Role of ProMED-mail in the Early Detection of Dengue Outbreaks in the South Asia Region: A Retrospective Descriptive Study

Role of ProMED-mail in the Early Detection of Dengue Outbreaks in the South Asia Region: A Retrospective Descriptive Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Role of ProMED-mail in the Early Detection of Dengue Outbreaks in the South Asia Region: A Retrospective Descriptive Study Nimasha Ekanayaka, Ranjan Premaratna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7177669/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Background: In South Asia, dengue led to substantial morbidity, mortality, and health system strain. Timely detection of outbreaks is crucial. We evaluated the utility of ProMED-mail in providing early warnings of dengue outbreaks across five South Asian countries. Methods: ProMED-mail reports (January 1, 2024-January 31, 2025) published as outbreak alerts, situation updates, or mortality events were reviewed for Bangladesh, India, Nepal, Pakistan, and Sri Lanka. National dengue trends from ministries of health and WHO-SEAR were analyzed by epidemiological week/month to identify outbreak onset, using a trend-based definition. Lead time was defined as time between the first ProMED-mail alert to estimated national outbreak start. Sensitivity analysis was performed by adjusting the outbreak start time by ±1 week. The median and interquartile range (IQR) of lead times was calculated to assess variability. Findings: 34/150 ProMED-mail reports were outbreak alerts. Calculated lead times ranged –8.6 weeks (Pakistan) to +5 weeks (Bangladesh and Event 2 Sri Lanka), and median (IQR) lead time 4.15(2-5) weeks. ProMED issued early alerts; Bangladesh (+5 weeks), India (+4 weeks), Nepal (~4.3 weeks), and Sri Lanka’s October case rise (+5 weeks). In Pakistan, ProMED identified the outbreak in the absence of publicly available surveillance data. Sensitivity analysis yielded median (IQR) lead times of 3.15(1-4) weeks and 5.15(3-6) weeks when outbreak onset was adjusted by –1 and +1 week, respectively. Conclusion: ProMED-mail provided meaningful lead time in detecting dengue outbreaks in South Asia, supporting its role as an effective early warning system and may facilitate enhance epidemic preparedness in resource-limited settings. Dengue ProMED-mail outbreak detection digital surveillance South Asia epidemic intelligence early warning systems Figures Figure 1 Figure 2 Introduction Dengue fever, a mosquito-borne viral infection, stands as the most widespread and rapidly expanding arboviral infection globally [ 1 ]. Of the 3.5 billion people living in dengue endemic countries and at risk of contracting dengue fever, 1.3 billion live in dengue endemic areas in ten countries within the WHO South-East Asia (SEA) Region. Notably, five countries - India, Indonesia, Myanmar, Sri Lanka and Thailand - rank among the 30 most highly endemic nations worldwide [ 2 ]. Despite ongoing control efforts, the global incidence of dengue has continued to rise, although improvements in case management have contributed to a reduction in the case fatality rate (CFR) to below 0.5% [ 3 ]. Over the past decade, countries in South-East Asian region (SEAR) have experienced recurrent and increasingly severe dengue outbreaks, placing substantial strain on healthcare systems. These outbreaks have often led to critical shortages in medical supplies, overcrowding of healthcare facilities, and insufficient hospital bed capacity [ 4 ]. Despite the presence of an efficient national notification system in Sri Lanka, the timely identification of outbreaks remains a major challenge due to several systemic and operational factors [ 5 ]. However, further augmentation of early detection of dengue outbreaks could significantly improve early introduction of control methods, and timely allocation of resources employed for case management and thus mitigate healthcare disruptions. In recent years, automated and digital public health surveillance approaches have demonstrated promise in improving the early detection of emerging infectious disease outbreaks. The growing and retrieval of information and their rapid dissemination through digital communication technologies has transformed the internet into a critical source for epidemic intelligence [ 6 ]. These digital surveillance systems aim to reduce the time lag between the emergence of disease signals and their formal recognition by public health authorities, thereby enabling more rapid investigation and response. Outbreak dynamics, especially for infectious diseases such as dengue, are often complex and non-linear, making them difficult to model with traditional statistical methods. These complexities are particularly pronounced in low- and middle-income countries where public health infrastructure may be limited [ 7 ]. Among existing digital surveillance tools, the Program for Monitoring Emerging Diseases; ProMED-mail stands out as one of the most widely used early alert systems. ProMED-mail is a qualitative, event-based reporting system through which clinicians and public health professionals report unusual disease events [ 8 ]. Launched in 1994 by the International Society for Infectious Diseases, ProMED-mail provides publicly accessible, global coverage of infectious disease outbreaks in humans, animals, and plants. It is moderated by subject matter-experts reviewers who evaluate information from a range of sources, including health professionals, government websites, and media reports. The platform supports the One Health approach, recognizing the interconnectedness of human, animal, and environmental health domains. Operating continuously across multiple time zones with contributors from over 30 countries, ProMED-mail has played a significant role in the early identification of several major outbreaks. While the system does not replace national surveillance programs, it acts as a complementary tool – frequently issuing alerts ahead of official announcements under the philosophy of ‘One Alert at a Time’ [ 9 ]. In recent years, it has collaborated with other digital surveillance platforms such as HealthMap and the WHO’s Epidemic Intelligence from Open Sources (EIOS) system, to enhance epidemic intelligence capacities. Given the increasing incidence of dengue in South Asia and the ongoing need for effective early warning systems, this study aims to evaluate the utility of ProMED-mail as a complementary surveillance tool for detecting emerging dengue outbreaks across the region. Methods Study Design and Data Sources We conducted a retrospective descriptive study to evaluate the use of ProMED-mail reports in detecting dengue outbreaks in South Asian countries during the period from January 1, 2024, to January 31, 2025. The primary dataset comprising all dengue-related reports from South Asian countries - India, Sri Lanka, Bangladesh, Nepal, and Pakistan - published during the study period - was obtained through a formal data access request submitted to the ProMED-mail team. Permission was granted to use and publish findings derived from the reports provided. The ProMED team did not participate in any aspect of the study design, data analysis, interpretation, or manuscript preparation. Their role was limited solely to data provision. For comparative purposes, dengue situation reports from the World Health Organization South-East Asia Region (WHO-SEAR) were reviewed with country level national surveillance updates to assess the alignment and timeliness of ProMED alerts relative to official outbreak notifications. Both ProMED-mail and WHO reports with national surveillance updates are publicly accessible platforms providing information for public health purposes. As no individual-level or confidential data were used, and all data was in the public domain, ethical clearance was not required for this study. Inclusion and Exclusion Criteria ProMED-mail reports were included if they met the following criteria: Reported on dengue-related outbreaks, case increases, or deaths in one of the South Asian countries, Published between January 1, 2024, and January 31, 2025, Written in English. Reports were excluded if they: Were duplicates or reposts of the same news, Described non-dengue events (e.g., malaria or chikungunya outbreaks), Contained editorial comments, research summaries, or news without case/event data, Were not related to countries classified under the South Asian region by WHO. Data Extraction and Variables Each eligible report was reviewed and manually extracted from the data provided into a data collection sheet. The following variables were recorded: Date of publication, Country and subnational region affected, Report classification (e.g., new outbreak alert, situational update, report of dengue-related death), Source of information (e.g., news outlet, government statement), Confirmation status (whether confirmed against official national surveillance sites such as the National Dengue Control Unit (NDCU) in Sri Lanka, India’s National Center for Vector Borne Disease Control, Bangladesh CDC, etc.). Statistical analysis Descriptive statistics, including frequencies and relative proportions, were calculated using Microsoft Excel (Version 365). Comparative timeline analysis was then performed by aligning ProMED report timelines with WHO country-level dengue case and mortality data, including national surveillance updates from each country, to assess temporal concordance and the lead time of ProMED alerts relative to confirmed outbreaks. Lead Time Calculation and sensitivity analysis To evaluate the timeliness of ProMED-mail in signaling dengue outbreaks, we calculated the ‘lead time’ between the publication of date of each ProMED-mail report and the estimated onset of the corresponding outbreak in weekly national dengue case data. Dengue case data were complied in weekly aggregates, standardized according to the ISO epidemiological week format. Each ProMED-mail report was assigned to its corresponding epidemiological week, based on the date of publication. In the absence of universally defined national outbreak thresholds across the five countries, a trend-based approach was used to define outbreak onset (‘Outbreak week’). Specifically, an outbreak was considered to have started during the first week of sustained increase in reported cases, typically