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Malizia V, de Vlas SJ, Roes KCB, Giardina F. Revisiting the impact of Schistosoma mansoni regulating mechanisms on transmission dynamics using SchiSTOP, a novel modelling framework. PLoS Negl Trop Dis 2024; 18:e0012464. [PMID: 39303001 DOI: 10.1371/journal.pntd.0012464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 08/15/2024] [Indexed: 09/22/2024] Open
Abstract
BACKGROUND The transmission cycle of Schistosoma is remarkably complex, including sexual reproduction in human hosts and asexual reproduction in the intermediate host (freshwater snails). Patterns of rapid recrudescence after treatment and stable low transmission are often observed, hampering the achievement of control targets. Current mathematical models commonly assume regulation of transmission to occur at worm level through density-dependent egg production. However, conclusive evidence on this regulating mechanism is weak, especially for S. mansoni. In this study, we explore the interplay of different regulating mechanisms and their ability to explain observed patterns in S. mansoni epidemiology. METHODOLOGY/PRINCIPAL FINDINGS We developed SchiSTOP: a hybrid stochastic agent-based and deterministic modelling framework for S. mansoni transmission in an age-structured human population. We implemented different models with regulating mechanisms at: i) worm-level (density-dependent egg production), ii) human-level (anti-reinfection immunity), and iii) snail-level (density-dependent snail dynamics). Additionally, we considered two functional choices for the age-specific relative exposure to infection. We assessed the ability of each model to reproduce observed epidemiological patterns pre- and post-control, and compared successful models in their predictions of the impact of school-based and community-wide treatment. Simulations confirmed that assuming at least one regulating mechanism is required to reproduce a stable endemic equilibrium. Snail-level regulation was necessary to explain stable low transmission, while models combining snail- and human-level regulation with an age-exposure function informed with water contact data were successful in reproducing a rapid rebound after treatment. However, the predicted probability of reaching the control targets varied largely across models. CONCLUSIONS/SIGNIFICANCE The choice of regulating mechanisms in schistosomiasis modelling largely determines the expected impact of control interventions. Overall, this work suggests that reaching the control targets solely through mass drug administration may be more challenging than currently thought. We highlight the importance of regulating mechanisms to be included in transmission models used for policy.
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Affiliation(s)
- Veronica Malizia
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Radboud University Medical Center, Department IQ Health, Biostatistics Research Group, Nijmegen, The Netherlands
| | - Sake J de Vlas
- Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Kit C B Roes
- Radboud University Medical Center, Department IQ Health, Biostatistics Research Group, Nijmegen, The Netherlands
| | - Federica Giardina
- Radboud University Medical Center, Department IQ Health, Biostatistics Research Group, Nijmegen, The Netherlands
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2
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Graham M, Ayabina D, Lucas TC, Collyer BS, Medley GF, Hollingsworth TD, Toor J. SCHISTOX: An individual based model for the epidemiology and control of schistosomiasis. Infect Dis Model 2021; 6:438-447. [PMID: 33665519 PMCID: PMC7897994 DOI: 10.1016/j.idm.2021.01.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 01/21/2021] [Indexed: 02/08/2023] Open
Abstract
A stochastic individual based model, SCHISTOX, has been developed for the study of schistosome transmission dynamics and the impact of control by mass drug administration. More novel aspects that can be investigated include individual level adherence and access to treatment, multiple communities, human sex population dynamics, and implementation of a potential vaccine. Many of the model parameters have been estimated within previous studies and have been shown to vary between communities, such as the age-specific contact rates governing the age profiles of infection. However, uncertainty remains as there are wide ranges for certain parameter values and a few remain relatively unknown. We analyse the model dynamics by parameterizing it with published parameter values. We also discuss the development of SCHISTOX in the form of a publicly available open-source GitHub repository. The next key development stage involves validating the model by calibrating to epidemiological data.
