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Guo B, Takala-Harrison S, O’Connor TD. Benchmarking and Optimization of Methods for the Detection of Identity-By-Descent in High-Recombining Plasmodium falciparum Genomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.04.592538. [PMID: 38746392 PMCID: PMC11092787 DOI: 10.1101/2024.05.04.592538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Genomic surveillance is crucial for identifying at-risk populations for targeted malaria control and elimination. Identity-by-descent (IBD) is increasingly being used in Plasmodium population genomics to estimate genetic relatedness, effective population size (N e ), population structure, and signals of positive selection. Despite its potential, a thorough evaluation of IBD segment detection tools for species with high recombination rates, such as P. falciparum, remains absent. Here, we perform comprehensive benchmarking of IBD callers - probabilistic (hmmIBD, isoRelate), identity-by-state-based (hap-IBD, phased IBD) and others (Refined IBD) - using population genetic simulations tailored for high recombination, and IBD quality metrics at both the IBD segment level and the IBD-based downstream inference level. Our results demonstrate that low marker density per genetic unit, related to high recombination relative to mutation, significantly compromises the accuracy of detected IBD segments. In genomes with high recombination rates resembling P. falciparum, most IBD callers exhibit high false negative rates for shorter IBD segments, which can be partially mitigated through optimization of IBD caller parameters, especially those related to marker density. Notably, IBD detected with optimized parameters allows for more accurate capture of selection signals and population structure; IBD-based N e inference is very sensitive to IBD detection errors, with IBD called from hmmIBD uniquely providing less biased estimates of N e in this context. Validation with empirical data from the MalariaGEN Pf 7 database, representing different transmission settings, corroborates these findings. We conclude that context-specific evaluation and parameter optimization are essential for accurate IBD detection in high-recombining species and recommend hmmIBD for quality-sensitive analysis, such as estimation of N e in these species. Our optimization and high-level benchmarking methods not only improve IBD segment detection in high-recombining genomes but also enhance overall genomic analysis, paving the way for more accurate genomic surveillance and targeted intervention strategies for malaria.
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Affiliation(s)
- Bing Guo
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Shannon Takala-Harrison
- Center for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore, MD USA
| | - Timothy D. O’Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
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2
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Fola AA, He Q, Xie S, Thimmapuram J, Bhide KP, Dorman J, Ciubotariu II, Mwenda MC, Mambwe B, Mulube C, Hawela M, Norris DE, Moss WJ, Bridges DJ, Carpi G. Genomics reveals heterogeneous Plasmodium falciparum transmission and selection signals in Zambia. COMMUNICATIONS MEDICINE 2024; 4:67. [PMID: 38582941 PMCID: PMC10998850 DOI: 10.1038/s43856-024-00498-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 03/28/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Genomic surveillance is crucial for monitoring malaria transmission and understanding parasite adaptation to interventions. Zambia lacks prior nationwide efforts in malaria genomic surveillance among African countries. METHODS We conducted genomic surveillance of Plasmodium falciparum parasites from the 2018 Malaria Indicator Survey in Zambia, a nationally representative household survey of children under five years of age. We whole-genome sequenced and analyzed 241 P. falciparum genomes from regions with varying levels of malaria transmission across Zambia and estimated genetic metrics that are informative about transmission intensity, genetic relatedness between parasites, and selection. RESULTS We provide genomic evidence of widespread within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in Zambia. Our analysis reveals country-level clustering of parasites from Zambia and neighboring regions, with distinct separation in West Africa. Within Zambia, identity by descent (IBD) relatedness analysis uncovers local spatial clustering and rare cases of long-distance sharing of closely related parasite pairs. Genomic regions with large shared IBD segments and strong positive selection signatures implicate genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Furthermore, differences in selection signatures, including drug resistance loci, are observed between eastern and western Zambian parasite populations, suggesting variable transmission intensity and ongoing drug pressure. CONCLUSIONS Our findings enhance our understanding of nationwide P. falciparum transmission in Zambia, establishing a baseline for analyzing parasite genetic metrics as they vary over time and space. These insights highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.
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Affiliation(s)
- Abebe A Fola
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Department of Pathology and Laboratory Medicine, Brown University, Providence, RI, USA
| | - Qixin He
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Shaojun Xie
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Jyothi Thimmapuram
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Ketaki P Bhide
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Jack Dorman
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Ilinca I Ciubotariu
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Mulenga C Mwenda
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Brenda Mambwe
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Conceptor Mulube
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Moonga Hawela
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Douglas E Norris
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - William J Moss
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Giovanna Carpi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Purdue Institute for Inflammation, Immunology, & Infectious Disease, Purdue University, West Lafayette, IN, USA.
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3
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Fola AA, He Q, Xie S, Thimmapuram J, Bhide KP, Dorman J, Ciubotariu II, Mwenda MC, Mambwe B, Mulube C, Hawela M, Norris DE, Moss WJ, Bridges DJ, Carpi G. Genomics reveals heterogeneous Plasmodium falciparum transmission and population differentiation in Zambia and bordering countries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.09.24302570. [PMID: 38370674 PMCID: PMC10871455 DOI: 10.1101/2024.02.09.24302570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Genomic surveillance plays a critical role in monitoring malaria transmission and understanding how the parasite adapts in response to interventions. We conducted genomic surveillance of malaria by sequencing 241 Plasmodium falciparum genomes from regions with varying levels of malaria transmission across Zambia. We found genomic evidence of high levels of within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in the country. We identified country-level clustering of parasites from Zambia and neighboring countries, and distinct clustering of parasites from West Africa. Within Zambia, our identity by descent (IBD) relatedness analysis uncovered spatial clustering of closely related parasite pairs at the local level and rare cases of long-distance sharing. Genomic regions with large shared IBD segments and strong positive selection signatures identified genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Together, our findings enhance our understanding of P. falciparum transmission nationwide in Zambia and highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.
