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.

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

            Parasites represent the most diverse and adaptable ecological group of the biosphere (Timm & Clauson, 1988; De Meeûs et al., 1998; Poulin & Morand, 2000; De Meeûs & Renaud, 2002). The human species is known to considerably alter biodiversity, though it hosts, and thus sustains the maintenance of a spectacular diversity of parasites (179 species for eukaryotic species only) (De Meeûs et al., 2009). Among these, the five species of malaria agents (genus Plasmodium) remain a major public health issue around the world. Plasmodium falciparum is the most prevalent and lethal of these (Liu et al., 2010). With a pick of up to 2 million deaths due to malaria in 2004, deaths decreased to around 1 million in 2010 (Murray et al., 2012), to reach 619,000 in 2021, most of which in sub-Saharan Africa, and 79% of which were among children aged under 5 years (World Health Organization, 2022). 

            As stressed by Taconet et al. (2023), reduction in malaria deaths is attributable to control measures, in particular against its vectors (mosquitoes of the genus Anopheles). Nevertheless, the success of vector control is hampered by several factors (biological, environmental and socio-economic), and in particular by the great propensity of targeted mosquitoes to evolve physiological or behavioral avoidance of anti-vectorial measures.

            In their paper Taconet et al. (2023) aims at understanding what are the main factors that determine the evolution of insecticide resistance in several malaria vectors, in relation to the biological determinisms of behavioral resistance and how fast such evolutions take place. To tackle these objectives, authors collected an impressive amount of data in two rural areas of West Africa. With appropriate modeling, Taconet et al. discovered, among many other results, a predominant role of public health measures, as compared to agricultural practices, in the evolution of physiological resistance. They also found that mosquito foraging activities are mostly explained by host availability and climate, with a poor, if any, association with genetic markers of physiological resistance to insecticides. These findings represent an important contribution to the field and should help at designing more efficient control strategies against malaria.

References

De Meeûs T, Michalakis Y, Renaud F (1998) Santa Rosalia revisited: or why are there so many kinds of parasites in “the garden of earthly delights”? Parasitology Today, 14, 10–13. https://doi.org/10.1016/S0169-4758(97)01163-0

De Meeûs T, Prugnolle F, Agnew P (2009) Asexual reproduction in infectious diseases. In: Lost Sex: The Evolutionary Biology of Parthenogenesis (eds Schön I, Martens K, van Dijk P), pp. 517-533. Springer, NY. https://doi.org/10.1007/978-90-481-2770-2_24

De Meeûs T, Renaud F (2002) Parasites within the new phylogeny of eukaryotes. Trends in Parasitology, 18, 247–251. https://doi.org/10.1016/S1471-4922(02)02269-9

Liu W, Li Y, Learn GH, Rudicell RS, Robertson JD, Keele BF, Ndjango JB, Sanz CM, Morgan DB, Locatelli S, Gonder MK, Kranzusch PJ, Walsh PD, Delaporte E, Mpoudi-Ngole E, Georgiev AV, Muller MN, Shaw GM, Peeters M, Sharp PM, Rayner JC, Hahn BH (2010) Origin of the human malaria parasite Plasmodium falciparum in gorillas. Nature, 467, 420–425. https://doi.org/10.1038/nature09442

Murray CJ, Rosenfeld LC, Lim SS, Andrews KG, Foreman KJ, Haring D, Fullman N, Naghavi M, Lozano R, Lopez AD (2012) Global malaria mortality between 1980 and 2010: a systematic analysis. The Lancet, 379, 413–431. https://doi.org/10.1016/S0140-6736(12)60034-8

Poulin R, Morand S (2000) The diversity of parasites. Quarterly Review of Biology, 75, 277–293. https://doi.org/10.1086/393500

Taconet P, Soma DD, Zogo B, Mouline K, Simard F, Koffi AA, Dabire RK, Pennetier C, Moiroux N (2023) Physiological and behavioural resistance of malaria vectors in rural West-Africa : a data mining study to address their fine-scale spatiotemporal heterogeneity, drivers, and predictability. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Infections. https://doi.org/10.1101/2022.08.20.504631

Timm RM, Clauson BL (1988) Coevolution: Mammalia. In: 1988 McGraw-Hill yearbook of science & technology, pp. 212–214. McGraw-Hill Book Company, New York.

