Case Report
THE INDONESIAN
JOURNAL OF INFECTIOUS DISEASES
p-ISSN: 2354-6.
, e-ISSN: 2599-1698 Spatial Distribution and Temporal Trends of Pneumonia in Indonesia: Descriptive Analysis of Early Warning and Response System (SKDR) Data, 2022-2024 Wawang1*.
Tri Yunis Miko Wahyono1.
Amelia Marisa1 1Department of Epidemiology.
Faculty of Public Health.
Universitas Indonesia.
Indonesia
ARTICLE INFO
Received: 20 Oct 2025 Reviewed: 22 Oct 2025 Accepted: 17 Nov 2025 Keywords:
Pneumonia.
Spatial Distribution.
Temporal Trends.
EWARS.
Indonesia *Corresponding author:
Wawang Email: wawangrafly@gmail.
ABSTRACT
Background: Pneumonia remains a significant public health burden in Indonesia.
This study aims to analyze the spatial distribution and temporal trends of pneumonia in Indonesia using data from the Early Warning and Response System (SKDR) for the period 2022Ae2024.
Methods: This descriptive study utilized secondary aggregate data from the Indonesian Ministry of Health's SKDR, covering all 38 provinces from 2022 to 2024.
Analyses were performed on the annual and cumulative distribution of pneumonia cases across provinces, visualized through ranking tables and choropleth maps, followed by a temporal analysis of quarterly trends Results: The SKDR recorded 1,204,023 pneumonia cases during the study period.
Temporally, a significant yearly increase was observed, alongside a predictable seasonal pattern, with Quarter 2 consistently the period of lowest incidence.
Spatially, extreme disparities were found, with the highest case hotspots concentrated predominantly on Java Island.
Conversely, coldspots in Eastern Indonesia .
Southwest Papua.
Maluk.
more likely reflect limitations in surveillance and healthcare access, indicating the presence of hidden high-risk areas.
Conclusion: Pneumonia in Indonesia exhibits dynamic temporal patterns and sharp spatial concentration.
These findings underscore the necessity of differentiated control strategies: proactive interventions based on seasonal patterns, intensive resource allocation in hotspots to control transmission, and strengthened surveillance systems and healthcare access in coldspots to address hidden high-risk
INTRODUCTION
Pneumonia remains a leading global cause of morbidity and mortality, particularly among vulnerable populations such as children and the elderly .
According to the World Health Organization (WHO), it is the single largest infectious cause of death in children worldwide, accounting for approximately 740,180 deaths in children under five in 2019 alone .
This acute infection of the lung parenchyma, caused by a variety of pathogens, poses a significant public health challenge due to its potential for severe complications and substantial health system costs .
Copyright: A 2025 by the authors.
This is an open access article distributed under the terms and conditions of the CC BY-NC 4.
In Indonesia, the burden of pneumonia remains substantial.
Data from the 2023 National Health Survey (SKI) revealed a symptom-based prevalence of 10.
8%, underscoring the disease's widespread occurrence in the population .
Alarmingly, pneumonia persists as a significant cause of mortality among toddlers .
-59 month.
, accounting for 1.
6% of deaths in this age group in 2023 .
Furthermore, a critical challenge is the low case detection rate of only 36.
in children under five, indicating a substantial number of undiagnosed and untreated cases .
Compounding this issue are the pronounced geographical disparities in disease burden across the archipelago, suggesting uneven resource allocation, healthcare access, or environmental risk factors .
Understanding the complete epidemiological profile of pneumonia, including its burden, distribution patterns, and temporal trends, is crucial for developing effective control strategies and resource allocation.
To monitor potential outbreaks, the Indonesian Ministry of Health established the Early Warning and Response System (SKDR), which collects comprehensive weekly pneumonia case data from all 38 provinces .
While the Early Warning and Response System (SKDR) represents a rich and valuable data source for epidemiological intelligence, its potential has not been fully A critical gap exists in the spatial and temporal analysis of this national data.
Most existing reports are limited to descriptive summaries and lack the integration of these dimensions, which is crucial for understanding disease dynamics.
Consequently, key questions remain unanswered: Where are the persistent hotspots of pneumonia? What are the specific seasonal patterns and temporal trends in different regions of Indonesia? Answering these questions through a focused descriptive analysis is essential, as it has proven to be significant for identifying high-risk areas, informing targeted surveillance, and guiding resource allocation for maximum public health impact .
To address this gap, this study aims to perform a descriptive analysis of pneumonia cases in Indonesia from 2022 to 2024 using SKDR data.