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Affiliation(s)
- Abebe A. Fola
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Qixin He
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Shaojun Xie
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Jyothi Thimmapuram
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Ketaki P. Bhide
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Jack Dorman
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | | | - Brenda Mambwe
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Conceptor Mulube
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Moonga Hawela
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Douglas E. Norris
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - William J. Moss
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Giovanna Carpi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Purdue Institute for Inflammation, Immunology, & Infectious Disease, Purdue University, West Lafayette, IN, USA
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Fornace KM, Topazian HM, Routledge I, Asyraf S, Jelip J, Lindblade KA, Jeffree MS, Ruiz Cuenca P, Bhatt S, Ahmed K, Ghani AC, Drakeley C. No evidence of sustained nonzoonotic Plasmodium knowlesi transmission in Malaysia from modelling malaria case data. Nat Commun 2023; 14:2945. [PMID: 37263994 PMCID: PMC10235043 DOI: 10.1038/s41467-023-38476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 05/02/2023] [Indexed: 06/03/2023] Open
Abstract
Reported incidence of the zoonotic malaria Plasmodium knowlesi has markedly increased across Southeast Asia and threatens malaria elimination. Nonzoonotic transmission of P. knowlesi has been experimentally demonstrated, but it remains unknown whether nonzoonotic transmission is contributing to increases in P. knowlesi cases. Here, we adapt model-based inference methods to estimate RC, individual case reproductive numbers, for P. knowlesi, P. falciparum and P. vivax human cases in Malaysia from 2012-2020 (n = 32,635). Best fitting models for P. knowlesi showed subcritical transmission (RC < 1) consistent with a large reservoir of unobserved infection sources, indicating P. knowlesi remains a primarily zoonotic infection. In contrast, sustained transmission (RC > 1) was estimated historically for P. falciparum and P. vivax, with declines in RC estimates observed over time consistent with local elimination. Together, this suggests sustained nonzoonotic P. knowlesi transmission is highly unlikely and that new approaches are urgently needed to control spillover risks.
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Affiliation(s)
- Kimberly M Fornace
- School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK.
- Saw Swee Hock School of Public Health, National University of, Singapore, Singapore.
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK.
| | - Hillary M Topazian
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Isobel Routledge
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- University of California, San Francisco, San Francisco, USA
| | - Syafie Asyraf
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
| | - Jenarun Jelip
- Vector-borne Disease Control Division, Ministry of Health Malaysia, Putrajaya, Malaysia
| | - Kim A Lindblade
- Global Malaria Programme, World Health Organization, Geneva, Switzerland
| | | | - Pablo Ruiz Cuenca
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Kamruddin Ahmed
- Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
| | - Chris Drakeley
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
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5
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Measurably recombining malaria parasites. Trends Parasitol 2023; 39:17-25. [PMID: 36435688 PMCID: PMC9893849 DOI: 10.1016/j.pt.2022.11.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/02/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022]
Abstract
Genomic epidemiology has guided research and policy for various viral pathogens and there has been a parallel effort towards using genomic epidemiology to combat diseases that are caused by eukaryotic pathogens, such as the malaria parasite. However, the central concept of viral genomic epidemiology, namely that of measurably mutating pathogens, does not apply easily to sexually recombining parasites. Here we introduce the related but different concept of measurably recombining malaria parasites to promote convergence around a unifying theoretical framework for malaria genomic epidemiology. Akin to viral phylodynamics, we anticipate that an inferential framework developed around recombination will help guide practical research and thus realize the full public health potential of genomic epidemiology for malaria parasites and other sexually recombining pathogens.
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Chua TH, Manin BO, Fornace K. Life table analysis of Anopheles balabacensis, the primary vector of Plasmodium knowlesi in Sabah, Malaysia. Parasit Vectors 2022; 15:442. [PMID: 36434625 PMCID: PMC9701013 DOI: 10.1186/s13071-022-05552-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 10/07/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Plasmodium knowlesi has become a major public health concern in Sabah, Malaysian Borneo, where it is now the only cause of indigenous malaria. The importance of P. knowlesi has spurred on a series of studies on this parasite, as well as on the biology and ecology of its principal vector, Anopheles balabacensis. However, there remain critical knowledge gaps on the biology of An. balabacensis, such as life history data and life table parameters. To fill these gaps, we conducted a life table study of An. balabacensis in the laboratory. Characterising vector life cycles and survival rates can inform more accurate estimations of the serial interval, the time between two linked cases, which is crucial to understanding and monitoring potentially changing transmission patterns. METHODS Individuals of An. balabacensis were collected in the field in Ranau district, Sabah to establish a laboratory colony. Induced mating was used, and the life history parameters of the progeny were recorded. The age-stage, two-sex life table approach was used in the analysis. The culture conditions in the laboratory were 9 h light:15 h dark, mean temperature 25.7 °C ± 0.05 and relative humidity 75.8% ± 0.31. RESULTS The eggs hatched within 2 days, and the larval stage lasted for 10.5 days in total, with duration of instar stages I, II, III and IV of 2.3, 3.7, 2.3, 2.2 days, respectively. The maximum total fecundity was 729 for one particular female, while the maximum female age-specific mean fecundity (mx) was 142 at age 59 days. The gross reproductive rate or number of offspring per individual was about 102. On average, each female laid 1.81 ± 0.19 (range 1-7) batches of eggs, with 63% of the females producing only one batch; only one female laid six batches, while one other laid seven. Each batch comprised 159 ± 17.1 eggs (range 5-224) and the female ratio of offspring was 0.28 ± 0.06. The intrinsic rate of increase, finite rate of increase, net reproductive rate, mean generation time and doubling time were, respectively, 0.12 ± 0.01 day-1, 1.12 ± 0.01 day-1, 46.2 ± 14.97, 33.02 ± 1.85 and 5.97 days. CONCLUSIONS Both the net reproductive rate and intrinsic rate of increase of An. balabacensis are lower than those of other species in published studies. Our results can be used to improve models of P. knowlesi transmission and to set a baseline for assessing the impacts of environmental change on malaria dynamics. Furthermore, incorporating these population parameters of An. balabacensis into spatial and temporal models on the transmission of P. knowlesi would provide better insight and increase the accuracy of epidemiological forecasting.
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Affiliation(s)
- Tock H. Chua
- grid.265727.30000 0001 0417 0814Department of Pathology and Microbiology, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Sabah, Malaysia
| | - Benny Obrain Manin
- grid.265727.30000 0001 0417 0814Department of Pathology and Microbiology, Faculty of Medicine and Health Sciences, Universiti Malaysia Sabah, Sabah, Malaysia
| | - Kimberly Fornace
- grid.8756.c0000 0001 2193 314XSchool of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, Glasgow, UK ,grid.4280.e0000 0001 2180 6431Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
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7
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Jorge DCP, Oliveira JF, Miranda JGV, Andrade RFS, Pinho STR. Estimating the effective reproduction number for heterogeneous models using incidence data. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220005. [PMID: 36133147 DOI: 10.6084/m9.figshare.c.6167795] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/16/2022] [Indexed: 05/25/2023]
Abstract
The effective reproduction number, R ( t ) , plays a key role in the study of infectious diseases, indicating the current average number of new infections caused by an infected individual in an epidemic process. Estimation methods for the time evolution of R ( t ) , using incidence data, rely on the generation interval distribution, g(τ), which is usually obtained from empirical data or theoretical studies using simple epidemic models. However, for systems that present heterogeneity, either on the host population or in the expression of the disease, there is a lack of data and of a suitable general methodology to obtain g(τ). In this work, we use mathematical models to bridge this gap. We present a general methodology for obtaining explicit expressions of the reproduction numbers and the generation interval distributions, within and between model sub-compartments provided by an arbitrary compartmental model. Additionally, we present the appropriate expressions to evaluate those reproduction numbers using incidence data. To highlight the relevance of such methodology, we apply it to the spread of COVID-19 in municipalities of the state of Rio de Janeiro, Brazil. Using two meta-population models, we estimate the reproduction numbers and the contributions of each municipality in the generation of cases in all others.
