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Enoe J, Sutherland M, Davis D, Ramlal B, Griffith-Charles C, Bhola KH, Asefa EM. A conceptional model integrating geographic information systems (GIS) and social media data for disease exposure assessment. GEOSPATIAL HEALTH 2024; 19. [PMID: 38551510 DOI: 10.4081/gh.2024.1264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/28/2024] [Indexed: 04/02/2024]
Abstract
Although previous studies have acknowledged the potential of geographic information systems (GIS) and social media data (SMD) in assessment of exposure to various environmental risks, none has presented a simple, effective and user-friendly tool. This study introduces a conceptual model that integrates individual mobility patterns extracted from social media, with the geographic footprints of infectious diseases and other environmental agents utilizing GIS. The efficacy of the model was independently evaluated for selected case studies involving lead in the ground; particulate matter in the air; and an infectious, viral disease (COVID- 19). A graphical user interface (GUI) was developed as the final output of this study. Overall, the evaluation of the model demonstrated feasibility in successfully extracting individual mobility patterns, identifying potential exposure sites and quantifying the frequency and magnitude of exposure. Importantly, the novelty of the developed model lies not merely in its efficiency in integrating GIS and SMD for exposure assessment, but also in considering the practical requirements of health practitioners. Although the conceptual model, developed together with its associated GUI, presents a promising and practical approach to assessment of the exposure to environmental risks discussed here, its applicability, versatility and efficacy extends beyond the case studies presented in this study.
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Affiliation(s)
- Jerry Enoe
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Michael Sutherland
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Dexter Davis
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Bheshem Ramlal
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Charisse Griffith-Charles
- Department of Geomatics Engineering and Land Management, The University of the West Indies, St. Augustine.
| | - Keston H Bhola
- Department of Computers and Technology, School of Arts and Science, St George's University.
| | - Elsai Mati Asefa
- School of Environmental Health, College of Health and Medical Sciences, Haramaya University, Harar.
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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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Lek D, Callery JJ, Nguon C, Debackere M, Sovannaroth S, Tripura R, Wojnarski M, Piola P, Khean ST, Manion K, Nguon S, Kunkel A, Vernaeve L, Peto TJ, Dantzer E, Davoeung C, Etienne W, Dondorp AM, Tuseo L, von Seidlein L, Guintran JO. Tools to accelerate falciparum malaria elimination in Cambodia: a meeting report. Malar J 2020; 19:151. [PMID: 32293452 PMCID: PMC7161105 DOI: 10.1186/s12936-020-03197-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 03/20/2020] [Indexed: 02/06/2023] Open
Abstract
Cambodia targets malaria elimination by 2025. Rapid elimination will depend on successfully identifying and clearing malaria foci linked to forests. Expanding and maintaining universal access to early diagnosis and effective treatment remains the key to malaria control and ultimately malaria elimination in the Greater Mekong Subregion (GMS) in the foreseeable future. Mass Drug Administration (MDA) holds some promise in the rapid reduction of Plasmodium falciparum infections, but requires considerable investment of resources and time to mobilize the target communities. Furthermore, the most practical drug regimen for MDA in the GMS—three rounds of DHA/piperaquine—has lost some of its efficacy. Mass screening and treatment benefits asymptomatic P. falciparum carriers by clearing chronic infections, but in its current form holds little promise for malaria elimination. Hopes that “highly sensitive” diagnostic tests would provide substantial advances in screen and treat programmes have been shown to be misplaced. To reduce the burden on P. falciparum and Plasmodium vivax infections in people working in forested areas novel approaches to the use of malaria prophylaxis in forest workers should be explored. During an October 2019 workshop in Phnom Penh researchers and policymakers reviewed evidence of acceptability, feasibility and effectiveness of interventions to target malaria foci and interrupt P. falciparum transmission and discussed operational requirements and conditions for programmatic implementation.