Specifically, we seek to: .
Describe the national and quarterly temporal trends of pneumonia cases.
Map the geographical distribution and identify high-risk clusters .
and low-risk areas .
Synthesize these findings on spatial distribution and temporal trends to provide evidence for targeted public health interventions and resource prioritization.
METHODS
This study employed a descriptive design using secondary aggregate data from the Indonesian Ministry of Health's Early Warning and Response System (SKDR).
SKDR collects pneumonia surveillance data through mandatory weekly reporting from health service facilities, including primary health centers (Puskesma.
, hospitals, clinics, and affiliated community health IJID | Vol 11 .
December 2025 networks.
Case reporting follows the national pneumonia case definition, and data are submitted electronically via the SKDR platform, using real-time or near-real-time inputs from trained surveillance officers.
These reports undergo routine validation at district and provincial levels before being aggregated at the national level .
The analysis focused on two complementary components.
First, an interprovincial distribution analysis was conducted by summarizing annual and cumulative case counts for all 38 provinces and visualizing them through ranking tables and choropleth maps to identify spatial clusters of high and low burden.
Second, a temporal analysis was performed using quarterly national trends across the three years .
2Ae2.
to detect seasonal patterns, peak periods, and year-to-year shifts in incidence.
All analyses were descriptive and based on aggregated data, without individual-level inference.
This study adopted a descriptive design because the available data consist of routinely collected aggregate surveillance reports across defined geographical and temporal units.
The strength of this approach lies in its ability to rapidly provide a comprehensive overview of disease distribution, both spatially and temporally, which is essential for immediate public health prioritization and targeted resource allocation.
However, this design inherently limits causal inference.
It carries the risk of ecological fallacy, meaning findings at the provincial level cannot be extrapolated to individual risk, thus positioning the results as hypotheses for future analytical research.
The analysis included all reported pneumonia cases from 38 provinces in Indonesia from 1 January 2022 to 31 December 2024 (Epidemiological Week 1 of 2022 to Week 52 of 2.
The dataset consisted of weekly aggregate reports by province, containing the variables:
year, province, epidemiological week, and number of cases.
For trend analysis, weekly data were aggregated into quarterly and annual totals.
Quarterly periods were defined as follows: Quarter 1 (Weeks 1Ae.
Quarter 2 (Weeks 14Ae.
Quarter 3 (Weeks 27Ae.
, and Quarter 4 (Weeks 40Ae The national total cases were calculated as the aggregate of weekly reports from all 38 provinces during the study period.
Given the use of secondary aggregate data, several aspects related to data quality and potential reporting biases were considered.
Data cleaning involved standardizing geographical units following provincial boundary changes during the study period and checking for extreme outliers in weekly reporting that may indicate data-entry errors.
however, no data points were excluded, as the analysis aimed to capture the full reported burden.
The validity of the aggregate SKDR data is acknowledged to be subject to under-reporting bias, particularly in rural and remote areas (Coldspot.
This is due to limited healthcare access, low awareness, and potential delays or incomplete electronic submissions by surveillance officers under resource constraints.
Conversely, over-reporting bias or misclassification may be more common in areas with highly motivated reporting units, though less so.
Crucially, the SKDR system is designed to track IJID | Vol 11 .
December 2025 syndromic trends rather than absolute incidence.
therefore, the low case counts observed in Eastern Indonesia are more likely an artifact of surveillance deficiency .
etection bia.
than a true reflection of low disease burden.
We relied on the routine validation steps already implemented at the district and provincial levels by the Ministry of Health before data The analysis was conducted by time and place.
Temporal analysis was performed using nationally aggregated quarterly data, visualized through a line chart to describe case trends over For spatial analysis, three-year cumulative cases per province were calculated and presented in ranking tables of the ten provinces with the highest and lowest case burdens.
choropleth map was generated in QGIS, a geographic information system, to illustrate the geographical distribution of pneumonia.
All data processing and descriptive analyses were performed using Microsoft Excel and QGIS.
RESULTS
Overview of Pneumonia Cases in Indonesia .
2Ae2.
During the three-year study period, from January 1, 2022, to December 31, 2024, the Early Warning and Response System (Sistem Kewaspadaan Dini dan Respon.
SKDR) recorded a total of 1,204,023 pneumonia cases across 38 provinces in Indonesia, averaging 401,341 cases per year.
This figure represents a substantial national disease burden and highlights the importance of continuous surveillance of pneumonia trends in Indonesia.
