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Aslan IH, Pourtois JD, Chamberlin AJ, Mitchell KR, Mari L, Lwiza KM, Wood CL, Mordecai EA, Yu A, Tuan R, Palasio RGS, Monteiro AMV, Kirk D, Athni TS, Sokolow SH, N’Goran EK, Diakite NR, Ouattara M, Gatto M, Casagrandi R, Little DC, Ozretich RW, Norman R, Allan F, Brierley AS, Liu P, Pereira TA, De Leo GA. Re-assessing thermal response of schistosomiasis transmission risk: Evidence for a higher thermal optimum than previously predicted. PLoS Negl Trop Dis 2024; 18:e0011836. [PMID: 38857289 PMCID: PMC11207148 DOI: 10.1371/journal.pntd.0011836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 06/26/2024] [Accepted: 05/23/2024] [Indexed: 06/12/2024] Open
Abstract
The geographical range of schistosomiasis is affected by the ecology of schistosome parasites and their obligate host snails, including their response to temperature. Previous models predicted schistosomiasis' thermal optimum at 21.7°C, which is not compatible with the temperature in sub-Saharan Africa (SSA) regions where schistosomiasis is hyperendemic. We performed an extensive literature search for empirical data on the effect of temperature on physiological and epidemiological parameters regulating the free-living stages of S. mansoni and S. haematobium and their obligate host snails, i.e., Biomphalaria spp. and Bulinus spp., respectively. We derived nonlinear thermal responses fitted on these data to parameterize a mechanistic, process-based model of schistosomiasis. We then re-cast the basic reproduction number and the prevalence of schistosome infection as functions of temperature. We found that the thermal optima for transmission of S. mansoni and S. haematobium range between 23.1-27.3°C and 23.6-27.9°C (95% CI) respectively. We also found that the thermal optimum shifts toward higher temperatures as the human water contact rate increases with temperature. Our findings align with an extensive dataset of schistosomiasis prevalence in SSA. The refined nonlinear thermal-response model developed here suggests a more suitable current climate and a greater risk of increased transmission with future warming for more than half of the schistosomiasis suitable regions with mean annual temperature below the thermal optimum.
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Affiliation(s)
- Ibrahim Halil Aslan
- Department of Biology, Stanford University, Stanford, California, United States of America
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
| | - Julie D. Pourtois
- Department of Biology, Stanford University, Stanford, California, United States of America
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
| | - Andrew J. Chamberlin
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
| | - Kaitlyn R. Mitchell
- Department of Biology, Stanford University, Stanford, California, United States of America
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
| | - Lorenzo Mari
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Kamazima M. Lwiza
- School of Marine and Atmospheric Sciences, Stony Brook University, New York, New York, United States of America
| | - Chelsea L. Wood
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, United States of America
| | - Erin A. Mordecai
- Department of Biology, Stanford University, Stanford, California, United States of America
- Woods Institute for the Environment, Stanford University, Stanford, California, United States of America
| | - Ao Yu
- Department of Earth System Science, Stanford University, Stanford, California, United States of America
| | - Roseli Tuan
- Pasteur Institute, São Paulo Health Public Office, São Paulo, Brazil
| | | | | | - Devin Kirk
- Department of Biology, Stanford University, Stanford, California, United States of America
| | - Tejas S. Athni
- Department of Biology, Stanford University, Stanford, California, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
| | - Susanne H. Sokolow
- Department of Biology, Stanford University, Stanford, California, United States of America
- Woods Institute for the Environment, Stanford University, Stanford, California, United States of America
| | | | | | | | - Marino Gatto
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Renato Casagrandi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - David C. Little
- Institute of Aquaculture, University of Stirling, Stirling, United Kingdom
| | - Reed W. Ozretich
- Institute of Aquaculture, University of Stirling, Stirling, United Kingdom
| | - Rachel Norman
- Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom
| | - Fiona Allan
- Department of Life Sciences, Natural History Museum, London, United Kingdom
| | - Andrew S. Brierley
- Scottish Oceans Institute, School of Biology, University of St. Andrews, St. Andrews, United Kingdom
| | - Ping Liu
- School of Marine and Atmospheric Sciences, Stony Brook University, New York, New York, United States of America
| | - Thiago A. Pereira
- Institute for Stem Cell Biology and Regenerative Medicine, School of Medicine, Stanford University, Stanford, California, United States of America
| | - Giulio A. De Leo
- Department of Biology, Stanford University, Stanford, California, United States of America
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
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Aslan IH, Pourtois JD, Chamberlin AJ, Mitchell KR, Mari L, Lwiza KM, Wood CL, Mordecai EA, Yu A, Tuan R, Palasio RGS, Monteiro AM, Kirk D, Athni TS, Sokolow SH, N’Goran EK, Diakite NR, Ouattara M, Gatto M, Casagrandi R, Little DC, Ozretich RW, Norman R, Allan F, Brierley AS, Liu P, Pereira TA, De Leo GA. Re-assessing thermal response of schistosomiasis transmission risk: evidence for a higher thermal optimum than previously predicted. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.04.24300851. [PMID: 38826336 PMCID: PMC11142288 DOI: 10.1101/2024.01.04.24300851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The geographical range of schistosomiasis is affected by the ecology of schistosome parasites and their obligate host snails, including their response to temperature. Previous models predicted schistosomiasis' thermal optimum at 21.7 °C, which is not compatible with the temperature in sub-Saharan Africa (SSA) regions where schistosomiasis is hyperendemic. We performed an extensive literature search for empirical data on the effect of temperature on physiological and epidemiological parameters regulating the free-living stages of S. mansoni and S. haematobium and their obligate host snails, i.e., Biomphalaria spp. and Bulinus spp., respectively. We derived nonlinear thermal responses fitted on these data to parameterize a mechanistic, process-based model of schistosomiasis. We then re-cast the basic reproduction number and the prevalence of schistosome infection as functions of temperature. We found that the thermal optima for transmission of S. mansoni and S. haematobium range between 23.1-27.3 °C and 23.6-27.9 °C (95 % CI) respectively. We also found that the thermal optimum shifts toward higher temperatures as the human water contact rate increases with temperature. Our findings align with an extensive dataset of schistosomiasis prevalence in SSA. The refined nonlinear thermal-response model developed here suggests a more suitable current climate and a greater risk of increased transmission with future warming for more than half of the schistosomiasis suitable regions with mean annual temperature below the thermal optimum.
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Affiliation(s)
- Ibrahim Halil Aslan
- Department of Biology, Stanford University, Stanford, CA, USA
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA
| | - Julie D. Pourtois
- Department of Biology, Stanford University, Stanford, CA, USA
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA
| | | | - Kaitlyn R. Mitchell
- Department of Biology, Stanford University, Stanford, CA, USA
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA
| | - Lorenzo Mari
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Kamazima M. Lwiza
- School of Marine and Atmospheric Sciences Stony Brook University, New York, NY, USA
| | - Chelsea L. Wood
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA
| | - Erin A. Mordecai
- Department of Biology, Stanford University, Stanford, CA, USA
- Woods Institute for the Environment, Stanford University, Stanford, CA, USA
| | - Ao Yu
- Department of Earth System Science, Stanford University, Stanford, CA, USA
| | - Roseli Tuan
- Pasteur Institute, São Paulo Health Public Office, São Paulo, SP, Brazil
| | | | | | - Devin Kirk
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Tejas S. Athni
- Department of Biology, Stanford University, Stanford, CA, USA
- Harvard Medical School, Boston, MA, USA
| | - Susanne H. Sokolow
- Department of Biology, Stanford University, Stanford, CA, USA
- Woods Institute for the Environment, Stanford University, Stanford, CA, USA
| | | | - Nana R. Diakite
- Université Félix Houphouët-Boigny, 22 BP 770, Abidjan 22, Côte d’Ivoire