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Affiliation(s)
- Matthew Graham
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.,Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Diepreye Ayabina
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Tim Cd Lucas
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.,Centre for Environment and Health, Imperial College London, London, United Kingdom
| | - Benjamin S Collyer
- Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, St Mary's Campus, Imperial College London, United Kingdom
| | - Graham F Medley
- Centre for Mathematical Modelling of Infectious Disease, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - T Deirdre Hollingsworth
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
| | - Jaspreet Toor
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.,MRC Centre for Global Infectious Disease Analysis; and the Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA), School of Public Health, Imperial College London, London, W2 1PG, United Kingdom
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3
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Borlase A, Webster JP, Rudge JW. Opportunities and challenges for modelling epidemiological and evolutionary dynamics in a multihost, multiparasite system: Zoonotic hybrid schistosomiasis in West Africa. Evol Appl 2018; 11:501-515. [PMID: 29636802 PMCID: PMC5891036 DOI: 10.1111/eva.12529] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 08/01/2017] [Indexed: 01/01/2023] Open
Abstract
Multihost multiparasite systems are evolutionarily and ecologically dynamic, which presents substantial trans-disciplinary challenges for elucidating their epidemiology and designing appropriate control. Evidence for hybridizations and introgressions between parasite species is gathering, in part in line with improvements in molecular diagnostics and genome sequencing. One major system where this is becoming apparent is within the Genus Schistosoma, where schistosomiasis represents a disease of considerable medical and veterinary importance, the greatest burden of which occurs in sub-Saharan Africa. Interspecific hybridizations and introgressions bring an increased level of complexity over and above that already inherent within multihost, multiparasite systems, also representing an additional source of genetic variation that can drive evolution. This has the potential for profound implications for the control of parasitic diseases, including, but not exclusive to, widening host range, increased transmission potential and altered responses to drug therapy. Here, we present the challenging case example of haematobium group Schistosoma spp. hybrids in West Africa, a system involving multiple interacting parasites and multiple definitive hosts, in a region where zoonotic reservoirs of schistosomiasis were not previously considered to be of importance. We consider how existing mathematical model frameworks for schistosome transmission could be expanded and adapted to zoonotic hybrid systems, exploring how such model frameworks can utilize molecular and epidemiological data, as well as the complexities and challenges this presents. We also highlight the opportunities and value such mathematical models could bring to this and a range of similar multihost, multi and cross-hybridizing parasites systems in our changing world.
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Affiliation(s)
- Anna Borlase
- Department of Pathobiology and Population SciencesCentre for Emerging, Endemic and Exotic DiseasesRoyal Veterinary CollegeUniversity of LondonLondonUK
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
| | - Joanne P. Webster
- Department of Pathobiology and Population SciencesCentre for Emerging, Endemic and Exotic DiseasesRoyal Veterinary CollegeUniversity of LondonLondonUK
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
| | - James W. Rudge
- Department of Infectious Disease EpidemiologyLondon Centre for Neglected Tropical Disease ResearchSchool of Public HealthImperial College LondonLondonUK
- Communicable Diseases Policy Research GroupLondon School of Hygiene and Tropical MedicineLondonUK
- Faculty of Public HealthMahidol UniversityBangkokThailand
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Perez-Saez J, Mari L, Bertuzzo E, Casagrandi R, Sokolow SH, De Leo GA, Mande T, Ceperley N, Froehlich JM, Sou M, Karambiri H, Yacouba H, Maiga A, Gatto M, Rinaldo A. A Theoretical Analysis of the Geography of Schistosomiasis in Burkina Faso Highlights the Roles of Human Mobility and Water Resources Development in Disease Transmission. PLoS Negl Trop Dis 2015; 9:e0004127. [PMID: 26513655 PMCID: PMC4625963 DOI: 10.1371/journal.pntd.0004127] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 09/08/2015] [Indexed: 12/28/2022] Open
Abstract
We study the geography of schistosomiasis across Burkina Faso by means of a spatially explicit model of water-based disease dynamics. The model quantitatively addresses the geographic stratification of disease burden in a novel framework by explicitly accounting for drivers and controls of the disease, including spatial information on the distributions of population and infrastructure, jointly with a general description of human mobility and climatic/ecological drivers. Spatial patterns of disease are analysed by the extraction and the mapping of suitable eigenvectors of the Jacobian matrix subsuming the stability of the disease-free equilibrium. The relevance of the work lies in the novel mapping of disease burden, a byproduct of the parametrization induced by regional upscaling, by model-guided field validations and in the predictive scenarios allowed by exploiting the range of possible parameters and processes. Human mobility is found to be a primary control at regional scales both for pathogen invasion success and the overall distribution of disease burden. The effects of water resources development highlighted by systematic reviews are accounted for by the average distances of human settlements from water bodies that are habitats for the parasite's intermediate host. Our results confirm the empirical findings about the role of water resources development on disease spread into regions previously nearly disease-free also by inspection of empirical prevalence patterns. We conclude that while the model still needs refinements based on field and epidemiological evidence, the proposed framework provides a powerful tool for large-scale public health planning and schistosomiasis management.