Distribution of Pneumonia Cases by Province The distribution of pneumonia cases across 38 provinces in Indonesia during 2022Ae2024 is presented in Table 1.
Overall, the number of cases exhibited a consistent annual upward trend, with almost all provinces showing increases over the three years.
The highest absolute disease burden was recorded on Java Island, where West Java.
Central Java, and East Java consistently ranked as the top three provinces throughout the study period.
These provinces contributed the largest share of the national total, forming the primary high-risk cluster .
of pneumonia in Indonesia.
Conversely, provinces in eastern Indonesia, such as Southwest Papua.
Maluku, and West Sulawesi, consistently ranked among the lowest across the three years.
The low number of reported cases in these areas may not solely reflect a lower disease burden but could also indicate disparities in surveillance and case reporting capacity between regions.
Cumulatively, 1,204,023 pneumonia cases were recorded nationwide during the 2022Ae 2024 period.
The concentration of cases in Java and parts of western Indonesia underscores the geographical disparity in disease burden, showing a clear gradient from west to east.
IJID | Vol 11 .
December 2025 Table 1.
Comprehensive Ranking of Pneumonia Cases by Province .
2, 2023, 2024, and Cumulativ.
Rank Province Cases Cases Cases Cumulative Number of Cases .
West Java Central Java East Java Jakarta Banten West Nusa Tenggara South Kalimantan DI Yogyakarta North Sumatra East Nusa Tenggara South Sulawesi Central Sulawesi Central Papua Bali West Sumatra East Kalimantan Gorontalo West Kalimantan Southeast Sulawesi Lampung Papua Riau Islands Riau South Papua North Maluku Bangka Belitung North Sulawesi West Papua Central Kalimantan Aceh South Sumatra Jambi North Kalimantan Highland Papua Bengkulu West Sulawesi Maluku Southwest Papua This pattern confirms the significant geographical disparities between western and eastern Indonesia, where densely populated, highly urbanized areas tend to report substantially higher case numbers than less-populated regions .
IJID | Vol 11 .
December 2025 Spatial Distribution of Pneumonia Cases Spatial visualization in Figures 1Ae3 illustrates the interprovincial dynamics of pneumonia distribution across study years.
In 2022 (Figure .
, pneumonia cases were highly concentrated on Java Island, particularly in West Java.
Central Java, and East Java.
Additional areas with very high disease burdens were also observed in West Nusa Tenggara and East Nusa Tenggara, forming a secondary cluster in the southern region.
Outside these primary clusters.
South Sulawesi and parts of South Kalimantan exhibited high disease burdens.
At the same time.
Bali.
Maluku.
Papua, and North Kalimantan fell into the low-to-moderately low disease burden category.
Overall, the 2022 spatial pattern demonstrated that the national pneumonia burden was dominated by western and southern regions, with decreasing intensity toward the eastern islands.
Figure 1.
Spatial Distribution of Pneumonia Cases by Province.
Indonesia, 2022 In 2023 (Figure .
, the spatial pattern remained dominated by very high-risk clusters in West Java.
Central Java, and East Java.
Although Java continued to dominate, other regions showed increased intensity, indicating an expansion of the disease burden.
Notably.
North Sumatra rose to the Auvery highAy category, joining South Kalimantan, which maintained its status from the previous year.
Additionally, the southern cluster, including Bali.
West Nusa Tenggara, and East Nusa Tenggara, collectively sustained and reinforced their high-burden categories, suggesting a continued expansion of pneumonia risk beyond Java.
IJID | Vol 11 .
December 2025 Figure 2.
Spatial Distribution of Pneumonia Cases by Province.
Indonesia, 2023 In 2024 (Figure .
, the distribution pattern showed persistent dominance in Java and western Indonesia, while eastern provinces such as Maluku.
Papua, and North Kalimantan continued to report low to moderately low burdens.
High-risk clusters outside Java, including North Sumatra.
South Sulawesi.
South Kalimantan.
East Kalimantan.
Bali.
West Nusa Tenggara, and East Nusa Tenggara, remained prominent.
These consistent spatial gradients underscore enduring geographic disparities throughout the study period.
IJID | Vol 11 .
December 2025 Figure 3.
Spatial Distribution of Pneumonia Cases by Province.
Indonesia, 2024 Taken together, the three annual maps demonstrate a relatively stable yet gradient spatial pattern, with the highest burdens concentrated in densely populated and economically active regions, while eastern Indonesia exhibited relatively lower risks.