| | - Mamadou Ouattara
- Université Félix Houphouët-Boigny, 22 BP 770, Abidjan 22, Côte d’Ivoire
| | - Marino Gatto
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Renato Casagrandi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milano, Italy
| | - David C. Little
- Institute of Aquaculture, University of Stirling, Stirling, UK
| | | | - Rachel Norman
- Computing Science and Mathematics, University of Stirling, Stirling, UK
| | - Fiona Allan
- Department of Life Sciences, Natural History Museum, London, UK
| | - Andrew S. Brierley
- Scottish Oceans Institute, School of Biology, University of St. Andrews, St. Andrews, UK
| | - Ping Liu
- School of Marine and Atmospheric Sciences Stony Brook University, New York, NY, USA
| | - Thiago A. Pereira
- Institute for Stem Cell Biology and Regenerative Medicine, School of Medicine, Stanford University, Stanford, CA, USA
| | - Giulio A. De Leo
- Department of Biology, Stanford University, Stanford, CA, USA
- Hopkins Marine Station, Stanford University, Pacific Grove, CA, USA
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Stella E, Pastres R, Pasetto D, Kolega M, Mejdandžić D, Čolak S, Musmanno A, Gustinelli A, Mari L, Bertuzzo E. A stratified compartmental model for the transmission of Sparicotyle chrysophrii (Platyhelminthes: Monogenea) in gilthead seabream ( Sparus aurata) fish farms †. ROYAL SOCIETY OPEN SCIENCE 2023; 10:221377. [PMID: 37206963 PMCID: PMC10189595 DOI: 10.1098/rsos.221377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 04/13/2023] [Indexed: 05/21/2023]
Abstract
The rapid development of intensive fish farming has been associated with the spreading of infectious diseases, pathogens and parasites. One such parasite is Sparicotyle chrysophrii (Platyhelminthes: Monogenea), which commonly infects cultured gilthead seabream (Sparus aurata)-a vital species in Mediterranean aquaculture. The parasite attaches to fish gills and can cause epizootics in sea cages with relevant consequences for fish health and associated economic losses for fish farmers. In this study, a novel stratified compartmental epidemiological model of S. chrysophrii transmission was developed and analysed. The model accounts for the temporal progression of the number of juvenile and adult parasites attached to each fish, as well as the abundance of eggs and oncomiracidia. We applied the model to data collected in a seabream farm, where the fish population and the number of adult parasites attached to fish gills were closely monitored in six different cages for 10 months. The model successfully replicated the temporal dynamics of the distribution of the parasite abundance within fish hosts and simulated the effects of environmental factors, such as water temperature, on the transmission dynamics. The findings highlight the potential of modelling tools for farming management, aiding in the prevention and control of S. chrysophrii infections in Mediterranean aquaculture.
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Affiliation(s)
- Elisa Stella
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, 30123 Venice, Italy
| | - Roberto Pastres
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, 30123 Venice, Italy
| | - Damiano Pasetto
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, 30123 Venice, Italy
| | | | | | | | - Antares Musmanno
- Department of Veterinary Medical Sciences, Alma Mater Studiorum Università di Bologna, 40064 Bologna, Italy
| | - Andrea Gustinelli
- Department of Veterinary Medical Sciences, Alma Mater Studiorum Università di Bologna, 40064 Bologna, Italy
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Enrico Bertuzzo
- Department of Environmental Sciences, Informatics and Statistics, Ca’ Foscari University of Venice, 30123 Venice, Italy
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Pulmonary Vascular Diseases Associated with Infectious Disease-Schistosomiasis and Human Immunodeficiency Viruses. Clin Chest Med 2021; 42:71-80. [PMID: 33541618 DOI: 10.1016/j.ccm.2020.11.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
A wide variety of infectious diseases are major contributors to the causation of pulmonary vascular disease and, consequently, pulmonary hypertension, especially in the developing world. Schistosomiasis and human immunodeficiency virus are the most common infections that are known to contribute to pulmonary hypertension worldwide. The resultant inflammation and immunologic milieu caused by infection are the main pathologic processes affecting the pulmonary vasculature.