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Affiliation(s)
- D C P Jorge
- Instituto de Física Teórica, Universidade Estadual Paulista-UNESP, R. Dr. Teobaldo Ferraz 271, São Paulo 01140-070, Brazil
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - J F Oliveira
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
| | - J G V Miranda
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - R F S Andrade
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - S T R Pinho
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
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8
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Jorge DCP, Oliveira JF, Miranda JGV, Andrade RFS, Pinho STR. Estimating the effective reproduction number for heterogeneous models using incidence data. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220005. [PMID: 36133147 DOI: 10.5281/zenodo.5822669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/16/2022] [Indexed: 05/25/2023]
Abstract
The effective reproduction number, R ( t ) , plays a key role in the study of infectious diseases, indicating the current average number of new infections caused by an infected individual in an epidemic process. Estimation methods for the time evolution of R ( t ) , using incidence data, rely on the generation interval distribution, g(τ), which is usually obtained from empirical data or theoretical studies using simple epidemic models. However, for systems that present heterogeneity, either on the host population or in the expression of the disease, there is a lack of data and of a suitable general methodology to obtain g(τ). In this work, we use mathematical models to bridge this gap. We present a general methodology for obtaining explicit expressions of the reproduction numbers and the generation interval distributions, within and between model sub-compartments provided by an arbitrary compartmental model. Additionally, we present the appropriate expressions to evaluate those reproduction numbers using incidence data. To highlight the relevance of such methodology, we apply it to the spread of COVID-19 in municipalities of the state of Rio de Janeiro, Brazil. Using two meta-population models, we estimate the reproduction numbers and the contributions of each municipality in the generation of cases in all others.
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Affiliation(s)
- D C P Jorge
- Instituto de Física Teórica, Universidade Estadual Paulista-UNESP, R. Dr. Teobaldo Ferraz 271, São Paulo 01140-070, Brazil
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - J F Oliveira
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
| | - J G V Miranda
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - R F S Andrade
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - S T R Pinho
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
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9
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Jorge DCP, Oliveira JF, Miranda JGV, Andrade RFS, Pinho STR. Estimating the effective reproduction number for heterogeneous models using incidence data. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220005. [PMID: 36133147 PMCID: PMC9449464 DOI: 10.1098/rsos.220005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/16/2022] [Indexed: 05/10/2023]
Abstract
The effective reproduction number, R ( t ) , plays a key role in the study of infectious diseases, indicating the current average number of new infections caused by an infected individual in an epidemic process. Estimation methods for the time evolution of R ( t ) , using incidence data, rely on the generation interval distribution, g(τ), which is usually obtained from empirical data or theoretical studies using simple epidemic models. However, for systems that present heterogeneity, either on the host population or in the expression of the disease, there is a lack of data and of a suitable general methodology to obtain g(τ). In this work, we use mathematical models to bridge this gap. We present a general methodology for obtaining explicit expressions of the reproduction numbers and the generation interval distributions, within and between model sub-compartments provided by an arbitrary compartmental model. Additionally, we present the appropriate expressions to evaluate those reproduction numbers using incidence data. To highlight the relevance of such methodology, we apply it to the spread of COVID-19 in municipalities of the state of Rio de Janeiro, Brazil. Using two meta-population models, we estimate the reproduction numbers and the contributions of each municipality in the generation of cases in all others.
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Affiliation(s)
- D. C. P. Jorge
- Instituto de Física Teórica, Universidade Estadual Paulista—UNESP, R. Dr. Teobaldo Ferraz 271, São Paulo 01140-070, Brazil
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - J. F. Oliveira
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
| | - J. G. V. Miranda
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - R. F. S. Andrade
- Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | - S. T. R. Pinho
- Instituto de Física, Universidade Federal da Bahia, Salvador, Bahia, Brazil
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10
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Park SW, Bolker BM, Funk S, Metcalf CJE, Weitz JS, Grenfell BT, Dushoff J. The importance of the generation interval in investigating dynamics and control of new SARS-CoV-2 variants. J R Soc Interface 2022; 19:20220173. [PMID: 35702867 PMCID: PMC9198506 DOI: 10.1098/rsif.2022.0173] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 05/19/2022] [Indexed: 12/19/2022] Open
Abstract
Inferring the relative strength (i.e. the ratio of reproduction numbers) and relative speed (i.e. the difference between growth rates) of new SARS-CoV-2 variants is critical to predicting and controlling the course of the current pandemic. Analyses of new variants have primarily focused on characterizing changes in the proportion of new variants, implicitly or explicitly assuming that the relative speed remains fixed over the course of an invasion. We use a generation-interval-based framework to challenge this assumption and illustrate how relative strength and speed change over time under two idealized interventions: a constant-strength intervention like idealized vaccination or social distancing, which reduces transmission rates by a constant proportion, and a constant-speed intervention like idealized contact tracing, which isolates infected individuals at a constant rate. In general, constant-strength interventions change the relative speed of a new variant, while constant-speed interventions change its relative strength. Differences in the generation-interval distributions between variants can exaggerate these changes and modify the effectiveness of interventions. Finally, neglecting differences in generation-interval distributions can bias estimates of relative strength.
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Affiliation(s)
- Sang Woo Park
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin M. Bolker
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
| | - Sebastian Funk
- Department for Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
| | - C. Jessica E. Metcalf
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Joshua S. Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
- School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
- Institut de Biologie, École Normale Supérieure, Paris, France
| | - Bryan T. Grenfell
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
- Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, Ontario, Canada
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
- M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, Ontario, Canada
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11
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Yukich JO, Lindblade K, Kolaczinski J. Receptivity to malaria: meaning and measurement. Malar J 2022; 21:145. [PMID: 35527264 PMCID: PMC9080212 DOI: 10.1186/s12936-022-04155-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 04/07/2022] [Indexed: 01/13/2023] Open
Abstract
"Receptivity" to malaria is a construct developed during the Global Malaria Eradication Programme (GMEP) era. It has been defined in varied ways and no consistent, quantitative definition has emerged over the intervening decades. Despite the lack of consistency in defining this construct, the idea that some areas are more likely to sustain malaria transmission than others has remained important in decision-making in malaria control, planning for malaria elimination and guiding activities during the prevention of re-establishment (POR) period. This manuscript examines current advances in methods of measurement. In the context of a decades long decline in global malaria transmission and an increasing number of countries seeking to eliminate malaria, understanding and measuring malaria receptivity has acquired new relevance.
