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Cabrera-Sosa L, Nolasco O, Kattenberg JH, Fernandez-Miñope C, Valdivia HO, Barazorda K, Rios SADL, Rodriguez-Ferrucci H, Vinetz JM, Rosanas-Urgell A, Geertruyden JPV, Gamboa D, Delgado-Ratto C. Genomic surveillance of malaria parasites in an indigenous community in the Peruvian Amazon. RESEARCH SQUARE 2024:rs.3.rs-3979991. [PMID: 38464169 PMCID: PMC10925399 DOI: 10.21203/rs.3.rs-3979991/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Hard-to-reach communities represent Peru's main challenge for malaria elimination, but information about transmission in these areas is scarce. Here, we assessed Plasmodium vivax (Pv) and P. falciparum (Pf) transmission dynamics, resistance markers, and Pf hrp 2/3 deletions in Nueva Jerusalén (NJ), a remote, indigenous community in the Peruvian Amazon with high population mobility. We collected samples from November 2019 to May 2020 by active (ACD) and passive case detection (PCD) in NJ. Parasites were identified with microscopy and PCR. Then, we analyzed a representative set of positive-PCR samples (Pv = 68, Pf = 58) using highly-multiplexed deep sequencing assays (AmpliSeq) and compared NJ parasites with ones from other remote Peruvian areas using population genetics indexes. The ACD intervention did not reduce malaria cases in the short term, and persistent malaria transmission was observed (at least one Pv infection was detected in 96% of the study days). In Nueva Jerusalen, the Pv population had modest genetic diversity (He = 0.27). Pf population had lower diversity (He = 0.08) and presented temporal clustering, one of these clusters linked to an outbreak in February 2020. Moreover, Pv and Pf parasites from NJ exhibited variable levels of differentiation (Pv Fst = 0.07-0.52 & Pf Fst = 0.11-0.58) with parasites from other remote areas. No artemisin resistance mutations but chloroquine (57%) and sulfadoxine-pyrimethamine (35-67%) were detected in NJ's Pf parasites. Moreover, pfhrp2/3 gene deletions were common (32-50% of parasites with one or both genes deleted). The persistent Pv transmission and the detection of a Pf outbreak with parasites genetically distinct from the local ones highlight the need for tailored interventions focusing on mobility patterns and imported infections in remote areas to eliminate malaria in the Peruvian Amazon.
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Sa-Ngamuang C, Lawpoolsri S, Su Yin M, Barkowsky T, Cui L, Prachumsri J, Haddawy P. Assessment of malaria risk in Southeast Asia: a systematic review. Malar J 2023; 22:339. [PMID: 37940923 PMCID: PMC10631000 DOI: 10.1186/s12936-023-04772-3] [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: 02/27/2023] [Accepted: 10/26/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Several countries in Southeast Asia are nearing malaria elimination, yet eradication remains elusive. This is largely due to the challenge of focusing elimination efforts, an area where risk prediction can play an essential supporting role. Despite its importance, there is no standard numerical method to quantify the risk of malaria infection. Thus, there is a need for a consolidated view of existing definitions of risk and factors considered in assessing risk to analyse the merits of risk prediction models. This systematic review examines studies of the risk of malaria in Southeast Asia with regard to their suitability in addressing the challenges of malaria elimination in low transmission areas. METHODS A search of four electronic databases over 2010-2020 retrieved 1297 articles, of which 25 met the inclusion and exclusion criteria. In each study, examined factors included the definition of the risk and indicators of malaria transmission used, the environmental and climatic factors associated with the risk, the statistical models used, the spatial and temporal granularity, and how the relationship between environment, climate, and risk is quantified. RESULTS This review found variation in the definition of risk used, as well as the environmental and climatic factors in the reviewed articles. GLM was widely adopted as the analysis technique relating environmental and climatic factors to malaria risk. Most of the studies were carried out in either a cross-sectional design or case-control studies, and most utilized the odds ratio to report the relationship between exposure to risk and malaria prevalence. CONCLUSIONS Adopting a standardized definition of malaria risk would help in comparing and sharing results, as would a clear description of the definition and method of collection of the environmental and climatic variables used. Further issues that need to be more fully addressed include detection of asymptomatic cases and considerations of human mobility. Many of the findings of this study are applicable to other low-transmission settings and could serve as a guideline for further studies of malaria in other regions.