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Huang Q, Gurarie D, Ndeffo-Mbah M, Li E, King CH. Schistosoma transmission in a dynamic seasonal environment and its impact on the effectiveness of disease control. J Infect Dis 2020; 225:1050-1061. [PMID: 33263735 PMCID: PMC8921996 DOI: 10.1093/infdis/jiaa746] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 11/30/2020] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND A seasonal transmission environment including seasonal variation of snail population density and human-snail contact patterns can affect the dynamics of Schistosoma infection and the success of control interventions. In projecting control outcomes, conventional modeling approaches have often ignored seasonality by using simplified intermediate-host modeling, or by restricting seasonal effects through use of yearly averaging. METHODS We used mathematical analysis and numerical simulation to estimate the impact of seasonality on disease dynamics and control outcomes, and to evaluate whether seasonal averaging or intermediate-host reduction can provide reliable predictions of control outcomes. We also examined whether seasonality could be used as leverage in creation of effective control strategies. RESULTS We found models that used seasonal averaging could grossly overestimate infection burden and underestimate control outcomes in highly seasonal environments. We showed that proper intra-seasonal timing of control measures could make marked improvement on the long-term burden reduction for Schistosoma transmission control, and we identified the optimal timing for each intervention. Seasonal snail control, implemented alone, was less effective than mass drug administration, but could provide additive impact in reaching control and elimination targets. CONCLUSION Seasonal variation makes Schistosoma transmission less sustainable and easier to control than predicted by earlier modeling studies.
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Affiliation(s)
- Qimin Huang
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, USA
| | - David Gurarie
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, USA.,Center for Global Health and Diseases, School of Medicine, Case Western Reserve University, Cleveland, USA
| | - Martial Ndeffo-Mbah
- Department of Veterinary and Integrative Biosciences, College of Veterinary and Biomedical Sciences, Texas A&M University, College Station, USA.,School of Public Health, Texas A&M University, College Station, USA
| | - Emily Li
- Ascension St. Vincent Indianapolis, Family Medicine Residency, Indianapolis, USA
| | - Charles H King
- Center for Global Health and Diseases, School of Medicine, Case Western Reserve University, Cleveland, USA.,Schistosomiasis Consortium for Operational Research and Evaluation, University of Georgia, Athens, USA.,WHO Collaborating Centre for Research and Training for Schistosomiasis Elimination, Cleveland, USA
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Direct onshore wind predicts daily swimmer's itch (avian schistosome) incidence at a Michigan beach. Parasitology 2020; 147:431-440. [PMID: 31965949 DOI: 10.1017/s0031182020000074] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Swimmer's itch (SI) is a painful rash caused by skin penetration by free-swimming infectious cercariae of avian schistosomes, snail-borne helminth parasites related to the causative agents of human schistosomiasis. The goal of this study was to determine if commonly collected environmental data could be used to predict daily fluctuations in SI incidence at an inland beach in northwestern Michigan. Lifeguards collected daily data over four summers, including the number of self-reported SI cases, total swimmers, water temperature, wind speed and wind direction. Mixed-effects binomial regression revealed that wind direction, wind speed and time of day were the best predictors of daily SI risk. Swimmers entering the water in the morning or on days with direct onshore wind perpendicular to the shoreline had the greatest SI risk. However, there was a negative effect of wind speed after accounting for direction, where SI risk was greatest on days with a gentle breeze originating directly offshore. These results suggest that at this beach, direct onshore winds generate a surface-water current that causes SI cercariae to aggregate in the shallow waters used by swimmers. Data are needed from additional sites to confirm whether the onshore wind is a generally important driver of SI incidence.
