Displaying all 18 publications

Abstract:
Sort:
  1. Spatio-temporal patterns of dengue in Malaysia: combining address and sub-district level
    Ling CY, Gruebner O, Krämer A, Lakes T
    Geospat Health, 2014 Nov;9(1):131-40.
    PMID: 25545931
    Spatio-temporal patterns of dengue risk in Malaysia were studied both at the address and the sub-district level in the province of Selangor and the Federal Territory of Kuala Lumpur. We geocoded laboratory-confirmed dengue cases from the years 2008 to 2010 at the address level and further aggregated the cases in proportion to the population at risk at the sub-district level. Kulldorff's spatial scan statistic was applied for the investigation that identified changing spatial patterns of dengue cases at both levels. At the address level, spatio-temporal clusters of dengue cases were concentrated at the central and south-eastern part of the study area in the early part of the years studied. Analyses at the sub-district level revealed a consistent spatial clustering of a high number of cases proportional to the population at risk. Linking both levels assisted in the identification of differences and confirmed the presence of areas at high risk for dengue infection. Our results suggest that the observed dengue cases had both a spatial and a temporal epidemiological component, which needs to be acknowledged and addressed to develop efficient control measures, including spatially explicit vector control. Our findings highlight the importance of detailed geographical analysis of disease cases in heterogeneous environments with a focus on clustered populations at different spatial and temporal scales. We conclude that bringing together information on the spatio-temporal distribution of dengue cases with a deeper insight of linkages between dengue risk, climate factors and land use constitutes an important step towards the development of an effective risk management strategy.
  2. Temporal and spatial mapping of hand, foot and mouth disease in Sarawak, Malaysia
    Sham NM, Krishnarajah I, Ibrahim NA, Lye MS
    Geospat Health, 2014 May;8(2):503-7.
    PMID: 24893027
    Hand, foot and mouth disease (HFMD) is endemic in Sarawak, Malaysia. In this study, a geographical information system (GIS) was used to investigate the relationship between the reported HFMD cases and the spatial patterns in 11 districts of Sarawak from 2006 to 2012. Within this 7-years period, the highest number of reported HFMD cases occurred in 2006, followed by 2012, 2008, 2009, 2007, 2010 and 2011, in descending order. However, while there was no significant distribution pattern or clustering in the first part of the study period (2006 to 2011) based on Moran's I statistic, spatial autocorrelation (P = 0.068) was observed in 2012.
  3. Mapping and modelling the geographical distribution of soil-transmitted helminthiases in Peninsular Malaysia: implications for control approaches
    Ngui R, Shafie A, Chua KH, Mistam MS, Al-Mekhlafi HM, Sulaiman WW, et al.
    Geospat Health, 2014 May;8(2):365-76.
    PMID: 24893014
    Soil-transmitted helminth (STH) infections in Malaysia are still highly prevalent, especially in rural and remote communities. Complete estimations of the total disease burden in the country has not been performed, since available data are not easily accessible in the public domain. The current study utilised geographical information system (GIS) to collate and map the distribution of STH infections from available empirical survey data in Peninsular Malaysia, highlighting areas where information is lacking. The assembled database, comprising surveys conducted between 1970 and 2012 in 99 different locations, represents one of the most comprehensive compilations of STH infections in the country. It was found that the geographical distribution of STH varies considerably with no clear pattern across the surveyed locations. Our attempt to generate predictive risk maps of STH infections on the basis of ecological limits such as climate and other environmental factors shows that the prevalence of Ascaris lumbricoides is low along the western coast and the southern part of the country, whilst the prevalence is high in the central plains and in the North. In the present study, we demonstrate that GIS can play an important role in providing data for the implementation of sustainable and effective STH control programmes to policy-makers and authorities in charge.
  4. A climate distribution model of malaria transmission in Sudan
    Musa MI, Shohaimi S, Hashim NR, Krishnarajah I
    Geospat Health, 2012 Nov;7(1):27-36.