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Affiliation(s)
- Chaitawat Sa-Ngamuang
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Saranath Lawpoolsri
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Myat Su Yin
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand
| | - Thomas Barkowsky
- Bremen Spatial Cognition Center (BSCC), University of Bremen, Bremen, Germany
| | - Liwang Cui
- Division of Infectious Diseases and International Medicine, Department of Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, USA
| | - Jetsumon Prachumsri
- Mahidol Vivax Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Peter Haddawy
- Faculty of Information and Communication Technology, Mahidol University, Nakhon Pathom, Thailand.
- Bremen Spatial Cognition Center (BSCC), University of Bremen, Bremen, Germany.
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Rorato AC, Dal'Asta AP, Lana RM, Dos Santos RBN, Escada MIS, Vogt CM, Neves TC, Barbosa M, Andreazzi CS, Dos Reis IC, Fernandes DA, da Silva-Nunes M, de Souza AR, Monteiro AMV, Codeço CT. Trajetorias: a dataset of environmental, epidemiological, and economic indicators for the Brazilian Amazon. Sci Data 2023; 10:65. [PMID: 36732347 PMCID: PMC9895449 DOI: 10.1038/s41597-023-01962-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 01/11/2023] [Indexed: 02/04/2023] Open
Abstract
The Trajetorias dataset is a harmonized set of environmental, epidemiological, and poverty indicators for all municipalities of the Brazilian Legal Amazon (BLA). This dataset is the result of a scientific synthesis research initiative conducted by scientists from several natural and social sciences fields, consolidating multidisciplinary indicators into a coherent dataset for integrated and interdisciplinary studies of the Brazilian Amazon. The dataset allows the investigation of the association between the Amazonian agrarian systems and their impacts on environmental and epidemiological changes, furthermore enhancing the possibilities for understanding, in a more integrated and consistent way, the scenarios that affect the Amazonian biome and its inhabitants.
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Affiliation(s)
- Ana C Rorato
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, 12227-900, Brazil.
| | - Ana Paula Dal'Asta
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, 12227-900, Brazil
| | | | | | - Maria Isabel S Escada
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, 12227-900, Brazil
| | - Camila M Vogt
- Departamento de Ciências Administrativas, Universidade Federal de Santa Maria, Santa Maria, Brazil
| | - Tatiana Campos Neves
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, 21040-900, Brazil
| | - Milton Barbosa
- Laboratório de Ecologia Evolutiva e Biodiversidade, Universidade Federal de Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Cecilia S Andreazzi
- Laboratório de Biologia e Parasitologia de Mamíferos Silvestres Reservatórios, Fundação Oswaldo Cruz, Rio de Janeiro, 21040-900, Brazil.,Departamento de Biodiversidad, Ecología y Evolución, Universidad Complutense de Madrid, Madrid, Spain
| | - Izabel C Dos Reis
- Laboratório de Imunologia Viral, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, 21040-900, Brazil
| | | | - Mônica da Silva-Nunes
- Departamento de Medicina, Centro de Ciências Biológicas e da Saúde, Universidade Federal de São Carlos, São Carlos, 13565-905, Brazil
| | - Anielli R de Souza
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, 12227-900, Brazil
| | - Antonio M V Monteiro
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, 12227-900, Brazil
| | - Claudia T Codeço
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, 21040-900, Brazil
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Cunha A, Batista FDC, Gianfelice PRDL, Oyarzabal RS, Grzybowski JMV, Macau EE. epidWaves: A code for fitting multi-wave epidemic models. SOFTWARE IMPACTS 2022; 14:100391. [PMID: 35909895 PMCID: PMC9316937 DOI: 10.1016/j.simpa.2022.100391] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 06/30/2022] [Accepted: 07/20/2022] [Indexed: 12/25/2022]
Abstract
The COVID-19 pandemic has given rise to a great demand for computational models capable of describing and inferring the evolution of an epidemic outbreak in the short term. In this sense, we introduce epidWaves, a package that provides a framework for fitting multi-wave epidemic models to data from actual outbreaks of COVID-19 and other infectious diseases.