evidenced by two or more consecutive weekly rise above baseline levels. Lead time was calculated using the following formula: Lead Time (weeks) = Outbreak Week − ProMED Alert Week A positive lead time indicated that the ProMED-mail report preceded the official outbreak signal, suggesting potential value for early warning. A zero or negative lead time indicated coincident or delayed reporting relative to national data. All lead time values were aggregated and summarized by country. All data cleaning, trend analysis, and calculations were performed using Microsoft Excel (Microsoft 365). Lead time values were summarized by country and outbreak cluster using descriptive statistics, including medians and interquartile ranges (IQR). To assess the robustness of lead time estimates between ProMED-mail alerts and national outbreak onset, a sensitivity analysis was conducted. Given the inherent variability in outbreak definitions and reporting lag across national surveillance systems, we simulated temporal uncertainty by adjusting key time points by ± 1 week. Two scenarios were tested: (A) outbreak onset shifted 1 week earlier, and (B) outbreak onset shifted 1 week later. For each scenario, lead time was recalculated as the difference (in weeks) between the adjusted outbreak onset and the ProMED alert date. Median lead times were then compared across scenarios. The results were used to evaluate whether small shifts in surveillance or alert timing materially affected the overall conclusion regarding ProMED-mail’s timeliness. Results Between January 1, 2024, and January 31, 2025, a total of 252 ProMED-mail reports related to South Asian countries (Sri Lanka, India, Bangladesh, Nepal, and Pakistan) were identified. After applying exclusion criteria, 102 reports were removed, including: 88 duplicate or redundant reports, 5 reports related to other infectious diseases (e.g., malaria, chikungunya), and 9 reports not published in English. The remaining 150 reports were categorized as follows: New outbreak detections: 34, Situation updates: 100, New death reports: 54. The majority of updates originated from verified media reports, which were cross validated with official national dengue surveillance data from the respective countries. Country-Specific Findings Bangladesh Among the 40 ProMED-mail reports on Bangladesh, 19 indicated rising case numbers, with new outbreak alerts issued in June, and rapid rising of cases in October 2024. Additionally, 34 reports provided situational updates, and 27 reports specifically mentioned newly reported dengue-related deaths. Approximately 98% of the reports were derived from online news sources, and 2% from print media. All reports were cross validated using the Bangladesh National Guidelines for the Clinical Management of Dengue Syndrome and the CDC's dengue surveillance updates. WHO reported rising cases from June 2024, peaking in October. The National Dengue Dynamic Dashboard showed cases rose from 29th week onwards (Fig. 2 (A)) [ 10 ]. Lead time analysis showed that ProMED-mail issued its June outbreak alert five weeks ahead of the observed rise in weekly case counts (Table 1). Additionally, the data indicated a seasonal pattern, with most outbreaks occurring during the monsoon period (June–September), which aligns with historically expected trends. However, a notable deviation was observed in the increased incidence of dengue during the ‘winter’ season, suggesting a possible shift in vector behavior or transmission dynamics. India A total of 43 ProMED-mail reports were related to India. Of these, 21 reports documented a rising trend in dengue cases, with five distinct new outbreak alerts: Karnataka (April 2024), South Goa (June 2024) Nashik, Maharashtra (July 2024), Bhopal, Madhya Pradesh (August 2024), Delhi (October 2024). There were also 36 situation update reports and 9 reports related to new dengue-related deaths. One report noted a declining trend in January 2025. WHO surveillance showed a major dengue outbreak beginning in 22nd week, peaking in 28th week, 2024 mainly at Karnataka and Kerala state in India [ 2 , 11 ]. Based on epidemiological case trends, ProMED-mail’s first major outbreak alert in April 2024 preceded the national outbreak onset in June by 4 weeks. Approximately 80% of the reports originated from Indian news media and were confirmed with national sources including: National Guidelines for Dengue Fever in India, National Centre for Vector Borne Disease Control, Directorate General of Health Services, and Ministry of Health & Family Welfare, Government of India. Nepal Dengue is endemic in Nepal, with year-round laboratory-confirmed cases showing variable intensity and post-monsoon peaks. Among the 7 ProMED-mail reports concerning Nepal: 5 reported rising case trends, particularly in Kathmandu Valley, where dengue incidence is increasing due to climate change, urbanization, rising temperatures, and mosquito adaptation to cooler climates. An outbreak alert was issued in May 2024, prior to a sharp rise in July-August, with the Mugu district reporting its first dengue occurrence. In August, a rise in cases during ‘winter’ months was noted, marking an atypical seasonal shift. 4 reports described new dengue-related deaths. All findings were verified against official data from Nepal's Epidemiology and Disease Control Division (EDCD), the national surveillance authority. Per WHO, the outbreak began in July and peaked in August. According to the situation report on dengue in Nepal published by the national Epidemiology and Disease Control Division (EDCD), there was a sharp rise in cases from 1,131 in July to 4,215 in August [ 12 ]. Based on monthly data, ProMED provided a lead time of approximately 1 month before the outbreak acceleration that is observed in July-August 2024. Pakistan A single, notable ProMED report from Rawalpindi district in November 2024 highlighted a worsening dengue outbreak that followed a sharp drop in ambient temperature in late October. The report suggested behavioral factors may have contributed to increased transmission, such as reduced use of electric fans with reduced temperature, which typically deter mosquito activity. The absence of air circulation may have created favorable conditions for mosquito bites during evening and night time hours. This report illustrates a possible link between climatic factors and outbreak severity. In Pakistan, the exact number of dengue fever cases in 2024 is not definitely stated in the search results. However, Pakistan experienced a significant dengue outbreak in 2024, with one news article reporting 2,795 new dengue cases within a single week in September [ 13 ]. According to the National Institute of Health (NIH), Pakistan, and Rawalpindi Medical University reports, a sharp rise in dengue cases was reported towards the latter part of the September 2024, with warnings for the following 3–4 weeks [ 14 , 15 ]. As per the official data however, the outbreak had begun in September, meaning the ProMED alert arrived approximately 2 months late. Sri Lanka Twenty ProMED-mail reports were identified for Sri Lanka, including 16 reports describing rising dengue trends. Outbreak alerts were issued in the Western Province in June and October 2024 with 18 reports provided situational updates.11 reports referenced newly reported deaths. Approximately 75% of the reports were sourced from online news platforms, and 25% from newspapers. All information was verified using data from the National Dengue Control Unit (NDCU) and the Sri Lanka Epidemiology Unit. WHO data indicated persistent year-round cases with peaks in January, June, November, and December 2024. The National Dengue Control Unit (NDCU) reported an upward trend of rising cases since 22nd week of 2024 with 12.8% rise from 22nd to 23rd week (early June), 20.5% by the 25th week which started resolution around 32nd week onwards (Event– 1 ), and there was another 25.5% rise from baseline was noted in the 47th week onwards (late November), (Event- 2 ) [ 15 ] In the first outbreak cluster (June), ProMED’s alert coincided with the beginning of a sustained rise in cases (lead time: 0 weeks). In the second cluster (November), ProMED issued an alert six weeks ahead of the official case surge (lead time: 5 weeks). Cross- Country Comparison of Lead Time Lead times between ProMED-mail alerts and the onset of dengue outbreaks were estimated for each country using a trend-based methodology. To ensure comparability, all outbreak onset data were converted to epidemiological weeks. Calculated lead times ranged from − 8.6 weeks (Pakistan) to + 5 weeks (Sri Lanka – Event 2). After standardizing to weeks, the median (IQR) lead time was 4.15 (2–5) weeks, suggesting that ProMED-mail typically issued outbreak alerts approximately one month prior to the observed rise in dengue cases. A detailed comparison of lead times is presented in Fig. 1 and Table 1. These findings highlight the potential value of ProMED-mail as a supplementary early warning tool to support dengue surveillance efforts across South Asia. To test the robustness of our lead time estimates, we conducted a sensitivity analysis by adjusting the outbreak onset dates by ± 1 week. Under the baseline scenario, the median (IQR) lead time was 4.15 (2–5) weeks. When the outbreak onset was advanced by 1-week (thereby reducing the lead time), the median (IQR) decreased to 3.15 (1–4) weeks. Conversely, delaying the outbreak onset by 1 week resulted in a median (IQR) lead time of 5.15 (3–6) weeks. Despite these variations, the overall pattern of ProMED-mail issuing alerts approximately one month prior to the observed case surge remains consistent. The notable exception is Pakistan, where ProMED alerts were delayed relative to outbreak onset regardless of these adjustments. Table 1 – Lead Time Between ProMED-mail Alerts and Dengue Outbreak Onsets in South Asia (2024 – 2025) Country ProMED Alert Date (2024) Alert Week/ Month Outbreak start Week/Month Lead time Weeks/ Months Scenario A: Outbreak – 1 wk Scenario B: Outbreak + 1 wk Bangladesh 21 June Week 25 Week 30 5 weeks 4 6 Nepal 17 May Month 5 (May) Month 6 (June) 1 month (4.3 weeks) 3.3 5.3 Sri Lanka – (Event 1) 1 June Week 22 Week 22 0 weeks -1 1 Sri Lanka – (Event 2) 19 October 2024 Week 42 Week 47 5 weeks 4 6 India 29 April 2024 Week 18 Week 22 4 weeks 3 5 Pakistan 4 