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Affiliation(s)
- Joshua O. Yukich
- grid.265219.b0000 0001 2217 8588Department of Tropical Medicine, Center for Applied Malaria Research and Evaluation, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA USA
| | - Kim Lindblade
- grid.3575.40000000121633745Global Malaria Programme, World Health Organization, Geneva, CH USA
| | - Jan Kolaczinski
- grid.3575.40000000121633745Global Malaria Programme, World Health Organization, Geneva, CH USA
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12
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Huber JH, Hsiang MS, Dlamini N, Murphy M, Vilakati S, Nhlabathi N, Lerch A, Nielsen R, Ntshalintshali N, Greenhouse B, Perkins TA. Inferring person-to-person networks of Plasmodium falciparum transmission: are analyses of routine surveillance data up to the task? Malar J 2022; 21:58. [PMID: 35189905 PMCID: PMC8860266 DOI: 10.1186/s12936-022-04072-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 01/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Inference of person-to-person transmission networks using surveillance data is increasingly used to estimate spatiotemporal patterns of pathogen transmission. Several data types can be used to inform transmission network inferences, yet the sensitivity of those inferences to different data types is not routinely evaluated. METHODS The influence of different combinations of spatial, temporal, and travel-history data on transmission network inferences for Plasmodium falciparum malaria were evaluated. RESULTS The information content of these data types may be limited for inferring person-to-person transmission networks and may lead to an overestimate of transmission. Only when outbreaks were temporally focal or travel histories were accurate was the algorithm able to accurately estimate the reproduction number under control, Rc. Applying this approach to data from Eswatini indicated that inferences of Rc and spatiotemporal patterns therein depend upon the choice of data types and assumptions about travel-history data. CONCLUSIONS These results suggest that transmission network inferences made with routine malaria surveillance data should be interpreted with caution.
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Affiliation(s)
- John H Huber
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
| | - Michelle S Hsiang
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, USA.,Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, CA, USA.,Department of Pediatrics, University of California, San Francisco,, CA, USA
| | - Nomcebo Dlamini
- National Malaria Elimination Programme, Ministry of Health, Manzini, Eswatini
| | - Maxwell Murphy
- Department of Medicine, University of California, San Francisco, CA, USA
| | | | - Nomcebo Nhlabathi
- National Malaria Elimination Programme, Ministry of Health, Manzini, Eswatini
| | - Anita Lerch
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Rasmus Nielsen
- Department of Integrative Biology and Statistics, University of California, Berkeley, CA, USA
| | | | - Bryan Greenhouse
- Department of Medicine, University of California, San Francisco, CA, USA.,Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - T Alex Perkins
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA.
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13
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Markwalter CF, Menya D, Wesolowski A, Esimit D, Lokoel G, Kipkoech J, Freedman E, Sumner KM, Abel L, Ambani G, Meredith HR, Taylor SM, Obala AA, O'Meara WP. Plasmodium falciparum importation does not sustain malaria transmission in a semi-arid region of Kenya. PLOS GLOBAL PUBLIC HEALTH 2022; 2:e0000807. [PMID: 36962553 PMCID: PMC10021402 DOI: 10.1371/journal.pgph.0000807] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/17/2022] [Indexed: 11/19/2022]
Abstract
Human movement impacts the spread and transmission of infectious diseases. Recently, a large reservoir of Plasmodium falciparum malaria was identified in a semi-arid region of northwestern Kenya historically considered unsuitable for malaria transmission. Understanding the sources and patterns of transmission attributable to human movement would aid in designing and targeting interventions to decrease the unexpectedly high malaria burden in the region. Toward this goal, polymorphic parasite genes (ama1, csp) in residents and passengers traveling to Central Turkana were genotyped by amplicon deep sequencing. Genotyping and epidemiological data were combined to assess parasite importation. The contribution of travel to malaria transmission was estimated by modelling case reproductive numbers inclusive and exclusive of travelers. P. falciparum was detected in 6.7% (127/1891) of inbound passengers, including new haplotypes which were later detected in locally-transmitted infections. Case reproductive numbers approximated 1 and did not change when travelers were removed from transmission networks, suggesting that transmission is not fueled by travel to the region but locally endemic. Thus, malaria is not only prevalent in Central Turkana but also sustained by local transmission. As such, interrupting importation is unlikely to be an effective malaria control strategy on its own, but targeting interventions locally has the potential to drive down transmission.
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Affiliation(s)
| | - Diana Menya
- School of Public Health, Moi University College of Health Sciences, Eldoret, Kenya
| | - Amy Wesolowski
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
| | - Daniel Esimit
- Department of Health Services and Sanitation, Turkana County, Kenya
| | - Gilchrist Lokoel
- Department of Health Services and Sanitation, Turkana County, Kenya
| | - Joseph Kipkoech
- Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - Elizabeth Freedman
- Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Kelsey M Sumner
- Duke University School of Medicine, Durham, North Carolina, United States of America
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Lucy Abel
- Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - George Ambani
- Academic Model Providing Access to Healthcare, Eldoret, Kenya
| | - Hannah R Meredith
- Duke Global Health Institute, Durham, North Carolina, United States of America
| | - Steve M Taylor
- Duke Global Health Institute, Durham, North Carolina, United States of America
- Duke University School of Medicine, Durham, North Carolina, United States of America
| | - Andrew A Obala
- School of Medicine, Moi University College of Health Sciences, Eldoret, Kenya
| | - Wendy P O'Meara
- Duke Global Health Institute, Durham, North Carolina, United States of America
- Duke University School of Medicine, Durham, North Carolina, United States of America
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14
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Routledge I, Unwin HJT, Bhatt S. Inference of malaria reproduction numbers in three elimination settings by combining temporal data and distance metrics. Sci Rep 2021; 11:14495. [PMID: 34262054 PMCID: PMC8280212 DOI: 10.1038/s41598-021-93238-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 06/11/2021] [Indexed: 11/10/2022] Open
Abstract
Individual-level geographic information about malaria cases, such as the GPS coordinates of residence or health facility, is often collected as part of surveillance in near-elimination settings, but could be more effectively utilised to infer transmission dynamics, in conjunction with additional information such as symptom onset time and genetic distance. However, in the absence of data about the flow of parasites between populations, the spatial scale of malaria transmission is often not clear. As a result, it is important to understand the impact of varying assumptions about the spatial scale of transmission on key metrics of malaria transmission, such as reproduction numbers. We developed a method which allows the flexible integration of distance metrics (such as Euclidian distance, genetic distance or accessibility matrices) with temporal information into a single inference framework to infer malaria reproduction numbers. Twelve scenarios were defined, representing different assumptions about the likelihood of transmission occurring over different geographic distances and likelihood of missing infections (as well as high and low amounts of uncertainty in this estimate). These scenarios were applied to four individual level datasets from malaria eliminating contexts to estimate individual reproduction numbers and how they varied over space and time. Model comparison suggested that including spatial information improved models as measured by second order AIC (ΔAICc), compared to time only results. Across scenarios and across datasets, including spatial information tended to increase the seasonality of temporal patterns in reproduction numbers and reduced noise in the temporal distribution of reproduction numbers. The best performing parameterisations assumed long-range transmission (> 200 km) was possible. Our approach is flexible and provides the potential to incorporate other sources of information which can be converted into distance or adjacency matrices such as travel times or molecular markers.