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Affiliation(s)
- Americo Cunha
- Rio de Janeiro State University, Rio de Janeiro, Brazil,Corresponding author
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5
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Carrasco-Escobar G, Rosado J, Nolasco O, White MT, Mueller I, Castro MC, Rodriguez-Ferruci H, Gamboa D, Llanos-Cuentas A, Vinetz JM, Benmarhnia T. Effect of out-of-village working activities on recent malaria exposure in the Peruvian Amazon using parametric g-formula. Sci Rep 2022; 12:19144. [PMID: 36351988 PMCID: PMC9645738 DOI: 10.1038/s41598-022-23528-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 11/01/2022] [Indexed: 11/11/2022] Open
Abstract
In the Amazon Region of Peru, occupational activities are important drivers of human mobility and may increase the individual risk of being infected while contributing to increasing malaria community-level transmission. Even though out-of-village working activities and other mobility patterns have been identified as determinants of malaria transmission, no studies have quantified the effect of out-of-village working activities on recent malaria exposure and proposed plausible intervention scenarios. Using two population-based cross-sectional studies in the Loreto Department in Peru, and the parametric g-formula method, we simulated various hypothetical scenarios intervening in out-of-village working activities to reflect their potential health benefits. This study estimated that the standardized mean outcome (malaria seroprevalence) in the unexposed population (no out-of-village workers) was 44.6% (95% CI: 41.7%-47.5%) and 66.7% (95% CI: 61.6%-71.8%) in the exposed population resulting in a risk difference of 22.1% (95% CI: 16.3%-27.9%). However, heterogeneous patterns in the effects of interest were observed between peri-urban and rural areas (Cochran's Q test = 15.5, p < 0.001). Heterogeneous patterns were also observed in scenarios of increased prevalence of out-of-village working activities and restriction scenarios by gender (male vs. female) and age (18 and under vs. 19 and older) that inform possible occupational interventions targetting population subgroups. The findings of this study support the hypothesis that targeting out-of-village workers will considerably benefit current malaria elimination strategies in the Amazon Region. Particularly, males and adult populations that carried out out-of-village working activities in rural areas contribute the most to the malaria seropositivity (recent exposure to the parasite) in the Peruvian Amazon.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA.
- Health Innovation Lab, Institute of Tropical Medicine "Alexander Von Humboldt", Universidad Peruana Cayetano Heredia, Lima, Peru.
| | - Jason Rosado
- G5 Épidémiologie Et Analyse Des Maladies Infectieuses, Département de Santé Globale, Institut Pasteur, 75015, Paris, France
| | - Oscar Nolasco
- Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación Y Desarrollo, Facultad de Ciencias Y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Michael T White
- G5 Épidémiologie Et Analyse Des Maladies Infectieuses, Département de Santé Globale, Institut Pasteur, 75015, Paris, France
| | - Ivo Mueller
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
- Population Health and Immunity Division, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia
| | - Marcia C Castro
- Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Dionicia Gamboa
- Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación Y Desarrollo, Facultad de Ciencias Y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
- Departamento de Ciencias Celulares Y Moleculares, Facultad de Ciencias Y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Alejandro Llanos-Cuentas
- Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Joseph M Vinetz
- Instituto de Medicina Tropical Alexander Von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
- Laboratorio ICEMR-Amazonia, Laboratorios de Investigación Y Desarrollo, Facultad de Ciencias Y Filosofía, Universidad Peruana Cayetano Heredia, Lima, Peru
- Section of Infectious Diseases, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California, San Diego, CA, 92037, USA
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Carrasco-Escobar G, Matta-Chuquisapon J, Manrique E, Ruiz-Cabrejos J, Barboza JL, Wong D, Henostroza G, Llanos-Cuentas A, Benmarhnia T. Quantifying the effect of human population mobility on malaria risk in the Peruvian Amazon. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211611. [PMID: 35875474 PMCID: PMC9297009 DOI: 10.1098/rsos.211611] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 06/29/2022] [Indexed: 06/15/2023]
Abstract
The impact of human population movement (HPM) on the epidemiology of vector-borne diseases, such as malaria, has been described. However, there are limited data on the use of new technologies for the study of HPM in endemic areas with difficult access such as the Amazon. In this study conducted in rural Peruvian Amazon, we used self-reported travel surveys and GPS trackers coupled with a Bayesian spatial model to quantify the role of HPM on malaria risk. By using a densely sampled population cohort, this study highlighted the elevated malaria transmission in a riverine community of the Peruvian Amazon. We also found that the high connectivity between Amazon communities for reasons such as work, trading or family plausibly sustains such transmission levels. Finally, by using multiple human mobility metrics including GPS trackers, and adapted causal inference methods we identified for the first time the effect of human mobility patterns on malaria risk in rural Peruvian Amazon. This study provides evidence of the causal effect of HPM on malaria that may help to adapt current malaria control programmes in the Amazon.
