Early Warning Alert and Response System for Detecting Infectious

Disease Outbreaks Based on Surveillance Attributes in

Lumajang, Indonesia

Cicik Agustini Juwita

, Anisa Maulida

, Verdian Nendra Dimas Pratama

Meilinda Alya’ Putri Haryanik

and Irma Prasetyowati

5* b

Sukodono Public Health Center, Lumajang District, East Java, Indonesia

Kunir Public Health Center, Lumajang District, Indonesia

Departement of Health, Population Control and Family Planning, Lumajang District, East Java, Indonesia

Epidemiology Master’s Program, FETP Specialization, Faculty of Public Health, Universitas Airlangga, Surabaya, Indonesia

Faculty of Public Health, Universitas Jember, Indonesia

Keywords: Attribute, Communicable Disease, EWARS, Surveillance, Outbreak Detection.

Abstract: Introduction, Early Warning Alerts and Response Systems (EWARS) is a surveillance tool to detect early

warning signals/threats of potential outbreaks of infectious disease. EWARS data for Lumajang District in

2022 shows that the accuracy of the EWARS report is the lowest in East Java 42.64%, while the completeness

84.48%, ranking 35th out of 38 district in East Java Province. There were 20 public health centers (80%) that

had report accuracy <80% and one of public health center (4%) had report completeness <90% of the 25

public health centers in Lumajang District. This research aimed to identify the implementation of EWARS in

the Lumajang District based on the attribute approach. Methods, This research was descriptive quantitative

research. The population was all 25 health center surveillance officers. Methods of data collection by filling

questioner forms and documentation. Data analysis used descriptive analysis. EC: No. 2389/UN25.

8/KEPK/DL/2024. Results of this research The implementation of EWARS only fulfilled the attributes of

simplicity (88%), flexible (64%), data quality(72%), acceptance (100%), completeness(96%), accuracy(88%)

and not fulfilling the attributes of representativeness(36%), stability(12%), utility(44%). There were 92% who

wanted regular meetings related to data evaluation, performance, and training to improve the quality and

benefits of EWARS. Conclusion Assessment according to EWARS attributes shows the system does not meet

representativeness, stability, and utility. Several recommendations can be made to address representativeness,

stability, and utility. Regularly assess whether data sources and surveillance points represent the entire

population or target area. Continuously training staff and ensuring there are sufficient human resources can

also be done to increase target achievement according to surveillance attributes. Design the output of the

system to provide actionable insights. This means not just collecting data, but analyzing it in ways that directly

support public health decisions and actions.

1 INTRODUCTION

Indonesia has a varied topography and high internal

and international mobility. This makes Indonesia very

vulnerable to various diseases, especially infectious

diseases. Infectious diseases can cause outbreaks and

extraordinary events without monitoring and

controlling(Nkengasong, Djoudalbaye, & Maiyegun,

https://orcid.org/0009-0003-1929-0917

https://orcid.org/0000-0003-0919-5070

2017). Without a good response at the national and

international levels, these outbreaks and

extraordinary events will become a threat. Through

the 2005 International Health Regulation (IHR),

WHO mandates that all member countries establish

basic capabilities in the field of monitoring and

response at all administrative levels to manage health

data to be conveyed to the public, especially diseases

Juwita, C. A., Maulida, A., Pratama, V. N. D., Haryanik, M. A. P. and Prasetyowati, I.

Early Warning Alert and Response System for Detecting Infectious Disease Outbreaks Based on Surveillance Attributes in Lumajang, Indonesia.