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15
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Malaria Elimination in Costa Rica: Changes in Treatment and Mass Drug Administration. Microorganisms 2020; 8:microorganisms8070984. [PMID: 32630155 PMCID: PMC7409053 DOI: 10.3390/microorganisms8070984] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/04/2020] [Accepted: 05/11/2020] [Indexed: 12/27/2022] Open
Abstract
Costa Rica is a candidate to eliminate malaria by 2020. The remaining malaria transmission hotspots are located within the Huétar Norte Region (HNR), where 90% of the country's 147 malaria cases have occurred since 2016, following a 33-month period without transmission. Here, we examine changes in transmission with the implementation of a supervised seven-day chloroquine and primaquine treatment (7DCPT). We also evaluate the impact of a focal mass drug administration (MDA) in January 2019 at Boca Arenal, the town in HNR reporting the greatest local transmission. We found that the change to a seven-day treatment protocol, from the prior five-day program, was associated with a 98% reduction in malaria transmission. The MDA helped to reduce transmission, keeping the basic reproduction number, RT, significantly below 1, for at least four months. However, following new imported cases from Nicaragua, autochthonous transmission resumed. Our results highlight the importance of appropriate treatment delivery to reduce malaria transmission, and the challenge that highly mobile populations, if their malaria is not treated, pose to regional elimination efforts in Mesoamerica and México.
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16
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Routledge I, Lai S, Battle KE, Ghani AC, Gomez-Rodriguez M, Gustafson KB, Mishra S, Unwin J, Proctor JL, Tatem AJ, Li Z, Bhatt S. Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach. PLoS Comput Biol 2020; 16:e1007707. [PMID: 32203520 PMCID: PMC7117777 DOI: 10.1371/journal.pcbi.1007707] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 04/02/2020] [Accepted: 02/03/2020] [Indexed: 01/02/2023] Open
Abstract
In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation.
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Affiliation(s)
| | - Shengjie Lai
- University of Southampton, Southampton, United Kingdom
| | | | | | | | - Kyle B. Gustafson
- Institute for Disease Modelling, Bellevue, Washington, United States of America
| | | | | | - Joshua L. Proctor
- Institute for Disease Modelling, Bellevue, Washington, United States of America
| | | | - Zhongjie Li
- Chinese Centers for Disease Control and Prevention, Beijing, China
| | - Samir Bhatt
- Imperial College London, London, United Kingom
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17
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Morgan AP, Brazeau NF, Ngasala B, Mhamilawa LE, Denton M, Msellem M, Morris U, Filer DL, Aydemir O, Bailey JA, Parr JB, Mårtensson A, Bjorkman A, Juliano JJ. Falciparum malaria from coastal Tanzania and Zanzibar remains highly connected despite effective control efforts on the archipelago. Malar J 2020; 19:47. [PMID: 31992305 PMCID: PMC6988337 DOI: 10.1186/s12936-020-3137-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 01/22/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Tanzania's Zanzibar archipelago has made significant gains in malaria control over the last decade and is a target for malaria elimination. Despite consistent implementation of effective tools since 2002, elimination has not been achieved. Importation of parasites from outside of the archipelago is thought to be an important cause of malaria's persistence, but this paradigm has not been studied using modern genetic tools. METHODS Whole-genome sequencing (WGS) was used to investigate the impact of importation, employing population genetic analyses of Plasmodium falciparum isolates from both the archipelago and mainland Tanzania. Ancestry, levels of genetic diversity and differentiation, patterns of relatedness, and patterns of selection between these two populations were assessed by leveraging recent advances in deconvolution of genomes from polyclonal malaria infections. RESULTS Significant decreases in the effective population sizes were inferred in both populations that coincide with a period of decreasing malaria transmission in Tanzania. Identity by descent analysis showed that parasites in the two populations shared long segments of their genomes, on the order of 5 cM, suggesting shared ancestry within the last 10 generations. Even with limited sampling, two of isolates between the mainland and Zanzibar were identified that are related at the expected level of half-siblings, consistent with recent importation. CONCLUSIONS These findings suggest that importation plays an important role for malaria incidence on Zanzibar and demonstrate the value of genomic approaches for identifying corridors of parasite movement to the island.
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Affiliation(s)
- Andrew P Morgan
- Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Nicholas F Brazeau
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Billy Ngasala
- Department of Parasitology and Medical Entomology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
| | - Lwidiko E Mhamilawa
- Department of Parasitology and Medical Entomology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania
- Department of Women's and Children's Health, International Maternal and Child Health (IMCH), Uppsala University, Uppsala, Sweden
| | - Madeline Denton
- Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Mwinyi Msellem
- Training and Research, Mnazi Mmoja Hospital, Zanzibar, Tanzania
| | - Ulrika Morris
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Dayne L Filer
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Ozkan Aydemir
- Department of Laboratory Medicine and Pathology, Brown University, Providence, RI, 02912, USA
| | - Jeffrey A Bailey
- Department of Laboratory Medicine and Pathology, Brown University, Providence, RI, 02912, USA
| | - Jonathan B Parr
- Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Andreas Mårtensson
- Department of Women's and Children's Health, International Maternal and Child Health (IMCH), Uppsala University, Uppsala, Sweden
| | - Anders Bjorkman
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Jonathan J Juliano
- Division of Infectious Diseases, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, 27599, USA.
- Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, 27599, USA.
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18
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Mordecai EA, Caldwell JM, Grossman MK, Lippi CA, Johnson LR, Neira M, Rohr JR, Ryan SJ, Savage V, Shocket MS, Sippy R, Stewart Ibarra AM, Thomas MB, Villena O. Thermal biology of mosquito-borne disease. Ecol Lett 2019; 22:1690-1708. [PMID: 31286630 PMCID: PMC6744319 DOI: 10.1111/ele.13335] [Citation(s) in RCA: 268] [Impact Index Per Article: 53.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 05/22/2019] [Accepted: 06/06/2019] [Indexed: 12/11/2022]
Abstract
Mosquito-borne diseases cause a major burden of disease worldwide. The vital rates of these ectothermic vectors and parasites respond strongly and nonlinearly to temperature and therefore to climate change. Here, we review how trait-based approaches can synthesise and mechanistically predict the temperature dependence of transmission across vectors, pathogens, and environments. We present 11 pathogens transmitted by 15 different mosquito species - including globally important diseases like malaria, dengue, and Zika - synthesised from previously published studies. Transmission varied strongly and unimodally with temperature, peaking at 23-29ºC and declining to zero below 9-23ºC and above 32-38ºC. Different traits restricted transmission at low versus high temperatures, and temperature effects on transmission varied by both mosquito and parasite species. Temperate pathogens exhibit broader thermal ranges and cooler thermal minima and optima than tropical pathogens. Among tropical pathogens, malaria and Ross River virus had lower thermal optima (25-26ºC) while dengue and Zika viruses had the highest (29ºC) thermal optima. We expect warming to increase transmission below thermal optima but decrease transmission above optima. Key directions for future work include linking mechanistic models to field transmission, combining temperature effects with control measures, incorporating trait variation and temperature variation, and investigating climate adaptation and migration.
