Lutfan Lazuardi

Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.

Carbon footprint of telemedicine solutions--unexplored opportunity for reducing carbon emissions in the health sector.

Background The healthcare sector is a significant contributor to global carbon emissions, in part due to extensive travelling by patients and health workers. Objectives To evaluate the potential of telemedicine services based on videoconferencing technology to reduce travelling and thus carbon emissions in the healthcare sector. Methods A life cycle inventory was performed to evaluate the carbon reduction potential of telemedicine activities beyond a reduction in travel related emissions. The study included two rehabilitation units at Umeå University Hospital in Sweden. Carbon emissions generated during telemedicine appointments were compared with care-as-usual scenarios. Upper and lower bound emissions scenarios were created based on different teleconferencing solutions and thresholds for when telemedicine becomes favorable were estimated. Sensitivity analyses were performed to pinpoint the most important contributors to emissions for different set-ups and use cases. Results Replacing physical visits with telemedicine appointments resulted in a significant 40–70 times decrease in carbon emissions. Factors such as meeting duration, bandwidth and use rates influence emissions to various extents. According to the lower bound scenario, telemedicine becomes a greener choice at a distance of a few kilometers when the alternative is transport by car. Conclusions Telemedicine is a potent carbon reduction strategy in the health sector. But to contribute significantly to climate change mitigation, a paradigm shift might be required where telemedicine is regarded as an essential component of ordinary health care activities and not only considered to be a service to the few who lack access to care due to geography, isolation or other constraints.

Contempo: A home care model to enhance the wellbeing of elderly people

Fast growing population of elderly people has recently been a serious issue in many countries and becomes a global concern in the world. Most elderly people require assistance in their daily life, including in maintaining their wellbeing, taking care of their health, or responding to emergency medical situations. The need for caring for elderly people, particularly to maintain their wellbeing, has been growing significantly. The objective of this research is to seek improvements in quality of life that can enhance the wellbeing of elderly people through innovations in information technology. This research is also expected to be a significant contribution for dealing with ageing population as a common issue in many countries. This paper deals with a pervasive health care technologies and ambient environment, which propose a model of Connected Technologies for Home Care for Elderly People (Contempo) to enhance the wellbeing of elderly people. A model of wellbeing assessment is also proposed to examine the proposed Contempo.

GIS for Dengue Surveillance: Strengthening Collaborations

Dear Sir: We congratulate Duncombe and colleagues1 for their interesting review concerning the role of the geographic information system (GIS) for dengue surveillance. They provide examples, particularly from the developing countries, showing the various functions of GIS to automate spatial identification, visualization, analysis, and decision making aiming to assist dengue-related public health actions. Despite the challenges, the authors advocate the use of open access technologies and international collaboration to improve dengue surveillance at the sub-national, country, and regional levels. Although we agree with the international collaboration proposition, on the contrary, we doubt that this initiative alone could lead to improvement of dengue surveillance at a sub-national level. Incomplete data and untimely reporting are the main challenges to surveillance systems in developing countries. Although the authors mention some technical challenges confronted by developing countries, they do not sufficiently recognize issues in their individual health systems,2 particularly decentralization policies, which interfere with comprehensiveness and accuracy of surveillance systems. Indonesia and the Philippines are among the dengue-burdened countries that have decentralized various health policies in the last decade, leading, in our view to at least three anecdotal phenomena. First, until well established, decentralization policy leads to structural and regional changes. Elimination, formation, amalgamation, and expansion of regions have problems in initial implementation, for example in causing troublesome spatial data processing and analyses. Second, more autonomous authority in the lower level has initiated idiosyncratic local rules and procedures. Unfortunately, national policies have not been implemented with decentralization of procedures and data collection/exchange. Third, policy makers at the national level are still confronted with inequities and gaps among regions in terms of human resources, fiscal capacity, local health problems, and infrastructures (including information and communication technologies) when commencing this reform. Finally, recommendations to foster international collaboration for GIS dengue surveillance should be balanced with efforts to strengthen surveillance capacity at national and subnational levels, particularly with regard to standard data collection, exchange and sharing.

Perspectives of human centered design and interoperability in ubiquitous home care for elderly people

In response to elderly people with independent living, healthcare services for elderly people is intended to be provided at homes, particularly for elderly with long-term care services and some disabilities. The health service can be facilitated utilizing the use of ubiquitous home care, equipped by connected technologies. Interoperability system has an important role in ubiquitous home care. By understanding the system interoperability requirements, the resulting ubiquitous home care system design becomes more effectively. Unfortunately, usability in designing ubiquitous home care is still less concern. Lack of usability concern will contribute in poorly designed of home care system for elderly. Elderly people will get difficulties in learning and operating the system. Poor designed system can create many human errors. Designing a user friendly ubiquitous home care system based on needs and preferences of elderly is such an important thing. The objective of this paper is to investigate a ubiquitous home care model for the elderly from the perspective of human centered design and interoperability. As a result, a well-designed home care for elderly people based on human centered approach will be able to encapsulate interoperability problems among medical devices.