    PMID: 23242678
    Malaria remains a major health problem in Sudan. With a population exceeding 39 million, there are around 7.5 million cases and 35,000 deaths every year. The predicted distribution of malaria derived from climate factors such as maximum and minimum temperatures, rainfall and relative humidity was compared with the actual number of malaria cases in Sudan for the period 2004 to 2010. The predictive calculations were done by fuzzy logic suitability (FLS) applied to the numerical distribution of malaria transmission based on the life cycle characteristics of the Anopheles mosquito accounting for the impact of climate factors on malaria transmission. This information is visualized as a series of maps (presented in video format) using a geographical information systems (GIS) approach. The climate factors were found to be suitable for malaria transmission in the period of May to October, whereas the actual case rates of malaria were high from June to November indicating a positive correlation. While comparisons between the prediction model for June and the case rate model for July did not show a high degree of association (18%), the results later in the year were better, reaching the highest level (55%) for October prediction and November case rate.
  5. Risk mapping of dengue in Selangor and Kuala Lumpur, Malaysia
    Hassan H, Shohaimi S, Hashim NR
    Geospat Health, 2012 Nov;7(1):21-5.
    PMID: 23242677
    Dengue fever is a recurring public health problem afflicting thousands of Malaysians annually. In this paper, the risk map for dengue fever in the peninsular Malaysian states of Selangor and Kuala Lumpur was modelled based on co-kriging and geographical information systems. Using population density and rainfall as the model's only input factors, the area with the highest risk for dengue infection was given as Gombak and Petaling, two districts located on opposite sides of Kuala Lumpur city that was also included in the risk assessment. Comparison of the modelled risk map with the dengue case dataset of 2010, obtained from the Ministry of Health of Malaysia, confirmed that the highest number of cases had been found in an area centred on Kuala Lumpur as predicted our risk profiling.
  6. Fulltext Spatial pattern of dengue cases: An analysis in Bangi District, Selangor, Malaysia
    Abd Majid N, Rainis R, Sahani M, Mohamed AF, Abdul Ghani Aziz SA, Muhamad Nazi N
    Geospat Health, 2021 03 11;16(1).
    PMID: 33706498 DOI: 10.4081/gh.2021.915
    In recent decades, dengue outbreaks have become increasingly common around the developing countries, including Malaysia. Thus, it is essential for rural as well as urbanised livelihood to understand the distribution pattern of this infection. The objective of this study is to determine the trend of dengue cases reported from the year 2014 to 2018 and the spatial pattern for this spread. Spatial statistical analyses conducted found that the distribution pattern and spatial mean centre for dengue cases were clustered in the eastern part of the Bangi region. Directional distribution observed that the elongated polygon of dengue cluster stretched from the Northeast to the Southwest of Bangi District. The standard distance observed for dengue cases was smallest in the year 2014 (0.017 m), and largest in 2016 (0.019 m), whereas in the year 2015, 2017 and 2018, it measured 0.018 m. The average nearest neighbour analysis also displayed clustered patterns for dengue cases in the Bangi District. The three spatial statistical analyses (spatial mean centre, standard distance and directional distribution) findings illustrate that the dengue cases from the year 2014 to 2018 are clustered in the Northeast to the Southwest of the study region.
  7. Fulltext Geographical distribution and spatio-temporal patterns of hospitalization due to dengue infection at a leading specialist hospital in Malaysia
    Low GKK, Papapreponis P, Isa RM, Gan SC, Chee HY, Te KK, et al.
    Geospat Health, 2018 05 07;13(1):642.