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Affiliation(s)
- Erin A. Mordecai
- Department of BiologyStanford University371 Serra MallStanfordCAUSA
| | | | - Marissa K. Grossman
- Department of Entomology and Center for Infectious Disease DynamicsPenn State UniversityUniversity ParkPA16802USA
| | - Catherine A. Lippi
- Department of Geography and Emerging Pathogens InstituteUniversity of FloridaGainesvilleFLUSA
| | - Leah R. Johnson
- Department of StatisticsVirginia Polytechnic and State University250 Drillfield DriveBlacksburgVAUSA
| | - Marco Neira
- Center for Research on Health in Latin America (CISeAL)Pontificia Universidad Católica del EcuadorQuitoEcuador
| | - Jason R. Rohr
- Department of Biological SciencesEck Institute of Global HealthEnvironmental Change InitiativeUniversity of Notre Dame, Notre DameINUSA
| | - Sadie J. Ryan
- Department of Geography and Emerging Pathogens InstituteUniversity of FloridaGainesvilleFLUSA
- School of Life SciencesUniversity of KwaZulu‐NatalDurbanSouth Africa
| | - Van Savage
- Department of Ecology and Evolutionary Biology and Department of BiomathematicsUniversity of California Los AngelesLos AngelesCA90095USA
- Santa Fe Institute1399 Hyde Park RdSanta FeNM87501USA
| | - Marta S. Shocket
- Department of BiologyStanford University371 Serra MallStanfordCAUSA
| | - Rachel Sippy
- Department of Geography and Emerging Pathogens InstituteUniversity of FloridaGainesvilleFLUSA
- Institute for Global Health and Translational SciencesSUNY Upstate Medical UniversitySyracuseNY13210USA
| | - Anna M. Stewart Ibarra
- Institute for Global Health and Translational SciencesSUNY Upstate Medical UniversitySyracuseNY13210USA
| | - Matthew B. Thomas
- Department of Entomology and Center for Infectious Disease DynamicsPenn State UniversityUniversity ParkPA16802USA
| | - Oswaldo Villena
- Department of StatisticsVirginia Polytechnic and State University250 Drillfield DriveBlacksburgVAUSA
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19
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Park SW, Champredon D, Weitz JS, Dushoff J. A practical generation-interval-based approach to inferring the strength of epidemics from their speed. Epidemics 2019; 27:12-18. [PMID: 30799184 DOI: 10.1016/j.epidem.2018.12.002] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 12/18/2018] [Accepted: 12/28/2018] [Indexed: 11/16/2022] Open
Abstract
Infectious disease outbreaks are often characterized by the reproduction number R and exponential rate of growth r. R provides information about outbreak control and predicted final size, but estimating R is difficult, while r can often be estimated directly from incidence data. These quantities are linked by the generation interval - the time between when an individual is infected by an infector, and when that infector was infected. It is often infeasible to obtain the exact shape of a generation-interval distribution, and to understand how this shape affects estimates of R. We show that estimating generation interval mean and variance provides insight into the relationship between R and r. We use examples based on Ebola, rabies and measles to explore approximations based on gamma-distributed generation intervals, and find that use of these simple approximations are often sufficient to capture the r-R relationship and provide robust estimates of R.
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Affiliation(s)
- Sang Woo Park
- Department of Mathematics & Statistics, McMaster University, Hamilton, Ontario, Canada
| | - David Champredon
- Department of Biology, McMaster University, Hamilton, Ontario, Canada; Department of Mathematics & Statistics, Agent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada
| | - Joshua S Weitz
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, Georgia, United States; School of Physics, Georgia Institute of Technology, Atlanta, Georgia, United States
| | - Jonathan Dushoff
- Department of Biology, McMaster University, Hamilton, Ontario, Canada.
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20
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Tennant W, Recker M. Robustness of the reproductive number estimates in vector-borne disease systems. PLoS Negl Trop Dis 2018; 12:e0006999. [PMID: 30557351 PMCID: PMC6312349 DOI: 10.1371/journal.pntd.0006999] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Revised: 12/31/2018] [Accepted: 11/14/2018] [Indexed: 01/30/2023] Open
Abstract
BACKGROUND The required efforts, feasibility and predicted success of an intervention strategy against an infectious disease are partially determined by its basic reproduction number, R0. In its simplest form R0 can be understood as the product of the infectious period, the number of infectious contacts and the per-contact transmission probability, which in the case of vector-transmitted diseases necessarily extend to the vector stages. As vectors do not usually recover from infection, they remain infectious for life, which places high significance on the vector's life expectancy. Current methods for estimating the R0 for a vector-borne disease are mostly derived from compartmental modelling frameworks assuming constant vector mortality rates. We hypothesised that some of the assumptions underlying these models can lead to unrealistic high vector life expectancies with important repercussions for R0 estimates. METHODOLOGY AND PRINCIPAL FINDINGS Here we used a stochastic, individual-based model which allowed us to directly measure the number of secondary infections arising from one index case under different assumptions about vector mortality. Our results confirm that formulas based on age-independent mortality rates can overestimate R0 by nearly 100% compared to our own estimate derived from first principles. We further provide a correction factor that can be used with a standard R0 formula and adjusts for the discrepancies due to erroneous vector age distributions. CONCLUSION Vector mortality rates play a crucial role for the success and general epidemiology of vector-transmitted diseases. Many modelling efforts intrinsically assume these to be age-independent, which, as clearly demonstrated here, can lead to severe over-estimation of the disease's reproduction number. Our results thus re-emphasise the importance of obtaining field-relevant and species-dependent vector mortality rates, which in turn would facilitate more realistic intervention impact predictions.