Google Trends Data on Dengue (Indonesia)

Digital traces are rapidly used for health monitoring purposes in recent years. This approach is growing as the consequence of increased use of mobile phone, Internet, and machine learning. Many studies reported the use of Google Trends data as a potential data source to assist traditional surveillance systems. The rise of Internet penetration (54.7%) and the huge utilization of Google (98%) indicate the potential use of Google Trends in Indonesia. Since no previous study exists on validating official dengue reports and Google Trends data in Indonesia and comparing them over time, this study aimed to cover this gap. This research was a quantitative study using time series data (2012-2016). Two sets of data were validated using Moving Average analysis in Microsoft Excel. Pearson and Time lag correlations were also used to measure the association between those data. Moving Average analysis showed that Google Trends data have a linear time series pattern with official dengue report. Pearson correlation indicated high correlation for 3 defined search terms with R-value range from 0.921 to 0.937 (p≤0.05, overall period) which showed increasing trend in epidemic periods (2015-2016). Time lag correlation also indicated that Google Trends data can potentially be used for an early warning system and novel tool to monitor public reaction before the increase of dengue cases and during the outbreak. Google Trends data have a linear time series pattern and statistically correlated with annual official dengue reports. Identification of information seeking behavior is needed to support the use of Google Trends for disease surveillance in Indonesia.

E-Referral System Modeling Using Fuzzy Multiple-Criteria Decision Making

Received Mar 3, 2018 Revised Apr 11, 2018 Accepted Apr 21, 2018 Paintball has gained a huge popularity in Malaysia with growing number of tournaments organized nationwide. Currently, Ideal Pro Event, one of the paintball organizer found difficulties to pair a suitable opponent to against one another in a tournament. This is largely due to the manual matchmaking method that only randomly matches one team with another. Consequently, it is crucial to ensure a balanced tournament bracket where eventual winners and losers not facing one another in the very first round. This study proposes an intelligent matchmaking using Particle Swarm Optimization (PSO) and tournament management system for paintball organizers. PSO is a swarm intelligence algorithm that optimizes problems by gradually improving its current solutions, therefore countenancing the tournament bracket to be continually improved until the best is produced. Indirectly, through the development of the system, it is consider as an intelligence business idea since it able to save time and enhance the company productivity. This algorithm has been tested using 3 size of population; 100, 1000 and 10,000. As a result, the speed of convergence is consistent and has not been affected through big population.N. N. S. Abdul Rahman, N.M. Saad, A. R. Abdullah, M. R. M. Hassan, M. S. S. M. Basir, N. S. M. Noor 1,2,4,6Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 2,3Center for Robotics and Industrial Automation, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 3,5Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, MalaysiaLight rail transit (LRT), or fast tram is urban public transport using rolling stock similar to a tramway, but operating at a higher capacity, and often on an exclusive right-of-way. Indonesia as one of developing countries has been developed the LRT in two cities of Indonesia, Palembang and Jakarta. There are opinions toward the development of LRT, negative and positive opinions. To reveal the level of LRT development acceptance, this research uses machine learning approach to analyze the data which is gathered through social media. By conducting this paper, the data is modeled and classified in order to analyze the social sentiment towards the LRT development.Mohamad, S., Nasir, F.M., Sunar, M.S., Isa, K., Hanifa, R.M., Shah, S.M., Ribuan, M.N., Ahmad, A. 1,4,6,7,8Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 1,2,3UTM-IRDA Digital Media Centre, Media and Game Innovation Centre of Excellence, Universiti Teknologi Malaysia, Johor, Malaysia 1,2,3Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia 5Centre for Diploma Studies, Universiti Tun Hussein Onn Malaysia, Johor, Malaysia 6Research Centre for Applied Electromagnetics, Universiti Tun Hussein Onn Malaysia, Johor, MalaysiaReceived Jan 31, 2018 Revised Apr 21, 2018 Accepted Apr 30, 2018 Bluetooth is an emerging mobile ad-hoc network that accredits wireless communication to connect various short range devices. A single hop network called piconet is the basic communication topology of bluetooth which allows only eight active devices for communication among them seven are active slaves controlled by one master. Multiple piconets are interconnected through a common node, known as Relay, to form a massive network called as Scatternet. It is obvious that the performance of Scatternet scheduling is highly dependent and directly proportionate with the performance of the Relay node. In contrary, by reducing the number of Relays, it may lead to poor performance, since every Relay has to perform and support several piconet connections. The primary focus of this study is to observe the performance metrics that affects the inter-piconet scheduling since the Relay node’s role is like switch between multiple piconets. In this paper, we address and analyze the performance issues to be taken into consideration for efficient data flow in Scatternet based on Relay node.