DOI: 10.5220/0013517900004612

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of BRIN’s 2nd International Conference for Health Research (ICHR 2024), pages 28-37

ISBN: 978-989-758-755-9

that have the potential to cause health emergencies

(Public Health Emergency International

Concern)(Andriarsa et al., 2022). As a member of the

WHO, Indonesia must implement the IHR regulations

by establishing an Early Warning Alert and Response

System (EWARS). This system is part of surveillance

activities that aim to detect health events that can

threaten public safety, namely diseases with the

potential for outbreaks and epidemics(Cahyadin &

Indriyanti, 2018). The types of diseases observed in

EWARS are types of diseases that have the potential

for outbreaks. According to the Ministry of Health,

17 types of diseases have the potential for outbreaks,

but the Ministry of Health can determine other types

of diseases in the future (Kemenkes,

2010)(Sutriyawan et al., 2020).

Extraordinary events and epidemics need

attention. Late and inadequate detection can increase

the number of cases, prolong the outbreak, increase

the number of deaths and increase the possibility of

the outbreak spreading to other areas nationally,

regionally and even internationally(Suharmida,

2018). Outbreaks and epidemics can also increase

morbidity and mortality rates, which, of course, also

have an impact on various fields such as tourism, the

economy and society. All related parties must pay

attention to and handle this matter. Outbreaks and

epidemics require early discovery and fast and

appropriate action. This identification increased

awareness and prepared the community to face

potential outbreaks(Wikansari, Santoso, Pramono, &

Widarsih, 2019). Therefore, it is necessary to have an

Emergency Warning and Response System

(EWARS) that is implemented and utilized correctly

because, in implementing effective surveillance,

there are two principles: early warning of disease

incidents and early detection.

Indonesia's 2021 health profile data states that

there were 235 cases of outbreaks in Indonesia for

VPDs of diphtheria, 8 cases of measles, 12 cases of

pertussis and 9 cases of neonatal tetanus. Apart from

outbreaks of disease, outbreaks of food poisoning

were also found, which had relatively high numbers.

It was found that there were 25 outbreaks of food

poisoning in 13 provinces in Indonesia in

2021(Kemenkes RI, 2022). In 2022, East Java

Province recorded 54 Extraordinary Events in several

cities/regencies, including Lumajang

Regency(Dinkesprov Jatim, 2021). If outbreaks are

still found, it is necessary to evaluate the

implementation of EWARS. East Java Province in

2022 shows that the accuracy of EWARS in

Lumajang Regency is the lowest in East Java at

42.64%, while its completeness is 84.48%, ranking it

35th out of 38 regencies.

Research on SKDR has been widely carried out

both abroad and in Indonesia. Research from Tejeda

et al. (2023) regarding EWARS in controlling dengue

fever in Mexico found that EWARS is a tool that can

coordinate efficient surveillance, prevention and

control of dengue fever(Sanchez Tejeda et al., 2023).

Another research, namely by Hassan (2023), using

the literature review method, discusses One Health

EWARS in efforts to handle zoonotic diseases with

the result that the implementation of One Health

EWARS requires good collaboration between

stakeholders, including non-governmental

organizations, international country offices, research

agencies, the private sector. as well as local

communities in the development of one health

EWARS(Hassan, de Balogh, & Winkler, 2023).

EWARS research in Indonesia was conducted by

Cahyadin and Indriyanti (2018) regarding EWARS in

Blora Regency, Central Java, with the result that the

obstacle in implementing EWARS in Blora Regency

is the difference in the source of reports for each

health centre and the number of village health

services or posts that collect reports. Weeks are

sometimes different(Cahyadin & Indriyanti, 2018).

Another research by Wardani (2018) regarding the

process of collecting PD3I surveillance data in

EWARS in the City of Surabaya resulted in the

accuracy, representativeness of the data and stability

of the EWARS in the City of Surabaya being

good(Wardani, 2021).

This research aims to evaluate the

implementation of the Early Warning Alert and

Response System (EWARS) in Lumajang Regency,

especially on the completeness and accuracy of

reporting by community health centers. Accurate and

comprehensive reporting is crucial for early detection

and effective response to outbreaks such as PD3I,

infectious diseases, and food poisoning. By assessing

EWARS implementation, the study seeks to provide

insights for improving and developing the system in

Lumajang Regency to enhance outbreak prevention

and control efforts.