November 2024 Month 11 (November) Month 9 (September) -2 months (-8.6 weeks) -9.6 -7.6 Median (IQR) 4.15 (2–5) weeks 3.15 (1–4) weeks 5.15 (3–6) weeks Lead time was calculated as the difference in weeks or months between the date of the first ProMED-mail alert indicating an outbreak and the date when national surveillance data crossed a predefined outbreak threshold. Sensitivity analyses were performed by shifting the outbreak start date one week earlier (Scenario A) and one week later (Scenario B) to assess robustness. Negative lead times indicate that ProMED-mail reports lagged behind national detectionLead time was calculated as the difference in weeks or months between the date of the first ProMED-mail alert indicating an outbreak and the date when national surveillance data crossed a predefined outbreak threshold. Sensitivity analyses were performed by shifting the outbreak start date one week earlier (Scenario A) and one week later (Scenario B) to assess robustness. Negative lead times indicate that ProMED-mail reports lagged behind national detection. This figure illustrates the temporal relationship between ProMED-mail alerts and national outbreak trends, highlighting lead time variations and supporting the platform’s potential role in early warning for dengue surveillance. Discussion The South Asian region remains heavily burdened by mosquito-borne viral diseases, particularly dengue, malaria, and chikungunya. Among these, seasonal dengue outbreaks especially in the post-monsoon period, the illness has become increasingly frequent and severe, placing considerable strain on public health systems. In response, each South Asian country has developed their own national dengue management guidelines, complying with WHO management protocols and regularly train healthcare providers to manage the disease effectively in various disciplines [ 10 – 16 ]. Most countries in the region maintain multi-tiered surveillance systems ranging from hospital and district (MOH) levels to national-level monitoring aimed at detecting outbreaks early and mobilizing resources for case management. These systems play a crucial role in allocating medical supplies, managing hospital bed capacity, and minimizing morbidity and mortality associated with sudden case surges [ 17 ]. However, a significant time lag often exists between the collection of surveillance data, preparation of surveillance documents and their dissemination to frontline clinicians, who are the first responders in outbreak scenarios. Contributing factors include delays in data reporting, limited time for clinicians to access weekly updates, and other system-level inefficiencies. This delay can compromise outbreak preparedness and response efforts, particularly in resource-limited settings. To bridge this gap, web-based epidemic intelligence platforms such as ProMED-mail, HealthMap, EPIWATCH, and the WHO's Epidemic Intelligence from Open Sources (EIOS) have emerged as valuable tools for real-time disease surveillance [ 18 ]. These platforms integrate information from official sources, news media, scientific literature, and social media, which are then reviewed by expert moderators. The result is an aggregated, timely, and accessible feed of disease alerts often available to users before formal notification systems issue updates. ProMED-mail, in particular, has become one of the most established digital surveillance systems for monitoring emerging and re-emerging infectious diseases. Moderated by infectious disease specialists, ProMED-mail disseminates validated outbreak reports through email alerts and its publicly available platform, allowing clinicians, policymakers, and researchers to access concise updates in near-real time. Our analysis demonstrated that ProMED-mail successfully detected dengue outbreak signals in South Asian countries approximately one month prior to peaks recorded in official WHO or national surveillance data. This early detection capability was evident across all five countries studied India, Sri Lanka, Bangladesh, Nepal, and Pakistan and included alerts for newly affected regions, changes in outbreak intensity, and unusual seasonal shifts, such as increasing dengue incidence during typically low transmission ‘winter’ months. In Sri Lanka, where dengue is endemic and cases occur around the year, ProMED-mail provided timely alerts during periods of case surges or rising in transmission. In the first outbreak cluster in early June 2024, the alert aligned with the beginning of a documented upward trend in case numbers. Notably, this period coincided with dengue control activities implemented by national dengue prevention campaign which initiated on 26th May, covering 71 MOH divisions in 17 districts. This proactive response may have contributed to flattening of the early outbreak curve. This clearly reflect how early awareness of case rises and timely mobilization of control measures and case management can mitigate transmission. In the second cluster, occurring in October–November 2024, ProMED issued an alert five weeks ahead of the national case surge, highlighting its potential as an anticipatory tool even beyond routine surveillance In India, ProMED-mail captured an outbreak signal in April 2024, preceding national-level trends that began rising notably in June. The early alert supported preparedness in multiple states, including Karnataka and Kerala, which experienced significant outbreaks during the peak transmission season [ 2 ]. This 4-week lead time, consistent with trends in Bangladesh and Nepal, strengthens the platform’s credibility in providing early warnings. In Nepal, ProMED’s May alert precede a sharp monthly increase observed in July–August 2024. The system successfully flagged activity even in non-traditional transmission months and regions, such as the Mugu district, indicating emerging geographic shifts in vector ecology and disease risk due to factors like climate change and urbanization. In Pakistan, where formal, real-time dengue surveillance data is limited or not publicly accessible, ProMED-mail was still able to report on a significant outbreak in Rawalpindi in November 2024. Although the alert lagged behind the estimated outbreak onset by over 8 weeks, this case underscores an important point: in countries lacking transparent or timely reporting systems, ProMED can still serve as a valuable source of outbreak intelligence, capturing events through local media and clinician reports when official data is delayed or absent. Across the five South Asian countries analyzed, the median lead time between ProMED-mail alerts and estimated national outbreak onset was 4.15 weeks (IQR: 2–5 weeks), suggesting that ProMED typically offered an approximately one-month advance window before observable case surges. This lead time, observed across diverse surveillance capacities and epidemiological profiles, reinforces ProMED-mail’s value as a complementary early warning system, particularly in low- and middle-income countries (LMICs) where structural delays in data collection, reporting, and dissemination may compromise the timeliness of outbreak responses. Sensitivity analysis confirmed the robustness of this signal, with adjusted medians ranging from 3.15 to 5.15 weeks, underscoring ProMED’s consistent potential to detect dengue activity in advance of formal recognition. Notably, in Pakistan, where official surveillance data were not publicly available, ProMED-mail issued alerts suggesting an ongoing outbreak, highlighting its added utility in data-scarce or conflict-affected settings. Collectively, these findings support the integration of digital epidemic intelligence tools like ProMED-mail into national and regional surveillance frameworks to strengthen early detection, enhance preparedness, and accelerate public health response in the face of emerging dengue threats From a practical standpoint, ProMED-mail offers several advantages: Free and public access; Centralized and concise summaries of diverse data sources; Early warning alerts that often precede official announcements; User-friendly interface and email delivery, making it accessible even to busy clinicians. These features make ProMED-mail a valuable supplementary tool for clinicians and public health officials, enhancing their ability to anticipate outbreaks, adjust resource planning, and implement timely interventions. This study has several limitations. First, the data analysis was based solely on ProMED reports, which depend on publicly available media and expert submissions. As such, reporting bias may exist, particularly favoring urban or high-profile outbreaks, while underrepresenting smaller or rural events. Second, the exclusion of non-English reports may have limited inclusion of some regionally relevant updates, especially in linguistically diverse national like India or Pakistan. Third, differences in national reporting practices and data publication delays may influence the apparent lead time of ProMED-mail alerts. For example, in Pakistan, the lack of accessible national dengue surveillance data may have contributed to the platform’s delayed alert in that setting, yet even without official data, ProMED was able to detect an outbreak, suggesting its utility in data-poor environments. We feel that further research to quantify the predictive accuracy of ProMED for other vector-borne diseases in the region, exploring the possibility of integrating such digital platform models with national disease surveillance platforms, and assessing clinician awareness and actual use of ProMED in outbreak response planning in order to further clarify its utility in outbreak prediction and mitigation in the region. Conclusion This study demonstrates the potential of digital epidemic intelligence tools such as ProMED-mail as reliable, timely, and easily accessible early-warning system for dengue surveillance. In multiple South Asian countries, ProMED-mail detected dengue activity several weeks ahead of official outbreak confirmation, suggesting it can effectively complement national surveillance systems. By offering expert-reviewed, real-time alerts that are publicly accessible, ProMED-mail enhances situational awareness and supports more proactive decision-making, especially in resource-constrained settings. Integrating such digital surveillance platforms into routine health information systems may bridge communication gaps and improve outbreak control strategies. Abbreviations CDC: Centers for Disease Control and Prevention CFR: Case Fatality Rate EIOS: Epidemic Intelligence from Open Source EDCD: Epidemiology and Disease Control Division IQR: Interquartile Range LMIC: Low- and Middle-Income Countries MOH: Medical Officer of Health NDCU: National Dengue Control Unit NIH: National Institute of Health ProMED-Mail: Program for Monitoring Emerging Diseases – Mail SEA: South-East Asia SEAR: South-East Asia Region WHO: World Health Organization Declarations Ethics approval and consent to participate Ethics approval was not required for this study, as it involved the analysis of publicly available, de-identified data with no human participant involvement. Patients or the public were not involved in the design, conduct, reporting, or dissemination plans of this research. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form. Availability of data and material The ProMED-mail data used in this study were obtained through a special data request approved by the ISID. These data are not publicly available but may be obtained from the corresponding author upon reasonable request and with permission from the ISID. Aggregated surveillance data from national health agencies used for comparison are publicly available through their official portals. Competing interests The authors declare no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors Authors’ Contributors N.E. conceptualized the study, led the design, conducted the initial literature searches, analyzed the data, and drafted the initial report. R.P. analyzed the data, updated the initial report, and provided expert epidemiological input. N.E. led the writing of the paper. R.P. updated and completed the paper. All authors were responsible for interpretation of data and reviewing and revising the manuscript critically for importantintellectual content. All authors read and approved the final manuscript. Acknowledgments We thank the International Society for Infectious Diseases (ISID) ProMED team for providing access to the dengue reports dataset. We also acknowledge the Ministry of Health, Sri Lanka, and regional epidemiological units in South Asia for supporting background surveillance data and contextual information. References Haider N, Hasan MN, Onyango J, et al. Global dengue epidemic worsens with record 14 million cases and 9000 deaths reported in 2024. Int J Infect Dis. 2025;158:107940. 10.1016/j.ijid.2025.107940 . World Health Organization, Regional office for South-East Asia. Epidemiological Bulletin: WHO Health Emergencies Programme, WHO South-East Asia Region. 1st ed. 15 Jan 2025. Reporting period: 23 Dec 2024–12 Jan 2025. https://www.who.int Ministry of Health Sri Lanka. 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Clin Infect Dis. 2004;39(2):227–32. 10.1086/422003 . You J, Expert P, Costelloe C. Using text mining to track outbreak trends in global surveillance of emerging diseases: ProMED-mail. J R Stat Soc Ser A. 2021;184:1189–211. 10.1111/rssa.12721 . Institute of Epidemiology, Disease Control and Research (IEDCR). Monthly dengue cases by month: Dengue Dynamic Dashboard, Bangladesh. [cited 2025 Jul 6]. National Centre for Vector Borne Diseases. Control (NCVBDC), India. Weekly vector-borne disease report. [cited 2025 Jul 6]. Epidemiology and Disease Control Division (EDCD). Nepal. Situation Report on Dengue in Nepal – 2024. Kathmandu: EDCD; 2024. Aftab S, Yaqoob E, Jared S. Dengue epidemic: Pakistan on alert. Lancet. 2024;404(10465):1807. National Institute of Health Pakistan. Integrated Disease Surveillance and Response (IDSR) Report – Week 16. 2024. Available from: https://www.nih.org.pk/wp-content/uploads/2024/05/Weekly%20Report-16-2024.pdf Yasin A. Next three to four weeks critical as dengue cases rise: Rawalpindi Medical University chief. Dawn [Internet]. 2024 Sep 23 [cited 2025 Jul 6]. Available from: https://www.dawn.com/news/1860475 National Dengue Control Unit (NDCU). Ministry of Health, Sri Lanka. Wkly Dengue Update. 2024;4(25):47. Talisma U, Mosharrafa R, Hossain RA, et al. Frequent outbreaks of dengue fever in South Asian countries – A correspondence analyzing causative factors and ways to avert. Health Sci Rep. 2023;6:e1598. 10.1002/hrs2.1598 . Bhatia S, Lassmann B, Cohn E, et al. Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread. NPJ Digit Med. 2021;4:73. 10.1038/s41746-021-00442-3 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7177669","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503255577,"identity":"5b27db9a-8f24-4ccb-b85b-ba644c6e9d3e","order_by":0,"name":"Nimasha Ekanayaka","email":"data:image/png;base64,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","orcid":"","institution":"Colombo North Teaching Hospital","correspondingAuthor":true,"prefix":"","firstName":"Nimasha","middleName":"","lastName":"Ekanayaka","suffix":""},{"id":503255578,"identity":"30abd3e2-c46d-403e-adae-9fe7dae742a5","order_by":1,"name":"Ranjan Premaratna","email":"","orcid":"","institution":"Colombo North Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ranjan","middleName":"","lastName":"Premaratna","suffix":""}],"badges":[],"createdAt":"2025-07-21 12:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7177669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7177669/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89971080,"identity":"28baea7c-3416-4505-b029-362a7116bcda","added_by":"auto","created_at":"2025-08-27 05:35:46","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34525,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLead time between ProMED-mail alerts and dengue outbreak onset across South Asian countries (2024–2025)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEach bar represents the time interval (in weeks) between the first ProMED-mail report referencing a dengue outbreak and the estimated week of outbreak onset, based on national surveillance trends. Positive values indicate early alerts preceding outbreak rise; negative values indicate alerts following onset. Lead times were derived using a trend-based method. ProMED demonstrated a median lead time of 4.15 weeks across countries.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7177669/v1/080fb9cf12765feb52476dcd.jpg"},{"id":89971082,"identity":"909efd7f-e9fe-45cf-b1a6-82431122ce59","added_by":"auto","created_at":"2025-08-27 05:35:46","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105101,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eComparison of ProMED-mail alerts and national dengue case trends across four South Asian countries (2024) Comparison of ProMED-mail alerts and national dengue case trends across four South Asian countries (2024)\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePanels A–D illustrate dengue case trends from official national and regional surveillance systems in Bangladesh, Nepal, Sri Lanka, and India. In each graph, the \u003cstrong\u003eblue arrow\u003c/strong\u003e marks the timing of the corresponding \u003cstrong\u003eProMED-mail alert\u003c/strong\u003e, allowing visual comparison with the onset of rising dengue cases.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePanel A\u003c/strong\u003e: Weekly dengue case counts from the \u003cem\u003eBangladesh National \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Dengue Dashboard\u003c/em\u003e (Directorate General of Health Services).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePanel B\u003c/strong\u003e: Monthly reported dengue cases from the \u003cem\u003eEpidemiology and \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Disease Control Division (EDCD), Nepal\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePanel C\u003c/strong\u003e: Weekly dengue surveillance data from the \u003cem\u003eDebussy Platform\u003c/em\u003e of \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;Sri Lanka’s \u003cem\u003eNational Dengue Control Unit (NDCU)\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePanel D\u003c/strong\u003e: Weekly dengue case counts in India, extracted from the \u003cem\u003eWHO \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;South-East Asia Regional (SEAR) Biweekly Dengue Situation Bulletin\u003c/em\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis figure illustrates the temporal relationship between ProMED-mail alerts and national outbreak trends, highlighting lead time variations and supporting the platform’s potential role in early warning for dengue surveillance.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7177669/v1/41e0c43719124482f7f06e81.jpg"},{"id":89973703,"identity":"b6ba117a-7ae4-4989-bc40-e88e3a387c4c","added_by":"auto","created_at":"2025-08-27 05:51:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":934775,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7177669/v1/f793f127-c65a-441f-83b7-c144fd3ded03.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Role of ProMED-mail in the Early Detection of Dengue Outbreaks in the South Asia Region: A Retrospective Descriptive Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDengue fever, a mosquito-borne viral infection, stands as the most widespread and rapidly expanding arboviral infection globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Of the 3.5\u0026nbsp;billion people living in dengue endemic countries and at risk of contracting dengue fever, 1.3\u0026nbsp;billion live in dengue endemic areas in ten countries within the WHO South-East Asia (SEA) Region. Notably, five countries - India, Indonesia, Myanmar, Sri Lanka and Thailand - rank among the 30 most highly endemic nations worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite ongoing control efforts, the global incidence of dengue has continued to rise, although improvements in case management have contributed to a reduction in the case fatality rate (CFR) to below 0.5% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOver the past decade, countries in South-East Asian region (SEAR) have experienced recurrent and increasingly severe dengue outbreaks, placing substantial strain on healthcare systems. These outbreaks have often led to critical shortages in medical supplies, overcrowding of healthcare facilities, and insufficient hospital bed capacity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite the presence of an efficient national notification system in Sri Lanka, the timely identification of outbreaks remains a major challenge due to several systemic and operational factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, further augmentation of early detection of dengue outbreaks could significantly improve early introduction of control methods, and timely allocation of resources employed for case management and thus mitigate healthcare disruptions.