3.1 Community-level blind spots

Current surveillance systems rely heavily on facility-based reporting. During 2006–2021, long-term surveillance data from Bangladesh indicate that Nipah detection has historically relied on hospital-based encephalitis surveillance, which has identified approximately 68% of reported Nipah cases [1]. While this system has been effective for case confirmation, it inherently captures cases at a later stage of disease progression rather than at the point of spillover, limiting its sensitivity to early community-level signals.

Early signals emerging from communities—where spillover events typically originate—are frequently overlooked. A recent community-based study in Bangladesh found that approximately 68% of individuals with Nipah-like illness did not seek care at a Nipah-surveillance hospital, while 24% first consulted informal providers [9]. This indicates that a substantial proportion of early cases may remain outside formal surveillance pathways. Consequently, small clusters of unusual illness may not be detected until severe clinical presentation occurs [8].

Community health workers could serve as critical sentinels for early warning by actively tracking local spike patterns, identifying initial clusters of severe encephalitis or respiratory distress, and serving as a direct link between rural communities and central epidemiological agencies [10]. Yet, in practice, they face structural barriers to doing so, including the absence of standardized reporting triggers for unusual clusters, limited integration into formal surveillance pipelines, and a lack of feedback mechanisms that would incentivize proactive reporting. This failure limits the system’s ability to recognize early risk amplification.

Public awareness activities on the Nipah virus have been implemented by government and non-governmental actors. However, community-based evidence suggests a persistent knowledge–behavior gap: in a survey of 545 respondents, only 29.2% had good knowledge and 33.0% reported correct preventive practices, while 53.2% still consumed raw date palm sap, leaving spillover risk largely unchanged [11].

3.2 Clinical uncertainty and diagnostic delays

Early clinical presentation of Nipah virus is non-specific, complicating timely recognition. Bangladesh has made substantial progress in expanding laboratory capacity for Nipah virus detection, including improvements in RT-PCR availability and national reference laboratory systems [1]. However, despite these advances, diagnostic confirmation remains largely centralized, and clinical decision-making at peripheral facilities still depends on referral pathways, which can delay escalation of suspected cases.

Additional delays may also occur between hospital presentation, sample collection, transport, and laboratory confirmation. Quantitative data from recent outbreaks in Bangladesh (2023–2025) indicate that the median time from symptom onset to hospital admission was 5 days (range: 2–9 days), while the median time from symptom onset to laboratory confirmation was 6 days (range: 3–13 days) [12]. This suggests that patients often remain under clinical evaluation prior to diagnostic confirmation, reflecting the challenges of recognizing the Nipah virus based on its non-specific early presentation. Importantly, the same study found that fatal cases had a shorter median detection lag (5 days) than survivors (9 days), underscoring that even brief delays in recognition may coincide with rapid clinical deterioration [12].

This variability in clinical outcomes is further complicated by the virological characteristics of Nipah virus infection. The incubation period of Nipah virus typically ranges from 3 to 14 days but may extend up to 45 days [4], complicating early detection and timely suspicion. Together, these factors create a critical gap between clinical suspicion and public health action, where early cases may not trigger alerts unless laboratory confirmation is obtained, potentially delaying response during the most actionable phase of transmission. For high-fatality zoonoses, it is essential to treat probable or suspected cases as meaningful early-warning signals. Otherwise, alerts are triggered only after transmission chains are established.

3.3 Fragmented surveillance across sectors

Despite the zoonotic nature of the Nipah virus, surveillance in Bangladesh remains siloed. Progress has been made under the One Health framework, with collaborative investigations between human and animal health sectors [13]. However, these efforts are largely event-driven and lack institutionalization. Routine integration of multi-sectoral data for early risk assessment is minimal, with human health signals rarely linked to animal, wildlife, or environmental information relevant to spillover risk [14].

This gap is particularly evident in the surveillance of reservoir hosts such as Pteropus bats. Monitoring of these species remains largely research-driven rather than being embedded within a routine, nationally coordinated early warning system [7]. Existing efforts rely on periodic ecological and virological studies, while biological and logistical constraints—including low viral detection rates, temporal variability in shedding, and the wide mobility of bat populations—further complicate systematic monitoring [7]. Consequently, data generated from bat studies are seldom incorporated into real-time risk assessment frameworks.

The core challenge is not data scarcity but the absence of operational mechanisms to synthesize and integrate signals across sectors in a timely manner. Strengthening early warning therefore requires institutionalized mechanisms for joint risk interpretation, shared situational awareness, and coordinated escalation, instead of building entirely new surveillance infrastructures.

