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Förbättrad forskningskapacitet och kompetens för utveckling av varningssystem för denguefeber

Forskningsprojekt Dengue är en virussjukdom som sprids till människor från myggor och som beror på vädermönster. Det finns idag inget vaccin mot dengue, och förbyggande åtgärder riktas mot att förhindra myggornas utbredning och på så sätt epidemier.

Det finns tydliga samband mellan klimatet och spridningsmönstret av dengue. Detta beror bland annat på inverkan av klimatet på myggornas livscykel och inkubationsperiod. Flera studier av forskare från Umeå universitet visar på starka positiva samband mellan dengue-epidemier och föregående meteorologiska förhållanden med en fördröjning av upp till 20 veckor. Syftet med detta projekt är att stärka forskningskapaciteten och kompetensen i Malaysia för att möjliggöra utveckling och implementering av klimatkänsliga sjukdomar som dengue.

Projektöversikt

Projektperiod

2015-01-01 2017-12-31

Finansiering

Vetenskapsrådet, 2015-2017: 750 000 kr

Forskningsämne

Folkhälsovetenskap och samhällsmedicin

Projektbeskrivning

Purpose and aims

Vector'borne diseases, principally dengue remain a major public health challenge in Malaysia. The nation experiences increase in intensity and magnitude of dengue outbreak despite the fact that concerted surveillance and control efforts, which are reinforced by health policy, legislation and enforcement mechanisms, are in place. Currently, vector control remains the only effective method to disrupt the chain of dengue transmission. Besides strengthening surveillance and control systems with enhanced capacity, an epidemic prediction capability is also required to allow timely mitigation and effective resources deployment. Such epidemic prediction capability is not demonstrated in current dengue surveillance system in Malaysia. An early warning with precise prediction enhances timely decision'making and empowers community or individual to adopt preventive measures to avoid or reduce the chance of a possible infection.

The aim of this project is to enhance research capacity and competence for developing an automated early warning system as a decision support mechanism for the control of climate'sensitive vector'borne disease, taking dengue as a case study.

Objectives of this project

1. Create a positive learning environment for knowledge transfer or exchange of disease forecasting techniques.
2. Facilitate activities to develop strategies for conducting studies on economic evaluation, automation and integration of an automated early warning system.
3. Set a platform for researchers from both institutions to advance knowledge of developing early warning of climate'sensitive vector'borne diseases.
4. Foster researchers'to'researchers relationship through activities that facilitate knowledge exchange and understanding of disease surveillance and control management.
5. Promote communication between researchers and policy makers for the development  of a functional and sustainable early warning system.

Survey of the field

The incidence, outbreak frequency, and distribution of many infectious diseases are generally expected to change as a consequence of climate change. In between 2007 and 2010, national infectious disease experts from 30 European Economic Area countries were surveyed about recent and projected infectious disease patterns in relation to climate change in their countries and the national capacity to cope with them. The ability to adapt depends on a number of factors, such as surveillance information, human resources, available technology, institutional capacity, economic resources, social equity, and political will. The majority of experts acknowledged research on climate'sensitive infectious diseases in their countries; however, only few countries are in place to monitor climate sensitive infectious diseases and the majority of these countries (including Sweden) need improvement. In 2012, dengue has been classified as the most important mosquito'borne viral disease in the world due to significant geographic spread of the virus and its vector into previously unaffected areas with significant socioeconomic and disease burden.

Dengue is a viral infectious disease transmitted by mosquitoes (vector) to human hosts. According to the report of World Health Organization (WHO) in 2012, populations residing in over 100 countries in the tropical and subtropical regions are at risk of dengue and about 50 to 100 million persons among these populations may be infected annually. Currently neither a vaccine against all serotypes of dengue virus nor an antiviral drug for treatment of dengue infection is available. Thus, effective vector control or elimination of mosquito breeding sources is the most effective method to disrupt the disease transmission. A success on dengue or vector control depends on numerous complex factors. Increasing regional population movement, degree of community commitment to vector control, evolution of dengue viruses, mosquito adaptation, and environmental changes such as climate change or global warming continue to challenge the effects of dengue control in endemic nations.

Clearly, many non'climatic factors such as herd immunity, vector control capacity, change of dominant circulating virus contribute to increase in dengue incidence. Nevertheless, several studies have documented that climate also contribute a part to the magnitude, temporal and spatial distribution of dengue. The influence of climate on dengue cases is translated through its direct impacts on the life cycle of mosquitoes and incubation period of dengue virus in mosquitoes. In a study, the applicants from Umeå University presented a lagged and non'linear relative risk relationship between climate and dengue incidence using data from Singapore. The findings show possible increase in relative risks of dengue cases in about 4 to 20 weeks after favorable climate conditions presence for mosquito breeding. The authors further demonstrated the possibility of using climate predictors such as weekly mean temperature and cumulative rainfall to forecast weekly dengue incidence up to 16 weeks in advance.

