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Rotejanaprasert C, Malaphone V, Mayxay M, Chindavongsa K, Banouvong V, Khamlome B, Vilay P, Vanisavaeth V, Maude RJ. Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection. Malar J 2024; 23:231. [PMID: 39098946 PMCID: PMC11298089 DOI: 10.1186/s12936-024-05064-0] [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: 12/16/2023] [Accepted: 07/30/2024] [Indexed: 08/06/2024] Open
Abstract
BACKGROUND The government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space-time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022. METHODS Spatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level. RESULTS Malaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010-2011, and with P. vivax incidence in the north and central regions during 2012-2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south. CONCLUSIONS Malaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.
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Affiliation(s)
- Chawarat Rotejanaprasert
- Department of Tropical Hygiene, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
| | - Vilayvone Malaphone
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
| | - Mayfong Mayxay
- Institute for Research and Education Development, University of Health Sciences, Vientiane, Lao PDR
- Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit, Microbiology Laboratory, Mahosot Hospital, Vientiane, Lao PDR
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | | | | | - Boualam Khamlome
- Center of Malariology, Parasitology, and Entomology, Vientiane, Lao PDR
| | - Phoutnalong Vilay
- Center of Malariology, Parasitology, and Entomology, Vientiane, Lao PDR
| | | | - Richard J Maude
- Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK
- The Open University, Milton Keynes, UK
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Wang H, Song C, Wang J, Gao P. A raster-based spatial clustering method with robustness to spatial outliers. Sci Rep 2024; 14:4103. [PMID: 38374209 PMCID: PMC10876529 DOI: 10.1038/s41598-024-53066-4] [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: 06/16/2023] [Accepted: 01/27/2024] [Indexed: 02/21/2024] Open
Abstract
Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the handling of spatial outliers is important. It is necessary to improve spatial integration so that each cluster is connected as much as possible, while protecting spatial outliers can help avoid the excessive masking of attribute differences This paper proposes a new spatial clustering method for raster data robust to spatial outliers. The method employs a sliding window to scan the entire region to determine spatial outliers. Additionally, a mechanism based on the range and standard deviation of the spatial units in each window is designed to judge whether the spatial integration should be further improved or the spatial outliers should be protected. To demonstrate the usefulness of the proposed method, we applied it in two case study areas, namely, Changping District and Pinggu District in Beijing. The results show that the proposed method can retain the spatial outliers while ensuring that the clusters are roughly contiguous. This method can be used as a simple but powerful and easy-to-interpret alternative to existing geographical spatial clustering methods.
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Affiliation(s)
- Haoyu Wang
- Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
| | - Changqing Song
- Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China.
| | - Jinfeng Wang
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| | - Peichao Gao
- Faculty of Geographical Science, Beijing Normal University, Beijing, 100875, China
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, 100875, China
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Kimi R, Beegum M, Nandi S, Dubal ZB, Sinha DK, Singh BR, Vinodhkumar OR. Spatio-temporal dynamics and distributional trend analysis of African swine fever outbreaks (2020-2021) in North-East India. Trop Anim Health Prod 2024; 56:39. [PMID: 38206527 DOI: 10.1007/s11250-023-03883-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 11/21/2023] [Indexed: 01/12/2024]
Abstract
African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern (NE) parts, since 47% of the Indian pig population is distributed in the NE region. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020-2021. The ASF outbreak data (2020-2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify the potential clusters, spatio-temporal aggregation, temporal distribution, disease spread, density maps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020-2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p < .01) and 2021 (z score = 4.89, p < .01). Spatial autocorrelation and Moran's I value (0.05-0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space-time analysis detected five, five, and two statistically significant (p < .01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north-south (2021), indicate the possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and disease control measures in NE Indian states.
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Affiliation(s)
- Rotluang Kimi
- Division of Epidemiology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, India
| | - Mufeeda Beegum
- Division of Epidemiology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, India
| | - S Nandi
- CADRAD, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Z B Dubal
- Division of Veterinary Public Health, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - D K Sinha
- Division of Epidemiology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, India
| | - B R Singh
- Division of Epidemiology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, India
| | - Obli Rajendran Vinodhkumar
- Division of Epidemiology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, Uttar Pradesh, India.