2 METHODS

This study used a quantitative descriptive method

with an attribute approach to evaluate the

implementation of the early warning alert and

response system (wars) in Lumajang Regency, East

Java. The research was conducted from January to

June 2022. The attribute approach refers to the theory

of the Centres for Disease Control and Prevention

Early Warning Alert and Response System for Detecting Infectious Disease Outbreaks Based on Surveillance Attributes in Lumajang,

Indonesia

(Klaucke et al., 2001), which includes nine main

attributes in evaluating a health surveillance system:

simplicity, flexibility, data quality, acceptability,

representativeness, completeness, accuracy, stability,

and utility.

Data were collected through questionnaires given

to 25 surveillance officers in all health centres in

Lumajang Regency and obtained through total

sampling.. The questionnaire was designed based on

indicators that were appropriate for each attribute. In

addition, secondary data was obtained from war

reporting documents owned by health centres and the

Lumajang health office to support the analysis. Each

attribute was assessed using a series of questions with

specific criteria, such as ease of understanding the

system (simplicity), the system's ability to adapt to

new threats (flexibility), the quality of the data

produced (data quality), and other attributes based on

the CDC's surveillance evaluation guidelines.

The use of secondary data had the potential for

bias, such as incomplete or inaccurate data due to

recording and reporting errors, and the possibility that

the data needed to be representative of all worked

areas. To overcome this limitation, the research

carried out cross-validation between secondary data

and primary data through interviews with

surveillance officers. In addition, the analysis was

carried out carefully by considering the context and

possible gaps in reporting.

The collected data was analyzed descriptively

using frequency distribution tables and narrative

explanations to identify attributes that had been met

and those that had not. The evaluation results were

compared with national guidelines and

recommendations from the CDC to measure the

effectiveness of wars in detecting and responding to

infectious disease outbreaks. This attribute approach

allows the study to comprehensively evaluate the

performance of the surveillance system in supporting

public health decision-making. This study obtained

ethical approval from the health research ethics

committee of the University of Jember with protocol

number ec: No.2389/UN25.8/KEPK/DL/2024. The

following are operational definitions and question

items for each variable:

Table 1: Question items and criteria based on each attribute variable.

No Suveillance Attributes Criteria

Simplicity

a. Do officers understand what data should be collected?

b. Do officers understand how recording should be done?

c. Do officers have EWARS guidelines?

d. Do officers make and save weekly reports using the Weekly Report (W2) format?

e. Does the officer make and keep a line/case list for the weekly W2 reports sent?

f. Do officers know how to analyze the data collected?

g. Is there evidence of any EWARS data analysis by public health centre officers?

h. Is the method of sending reports via WhatsApp easy to do?

i. Do you understand the Operational Definition (DO) of the 23 potential outbreak

diseases you reported?

j. Do you also report the same disease with EWARS using another reporting

system/format/form?

• Meet = Answer “Yes” ≥ 6

• Unmeet = Answer “Yes” <

Flexibility

a. If there is a threat of a new disease not on the list of 23 diseases, can you

identify/include it in the EWARS reporting?

b. If additional information, such as laboratory data, exists, can you include it in the

EWARS reporting?

c. If the officer responsible for EWARS moves or is not there, can SKDR data

collection, reporting, and analysis continue?

• Meet = Answer “Yes” ≥ 2

• Unmeet = Answer “Yes” <

Data Quality

a. Do officers understand the Operational Definition (DO) of diseases on the EWARS

list?

b. Do officers understand the data source of the EWARS report?

c. Has any feedback from the above levels ever regarding irregularities in the reported

data and incompleteness?

d. Has there ever been an ERROR or irregularity caused by the EWARS application

system?

e. What training have the Public Health Center surveillance officers received?