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Affiliation(s)
- Gabriel Carrasco-Escobar
- Health Innovation Lab, Institute of Tropical Medicine ‘Alexander von Humboldt’, Universidad Peruana Cayetano Heredia, Lima, Peru
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Jose Matta-Chuquisapon
- Health Innovation Lab, Institute of Tropical Medicine ‘Alexander von Humboldt’, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Edgar Manrique
- Health Innovation Lab, Institute of Tropical Medicine ‘Alexander von Humboldt’, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jorge Ruiz-Cabrejos
- Health Innovation Lab, Institute of Tropical Medicine ‘Alexander von Humboldt’, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Jose Luis Barboza
- Health Innovation Lab, Institute of Tropical Medicine ‘Alexander von Humboldt’, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Daniel Wong
- Health Innovation Lab, Institute of Tropical Medicine ‘Alexander von Humboldt’, Universidad Peruana Cayetano Heredia, Lima, Peru
| | | | - Alejandro Llanos-Cuentas
- Instituto de Medicinal Tropical Alexander von Humboldt, Universidad Peruana Cayetano Heredia, Lima, Peru
- Facultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California, San Diego, CA, USA
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7
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Laporta GZ, Grillet ME, Rodovalho SR, Massad E, Sallum MAM. Reaching the malaria elimination goal in Brazil: a spatial analysis and time-series study. Infect Dis Poverty 2022; 11:39. [PMID: 35382896 PMCID: PMC8981179 DOI: 10.1186/s40249-022-00945-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 02/06/2022] [Indexed: 11/10/2022] Open
Abstract
Background Since 2015, the Global Technical Strategy (GTS) for Malaria 2016–2030 has been adopted by the World Health Organization (WHO) as a comprehensive framework to accelerate progress for malaria elimination in endemic countries. This strategy sets the target of reducing global malaria incidence and mortality rates by 90% in 2030. Here it is sought to evaluate Brazil’s achievements towards reaching the WHO GTS milestone in 2030. Considering the total number of new malaria cases in 2015, the main research question is: will Brazil reach the malaria elimination goal in 2030? Methods Analytical strategies were undertaken using the SIVEP-malaria official databases of the Brazilian Malaria Control Programme for the Brazilian Amazon region from 2009 to 2020. Spatial and time-series analyses were applied for identifying municipalities that support the highest numbers of malaria cases over the years. Forecast analysis was used for predicting the estimated number of new cases in Brazil in 2025–2050. Results Brazil has significantly reduced the number of new malaria cases in 2020 in comparison with 2015 in the states of Acre (− 56%), Amapá (− 75%), and Amazonas (− 21%); however, they increased in the states of Pará (156%), Rondônia (74%), and Roraima (362%). Forecast of the predicted number of new malaria cases in 2030 is 74,764 (95% CI: 41,116–141,160) in the Brazilian Amazon. Conclusions It is likely that Brazil will reduce the number of new malaria cases in the Brazilian Amazon in 2030 in relation to that in 2015. Herein forecast shows a reduction by 46% (74,754 in 2030 forecast/137,982 in 2015), but this reduction is yet far from the proposed reduction under the WHO GTS 2030 milestone (90%). Stable and unbeatable transmission in the Juruá River Valley, Manaus, and Lábrea still support endemic malaria in the Brazilian Amazon. Today’s cross-border malaria is impacting the state of Roraima unprecedently. If this situation is maintained, the malaria elimination goal (zero cases) may not be reached before 2050. An enhanced political commitment is vital to ensure optimal public health intervention designs in the post-2030 milestones for malaria elimination. Graphical Abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s40249-022-00945-5.