    PMID: 29772885 DOI: 10.4081/gh.2018.642
    Increasing numbers of dengue infection worldwide have led to a rise in deaths due to complications caused by this disease. We present here a cross-sectional study of dengue patients who attended the Emergency and Trauma Department of Ampang Hospital, one of Malaysia's leading specialist hospitals. The objective was to search for potential clustering of severe dengue, in space and/or time, among the annual admissions with the secondary objective to describe the spatio-temporal pattern of all dengue cases admitted to this hospital. The dengue status of the patients was confirmed serologically with the geographic location of the patients determined by residency, but not more specific than the street level. A total of 1165 dengue patients were included in the analysis using SaTScan software. The mean age of these patients was 27.8 years, with a standard deviation of 14.2 years and an age range from 1 to 77 years, among whom 54 (4.6%) were cases of severe dengue. A cluster of general dengue cases was identified occurring from October to December in the study year of 2015 but the inclusion of severe dengue in that cluster was not statistically significant (P=0.862). The standardized incidence ratio was 1.51. General presence of dengue cases was, however, detected to be concentrated at the end of the year, which should be useful for hospital planning and management if this pattern holds.
  8. Fulltext Correlation analysis of air pollutant index levels and dengue cases across five different zones in Selangor, Malaysia
    Thiruchelvam L, Dass SC, Zaki R, Yahya A, Asirvadam VS
    Geospat Health, 2018 05 07;13(1):613.
    PMID: 29772882 DOI: 10.4081/gh.2018.613
    This study investigated the potential relationship between dengue cases and air quality - as measured by the Air Pollution Index (API) for five zones in the state of Selangor, Malaysia. Dengue case patterns can be learned using prediction models based on feedback (lagged terms). However, the question whether air quality affects dengue cases is still not thoroughly investigated based on such feedback models. This work developed dengue prediction models using the autoregressive integrated moving average (ARIMA) and ARIMA with an exogeneous variable (ARIMAX) time series methodologies with API as the exogeneous variable. The Box Jenkins approach based on maximum likelihood was used for analysis as it gives effective model estimates and prediction. Three stages of model comparison were carried out for each zone: first with ARIMA models without API, then ARIMAX models with API data from the API station for that zone and finally, ARIMAX models with API data from the zone and spatially neighbouring zones. Bayesian Information Criterion (BIC) gives goodness-of-fit versus parsimony comparisons between all elicited models. Our study found that ARIMA models, with the lowest BIC value, outperformed the rest in all five zones. The BIC values for the zone of Kuala Selangor were -800.66, -796.22, and -790.5229, respectively, for ARIMA only, ARIMAX with single API component and ARIMAX with API components from its zone and spatially neighbouring zones. Therefore, we concluded that API levels, either temporally for each zone or spatio- temporally based on neighbouring zones, do not have a significant effect on dengue cases.
  9. Fulltext Development of a web-geographical information system application for plotting tuberculosis cases
    Mohidem NA, Osman M, Muharam FM, Mohd Elias S, Shaharudin R, Hashim Z
    Geospat Health, 2021 Oct 19;16(2).
    PMID: 34672178 DOI: 10.4081/gh.2021.980
    In the last few decades, public health surveillance has increasingly applied statistical methods to analyze the spatial disease distributions. Nevertheless, contact tracing and follow up control measures for tuberculosis (TB) patients remain challenging because public health officers often lack the programming skills needed to utilize the software appropriately. This study aimed to develop a more user-friendly application by applying the CodeIgniter framework for server development, ArcGIS JavaScript for data display and a web application based on JavaScript and Hypertext Preprocessor to build the server's interface, while a webGIS technology was used for mapping. The performance of this approach was tested based on 3325 TB cases and their sociodemographic data, such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency status, and smoking status between 1st January 2013 and 31st December 2017 in Gombak, Selangor, Malaysia. These data were collected from the Gombak District Health Office and Rawang Health Clinic. Latitude and longitude of the location for each case was geocoded by uploading spatial data using Google Earth and the main output was an interactive map displaying location of each case. Filters are available for the selection of the various sociodemographic factors of interest. The application developed should assist public health experts to utilize spatial data for the surveillance purposes comprehensively as well as for the drafting of regulations aimed at to reducing mortality and morbidity and thus minimizing the public health impact of the disease.
  10. Fulltext Spatial accessibility to health care services among children with cerebral palsy in Johor, Peninsular Malaysia
    Abu Bakar MA, Samat N, Yaacob NS
    Geospat Health, 2021 10 19;16(2).