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Rabone M, Wiethase JH, Allan F, Gouvras AN, Pennance T, Hamidou AA, Webster BL, Labbo R, Emery AM, Garba AD, Rollinson D. Freshwater snails of biomedical importance in the Niger River Valley: evidence of temporal and spatial patterns in abundance, distribution and infection with Schistosoma spp. Parasit Vectors 2019; 12:498. [PMID: 31640811 PMCID: PMC6805334 DOI: 10.1186/s13071-019-3745-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 10/09/2019] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Sound knowledge of the abundance and distribution of intermediate host snails is key to understanding schistosomiasis transmission and to inform effective interventions in endemic areas. METHODS A longitudinal field survey of freshwater snails of biomedical importance was undertaken in the Niger River Valley (NRV) between July 2011 and January 2016, targeting Bulinus spp. and Biomphalaria pfeifferi (intermediate hosts of Schistosoma spp.), and Radix natalensis (intermediate host of Fasciola spp.). Monthly snail collections were carried out in 92 sites, near 20 localities endemic for S. haematobium. All bulinids and Bi. pfeifferi were inspected for infection with Schistosoma spp., and R. natalensis for infection with Fasciola spp. RESULTS Bulinus truncatus was the most abundant species found, followed by Bulinus forskalii, R. natalensis and Bi. pfeifferi. High abundance was associated with irrigation canals for all species with highest numbers of Bulinus spp. and R. natalensis. Seasonality in abundance was statistically significant in all species, with greater numbers associated with dry season months in the first half of the year. Both B. truncatus and R. natalensis showed a negative association with some wet season months, particularly August. Prevalences of Schistosoma spp. within snails across the entire study were as follows: Bi. pfeifferi: 3.45% (79/2290); B. truncatus: 0.8% (342/42,500); and B. forskalii: 0.2% (24/11,989). No R. natalensis (n = 2530) were infected. Seasonality of infection was evident for B. truncatus, with highest proportions shedding in the middle of the dry season and lowest in the rainy season, and month being a significant predictor of infection. Bulinus spp. and Bi. pfeifferi showed a significant correlation of snail abundance with the number of snails shedding. In B. truncatus, both prevalence of Schistosoma spp. infection, and abundance of shedding snails were significantly higher in pond habitats than in irrigation canals. CONCLUSIONS Evidence of seasonality in both overall snail abundance and infection with Schistosoma spp. in B. truncatus, the main intermediate host in the region, has significant implications for monitoring and interrupting transmission of Schistosoma spp. in the NRV. Monthly longitudinal surveys, representing intensive sampling effort have provided the resolution needed to ascertain both temporal and spatial trends in this study. These data can inform planning of interventions and treatment within the region.
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Affiliation(s)
- Muriel Rabone
- Department of Life Sciences, Natural History Museum, Cromwell Rd, South Kensington, London, SW7 5BD UK
| | - Joris Hendrik Wiethase
- Department of Life Sciences, Natural History Museum, Cromwell Rd, South Kensington, London, SW7 5BD UK
| | - Fiona Allan
- Department of Life Sciences, Natural History Museum, Cromwell Rd, South Kensington, London, SW7 5BD UK
| | - Anouk Nathalie Gouvras
- Department of Life Sciences, Natural History Museum, Cromwell Rd, South Kensington, London, SW7 5BD UK
| | - Tom Pennance
- Department of Life Sciences, Natural History Museum, Cromwell Rd, South Kensington, London, SW7 5BD UK
- School of Biosciences, Cardiff University, Cardiff, CF10 3AT UK
| | - Amina Amadou Hamidou
- Réseau International Schistosomoses, Environnement Aménagement et Lutte (RISEAL-Niger), 333, Avenue des Zarmakoye, B.P. 13724, Niamey, Niger
| | - Bonnie Lee Webster
- Department of Life Sciences, Natural History Museum, Cromwell Rd, South Kensington, London, SW7 5BD UK
| | - Rabiou Labbo
- Réseau International Schistosomoses, Environnement Aménagement et Lutte (RISEAL-Niger), 333, Avenue des Zarmakoye, B.P. 13724, Niamey, Niger
- Centre de Recherche Médicale et Sanitaire (CERMES), Institut Pasteur International Network, 634 Bd de la Nation, BP 10887, Niamey, Niger
| | - Aidan Mark Emery
- Department of Life Sciences, Natural History Museum, Cromwell Rd, South Kensington, London, SW7 5BD UK
| | - Amadou Djirmay Garba
- Réseau International Schistosomoses, Environnement Aménagement et Lutte (RISEAL-Niger), 333, Avenue des Zarmakoye, B.P. 13724, Niamey, Niger
- World Health Organization, Geneva, Switzerland