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Affiliation(s)
- Javier Perez-Saez
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lorenzo Mari
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Enrico Bertuzzo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Renato Casagrandi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Susanne H. Sokolow
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
- Marine Science Institute, University of California Santa Barbara, California, United States of America
| | - Giulio A. De Leo
- Hopkins Marine Station, Stanford University, Pacific Grove, California, United States of America
- Woods Institute for the Environment, Stanford University, California, United States of America
| | - Theophile Mande
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Natalie Ceperley
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean-Marc Froehlich
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mariam Sou
- Institute International d’Ingénierie de l’Eau et de l’Environment, Ouagadougou, Burkina Faso
| | - Harouna Karambiri
- Institute International d’Ingénierie de l’Eau et de l’Environment, Ouagadougou, Burkina Faso
| | - Hamma Yacouba
- Institute International d’Ingénierie de l’Eau et de l’Environment, Ouagadougou, Burkina Faso
| | - Amadou Maiga
- Institute International d’Ingénierie de l’Eau et de l’Environment, Ouagadougou, Burkina Faso
| | - Marino Gatto
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy
| | - Andrea Rinaldo
- Laboratory of Ecohydrology, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Dipartimento ICEA, Università di Padova, Padova, Italy
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Xu JF, Lv S, Wang QY, Qian MB, Liu Q, Bergquist R, Zhou XN. Schistosomiasis japonica: modelling as a tool to explore transmission patterns. Acta Trop 2015; 141:213-22. [PMID: 25004441 DOI: 10.1016/j.actatropica.2014.06.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2013] [Revised: 06/22/2014] [Accepted: 06/27/2014] [Indexed: 11/26/2022]
Abstract
Modelling is an important tool for the exploration of Schistosoma japonicum transmission patterns. It provides a general theoretical framework for decision-makers and lends itself specifically to assessing the progress of the national control programme by following the outcome of surveys. The challenge of keeping up with the many changes of social, ecological and environmental factors involved in control activities is greatly facilitated by modelling that can also indicate which activities are critical and which are less important. This review examines the application of modelling tools in the epidemiological study of schistosomiasis japonica during the last 20 years and explores the application of enhanced models for surveillance and response. Updated and timely information for decision-makers in the national elimination programme is provided but, in spite of the new modelling techniques introduced, many questions remain. Issues on application of modelling are discussed with the view to improve the current situation with respect to schistosomiasis japonica.
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Mellor J, Abebe L, Ehdaie B, Dillingham R, Smith J. Modeling the sustainability of a ceramic water filter intervention. WATER RESEARCH 2014; 49:286-99. [PMID: 24355289 PMCID: PMC3924855 DOI: 10.1016/j.watres.2013.11.035] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2013] [Revised: 11/21/2013] [Accepted: 11/22/2013] [Indexed: 05/04/2023]
Abstract
Ceramic water filters (CWFs) are a point-of-use water treatment technology that has shown promise in preventing early childhood diarrhea (ECD) in resource-limited settings. Despite this promise, some researchers have questioned their ability to reduce ECD incidences over the long term since most effectiveness trials conducted to date are less than one year in duration limiting their ability to assess long-term sustainability factors. Most trials also suffer from lack of blinding making them potentially biased. This study uses an agent-based model (ABM) to explore factors related to the long-term sustainability of CWFs in preventing ECD and was based on a three year longitudinal field study. Factors such as filter user compliance, microbial removal effectiveness, filter cleaning and compliance declines were explored. Modeled results indicate that broadly defined human behaviors like compliance and declining microbial effectiveness due to improper maintenance are primary drivers of the outcome metrics of household drinking water quality and ECD rates. The model predicts that a ceramic filter intervention can reduce ECD incidence amongst under two year old children by 41.3%. However, after three years, the average filter is almost entirely ineffective at reducing ECD incidence due to declining filter microbial removal effectiveness resulting from improper maintenance. The model predicts very low ECD rates are possible if compliance rates are 80-90%, filter log reduction efficiency is 3 or greater and there are minimal long-term compliance declines. Cleaning filters at least once every 4 months makes it more likely to achieve very low ECD rates as does the availability of replacement filters for purchase. These results help to understand the heterogeneity seen in previous intervention-control trials and reemphasize the need for researchers to accurately measure confounding variables and ensure that field trials are at least 2-3 years in duration. In summary, the CWF can be a highly effective tool in the fight against ECD, but every effort should be made by implementing agencies to ensure consistent use and maintenance.