• Meet = Answer “Yes” ≥ 4

• Unmeet = Answer “Yes” <

Acceptance

a. Do you think that implementing the EWARS system is a burden for you or your

institution?

• Burdened = Answer “Yes”

• Unburdened = Answer

“No”

ICHR 2024 - BRIN’s International Conference for Health Research (ICHR)

No Suveillance Attributes Criteria

Representativeness

a. Does the weekly health centre EWARS report come from the recapitulation results

received by all Puskesmas work areas (Service Units/Networks/ Networks)?

• Meet = Answer “Yes”

• Unmeet = Answer “No” <

Completeness

a. Do you know the completeness of the reports you submitted this year and the

previous year?

b. Do officials report the number of cases of all potential disease outbreaks (with zero

(0) for no cases)?

• Meet = Answer “Yes” = 2

• Unmeet = Answer “Yes” <

Accuracy

a. Do you know the accuracy of the reports you submitted this year and the previous

year?

• Meet = Answer “Yes”

• Unmeet = Answer “No”

Stability

a. Are there any problems sending weekly EWARS reports using the WhatsApp

application?

b. If the officer in charge of the health centre EWARS does not come to work/is

sick/absent), is there an officer who can replace him?

c. Are there funds/costs to support EWARS from year to year?

d. Are there communication facilities provided by the Puskesmas/Health Service?

• Meet = Answer “Yes” ≥ 3

• Unmeet = Answer “Yes” <

Utility/Benefits

a. Do you feel any benefit from the EWARS data you collect and report to detect

outbreak signals in your area?

b. Have you ever encountered any KLB alerts/suspects from this system?

c. Have any rumours of disease/disease outbreaks been reported from the

Puskesmas/District?

d. Have any bulletins or reports been submitted to relevant leaders/

programs/stakeholders?

e. If so, is there any feedback from the leadership/program manager/relevant

stakeholders?

• Meet = Answer “Yes” ≥ 4

• Unmeet = Answer “Yes” <

3 RESULTS

In this study, 25 EWARS coordinator surveillance

officers participated. Table 2 shows the distribution

of respondent characteristics, such as age, gender,

education, and length of surveillance work. The age

groups were early adulthood (20-40) and middle-aged

(41-60). Gender was categorized as male and female.

Education was divided into DIII/S1, and the

surveillance work period was grouped into ≥ 2 years

and < 2 years. According to Table 2, most

respondents were in early adulthood, accounting for

16 people (64%). There were 13 females (52%), 14

respondents with DIII education (56%), and most

respondents had worked as surveillance officers for

more than two years, totalling 18 people (72%).

This research aims to identify the implementation

of EWARS in Lumajang Regency using an attribute

approach. The identification results using the attribute

approach include simplicity, flexibility, data quality,

acceptability, representativeness, completeness,

accuracy, stability, and utility or benefits, as

presented in Table 3. The attributes, simplicity,

flexibility, data quality, representativeness,

completeness, accuracy, stability, and Benefits are

Table 2: Distribution of Respondent Characteristics for

Lumajang Regency EWARS Coordinator Surveillance

Officers.

EWARS Coordinator

Characteristics

n %

1 Age

Early Adulth (20 - 40 Years)

Middle A

(

–

60 Years

)

2 Gender

Male

Female

3 Education

DIII

DIV/S1

4 Surveillance Work Periods

≥ 2 Years (Old)

< 2 Years (New)

grouped into met and unmet. In contrast, acceptance

surveillance attributes are grouped into burdened and

unburdened.

Table 3 provides information on the distribution

of answers from respondents to each question item

according to the variable. Based on Table 3, it is

found that the majority of respondents for the

variables simplicity, flexibility, data quality,

representativeness, accuracy, completeness,

Early Warning Alert and Response System for Detecting Infectious Disease Outbreaks Based on Surveillance Attributes in Lumajang,

Indonesia

Table 3: Distribution of Respondents' Answers to each question item.