5.1 From detection to anticipation

Early warning should incorporate predictive elements grounded in historical patterns, seasonality, and ecological risk. Periods associated with known drivers of spillover should trigger heightened vigilance and preparedness before human cases are detected [18]. In Bangladesh, Nipah virus spillover shows strong seasonal clustering during the winter months (December–March), and winter temperature alone explains 53% of the annual variation in spillover events, supporting a shift from passive detection to anticipatory preparedness [6,18]. Spillover is also not evenly distributed in time or space: temperature explains about 36% of year-to-year variation, while distance to surveillance hospitals explains about 45% of spatial variation, indicating that both ecological and access-related factors shape observed risk [18].

Operationally, a period of heightened alert could be defined annually during this high-risk season, particularly in districts with prior outbreaks. During this period, preparedness measures at the upazila level should be proactively strengthened, including (i) reinforcement of community risk communication on safe consumption practices, (ii) readiness of isolation facilities in Upazila Health Complexes (UHCs), and (iii) pre-positioning of sample collection and transport systems. Such seasonal activation may enable early warning systems to function as time-bound preparedness mechanisms rather than solely case-based response tools.

5.2 Operationalizing One Health

One Health should move beyond policy rhetoric to operational integration across sectors. This requires formalized and routine data-sharing mechanisms between human health authorities, such as the Institute of Epidemiology, Disease Control and Research (IEDCR), research institutions such as the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), and sectors responsible for livestock, wildlife, and environmental monitoring [19].

Currently, surveillance of Pteropus bats in Bangladesh remains largely fragmented and research-driven rather than systematically incorporated into national surveillance systems [7]. A feasible step toward operationalization is the establishment of structured joint risk assessment processes at the district level. These would be coordinated through Civil Surgeon offices, where human clinical signals, ecological observations such as bat roost proximity, and behavioral risk indicators including date palm sap consumption practices are periodically reviewed. These assessments could be triggered during the defined high-risk season or upon detection of suspected encephalitis cases at the upazila level, with Civil Surgeons responsible for convening cross-sectoral discussions and notifying national authorities where elevated risk is identified. Such integration would support earlier identification of spillover risk prior to laboratory confirmation [19].

5.3 Redefining alert thresholds

Alert criteria should be explicitly defined in operational terms and should account for uncertainty. In the Bangladesh context, early warning triggers at the upazila level can be operationalized as the occurrence of at least one suspected Nipah case presenting with encephalitis and a relevant epidemiological exposure, such as the consumption of raw date palm sap [1], or the identification of two or more unexplained encephalitis cases within a seven-day period in the same locality [5]. Additionally, a single probable case occurring during the high-risk season in a previously affected district could prompt early action [2].

Once such thresholds are reached, Upazila Health and Family Planning Officers (UHFPOs) could initiate preliminary response measures, including case isolation, risk communication, and rapid reporting to national authorities, without waiting for laboratory confirmation from IEDCR [5]. This approach requires acceptance of a limited number of false alarms as a necessary trade-off, given the high case fatality and the consequences of a delayed response in Nipah virus outbreaks [20]. However, this trade-off must also be understood in the context of resource prioritization constraints within the health system, including limited workforce capacity, diagnostic resources, and outbreak response funding—particularly in settings where multiple competing health priorities may require simultaneous attention. Such a trade-off does not negate the value of Bangladesh’s existing surveillance system, which has contributed to outbreak detection and containment in past human-to-human transmission events; rather, it highlights that increasing surveillance sensitivity must be carefully balanced against the risk of alert fatigue, unintended stigma for affected communities, and the diversion of these limited public health resources away from competing health priorities.

5.4 Linking warning to action

Early warning is meaningful only if it triggers predefined and time-bound measures of preparedness. Clear response protocols—covering infection control, diagnostics, and risk communication—should be activated automatically once predefined alert thresholds are met [3]. In Bangladesh, this requires linking risk signals to existing institutional structures across the health system. At the sub-national level, UHCs and district health authorities should be positioned to initiate initial response measures, while at the national level, IEDCR provides laboratory confirmation and technical guidance. In parallel, community awareness platforms led by the government and non-governmental organizations (NGOs), including icddr,b, represent an important preparedness asset that can be rapidly mobilized for risk communication and community engagement [11].

Strengthening these linkages is important to ensure that early-warning signals translate into timely, coordinated action. However, implementation of such a system should also acknowledge existing constraints, including limited diagnostic capacity outside central laboratories, resource limitations for rapid deployment, and the absence of fully codified trigger-based response protocols.

7.1 Scope of this perspective

This article is based on a synthesis of published literature and documented surveillance experiences in Bangladesh and comparable settings. It is intended to provide a conceptual framework for strengthening early warning systems for Nipah virus, rather than prescriptive operational guidance. The perspectives presented should therefore be interpreted in conjunction with context-specific public health, epidemiological, and veterinary expertise.