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Affiliation(s)
- Jonathan Mellor
- Department of Civil and Environmental Engineering, University of Virginia, Thornton Hall, P.O. Box 400742, Charlottesville, VA 22904, USA.
| | - Lydia Abebe
- Department of Civil and Environmental Engineering, University of Virginia, Thornton Hall, P.O. Box 400742, Charlottesville, VA 22904, USA
| | - Beeta Ehdaie
- Department of Civil and Environmental Engineering, University of Virginia, Thornton Hall, P.O. Box 400742, Charlottesville, VA 22904, USA
| | - Rebecca Dillingham
- The Center for Global Health, Carter-Harrison Research Building, MR-6, Room 2526, 345 Crispell Drive, P.O. Box 801379, University of Virginia Health System, Charlottesville, VA 22908-1379, USA
| | - James Smith
- Department of Civil and Environmental Engineering, University of Virginia, Thornton Hall, P.O. Box 400742, Charlottesville, VA 22904, USA
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Xu JF, Xu J, Li SZ, Jia TW, Huang XB, Zhang HM, Chen M, Yang GJ, Gao SJ, Wang QY, Zhou XN. Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model. PLoS Negl Trop Dis 2013; 7:e2123. [PMID: 23556015 PMCID: PMC3605232 DOI: 10.1371/journal.pntd.0002123] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Accepted: 02/04/2013] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. METHODOLOGY/PRINCIPAL FINDINGS We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. CONCLUSION/SIGNIFICANCE Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control.
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Affiliation(s)
- Jun-Fang Xu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China
- WHO Collaborating Center for Malaria, Schistosomiasis and Filariasis; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China
| | - Jing Xu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China
- WHO Collaborating Center for Malaria, Schistosomiasis and Filariasis; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China
| | - Shi-Zhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China
- WHO Collaborating Center for Malaria, Schistosomiasis and Filariasis; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China
| | - Tia-Wu Jia
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China
- WHO Collaborating Center for Malaria, Schistosomiasis and Filariasis; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China
| | - Xi-Bao Huang
- Hubei Center for Disease Control and Prevention, Wuhan, Hubei, People's Republic of China
| | - Hua-Ming Zhang
- Jiangling Institute of Schistosomiasis Control, Jiangling County, Hubei, People's Republic of China
| | - Mei Chen
- Jiangling Institute of Schistosomiasis Control, Jiangling County, Hubei, People's Republic of China
| | - Guo-Jing Yang
- School of Public Health and Primary Care, The Jockey Club Chinese University of Hong Kong, Shatin, Hong Kong
- Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu, People's Republic of China
| | - Shu-Jing Gao
- Normal University of Gannan, Ganzhou, Jiangxi, People's Republic of China
| | - Qing-Yun Wang
- Normal University of Gannan, Ganzhou, Jiangxi, People's Republic of China
| | - Xiao-Nong Zhou
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People's Republic of China
- WHO Collaborating Center for Malaria, Schistosomiasis and Filariasis; Laboratory of Parasite and Vector Biology, Ministry of Health, Shanghai, People's Republic of China
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Bergquist R. New tools for epidemiology: a space odyssey. Mem Inst Oswaldo Cruz 2011; 106:892-900. [DOI: 10.1590/s0074-02762011000700016] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2011] [Accepted: 05/20/2011] [Indexed: 11/22/2022] Open
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