No Suveillance Attributes

Answer

Yes No

Simplicity

a. Do officers understand what data should be collected?

b. Do officers understand how recording should be done?

c. Do officers have EWARS guidelines?

d. Do officers make and save weekly reports using the Weekly Report (W2) format?

e. Does the officer make and keep a line/case list for the weekly W2 reports sent?

f. Do officers know how to analyze the data collected?

g. Is there evidence of any EWARS data analysis by public health centre officers?

h. Is the method of sending reports via WhatsApp easy to do?

i. Do you understand the Operational Definition (DO) of the 23 potential outbreak diseases

you reported?

j. Do you also report the same disease with EWARS using another reporting

system/format/form?

Flexibility

a. If there is a threat of a new disease not on the list of 23 diseases, can you identify/include it

in the EWARS reporting?

b. If additional information, such as laboratory data, exists, can you include it in the EWARS

reporting?

c. If the officer responsible for EWARS moves or is not there, can SKDR data collection,

reporting, and analysis continue?

Data Quality

a. Do officers understand the Operational Definition (DO) of diseases on the EWARS list?

b. Do officers understand the data source of the EWARS report?

c. Has any feedback from the above levels ever regarding irregularities in the reported data and

incompleteness?

d. Has there ever been an ERROR or irregularity caused by the EWARS application system?

e. What training have the Public Health Center surveillance officers received?

Acceptance

a. Do you think that implementing the EWARS system is a burden for you or your institution?

Representativeness

a. Does the weekly health centre EWARS report come from the recapitulation results received

y all Puskesmas work areas (Service Units/Networks/Networks)?

Completeness

a. Do you know the completeness of the reports you submitted this year and the previous year?

b. Do officials report the number of cases of all potential disease outbreaks (with zero (0) for

no cases)?

Accuracy

a. Do you know the accuracy of the reports you submitted this year and the previous year?

Stability

a. Are there any problems sending weekly EWARS reports using the WhatsApp application?

b. If the officer in charge of the health centre EWARS does not come to work/is sick/absent),

is there an officer who can replace him?

c. Are there funds/costs to support EWARS from year to year?

d. Are there communication facilities provided by the Puskesmas/Health Service?

Utility/Benefits

a. Do you feel any benefit from the EWARS data you collect and report to detect outbreak

signals in your area?

b. Have you ever encountered any KLB alerts/suspects from this system?

c. Have any rumours of disease/disease outbreaks been reported from the Puskesmas/District?

d. Have any bulletins or reports been submitted to relevant leaders/programs/stakeholders?

e. If so, is there any feedback from the leadership/program manager/relevant stakeholders?

stability and utility answered "Yes". Acceptance

variable showed 100% respondent answered "no"

(unfavorable question), this indicates that they agree

with EWARS and that this is not a burden for

surveillance holders to implement.

ICHR 2024 - BRIN’s International Conference for Health Research (ICHR)

Table 4: Evaluation Results of the Lumajang Regency early

Awareness and Response System (SKDR).

No Suveillance Attributes n %

1 Simplicity

Met

Unmet

2 Flexibility

Met

Unmet

3 Data Quality

Met

Unmet

4 Acceptance

Burdened

Unburdene

100

5 Representativeness

Met

Unmet

6 Completeness

Met

Unmet

7 Accuracy

Met

Unmet

8 Stability

Met

Unmet

9 Utility/Benefits

Met

Unmet

Based on Table 4, it is found that the simplicity

attribute is fulfilled chiefly, namely 88%; the most

flexible is fulfilled, namely 64%; the data quality is

fulfilled chiefly, namely 72%; the acceptance

attribute is 100% fulfilled, the representativeness

attribute is mostly not yet fulfilled, namely 64%, most

of the completeness attributes have been fulfilled,

60%. Most of the accuracy attributes have met 88%,

most stability attributes have not met 88%, and most

utility attributes have not met 56%.

