In recent years, the Zika virus has captured global scientific attention due to its rapid spread and significant public health implications, especially in tropical regions. Now, a groundbreaking study by Tsang, Rojas, Xu, and colleagues has provided new insights into how transmissible the virus was in Colombia, a nation profoundly affected by the Zika epidemic, even when navigating the often challenging waters of surveillance bias. Published in Nature Communications in 2025, this research represents a pivotal step in epidemiological modeling, employing advanced statistical methodologies to decipher viral spread amid incomplete and potentially skewed data.
The Zika virus, primarily transmitted through Aedes mosquito bites, unleashed an epidemic wave that alarmed public health officials around the world. Although its clinical manifestations are often mild, the virus’s greatest threat lies in its teratogenic risks, particularly its association with microcephaly in newborns and other severe neurological complications. Accurately estimating the virus’s transmissibility is crucial for formulating control strategies and predicting potential outbreak trajectories. However, capturing the true scale of transmission is notoriously complicated by surveillance bias—an inherent issue where some infections go undetected due to asymptomatic cases, underreporting, or limitations in healthcare infrastructure.
Tsang and colleagues confronted this formidable challenge by developing an innovative framework that integrates epidemiological data with modeling techniques designed to adjust for surveillance bias. Their approach centered on utilizing temporal and spatial patterns of reported Zika cases in Colombia, taking into account heterogeneities in reporting quality across different regions. Traditional models often assume perfect case detection or apply uniform correction factors, yet this study advances beyond these assumptions by introducing probabilistic mechanisms that dynamically estimate underreporting rates over time and space, thereby refining estimates of key transmission parameters like the effective reproduction number.
Central to the team’s strategy was the incorporation of serological survey data, which provide snapshots of population-level immunity by detecting past infection exposure. These serosurveys, despite being limited in sample size or frequency, serve as a vital benchmark to anchor model estimates. By calibrating their models against these independent data sources, Tsang et al. achieved a more realistic quantification of the proportion of infections that escaped official surveillance. This methodological innovation allows for a more honest reflection of the epidemic’s scale and provides a template for addressing surveillance bias in the context of emerging epidemics caused by pathogens with high asymptomatic fractions.
The authors’ findings revealed that Zika transmissibility in Colombia had been underestimated in prior reports, primarily due to substantial surveillance gaps. The refined models suggest that the virus’s reproductive number—an epidemiological metric indicating the average number of secondary infections generated by a single case—was significantly higher during the peak transmission periods than earlier estimates indicated. This elevated transmissibility implies a more aggressive epidemic dynamics, underlining the urgent need for strengthening surveillance systems and vector control programs, particularly in regions that experience similar surveillance challenges.
An intriguing component of the study involved analyzing the heterogeneity of transmission across different Colombian departments. The researchers observed that regions with better healthcare infrastructure and more intensive surveillance efforts recorded apparently lower transmission rates, a phenomenon attributable not to diminished viral spread but rather to improved case detection. In contrast, rural and under-resourced areas with less comprehensive reporting infrastructures exhibited greater apparent epidemic sizes post-adjustment. Such spatial stratification underscores the vital importance of contextualizing epidemiological data within local realities and avoiding simplistic comparisons that overlook surveillance biases.
Mathematical modeling in infectious disease epidemiology often grapples with the tension between complexity and interpretability. In this context, Tsang et al. took a balanced approach, employing a semi-mechanistic modeling framework that captures essential transmission dynamics without becoming intractably complex. The model relies on a renewal equation formulation, which links past incidence to future case generation, coupled with a reporting model that probabilistically accounts for the likelihood of case detection. This combination allows for robust estimation of time-varying transmissibility metrics while simultaneously quantifying uncertainty inherent in surveillance data.
Beyond the immediate epidemiological implications, this research bears significant consequences for public health policymaking and preparedness. Understanding the true magnitude of Zika virus spread helps identify at-risk populations, allocate resources more effectively, and tailor intervention strategies accordingly. For example, an underestimation of transmissibility may lead to insufficient vector control efforts, inadequate public awareness campaigns, and delayed mobilization of healthcare resources, potentially exacerbating the epidemic burden.