| | - David Rollinson
- Department of Life Sciences, Natural History Museum, Cromwell Rd, South Kensington, London, SW7 5BD UK
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Arakala A, Hoover CM, Marshall JM, Sokolow SH, De Leo GA, Rohr JR, Remais JV, Gambhir M. Estimating the elimination feasibility in the 'end game' of control efforts for parasites subjected to regular mass drug administration: Methods and their application to schistosomiasis. PLoS Negl Trop Dis 2018; 12:e0006794. [PMID: 30418968 PMCID: PMC6258430 DOI: 10.1371/journal.pntd.0006794] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Revised: 11/26/2018] [Accepted: 08/27/2018] [Indexed: 11/18/2022] Open
Abstract
Progress towards controlling and eliminating parasitic worms, including schistosomiasis, onchocerciasis, and lymphatic filariasis, is advancing rapidly as national governments, multinational NGOs, and pharmaceutical companies launch collaborative chemotherapeutic control campaigns. Critical questions remain regarding the potential for achieving elimination of these infections, and analytical methods can help to quickly estimate progress towards-and the probability of achieving-elimination over specific timeframes. Here, we propose the effective reproduction number, Reff, as a proxy of elimination potential for sexually reproducing worms that are subject to poor mating success at very low abundance (positive density dependence, or Allee effects). Reff is the number of parasites produced by a single reproductive parasite at a given stage in the transmission cycle, over the parasite's lifetime-it is the generalized form of the more familiar basic reproduction number, R0, which only applies at the beginning of an epidemic-and it can be estimated in a 'model-free' manner by an estimator ('ε'). We introduce ε, demonstrate its estimation using simulated data, and discuss how it may be used in planning and evaluation of ongoing elimination efforts for a range of parasitic diseases.
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Affiliation(s)
- Arathi Arakala
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
| | - Christopher M. Hoover
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, United States of America
| | - John M. Marshall
- Division of Epidemiology and Biostatistics, School of Public Health, University of California, Berkeley, California, United States of America
| | - Susanne H. Sokolow
- Department of Biology—Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
| | - Giulio A. De Leo
- Department of Biology—Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
| | - Jason R. Rohr
- Department of Integrative Biology, University of Southern Florida, Tampa, Florida, United States of America
| | - Justin V. Remais
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, California, United States of America
| | - Manoj Gambhir
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia
- Health Modelling and Analytics, IBM Research Australia, Melbourne, Australia
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9
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Mari L, Casagrandi R, Rinaldo A, Gatto M. Epidemicity thresholds for water-borne and water-related diseases. J Theor Biol 2018; 447:126-138. [PMID: 29588168 DOI: 10.1016/j.jtbi.2018.03.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2017] [Revised: 02/02/2018] [Accepted: 03/16/2018] [Indexed: 12/18/2022]
Abstract
Determining the conditions that favor pathogen establishment in a host community is key to disease control and eradication. However, focusing on long-term dynamics alone may lead to an underestimation of the threats imposed by outbreaks triggered by short-term transient phenomena. Achieving an effective epidemiological response thus requires to look at different timescales, each of which may be endowed with specific management objectives. In this work we aim to determine epidemicity thresholds for some prototypical examples of water-borne and water-related diseases, a diverse family of infections transmitted either directly through water infested with pathogens or by vectors whose lifecycles are closely associated with water. From a technical perspective, while conditions for endemicity are determined via stability analysis, epidemicity thresholds are defined through generalized reactivity analysis, a recently proposed method that allows the study of the short-term instability properties of ecological systems. Understanding the drivers of water-borne and water-related disease dynamics over timescales that may be relevant to epidemic and/or endemic transmission is a challenge of the utmost importance, as large portions of the developing world are still struggling with the burden imposed by these infections.