This research also discusses suggestions given by

officers holding the EWARS program to improve the

quality and program improvements of EWARS

implementation in the future. This aims to produce

information useful in developing programs or

controlling infectious diseases. These suggestions

consist of regular meetings, availability of

guidebooks, computer facilities and applications,

funds for communication, supervision visits (central,

provincial or city), availability of reporting,

availability of personnel, or the distribution of

suggestions from surveillance officers, as presented

in Table 5.

Table 5: Distribution of Input and Suggestions for

improving EWARS Implementation in Lumajang Regency

in 2022.

No Feedback and Su

estions n %

1 Regular meetings for evaluastion

of data, performance and

Trainnin

23 90

2 Availability of handbook and

ortin

for

15 15

3 Computer facilities and

application for reporting and data

analysis

18 18

4 Funds for communications 19 19

5 Supervision visits from

center/Province/Re

enc

5 5

6 Availability of personer/HR 10 10

7 Othe

0 0

Based on Table 5, which provides input and

suggestions for improving EWARS in Lumajang

Regency in 2022, the majority, 23 (92%) respondents,

suggested holding regular meetings to evaluate data

and performance and provide training.

4 DISCUSSION

The research results show that most students came

from early adulthood, aged 20 to 40 years. Age is

related to the performance of health workers. As age

increases, work productivity will decrease due to

decreased work motivation. Meanwhile, if you are

still young, your motivation is still high, and your

desire to apply knowledge is still relatively

high(Handayani, Fannya, & Nazofah, 2018). The

results also show that the majority of respondents are

female. This aligns with the results of research

conducted in Tanah Bumbu Regency and Banjar

Regency, which found that most EWARS officers are

female, 57%(Andiarsa & Hidayat, 2023). Gender has

an indirect influence on the performance of health

workers. According to him, there are differences in

characteristics between women and men. Women

also have freedom in developing their roles; however,

marriage factors make women more focused on

handling the household, while men tend to be more

focused on their work(Julianti, Duarsa, & Yuliyatni,

2021).

Most of the highest education is DIII. The higher

the level of education one has, the higher a person's

performance will be. This is related to the higher

awareness and responsibility that the person has for

their tasks, resulting in better performance compared

to officers who do not have higher education(Tua &

Mardhiyah, 2022). Most of the respondents' work

Early Warning Alert and Response System for Detecting Infectious Disease Outbreaks Based on Surveillance Attributes in Lumajang,

Indonesia

period was more than two years. This shows that the

officers are experienced enough in their field and

have more insight regarding the EWARS program.

Apart from that, working for more than two years

shows they have had more work experience and a

more mature mindset due to experience(Budiman,

Makaginsar, & Putra, 2022).

The evaluation results indicating that most

aspects meet the simplicity requirements suggest that

the EWARS system in Lumajang Regency is user-

friendly and easy to implement. This simplicity likely

facilitates better adoption and usage by community

health centers, minimizing technical or procedural

barriers. However, it is essential to ensure that

simplicity does not compromise the system's

accuracy or comprehensiveness, and continuous

monitoring is needed to maintain this balance while

addressing other areas requiring improvement. This

aligns with research conducted in Yemen, which

shows that EWARS officers can collect and process

data without significant disruption, and the reporting

system is simple and easy to use. Where officers

obtain data using cell phones and enter it using

computers, and all officers in health services receive

training to collect and input data(Dureab et al., 2020).

The results showing compliance with the

flexibility attribute indicate that the EWARS system

in Lumajang Regency can adapt to the varying needs

and capacities of public health centers. This

flexibility likely enhances the system's effectiveness

in accommodating diverse local conditions and

challenges. However, continuous evaluation is

necessary to ensure that this adaptability remains

aligned with the core objectives of accurate and

timely outbreak detection and response. This aligns

with research conducted in Yamen, which shows that

the EWARS system meets flexibility requirements. It

is stated that the EWARS system is easy to maintain

and flexible enough if it is to be widely used in new

health facilities. It is also easy to adjust, for example,

by adding new priority diseases(Dureab et al., 2020).