The study also contributes methodologically to the broader scientific community’s efforts in outbreak analytics. Surveillance bias is not unique to Zika virus epidemics; it is a pervasive issue across many infectious diseases, from influenza to COVID-19. The modeling framework developed here offers a replicable blueprint for disentangling the true epidemic dynamics in the face of imperfect data, a challenge that has stymied public health responses during multiple outbreaks worldwide. Moreover, the team’s emphasis on integrating seroprevalence data into transmissibility estimation paves the way for more informed epidemic forecasting and retrospective analyses.
One of the more nuanced insights from the paper concerns the temporal evolution of surveillance sensitivity throughout the Colombian outbreak. The authors noted that detection rates appeared to improve as the epidemic progressed, likely reflecting increased awareness, enhanced diagnostic capacity, and intensified reporting efforts. This dynamic surveillance performance underscores the importance of incorporating time-varying detection probabilities within epidemiological models. Static assumptions risk mischaracterizing epidemic trajectories and could mislead interventions.
Additionally, the authors explored the implications of their findings on the basic reproduction number (R0), which represents transmissibility in a fully susceptible population. By adjusting for underreporting, they derived estimates suggesting that Zika’s intrinsic transmissibility might have been underestimated during the initial phases of the epidemic. Such insights recalibrate long-term risk assessments and evolutionary projections of the virus, perhaps influencing vaccine development targets and transmission mitigation strategies.
The work also acknowledges limitations inherent in modeling efforts, including reliance on timely and accurate serosurvey data, assumptions about spatial mixing, and challenges in separating true transmission heterogeneity from surveillance noise. However, the study exhibits commendable transparency in addressing these caveats and discusses potential avenues for future improvement, such as incorporating mobility data, genomic surveillance, and more granular environmental factors driving mosquito populations.
Perhaps most compelling is the study’s broader relevance to global health security. As climate change and urbanization continue to shape mosquito habitats, arboviral diseases like Zika are poised to expand their geographical reach, especially in vulnerable regions. This research equips epidemiologists and public health officials with more reliable tools for quantifying transmission risks under real-world conditions, facilitating timely and optimized responses to emergent outbreaks.
In sum, Tsang et al.’s study stands as a landmark contribution to the understanding of Zika virus epidemic dynamics. By deftly navigating and correcting for surveillance bias, the researchers have unveiled a more accurate portrait of the virus’s transmissibility in Colombia, illuminating both the epidemiological challenges and opportunities inherent in studying infectious diseases within imperfect data landscapes. Their work not only enhances our scientific comprehension of Zika but also fortifies the epidemiological toolkit required to confront future infectious threats with rigor and precision.
As the scientific community continues to grapple with the complexities of infectious disease transmission, studies such as this remind us of the indispensable role of meticulous data integration, innovative modeling, and contextual awareness in interpreting epidemic phenomena. The findings underscore that beneath every reported case lies a larger, often elusive picture of viral spread that can only be revealed through rigorous methodological advancements and interdisciplinary collaboration.
This research is likely to influence ongoing surveillance strategies and vector control programs in Colombia and beyond, setting a high standard for epidemic analytics in the presence of imperfect information. With global health increasingly challenged by emerging and re-emerging pathogens, the ability to accurately estimate transmissibility in the face of surveillance obstacles is not just an academic exercise—it is a practical necessity for safeguarding human populations worldwide.
Subject of Research: Zika virus transmissibility estimation under surveillance bias
Article Title: Estimating transmissibility of Zika virus in Colombia in the presence of surveillance bias
Article References:
Tsang, T.K., Rojas, D.P., Xu, F. et al. Estimating transmissibility of Zika virus in Colombia in the presence of surveillance bias. Nat Commun 16, 4299 (2025). https://doi.org/10.1038/s41467-025-59655-9
Image Credits: AI Generated