This pattern indicates the influence of demographic, urbanization, and healthcare accessibility factors on disease National Temporal Trend of Pneumonia Cases The national temporal trend of pneumonia cases during 2022Ae2024, illustrated in Figure 4, reveals consistent annual increases along with clear seasonal variation.
In general, pneumonia cases rose steadily each year, peaking in Quarter IV of 2022 and 2023, before shifting to Quarter i in 2024.
This shift suggests temporal variability in disease incidence, potentially influenced by environmental factors and seasonal changes.
IJID | Vol 11 .
December 2025 Number of Pneumonia Cases (N) Quarterly Temporal Trend of National Pneumonia Cases, 2022Ae2024 Quarter 1 Quarter 2 Quarter 3 Quarter 4 Quarterly Period Figure 4.
Quarterly Temporal Trend of National Pneumonia Cases, 2022Ae2024 Beyond the annual increases, the quarterly analysis revealed a consistent decline in cases during Quarter II of each observation year.
This recurring seasonal dip may reflect the influence of tropical climatic conditions on the transmission of pneumonia-causing pathogens in Indonesia.
Consequently, the temporal analysis provides an important basis for early warning strategies and the optimal timing of public health interventions.
DISCUSSION
This study delineated the epidemiological characteristics of pneumonia in Indonesia from 2022 to 2024 using a comprehensive descriptive approach.
The overall increase in pneumonia cases over the three years reflects a broader post-pandemic resurgence of respiratory infections globally, particularly in tropical-climate countries where environmental and behavioral determinants strongly influence transmission .
The consistent decline in cases observed in Quarter 2 each year aligns with seasonal epidemiological patterns described in Southeast Asia, where lower rainfall, reduced humidity, and shifts in population mobility contribute to a temporary reduction in respiratory pathogen circulation .
Understanding these predictable seasonal windows is essential for implementing early-warning systems and scheduling preventive interventions, consistent with WHO recommendations for respiratory disease preparedness .
A clear understanding of this seasonal pattern creates a strategic window of opportunity for pneumonia control programs.
This proactive approach, supported by the identification of a clear seasonal pattern, aligns with broader global health strategies outlined by the WHO to reduce morbidity and prepare for annual respiratory epidemics .
Multiple factors may drive the sustained year-to-year increase in pneumonia cases.
The concept of immunity debt, a global phenomenon characterized by heightened susceptibility to IJID | Vol 11 .
December 2025 respiratory infections following prolonged periods of reduced exposure during the COVID-19 pandemic, has been widely reported in Asia and Europe and may partially explain the upward trend observed in Indonesia.
At the same time, improvements in the SKDR surveillance system, including expanded reporting coverage, better diagnostic availability, and strengthened digital reporting platforms, may have enhanced case detection, producing a more complete picture of the national pneumonia burden .
The shift in peak incidence from Quarter 4 to Quarter 3 in 2024 also suggests dynamic interplays between climatic variation and circulating respiratory viruses, such as RSV.
Influenza, and adenoviruses, patterns similarly documented in studies from Thailand.
Malaysia, and the Philippines .
Therefore, this shift highlights the need for real-time monitoring to manage the temporal uncertainty of outbreaks from year to year, while also providing a foundation for further analytical investigations to specifically confirm the relative contributions of climatic and virological factors.
Beyond temporal patterns, this study revealed pronounced spatial disparities in pneumonia burden across Indonesia.
The concentration of high-burden clusters in Java is consistent with prior studies linking population density, urban crowding, and industrial air pollution to elevated risks of respiratory infections in Southeast Asia .
JavaAos extremely high population density increases person-to-person contact rates, accelerating pathogen In contrast, its high levels of PM2.
5 pollution have been repeatedly linked to increased pneumonia mortality and hospitalization in regional meta-analyses .
Nevertheless, the emergence of West Nusa Tenggara.
East Nusa Tenggara.
North Sumatra, and South Kalimantan as secondary high-risk clusters highlights that context-specific determinants beyond Java, particularly socio-economic disparities and limited healthcare access, play a critical role in shaping pneumonia risk.
Ae.
Furthermore, studies in rural settings in Indonesia and Bangladesh similarly show that delayed care-seeking, malnutrition, indoor biomass fuel exposure, and low-quality primary care contribute significantly to pneumonia incidence in nonurban settings .
,27,.
In contrast, provinces categorized as coldspots, such as Southwest Papua.
Maluku, and West Sulawesi, require careful interpretation.