Data quality attributes based on the results show

that most public health centres in the Jember Regency

have complied. This is in line with research

conducted in West Papua, which stated that almost all

public health centres in West Papua met data quality

guidelines. Officers understand the definition of a

case that is classified as an outbreak or outbreak.

Apart from that, the data quality is adequate,

indicating that the level of knowledge possessed by

the officer in charge is quite good. This needs to be

maintained and improved, one of which is by

providing regular feedback to the public health centre

and monthly reports back to the public health centre

or posts(Manurung, Reo, Pardosi, & Muscatello,

2020).

The findings on the acceptance attribute indicate

that the EWARS program is well-received, with no

significant burden felt by the users. This positive

perception likely contributes to better engagement

and adherence to the program, ensuring consistent

reporting and monitoring. However, it is essential to

maintain this level of acceptance by addressing any

emerging challenges promptly and ensuring that the

system continues to meet the needs of its users

effectively. This aligns with research conducted in

West Papua, which stated that officers accepted the

EWARS program. This could be because all

respondents knew that the EWARS system was handy

and essential in detecting infectious disease

outbreaks(Manurung et al., 2020).

The results showed that the EWARS

representation attributes of public health centres in

Lumajang Regency could have been more fulfilled.

This is in line with research, which states that the

EWARS report needs to meet the requirements for

representation. This could be because several private

health services and general and specialist practice

clinics do not provide reports to the health centre, so

the data received by EWARS officers does not

represent data in the work area of the health centre.

The report completeness of 60% in most Public

Health Centers in Lumajang Regency, while a

positive achievement, falls significantly short of the

national EWARS target of 90%. This gap highlights

a need for stronger efforts to improve data collection,

reporting systems, and resource allocation.

Addressing barriers such as limited staff capacity,

training gaps, or technical challenges is critical to

achieving the national standard and ensuring the

effectiveness of EWARS in detecting and responding

to outbreaks. These results are in line with research

from Paramita (2017), which shows that the majority

of public health centres studied have fulfilled the

completeness of the report, namely 87.9%, which also

does not meet the national minimum requirements. Of

course, efforts need to be made to increase the

completeness of reports. This is because the

completeness of the report will also determine the

quality of the data produced in EWARS.

The reporting accuracy of 88% in some Public

Health Centers in Lumajang Regency exceeds the

national minimum requirement of 80%, indicating

that the data reported through EWARS is generally

reliable and precise. This achievement reflects

effective training and implementation in these

centers. However, efforts should continue to maintain

or improve accuracy, especially in centers that may

ICHR 2024 - BRIN’s International Conference for Health Research (ICHR)

fall below this threshold, ensuring consistent and

high-quality reporting across all facilities (Marullyta

& Rohananingsih, 2022). This result is in line with

research conducted in Bangladesh, which showed a

result of 82%. Where it was recorded that there were

no significant delays except during Ramadan. Only

29% of service facilities need to be on time in

reporting(Wijekoon et al., 2020). The stability

attribute shows that most of the results could be more

satisfactory. These results align with research

conducted in South Sudan, which stated that the level

of stability of EWARS was shallow(Rumunu et al.,

2022). This could be because most EWARS officers

need training in good data analysis and processing, so

the reports produced are unstable. Therefore, efforts

are needed to increase its stability, one of which is by

holding training for EWARS officers and providing

facilities and infrastructure to support the

implementation of the EWARS system(Polak,

Sumampouw, & Pinontoan, 2020).