Malaysia is experiencing double diseases burden, communicable and non'communicable. In the past decade, strategic disease control programs have contributed to downward trend of most of the notifiable communicable diseases, with the exception of tuberculosis and dengue. The incidence rate of dengue fever in Malaysia increased from 24 per 100,000 population in 1990 to 44 and 181 per 100,000 in 1999 and 2007, respectively, with case fatality rate about 0.2%. Majority cases were reported from urban areas. Currently Malaysia is experiencing a surge of dengue cases with 43,346 cases reported in 2013, which is almost double compared to 21,900 cases in 2012 (MOH press statement, 2013). There has been a three'fold increase in dengue mortality in 2013 compared to 2012, with 92 deaths reported in 2013 compared to 35 in 2012 (MOH press statement, 2013). Highest incidence rate of dengue was reported from the state of Selangor. In year 2010, the incidence rate in the state of Selangor reached approximately 309 cases per 100.000 populations, given the national incidence rate of 163 cases per 100.000 populations.

Endemic and epidemic of dengue pose threats to the social and economical wellbeing of individuals, family, and even community, especially in a nation such as Malaysia where about 41% of the private households pays medical bills out of pocket. It has been suggested that communities with lower socioeconomic are more susceptible to vector'borne diseases, due to poor sanitation, water storage behavior, and living environment. A recent study by Shepard et# al. shows that the economic burden of dengue illness costs Malaysia about US$102 million per year or around US$3.72 per capita . This amount excludes the costs of dengue surveillance and control.

To combat upsurge dengue incidence, health authority in Malaysia has implemented strategies including early detection based on case, laboratory and mosquito surveillance, vector control programs, community mobilization and promotion, and mechanisms for multisectoral collaboration. The Vector Control Unit, an arm of the Disease Control Division, is a key driver to implement vector control program to eliminate mosquitoes based on a dengue surveillance, risk assessment and response framework. Current vector control includes mosquito breeding source reduction exercises, chemical spray, community participation campaigns, and installation of Ovitraps (for monitoring of mosquito density by species) in strategic locations. In recent years, the Ministry of Health, Malaysia has established the Crisis Preparedness and Recovery Centre (CPRC) to handle infectious disease outbreaks. Nevertheless, CPRC works independently of other branches of government and transport links, and it focuses only on a few diseases at any one time and lacks the capacity to monitor or accurately predict disease outbreaks. The lack of a systematic real'time outbreak risk monitoring and analysis poses difficulty for timely response.

While the Malaysian government formulated and implemented a series of strategies for dengue control measures, an early warning of outbreak that allow timely and effective response is lacking. An early warning with precise prediction of outbreak enhances timely mitigation and empowers community or individual to adopt preventive measures to avoid or reduce the chance of a possible infection. Furthermore, a system that improves decision'making in terms of manpower deployment, scale of control exercises, and location specific could largely improve existing vector surveillance and control system. Disease forecast modelling is a relatively new research area in Malaysia; thus, it is not uncommon that a data driven model'based forecast or early warning of infectious disease is not employed to enhance current surveillance and response. The needs to identify operational gaps and strengthen capacity of vector surveillance and control system continue to pose challenges to the Ministry of Health Malaysia.

In brief, Malaysia continues to experience cyclical and seasonal epidemic of dengue in recent decade, despite a series of dengue control measures are in place. The needs to improve existing dengue surveillance and control measures were highlighted. An early warning with sufficient lead'time and precise prediction of outbreak may contribute a part to dengue control.

Project description and mode of cooperation

This project fosters researchers'to'researchers relationship between Umeå University and University of Malaya and enhances research competence and skills required for the development of an automated early warning system. A long'term benefit of this project can be demonstrated through an effort of the university of Malaya to integrate disease forecast modeling as course modules for graduate students and to promote sustained research on disease control. Overall, this project creates a platform for long'term collaboration between the two institutions. Upon completion of the project, both nations could anticipate following benefits including, not exhaustive:

Sweden and Malaysia (mutual benefits):
- Researchers from both institutions gain new knowledge and skills through the process of development and implementation of an automated dengue early warning system based on mixed forecasting methods,
- Exposure to challenges and limitations in the project may inspire new or innovative ideas for future research on disease prevention measures, which may benefit both nations
- Enhanced communication and mutual understanding among researchers strengthen relationship and encourage long'term research partnership between Umeå University and University of Malaya
- Acquire advance knowledge of disease surveillance and outbreak control management in different settings

Sweden:
- Increase the scope of exposure and gain deeper knowledge pertaining to climate' sensitive vector'borne diseases that could pose potential threat to some European countries as a consequence of global warming
- Extended knowledge of vector control management related to vector'borne diseases.
- Detailed knowledge and valuable experience of an automated early warning and heightened understanding of strengths and limitations of prevention and control measures in different settings; the information could serve as references for Sweden
- Access to data, which is valuable for research, for disease modeling and analysis
- Joint publications with University of Malaya to report findings in a study on an early warning system for dengue

Malaysia:
- Enhanced research capacity and competency in disease modeling
- Integration of forecast allows timely decision'making and enhance effectiveness of mitigation
- Development of a course module on disease modeling, economic evaluation and
projection, and development of a dengue early warning system
- Joint publication with Umea University pertaining to findings related to infectious disease

The aims and objectives of this project are materialized through a series of joint activities described hereunder.