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Affiliation(s)
- Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy.
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland; Dipartimento ICEA, Università di Padova, Padova 35131, Italy
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano 20133, Italy
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Ciddio M, Mari L, Sokolow SH, De Leo GA, Casagrandi R, Gatto M. The spatial spread of schistosomiasis: A multidimensional network model applied to Saint-Louis region, Senegal. ADVANCES IN WATER RESOURCES 2017; 108:406-415. [PMID: 29056816 PMCID: PMC5637889 DOI: 10.1016/j.advwatres.2016.10.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Revised: 09/13/2016] [Accepted: 10/10/2016] [Indexed: 05/06/2023]
Abstract
Schistosomiasis is a parasitic, water-related disease that is prevalent in tropical and subtropical areas of the world, causing severe and chronic consequences especially among children. Here we study the spatial spread of this disease within a network of connected villages in the endemic region of the Lower Basin of the Senegal River, in Senegal. The analysis is performed by means of a spatially explicit metapopulation model that couples local-scale eco-epidemiological dynamics with spatial mechanisms related to human mobility (estimated from anonymized mobile phone records), snail dispersal and hydrological transport of schistosome larvae along the main water bodies of the region. Results show that the model produces epidemiological patterns consistent with field observations, and point out the key role of spatial connectivity on the spread of the disease. These findings underline the importance of considering different transport pathways in order to elaborate disease control strategies that can be effective within a network of connected populations.
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Affiliation(s)
- Manuela Ciddio
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Susanne H. Sokolow
- Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, United States
- Marine Science Institute, University of California, Santa Barbara, CA 93106, United States
| | - Giulio A. De Leo
- Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, United States
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
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Mari L, Ciddio M, Casagrandi R, Perez-Saez J, Bertuzzo E, Rinaldo A, Sokolow SH, De Leo GA, Gatto M. Heterogeneity in schistosomiasis transmission dynamics. J Theor Biol 2017; 432:87-99. [PMID: 28823529 PMCID: PMC5595357 DOI: 10.1016/j.jtbi.2017.08.015] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/30/2017] [Accepted: 08/15/2017] [Indexed: 01/30/2023]
Abstract
Transmission dynamics of schistosomiasis presents multiple heterogeneity sources. A comprehensive framework for heterogeneous disease transmission is proposed. Heterogeneous multigroup communities can be more prone to parasite transmission. Presence of multiple water sources can hinder parasite transmission. Spatial and temporal heterogeneities can have nontrivial implications for endemicity.
Simple models of disease propagation often disregard the effects of transmission heterogeneity on the ecological and epidemiological dynamics associated with host-parasite interactions. However, for some diseases like schistosomiasis, a widespread parasitic infection caused by Schistosoma worms, accounting for heterogeneity is crucial to both characterize long-term dynamics and evaluate opportunities for disease control. Elaborating on the classic Macdonald model for macroparasite transmission, we analyze families of models including explicit descriptions of heterogeneity related to differential transmission risk within a community, water contact patterns, the distribution of the snail host population, human mobility, and the seasonal fluctuations of the environment. Through simple numerical examples, we show that heterogeneous multigroup communities may be more prone to schistosomiasis than homogeneous ones, that the availability of multiple water sources can hinder parasite transmission, and that both spatial and temporal heterogeneities may have nontrivial implications for disease endemicity. Finally, we discuss the implications of heterogeneity for disease control. Although focused on schistosomiasis, results from this study may apply as well to other parasitic infections with complex transmission cycles, such as cysticercosis, dracunculiasis and fasciolosis.