    PMID: 34672180 DOI: 10.4081/gh.2021.987
    Cerebral palsy (CP) is one of the most common causes of disability in childhood, leading to functional limitations and poor nutritional status. Families with CP children face challenges in providing proper care. Thus, accessibility of CP patients to health facilities is important to ensure that they can maintain regular visits to health facilities for proper treatment and care. The current study aimed to map the spatial distribution of CP in Johor, Malaysia and measure the accessibility of CP patients to nearby hospitals, health clinics and community-based rehabilitation centres. The study is based on CP cases in 2017 obtained from the Department of Social Welfare, Malaysia and analysed using the average nearest neighbour, buffer analysis and Kernel Density Estimation. Results indicate that there is generally good access to health care services for many of the CP children in Johor, but for 25% of those living more than 10 km away from the health clinics or community-based rehabilitation centres, regular visits can be a problem. This information should be used for targeted intervention and planning for health care strategies. Furthermore, information on hospital accessibility of CP children would allow for planning of proper and regular treatment for these patients. The study has shown that it is possible to improve the understanding of the distribution of CP cases by integrating spatial analysis using geographical information systems without relying on official information about the density of populations.
  11. Fulltext Detecting dengue outbreaks in Malaysia using geospatial techniques
    Nellis S, Loong SK, Abd-Jamil J, Fauzi R, AbuBakar S
    Geospat Health, 2021 11 03;16(2).
    PMID: 34730321 DOI: 10.4081/gh.2021.1008
    Dengue is a complex disease with an increasing number of infections worldwide. This study aimed to analyse spatiotemporal dengue outbreaks using geospatial techniques and examine the effects of the weather on dengue outbreaks in the Klang Valley area, Kuala Lumpur, Malaysia. Daily weather variables including rainfall, temperature (maximum and minimum) and wind speed were acquired together with the daily reported dengue cases data from 2001 to 2011 and converted into geospatial format to identify whether there was a specific pattern of the dengue outbreaks. The association between these variables and dengue outbreaks was assessed using Spearman's correlation. The result showed that dengue outbreaks consistently occurred in the study area during a 11-year study period. And that the strongest outbreaks frequently occurred in two high-rise apartment buildings located in Kuala Lumpur City centre. The results also show significant negative correlations between maximum temperature and minimum temperature on dengue outbreaks around the study area as well as in the area of the high-rise apartment buildings in Kuala Lumpur City centre.
  12. Fulltext Vaccine hesitancy in East Malaysia (Sabah): A survey of the national COVID-19 immunisation programme
    Jafar A, Mapa MT, Sakke N, Dollah R, Joko EP, Atang C, et al.
    Geospat Health, 2022 01 14;17(s1).
    PMID: 35147010 DOI: 10.4081/gh.2022.1037
    The Malaysian government has introduced the National COVID-19 Immunisation Programme (PICK) as a new mechanism to address the transmission of coronavirus disease 2019 (COVID-19). Unfortunately, the number of PICK registrations is still unsatisfactory and is now even lower. The low level of participation of the Sabah (East Malaysia) population significantly impacts the PICK registrations. Therefore, this study aims to identify the factors that cause vaccine hesitancy among the people of Sabah. This study seeks to identify these trends based on zone and district boundaries. A total of 1024 respondents were sampled in this study. Raw data collected through the survey method were analysed using K-means clustering, principal component analysis (PCA), and spatial analysis. The study discovered that factors including confidence, authority, mainstream media, complacency, social media, and convenience are the top causes of vaccine hesitancy among respondents. This study also revealed that the Sabah population's key variables causing vaccine hesitancy to vary by region (zones and districts). The conclusion is significant as a source of supporting data for stakeholders seeking to identify the Sabah population's constraints in each region and therefore, it would help improve PICK management's performance in Sabah.
  13. Fulltext Future malaria spatial pattern based on the potential global warming impact in South and Southeast Asia
    Khormi HM, Kumar L
    Geospat Health, 2016 11 21;11(3):416.