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Affiliation(s)
- Gabriel Zorello Laporta
- Graduate Research and Innovation Program, Centro Universitario FMABC, Santo André, SP, Brazil.
| | - Maria Eugenia Grillet
- Laboratory of Parasite and Vector Biology, Institute of Zoology and Tropical Ecology, School of Sciences, Central University of Venezuela, Caracas, Venezuela
| | - Sheila Rodrigues Rodovalho
- Technical Unit of Transmissible Diseases and Current Health Assessment, Pan American Health Organization (PAHO/WHO), Brasília, DF, Brazil
| | - Eduardo Massad
- School of Applied Mathematics, Getulio Vargas Foundation, Rio de Janeiro, RJ, Brazil
| | - Maria Anice Mureb Sallum
- Epidemiology Department, School of Public Health, University of São Paulo, São Paulo, SP, Brazil.
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8
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Chang HH, Chang MC, Kiang M, Mahmud AS, Ekapirat N, Engø-Monsen K, Sudathip P, Buckee CO, Maude RJ. Low parasite connectivity among three malaria hotspots in Thailand. Sci Rep 2021; 11:23348. [PMID: 34857842 PMCID: PMC8640040 DOI: 10.1038/s41598-021-02746-6] [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: 07/31/2021] [Accepted: 11/17/2021] [Indexed: 12/25/2022] Open
Abstract
Identifying sources and sinks of malaria transmission is critical for designing effective intervention strategies particularly as countries approach elimination. The number of malaria cases in Thailand decreased 90% between 2012 and 2020, yet elimination has remained a major public health challenge with persistent transmission foci and ongoing importation. There are three main hotspots of malaria transmission in Thailand: Ubon Ratchathani and Sisaket in the Northeast; Tak in the West; and Yala in the South. However, the degree to which these hotspots are connected via travel and importation has not been well characterized. Here, we develop a metapopulation model parameterized by mobile phone call detail record data to estimate parasite flow among these regions. We show that parasite connectivity among these regions was limited, and that each of these provinces independently drove the malaria transmission in nearby provinces. Overall, our results suggest that due to the low probability of domestic importation between the transmission hotspots, control and elimination strategies can be considered separately for each region.
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Affiliation(s)
- Hsiao-Han Chang
- grid.38348.340000 0004 0532 0580Institute of Bioinformatics and Structural Biology and Department of Life Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Meng-Chun Chang
- grid.38348.340000 0004 0532 0580Institute of Bioinformatics and Structural Biology and Department of Life Science, National Tsing Hua University, Hsinchu, Taiwan
| | - Mathew Kiang
- grid.168010.e0000000419368956Department of Epidemiology and Population Health, Stanford University, Stanford, CA USA
| | - Ayesha S. Mahmud
- grid.47840.3f0000 0001 2181 7878Department of Demography, University of California, Berkeley, USA
| | - Nattwut Ekapirat
- grid.10223.320000 0004 1937 0490Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | | | - Prayuth Sudathip
- grid.415836.d0000 0004 0576 2573Division of Vector Borne Diseases, Ministry of Public Health, Nonthaburi, Thailand
| | - Caroline O. Buckee
- grid.38142.3c000000041936754XHarvard TH Chan School of Public Health, Harvard University, Boston, USA
| | - Richard J. Maude
- grid.10223.320000 0004 1937 0490Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand ,grid.38142.3c000000041936754XHarvard TH Chan School of Public Health, Harvard University, Boston, USA ,grid.4991.50000 0004 1936 8948Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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Codeço CT, Dal'Asta AP, Rorato AC, Lana RM, Neves TC, Andreazzi CS, Barbosa M, Escada MIS, Fernandes DA, Rodrigues DL, Reis IC, Silva-Nunes M, Gontijo AB, Coelho FC, Monteiro AMV. Epidemiology, Biodiversity, and Technological Trajectories in the Brazilian Amazon: From Malaria to COVID-19. Front Public Health 2021; 9:647754. [PMID: 34327184 PMCID: PMC8314010 DOI: 10.3389/fpubh.2021.647754] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 06/14/2021] [Indexed: 11/13/2022] Open
Abstract
The Amazon biome is under severe threat due to increasing deforestation rates and loss of biodiversity and ecosystem services while sustaining a high burden of neglected tropical diseases. Approximately two thirds of this biome are located within Brazilian territory. There, socio-economic and environmental landscape transformations are linked to the regional agrarian economy dynamics, which has developed into six techno-productive trajectories (TTs). These TTs are the product of the historical interaction between Peasant and Farmer and Rancher practices, technologies and rationalities. This article investigates the distribution of the dominant Brazilian Amazon TTs and their association with environmental degradation and vulnerability to neglected tropical diseases. The goal is to provide a framework for the joint debate of the local economic, environmental and health dimensions. We calculated the dominant TT for each municipality in 2017. Peasant trajectories (TT1, TT2, and TT3) are dominant in ca. fifty percent of the Amazon territory, mostly concentrated in areas covered by continuous forest where malaria is an important morbidity and mortality cause. Cattle raising trajectories are associated with higher deforestation rates. Meanwhile, Farmer and Rancher economies are becoming dominant trajectories, comprising large scale cattle and grain production. These trajectories are associated with rapid biodiversity loss and a high prevalence of neglected tropical diseases, such as leishmaniasis, Aedes-borne diseases and Chagas disease. Overall, these results defy simplistic views that the dominant development trajectory for the Amazon will optimize economic, health and environmental indicators. This approach lays the groundwork for a more integrated narrative consistent with the economic history of the Brazilian Amazon.
