gabon. PLoS ONE, 4, e4691. https://doi.org/10.1371/journal.pone.0004691

5.       Paupy C, Kassa FK, Caron M, Nkoghé D, Leroy EM (2012) A Chikungunya Outbreak Associated with the Vector Aedes albopictus in Remote Villages of Gabon. Vector-Borne and Zoonotic Diseases, 12, 167–169. https://doi.org/10.1089/vbz.2011.0736

6.       Mombouli J-V, Bitsindou P, Elion DOA, Grolla A, Feldmann H, Niama FR, Parra H-J, Munster VJ. (2013). Chikungunya Virus Infection, Brazzaville, Republic of Congo, 2011. Emerging Infectious Diseases, 19, 1542–1543. https://doi.org/10.3201/eid1909.130451

7.       Venturi G, Luca MD, Fortuna C, Remoli ME, Riccardo F, Severini F, Toma L, Manso MD, Benedetti E, Caporali MG, Amendola A, Fiorentini C, Liberato CD, Giammattei R, Romi R, Pezzotti P, Rezza G, Rizzo C. (2017). Detection of a chikungunya outbreak in Central Italy, August to September 2017. Eurosurveillance, 22, 17–00646. https://doi.org/10.2807/1560-7917.es.2017.22.39.17-00646

8.       de Lima Cavalcanti, T.Y.V.; Pereira, M.R.; de Paula, S.O.; Franca, R.F.d.O. (2022). A Review on Chikungunya Virus Epidemiology, Pathogenesis and Current Vaccine Development. Viruses 2022, 14, 969. https://doi.org/10.3390/v14050969

9.       Barbara Viginier, Lucie Cappuccio, Celine Garnier, Edwige Martin, Carine Maisse, Claire Valiente Moro, Guillaume Minard, Albin Fontaine, Sebastian Lequime, Maxime Ratinier, Frederick Arnaud, Vincent Raquin. (2023). Chikungunya intra-vector dynamics in Aedes albopictus from Lyon (France) upon exposure to a human viremia-like dose range reveals vector barrier permissiveness and supports local epidemic potential. medRxiv, ver.3, peer-reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2022.11.06.22281997

10.     Appassakij H, Khuntikij P, Kemapunmanus M, Wutthanarungsan R, Silpapojakul K (2013) Viremic profiles in CHIKV-infected cases. Transfusion, 53, 2567–2574. https://doi.org/10.1111/j.1537-2995.2012.03960.x

11.     Riswari SF, Ma’roef CN, Djauhari H, Kosasih H, Perkasa A, Yudhaputri FA, Artika IM, Williams M, Ven A van der, Myint KS, Alisjahbana B, Ledermann JP, Powers AM, Jaya UA (2015) Study of viremic profile in febrile specimens of chikungunya in Bandung, Indonesia. Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology, 74, 61–5. https://doi.org/10.1016/j.jcv.2015.11.017

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