    PMID: 27903054 DOI: 10.4081/gh.2016.416
    We used the Model for Interdisciplinary Research on Climate-H climate model with the A2 Special Report on Emissions Scenarios for the years 2050 and 2100 and CLIMEX software for projections to illustrate the potential impact of climate change on the spatial distributions of malaria in China, India, Indochina, Indonesia, and The Philippines based on climate variables such as temperature, moisture, heat, cold and dryness. The model was calibrated using data from several knowledge domains, including geographical distribution records. The areas in which malaria has currently been detected are consistent with those showing high values of the ecoclimatic index in the CLIMEX model. The match between prediction and reality was found to be high. More than 90% of the observed malaria distribution points were associated with the currently known suitable climate conditions. Climate suitability for malaria is projected to decrease in India, southern Myanmar, southern Thailand, eastern Borneo, and the region bordering Cambodia, Malaysia and the Indonesian islands, while it is expected to increase in southern and south-eastern China and Taiwan. The climatic models for Anopheles mosquitoes presented here should be useful for malaria control, monitoring, and management, particularly considering these future climate scenarios.
  14. Fulltext Spatial clustering of colorectal cancer in Malaysia
    Syed Soffian SS, Mohammed Nawi A, Hod R, Abdul Maulud KN, Mohd Azmi AT, Hasim Hashim MH, et al.
    Geospat Health, 2023 May 25;18(1).
    PMID: 37246545 DOI: 10.4081/gh.2023.1158
    INTRODUCTION: The rise in colorectal cancer (CRC) incidence becomes a global concern. As geographical variations in the CRC incidence suggests the role of area-level determinants, the current study was designed to identify the spatial distribution pattern of CRC at the neighbourhood level in Malaysia.

    METHOD: Newly diagnosed CRC cases between 2010 and 2016 in Malaysia were identified from the National Cancer Registry. Residential addresses were geocoded. Clustering analysis was subsequently performed to examine the spatial dependence between CRC cases. Differences in socio-demographic characteristics of individuals between the clusters were also compared. Identified clusters were categorized into urban and semi-rural areas based on the population background.

    RESULT: Most of the 18 405 individuals included in the study were male (56%), aged between 60 and 69 years (30.3%) and only presented for care at stages 3 or 4 of the disease (71.3%). The states shown to have CRC clusters were Kedah, Penang, Perak, Selangor, Kuala Lumpur, Melaka, Johor, Kelantan, and Sarawak. The spatial autocorrelation detected a significant clustering pattern (Moran's Index 0.244, p< 0.01, Z score >2.58). CRC clusters in Penang, Selangor, Kuala Lumpur, Melaka, Johor, and Sarawak were in urbanized areas, while those in Kedah, Perak and Kelantan were in semi-rural areas.

    CONCLUSION: The presence of several clusters in urbanized and semi-rural areas implied the role of ecological determinants at the neighbourhood level in Malaysia.  Such findings could be used to guide the policymakers in resource allocation and cancer control.

  15. Fulltext Prediction of dengue cases using the attention-based long short-term memory (LSTM) approach
    Majeed MA, Shafri HZM, Wayayok A, Zulkafli Z
    Geospat Health, 2023 May 25;18(1).
    PMID: 37246539 DOI: 10.4081/gh.2023.1176
    This research proposes a 'temporal attention' addition for long-short term memory (LSTM) models for dengue prediction. The number of monthly dengue cases was collected for each of five Malaysian states i.e. Selangor, Kelantan, Johor, Pulau Pinang, and Melaka from 2011 to 2016. Climatic, demographic, geographic and temporal attributes were used as covariates. The proposed LSTM models with temporal attention was compared with several benchmark models including a linear support vector machine (LSVM), a radial basis function support vector machine (RBFSVM), a decision tree (DT), a shallow neural network (SANN) and a deep neural network (D-ANN). In addition, experiments were conducted to analyze the impact of look-back settings on each model performance. The results showed that the attention LSTM (A-LSTM) model performed best, with the stacked, attention LSTM (SA-LSTM) one in second place. The LSTM and stacked LSTM (S-LSTM) models performed almost identically but with the accuracy improved by the attention mechanism was added. Indeed, they were both found to be superior to the benchmark models mentioned above. The best results were obtained when all attributes were included in the model. The four models (LSTM, S-LSTM, A-LSTM and SA-LSTM) were able to accurately predict dengue presence 1-6 months ahead. Our findings provide a more accurate dengue prediction model than previously used, with the prospect of also applying this approach in other geographic areas.
