The number of outbreaks has increased over time in the world including in Indonesia. Since 1980, 88% of 215 datasets of diseases have caused the outbreak over the world. The source of the outbreak varied from bacteria, viruses as well as zoonotic diseases that were transmitted to the vector host. However, the majority of the increasing number of the outbreak is due to human-specific diseases such as measles, rubella, malaria, pertussis, cholera, hepatitis B, and others. A study shows the risk of an outbreak is mostly influenced by how the surveillance system capabilities in a country. Furthermore, it is also influenced by the role of the digital infrastructure to analyze any changes in infectious disease over space and time.
“Public health surveillance is the continuous, systematic collection, analysis, and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice”.
The key to the success of outbreak response in a surveillance system is a timely response in case detection. Delays in outbreak alert and response will give an impact on the direct and indirect costs to the country. Hence, there is an urgency to invest in strengthening the architecture system in public health surveillance to reduce the outbreak’s impact on the community.
As a part of the surveillance system, World Health Organization has designed the best prediction tool alert system to give a signal when an outbreak happened or known as EWARS. EWARS (Early Warning Alert and Response System) has been implemented in some countries to be specific in some circumstances areas. This system has established to help health workers to get an early alert for any outbreak that happened in the community and to give any public health response in time.
EWARS in Indonesia
EWARS has been implemented in some countries such as the Democratic Republic of Congo, Zanzibar, Uganda, Liberia, and Indonesia. In Indonesia, EWARS has been created since 2009 in the frame of a web-based system known as SKDR (Sistem Kewaspadaan Dini dan Respon), to provide any potential epidemic disease in any emergency setting. The health worker has a chance to report the disease through the message based on the case definition within. The system has an algorithm system to read specific disease diagnoses (23 infectious diseases that have been identified might become an outbreak in the community) reported by health workers and the system will respond algorithm based on the disease as well as give an alert
based on the algorithm’s outbreak response. Hence it will give an alert notification to health workers once the system identifies the increasing number of cases in the area.
The study showed EWARS is highly important for puskesmas (primary healthcare) to detect earlier any infectious disease outbreak, minimize mortality and any health risks due to the outbreak situation. Adequate and timely response to every signal alarm has influenced the maximum benefit of EWARS implementation in Indonesia. However, there are some challenges to maximizing this EWARS function such as the burden among health workers in Puskesmas to monitor and analyze the EWARS, a discrepancy in the quality of surveillance reports in terms of completeness and timeliness and also the challenges due to the lack of mobile network, especially in remoteness area. Moreover, currently, EWARS is only managed by a Puskesmas person and not linked to the laboratory system as well as to all hospitals or private clinics. Hence the case report is only centralized among the health workers at Puskesmas level.
How EWARS utilization would save the economic burden in a country
The outbreak could impact a huge economic burden on a country. The Zika outbreak that happened in Latin America in 2015-2017 cost around 7-18 billion dollars while the dengue outbreak cost the burden around $1.56 per capita. Dengue disease also give an impact on the indirect cost calculating the loss of productivity due to dengue illness estimated at $10.93/day in 2010 and $11.06/day in 2011 as per work day. Furthermore, in Colombia, the chikungunya outbreak leads to an economic burden with a cost equivalent to 0.04% of the National Gross Domestic Product in 2013.
However, economic loss due to the outbreak will be lower by putting public health control in a country. The study has shown how the higher cost in an uncontrol outbreak than the measurable one. As a comparison, a four to six weeks delay in dengue outbreak response resulted in 86 times (13 million USD) and 346 times (382 million USD) higher dengue illness costs than a country doing active surveillance and response within two weeks. Within two weeks gap only, a country can save a lot of loss when the outbreak occurs.
EWARS has shown a promising capability of timely disease outbreak prediction if it is used in an adequate and timely response, especially in an emergency setting. It has helped control the outbreak after cyclone Winston in Fuji by detecting three confirmed outbreaks, as a result, no large outbreak case was missed. EWARS has successfully alerted correctly 100% of the dengue outbreak and 97% of the zika outbreak in Mexico. By having this system, every immediate response action has been taken to control disease transmission and prevent any loss. The study presents, a district with an alarm sign through EWARS then gives an adequate and timely response has shown a successful record of outbreak prevention. Hence it is clear how the utilization of the alert system as part of surveillance and response will save economic burden in a country.
Opportunities to advancing EWARS in Indonesia
EWARS utilization in Indonesia has been evaluated and shows an eye for usefulness in terms of EWARS objectives. The feedback given by health workers showed how the EWARS gave an insight into the trend of infectious disease, the impact of the disease control program, and too quickly doing the outbreak response in a community. However, some limitations should be solved in the EWARS development system.
An early Warning Alert Response System needs to be established by these four key element factors; risk knowledge, monitoring and warning service, dissemination and communication, and response capability. One failure in one of the element factors might give an impact on the whole system.
In terms of risk knowledge, outbreak prediction needs to involve the assessment of risk as part of the variable in the EWARS system to identify vulnerable populations as a higher-risk group. In the measles outbreak, putting the immunization coverage in the past few years as well as the severely malnourished status will give a better prediction for the potential outbreak in some setting areas. For instance, the incidence of measles in Kediri City has been found in the area where the average proportion of the population was not immunized, also correlated with the high level of population density, and frequently occurred in the range from October to April. It is necessary for the EWARS system able to capture a risk assessment by considering multiple hazards and vulnerabilities in the population.
Monitoring and warning services require continuous monitoring every day by health workers. Most health workers at the Puskesmas level have multiple jobs and this situation gives a major impact on the lack of monitoring and response of the alert in EWARS. Moreover, network unavailability and remote access create difficulties in accessing the system on time on their devices. Innovation has been made through EWARS in a box in some emergency setting areas in other countries. EWARS in a box has been made with a kit that includes solar power to charge, a phone, laptop, surveillance tools, and a local server that spreads in 50 fixed or mobile clinics that serve 500000 people. This also can give a line-listing of cases when an outbreak is declared, giving an automated EWARS bulletin and GIS mapping capabilities. Hence, there is an urgent need for an expansion of the system to create a procedural network on this kind of element.
Dissemination and communication as well as response capability are essential to be involved. The warning risk must reach the community so the management case plan can be addressed in a proper time to reduce the transmission. A direct engagement with the community or individual as a part of the risk communication needs to be addressed. This also will be impactful in mitigating the risk of behavior in a community since currently all the information is only managed by health workers in puskesmas.
In the end, EWARS required strong details of architecture development to achieve the maximum benefit of the system. With the current challenges of EWARS in Indonesia, there is a potential that we will miss controlling the outbreak in the future and a lot of costs will be lost too. Hence strong collaboration among various stakeholders is needed to advance the EWARS in Indonesia.