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Affiliation(s)
- Dysoley Lek
- Centre for Parasitology, Entomology and Malaria Control, Phnom Penh, Cambodia
| | - James J Callery
- Mahidol-Oxford University Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand
| | - Chea Nguon
- Centre for Parasitology, Entomology and Malaria Control, Phnom Penh, Cambodia
| | | | - Siv Sovannaroth
- Centre for Parasitology, Entomology and Malaria Control, Phnom Penh, Cambodia
| | - Rupam Tripura
- Mahidol-Oxford University Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.,Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Marius Wojnarski
- Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | | | - Soy Ty Khean
- University Research Company Ltd., Phnom Penh, Cambodia.,London School of Hygiene and Tropical Medicine, London, UK
| | | | - Sokomar Nguon
- University Research Company Ltd., Phnom Penh, Cambodia
| | - Amber Kunkel
- Institut Pasteur du Cambodge, Phnom Penh, Cambodia.,Emerging Diseases Epidemiology Unit, Institut Pasteur, Paris, France
| | | | - Thomas J Peto
- Mahidol-Oxford University Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.,Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - Emily Dantzer
- University of California at San Francisco, San Francisco, USA
| | | | | | - Arjen M Dondorp
- Mahidol-Oxford University Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.,Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | | | - Lorenz von Seidlein
- Mahidol-Oxford University Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand. .,Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK.
| | - Jean-Olivier Guintran
- Centre for Parasitology, Entomology and Malaria Control, Phnom Penh, Cambodia. .,Mahidol-Oxford University Tropical Medicine Research Unit, Mahidol University, Bangkok, Thailand.
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Okami S, Kohtake N. Spatiotemporal Modeling for Fine-Scale Maps of Regional Malaria Endemicity and Its Implications for Transitional Complexities in a Routine Surveillance Network in Western Cambodia. Front Public Health 2017; 5:262. [PMID: 29034229 PMCID: PMC5627027 DOI: 10.3389/fpubh.2017.00262] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Accepted: 09/13/2017] [Indexed: 11/24/2022] Open
Abstract
Due to the associated and substantial efforts of many stakeholders involved in malaria containment, the disease burden of malaria has dramatically decreased in many malaria-endemic countries in recent years. Some decades after the past efforts of the global malaria eradication program, malaria elimination has again featured on the global health agenda. While risk distribution modeling and a mapping approach are effective tools to assist with the efficient allocation of limited health-care resources, these methods need some adjustment and reexamination in accordance with changes occurring in relation to malaria elimination. Limited available data, fine-scale data inaccessibility (for example, household or individual case data), and the lack of reliable data due to inefficiencies within the routine surveillance system, make it difficult to create reliable risk maps for decision-makers or health-care practitioners in the field. Furthermore, the risk of malaria may dynamically change due to various factors such as the progress of containment interventions and environmental changes. To address the complex and dynamic nature of situations in low-to-moderate malaria transmission settings, we built a spatiotemporal model of a standardized morbidity ratio (SMR) of malaria incidence, calculated through annual parasite incidence, using routinely reported surveillance data in combination with environmental indices such as remote sensing data, and the non-environmental regional containment status, to create fine-scale risk maps. A hierarchical Bayesian frame was employed to fit the transitioning malaria risk data onto the map. The model was set to estimate the SMRs of every study location at specific time intervals within its uncertainty range. Using the spatial interpolation of estimated SMRs at village level, we created fine-scale maps of two provinces in western Cambodia at specific time intervals. The maps presented different patterns of malaria risk distribution at specific time intervals. Moreover, the visualized weights estimated using the risk model, and the structure of the routine surveillance network, represent the transitional complexities emerging from ever-changing regional endemic situations.
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Affiliation(s)
- Suguru Okami
- Graduate School of System Design and Management, Keio University, Kanagawa, Japan
| | - Naohiko Kohtake
- Graduate School of System Design and Management, Keio University, Kanagawa, Japan
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