  16. Fulltext Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)
    Ullah S, Mohd Nor NH, Daud H, Zainuddin N, Gandapur MSJ, Ali I, et al.
    Geospat Health, 2021 May 05;16(1).
    PMID: 33969966 DOI: 10.4081/gh.2021.961
    Coronavirus disease 2019 (COVID-19) is the current worldwide pandemic as declared by the World Health Organization (WHO) in March 2020. Being part of the ongoing global pandemic, Malaysia has recorded a total of 8639 COVID-19 cases and 121 deaths as of 30th June 2020. This study aims to detect spatial clusters of COVID-19 in Malaysia using the Spatial Scan Statistic (SaTScan™) to guide control authorities on prioritizing locations for targeted interventions. The spatial analyses were conducted on a monthly basis at the state-level from March to September 2020. The results show that the most likely cluster of COVID-19 occurred in West Malaysia repeatedly from March to June, covering three counties (two federal territories and one neighbouring state) and moved to East Malaysia in July covering two other counties. The most likely cluster shows a tendency of having moved from the western part to the eastern part of the country. These results provide information that can be used for the evidence- based interventions to control the spread of COVID-19 in Malaysia. A Correction has been published: https://doi.org/10.4081/gh.2023.1233
  17. Fulltext Correction. Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)
    Publisher T
    Geospat Health, 2023 Aug 01;18(2).
    PMID: 37526033 DOI: 10.4081/gh.2023.1233
    In the Article titled "Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020)." published May 5th, 2021, in Vol. 16(1) of Geospatial Health, an author's name was misspelled. The seventh author's name should be "Alamgir".   Reference: Ullah S, Mohd Nor NH, Daud H, Zainuddin N, Gandapur MS J, Ali I, Khalil A, 2021. Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020). Geospatial Health, 16:961. https://doi.org/10.4081/gh.2021.961.
  18. Fulltext Mitigating infectious disease risks through non-stationary flood frequency analysis: a case study in Malaysia based on natural disaster reduction strategy
    Mat Jan NA, Marsani MF, Thiruchelvam L, Zainal Abidin NB, Shabri A, Abdullah Sani SA
    Geospat Health, 2023 Nov 13;18(2).
    PMID: 37961980 DOI: 10.4081/gh.2023.1236
    The occurrence of floods has the potential to escalate the transmission of infectious diseases. To enhance our comprehension of the health impacts of flooding and facilitate effective planning for mitigation strategies, it is necessary to explore the flood risk management. The variability present in hydrological records is an important and neglecting non-stationary patterns in flood data can lead to significant biases in estimating flood quantiles. Consequently, adopting a non-stationary flood frequency analysis appears to be a suitable approach to challenge the assumption of independent and identically distributed observations in the sample. This research employed the generalized extreme value (GEV) distribution to examine annual maximum flood series. To estimate non-stationary models in the flood data, several statistical tests, including the TL-moment method was utilized on the data from ten stream-flow stations in Johor, Malaysia, which revealed that two stations, namely Kahang and Lenggor, exhibited non-stationary behaviour in their annual maximum streamflow. Two non-stationary models efficiently described the data series from these two specific stations, the control of which could reduce outbreak of infectious diseases when used for controlling the development measures of the hydraulic structures. Thus, the application of these models may help prevent biased prediction of flood occurrences leading to lower number of cases infected by disease.
Related Terms
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links