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Kitawa YS, Asfaw ZG. Space-time modelling of monthly malaria incidence for seasonal associated drivers and early epidemic detection in Southern Ethiopia. Malar J 2023; 22:301. [PMID: 37814300 PMCID: PMC10563281 DOI: 10.1186/s12936-023-04742-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 10/04/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Although Ethiopia has made great strides in recent years to reduce the threat of malaria, the disease remains a significant issue in most districts of the country. It constantly disappears in parts of the areas before reappearing in others with erratic transmission rates. Thus, developing a malaria epidemic early warning system is important to support the prevention and control of the incidence. METHODS Space-time malaria risk mapping is essential to monitor and evaluate priority zones, refocus intervention, and enable planning for future health targets. From August 2013 to May 2019, the researcher considered an aggregated count of genus Plasmodium falciparum from 149 districts in Southern Ethiopia. Afterwards, a malaria epidemic early warning system was developed using model-based geostatistics, which helped to chart the disease's spread and future management. RESULTS Risk factors like precipitation, temperature, humidity, and nighttime light are significantly associated with malaria with different rates across the districts. Districts in the southwest, including Selamago, Bero, and Hamer, had higher rates of malaria risk, whereas in the south and centre like Arbaminch and Hawassa had moderate rates. The distribution is inconsistent and varies across time and space with the seasons. CONCLUSION Despite the importance of spatial correlation in disease risk mapping, it may occasionally be a good idea to generate epidemic early warning independently in each district to get a quick picture of disease risk. A system like this is essential for spotting numerous inconsistencies in lower administrative levels early enough to take corrective action before outbreaks arise.
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Affiliation(s)
- Yonas Shuke Kitawa
- Department of Statistics, College of Natural and Computational Sciences, Hawassa University, Hawassa, Ethiopia.
| | - Zeytu Gashaw Asfaw
- Department of Bio-statistics and Epidemiology, School of Public Health, Addis Ababa University, Addis Ababa, Ethiopia
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Nigussie TZ, Zewotir TT, Muluneh EK. Seasonal and spatial variations of malaria transmissions in northwest Ethiopia: Evaluating climate and environmental effects using generalized additive model. Heliyon 2023; 9:e15252. [PMID: 37089331 PMCID: PMC10114238 DOI: 10.1016/j.heliyon.2023.e15252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 03/16/2023] [Accepted: 03/31/2023] [Indexed: 04/25/2023] Open
Abstract
The impacts of climate change and environmental predictors on malaria epidemiology remain unclear and not well investigated in the Sub-Sahara African region. This study was aimed to investigate the nonlinear effects of climate and environmental factors on monthly malaria cases in northwest Ethiopia, considering space-time interaction effects. The monthly malaria cases and populations sizes of the 152 districts were obtained from the Amhara public health institute and the central statistical agency of Ethiopia. The climate and environmental data were retrieved from US National Oceanic and Atmospheric Administration. The data were analyzed using a spatiotemporal generalized additive model. The spatial, temporal, and space-time interaction effects had higher contributions in explaining the spatiotemporal distribution of malaria transmissions. Malaria transmission was seasonal, in which a higher number of cases occurred from September to November. The long-term trend of malaria incidence has decreased between 2012 and 2018 and has turned to an increased pattern since 2019. Areas neighborhood to the Abay gorge and Benshangul-Gumuz, South Sudan, and Sudan border have higher spatial effects. Climate and environmental predictors had significant nonlinear effects, in which their effects are not stationary through the ranges of values of variables, and they had a smaller contributions in explaining the variabilities of malaria incidence compared to seasonal, spatial and temporal effects. Effects of climate and environmental predictors were nonlinear and varied across areas, ecology, and landscape of the study sites, which had little contribution to explaining malaria transmission variabilities with an account of space and time dimensions. Hence, exploring and developing an early warning system that predicts the outbreak of malaria transmission would have an essential role in controlling, preventing, and eliminating malaria in areas with lower and higher transmission levels and ultimately lead to the achievement of malaria GTS milestones.
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Affiliation(s)
- Teshager Zerihun Nigussie
- Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia
- Department of Statistics, Faculty of Natural and Computational Sciences, Debre Tabor University, Debre Tabor, Ethiopia
- Corresponding author. Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
| | - Temesgen T. Zewotir
- School of Mathematics, Statistics and Computer Science, College of Agriculture Engineering and Science, University of KwaZulu-Natal, Durban, South Africa
| | - Essey Kebede Muluneh
- School of Public Health, College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia
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Luo W, Liu Z, Zhou Y, Zhao Y, Li YE, Masrur A, Yu M. Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method. JMIR Public Health Surveill 2022; 8:e35840. [PMID: 35861674 PMCID: PMC9364972 DOI: 10.2196/35840] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/19/2022] [Accepted: 07/19/2022] [Indexed: 12/18/2022] Open
Abstract
Background The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. Objective The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. Methods We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. Results We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. Conclusions The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.
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Affiliation(s)
- Wei Luo
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Zhaoyin Liu
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Yuxuan Zhou
- Department of Geography, National University of Singapore, Singapore, Singapore
| | - Yumin Zhao
- Department of Civil and Environmental Engineering, National University of Singapore, Singapore, Singapore
| | - Yunyue Elita Li
- Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, United States
| | - Arif Masrur
- Department of Geography, Pennsylvania State University, State College, PA, United States
| | - Manzhu Yu
- Department of Geography, Pennsylvania State University, State College, PA, United States
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