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Affiliation(s)
- Warren Tennant
- Centre for Mathematics and the Environment, University of Exeter, Penryn Campus, Penryn, United Kingdom
| | - Mario Recker
- Centre for Mathematics and the Environment, University of Exeter, Penryn Campus, Penryn, United Kingdom
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21
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Okell LC, Reiter LM, Ebbe LS, Baraka V, Bisanzio D, Watson OJ, Bennett A, Verity R, Gething P, Roper C, Alifrangis M. Emerging implications of policies on malaria treatment: genetic changes in the Pfmdr-1 gene affecting susceptibility to artemether-lumefantrine and artesunate-amodiaquine in Africa. BMJ Glob Health 2018; 3:e000999. [PMID: 30397515 PMCID: PMC6202998 DOI: 10.1136/bmjgh-2018-000999] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/21/2018] [Accepted: 08/31/2018] [Indexed: 11/04/2022] Open
Abstract
Artemether–lumefantrine (AL) and artesunate–amodiaquine (AS-AQ) are the most commonly used artemisinin-based combination therapies (ACT) for treatment of Plasmodium falciparum in Africa. Both treatments remain efficacious, but single nucleotide polymorphisms (SNPs) in the Plasmodium falciparum multidrug resistance 1 (Pfmdr1) gene may compromise sensitivity. AL and AS-AQ exert opposing selective pressures: parasites with genotype 86Y, Y184 and 1246Y are partially resistant to AS-AQ treatment, while N86, 184 F and D1246 are favoured by AL treatment. Through a systematic review, we identified 397 surveys measuring the prevalence of Pfmdr1 polymorphisms at positions 86 184 or 1246 in 30 countries in Africa. Temporal trends in SNP frequencies after introduction of AL or AS-AQ as first-line treatment were analysed in 32 locations, and selection coefficients estimated. We examined associations between antimalarial policies, consumption, transmission intensity and rate of SNP selection. 1246Y frequency decreased on average more rapidly in locations where national policy recommended AL (median selection coefficient(s) of −0.083), compared with policies of AS-AQ or both AL and AS-AQ (median s=−0.035 and 0.021, p<0.001 respectively). 86Y frequency declined markedly after ACT policy introduction, with a borderline significant trend for a more rapid decline in countries with AL policies (p=0.055). However, these trends could also be explained by a difference in initial SNP frequencies at the time of ACT introduction. There were non-significant trends for faster selection of N86 and D1246 in areas with higher AL consumption and no trend with transmission intensity. Recorded consumption of AS-AQ was low in the locations and times Pfmdr1 data were collected. SNP trends in countries with AL policies suggest a broad increase in sensitivity of parasites to AS-AQ, by 7–10 years after AL introduction. Observed rates of selection have implications for planning strategies to cycle drugs or use multiple first-line therapies to maintain drug efficacy.
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Affiliation(s)
- Lucy C Okell
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Lisa Malene Reiter
- Global Health Section, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Lene Sandø Ebbe
- Centre for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark.,Department of Infectious Disease, Copenhagen University Hospital, Copenhagen, Denmark
| | - Vito Baraka
- Department of Biomedical Sciences, National Institute for Medical Research, Tanga, United Republic of Tanzania
| | - Donal Bisanzio
- RTI International, Washington, District of Columbia, USA
| | - Oliver J Watson
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Adam Bennett
- Malaria Elimination Initiative, Global Health Group, University of San FranciscO, San Francisco, California, USA
| | - Robert Verity
- MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
| | - Peter Gething
- Malaria Atlas Project, Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Cally Roper
- Department of Pathogen Molecular Biology, London School of Hygiene and Tropical Medicine, London, UK
| | - Michael Alifrangis
- Centre for Medical Parasitology, Department of Immunology and Microbiology, University of Copenhagen, Copenhagen, Denmark
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22
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Chitnis N, Schapira A, Schindler C, Penny MA, Smith TA. Mathematical analysis to prioritise strategies for malaria elimination. J Theor Biol 2018; 455:118-130. [PMID: 30006002 PMCID: PMC6117457 DOI: 10.1016/j.jtbi.2018.07.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 06/21/2018] [Accepted: 07/09/2018] [Indexed: 11/27/2022]
Abstract
Malaria and some other tropical diseases are currently targeted for elimination and eventually eradication. Since resources are limited, prioritisation of countries or areas for elimination is often necessary. However, this prioritisation is frequently conducted in an ad hoc manner. Lower transmission areas are usually targeted for elimination first, but for some areas this necessitates long and potentially expensive surveillance programs while transmission is eliminated from neighbouring higher transmission areas. We use a mathematical model to compare the implications of prioritisation choices in reducing overall burden and costs. We show that when the duration of the elimination program is independent of the transmission potential, burden is always reduced most by targeting high transmission areas first, but to reduce costs the optimal ordering depends on the actual transmission levels. In general, when overall transmission potential is low and the surveillance cost per secondary case compared to the cost per imported case is low, targeting the higher transmission area for elimination first is favoured.
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Affiliation(s)
- Nakul Chitnis
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland.
| | - Allan Schapira
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland
| | - Christian Schindler
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland
| | - Melissa A Penny
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland
| | - Thomas A Smith
- Swiss Tropical and Public Health Institute, Socinstrasse 57, Basel 4002, Switzerland; University of Basel, Basel 4003, Switzerland
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23
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Routledge I, Chevéz JER, Cucunubá ZM, Rodriguez MG, Guinovart C, Gustafson KB, Schneider K, Walker PGT, Ghani AC, Bhatt S. Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting. Nat Commun 2018; 9:2476. [PMID: 29946060 PMCID: PMC6018772 DOI: 10.1038/s41467-018-04577-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 05/02/2018] [Indexed: 01/08/2023] Open
Abstract
In 2016 the World Health Organization identified 21 countries that could eliminate malaria by 2020. Monitoring progress towards this goal requires tracking ongoing transmission. Here we develop methods that estimate individual reproduction numbers and their variation through time and space. Individual reproduction numbers, Rc, describe the state of transmission at a point in time and differ from mean reproduction numbers, which are averages of the number of people infected by a typical case. We assess elimination progress in El Salvador using data for confirmed cases of malaria from 2010 to 2016. Our results demonstrate that whilst the average number of secondary malaria cases was below one (0.61, 95% CI 0.55–0.65), individual reproduction numbers often exceeded one. We estimate a decline in Rc between 2010 and 2016. However we also show that if importation is maintained at the same rate, the country may not achieve malaria elimination by 2020. Twenty one countries have been identified for malaria elimination by 2020 and their progress needs to be constantly evaluated. Here, the authors present a method that estimates individual reproduction numbers and their variation through time and space and use it to monitor elimination success in El Salvador between 2010 and 2016.
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Affiliation(s)
- Isobel Routledge
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK.
| | | | - Zulma M Cucunubá
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
| | | | | | | | | | - Patrick G T Walker
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
| | - Azra C Ghani
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
| | - Samir Bhatt
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
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24
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Codeço CT, Villela DAM, Coelho FC. Estimating the effective reproduction number of dengue considering temperature-dependent generation intervals. Epidemics 2018; 25:101-111. [PMID: 29945778 DOI: 10.1016/j.epidem.2018.05.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 05/14/2018] [Accepted: 05/29/2018] [Indexed: 01/17/2023] Open
Abstract
The effective reproduction number, Rt, is a measure of transmission that can be calculated from standard incidence data to timely detect the beginning of epidemics. It has being increasingly used for surveillance of directly transmitted diseases. However, current methods for Rt estimation do not apply for vector borne diseases, whose transmission cycle depends on temperature. Here we propose a method that provides dengue's Rt estimates in the presence of temperature-mediated seasonality and apply this method to simulated and real data from two cities in Brazil where dengue is endemic. The method shows good precision in the simulated data. When applied to the real data, it shows differences in the transmission profile of the two cities and identifies periods of higher transmission.