\u003c/p\u003e\u003cp\u003eIn recent years, automated and digital public health surveillance approaches have demonstrated promise in improving the early detection of emerging infectious disease outbreaks. The growing and retrieval of information and their rapid dissemination through digital communication technologies has transformed the internet into a critical source for epidemic intelligence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These digital surveillance systems aim to reduce the time lag between the emergence of disease signals and their formal recognition by public health authorities, thereby enabling more rapid investigation and response.\u003c/p\u003e\u003cp\u003eOutbreak dynamics, especially for infectious diseases such as dengue, are often complex and non-linear, making them difficult to model with traditional statistical methods. These complexities are particularly pronounced in low- and middle-income countries where public health infrastructure may be limited [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among existing digital surveillance tools, the Program for Monitoring Emerging Diseases; ProMED-mail stands out as one of the most widely used early alert systems. ProMED-mail is a qualitative, event-based reporting system through which clinicians and public health professionals report unusual disease events [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eLaunched in 1994 by the International Society for Infectious Diseases, ProMED-mail provides publicly accessible, global coverage of infectious disease outbreaks in humans, animals, and plants. It is moderated by subject matter-experts reviewers who evaluate information from a range of sources, including health professionals, government websites, and media reports. The platform supports the One Health approach, recognizing the interconnectedness of human, animal, and environmental health domains.\u003c/p\u003e\u003cp\u003eOperating continuously across multiple time zones with contributors from over 30 countries, ProMED-mail has played a significant role in the early identification of several major outbreaks. While the system does not replace national surveillance programs, it acts as a complementary tool – frequently issuing alerts ahead of official announcements under the philosophy of ‘One Alert at a Time’ [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In recent years, it has collaborated with other digital surveillance platforms such as HealthMap and the WHO’s Epidemic Intelligence from Open Sources (EIOS) system, to enhance epidemic intelligence capacities.\u003c/p\u003e\u003cp\u003eGiven the increasing incidence of dengue in South Asia and the ongoing need for effective early warning systems, this study aims to evaluate the utility of ProMED-mail as a complementary surveillance tool for detecting emerging dengue outbreaks across the region.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Data Sources\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted a retrospective descriptive study to evaluate the use of ProMED-mail reports in detecting dengue outbreaks in South Asian countries during the period from January 1, 2024, to January 31, 2025.\u003c/p\u003e\u003cp\u003eThe primary dataset comprising all dengue-related reports from South Asian countries - India, Sri Lanka, Bangladesh, Nepal, and Pakistan - published during the study period - was obtained through a formal data access request submitted to the ProMED-mail team. Permission was granted to use and publish findings derived from the reports provided. The ProMED team did not participate in any aspect of the study design, data analysis, interpretation, or manuscript preparation. Their role was limited solely to data provision.\u003c/p\u003e\u003cp\u003eFor comparative purposes, dengue situation reports from the World Health Organization South-East Asia Region (WHO-SEAR) were reviewed with country level national surveillance updates to assess the alignment and timeliness of ProMED alerts relative to official outbreak notifications.\u003c/p\u003e\u003cp\u003eBoth ProMED-mail and WHO reports with national surveillance updates are publicly accessible platforms providing information for public health purposes. As no individual-level or confidential data were used, and all data was in the public domain, ethical clearance was not required for this study.\u003c/p\u003e\u003cp\u003e\u003cem\u003eInclusion and Exclusion Criteria\u003c/em\u003e\u003c/p\u003e\u003cp\u003eProMED-mail reports were included if they met the following criteria:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eReported on dengue-related outbreaks, case increases, or deaths in one of the South Asian countries,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePublished between January 1, 2024, and January 31, 2025,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWritten in English.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eReports were excluded if they:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eWere duplicates or reposts of the same news,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDescribed non-dengue events (e.g., malaria or chikungunya outbreaks),\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eContained editorial comments, research summaries, or news without case/event data,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eWere not related to countries classified under the South Asian region by WHO.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003cem\u003eData Extraction and Variables\u003c/em\u003e\u003c/p\u003e\u003cp\u003eEach eligible report was reviewed and manually extracted from the data provided into a data collection sheet. The following variables were recorded:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eDate of publication,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCountry and subnational region affected,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eReport classification (e.g., new outbreak alert, situational update, report of dengue-related death),\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSource of information (e.g., news outlet, government statement),\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eConfirmation status (whether confirmed against official national surveillance sites such as the National Dengue Control Unit (NDCU) in Sri Lanka, India’s National Center for Vector Borne Disease Control, Bangladesh CDC, etc.).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics, including frequencies and relative proportions, were calculated using Microsoft Excel (Version 365). Comparative timeline analysis was then performed by aligning ProMED report timelines with WHO country-level dengue case and mortality data, including national surveillance updates from each country, to assess temporal concordance and the lead time of ProMED alerts relative to confirmed outbreaks.\u003c/p\u003e\u003cp\u003e\u003cem\u003eLead Time Calculation and sensitivity analysis\u003c/em\u003e\u003c/p\u003e\u003cp\u003eTo evaluate the timeliness of ProMED-mail in signaling dengue outbreaks, we calculated the ‘lead time’ between the publication of date of each ProMED-mail report and the estimated onset of the corresponding outbreak in weekly national dengue case data.\u003c/p\u003e\u003cp\u003eDengue case data were complied in weekly aggregates, standardized according to the ISO epidemiological week format. Each ProMED-mail report was assigned to its corresponding epidemiological week, based on the date of publication.\u003c/p\u003e\u003cp\u003eIn the absence of universally defined national outbreak thresholds across the five countries, a trend-based approach was used to define outbreak onset (‘Outbreak week’). Specifically, an outbreak was considered to have started during the first week of sustained increase in reported cases, typically evidenced by two or more consecutive weekly rise above baseline levels.\u003c/p\u003e\u003cp\u003eLead time was calculated using the following formula:\u003c/p\u003e\u003cp\u003eLead Time (weeks) = Outbreak Week − ProMED Alert Week\u003c/p\u003e\u003cp\u003eA positive lead time indicated that the ProMED-mail report preceded the official outbreak signal, suggesting potential value for early warning. A zero or negative lead time indicated coincident or delayed reporting relative to national data. All lead time values were aggregated and summarized by country.\u003c/p\u003e\u003cp\u003eAll data cleaning, trend analysis, and calculations were performed using Microsoft Excel (Microsoft 365). Lead time values were summarized by country and outbreak cluster using descriptive statistics, including medians and interquartile ranges (IQR). To assess the robustness of lead time estimates between ProMED-mail alerts and national outbreak onset, a sensitivity analysis was conducted. Given the inherent variability in outbreak definitions and reporting lag across national surveillance systems, we simulated temporal uncertainty by adjusting key time points by ± 1 week. Two scenarios were tested: (A) outbreak onset shifted 1 week earlier, and (B) outbreak onset shifted 1 week later.\u003c/p\u003e\u003cp\u003eFor each scenario, lead time was recalculated as the difference (in weeks) between the adjusted outbreak onset and the ProMED alert date. Median lead times were then compared across scenarios. The results were used to evaluate whether small shifts in surveillance or alert timing materially affected the overall conclusion regarding ProMED-mail’s timeliness.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween January 1, 2024, and January 31, 2025, a total of 252 ProMED-mail reports related to South Asian countries (Sri Lanka, India, Bangladesh, Nepal, and Pakistan) were identified. After applying exclusion criteria, 102 reports were removed, including: 88 duplicate or redundant reports, 5 reports related to other infectious diseases (e.g., malaria, chikungunya), and 9 reports not published in English.