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Affiliation(s)
- Claudia T. Codeço
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Ana P. Dal'Asta
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, Sao Jose dos Campos, Brazil
| | - Ana C. Rorato
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, Sao Jose dos Campos, Brazil
- Centro de Ciência do Sistema Terrestre, Instituto Nacional de Pesquisas Espaciais, Sao Jose dos Campos, Brazil
| | - Raquel M. Lana
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Tatiana C. Neves
- Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Cecilia S. Andreazzi
- Laboratório de Biologia e Parasitologia de Mamíferos Silvestres Reservatórios, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | - Milton Barbosa
- Ecologia Evolutiva e Biodiversidade, DGEE, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Maria I. S. Escada
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, Sao Jose dos Campos, Brazil
| | - Danilo A. Fernandes
- Instituto de Ciências Sociais Aplicadas e Núcleo de Altos Estudos Amazônicos, Universidade Federal do Pará, Belem, Brazil
| | - Danuzia L. Rodrigues
- Instituto de Estudos em Desenvolvimento Agrário e Regional, Universidade Federal do Sul e Sudeste do Pará, Maraba, Brazil
| | - Izabel C. Reis
- Laboratório de Mosquitos Transmissores de Hematozoários, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
| | | | - Alexandre B. Gontijo
- Laboratório de Produtos Florestais, Serviço Florestal Brasileiro, Brasília, Brazil
| | - Flavio C. Coelho
- Escola de Matemática Aplicada, Fundação Getúlio Vargas, Rio de Janeiro, Brazil
| | - Antonio M. V. Monteiro
- Laboratório de Investigação em Sistemas Socioambientais, Instituto Nacional de Pesquisas Espaciais, Sao Jose dos Campos, Brazil
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Lee SA, Jarvis CI, Edmunds WJ, Economou T, Lowe R. Spatial connectivity in mosquito-borne disease models: a systematic review of methods and assumptions. J R Soc Interface 2021; 18:20210096. [PMID: 34034534 PMCID: PMC8150046 DOI: 10.1098/rsif.2021.0096] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 12/14/2022] Open
Abstract
Spatial connectivity plays an important role in mosquito-borne disease transmission. Connectivity can arise for many reasons, including shared environments, vector ecology and human movement. This systematic review synthesizes the spatial methods used to model mosquito-borne diseases, their spatial connectivity assumptions and the data used to inform spatial model components. We identified 248 papers eligible for inclusion. Most used statistical models (84.2%), although mechanistic are increasingly used. We identified 17 spatial models which used one of four methods (spatial covariates, local regression, random effects/fields and movement matrices). Over 80% of studies assumed that connectivity was distance-based despite this approach ignoring distant connections and potentially oversimplifying the process of transmission. Studies were more likely to assume connectivity was driven by human movement if the disease was transmitted by an Aedes mosquito. Connectivity arising from human movement was more commonly assumed in studies using a mechanistic model, likely influenced by a lack of statistical models able to account for these connections. Although models have been increasing in complexity, it is important to select the most appropriate, parsimonious model available based on the research question, disease transmission process, the spatial scale and availability of data, and the way spatial connectivity is assumed to occur.
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Affiliation(s)
- Sophie A. Lee
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Christopher I. Jarvis
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - W. John Edmunds
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | | | - Rachel Lowe
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
- Centre on Climate Change and Planetary Health, London School of Hygiene & Tropical Medicine, London, UK
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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