Designing and collecting data for a longitudinal study : the Sleman Health and Demographic Surveillance System (HDSS).

Background: This paper describes the methodological considerations of developing an urban Health and Demographic Surveillance System (HDSS), in the Sleman District of Yogyakarta, Indonesia. Methods: 1) The Sleman District was selected because it is mostly an urban area. 2) The minimum sample size was calculated to measure infant mortality as the key variable and resulted in a sample of 4942 households. A two-stage cluster sampling procedure with probability proportionate to size was applied; first, 216 Censuses Blocks (CBs) were selected, and second, 25 households in each CB were selected. 3) A baseline survey was started in 2015, and collected data on demographic and economic characteristics and verbal autopsy (VA); the 2nd cycle collected updated demographic data, VA, type of morbidity (communicable and non-communicable diseases, disability and injury) and health access. 4) The data were collected at a home visit through a Computer-Assisted Personal Interview (CAPI) on a tablet device, and the data were transferred to the server through the Internet. 5) The quality control consisted of spot-checks of 5% of interviews to control for adherence to the protocol, re-checks to ensure the validity of the interview, and computer-based data cleaning. 6) A utilization system was designed for policy-makers (government) and researchers. Results: In total, 5147 households participated in the baseline assessment in 2015, and 4996 households participated in the second cycle in 2016 (97.0% response rate). Conclusions: Development of an urban HDSS is possible and is beneficial in providing data complementary to the existing demographic and health information system at local, national and global levels.

Analisis Determinan Ketersediaan Dokter Spesialis dan Gambaran Fasilitas Kesehatan di RSU Pemerintah Kabupaten/Kota Indonesia (Analisis Data Rifaskes 2011)

Determinants of specialist availability in public hospitals: analysis of 2011 Rifaskes ABSTRACT Background:Indonesia still faces theproblem of unequal distribution of specialist doctors. The ratio of health workers per 100.000 population has not met the target. In 2008, the ratio of health workers to medical specialist per 100.000 population amounted to 7,73 compared to the target which is 9. Some areas of development in underserved areas, such as low economic power, lack of hospital system capacity and hospital medical equipment, have been neglected by government. Engagement of stakeholder to improve hospital quality system is a critical element to contribute to the policy of specialist doctors dsitribution, typically to increase the number of specialist doctors practising in rural and remote areas. Objective: To assess the determinants ofavailability of specialist doctors in government/public hospitals and to find out the correlation of variable factors. Methods: A cross sectional design was adopted for this study, in which 7 factors were chosen to assess determinant of availability of specialist doctors using a Health Facilities Research (Rifaskes) conducted Bay the HealthMinistry in 2011 and to describe availibility of hospital facilities in the Indonesian public hospitals. Results: Bivariate analysis indicated that level of district, hospital accredited, BLU versus Non-BLU, remuneration, hospital facilities, dan GNP significantly affect to the number of specialist doctors (p <0,05). Logistic regression indicated that the strongest predictors of availibility specialist is accredited public hospital with 12 standard of care (odds ratio 9,32 ; 95% CI: 1,2-72,4) ; p < 0.03). Level of district have significantly associated to availibility specialist in public hospital (odds ratio 2,15 ; (95% CI: 1,36-3,39) ; p = 0,001). Conclusion: The current study makes an important contribution to the literature in finding the determinants of distribution of specialist doctors in public hospital in Indonesia to address maldistribution between urban and rural barriers. Additional research is needed to examine preference to choose rural location and the incorporation of other retention strategies, such as medical educationinitiatives, community and professional support, differential rural fees and alternate funding models. Keywords: Availability,specialist doctors, specialistic facilities

Emergency alert prediction for elderly based on supervised learning

At the older age, the likelihood of disability increases and hence the increasing need for long-term care and facilities to assist elderly people who endure gradual loss of body function. Early detection of changes in health condition of elderly can increase safety for elderly people in emergency conditions. Alert prediction can be viewed as an assistive technology that will deliver appropriate escalation in the earliest time so that elderly can receive immediate responses. Supervised learning can be used as a tool to predict alert in emergency condition by training historical data of elderly behaviors and conditions. This paper proposed emergency alert prediction using supervised learning algorithms. Three algorithms of supervised learning, namely deep learning, k-NN, and LVQ were used to simulate the proposed system. The objective of this paper is to investigate the performance of three algorithms in making emergency alert prediction for elderly living independently. We conducted experiments for 30 days to elderly living independently and we obtained 1038 datasets. The simulation results showed deep learning performed the best accuracy 99.57% correct. Whereas k-NN obtained the best accuracy 90.79% correct, and LVQ obtained the best accuracy 80.32%.