The utility or benefit attributes of EWARS in

most Public Health Centers in Lumajang Regency

still falling short suggests that the system's practical

impact on outbreak prevention and response may be

limited. This gap could stem from issues such as

inadequate training, lack of integration with other

health initiatives, or insufficient feedback

mechanisms. Addressing these challenges is essential

to enhance the perceived and actual benefits of

EWARS, ensuring it effectively supports public

health efforts at the local level. This is in line with

research stating that EWARS needs to be

appropriately utilized in formulating policies and

creating treatment solutions. According to research

conducted in the United States, EWARS can be

maximized in making decisions and providing

information regarding current conditions related to

the spread of infectious diseases(Ricks et al., 2022).

The results of research regarding suggestions

and input from surveillance officers in improving the

implementation of EWARS in Lumajang Regency in

2022 mainly chose to hold regular meetings to

evaluate data, performance and training. Critical data

is evaluated periodically to assess the objectives and

targeted benefits and check the accuracy and

completeness of the data(Rahajeng & Wahidin,

2020). Apart from that, it is also essential to assess the

performance of surveillance officers to determine

whether the implementation of surveillance is

effective. If it is not practical, through performance

evaluation, we can find out what obstacles are causing

the implementation of surveillance activities to run

poorly. Providing training for EWARS officers has

had a positive impact. This is in line with research

conducted, which states that training for surveillance

officers provides increased understanding of potential

epidemic diseases, identification of risk factors, data

management and participation in epidemiological

studies with simple presentations. In addition,

training provides capacity strengthening and

increased motivation for officers to carry out their

duties, which improves the quality of EWARS reports

(Syafruddin et al., 2022).

The study results indicate that the

representativeness, stability, and utility attributes of

EWARS in Lumajang Regency still need to meet the

standards, which impacts the effectiveness of early

detection and response to outbreaks. Data

unrepresentativeness hinders data-based decision-

making, increasing the risk of late detection and

spread of outbreaks. Due to a lack of training and

infrastructure, low system stability increases the risk

of unpreparedness for health emergencies. In

addition, the limited utility shows that EWARS data

could be more optimal in supporting strategic

decisions, so opportunities for outbreak prevention

are not maximized. Therefore, routine training,

strengthening infrastructure, and increasing

collaboration with the private sector are needed to

ensure more representative and stable reporting.

These steps are essential to mitigate the risk of health

emergencies and increase the effectiveness of

EWARS in supporting a rapid and appropriate public

health response (Cardenas, Hussain-Alkhateeb,

Benitez-Valladares, Sánchez-Tejeda, & Kroeger,

2022).

This study has limitations, such as potential

inaccuracies in secondary data, a limited research

scope to one district, and a short research time. Cross-

validation with primary data is carried out to reduce

data bias, while broader and longitudinal research is

recommended for more comprehensive results.

However, this research provides important insights

for strengthening EWARS through regular training,

strengthening infrastructure, and better coordination

to support early detection and effective response to

infectious disease outbreaks.

5 CONCLUSIONS

The implementation of EWARS in Lumajang

Regency in 2022 requires improvement, particularly

in representativeness, stability, and utility, which

impact the effectiveness of outbreak detection and

response. Efforts to address these issues include

routine training, enhanced data collection, regular

evaluations, infrastructure strengthening, improved

Early Warning Alert and Response System for Detecting Infectious Disease Outbreaks Based on Surveillance Attributes in Lumajang,

Indonesia

supervision, and adequate human resources. Health

centres are advised to conduct routine training,

increase the scope of data collection, and conduct

regular report evaluations. The District Health Office

needs to provide infrastructure support, improve

supervision, and ensure the availability of human

resources. These steps aim to optimize EWARS

functionality, support public health decision-making,

and strengthen community health resilience. Further

research is needed to explore additional variables and

approaches for improving EWARS implementation.

ACKNOWLEDGEMENTS

The author would like to thank the participants in the

research and thank the Head of the Lumajang Health

Office and the Dean of Public Health Faculty at

Universitas Jember for the support provided.

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