1) Disease forecast modeling workshop
We schedule the disease'modeling workshop throughout the research period to achieve three goals:

a. Umea University conduct modeling workshop in Sweden: In the first year, Umea researchers plan and transfer knowledge of quantitative forecast modeling techniques such as identification, estimation, selection, and validation of model to researchers from Malaya University. The purpose of the workshop is to provide hands'on training to PhD researchers who are taking part in developing a statistical forecasting model in Malaysia.

b. Malaysia researchers conduct similar workshop in University of Malaya: During year 2, the Malaysian researchers will conduct similar workshop to train local graduate students and researchers in Malaysia, whereas the Umeå researchers or modelers will provide support, evaluation and feedback. The workshop’s goal is to support, evaluate and provide constructive feedback to Malaysia researchers or trainers regarding their skills of facilitating a disease'modeling workshop for students and other researchers.

c. Pedagogy: At the end of this project, produce a course module on infectious disease forecast modeling to teach graduate students in the Faculty of Medicine, University Malaya as well as for other researchers.

The Statistical forecasting model will be developed using climate and reported cases as main predictors. Dr. Yien Ling Hii and Ass. Prof Joacim Rocklöv from Umeå University will design and develop a disease modeling training modules tailored for researchers with basic background knowledge of statistics or modeling techniques. Basic understanding of multivariate regression methods with focus on Poisson or Negative Binomial regression will be included in the training module. The modeling workshop is designed to include data examination, model identification, estimation, selection, validation, and sensitivity test. At the end of the workshop, trainees will be able to determine risk pattern between variable and predictors as well as forecast cases with optimal lead'time for vector control based on lagged exposure'response relationship between predictors and reported cases. Training will be conducted in a 5'day workshop. Subsequently, the trainees will have hands'on experience of developing a forecasting model using actual data provided by the Ministry of Health Malaysia. Dr. Rafdzah Ahmad Zaki, Dr. Abqariyah Yahya, and Prof Dr. Awang Bulgiba from University of Malaya will plan a training workshop in Malaysia during 2016. Whereas, researchers from University of Malaya and Umeå University will contribute to course or module outlines and strategies for introducing and promoting the module to graduate students.

2) Umeå University and University of Malaya jointly discuss strategies for an economic
evaluation of dengue early warning
An economic evaluation is essential to justify allocation of scarce public health resources. Assessment and cost'effectiveness analysis are to be performed to understand costs and savings for implementation of an intervention such as dengue early warning system and the impacts of the system on public health. Besides, threshold level for false prediction is analyzed. Prof. Lars Lindholm (Umeå University), Assoc Prof Dr. Ng Chiu Wan (University of Malaya), and Dr. Yien Ling Hii (Umeå University) will plan and conduct the workshops jointly to plan and discuss issues regarding opportunities and barriers for data collection from local authorities, study design, appropriateness of proposed economic models or methods for the study, time horizon, assumptions used in the study, detailed roles of researchers, and etc.

3) Umeå University conducts seminar for automation of a forecasting system
Automation is a key to sustainability of an early warning system. This allows access of information even by layman officers. Ass Prof. Francisco Hernandaz (Umea University) will be the leading researcher for the process of automation. Ass Prof. Francisco Hernandaz has rich experiences in development of computing distribution system. Workshop, seminar, and a series of meetings will be arranged together with the MOH to discuss the design of information flow according to the needs specified by MOH.

4) Field visits to the vectorBcontrol unit
Field visit to the local vector control unit aims to bridge communication between scientists and policy'makers. The research team from University of Malaya has been in partnership with the Ministry of Health to conduct research on infectious diseases. Several days visit shall be arranged for further understanding of vector control efforts. This will strengthen relationship between scientists and policy'makers and improve communication and understand. Another reason for the visit is to identify optimal lead'time required by the authority to carry out effective vector or dengue control. This information will be considered for developing a functional forecasting model.

5) Joint activities to plan a pilot study
Integration, implementation and evaluation of an early warning system are essential steps for developing a sustainable early warning system. Integration provides easy access of forecast information to health officers. Researchers run the early warning system and evaluate flow of the system, precision of information, identify unexpected challenges or difficulty, and modify or calibrate the models or system accordingly. A control will be designed to allow researchers to arrest issues surface due to faulty of the system. Dr Yien Ling Hii (Umeå University) and Dr. Rafdzah Ahmad Zaki (University of Malaya) will be in charge of the pilot study. Additionally, Periodical meetings will be arranged to