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Affiliation(s)
- Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy.
| | - Manuela Ciddio
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
| | - Javier Perez-Saez
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Enrico Bertuzzo
- Dipartimento di Scienze Ambientali, Informatica e Statistica, Università Ca' Foscari Venezia, 30170 Venezia Mestre, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland; Dipartimento ICEA, Università di Padova, 35131 Padova, Italy
| | - Susanne H Sokolow
- Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA; Marine Science Institute, University of California, Santa Barbara, CA 93106, USA
| | - Giulio A De Leo
- Hopkins Marine Station, Stanford University, Pacific Grove, CA 93950, USA
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milano, Italy
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Big-data-driven modeling unveils country-wide drivers of endemic schistosomiasis. Sci Rep 2017; 7:489. [PMID: 28352101 PMCID: PMC5428445 DOI: 10.1038/s41598-017-00493-1] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2016] [Accepted: 02/27/2017] [Indexed: 11/09/2022] Open
Abstract
Schistosomiasis is a parasitic infection that is widespread in sub-Saharan Africa, where it represents a major health problem. We study the drivers of its geographical distribution in Senegal via a spatially explicit network model accounting for epidemiological dynamics driven by local socioeconomic and environmental conditions, and human mobility. The model is parameterized by tapping several available geodatabases and a large dataset of mobile phone traces. It reliably reproduces the observed spatial patterns of regional schistosomiasis prevalence throughout the country, provided that spatial heterogeneity and human mobility are suitably accounted for. Specifically, a fine-grained description of the socioeconomic and environmental heterogeneities involved in local disease transmission is crucial to capturing the spatial variability of disease prevalence, while the inclusion of human mobility significantly improves the explanatory power of the model. Concerning human movement, we find that moderate mobility may reduce disease prevalence, whereas either high or low mobility may result in increased prevalence of infection. The effects of control strategies based on exposure and contamination reduction via improved access to safe water or educational campaigns are also analyzed. To our knowledge, this represents the first application of an integrative schistosomiasis transmission model at a whole-country scale.
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Hydrology and density feedbacks control the ecology of intermediate hosts of schistosomiasis across habitats in seasonal climates. Proc Natl Acad Sci U S A 2016; 113:6427-32. [PMID: 27162339 DOI: 10.1073/pnas.1602251113] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
We report about field and theoretical studies on the ecology of the aquatic snails (Bulinus spp. and Biomphalaria pfeifferi) that serve as obligate intermediate hosts in the complex life cycle of the parasites causing human schistosomiasis. Snail abundance fosters disease transmission, and thus the dynamics of snail populations are critically important for schistosomiasis modeling and control. Here, we single out hydrological drivers and density dependence (or lack of it) of ecological growth rates of local snail populations by contrasting novel ecological and environmental data with various models of host demography. Specifically, we study various natural and man-made habitats across Burkina Faso's highly seasonal climatic zones. Demographic models are ranked through formal model comparison and structural risk minimization. The latter allows us to evaluate the suitability of population models while clarifying the relevant covariates that explain empirical observations of snail abundance under the actual climatic forcings experienced by the various field sites. Our results link quantitatively hydrological drivers to distinct population dynamics through specific density feedbacks, and show that statistical methods based on model averaging provide reliable snail abundance projections. The consistency of our ranking results suggests the use of ad hoc models of snail demography depending on habitat type (e.g., natural vs. man-made) and hydrological characteristics (e.g., ephemeral vs. permanent). Implications for risk mapping and space-time allocation of control measures in schistosomiasis-endemic contexts are discussed.
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Anderies JM, Kaper HG, Shuckburgh EF, Zagaris A. Introduction to focus issue: Nonlinear dynamics for planet Earth. CHAOS (WOODBURY, N.Y.) 2015; 25:036201. [PMID: 25833438 DOI: 10.1063/1.4915260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Affiliation(s)
- John M Anderies
- School of Human Evolution and Social Change, and School of Sustainability, Arizona State University, P.O. Box 872402, Tempe, AZ 85287-2402, USA
| | - Hans G Kaper
- Department of Mathematics and Statistics, Georgetown University, Washington, DC 20057, USA
| | | | - Antonios Zagaris
- Department of Applied Mathematics, Universiteit Twente, 7522 NB Enschede, The Netherlands
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