\u003c/p\u003e\n\u003cp\u003eThe remaining 150 reports were categorized as follows: New outbreak detections: 34, Situation updates: 100, New death reports: 54. The majority of updates originated from verified media reports, which were cross validated with official national dengue surveillance data from the respective countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCountry-Specific Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBangladesh\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 40 ProMED-mail reports on Bangladesh, 19 indicated rising case numbers, with new outbreak alerts issued in June, and rapid rising of cases in October 2024. Additionally, 34 reports provided situational updates, and 27 reports specifically mentioned newly reported dengue-related deaths. Approximately 98% of the reports were derived from online news sources, and 2% from print media. All reports were cross validated using the Bangladesh National Guidelines for the Clinical Management of Dengue Syndrome and the CDC\u0026apos;s dengue surveillance updates. WHO reported rising cases from June 2024, peaking in October. The National Dengue Dynamic Dashboard showed cases rose from 29th week onwards (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (A)) [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Lead time analysis showed that ProMED-mail issued its June outbreak alert five weeks ahead of the observed rise in weekly case counts (Table\u0026nbsp;1).\u003c/p\u003e\n\u003cp\u003eAdditionally, the data indicated a seasonal pattern, with most outbreaks occurring during the monsoon period (June\u0026ndash;September), which aligns with historically expected trends. However, a notable deviation was observed in the increased incidence of dengue during the \u0026lsquo;winter\u0026rsquo; season, suggesting a possible shift in vector behavior or transmission dynamics.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIndia\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 43 ProMED-mail reports were related to India. Of these, 21 reports documented a rising trend in dengue cases, with five distinct new outbreak alerts: Karnataka (April 2024), South Goa (June 2024) Nashik, Maharashtra (July 2024), Bhopal, Madhya Pradesh (August 2024), Delhi (October 2024). There were also 36 situation update reports and 9 reports related to new dengue-related deaths. One report noted a declining trend in January 2025. WHO surveillance showed a major dengue outbreak beginning in 22nd week, peaking in 28th week, 2024 mainly at Karnataka and Kerala state in India [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Based on epidemiological case trends, ProMED-mail\u0026rsquo;s first major outbreak alert in April 2024 preceded the national outbreak onset in June by 4 weeks.\u003c/p\u003e\n\u003cp\u003eApproximately 80% of the reports originated from Indian news media and were confirmed with national sources including: National Guidelines for Dengue Fever in India, National Centre for Vector Borne Disease Control, Directorate General of Health Services, and Ministry of Health \u0026amp; Family Welfare, Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNepal\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDengue is endemic in Nepal, with year-round laboratory-confirmed cases showing variable intensity and post-monsoon peaks. Among the 7 ProMED-mail reports concerning Nepal: 5 reported rising case trends, particularly in Kathmandu Valley, where dengue incidence is increasing due to climate change, urbanization, rising temperatures, and mosquito adaptation to cooler climates. An outbreak alert was issued in May 2024, prior to a sharp rise in July-August, with the Mugu district reporting its first dengue occurrence. In August, a rise in cases during \u0026lsquo;winter\u0026rsquo; months was noted, marking an atypical seasonal shift. 4 reports described new dengue-related deaths. All findings were verified against official data from Nepal\u0026apos;s Epidemiology and Disease Control Division (EDCD), the national surveillance authority.\u003c/p\u003e\n\u003cp\u003ePer WHO, the outbreak began in July and peaked in August. According to the situation report on dengue in Nepal published by the national Epidemiology and Disease Control Division (EDCD), there was a sharp rise in cases from 1,131 in July to 4,215 in August [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Based on monthly data, ProMED provided a lead time of approximately 1 month before the outbreak acceleration that is observed in July-August 2024.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePakistan\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA single, notable ProMED report from Rawalpindi district in November 2024 highlighted a worsening dengue outbreak that followed a sharp drop in ambient temperature in late October. The report suggested behavioral factors may have contributed to increased transmission, such as reduced use of electric fans with reduced temperature, which typically deter mosquito activity. The absence of air circulation may have created favorable conditions for mosquito bites during evening and night time hours. This report illustrates a possible link between climatic factors and outbreak severity.\u003c/p\u003e\n\u003cp\u003eIn Pakistan, the exact number of dengue fever cases in 2024 is not definitely stated in the search results. However, Pakistan experienced a significant dengue outbreak in 2024, with one news article reporting 2,795 new dengue cases within a single week in September [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. According to the National Institute of Health (NIH), Pakistan, and Rawalpindi Medical University reports, a sharp rise in dengue cases was reported towards the latter part of the September 2024, with warnings for the following 3\u0026ndash;4 weeks [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. As per the official data however, the outbreak had begun in September, meaning the ProMED alert arrived approximately 2 months late.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSri Lanka\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTwenty ProMED-mail reports were identified for Sri Lanka, including 16 reports describing rising dengue trends. Outbreak alerts were issued in the Western Province in June and October 2024 with 18 reports provided situational updates.11 reports referenced newly reported deaths. Approximately 75% of the reports were sourced from online news platforms, and 25% from newspapers. All information was verified using data from the National Dengue Control Unit (NDCU) and the Sri Lanka Epidemiology Unit.\u003c/p\u003e\n\u003cp\u003eWHO data indicated persistent year-round cases with peaks in January, June, November, and December 2024. The National Dengue Control Unit (NDCU) reported an upward trend of rising cases since 22nd week of 2024 with 12.8% rise from 22nd to 23rd week (early June), 20.5% by the 25th week which started resolution around 32nd week onwards \u003cem\u003e(Event\u0026ndash; 1\u003c/em\u003e), and there was another 25.5% rise from baseline was noted in the 47th week onwards (late November), \u003cem\u003e(Event- 2\u003c/em\u003e) [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n\u003cp\u003eIn the first outbreak cluster (June), ProMED\u0026rsquo;s alert coincided with the beginning of a sustained rise in cases (lead time: 0 weeks). In the second cluster (November), ProMED issued an alert six weeks ahead of the official case surge (lead time: 5 weeks).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross- Country Comparison of Lead Time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLead times between ProMED-mail alerts and the onset of dengue outbreaks were estimated for each country using a trend-based methodology. To ensure comparability, all outbreak onset data were converted to epidemiological weeks. Calculated lead times ranged from \u0026minus;\u0026thinsp;8.6 weeks (Pakistan) to +\u0026thinsp;5 weeks (Sri Lanka \u0026ndash; Event 2). After standardizing to weeks, the median (IQR) lead time was 4.15 (2\u0026ndash;5) weeks, suggesting that ProMED-mail typically issued outbreak alerts approximately one month prior to the observed rise in dengue cases.\u003c/p\u003e\n\u003cp\u003eA detailed comparison of lead times is presented in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and Table 1. These findings highlight the potential value of ProMED-mail as a supplementary early warning tool to support dengue surveillance efforts across South Asia.\u003c/p\u003e\n\u003cp\u003eTo test the robustness of our lead time estimates, we conducted a sensitivity analysis by adjusting the outbreak onset dates by \u0026plusmn;\u0026thinsp;1 week. Under the baseline scenario, the median (IQR) lead time was 4.15 (2\u0026ndash;5) weeks. When the outbreak onset was advanced by 1-week (thereby reducing the lead time), the median (IQR) decreased to 3.15 (1\u0026ndash;4) weeks. Conversely, delaying the outbreak onset by 1 week resulted in a median (IQR) lead time of 5.15 (3\u0026ndash;6) weeks. Despite these variations, the overall pattern of ProMED-mail issuing alerts approximately one month prior to the observed case surge remains consistent. The notable exception is Pakistan, where ProMED alerts were delayed relative to outbreak onset regardless of these adjustments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 1 \u0026ndash; Lead Time Between ProMED-mail Alerts and Dengue Outbreak Onsets in South Asia (2024 \u0026ndash; 2025)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProMED Alert Date\u003c/p\u003e\n \u003cp\u003e(2024)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAlert Week/ Month\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutbreak start Week/Month\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLead time\u003c/p\u003e\n \u003cp\u003eWeeks/ Months\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScenario A:\u003c/p\u003e\n \u003cp\u003eOutbreak \u0026ndash; 1 wk\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eScenario B:\u003c/p\u003e\n \u003cp\u003eOutbreak\u0026thinsp;+\u0026thinsp;1 wk\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBangladesh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 