The low number of reported cases is unlikely to indicate a genuinely low disease burden, but rather reflects persistent gaps in surveillance sensitivity, health system reach, and service accessibility.
These regions face structural challenges, including geographic isolation, limited transportation networks, understaffed health facilities, and logistical constraints in laboratory confirmation factors repeatedly highlighted in national assessments of surveillance performance in Eastern Indonesia .
This condition indicates that the recorded pneumonia burden is likely far lower than the actual incidence in the community .
In addition, lower health-seeking behavior, reliance on traditional healers, and incomplete reporting from remote Puskesmas contribute to under-detection of pneumonia cases .
IJID | Vol 11 .
December 2025 Similar challenges related to the under-detection of respiratory infections in remote and underserved areas have been reported across several countries in Southeast Asia and the Pacific.
Studies from Papua New Guinea and the Philippines, for example, show that geographical isolation, limited diagnostic capacity, and weak routine surveillance contribute to substantial underreporting of pneumonia and other acute respiratory infections .
Thus, the coldspot classification likely reflects underreporting rather than lower incidence, and these provinces should be considered priority areas for surveillance strengthening.
Taken together, these temporal and spatial findings offer critical insights for pneumonia control in Indonesia.
The predictable seasonal patterns provide clear opportunities for targeted interventions such as vaccination drives, reinforcement of primary care readiness, and resource pre-positioning before peak months .
Spatial mapping allows the Ministry of Health to allocate resources in a differentiated manner: high-burden urban clusters such as Java require intensive transmission control, air quality interventions, and hospital surge preparedness, while underserved regions in Eastern Indonesia require investments in surveillance sensitivity, diagnostic capacity, and accessibility of essential health services.
Strengthening the integration between routine SKDR reporting and community-level case detection, especially through active surveillance and digital reporting innovations, could substantially reduce reporting gaps.
Finally, although this study is descriptive and cannot establish causal inferences, its findings are consistent with global evidence on pneumonia epidemiology and highlight key structural determinants that warrant policy attention.
The observed disparities reinforce WHOAos call for targeted, equity-driven strategies for respiratory disease control, especially in geographically diverse lower-middle-income countries.
Future studies using individual-level data or spatial-temporal modeling are needed to deepen the understanding of the underlying drivers and to support more precise risk stratification.
CONCLUSION
Based on the analysis of Early Warning and Response System (EWARS/SKDR) data from 2022Ae2024, it is concluded that pneumonia in Indonesia exhibits distinct epidemiological characteristics, marked by a yearly increasing temporal trend and a consistent seasonal pattern .
predictable decline in Quarter II).
Extreme spatial disparities establish Java Island as the national epicenter .
, driven primarily by demographic and environmental factors such as urbanization and air pollution.
Conversely, the low number of reported cases in Eastern Indonesia .
Southwest Papua.
Maluk.
more likely reflects limitations in surveillance systems and healthcare access, indicating the existence of hidden high-risk areas.
These findings underscore the necessity of a differentiated, highly specific control strategy.
Temporally, proactive interventions such as the mobilization of vaccination campaigns IJID | Vol 11 .
December 2025 (PCV/Influenz.
and logistics preparation .
, ensuring medical supply stoc.
must be strategically scheduled during Quarter II to maximize preparedness before the peak transmission Spatially, resource allocation must be tailored:
For Hotspots (Jav.
: Focus on intensive transmission control and mitigating modifiable environmental risk factors, including enforcing policies to address non-infectious triggers like air pollution (PM2.
For Coldspots (Eastern Indonesi.
: The priority is strengthening the detection system.
This requires specific actions, including detailed training for surveillance personnel on national case definitions and real-time SKDR reporting, as well as the mobilization of diagnostic logistics and improved service access to uncover the true burden of disease.
This study thus provides a robust, actionable evidence base to improve pneumonia control resource allocation in Indonesia.
DECLARATIONS
Ethics approval This study was approved by the Ethical approval obtained from the Research and Community Service Ethics Committee.
Faculty of Public Health.
Universitas Indonesia (No.
Ket-592/UN2.
F10.
D11/PPM.
02/2.
Conflict of interest.
The authors declare no conflict of interest.
Funding This research did not receive any specific grants from funding agencies in the public, commercial, or nonprofit sectors.
Acknowledgments The authors gratefully acknowledge the Directorate of Health Surveillance and Quarantine.
Directorate General of Disease Prevention and Control.
Ministry of Health, for their support and assistance in providing the data and information essential for this research.
REFERENCES