World Health Organization (2022) World malaria report 2022. Geneva: World Health Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO. https://iris.who.int/bitstream/handle/10665/365169/9789240064898-eng.pdf?sequence=1.

Most infectious diseases of humans are zoonoses, and many of these come from particularly species diverse reservoir taxa, such as bats, birds, and rodents (1). Because of our changing landscape, there is increased exposure of humans to wildlife diseases reservoirs, yet we have little basic information about prevalence, hotspots, and transmission factors of most zoonotic pathogens. Viruses are particularly worrisome as a public health risk due to their fast mutation rates and well-known cross-species transmission abilities. There is a global push to better survey wildlife for viruses (2), but these studies are difficult, and the problem is vast. Astroviruses (AstVs) comprise a diverse family of ssRNA viruses known from mammals and birds. Astroviruses can cause gastroenteritis in humans and are more common in elderly and young children, but the relationship of human to non-human Astroviridae as well as transmission routes are unclear.  AstVs have been detected at high prevalence in bats in multiple studies (3,4), but it is unclear what factors, such as co-infecting viruses and bat reproductive phenology, influence viral shedding and prevalence.
In this recommended study, Vimbiso et al. (5) study the prevalence and diversity of astroviruses in different insectivorous and frugivorous chiropteran species roosting in trees, caves and building basements across Zimbabwe, a region never investigated for astroviruses. Using both pooled population samples and individual samples from 11 different sites, the authors screened for astrovirus prevalence via RT-PCR and identified bat taxa using mitochondrial gene sequencing. An overall prevalence of 10-14% infection was recorded. No clear association of increased astrovirus and coronavirus coinfection was detected, and although astrovirus infection varied over the season, it did not do so in consistent ways across the two primary sampling sites, Magweto and Chirundu. A phylogeny generated by sequencing all of the astrovirus positive samples showed evidence that most of the viral lineages are transmitting within species but across Zibabwae such that most phylogenetic lineages grouped viruses from the same host species together. However, there was ample evidence for interspecies transmission between bats. Finally, a small percentage of the total astrovirus diversity from Zibabwae clustered with sequences from humans. The timing and direction of the transmission between humans and bats need further investigation.
 
This study provides important baseline data about viral diversity and does an excellent job of capturing the spatial, temporal, host species, and sequence level dynamics of the astroviruses. There are clear limitations on how this study can be interpreted due to different sampling regimes and, in particular, the fact that each of the two primary sites was only explored for temporal variation over a single calendar year. That said, the grand diversity of astroviruses demonstrated in insectivorous bats in Zibabwae shows that we are only seeing the very tip of the iceberg with respect to viral diversity with zoonotic potential. As suggested by the reviewers, more studies like this are needed to understand the basic ecology of viruses and to aid in predicting epidemics.

References

1. Mollentze N, Streicker DG. Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts. Proceedings of the National Academy of Sciences. 2020 Apr 28;117(17):9423-30. https://doi.org/10.1073/pnas.1919176117
2. Carroll D, Daszak P, Wolfe ND, Gao GF, Morel CM, Morzaria S, et al. The Global Virome Project. Science. 2018 Feb 23;359(6378):872-4. https://doi.org/10.1126/science.aap7463
3. Lee SY, Son KD, Yong-Sik K, Wang SJ, Kim YK, Jheong WH, et al. Genetic diversity and phylogenetic analysis of newly discovered bat astroviruses in Korea. Arch Virol. 2018;163(11):3065-72. https://doi.org/10.1007/s00705-018-3992-6
4. Seltmann A, Corman VM, Rasche A, Drosten C, Czirják GÁ, Bernard H, et al. Seasonal Fluctuations of Astrovirus, But Not Coronavirus Shedding in Bats Inhabiting Human-Modified Tropical Forests. EcoHealth. 2017 Jun 1;14(2):272-84. https://doi.org/10.1007/s10393-017-1245-x
5. Vimbiso C, Hélène DN, Malika A, Getrude M, Valérie P, Ngoni C, et al. Longitudinal Survey of Astrovirus infection in different bat species in Zimbabwe: Evidence of high genetic Astrovirus diversity. bioRxiv, 2023.04.14.536987, ver. 6 peer-reviewed and recommended by Peer Community In Infections. https://doi.org/10.1101/2023.04.14.536987

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