June\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNepal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 May\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonth 5 (May)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonth 6 (June)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 month (4.3 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSri Lanka \u0026ndash; (Event 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 June\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSri Lanka \u0026ndash; (Event 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 October 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 April 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeek 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 November 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonth 11 (November)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonth 9 (September)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2 months (-8.6 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.15\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(2\u0026ndash;5) weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.15\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(1\u0026ndash;4) weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.15\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(3\u0026ndash;6) weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eLead time was calculated as the difference in weeks or months between the date of the first ProMED-mail alert indicating an outbreak and the date when national surveillance data crossed a predefined outbreak threshold. Sensitivity analyses were performed by shifting the outbreak start date one week earlier (Scenario A) and one week later (Scenario B) to assess robustness. Negative lead times indicate that ProMED-mail reports lagged behind national detectionLead time was calculated as the difference in weeks or months between the date of the first ProMED-mail alert indicating an outbreak and the date when national surveillance data crossed a predefined outbreak threshold. Sensitivity analyses were performed by shifting the outbreak start date one week earlier (Scenario A) and one week later (Scenario B) to assess robustness. Negative lead times indicate that ProMED-mail reports lagged behind national detection.\u003c/p\u003e\n\u003cp\u003eThis figure illustrates the temporal relationship between ProMED-mail alerts and national outbreak trends, highlighting lead time variations and supporting the platform\u0026rsquo;s potential role in early warning for dengue surveillance.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe South Asian region remains heavily burdened by mosquito-borne viral diseases, particularly dengue, malaria, and chikungunya. Among these, seasonal dengue outbreaks especially in the post-monsoon period, the illness has become increasingly frequent and severe, placing considerable strain on public health systems. In response, each South Asian country has developed their own national dengue management guidelines, complying with WHO management protocols and regularly train healthcare providers to manage the disease effectively in various disciplines [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Most countries in the region maintain multi-tiered surveillance systems ranging from hospital and district (MOH) levels to national-level monitoring aimed at detecting outbreaks early and mobilizing resources for case management. These systems play a crucial role in allocating medical supplies, managing hospital bed capacity, and minimizing morbidity and mortality associated with sudden case surges [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, a significant time lag often exists between the collection of surveillance data, preparation of surveillance documents and their dissemination to frontline clinicians, who are the first responders in outbreak scenarios. Contributing factors include delays in data reporting, limited time for clinicians to access weekly updates, and other system-level inefficiencies. This delay can compromise outbreak preparedness and response efforts, particularly in resource-limited settings.\u003c/p\u003e\u003cp\u003eTo bridge this gap, web-based epidemic intelligence platforms such as ProMED-mail, HealthMap, EPIWATCH, and the WHO's Epidemic Intelligence from Open Sources (EIOS) have emerged as valuable tools for real-time disease surveillance [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These platforms integrate information from official sources, news media, scientific literature, and social media, which are then reviewed by expert moderators. The result is an aggregated, timely, and accessible feed of disease alerts often available to users before formal notification systems issue updates.\u003c/p\u003e\u003cp\u003eProMED-mail, in particular, has become one of the most established digital surveillance systems for monitoring emerging and re-emerging infectious diseases. Moderated by infectious disease specialists, ProMED-mail disseminates validated outbreak reports through email alerts and its publicly available platform, allowing clinicians, policymakers, and researchers to access concise updates in near-real time.\u003c/p\u003e\u003cp\u003eOur analysis demonstrated that ProMED-mail successfully detected dengue outbreak signals in South Asian countries approximately one month prior to peaks recorded in official WHO or national surveillance data. This early detection capability was evident across all five countries studied India, Sri Lanka, Bangladesh, Nepal, and Pakistan and included alerts for newly affected regions, changes in outbreak intensity, and unusual seasonal shifts, such as increasing dengue incidence during typically low transmission \u0026lsquo;winter\u0026rsquo; months.\u003c/p\u003e\u003cp\u003eIn Sri Lanka, where dengue is endemic and cases occur around the year, ProMED-mail provided timely alerts during periods of case surges or rising in transmission. In the first outbreak cluster in early June 2024, the alert aligned with the beginning of a documented upward trend in case numbers. Notably, this period coincided with dengue control activities implemented by national dengue prevention campaign which initiated on 26th May, covering 71 MOH divisions in 17 districts. This proactive response may have contributed to flattening of the early outbreak curve. This clearly reflect how early awareness of case rises and timely mobilization of control measures and case management can mitigate transmission. In the second cluster, occurring in October\u0026ndash;November 2024, ProMED issued an alert five weeks ahead of the national case surge, highlighting its potential as an anticipatory tool even beyond routine surveillance\u003c/p\u003e\u003cp\u003eIn India, ProMED-mail captured an outbreak signal in April 2024, preceding national-level trends that began rising notably in June. The early alert supported preparedness in multiple states, including Karnataka and Kerala, which experienced significant outbreaks during the peak transmission season [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This 4-week lead time, consistent with trends in Bangladesh and Nepal, strengthens the platform\u0026rsquo;s credibility in providing early warnings.\u003c/p\u003e\u003cp\u003eIn Nepal, ProMED\u0026rsquo;s May alert precede a sharp monthly increase observed in July\u0026ndash;August 2024. The system successfully flagged activity even in non-traditional transmission months and regions, such as the Mugu district, indicating emerging geographic shifts in vector ecology and disease risk due to factors like climate change and urbanization.\u003c/p\u003e\u003cp\u003eIn Pakistan, where formal, real-time dengue surveillance data is limited or not publicly accessible, ProMED-mail was still able to report on a significant outbreak in Rawalpindi in November 2024. Although the alert lagged behind the estimated outbreak onset by over 8 weeks, this case underscores an important point: in countries lacking transparent or timely reporting systems, ProMED can still serve as a valuable source of outbreak intelligence, capturing events through local media and clinician reports when official data is delayed or absent.\u003c/p\u003e\u003cp\u003eAcross the five South Asian countries analyzed, the median lead time between ProMED-mail alerts and estimated national outbreak onset was 4.15 weeks (IQR: 2\u0026ndash;5 weeks), suggesting that ProMED typically offered an approximately one-month advance window before observable case surges. This lead time, observed across diverse surveillance capacities and epidemiological profiles, reinforces ProMED-mail\u0026rsquo;s value as a complementary early warning system, particularly in low- and middle-income countries (LMICs) where structural delays in data collection, reporting, and dissemination may compromise the timeliness of outbreak responses. Sensitivity analysis confirmed the robustness of this signal, with adjusted medians ranging from 3.15 to 5.15 weeks, underscoring ProMED\u0026rsquo;s consistent potential to detect dengue activity in advance of formal recognition. Notably, in Pakistan, where official surveillance data were not publicly available, ProMED-mail issued alerts suggesting an ongoing outbreak, highlighting its added utility in data-scarce or conflict-affected settings. Collectively, these findings support the integration of digital epidemic intelligence tools like ProMED-mail into national and regional surveillance frameworks to strengthen early detection, enhance preparedness, and accelerate public health response in the face of emerging dengue threats\u003c/p\u003e\u003cp\u003eFrom a practical standpoint, ProMED-mail offers several advantages: Free and public access; Centralized and concise summaries of diverse data sources; Early warning alerts that often precede official announcements; User-friendly interface and email delivery, making it accessible even to busy clinicians. These features make ProMED-mail a valuable supplementary tool for clinicians and public health officials, enhancing their ability to anticipate outbreaks, adjust resource planning, and implement timely interventions.