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Affiliation(s)
- Claudia T Codeço
- Scientific Computing Program/Oswaldo Cruz Foundation, Rio de Janeiro, Brazil.
| | - Daniel A M Villela
- Scientific Computing Program/Oswaldo Cruz Foundation, Rio de Janeiro, Brazil
| | - Flavio C Coelho
- School of Applied Mathematics/Getulio Vargas Foundation, Rio de Janeiro, Brazil
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25
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Cohen JM, Le Menach A, Pothin E, Eisele TP, Gething PW, Eckhoff PA, Moonen B, Schapira A, Smith DL. Mapping multiple components of malaria risk for improved targeting of elimination interventions. Malar J 2017; 16:459. [PMID: 29132357 PMCID: PMC5683539 DOI: 10.1186/s12936-017-2106-3] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2017] [Accepted: 11/02/2017] [Indexed: 11/13/2022] Open
Abstract
There is a long history of considering the constituent components of malaria risk and the malaria transmission cycle via the use of mathematical models, yet strategic planning in endemic countries tends not to take full advantage of available disease intelligence to tailor interventions. National malaria programmes typically make operational decisions about where to implement vector control and surveillance activities based upon simple categorizations of annual parasite incidence. With technological advances, an enormous opportunity exists to better target specific malaria interventions to the places where they will have greatest impact by mapping and evaluating metrics related to a variety of risk components, each of which describes a different facet of the transmission cycle. Here, these components and their implications for operational decision-making are reviewed. For each component, related mappable malaria metrics are also described which may be measured and evaluated by malaria programmes seeking to better understand the determinants of malaria risk. Implementing tailored programmes based on knowledge of the heterogeneous distribution of the drivers of malaria transmission rather than only consideration of traditional metrics such as case incidence has the potential to result in substantial improvements in decision-making. As programmes improve their ability to prioritize their available tools to the places where evidence suggests they will be most effective, elimination aspirations may become increasingly feasible.
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Affiliation(s)
- Justin M Cohen
- Clinton Health Access Initiative, 383 Dorchester Ave., Suite 400, Boston, MA, 02127, USA.
| | - Arnaud Le Menach
- Clinton Health Access Initiative, 383 Dorchester Ave., Suite 400, Boston, MA, 02127, USA
| | - Emilie Pothin
- Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051, Basel, Switzerland
| | - Thomas P Eisele
- Center for Applied Malaria Research and Evaluation, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St (2300), New Orleans, LA, 70112, USA
| | - Peter W Gething
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, OX3 7LF, UK
| | - Philip A Eckhoff
- Institute for Disease Modeling, Building IV, 3150 139th Ave SE, Bellevue, WA, 98005, USA
| | - Bruno Moonen
- Bill & Melinda Gates Foundation, PO Box 23350, Seattle, WA, 98102, USA
| | | | - David L Smith
- Institute for Health Metrics and Evaluation, University of Washington, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, USA
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26
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Siraj AS, Oidtman RJ, Huber JH, Kraemer MUG, Brady OJ, Johansson MA, Perkins TA. Temperature modulates dengue virus epidemic growth rates through its effects on reproduction numbers and generation intervals. PLoS Negl Trop Dis 2017; 11:e0005797. [PMID: 28723920 PMCID: PMC5536440 DOI: 10.1371/journal.pntd.0005797] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2017] [Revised: 07/31/2017] [Accepted: 07/11/2017] [Indexed: 12/16/2022] Open
Abstract
Epidemic growth rate, r, provides a more complete description of the potential for epidemics than the more commonly studied basic reproduction number, R0, yet the former has never been described as a function of temperature for dengue virus or other pathogens with temperature-sensitive transmission. The need to understand the drivers of epidemics of these pathogens is acute, with arthropod-borne virus epidemics becoming increasingly problematic. We addressed this need by developing temperature-dependent descriptions of the two components of r—R0 and the generation interval—to obtain a temperature-dependent description of r. Our results show that the generation interval is highly sensitive to temperature, decreasing twofold between 25 and 35°C and suggesting that dengue virus epidemics may accelerate as temperatures increase, not only because of more infections per generation but also because of faster generations. Under the empirical temperature relationships that we considered, we found that r peaked at a temperature threshold that was robust to uncertainty in model parameters that do not depend on temperature. Although the precise value of this temperature threshold could be refined following future studies of empirical temperature relationships, the framework we present for identifying such temperature thresholds offers a new way to classify regions in which dengue virus epidemic intensity could either increase or decrease under future climate change. Recurrent, rapidly intensifying epidemics of dengue–the world’s most prevalent mosquito-borne viral disease–pose a challenge to healthcare systems throughout the tropical and subtropical world. An acute disease that tends to respond well to proper treatment, the sometimes intense nature of dengue epidemics has been known to overwhelm healthcare systems and elevate the morbidity and mortality of patients left without adequate medical treatment under peak epidemic conditions. Here, we quantify the temperature dependence of dengue epidemic intensity by quantifying two distinct determinants of epidemic growth rate: the average number of secondary infections arising from each primary infection and the average time between successive infections in humans. Our results show that the time between successive infections in humans decreases steadily with increasing temperatures, whereas the average number of secondary infections peaks at intermediate temperatures. Altogether, this suggests a peak temperature for dengue epidemic intensity. Applying this result to global temperature projections under future climate change scenarios suggests that dengue epidemics in many regions of the world could become more intense under future temperature increases.
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Affiliation(s)
- Amir S. Siraj
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, United States of America
- * E-mail: (ASS); (TAP)
| | - Rachel J. Oidtman
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, United States of America
| | - John H. Huber
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, United States of America
| | - Moritz U. G. Kraemer
- Department of Zoology, University of Oxford, Oxford, United Kingdom
- Department of Pediatrics, Harvard Medical School, Boston, United States of America
- Department of Informatics, Boston Children’s Hospital, Boston, United States of America
| | - Oliver J. Brady
- Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Michael A. Johansson
- Division of Vector-Borne Diseases, Centers for Disease Control and Prevention, San Juan, Puerto Rico
- Center for Communicable Disease Dynamics, Harvard TH Chan School of Public Health, Boston, United States of America
| | - T. Alex Perkins
- Department of Biological Sciences and Eck Institute for Global Health, University of Notre Dame, Notre Dame, United States of America
- * E-mail: (ASS); (TAP)
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