\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, the data analysis was based solely on ProMED reports, which depend on publicly available media and expert submissions. As such, reporting bias may exist, particularly favoring urban or high-profile outbreaks, while underrepresenting smaller or rural events. Second, the exclusion of non-English reports may have limited inclusion of some regionally relevant updates, especially in linguistically diverse national like India or Pakistan. Third, differences in national reporting practices and data publication delays may influence the apparent lead time of ProMED-mail alerts. For example, in Pakistan, the lack of accessible national dengue surveillance data may have contributed to the platform\u0026rsquo;s delayed alert in that setting, yet even without official data, ProMED was able to detect an outbreak, suggesting its utility in data-poor environments.\u003c/p\u003e\u003cp\u003eWe feel that further research to quantify the predictive accuracy of ProMED for other vector-borne diseases in the region, exploring the possibility of integrating such digital platform models with national disease surveillance platforms, and assessing clinician awareness and actual use of ProMED in outbreak response planning in order to further clarify its utility in outbreak prediction and mitigation in the region.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates the potential of digital epidemic intelligence tools such as ProMED-mail as reliable, timely, and easily accessible early-warning system for dengue surveillance. In multiple South Asian countries, ProMED-mail detected dengue activity several weeks ahead of official outbreak confirmation, suggesting it can effectively complement national surveillance systems. By offering expert-reviewed, real-time alerts that are publicly accessible, ProMED-mail enhances situational awareness and supports more proactive decision-making, especially in resource-constrained settings.\u003c/p\u003e\u003cp\u003eIntegrating such digital surveillance platforms into routine health information systems may bridge communication gaps and improve outbreak control strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCDC: Centers for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eCFR: Case Fatality Rate\u003c/p\u003e\n\u003cp\u003eEIOS: Epidemic Intelligence from Open Source\u003c/p\u003e\n\u003cp\u003eEDCD: Epidemiology and Disease Control Division\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile Range\u003c/p\u003e\n\u003cp\u003eLMIC: Low- and Middle-Income Countries\u003c/p\u003e\n\u003cp\u003eMOH: Medical Officer of Health\u003c/p\u003e\n\u003cp\u003eNDCU: National Dengue Control Unit\u003c/p\u003e\n\u003cp\u003eNIH: National Institute of Health\u003c/p\u003e\n\u003cp\u003eProMED-Mail: Program for Monitoring Emerging Diseases \u0026ndash; Mail\u003c/p\u003e\n\u003cp\u003eSEA: South-East Asia\u003c/p\u003e\n\u003cp\u003eSEAR: South-East Asia Region\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval was not required for this study, as it involved the analysis of publicly available, de-identified data with no human participant involvement. Patients or the public were not involved in the design, conduct, reporting, or dissemination plans of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain any individual person’s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ProMED-mail data used in this study were obtained through a special data request approved by the ISID. These data are not publicly available but may be obtained from the corresponding author upon reasonable request and with permission from the ISID. Aggregated surveillance data from national health agencies used for comparison are publicly available through their official portals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.E. conceptualized the study, led the design, conducted the initial literature searches, analyzed the data, and drafted the initial\u0026nbsp;report. R.P. analyzed the data, updated the initial report, and provided expert epidemiological input. N.E. led the writing of the paper. R.P. updated and completed the paper. All authors were responsible for interpretation of data and reviewing and revising the manuscript critically for importantintellectual content. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the International Society for Infectious Diseases (ISID) ProMED team for providing access to the dengue reports dataset. We also acknowledge the Ministry of Health, Sri Lanka, and regional epidemiological units in South Asia for supporting background surveillance data and contextual information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHaider N, Hasan MN, Onyango J, et al. Global dengue epidemic worsens with record 14 million cases and 9000 deaths reported in 2024. 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J Med Internet Res. 2023;25:e44649. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/44649\u003c/span\u003e\u003cspan address=\"10.2196/44649\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahman MS, Shiddik MAB. Explainable artificial intelligence for predicting dengue outbreaks in Bangladesh using eco-climatic triggers. Glob Epidemiol. 2025;10:100210. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.gloepi.2025.100210\u003c/span\u003e\u003cspan address=\"10.1016/j.gloepi.2025.100210\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMadoff LC. ProMED-mail: An early warning system for emerging diseases. 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Wkly Dengue Update. 2024;4(25):47.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTalisma U, Mosharrafa R, Hossain RA, et al. Frequent outbreaks of dengue fever in South Asian countries \u0026ndash; A correspondence analyzing causative factors and ways to avert. Health Sci Rep. 2023;6:e1598. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/hrs2.1598\u003c/span\u003e\u003cspan address=\"10.1002/hrs2.1598\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBhatia S, Lassmann B, Cohn E, et al. Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread. NPJ Digit Med. 2021;4:73. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41746-021-00442-3\u003c/span\u003e\u003cspan address=\"10.1038/s41746-021-00442-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dengue, ProMED-mail, outbreak detection, digital surveillance, South Asia, epidemic intelligence, early warning systems","lastPublishedDoi":"10.21203/rs.3.rs-7177669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7177669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003cbr\u003e\nIn South Asia, dengue led to substantial morbidity, mortality, and health system strain. Timely detection of outbreaks is crucial. We evaluated the utility of ProMED-mail in providing early warnings of dengue outbreaks across five South Asian countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\nProMED-mail reports (January 1, 2024-January 31, 2025) published as outbreak alerts, situation updates, or mortality events were reviewed for Bangladesh, India, Nepal, Pakistan, and Sri Lanka. National dengue trends from ministries of health and WHO-SEAR were analyzed by epidemiological week/month to identify outbreak onset, using a trend-based definition. Lead time was defined as time between the first ProMED-mail alert to estimated national outbreak start. Sensitivity analysis was performed by adjusting the outbreak start time by ±1 week. The median and interquartile range (IQR) of lead times was calculated to assess variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003e\u003cbr\u003e\n34/150 ProMED-mail reports were outbreak alerts. Calculated lead times ranged –8.6 weeks (Pakistan) to +5 weeks (Bangladesh and Event 2 Sri Lanka), and median (IQR) lead time 4.15(2-5) weeks. ProMED issued early alerts; Bangladesh (+5 weeks), India (+4 weeks), Nepal (~4.3 weeks), and Sri Lanka’s October case rise (+5 weeks). In Pakistan, ProMED identified the outbreak in the absence of publicly available surveillance data. Sensitivity analysis yielded median (IQR) lead times of 3.15(1-4) weeks and 5.15(3-6) weeks when outbreak onset was adjusted by –1 and +1 week, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003cbr\u003e\nProMED-mail provided meaningful lead time in detecting dengue outbreaks in South Asia, supporting its role as an effective early warning system and may facilitate enhance epidemic preparedness in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Role of ProMED-mail in the Early Detection of Dengue Outbreaks in the South Asia Region: A Retrospective Descriptive Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 05:35:42","doi":"10.21203/rs.3.rs-7177669/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-16T13:54:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-06T10:44:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118799676091557837000150208499786780491","date":"2025-08-28T03:44:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T01:19:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51294582234470253579061989441344098655","date":"2025-08-27T01:04:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104689674818481627921028395757451413204","date":"2025-08-25T02:26:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275289662578774254800415067274115107634","date":"2025-08-21T04:57:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-20T17:04:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33843278353290071374617907756169437326","date":"2025-08-18T07:19:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257719416304971897484278905296115972613","date":"2025-08-14T18:14:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-14T07:36:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-24T07:38:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-23T12:33:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-23T12:32:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-07-21T12:45:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"42180abf-f4c7-44e5-a42d-0855456586ed","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T15:23:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 05:35:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7177669","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7177669","identity":"rs-7177669","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}